tag:blogger.com,1999:blog-85822642287051476352024-03-19T03:39:11.107-07:00Indonesia Tourism and AgricultureWhat is Agriculture, Farming, Holticulture , and Husbandry. This site will inform you the product and Information.Unknownnoreply@blogger.comBlogger59125tag:blogger.com,1999:blog-8582264228705147635.post-6490682195727617482017-08-20T00:41:00.002-07:002017-08-20T00:41:23.979-07:00The Peace and Silence of Sipatahunan Lake IndonesiaIndonesia has so many lakes. Many of the lakes are created from ancient volcanoes. Danau means Lake in Indonesian. In local west Java, according to sundanese, Situ means Danau. Situ Sipatahunan is one of the closest lake to Bandung. The lake is very peaceful and silent since it is not too many people visit. The peace of the lake and the great scenery of Baleendah mountain will stun you.<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhX1vaVYKSyT5NFDqWbZb3-iIkUdZEHjbB_GKw8awnQICb98cLPFPqBwAH25APyOwDLtofhZL_3_EGgsI4Ju746Fimd15AZh9CMFqL8GnljH9KQt6Im5oWcQJhSWYW_rdXM5icnQ_bxIKw/s1600/2012-05-17+11.30.54.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1200" data-original-width="1600" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhX1vaVYKSyT5NFDqWbZb3-iIkUdZEHjbB_GKw8awnQICb98cLPFPqBwAH25APyOwDLtofhZL_3_EGgsI4Ju746Fimd15AZh9CMFqL8GnljH9KQt6Im5oWcQJhSWYW_rdXM5icnQ_bxIKw/s320/2012-05-17+11.30.54.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Sipatahunan lake scenery</td></tr>
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Unlike any other tourism spots in Bandung Regency, such as Cileunca lake or Patengan lake, there is no direction signs to the potential Sipatahunan lake. Even with the development of times, everything has been made easy, including access to a place, still, a direction to a location is really needed. Moreover, it will be a guide tool for a person who does not know how to use the technology, it will also improve the technology with many problems.<br />
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<a name='more'></a><br /><br />It is indeed very unfortunate, this thing has an effect to a low knowledge of Sipatahunan lake, in the great Bandung folks and people, especially. Even some times ago, the area of Sipatahunan lake becomes a law suit between the government and privates, as reported by one of the online media. Out of the blue, which news is true, but based on the news, it raises cares from some people in Baleendah.<br /><br /><br /><br />
<br />Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-5682066848791239122017-08-04T03:39:00.001-07:002017-08-22T02:32:23.862-07:00Pear Sampire South Africa at Glance<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgmzoUV-v3lu9LVApdCD5AU22ECOKBDdWDiNR8cQXg7BCGx7Imur3xk5afk_hqj1uMufTAiaGBnI5wX3cXv8mN06OvMOiEE4pjzyR3_bRPl9KbOMM5Z4oCBoYkND9niEftFSnJI5fol400/s1600/IMAG0014.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1600" data-original-width="911" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgmzoUV-v3lu9LVApdCD5AU22ECOKBDdWDiNR8cQXg7BCGx7Imur3xk5afk_hqj1uMufTAiaGBnI5wX3cXv8mN06OvMOiEE4pjzyR3_bRPl9KbOMM5Z4oCBoYkND9niEftFSnJI5fol400/s320/IMAG0014.jpg" width="182" /></a></div>
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Discount on Pear Sampire from South Africa at Lulu Hypermarket forced me to try the fruit. It is only 4.45 SAR/Kg or 1.19 USD persuaded my to take 1 kilo of them.<br />
It tasted not like a pear since there is no sourness. The fruit skin fruit is not yellowish. It has some bitter sweet like Guava.<br />
I Like the taste but the fruit is so small that four bites are enough to finish the whole fruit.M. Edwardhttp://www.blogger.com/profile/02246924637307940746noreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-52215789349465966582011-11-21T22:24:00.001-08:002017-07-28T14:26:37.938-07:00Heavenly Scenery of Green Canyon Indonesia<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgCEqgwtp_SARUuA3yoJYg49U5nlSbboeI_nXyzRg0gQM3FMZVuAQZ7g19lAXy0fwQwxW28UYEdtQxJ23LaLh98n35FFe7-KU3KwNqHFajuHpROPeUdsVQgDcXbTBoJsgn_HMy-21PX2dA/s1600/green-canyon1.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="300" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgCEqgwtp_SARUuA3yoJYg49U5nlSbboeI_nXyzRg0gQM3FMZVuAQZ7g19lAXy0fwQwxW28UYEdtQxJ23LaLh98n35FFe7-KU3KwNqHFajuHpROPeUdsVQgDcXbTBoJsgn_HMy-21PX2dA/s400/green-canyon1.jpg" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Green Canyon, Pangandaran, Indonesia</td></tr>
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<br />M. Edwardhttp://www.blogger.com/profile/02246924637307940746noreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-30525667702157198642011-11-21T22:18:00.001-08:002011-11-21T22:21:12.714-08:00Cranberry<a href="http://upload.wikimedia.org/wikipedia/commons/3/3a/Cranberry_bog.jpg" target="_blank">Cranberry</a><br />
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiqHrRTQ3w5LVn68UI_j8txYK7kVXAF0VI1ooVlr3nAqC-RVSfqRM9jx2D5v5satsMKkBqtT5WL1szbsK6v7RySMEbsgXmNwfwxtJm15FTKLwOYQT-s-2w3vgV9yjduuaOGl2BTEXZP5aE/s1600/800px-Cranberry_bog.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="211" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiqHrRTQ3w5LVn68UI_j8txYK7kVXAF0VI1ooVlr3nAqC-RVSfqRM9jx2D5v5satsMKkBqtT5WL1szbsK6v7RySMEbsgXmNwfwxtJm15FTKLwOYQT-s-2w3vgV9yjduuaOGl2BTEXZP5aE/s320/800px-Cranberry_bog.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Cranberry</td></tr>
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<br />M. Edwardhttp://www.blogger.com/profile/02246924637307940746noreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-16959650649684545942011-11-20T21:27:00.001-08:002011-11-20T21:29:48.076-08:00Anak Krakatau on Sunda Strait<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjIe9pU2XdpdhhqWZz5al68YtPXtOFnWvLtcZnV9HBoT1ZvVRI0duhdSAo3r5fv-S5nwivyaFMbqWbxzaAvODZpUFabFeVriGw25vDFDKyrBNKGsmHHdvR7HU_KKhnLhcSurCAO-LGGDAQ/s1600/800px-Indonesia%252C_Sunda_Straits.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="219" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjIe9pU2XdpdhhqWZz5al68YtPXtOFnWvLtcZnV9HBoT1ZvVRI0duhdSAo3r5fv-S5nwivyaFMbqWbxzaAvODZpUFabFeVriGw25vDFDKyrBNKGsmHHdvR7HU_KKhnLhcSurCAO-LGGDAQ/s320/800px-Indonesia%252C_Sunda_Straits.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Anak Krakatau in Sunda Strait</td></tr>
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<a href="http://upload.wikimedia.org/wikipedia/commons/4/4a/Indonesia%2C_Sunda_Straits.jpg">Anak Krakatau in Sunda Strait</a>M. Edwardhttp://www.blogger.com/profile/02246924637307940746noreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-83272065857761540382011-11-20T21:23:00.001-08:002011-11-20T21:24:50.710-08:00Satay : Indonesian Steak<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjTFFou_GLrs8oB8t5YKN7jc6cc9dmJZg4vUoXuET3G_yXJzLFLrRe7YpaEheivqHaKeMFd_Sp80O_zEhK6_-kAi9THbAzKxe2f4_RvXHS9scAOTsdviAXRDMSHIdOKL9pCTeP5HVSCwOE/s1600/799px-Sate_Ponorogo.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjTFFou_GLrs8oB8t5YKN7jc6cc9dmJZg4vUoXuET3G_yXJzLFLrRe7YpaEheivqHaKeMFd_Sp80O_zEhK6_-kAi9THbAzKxe2f4_RvXHS9scAOTsdviAXRDMSHIdOKL9pCTeP5HVSCwOE/s320/799px-Sate_Ponorogo.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Satay</td></tr>
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SatayM. Edwardhttp://www.blogger.com/profile/02246924637307940746noreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-76490873105859034092011-11-18T21:32:00.001-08:002011-11-18T21:33:23.888-08:00White Peach<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi5CrdNRPu-8Zu-nM60vXMCnbZq3eyCTnLaxjU7vkMESh2Uw4orFQblZSTJbJ23V3e3YPqHsTbwrAXQSQZR6ER_T5caNmvSzbg53qEWe52ehTRHLSAUC2TYm21vowkJUGu5ZSNAUHV24bg/s1600/White_peach_and_cross_section_edit.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="161" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi5CrdNRPu-8Zu-nM60vXMCnbZq3eyCTnLaxjU7vkMESh2Uw4orFQblZSTJbJ23V3e3YPqHsTbwrAXQSQZR6ER_T5caNmvSzbg53qEWe52ehTRHLSAUC2TYm21vowkJUGu5ZSNAUHV24bg/s400/White_peach_and_cross_section_edit.jpg" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">White Peach</td></tr>
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White PeachM. Edwardhttp://www.blogger.com/profile/02246924637307940746noreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-13189253814673650932011-11-18T21:27:00.001-08:002017-07-29T08:11:59.649-07:00Autumn Red Peach<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh3Iff1ovFUsuhPRwsOOT-gKdE5-KAqrxEJsNftR9dCmhSD1F6DNrWrYwlAD2yqNPk9wfr4hZFsr01IiuIObLqd2J7iPLgepnzK6wrCJ3dPh1S6UdeJYuYim-r3OEjuVEoaCA4svT4px68/s1600/Autumn_Red_peaches.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="270" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh3Iff1ovFUsuhPRwsOOT-gKdE5-KAqrxEJsNftR9dCmhSD1F6DNrWrYwlAD2yqNPk9wfr4hZFsr01IiuIObLqd2J7iPLgepnzK6wrCJ3dPh1S6UdeJYuYim-r3OEjuVEoaCA4svT4px68/s400/Autumn_Red_peaches.jpg" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Autumn Red Peach</td></tr>
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Autumn Red PeachM. Edwardhttp://www.blogger.com/profile/02246924637307940746noreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-7943970400674549642011-11-17T18:21:00.001-08:002017-07-29T08:12:46.009-07:00Apricot Fruit<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjtSCTjwOLjziV8mAsR4CueRzpLtBrrZlynKoAglzoxYZeCN-9iIEmOYkqk7R2B1FRNul0t28X79feb5qi2Bcv4SndLqMBn5oVAghLC2f2wmHAu_ps8D4lbNjLuDdDalS7B0V9P_Qe1xns/s1600/1024px-Apricot_and_cross_section.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="205" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjtSCTjwOLjziV8mAsR4CueRzpLtBrrZlynKoAglzoxYZeCN-9iIEmOYkqk7R2B1FRNul0t28X79feb5qi2Bcv4SndLqMBn5oVAghLC2f2wmHAu_ps8D4lbNjLuDdDalS7B0V9P_Qe1xns/s400/1024px-Apricot_and_cross_section.jpg" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Apricot Fruit</td></tr>
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<br />M. Edwardhttp://www.blogger.com/profile/02246924637307940746noreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-3581884880679310902011-11-17T18:14:00.001-08:002011-11-17T18:15:43.250-08:00Pineapple<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi0Zl_ZgwZP-m2wxGiQ_R0lltjLuX1Qn-6O0rtXfw7goBoBrxqZeM9j4Oezn1aKpGO8K8f-lTVIW2PY3yV23zu7E1-mD3xRdWq8MG-yW4A3o2JXvQ2cWhTkTpudeeFuTpOk_TjXTLPUWZ8/s1600/1220px-Pineapple_and_cross_section.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="335" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi0Zl_ZgwZP-m2wxGiQ_R0lltjLuX1Qn-6O0rtXfw7goBoBrxqZeM9j4Oezn1aKpGO8K8f-lTVIW2PY3yV23zu7E1-mD3xRdWq8MG-yW4A3o2JXvQ2cWhTkTpudeeFuTpOk_TjXTLPUWZ8/s400/1220px-Pineapple_and_cross_section.jpg" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Pineapple</td></tr>
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<br />M. Edwardhttp://www.blogger.com/profile/02246924637307940746noreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-8271737848290584562011-11-16T21:53:00.001-08:002011-11-16T21:54:27.670-08:00Table Grape<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhi8p2-3bp4UP3nptGRpqy43V7d3OH14DPIvKH9bKZQjOEekguOo6GNmwULzhS2-ZK5kr9j1hk4A-nzQ0Q8dukMfZ0wbq-nyWFbZKESNg-CFTPkkhEl3-ayRSX6CrALO9HUNRDCCQWrlZo/s1600/Table_grapes_on_white.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="266" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhi8p2-3bp4UP3nptGRpqy43V7d3OH14DPIvKH9bKZQjOEekguOo6GNmwULzhS2-ZK5kr9j1hk4A-nzQ0Q8dukMfZ0wbq-nyWFbZKESNg-CFTPkkhEl3-ayRSX6CrALO9HUNRDCCQWrlZo/s400/Table_grapes_on_white.jpg" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Table Grape</td></tr>
</tbody></table>
<br />M. Edwardhttp://www.blogger.com/profile/02246924637307940746noreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-20459468714747652022011-11-15T18:17:00.001-08:002011-11-15T18:24:12.910-08:00Bee Orchid(OphrysApifera)<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEio8YWr3aZn2HuP1knrNbytOUVZ106BF4arHJtvlGeb_zoeCXrTQB_hQHm-RHh0yQwJvbF1SbYCA6cvbKMowpkiIK1_3JgD7zHF0QPTbYS5BxhZg-lgg2jDK4oyPQ3FIrAS2cZKboeKD2r0/s1600/Ophrys_apifera_%2528pink_colour_form%2529.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEio8YWr3aZn2HuP1knrNbytOUVZ106BF4arHJtvlGeb_zoeCXrTQB_hQHm-RHh0yQwJvbF1SbYCA6cvbKMowpkiIK1_3JgD7zHF0QPTbYS5BxhZg-lgg2jDK4oyPQ3FIrAS2cZKboeKD2r0/s320/Ophrys_apifera_%2528pink_colour_form%2529.jpg" width="239" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Bee Orchid (OphrysApifera)</td></tr>
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<br />Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-84440929033440487262011-09-24T18:29:00.000-07:002017-07-29T08:14:16.557-07:00Kangkung: water spinach farming<b>Kangkung </b>or<b> water spinach </b>is asian <b>vegetable </b>which has so many benefits. more over, it is very easy to cultivate water spinach. you just need to put a part of the plant to soil. the soil must be kept watered every day and the plant will grow fast. the branch will grow as soon as the root grows, the plant will grow branch a lot. the most important condition is to keep the moisture of soil. you can <b>harvest</b> the water spinach in a month.<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgpgca4paT55wRKNje4AMTJSlXx5cubac6XxR3p_aF2ZpxNWAqw-wwceNDcRoP9xABrCkpyxC527_QX3UYad8ux6plhqCETl2r07p1Heactu-nPVaATPCsukA6BOzPb7Fecbd3CeEjcXwG5/s1600/2011-09-25+06.02.11.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgpgca4paT55wRKNje4AMTJSlXx5cubac6XxR3p_aF2ZpxNWAqw-wwceNDcRoP9xABrCkpyxC527_QX3UYad8ux6plhqCETl2r07p1Heactu-nPVaATPCsukA6BOzPb7Fecbd3CeEjcXwG5/s320/2011-09-25+06.02.11.jpg" width="240" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Kangkung or water spinach</td><td class="tr-caption" style="text-align: center;"><br /></td></tr>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgHaSZN6jw2vOgEDwddqowK9m5yltKgQpWOCnle7xiB_8QJ39-hy42dxRGNOgmYUcpq5EvWb1YcdcckKEMo41ReiTuvaQuFFIuUUepeGyVVxeCmxodoCivUiU_0FmK26s0JWpzpZnPo3l9N/s1600/2011-09-25+06.11.51.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgHaSZN6jw2vOgEDwddqowK9m5yltKgQpWOCnle7xiB_8QJ39-hy42dxRGNOgmYUcpq5EvWb1YcdcckKEMo41ReiTuvaQuFFIuUUepeGyVVxeCmxodoCivUiU_0FmK26s0JWpzpZnPo3l9N/s320/2011-09-25+06.11.51.jpg" width="240" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">harvesting time</td></tr>
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<br />
<br />
another part of <b>kangkung </b>lifecycle is flowering season. when the <b>water spinach</b> got nutrition enough the plant will grow flower. the flower is Saxophone-like shaped. the flower blossom means the <b>kangkung </b>is ready to be harvested.<br />
<br />Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-82910363574389349132011-09-07T04:57:00.000-07:002017-07-29T08:14:28.334-07:00Analysis of genes for stigma coloration in riceThe anthocyanic pigmentation of the rice apiculus is controlled by three complementary<br />
genes— C, A, and P —which serve as the basic coloration genes. The<br />
genetic control of stigma color is more complex. To shed more light on genes for<br />
stigma coloration, F 1 and F 2 data for 196 varietal crosses were investigated to<br />
explain the whole pattern of segregation by assuming certain Mendelian genes. In<br />
ordinary cases, the stigma is colored only in plants having C, A, and P. Two<br />
independent genes, Ps-2 and Ps-3, take part in stigma coloration, Ps-2 being<br />
relatively frequent in indicas and Ps-3 in japonicas. For Ps-3, an inhibitor, I-Ps-3,<br />
was found, which seems to have two loci according to variety. In addition, two<br />
complementary inhibitors are assumed to be present in some of the varieties. A<br />
Japanese upland variety, Gaisen-mochi, having a colorless apiculus and colored<br />
stigma, has Ps-1, which expresses stigma color even when P is absent (recessive).<br />
An inhibitor for this gene, I-Ps-1, needs P to function. Four genes— P, Ps-3,<br />
Ph, and Ps-1 —are located in linkage group II in that order. Their recombination<br />
values were estimated.<br />
<a name='more'></a><br />
Most rice varieties and derivatives from varietal crosses express anthocyanin color in<br />
the stigma only when the apiculus is colored. The coloration of the stem node and some<br />
other plant parts also shows such a relation, and the genes for apiculus color can be<br />
considered as basic for anthocyanin formation (Takahashi 1964). Apiculus color at<br />
heading ranges from pink to dark purple, for which several isoalleles have been<br />
described, but when only the presence or absence of color is considered, it is controlled<br />
simply by three complementary genes: C (chromogen production), A (activation), and<br />
P (spreading pigment). These three genes are located in linkage groups I, III, and II,<br />
respectively (cf. Kinoshita 1984, pp. 29–30). Accordingly, all varieties with colored<br />
apiculus have the genotype CAP, while colorless varieties can have varying genotypes<br />
(Oka 1989a). Setty and Misro (1973) reported that the P gene has different loci<br />
according to variety. In the material used in this study, however, all F 2 plants derived<br />
from crosses between apiculus-colored parents had a colored apiculus, and the<br />
different loci of gene P were not confirmed.<br />
97<br />
In contrast to apiculus color, the genetic control of stigma color is more complex,<br />
as reported in this paper, but space is too limited to describe the procedures for<br />
identifying genes and determining parental genotypes for all crosses observed.<br />
Materials and methods<br />
Forty-one strains were used as parents, including 21 indicas and 19 japonicas ( Oryza<br />
sativa ), and an Indian wild annual type of O. rufipogon (Table 1). The F 1 and F 2 plants<br />
of 196 crosses were observed for apiculus and stigma color at heading, pollen and seed<br />
fertilities, phenol reaction, and some other traits. In 20 cases, reciprocal crosses were<br />
made and the F 2 populations were observed more than once, even though no significant<br />
differences were detected between reciprocal crosses and replications. In each cross,<br />
about 80–300 F 2 plants were grown and observed in Taichung, Taiwan, China, with the<br />
assistance of several students of Chung Hsing University.<br />
Purple to dark red (P) and red to pink (R) color tones were distinguished, but it was<br />
difficult to classify them into several grades to identify multiple alleles at the C and A<br />
loci. This was largely because distant crosses were tested under varying conditions<br />
during the winter and summer cropping seasons.<br />
In distant crosses, F 2 ratios are often distorted (Oka 1989b). Yet, the underlying<br />
genes could be deduced in many cases from observed segregation patterns. To estimate<br />
recombination values from distorted ratios, the methods described by Oka (1989c)<br />
were employed. To confirm the parental genotypes presumed from the F 2 data, F 3 lines<br />
from selected F 2 plants were examined in 001/325, 108/532, and 5 other crosses, and<br />
some colorless F 3 lines were intercrossed.<br />
Results<br />
The results of the experiments are presented in two sections: one discussing ordinary<br />
varieties in which stigma coloration occurs only in plants with a colored apiculus, and<br />
the other discussing a special variety, Gaisen-mochi (532), and its crosses, in which<br />
plants with colorless apiculus and colored stigma occur.<br />
Crosses between ordinary varieties<br />
In the F 2 populations of crosses between ordinary varieties, three types of segregants<br />
are found: + + (apiculus and stigma both colored), + - (apiculus colored, stigma<br />
colorless), and - - (apiculus and stigma both colorless). No - + (apiculus colorless,<br />
stigma colored) type occurs. The parental genotype for apiculus coloration (the<br />
combination of C, A, and P ) can be judged by the F 1 phenotypes from crosses with<br />
known genotypes and by observation of F 2 plants when necessary (Oka 1989a). The<br />
F 2 ratios for stigma color observed among segregants with colored apiculus (+ + and<br />
+ - types) are summarized in Table 2, excluding those with variety 532.<br />
Different segregation patterns were found; representative ones are briefly described<br />
in Table 3.<br />
98 H. I. Oka<br />
Table 1. Varieties used as parents and their apiculus and stigma color. a<br />
Code Origin Vernacular name<br />
Color<br />
Apiculus Stigma<br />
001<br />
022<br />
060<br />
101<br />
108 b<br />
124<br />
143<br />
160<br />
414 b<br />
421<br />
435<br />
451<br />
612<br />
619<br />
706<br />
717<br />
719<br />
724<br />
727<br />
761<br />
1091<br />
219<br />
221<br />
236<br />
242<br />
318<br />
325<br />
T65 b<br />
501<br />
521 b<br />
532<br />
535<br />
545 b<br />
552<br />
563 b<br />
571<br />
647 b<br />
701<br />
703<br />
871<br />
W106 b<br />
Vietnam<br />
Vietnam<br />
Vietnam<br />
Taiwan<br />
Taiwan<br />
Taiwan<br />
Taiwan<br />
Taiwan<br />
India<br />
India<br />
India<br />
India<br />
Sulawesi<br />
Sulawesi<br />
North China<br />
China<br />
China<br />
South China<br />
South China<br />
Hainan<br />
South China<br />
Philippines<br />
Philippines<br />
Philippines<br />
Philippines<br />
Indonesia<br />
Indonesia<br />
Taiwan<br />
Japan<br />
Japan<br />
Japan<br />
Japan<br />
Japan<br />
Japan<br />
Japan<br />
Japan<br />
Sulawesi<br />
North China<br />
North China<br />
Taiwan<br />
India<br />
lndicas<br />
70 a som cau<br />
II dauh<br />
RTS23<br />
U-kuh-tsing-you<br />
Peh-ku<br />
Shuang-chiang<br />
Lui-tou-tzu<br />
Hong-ka-chiu<br />
PTB10<br />
PTB8<br />
Pachchai perumal<br />
Surjamkhi<br />
Padi bali<br />
Padi hotjong<br />
He-nan-tsao<br />
Nan-chang-wan<br />
Chin-sen<br />
Kunming tsieh-huan<br />
Chin-tsao<br />
Siao-chung-kuh<br />
Lui-kung sen<br />
Japonicas<br />
Garumbalay<br />
lnakupa<br />
Olag ayau<br />
Malagkit pirurutong<br />
Boegi inda<br />
Kaniranga<br />
Taichung 65<br />
Urasan<br />
Kisshin<br />
Gaisen-mochi<br />
Hirayama<br />
Shinriki<br />
Aikoku<br />
Kinoshita-mochi<br />
Mansaku<br />
Padi ase-banda<br />
Tatung-tsailai<br />
Tamao tao<br />
Nabeshi<br />
O. rufipogon<br />
Cuttack annual type<br />
P<br />
P<br />
P<br />
P<br />
P<br />
P<br />
P<br />
P<br />
P<br />
P<br />
P<br />
R<br />
R<br />
P<br />
P<br />
P<br />
P<br />
P<br />
P<br />
P<br />
P<br />
P<br />
P<br />
P<br />
P<br />
P<br />
R<br />
P<br />
P<br />
a P = purple to dark red, R = red to pink, (–) = colorless. b Used as maternal parent and<br />
intercrossed.<br />
Analysis of genes for stigma coloration in rice 99<br />
– –<br />
– –<br />
– –<br />
– –<br />
– –<br />
– –<br />
– –<br />
–<br />
– –<br />
–<br />
– –<br />
– –<br />
– –<br />
– –<br />
– –<br />
–<br />
– –<br />
– –<br />
– –<br />
– –<br />
– –<br />
–<br />
– –<br />
–<br />
– –<br />
– –<br />
– –<br />
– –<br />
– –<br />
Table 2. Pooled F 2 ratios for stigma color (++:+–) among segregants with colored apiculus<br />
and genotypes presumed for respective strains. a<br />
P 2<br />
P 1<br />
001 545 647 219 521 T65 W106<br />
Genotype of P 2 strain<br />
Common genes b Extra c<br />
Group A d<br />
O22<br />
Group B e<br />
421<br />
619<br />
Group C f<br />
545<br />
871<br />
647<br />
219<br />
242<br />
325<br />
501<br />
521<br />
571<br />
236<br />
703<br />
535<br />
552<br />
T65<br />
W106<br />
0<br />
0<br />
1:0<br />
49:15<br />
49:15<br />
1:0<br />
3:1<br />
3:1<br />
51:13<br />
0<br />
3:1<br />
0<br />
1:0<br />
0<br />
0<br />
3:1<br />
1:0<br />
1:0<br />
3:1<br />
3:1<br />
3:1<br />
51:13<br />
3:61<br />
51:13<br />
0<br />
0:1<br />
3:1<br />
0<br />
0<br />
201:55 j<br />
0<br />
51:13<br />
1:0<br />
1:3<br />
1:3<br />
1:0<br />
0<br />
7:57 g<br />
7:57 g<br />
15:1<br />
21:43 h<br />
0<br />
0<br />
0<br />
15:1<br />
3:1<br />
13:3<br />
1:15<br />
13:3<br />
0:1<br />
0:1<br />
1:15<br />
51:13<br />
0:1<br />
3:13<br />
0<br />
0<br />
51:13<br />
3:61<br />
(3:61)<br />
51:13<br />
0<br />
0<br />
0:1<br />
3:1<br />
0<br />
0<br />
3:1<br />
0:1<br />
15:1<br />
3:13<br />
15:1<br />
1:1<br />
1:0<br />
(61:3)<br />
1:0<br />
0 1:0<br />
21:1235 i<br />
0<br />
0<br />
0<br />
0<br />
0:1<br />
0:1<br />
(63:1)<br />
(63:1)<br />
1:0<br />
(63:1)<br />
c A P Ps-2 ps-3 I-3<br />
c A P ps-2 ps-3 I-3<br />
C A P ps-2 ps-3 I-3'<br />
C A P ps-2 ps-3 I-3'<br />
C A P Ps-2 Ps-3 i-3<br />
C A P Ps-2 Ps-3 i-3<br />
C a P ps-2 ps-3 I-3<br />
C a P ps-2 ps-3 I-3<br />
C a P ps-2 Ps-3 i-3<br />
C A P ps-2 Ps-3 I-3<br />
c A P ps-2 Ps-3 I-3'<br />
C A P Ps-2 ps-3 i-3<br />
c A P ps-2 ps-3 i-3<br />
c a P ps-2 ps-3 l-3<br />
c a P ps-2 ps-3 i-3<br />
C A p ps-2 ps-3 i-3<br />
C a p Ps-2 ? ?<br />
C Br A P Ps-2 Ps-3 i-3<br />
C Br A P ps-2 ps-3 i-3<br />
C Br a P ps-2 Ps-3 i-3<br />
C A P Ps-2 Ps-3 i-3<br />
(l-a)<br />
(l-a)<br />
(l-b)<br />
(l-b)<br />
(l-b)<br />
(l-a)<br />
(Ps-1)<br />
a Ratios in parentheses = assumed without confirmation. b I-3/i-3 = I-Ps-3/i-ps-3, l-3' = I-Ps-3' supposedly having<br />
a locus different from that of I-Ps-3. l-a and I-b = complementary inhibitors for Ps-3, corresponding to I-Ps-a and<br />
= 001, 060, 101, 108, 124, 143, 414, 612, 717, 719, 724, 727, and 761. e Group B = 160, 435, 451, 706, and 1090.<br />
I-Ps-b in Kinoshita (1984, p. 30). c Strains with no "extra" gene are expected to have i-a, i-b, and ps-i. d Group A<br />
f Group C = 221, 318, and 701. g 7:57 = (1:3) (7:9). (1:3) comes from cosegregation for l-3/i-3 (given in b). (7:9)<br />
comes from complementary inhibitors I-a/i-a and I-b/i-b (given in b). h 21:43 = (3:1) (7:9). (3:1) comes from Ps-<br />
3/ps-3. (7:9) is expected from l-a and I-b as mentioned above. j 21:235 = (3:13) (7:9). (3:13) comes from interaction<br />
between Ps-3/ps-3 (3:1) and I-3/i-3 (1:3). (7:9) is expected from l-a and I-b as mentioned above. j 201:55 = (3:1)<br />
(9:7) (1:3). (3:1) comes from Ps-2/ps-2. (9:7) is due to C/C Br and Ps-3/ps-3, where plants with C and Ps-3<br />
are expected to show stigma coloration. (1:3) is expected from I-3/i-3 as mentioned above (b and g ).<br />
Table 3. Representative segregation patterns, F 2 ratios, and genes for stigma<br />
color.<br />
Case P 1 P 2 F 1<br />
0<br />
1<br />
2<br />
3<br />
4<br />
5<br />
6<br />
7<br />
– –<br />
– –<br />
– –<br />
– –<br />
+–<br />
– –<br />
– –<br />
+–<br />
– –<br />
++<br />
– –<br />
+–<br />
– –<br />
++<br />
– –<br />
– –<br />
– –<br />
++<br />
++<br />
+–<br />
+–<br />
++<br />
++<br />
+–<br />
F 2 ratio<br />
++:+–<br />
"0"<br />
1:0<br />
3:1<br />
1:3<br />
3:13<br />
13:3<br />
51:13<br />
0:1<br />
Example Genes for stigma color<br />
Many (No apiculus-colored segregants)<br />
108/160 Same gene in both parents<br />
108/545 Segregation for a coloration gene<br />
647/421 Segregation for an inhibitor<br />
219/571 A coloration gene and an inhibitor<br />
219/221 A color gene and an inhibitor for<br />
another color gene<br />
108/647 Two color genes and an inhibitor<br />
219/521 Same inhibitor in both parents<br />
100 H. I. Oka<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
Underlying genes in these different cases are presumed as follows:<br />
• Crosses between colorless indicas produced no colored hybrids, because both<br />
parents have c A P (group A; Table 3, case 0). In their crosses with colored indicas<br />
(group B) or colored japonicas (group C), the F 1 and all F 2 plants with colored<br />
apiculus had colored stigmas (+ +:+ – = 1:0; Table 2; Table 3, case 1). In this case,<br />
both parents are thought to have the same gene for stigma coloration, assigned<br />
the symbol Ps-2 to agree with the description in Kinoshita (1984, p. 30).<br />
• When varieties of group A ( c A P Ps-2 ) were crossed with variety 545 ( C a P ),<br />
the F 1 was colored (+ +) and the F 2 plants with colored apiculus segregated into<br />
3 + +:1 + – type (Table 3, case 2). Variety 871 showed the same pattern. These<br />
varieties seem to have ps-2. Variety 647, whose F 2 ratios in crosses with group<br />
A varieties were near 3:1, may also be considered to have ps-2.<br />
• When 647 was crossed with varieties of groups B and C, all F 2 plants with colored<br />
apiculus had colored stigmas (1:0; Table 2; Table 3, case 1). This cannot be<br />
explained without assuming another gene for stigma coloration, Ps-3, present in<br />
both parents (to agree with Kinoshita 1984, p. 30), since 647 would have ps-2.<br />
Then the crosses of 647 ( ps-2 Ps-3 ) with group A varieties ( Ps-2 ps-3 ) are<br />
expected to show a 15:1 ratio in the F 2 . The occurrence of nearly 3:l ratios in<br />
those crosses suggests that group A varieties have an inhibitor for Ps-3, I-Ps-3<br />
(abbreviated I-3 ); in view of the 1:0 pattern in crosses with groups B and C<br />
( Ps-3 Ps-2 ), 647 cannot have I-3. Accordingly, the nearly 3:1 ratio observed in<br />
crosses between 647 and group A varieties may be considered to be 51:13 (Table<br />
2; Table 3, case 6) resulting from 3:1 for Ps-2 and 3:13 for Ps-3 and I-3. The<br />
observed ratio of 374:105 (pooled for 4 crosses whose ratios were homogeneous)<br />
agrees with this expectation ( c 2 = 0.8, P > 0.5).<br />
• A colored variety 325, showing a 15:1 F 2 ratio in its cross with 647 and the 1:0<br />
pattern with group A varieties, is considered to have Ps-2 ps-3 i-3. Variety 545<br />
is thought to have ps-2 ps-3 I-3, because its crosses with 325 and group A<br />
varieties gave a 3:1 ratio, and its crosses with varieties of groups B and C gave<br />
a nearly 3:1 ratio (51:13; Table 2; Table 3, case 6). The observed F 2 ratios in<br />
crosses of 545 with groups B and C—468:132 (4 crosses pooled, homogeneous)—<br />
agree well with 51:13 ( c 2 = 1.2, P > 0.25).<br />
• When variety 219 was crossed with group A varieties, the F 1 was colored (+ +)<br />
and the F 2 showed a 3 + +:1 + – ratio (568:145, c 2 = 1.2, P > 0.25; 4 homogeneous<br />
crosses pooled). This suggests that 219 has ps-2 Ps-3 I-3. The F 1 and F 2 plants<br />
from 219/545 and 219/871 showed no stigma color, because they had I-3 in<br />
common (Table 3, case 7).<br />
• Varieties 242 and 421 showed nearly 1:15 F 2 ratios for stigma color in their<br />
crosses with 219, suggesting that they have an inhibitor for Ps-3 at a locus<br />
different from that in 219, which is tentatively designated as I-Ps-3' (abbreviated<br />
I-3' ). Variety 619, which showed a segregation pattern similar to that of 421, is<br />
also assumed to have I-3'. When 1:15 is combined with 3:1, a 49:15 ratio is<br />
expected, although it is not distinguishable from 3:1 (e.g., 421/group A,<br />
Table 2).<br />
Analysis of genes for stigma coloration in rice 101<br />
• F 2 ratios lying between 1:3 and 1:15, and between 1:3 and 1:1 were found in some<br />
crosses. These may be regarded as resulting from segregation distortion, but<br />
some of these crosses showed high F 1 fertilities. Hsieh (1960, 1961) pointed out<br />
the presence of a set of complementary inhibitors (I ps and I ps 2; redesignated IPs-<br />
a and I-Ps-b in Kinoshita (1984, p. 30); I-PS-3-a and I-PS-3-b or I-a and I-b<br />
in this paper), based on the finding that T65, which seemed to have no ordinary<br />
inhibitor, showed in its crosses with some colored-stigma strains (7111 and two<br />
others) colorless F 1 plants, and nearly 1:2 F 2 ratios. Such complementary<br />
inhibitors will bring about a 7:9 ratio in the F 2 and will give rise to 21:42, 7:57,<br />
and other ratios when combined with 3:1,1:3, and others. Although the data from<br />
this study give no conclusive evidence for their existence, this assumption<br />
facilitates explanation of some of the F 2 ratios observed. Tentatively, it is<br />
assumed that varieties T65, 545, and 647 have I-a; and that 219, 242, and 501<br />
have I-b (Table 2). Then, for instance, the F 2 ratio found in 647/219 (22:226) can<br />
be regarded as 7:57 ( c 2 = 1.1, P>0.25), and that observed in 647/501 (66:159)<br />
may be regarded as 21:43 ( c 2 = 1.2, P>0.25). Probably, if such complementary<br />
inhibitors exist, they would be distributed more commonly, although their<br />
detection is not always feasible.<br />
• When a colorless japonica, 521, having c a P, was crossed with varieties of<br />
groups B and C, the F 2 ratios (++:+–) observed were 89:5, 86:7, 59:5, etc. Its cross<br />
with 219 produced no colored-stigma F 2 plants (0: 1; Table 3, case 7), while its<br />
cross with 421 ( CAP Ps-2 Ps-3 I-3’ ) gave a nearly 1:15 (3:61) ratio. Variety 521<br />
is then assumed to have ps-2 ps-3 I-3, like 545, and the above ratios with groups<br />
B and C are assumed to be 51:13. The observed ratio in 521/451 (86:17) agrees<br />
with 51:13 ( c 2 = 0.9, P>0.25), but others do not. This is considered due to<br />
segregation distortion, since the crosses with 521 showed low F 1 pollen fertilities<br />
(20–50%). A similar colorless japonica, 571, was assumed to have ps-2 ps-3 i-<br />
3, because its F 2 with 219 gave a 3:13 ratio (13:95. c 2 = 3.2, P>0.05). Another<br />
colorless japonica, 703, having C a p, showed the 1:0 pattern in its F 2 s with group<br />
A varieties, suggesting that it has Ps-2. However. its alleles at loci Ps-3 and I-3<br />
remain unknown. Through similar exercises in symbolic logic, the genotypes for<br />
stigma coloration of several varieties were presumed, e.g., 236 = C A p ps-2 ps-<br />
3 i-3 and 501 = c A P ps-2 ps-3 i-3 I-b (Table 2).<br />
• Varieties showing pink color at the apiculus may be assumed to have C Br , while<br />
plants with C Br/ C Br or C Br/ c and A P are thought to express no stigma color even<br />
if they have a gene for stigma color (Takahashi 1958; 1964, p. 217). T65 is known<br />
to have C Br a P for apicuius color (Hsieh 1960; 1961, p. 85); varieties 535 and<br />
552 with pink apiculus may also be assumed to have C Br. This study suggests,<br />
however, that the suspending effect of C Br on stigma coloration is limited to Ps-<br />
3, leaving Ps-2 unaffected. For instance, the F 2 from 108/T65 segregating for Ps-<br />
2 showed a 3:1 ratio (95:32) for stigma color. On the assumption that the<br />
genotype of T65 is C Br a P ps-2 Ps-3 i-3 I-a, its crosses with groups B and C are<br />
expected to show a 15:1 F 2 ratio for stigma color. This agrees well with the data<br />
102 H. I. Oka<br />
(121:9, 2 homogeneous crosses pooled, c 2 = 0.1, P>0.5). Together with the<br />
complementary inhibitors, the suspending effect of C Br on Ps-3 brings about a<br />
complication in F 2 ratios. For instance, the F 2 of 219/T65 involving C Br / C and Ia,<br />
I-b is expected to give a 21:235 ratio (Table 2, footnote i ), which agrees with<br />
the data (12:108). Variety 022, which showed the 0:1 pattern in its F 2 with T65,<br />
would have ps-2 ps-3 I-3. Variety 535 may be considered to have C Br A P Ps-<br />
2 Ps-3 i-3, because its F 2 with 647 showed a 15:1 ratio (82:3). Its cross with 545<br />
is expected to give an F 2 ratio of 201:55 (Table 2, footnote j ), which agrees with<br />
the data (139:46; c 2 = 1.3, P>0.25).<br />
• Variety 563 shows purple-tawny color spread over the hulls at maturity, which<br />
is expressed by gene Pr (linkage group II, Yen and Hsieh 1968), and pink color<br />
in the stigma. The gene for stigma color of this variety could not be identified,<br />
although more than 20 crosses with different varieties were observed. It seems<br />
that Pr/pr has some effect on stigma color, but the variation in color tone is<br />
continuous from pink to colorless or to purple. Accordingly, the data for 563<br />
crosses are not presented in this paper.<br />
• The crosses of W106 with different cultivars all produced colored F 1 plants, and<br />
when the F 2 segregated for apiculus color, all colored-apiculus segregants had<br />
colored stigmas (1:0) in most crosses. When crossed with varieties having I-3,<br />
there were a few segregants with colorless stigmas (+ – type). W106 is then<br />
considered to have C A P Ps-2 Ps-3 i-3. In addition, it may have Ps-1, because<br />
its cross with varieties having C A p produced a few F 2 segregants of – + type.<br />
Accordingly, the occurrence of many + + and a few + – F 2 plants is assumed to<br />
represent a 63:1 or 61:3 ratio (Table 2). Several other wild strains (e.g., W120,<br />
W134, and W145) also showed the same segregation patterns, suggesting that<br />
their genotypes for stigma coloration genes are similar to that presumed for<br />
W106.<br />
Crosses of Gaisen-mochi (532) with other varieties and linkages<br />
Variety 532, having colorless apiculus and purple stigma (– + type), is thought to have<br />
Ps-1 (called Ps by Takahashi [1958]), which causes stigma pigmentation without P as<br />
long as C and A are present. This variety, having C A p Ps-1, was crossed with several<br />
other varieties. The F 2 patterns observed were complex (Table 4) but could be analyzed<br />
by using the linkage of relevant genes with Ph for phenol reaction (532 had Ph ).<br />
P and Ph are linked with a mean recombination value of 27.2+5.6% (Table 5). Also,<br />
Ps-2 and Ph are linked with a mean recombination value of 21.1 ± 4.0% (Table 6).<br />
Accordingly, P and Ps-2 are linked (9.0 ± 5.4%).<br />
The F 2 data obtained in 545/532 and 219/532 suggest that gene Ps-1 carried by 532<br />
is also linked with Ph, as most segregants of – + type showed positive phenol reaction<br />
(Table 4). In both F 2 populations, + + plants were fewer than + – plants, suggesting that<br />
an inhibitor for Ps-1 ( I-Ps-1 ) is present in 545 and 219. However, – + segregants are<br />
more numerous (19% in 545/532) than expected when Ps-1 is completely suppressed<br />
by I-Ps-1 (about 6%). To account for the observed patterns, an assumption is needed<br />
Analysis of genes for stigma coloration in rice 103<br />
Table 4. F 2 segregation patterns for apiculus (Ap) and stigma (St) color and<br />
phenol reaction (Ph) observed in crosses with Gaisen-mochi (532).<br />
F 1<br />
a Plants (no.) with given segregation pattern<br />
Cross<br />
Ap St F 2 Ap:<br />
St:<br />
Ph:<br />
+<br />
+<br />
+<br />
+<br />
+<br />
+<br />
+<br />
+<br />
+<br />
+<br />
+<br />
+<br />
Plants<br />
(no.)<br />
545/532 P<br />
219/532 P<br />
108/532 P<br />
414/532 P<br />
(pooled)<br />
521/532 P<br />
647/532 P<br />
(R)<br />
(R)<br />
P<br />
P<br />
P<br />
P<br />
26<br />
67<br />
102<br />
67<br />
169<br />
20<br />
47<br />
6<br />
5<br />
14<br />
31<br />
64<br />
77<br />
0<br />
3<br />
3<br />
9<br />
11<br />
31<br />
33<br />
7<br />
3<br />
45<br />
55<br />
12<br />
8<br />
20<br />
16<br />
24<br />
1<br />
2<br />
0<br />
3<br />
48<br />
0<br />
123<br />
94<br />
217<br />
53<br />
41<br />
22<br />
4<br />
9<br />
14<br />
243<br />
243<br />
237<br />
172<br />
409<br />
128<br />
174<br />
a P = purple to dark red, (R) = faint pink<br />
Table 5. Linkage relations estimated between loci P and Ph. a<br />
Cross<br />
(P 1 /P 2 )<br />
Parental<br />
genotype<br />
F 2 phenotype b<br />
Ap:<br />
Ph:<br />
+<br />
+<br />
+<br />
+<br />
Plants<br />
(no.)<br />
Recombination<br />
value (%)<br />
Distortion<br />
considered<br />
for<br />
001/703<br />
108/703<br />
414/703<br />
(pooled)<br />
545/532<br />
P 1 : cAP-Ph<br />
P 2 : Cap-ph<br />
P 1 : CaP-ph<br />
P 2 : CAp-Ph<br />
Obs.<br />
Exp.<br />
Obs.<br />
Exp.<br />
149<br />
150.5<br />
90<br />
97.0<br />
25<br />
23.6<br />
37<br />
39.7<br />
266<br />
274.0<br />
93<br />
85.3<br />
126<br />
117.9<br />
23<br />
21.0<br />
566<br />
c 2 = 0.9,<br />
243<br />
c 2 = 1.4,<br />
22.5 ± 4.5<br />
P > 0.75<br />
35.8 ± 9.9<br />
P > 0.5<br />
219/532<br />
647/532<br />
P 1 : CAP-ph Obs. 144 38 59 243 24.5 ± 6.0<br />
P 2 : CAp-Ph Exp. 136.4 36.5 66.5 c 2 = 2.0, P > 0.5<br />
P 1 : CaP-ph Obs. 58 34 65 174 26.4 ± 15.7<br />
P 2 : CAp-Ph Exp. 67.5 30.4 63.0 c 2 = 3.0, P > 0.25<br />
a Mean = 27.2 ± 5.6%, weighted according to the number of plants observed. Standard deviation for mean =<br />
standing for the variation among estimates. Ap = apiculus color, Ph = phenol reactlon, "-" shows linkage. b Obs.<br />
= observed, Exp. = expected.<br />
2<br />
3.6<br />
17<br />
13.1<br />
C:c<br />
Ph:ph<br />
that I-Ps-1 requires P for its function. The F 2 data for 219/532 are also explained by the<br />
same assumption favorably.<br />
In 545/532, – – segregants would be mostly the aa plants: in 219/532, in which no<br />
aa plant occurs, – – segregants were few (Table 4). This suggests the linkages of p with<br />
Ps-1 as expected from the linkage of Ph with P and Ps-1. Thus, the parental genotypes<br />
are assumed to be: 532= C A p—Ph—Ps-1—ps-2 ps-3 i-3 i-Ps-1 ("—"shows linkage),<br />
545 = C a P—ph—ps-1—ps-2 ps-3 I-3 I-Ps-1, and 219 = C A P—ph—ps-1—ps-<br />
104 H. I. Oka<br />
–<br />
– –<br />
–<br />
–<br />
– –<br />
–<br />
– –<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
Table 6. Linkage relations estimated between loci Ps-2 and Ph. a<br />
F 2 phenotype<br />
Cross<br />
P 1 /P 2<br />
Parental genotype<br />
St:<br />
Ph:<br />
+<br />
+<br />
+<br />
+<br />
Plants<br />
(no.)<br />
Recombination<br />
value (%)<br />
219/160<br />
219/435<br />
(pooled)<br />
647/108<br />
647/143<br />
(pooled)<br />
545/001<br />
545/124<br />
545/414<br />
(pooled)<br />
219/108<br />
219/124<br />
219/414<br />
(pooled)<br />
647/435<br />
P 1 : ps-2—ph Ps-3 l-3<br />
P 2 : Ps-2—Ph Ps-3 i-3<br />
P 1 : ps-2—ph Ps-3 i-3<br />
P 2 : Ps-2—Ph Ps-3 I-3<br />
P 1 : ps-2—ph<br />
P 2 : Ps-2—Ph<br />
P 1 : ps-2—ph<br />
P 2 : Ps-2—Ph<br />
P 1 : ps-2—ph Ps-3 i-3<br />
P 2 : Ps-2—Ph Ps-3 I-3<br />
Obs.<br />
Exp.<br />
Obs.<br />
Exp.<br />
Obs.<br />
Exp.<br />
Obs.<br />
Exp.<br />
Obs.<br />
Exp.<br />
279<br />
285.9<br />
113<br />
116.5<br />
100<br />
96.9<br />
241<br />
237.4<br />
38<br />
40.6<br />
31<br />
40.2<br />
20<br />
21.3<br />
14<br />
11.9<br />
55<br />
45.4<br />
8<br />
7.3<br />
36<br />
29.1<br />
15<br />
13.2<br />
9<br />
11.9<br />
34<br />
45.4<br />
4<br />
3.6<br />
43<br />
33.8<br />
25<br />
21.9<br />
22<br />
24.4<br />
47<br />
48.8<br />
9<br />
7.5<br />
389<br />
c 2 = 6.4,<br />
P > 0.05<br />
173<br />
c 2 = 0.9,<br />
P > 0.75<br />
145<br />
c 2 = 1.4,<br />
P > 0.5<br />
377<br />
c 2 = 5.0,<br />
P > 0.1<br />
59<br />
c 2 = 0.58<br />
P > 0.9<br />
21 .0 ± 3.8 b<br />
20.7 ± 4.3<br />
18.0 ± 3.6<br />
27.8 ± 2.8<br />
18.0 ± 7.3<br />
a Mean = 21.1 ± 4.0%, weighted according to the number of plants observed. Standard deviation for mean =<br />
observed, Exp. = expected. b Segregation distortion for Ph:ph was considered in computation (cf. Oka 1989c).<br />
standing for the variation among estimates. St = stigma color, Ph = phenol reaction, "–"shows linkage. Obs. =<br />
2 Ps-3 I-3 I-Ps-1. The inhibitors are assumed to be independent of coloration genes so<br />
as to better explain the data.<br />
In 545/532, since both parents have ps-3 (recessive), this gene and its inhibitor can<br />
be neglected in analyzing the segregation pattern. Letting the recombination value<br />
between P and ps-1 (repulsion) be p , the frequencies of genotypes for four color classes<br />
are expected to be as in Table 7.<br />
The maximum likelihood estimate of p is 36.1 ± 14.6%, and the expected values<br />
show no significant deviation from observed ones. Letting the recombination value<br />
between Ps-1 and Ph (coupling) be q , the expected frequencies are as shown in Table<br />
8, in which the genes controlling color phenotype are the same as in Table 7. In<br />
estimating q , the P—ps-1 recombination ( p ) is assumed to be 0.30 to be close to the<br />
mean for different crosses (Table 9).<br />
The q value for Ps-1 and Ph recombination was 14.1 ± 5.7%. Similarly, the<br />
recombination value between P and Ph was 35.8 + 9.9% (Table 5). Thus, the observed<br />
segregation pattern could be explained by analyzing linkage relations.<br />
In 219/532, the computation procedures were more intricate, since I-3 for Ps-3 was<br />
involved in addition to I-Ps-1, and distorted segregation for Ph/ph had to be considered.<br />
It must be determined whether I-Ps-1 is independent of I-3 or whether they are the same<br />
Analysis of genes for stigma coloration in rice 105<br />
–<br />
–<br />
–<br />
–<br />
Table 7. Frequencies of genotypes for 4 color classes in 545/532.<br />
Phenotype Genes Frequency<br />
(Ap) (St) concerned expected<br />
Plants (no.)<br />
Observed Expected<br />
+<br />
+<br />
–<br />
–<br />
+ A P-Ps-1 i-Ps-1<br />
A P-ps-1<br />
A P- Ps- 1 l-Ps-1<br />
+ A p-PS-1<br />
– A p-ps-1 and aa<br />
Total<br />
3<br />
4<br />
3<br />
4<br />
3<br />
4<br />
3<br />
4<br />
3<br />
4<br />
1<br />
4<br />
1<br />
4<br />
3<br />
4<br />
1<br />
4<br />
1<br />
4<br />
1<br />
4<br />
1<br />
4<br />
p 2<br />
(2 + p 2 )<br />
(1 – p 2 )<br />
(2 + p 2 )<br />
(1 – p 2 )<br />
+ 1<br />
4<br />
= 0.0938 + 0.0496 p 2<br />
= 0.4687 – 0.0496 p 2<br />
= 0.1875 – 0.1875 p 2<br />
= 0.25 + 0.1875 p 2<br />
1.0 ( p = 0.361)<br />
32<br />
95<br />
66.7<br />
c 2 = 6.9,<br />
P > 0.05<br />
46<br />
70<br />
243<br />
24.3<br />
112.4<br />
39.6<br />
Table 8. Expected frequencies in 545/532.<br />
Phenotype Expected frequency a Plants (no.)<br />
(Ap) (St) (Ph) ( p = 0.30) Observed Expected<br />
Remarks<br />
+<br />
+<br />
+<br />
+<br />
–<br />
–<br />
–<br />
–<br />
+<br />
+<br />
–<br />
–<br />
+<br />
+<br />
–<br />
–<br />
+<br />
–<br />
+<br />
–<br />
+<br />
–<br />
+<br />
–<br />
0.0980 (3-2 q + q 2 ) • 1/3<br />
0.0980 (2 q + q 2 ) • 1/3<br />
(0.2939 (3–2 q + q 2 ) • 1/3)<br />
(0.1706 (2 q-q 2 )<br />
(0.2939 (2 q–q 2 ) • 1/3<br />
(0.1706 (1–2 q+q 2 )<br />
0.1706 (3–2 q+q 2 ) • 1/3<br />
0.1706 (2 q–q 2 ) • 1/3<br />
0.0169 (1–2 q+q 2 ) + 0.0625<br />
0.0169 (2 q–q 2 ) + 0.1875<br />
26<br />
6<br />
64<br />
31<br />
45<br />
1<br />
22<br />
243<br />
48<br />
2.1<br />
71.9<br />
41.0<br />
3.6<br />
46.6<br />
18.2<br />
Pooled in<br />
computing<br />
c 2 value<br />
Pooled in<br />
computing<br />
c 2 value<br />
Total 1.0 ( q = 0.141) c2 = 7.3, P > 0.1 (df = 5)<br />
a 1/3 is a multiplier necessary for estimating the second recombination value by using the estimate of first<br />
recombination value. To forget this is a pitfall in computing 2 linkage relations consecutively.<br />
gene (or closely linked). The expected numbers for eight phenotypic classes gave a<br />
good fit to the observed numbers when I-Ps-1 and I-3 were assumed to be independent,<br />
but deviated significantly from the observed numbers when they were assumed to be<br />
closely linked, as shown in Table 10.<br />
106 H. I. Oka<br />
21.7<br />
37.9<br />
– { }<br />
}<br />
}<br />
{ }<br />
{ }<br />
Table 9. Recombination values obtained between Ps-1 and 3 other loci. a<br />
Cross Recombination Plants Genes controlling F 2 phenotypes<br />
value (%) (no.) (showing dominant genes only)<br />
545/532<br />
219/532<br />
647/532<br />
521 /532<br />
Mean<br />
219/532<br />
545/532<br />
Mean<br />
108/532<br />
414/532<br />
(pooled)<br />
243<br />
243<br />
172<br />
Ps-1—P (repulsion) Apiculus color : stigma color<br />
36.1 ± 14.6 243 A P—Ps-1 I-PS-1<br />
26.4 ± 6.2 243 P—Ps-1 I-PS-1 Ps-3 I-3<br />
31.3 ± 14.5 174 A P—PS-1 Ps-3<br />
31.4 ± 21.9 128 C A P—Ps-1<br />
31.3 ± 3.9 (standard deviation for variation among estimates)<br />
Ps-1—Ph (coupling) Apiculus/stigma color : phenol reaction<br />
14.9 ± 6.1 P—Ph—Ps-1 I-Ps-1 Ps-3 I-3<br />
14.1 ± 5.7 A P—Ph—Ps-1 I-Ps-1<br />
14.5 ± 4.2 (standard error from two standard deviations)<br />
Ps-1—Ps-2 (repulsion) Apiculus color : stigma color<br />
23.9 ± 6.8 C P–Ps-2–Ps-1<br />
(distortion for Ph:ph considered)<br />
(distortion for C:c considered)<br />
P–Ps-2 (repulsion)<br />
9.0 ± 5.4 172 C P—Ps-2—Ps-1<br />
(distortion for C:c considered)<br />
a "–" shows linkage.<br />
Table 10. Phenotypic classes of 219/532.<br />
Apiculus<br />
Stigma<br />
Phenol<br />
Observed no.<br />
+<br />
+<br />
+<br />
67<br />
+<br />
+<br />
–<br />
5<br />
+<br />
–<br />
+<br />
77<br />
+<br />
–<br />
–<br />
33<br />
–<br />
+<br />
+<br />
55<br />
–<br />
+<br />
–<br />
–<br />
–<br />
+<br />
–<br />
–<br />
–<br />
2 0 4<br />
(pooled)<br />
Total<br />
(no.)<br />
243<br />
Expected no., when I-Ps-1 and I-3 were assumed:<br />
Independent 53.2 8.0 90.5 26.8 58.2 3.2 1.1 2.0 c 2 = 8.3, P > 0.1<br />
Closely linked 35.0 7.0 104.9 27.1 56.5 4.3 1.4 1.8 c 2 = 41.3, P < 0.01<br />
This comparison shows that the two inhibitors are independent. The Ps-1–Ph<br />
recombination value was estimated to be 14.9 ± 6.1%. The P-Ps-a1n d P–Ph values<br />
were also estimated in this cross as shown in Tables 5 and 9. The good fit of expected<br />
to observed numbers may serve as a verification of the assumption of relevant genes.<br />
Similarly, the segregation patterns observed in 108/532, 414/532, 521/532, and<br />
647/532 were analyzed. In these crosses, inhibitor I-Ps-1 does not seem to be involved<br />
(it is recessive in both parents). The data from 108/532 and 414/532 were pooled, since<br />
they were homogeneous. In these two crosses, stigma color is controlled by Ps-1 and<br />
Ps-2, which are linked, Ps-3 being recessive in both parents. The Ps-1–Ps-2<br />
recombination value is estimated to be 23.9 ± 6.8%. The P–Ps-3 recombination is<br />
estimated to be 9.0 ± 5.4%. All these genes belong to linkage group II; their sequence<br />
is mapped in Figure 1.<br />
Analysis of genes for stigma coloration in rice 107<br />
1. Mapping of 5 genes in linkage group II. a Yen and Hsieh (1968). b Takahashi (1964, p. 224). c Standard<br />
deviation standing for variation among several estimates derived from different crosses.<br />
Discussion<br />
This study has shown that there are three genes for stigma coloration: Ps-1, Ps-2, and<br />
Ps-3. This is in agreement with the description in Kinoshita (1984, p. 30), which is<br />
based on a survey of literature by T. Kinoshita. Ps-1, a special gene detected in Gaisenmochi,<br />
confers stigma color even when P is recessive if C and A are dominant, as<br />
reported by Takahashi (1958). Ps-2 and Ps-3 require the dominant combination of C,<br />
A, and P for expressing stigma color. Ps-2 seems to be relatively frequent in indica<br />
varieties (86%; Table 2, group A to 619), while Ps-3 is common in japonicas (50%;<br />
group C to T65), although many colored varieties of indicas and japonicas (groups B<br />
and C) have both genes.<br />
This study has also shown that Ps-1 and Ps-2 belong to linkage group II and are<br />
linked with Ph for phenol reaction. In Kinoshita (1984, p. 30) and Committee on Gene<br />
Symbolization, Nomenclature, and Linkage Groups (1987, p. 15), Ps-2 and Ps-3 are<br />
considered to belong to linkage group II, referring to Hsieh (1961) and Yen and Hsieh<br />
(1968). But Hsieh gives no such information on Ps-3, mentioning, on the contrary, that<br />
“presumably, Ps 2 [corresponding to Ps-3 ] is not linked with Ps 1 , [corresponding to Ps-<br />
2 ] nor with the phenol reaction gene” (Hsieh 1961, p. 128); he reported the linkage of<br />
Ps 1 (= Ps-2 ) with A (corresponding to P ) and Ph. In this study, also, no linkage was<br />
found between Ps-3 and other genes, and the location of Ps-3 remains unknown.<br />
Ps-1 in Gaisen-mochi is thought to belong to linkage group V (Kinoshita 1984, p.<br />
30), based mainly on the linkage of Ps (= Ps-1 ) with I-Bf (inhibitor for brown furrows<br />
on glume, Nagao and Takahashi 1963; Takahashi 1964, p. 225). But the recombination<br />
value of 42.1±3.3% is too high to demonstrate the linkage decisively. Referring to this<br />
linkage, Shastry et al (1975) reported that Ps (= Ps-1 ) is also linked with sd ( sd-1 in<br />
IR20, for semidwarfism), but sd-1 is known to belong to linkage group III (Committee<br />
on Gene Symbolization, Nomenclature and Linkage Groups 1987, p. 17). The present<br />
study has clearly shown that Ps-1 is linked with Ph and several other loci belonging to<br />
linkage group II.<br />
108 H. I. Oka<br />
In this study, inhibitors for stigma color were detected for Ps-1 and Ps-3, but not for<br />
Ps-2. The inhibitors appeared to be independent of each other and of other coloration<br />
genes, although their loci remain unknown. The inhibitor for Ps-3, I-Ps-3 (or I-3 ),<br />
seemed to have a different locus in a few varieties, giving a 1:15 ratio for stigma color<br />
in crosses with varieties having I-3 at the ordinary locus. The dual locations of I-3 may<br />
be regarded as corresponding to 1 ps 2a and 1 ps 2b assumed by Hsieh (1961, p. 129).<br />
Furthermore, Hsieh (1960; 1961, p. 87) has considered that there is a set of complementary<br />
inhibitors, one of which is carried by T65. In the present study, no critical evidence<br />
was obtained for this, but this assumption was adopted for T65 and a few other varieties<br />
to account for F 2 ratios that were otherwise not accountable. It seems certain that there<br />
are two inhibitors, I-Ps-3 and I-Ps-1, but the assumption of inhibitors beyond these two<br />
is still provisional. In this kind of genic analysis, we must elucidate all observed<br />
segregation patterns by assuming a minimum number of genes. The web of inhibitors<br />
for Ps-3 is left for more elaborate analysis in the future. It is also an unanswered<br />
question why the genes for stigma color are more complicated than those for apiculus<br />
color.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-87135037407979737442011-09-05T17:27:00.000-07:002017-07-29T08:15:11.574-07:00Meat Products8.1 Introduction<br />
Meat is an important international commodity, consisting of fresh (chilled and frozen) meats and a<br />
wide variety of fermented, dry-cured and smoked, as well as cooked products. Shipping whole lamb<br />
carcasses and parts occurs. Beef and pork may also be shipped as half-carcasses or converted into<br />
primal cuts, retail cuts, boneless meat and trimmings. Raw meat is an important source of human<br />
enteric diseases caused by salmonellae, thermophilic Campylobacter spp., toxigenic E. coli O157:H7<br />
and other enterohemorrhagic E. coli (EHEC) strains and Yersinia enterocolitica. In general, foodborne<br />
disease from these pathogens is due to under cooking or under processing (e.g., improperly<br />
fermented meats). The pathogens also may be transferred from the raw meat to ready-to-eat foods.<br />
Outgrowth of surviving spores of Clostridium perfringens during slow chilling or improper holding<br />
of cooked meats is also a problem in foodservice and home settings.<br />
Fresh chilled meat is highly perishable and will spoil under the best of conditions unless frozen.<br />
Meat is preserved by adding salt and other ingredients and processing (e.g., fermenting, drying, cooking,<br />
<a name='more'></a><br />
canning) in many regions of the world. The conditions of processing and holding can lead to<br />
other risks of foodborne illness that are discussed under each product category.<br />
Raw meat is often purchased as an ingredient in chilled or frozen form. While microbiological<br />
testing can be performed on the meat, this is an ineffective approach to controlling quality. A preferred<br />
approach is for the buyer and supplier to agree on a purchase specification that includes the<br />
maximum number of days from slaughter (e.g., 3–10 days), microbial data on process hygiene, and<br />
the conditions of chilling, storage and distribution (e.g., £5°C). By controlling time and temperature,<br />
microbial safety and quality may be better assured for the intended purpose. While there are no standardized<br />
procedures for establishing such specifications, they must take into account practical considerations<br />
such as the time required to convert carcasses into the desired cuts of chilled meat and<br />
shipping, including allowance for nonworking days (e.g., weekends and holidays). The temperature<br />
of the meat may vary with the method of chilling (e.g., air chill, CO2 snow) and the size of the portions<br />
of meat but, typically internal temperatures of £5°C are common when received by customers.<br />
An exception may be large beef rounds that are chilled for £24 h (at minimum £7°C) before<br />
shipping.<br />
Another alternative is to purchase frozen raw meat from suppliers that have procedures that control<br />
the freezing rate. The method of packing, palletizing and freezing can influence whether microbial<br />
growth and spoilage occurs before the meat is frozen in the center of the pack. Manufacturers of<br />
certain cooked products prefer mixing chilled and frozen meat to achieve desired temperatures and<br />
conditions during processing. Chilled and frozen products may also be mixed during production of<br />
products such as salami to keep the fat cold and thus prevent smear when filled into the casing.<br />
Chapter 8<br />
Meat Products<br />
76 8 Meat Products<br />
Additional information on the microbiology of meat products is available (ICMSF 2005).<br />
The Codex Alimentarius Commission’s Code of Hygienic Practice for Meat (Codex Alimentarius<br />
2005) provides guidance for managing microbiological risks associated with meat products.<br />
8.2 Primary Production<br />
Conditions for raising livestock differ significantly throughout the world and range from small familyowned<br />
farms having one or more animals to large specialized livestock operations. As farm sizes<br />
increase and become more specialized, financial investment and concern for animal disease increases.<br />
Larger farms must implement more stringent controls to achieve faster growth rates at lower cost with<br />
greater yields of higher quality meat and other products. With fewer but larger farms, there is an<br />
opportunity to establish national on-farm control programs to improve the conditions necessary to<br />
reduce pathogens of concern to human health as well as livestock. For example, regulations that<br />
prevent feeding raw, uncooked garbage to pigs successfully reduced the prevalence of Trichinella<br />
spiralis in pigs and, thereby, reduced the risk of trichinosis among humans in the USA. Likewise,<br />
programs adopted in certain countries to improve control of Salmonella in livestock at the farm level,<br />
for example EU Regulation 2160/2003/EC on control of Salmonella or other specified foodborne<br />
zoonotic agents.<br />
8.3 Raw Meat Products, Excluding Comminuted Meats<br />
This section covers fresh chilled or frozen meat product other than comminuted meats that are typically<br />
intended to be cooked.<br />
8.3.1 Significant Organisms<br />
8.3.1.1 Hazards and Controls<br />
Significant hazards for fresh meat are salmonellae and campylobacters. In beef, E. coli O157:H7 and<br />
other EHEC strains are also a concern, especially in products that may not receive sufficient heat<br />
to render the product safe. Fresh pork is a primary source for T. spiralis and pathogenic strains of<br />
Y. enterocolitica. The microbiological content of packaged fresh meat reflects the conditions of the<br />
incoming livestock, slaughtering, chilling, cutting, deboning, etc. Control consists of on-farm good<br />
animal husbandry practices, contamination prevention during slaughter and microbial contamination<br />
reduction by surface treatment of carcasses before chilling. Some surface treatments (e.g., steam, hot<br />
water, acid sprays and dips) are not permitted in certain countries.<br />
The Codex Alimentarius Commission’s Code of Hygienic Practice for Meat (Codex Alimentarius<br />
2005) provides guidance for managing microbiological risks associated with raw meat.<br />
8.3.1.2 Spoilage and Controls<br />
Four factors influence the microbial spoilage of raw meat at refrigeration temperatures, (1) the numbers<br />
and types of psychrotrophic bacteria, (2) the inherent pH of the meat, (3) the storage temperature<br />
and (4) the type of packaging, including modified atmosphere or vacuum packaging. These factors<br />
should be controlled. Effective implementation of GHP is the primary factor affecting the number and<br />
type of psychrotrophic bacteria on raw meat. Equipment should be designed for ease of maintenance<br />
and cleanability, and the equipment and processing environment must be cleaned and disinfected at<br />
8.3 Raw Meat Products, Excluding Comminuted Meats 77<br />
intervals that can maintain low levels of the psychrotrophic spoilage bacteria. Rooms used for cutting,<br />
trimming or deboning chilled carcasses should be maintained at chill temperatures.<br />
The inherent pH of muscle tissue (e.g., pH 5.4–6.5) cannot be altered but should be understood since<br />
it is an important factor influencing shelf life of raw, refrigerated meats. Storage temperature, however,<br />
can be controlled and storage below 4°C can have a profound beneficial impact on keeping quality. Shelf<br />
life is maximized at temperatures approaching the freezing point of meat (about −1.5°C).<br />
The type of packaging can influence the rate of growth and the microorganisms that ultimately<br />
cause spoilage. For example, raw meat has a longer shelf life when vacuum packaged or packaged<br />
with a gas atmosphere containing carbon dioxide compared with packaging in an oxygen permeable<br />
film. Trace amounts of oxygen can influence the rate of spoilage in vacuum packaged meats. Frozen<br />
meat typically does not undergo microbial spoilage.<br />
The above information also applies to offal and other by-products (livers, hearts, kidneys, head<br />
meat, etc.). Slaughtering operations must provide removal and chilling of these internal organs and<br />
meats in a timely manner to prevent incipient spoilage.<br />
8.3.2 Microbial Data<br />
Table 8.1 summarizes useful testing for fresh chilled and frozen meat products, excluding comminuted<br />
meats, for microbiological safety and quality.<br />
8.3.2.1 Critical Ingredients<br />
Fresh meats available in international commerce, by definition, should not contain added ingredients.<br />
Some retail products include added spices or flavorings to marinate the product during refrigerated<br />
distribution, storage and display. These ingredients are not likely to influence shelf life unless they<br />
introduce psychrotrophic bacteria capable of growing on the product under the conditions of packaging.<br />
Certain ingredients, such as vinegar and salt, could reduce the spoilage rate, if present in sufficiently<br />
high concentration.<br />
8.3.2.2 In-Process<br />
The most common sampling times for control of slaughter process hygiene are before or after the<br />
carcasses are chilled. Prechill samples can reflect the level of slaughter process hygiene related to<br />
meat safety (e.g., the numbers of E. coli or Enterobacteriaceae which indicate fecal contamination).<br />
Postchill samples reflect all the previous effort to minimize contamination during the slaughtering<br />
and chilling. Samples typically consist of swabs, sponges or tissue samples from specified locations<br />
on the carcass. Subsequent tissue samples can be collected after the carcasses are cut into portions<br />
for further processing or retail packages. Typical levels encountered in operations that apply multiple<br />
hurdles during slaughter are an aerobic colony count of <103 CFU/cm2 carcass surface or <104 CFU/g<br />
of tissue from cut meat when plates are incubated at 35°C. These counts can vary considerably<br />
depending on the temperature of incubation and the processing methods used in the region. Because<br />
of this, regional or internal company standards will vary and specific recommendations are not possible<br />
for this category of products.<br />
8.3.2.3 Processing Environment<br />
Swab or sponge samples should be collected before the start of operation to verify the effectiveness of<br />
cleaning and disinfecting the meat-contact surfaces and equipment used for cutting, trimming, deboning<br />
and other steps in converting carcasses to packaged fresh meat. Analysis for aerobic colony counts is<br />
78 8 Meat Products<br />
Table 8.1 Testing of fresh chilled and frozen meat products, excluding comminuted meats, for microbiological safety<br />
and quality<br />
Relative importance Useful testing<br />
Critical<br />
ingredients<br />
Low Fresh meats generally do not contain added ingredients<br />
In-process Medium Swab, sponge or tissue samples from carcasses before or after entering the chiller,<br />
or tissue samples from cut portions can be useful to assess hygiene process control<br />
and conditions that affect microbial levels of subsequent product (ISO 17604).<br />
See text for typical levels encountered<br />
Processing<br />
environment<br />
Medium Sample equipment surfaces before start-up to verify efficacy of cleaning and<br />
disinfecting. See text for typical levels encountered<br />
Shelf life Low Routine shelf life testing of refrigerated raw meat is not recommended. Shelf life testing<br />
may be useful to validate code dates of new retail products or when new packaging<br />
systems are implemented<br />
End product Medium Test for indicators or utility organisms for on-going process control and trend analysis<br />
of freshly packaged product using internally developed guidelines (see text). Levels<br />
developed for processing do not apply during distribution or at retail (see text)<br />
Product Microorganism<br />
Analytical<br />
methoda Case<br />
Sampling plan &<br />
limits/gb,c<br />
n c m M<br />
Raw, noncomminuted<br />
meat<br />
E. coli ISO 16649-2 4 5 3 10 102<br />
Medium Routine lot acceptance sampling is not recommended for salmonellae on raw meat<br />
products. In countries or regions that have established performance criteria for<br />
salmonellae, use the required sampling plan and tests. Test in regions where ground<br />
beef is a continuing source of E. coli O157:H7 illness<br />
Product Microorganism<br />
Analytical<br />
methoda Case<br />
Sampling plan &<br />
limits/25gb,c<br />
n c m M<br />
Beef trimmings used<br />
in ground beef<br />
E. coli O157:H7 ISO 16654 14 30d 0 0 –<br />
a Alternative methods may be used when validated against ISO methods<br />
b Refer to Appendix A for performance of these sampling plans<br />
c Swab or sponge samples could also be considered<br />
d Individual 25 g analytical units (see Sect. 7.5.2 for compositing)<br />
commonly used, but other tests (e.g., ATP-bioluminescence), coliforms, Enterobacteriaceae, occasionally<br />
staphylococci may provide useful information. A typical level encountered on thoroughly cleaned,<br />
disinfected stainless steel is an aerobic colony count of <500 CFU/cm2. Higher numbers may be<br />
encountered on other surfaces (e.g., nonmetal conveyor belts). Regulatory standards may be established<br />
in some regions.<br />
8.3.2.4 Shelf Life<br />
Shelf life testing may be performed on refrigerated meats, should the company deem this useful, but<br />
testing frozen raw meat is not necessary. Shelf life testing may be useful to validate code dates of new<br />
retail products or when new packaging systems are installed. The term “code date” may include “use<br />
by,” “sell by” and “best-before” dates, depending on the region. Verification of the code date can be<br />
based simply on sensory evaluation. Microbiological analysis for specific spoilage microorganisms<br />
8.3 Raw Meat Products, Excluding Comminuted Meats 79<br />
may be helpful for certain products. Another method is to conduct in-store surveys to verify sensory<br />
acceptability relative to the code dates.<br />
8.3.2.5 End Product<br />
Many companies and governments have established criteria for indicators of quality or process<br />
hygiene (e.g., aerobic colony count, Enterobacteriaceae, generic E. coli). The criteria may be<br />
intended for one or more steps in the food chain from slaughter through retail display. Such tests<br />
reflect the conditions of slaughter, chilling, and the time and temperature of storage. These values<br />
are poor indicators of the prevalence or concentration of enteric pathogens in fresh meats. Also, since<br />
psychrotrophic microorganisms increase during storage, distribution and retail display, samples collected<br />
at these stages cannot be used to estimate the hygienic conditions during processing and<br />
packaging. Samples yielding unacceptable results in distribution and retail display should lead to<br />
investigative sampling to determine why they occurred, so that appropriate corrective actions can be<br />
implemented. For example, if high levels of E. coli are encountered at retail, this may be caused by<br />
poor hygienic conditions during manufacture or storage at elevated temperatures (e.g., >7–8°C) that<br />
permit growth. Typical levels encountered in operations that apply multiple hurdles during slaughter<br />
are an aerobic colony count (incubated at 35°C) of <104 CFU/g and generic E. coli of <10 CFU/g.<br />
These counts can vary considerably depending on the temperature of incubation and the processing<br />
methods used or allowed in the region. Because of this, regional or internal company standards will<br />
vary and specific recommendations are not possible for this category of products.<br />
Indicator tests of frozen products reflect the microbial population at time of freezing and any<br />
decrease that may have occurred during distribution and retail display.<br />
There are considerable differences in prevalence rates for salmonellae on fresh meat in different<br />
regions and countries. While routine lot acceptance sampling is not recommended for salmonellae on<br />
fresh meat products, unique situations can occur where information on the presence/prevalence of<br />
salmonellae can provide useful information, such as for outbreak investigations and new supplier<br />
qualification.<br />
Of increasing interest is the effort to improve food safety through the application of criteria<br />
(e.g., performance objectives) for foodborne pathogens (e.g., salmonellae) at specific steps in the food<br />
chain. The growing support for this approach led the Codex Alimentarius Commission to provide<br />
guidance to governments for verification of process control of meat hygiene by microbiological testing<br />
(Codex Alimentarius 2005). While specific microbiological criteria are not provided, the guidance<br />
states that “Establishment of microbiological testing requirements, including performance<br />
objectives<br />
or performance criteria should be the responsibility of competent authorities, in consultationwith<br />
relevant interested parties, and may consist of guidelines or regulatory standards.” Furthermore,<br />
“The competent authority should verify compliance with microbiological testing requirements where<br />
they are specified in regulation e.g., microbiological statistical process control requirements, standards<br />
for Salmonella spp.”<br />
Trend analysis is an important component, because the data can be used to measure changes in<br />
prevalence rates as industry implements procedures to meet the established requirements. Some<br />
countries or regions (e.g., USA, EU) have initiated long-term continuous improvement programs to<br />
reduce the prevalence of salmonellae on raw beef and pork products (USDA 1996, 2008; EU 2003,<br />
2005). Ideally, such programs are coupled with guidance that provides science-based, best practices<br />
from farm through slaughter and chilling, and relate to a public health goal. It is uncertain whether<br />
the approaches (control at the farm, control at the slaughtering plant or a combination of the two)<br />
applied by different countries will lead to different degrees of pathogen control and consumer protection.<br />
For example, adoption of performance objectives at the plant level for raw meat and poultry has<br />
yet to result in reduction of human salmonellosis in the USA that was expected when the pathogen<br />
reduction regulation (USDA 1996) was finalized (Cole and Tompkin 2005, CDC 2009).<br />
80 8 Meat Products<br />
Lot acceptance sampling of beef trimmings is being used by industry in the USA as a control<br />
measure in a comprehensive management system to reduce the risk of E. coli O157:H7 in ground<br />
beef. For countries or regions where E. coli O157:H7 or other EHEC are a pathogen of concern in<br />
ground beef, guidance is available for establishing an appropriate sampling plan (ICMSF 2002, Cole<br />
and Tompkin 2005, Butler et al. 2006). Epidemiologic data in the USA suggests this practice has<br />
contributed to the reduction in disease from E. coli O157:H7 in the USA (Cole and Tompkin 2005).<br />
8.4 Raw Comminuted Meats<br />
8.4.1 Significant Organisms<br />
8.4.1.1 Hazards and Controls<br />
A wide variety of raw comminuted meat products are produced containing beef, pork, lamb, veal and<br />
other meats. The products may contain extenders (e.g., rice, wheat flour, soy protein), spices, herbs<br />
and flavoring agents, and are available in many different shapes, sizes and packaging. The hazards of<br />
significance in raw comminuted meat products are salmonellae, campylobacters, and when beef and<br />
other ruminant species are added, E. coli O157:H7 and other EHEC strains. In certain regions, pork<br />
products may contain pathogenic strains of Y. enterocolitica or T. spiralis. Both pathogens can be<br />
inactivated by cooking.<br />
8.4.1.2 Spoilage and Controls<br />
See Sect. 8.3.1.2.<br />
8.4.2 Microbial Data<br />
Table 8.2 summarizes useful testing for raw comminuted meats. Refer to the text for important details<br />
related to specific recommendations.<br />
8.4.2.1 Critical Ingredients<br />
There are no critical nonmeat ingredients. The primary source of microbial hazards is the raw meat.<br />
Since beef trimmings are the primary source of E. coli O157:H7, the sampling plan in Table 8.1 is<br />
recommended for trimmings to be used for manufacturing ground beef in regions where illness is a<br />
concern. Other sampling plans may be proposed. For example, the USDA-FSIS (USDA 2010) refers<br />
to “robust” sampling using n = 60, where each sample is a 1 × 3 × 0.125 in. (2.5 × 7.6 × 0.32 cm) surface<br />
sample (approximately 340 g). Analysis of trimmings can be used to select suppliers. Working with<br />
approved suppliers can lead to improved microbial control of the end products.<br />
8.4.2.2 In-Process<br />
Routine in-process samples are not normally collected. Samples of meat at various stages of processing<br />
can be used to establish a baseline and understand changes in the microbial population during<br />
processing.<br />
8.4 Raw Comminuted Meats 81<br />
8.4.2.3 Processing Environment<br />
Samples from equipment surfaces before start-up should be used to verify the efficacy of cleaning<br />
and disinfecting procedures. Typical aerobic colony counts on thoroughly cleaned, disinfected stainless<br />
steel are <500 CFU/cm2. Higher numbers may be encountered on other surfaces (e.g., nonmetal<br />
conveyor belts).<br />
8.4.2.4 Shelf Life<br />
Shelf life testing of refrigerated raw comminuted meat may be performed if the company finds this<br />
useful, but testing of frozen products is not recommended. Shelf life testing may be useful to validate<br />
code dates of new retail products or when new packaging systems are installed. Shelf life tests can<br />
be performed to periodically verify the code dates applied to retail products.<br />
8.4.2.5 End Product<br />
Testing for indicators can be useful for on-going process control and trend analysis of freshly packaged<br />
product. Typical levels encountered in operations that apply multiple hurdles during slaughter<br />
are an aerobic colony count (incubated at 35°C) of <105 CFU/g and generic E. coli of <102 CFU/g.<br />
Table 8.2 Testing of raw comminuted meats for microbiological safety and quality<br />
Relative importance Useful testing<br />
Critical<br />
ingredients<br />
Low to<br />
high<br />
Pretesting beef trimmings for E. coli O157:H7 may be useful when confidence in<br />
supplier control programs is low (see text)<br />
In-process Low Routine in-process samples are not normally collected. Samples of meat at various<br />
stages of processing can be used to establish a baseline and understand changes in<br />
the microbial population during processing<br />
Processing<br />
environment<br />
Low Sample equipment surfaces before start-up to verify efficacy of cleaning and<br />
disinfecting (see text for typical levels encountered)<br />
Shelf life Low Routine shelf life testing of refrigerated raw meat is not recommended. Shelf life<br />
testing may be useful to validate code dates of new retail products or when new<br />
packaging systems are installed<br />
End product Medium Test for indicators or utility organisms for on-going process control and trend analysis<br />
of freshly packaged product using internally developed guidelines (see text). Levels<br />
developed for processing do not apply during distribution or at retail (see text)<br />
Product Microorganism<br />
Analytical<br />
methoda Case<br />
Sampling plan &<br />
limits/gb<br />
n c m M<br />
Raw, noncomminuted meat E. coli ISO 16649-2 4 5 3 10 102<br />
Medium Routine testing is not recommended for salmonellae on raw comminuted meat<br />
products (see text). In regions where ground beef is a continuing source of E. coli<br />
O157:H7 illness, the following criteria are recommended<br />
Product Microorganism<br />
Analytical<br />
methoda Case<br />
Sampling plan &<br />
limits/25 gb<br />
n c m M<br />
Ground beef E. coli O157:H7 ISO 16654 14 30c 0 0 –<br />
aAlternative methods may be used when validated against ISO methods<br />
bRefer to Appendix A for performance of these sampling plans<br />
cIndividual 25 g analytical units (see Sect. 7.5.2 for compositing)<br />
82 8 Meat Products<br />
These counts can vary considerably depending on the temperature of incubation and the processing<br />
methods used or allowed in the region. Because of this, regional or internal company standards will<br />
vary and specific recommendations are not possible for this category of products.<br />
Indicator tests (e.g., aerobic colony count, E. coli) of comminuted meats during distribution and<br />
retail display cannot be used to assess the hygienic conditions during time of manufacture. If high<br />
levels of E. coli are encountered at retail, investigational samples are necessary to determine the<br />
reason such as poor hygienic conditions during manufacture and/or storage at elevated temperatures<br />
(e.g., >7–8°C) that permit growth. Indicator tests of frozen products reflect the microbial population<br />
at the time of freezing and any decrease that may have occurred during distribution and retail<br />
display.<br />
There are considerable differences in prevalence rates for salmonellae in raw comminuted meats<br />
in different regions and countries. A microbiological risk assessment has not been conducted to estimate<br />
the risk of salmonellosis as different sampling plans are applied. While routine lot acceptance<br />
sampling is not recommended for salmonellae on raw comminuted meats, unique situations (e.g.,<br />
outbreak investigations, new supplier certification) can occur where data on the prevalence of salmonellae<br />
can provide useful information.<br />
The information in Sect. 8.3.2.5 is generally applicable to raw comminuted meats. Due to the public<br />
health risk associated with E. coli O157:H7 in ground beef, sampling for this pathogen may be appropriate<br />
in regions where epidemiological data indicate this can be beneficial. It is important to recognize that<br />
the recommended sampling plan cannot ensure that E. coli O157:H7 will be absent from the entire lot,<br />
particularly with the expected low prevalence. The purpose of the sampling plan is to detect and remove<br />
lots of ground beef that have a higher than normal prevalence or concentration of E. coli O157:H7 and<br />
that will more likely result in illness. Normally, case 13 would apply since ground beef is usually cooked<br />
before eating; however, case 14 may be appropriate for regions where E. coli O157:H7 or other EHEC<br />
are a recognized hazard and undercooking and/or cross-contamination to ready-to-eat foods is likely to<br />
occur in homes and food service establishments (ICMSF 2002).<br />
8.5 Raw Cured Shelf-Stable Meats<br />
8.5.1 Significant Organisms<br />
8.5.1.1 Hazards and Controls<br />
Two groups of shelf-stable meat products are discussed in this section: (1) traditional raw dry cured<br />
hams and (2) dry fermented sausages. The hazards to consider in raw cured shelf-stable meats are<br />
salmonellae, EHEC, Y. enterocolitica, Staphylococcus aureus, Clostridium botulinum and T. spiralis.<br />
The pathogens of concern depend upon the type of meat (e.g., beef, pork) and the method of manufacture<br />
(e.g., dry curing, fermenting, mild heating). While L. monocytogenes has been detected in raw<br />
cured hams and raw fermented sausages, the product characteristics (e.g., low aW) prevent multiplication.<br />
A risk assessment and a risk categorization placed these products in the low category of risk as<br />
sources of foodborne listeriosis (FDA-FSIS 2003, FAO/WHO 2004). For dry cured hams, the methods<br />
of control are based on traditional practices that have evolved over hundreds of years. Initially, the<br />
meat (e.g., pork) is externally coated with salt, which may contain nitrate, nitrite and spices, and held<br />
at low temperatures for times sufficient to allow the salt to penetrate throughout the meat. Subsequent<br />
drying and aging at higher temperatures for relatively long periods of time (e.g., months) allows additional<br />
growth of microorganisms typical for the products (e.g., lactic acid-producing bacteria)<br />
and<br />
elimination of enteric pathogens.<br />
For dry fermented sausages, use of a commercial starter culture or glucono-delta-lactone (GDL) and<br />
processing conditions (e.g., amount of added salt, temperature of fermentation) that favor growth of the<br />
8.5 Raw Cured Shelf-Stable Meats 83<br />
culture, limits growth of S. aureus by acidulation process (e.g., pH £ 5.3) at a defined period of time and<br />
temperature. Another somewhat less reliable method to control S. aureus is to hold the sausages at lower<br />
temperatures until the moisture content is reduced and, more importantly, enable the indigenous lactic<br />
population to multiply. This reduces the likelihood that S. aureus will multiply when the temperature is<br />
subsequently increased for further processing. Other procedures can be applied.<br />
Survival of Salmonella, E. coli O157:H7 and Y. enterocolitica in improperly manufactured<br />
fermented<br />
sausage has resulted in illness. These enteric pathogens can be controlled in fermented<br />
sausages by applying processes that have been validated to kill the pathogen at levels expected in the<br />
raw meat blends and then applying HACCP systems to verify that the required conditions of manufacture<br />
are met. Some countries (e.g., Canada, USA) have requirements for validating control of<br />
EHEC in fermented meats because the product has been implicated in EHEC infections. These processes<br />
may include a mild heating step that may cause the product to lose the raw meat texture traditionally<br />
associated with the product. In regions where T. spiralis occurs in raw pork, procedures can<br />
be applied to inactivate the parasite. One option is to use pork that has been frozen and held for a<br />
prescribed time. Another option is to apply processing conditions specified in guidelines or regulations<br />
to inactivate the parasite.<br />
8.5.1.2 Spoilage and Controls<br />
By definition these products are shelf-stable and generally do not undergo microbial spoilage during<br />
storage and distribution. The method of packaging may be a factor for certain products. Exposure to<br />
high humidity can lead to mold spoilage.<br />
8.5.2 Microbial Data<br />
Table 8.3 summarizes useful testing for raw cured shelf-stable meats. Refer to the text for important<br />
details related to specific recommendations.<br />
8.5.2.1 Critical Ingredients<br />
The manufacturing processes for meat used in raw, cured, shelf-stable meats should be validated for<br />
control of pathogens that occur in the meat. The nonmeat ingredients added to these products are rarely<br />
a source of significant pathogens or spoilage organisms. The quantity of some ingredients (e.g., salt,<br />
Table 8.3 Testing of raw cured shelf-stable meats for microbiological safety and quality<br />
Relative importance Useful testing<br />
Critical ingredients Low These products do not contain nonmeat ingredients of significance for<br />
microbiological safety or quality<br />
In-process Low Routine sampling of intermediate products for microbiological testing is<br />
not recommended. Critical factors such as time, temperature, rate of pH<br />
decline, aW, addition of correct amount of salt and curing agent, must be<br />
monitored for safety<br />
Processing environment Low Routine sampling of equipment and the environment is not recommended<br />
Shelf life Low These products are inherently shelf-stable<br />
End product Low Routine sampling of the end products is not recommended<br />
84 8 Meat Products<br />
sodium nitrite) is, however, critical in certain products. Insufficient amounts of salt can lead to pathogen<br />
survival and growth. An excessive amount of salt during formulation of sausages to be fermented can<br />
slow or prevent the development of the lactic acid bacteria and favor the growth of S. aureus.<br />
8.5.2.2 In-Process<br />
For dry cured hams, routine microbial testing at various stages of processing is not performed. Such<br />
samples, however, can be helpful in the event a problem occurs and microbiological data are needed.<br />
For dry fermented meats, monitoring time, temperature and rate of acid production (decreasing pH)<br />
is very important. Routine sampling for pathogens is not recommended since the risk associated with<br />
these pathogens is controllable through GHP and the HACCP system. Validated processing conditions<br />
should be used for pathogen control.<br />
8.5.2.3 Processing Environment<br />
Sampling the processing environment is generally not recommended for these traditional products.<br />
Many of the facilities have a natural flora that has evolved over time and may be beneficial to the<br />
process.<br />
8.5.2.4 Shelf Life<br />
These traditional products typically have extended code dates reflecting their stability at ambient<br />
temperatures. Shelf life tests are not recommended.<br />
8.5.2.5 End Product<br />
Routine microbiological sampling of these products is not recommended for either safety or quality.<br />
Emphasis should be placed on application of GHP, validated processes and monitoring CCPs within<br />
the HACCP plan for control of microbiological safety and quality.<br />
8.6 Dried Meat Products<br />
8.6.1 Significant Organisms<br />
8.6.1.1 Hazards and Controls<br />
Three general groups of dried meats are produced. The first includes cooked dried meats that are used<br />
as ingredients in dried soups and other foods. Cooking and preventing recontamination are important<br />
control factors for this class of product.<br />
The second group includes strips of meat or thin sausages that are cooked before drying. These<br />
products are sold as snacks or basic ingredients in certain dishes. They may be produced in large<br />
quantities in continuous systems or in smaller quantities in batch processing equipment. This product<br />
is also produced throughout the world in very small operations, primarily for personal use or local<br />
distribution, but this practice can involve fairly wide consumer exposure.<br />
The third group includes a variety of traditional products that are unique to certain regions and<br />
have not been cooked (e.g., biltong, charqui).<br />
8.6 Dried Meat Products 85<br />
The microbial hazards to consider in dried meat products are Salmonella, EHEC and S. aureus.<br />
L. monocytogenes is not a hazard of concern because the low aw prevents its multiplication in these<br />
products. A risk assessment and a risk categorization have placed these products in the low risk category<br />
for foodborne listeriosis (FDA-FSIS 2003, FAO/WHO 2004). Cooking is a CCP for most of<br />
these products. Uncontrolled salting and drying conditions can permit growth and enterotoxin production<br />
by S. aureus. Additional control consists of applying GHP to prevent contamination with<br />
enteric pathogens. Extended storage at ambient temperature with high salt (i.e., low aW) can reduce<br />
enteric pathogen levels.<br />
8.6.1.2 Spoilage and Controls<br />
Dried meat products are microbiologically stable, although exposure to conditions of high humidity<br />
can lead to spoilage by molds.<br />
8.6.2 Microbial Data<br />
Table 8.4 summarizes useful testing for dried meat products. Refer to the text for important details<br />
related to specific recommendations.<br />
8.6.2.1 Critical Ingredients<br />
Manufacturing processes for dried meat products should be validated for control of pathogens that<br />
occur in the meat. There are no critical nonmeat ingredients.<br />
8.6.2.2 In-Process<br />
Routine in-process samples should not be necessary, but can be helpful in the event of a problem and<br />
the source(s) of microbial contamination must be determined.<br />
8.6.2.3 Processing Environment<br />
Routine environmental samples for salmonellae should not be necessary in a controlled operation<br />
operating under GHP with adequate separation between raw meat processing areas and where cooked<br />
meat products are exposed. Environmental sampling, however, can be helpful in the event a problem<br />
does occur and the source(s) of contamination must be determined.<br />
Swab or sponge samples should be collected to verify the effectiveness of cleaning and disinfecting<br />
equipment before the start of operation. Analysis for aerobic colony count is typical, but other<br />
tests (e.g., ATP-bioluminescence) may provide useful information.<br />
Typical aerobic colony count levels encountered on thoroughly cleaned, disinfected stainless steel<br />
are <500 CFU/cm2. Higher numbers may be encountered on other surfaces (e.g., nonmetal conveyor<br />
belts).<br />
8.6.2.4 Shelf Life<br />
The final moisture content (i.e., <10%) and low aW levels make these products microbiologically<br />
stable. The strips and thin sausage-shaped products may be higher in moisture for better palatability<br />
86 8 Meat Products<br />
as snacks. If aW levels are sufficiently high (e.g., >0.70), these products must be packaged in a low<br />
oxygen atmosphere to prevent the growth of mold during extended storage or be formulated with a<br />
mold inhibitor. Defective packaging seals can contribute to mold spoilage of these products during<br />
storage, distribution and retail display.<br />
8.6.2.5 End Product<br />
These products are of low risk to public health and routine sampling is not recommended. If there is<br />
reason to question whether GHP and HACCP are being applied in a manner to control enteric pathogens,<br />
then sampling for an indicator (e.g., E. coli) or salmonellae is recommended. Recommended<br />
testing for these products is summarized in Table 8.4.<br />
8.7 Cooked Meat Products<br />
8.7.1 Significant Organisms<br />
8.7.1.1 Hazards and Controls<br />
These products are perishable and must be refrigerated or frozen for storage or distribution. Cured<br />
and uncured products are included in this section. The microbial hazards to consider in cooked perishable<br />
meats include Salmonella, EHEC, L. monocytogenes and C. perfringens. Control of Salmonella,<br />
EHEC and L. monocytogenes requires validated cooking procedures and recontamination prevention;<br />
Table 8.4 Testing of dried meat products for microbiological safety and quality<br />
Relative importance Useful testing<br />
Critical<br />
ingredients<br />
Low These products do not contain nonmeat ingredients of significance for<br />
microbiological safety or quality<br />
In-process Low Routine in-process samples are not recommended<br />
Processing<br />
environment<br />
Medium Sample equipment surfaces before start-up to verify efficacy of cleaning and<br />
disinfecting. (See text for typical levels encountered)<br />
Shelf life Low These products are inherently shelf-stable when properly dried and protected from<br />
high humidity. The higher aw of snack products may require verification of<br />
stability<br />
End product Low Routine sampling is not recommended. If application of GHP and HACCP is in<br />
question, sampling for an indicator (e.g., E. coli) or Salmonella should be<br />
considered<br />
Product Microorganism<br />
Analytical<br />
methoda Case<br />
Sampling plan & limit/gb<br />
n c m M<br />
Low Dried meat E. coli ISO 16649-2 5 5 2 10 102<br />
Sampling plan &<br />
limit/25 gb<br />
n c m M<br />
Low Dried meat Salmonella ISO 6579 11 10c 0 0 –<br />
aAlternative methods may be used when validated against ISO methods<br />
bRefer to Appendix A for performance of these sampling plans<br />
cIndividual 25 g analytical units (see Sect. 7.5.2 for compositing)<br />
8.7 Cooked Meat Products 87<br />
with cooking managed through the HACCP plan and recontamination managed through effective<br />
application of GHP with verification through environmental monitoring (Codex Alimentarius 2009a).<br />
Some products are given a final in-package listericidal treatment. Additives may also be used in<br />
some countries to inactivate or restrict the growth of L. monocytogenes. Salmonella and EHEC<br />
can survive on cooked refrigerated meat products but cannot multiply if the products are maintained<br />
at <7°C.<br />
Control of C. perfringens requires chilling cooked meat products at a rate that prevents unacceptable<br />
multiplication of surviving spores and storing at <12°C. Historically, a vast majority of C. perfringens<br />
outbreaks have occurred due to improper chilling or holding in foodservice operations (Brett 1998, Bates<br />
and Bodnaruk 2003, Golden et al. 2009). Cured meat products contain sodium nitrite and generally have<br />
a higher salt content than uncured products such as roast beef. As a result, cured meat or poultry products<br />
rarely are implicated as a source of C. perfringens illness.<br />
The microbial hazards on frozen cooked uncured meat products are similar to those for refrigerated<br />
products except the vegetative cells of C. perfringens are quite sensitive to freezing and decline<br />
during frozen storage. Also, L. monocytogenes cannot multiply while the product remains frozen.<br />
The Codex Alimentarius Commission’s Code of Hygienic Practice for Meat (Codex Alimentarius<br />
2005) provides guidance for managing microbiological risks associated with cooked meat products.<br />
8.7.1.2 Spoilage and Controls<br />
The rate of spoilage is influenced by many factors, such as storage temperature, initial number and<br />
type of microorganisms when packaged, type of packaging and chemical composition. Spoilage by<br />
psychrotrophic clostridia and lactic acid bacteria has occurred in commercial products having<br />
extended refrigerated shelf life (e.g., ³35 days). Control consists of determining the source of the<br />
spoilage bacteria, such as the raw meat or harborage sites in the raw processing environment, and<br />
implementing appropriate controls.<br />
8.7.2 Microbial Data<br />
Table 8.5 summarizes useful testing for cooked meat products. Refer to the text for important details<br />
related to specific recommendations.<br />
8.7.2.1 Critical Ingredients<br />
The nonmeat ingredients in cooked meat products are rarely a source of significant pathogens or<br />
spoilage flora. Some ingredients (e.g., salt, sodium nitrite, sodium lactate, sodium diacetate) can<br />
reduce the rate of spoilage and growth of L. monocytogenes and clostridia.<br />
8.7.2.2 In-Process<br />
The relative value of testing in-process samples versus processing environment samples for routine<br />
assessment of Listeria spp. control is debatable. The decision to rely more on in-process over environmental<br />
samples may be influenced by regulatory policies and the complexity of the equipment and<br />
steps in the process after cooking. Routine in-process sampling is not performed by some manufacturers,<br />
while others rely on in-process samples for assessing control. In-process samples can be helpful<br />
when investigating a problem and are recommended. Routine sampling for salmonellae,<br />
S. aureus or C. perfringens is not recommended, since the risk associated with these pathogens is<br />
controllable through GHP and HACCP.<br />
88 8 Meat Products<br />
8.7.2.3 Processing Environment<br />
The relative importance of verifying control of the processing environment depends on the risk to<br />
consumers if the product becomes contaminated between cooking and final packaging. The products<br />
of highest concern are those that support the growth of L. monocytogenes during normal storage and<br />
distribution and do not have a listericidal treatment after final packaging, especially if the intended<br />
consumers are highly susceptible to listeriosis. The frequency and extent of sampling also should<br />
reflect consumer risk.<br />
Monitoring programs that include sampling equipment and other surfaces that come into contact<br />
with exposed cooked products before final packaging are recommended. Sponge samples from large<br />
Table 8.5 Testing of cooked meat products for microbiological safety and quality<br />
Relative importance Useful testing<br />
Critical<br />
ingredients<br />
Low These products do not contain nonmeat ingredients of significance for<br />
microbiological safety or quality<br />
In-process High Monitoring the cooking parameters is essential<br />
Medium For products that support L. monocytogenes growth, postcook samples can assess<br />
control of Listeria spp. Typical levels encountered postcook:<br />
• Listeria spp. – absent<br />
Processing<br />
environment<br />
High For products that support L. monocytogenes growth, during production sample<br />
product contact surfaces where cooked products are exposed to potential<br />
contamination before packaging. Sponge or swab samples from floors, drains and<br />
other nonproduct contact surfaces can provide an early indication of the level of<br />
control and a potential risk of contamination for equipment and product. Typical<br />
levels encountered:<br />
• Listeria spp. – absent<br />
Medium Sample equipment surfaces before start-up to verify efficacy of cleaning and<br />
disinfecting. (See text for typical levels encountered)<br />
Shelf life Medium Shelf life testing may be useful for refrigerated products with extended code dates<br />
(see text). Shelf life testing of frozen cooked meats is not necessary<br />
End product Medium Test for indicators for ongoing process control and trend analysis (see text)<br />
Product Microorganism<br />
Analytical<br />
methoda Case<br />
Sampling plan &<br />
limits/gb<br />
n c m M<br />
Cooked meat Aerobic colony<br />
count<br />
ISO 4833 2 5 2 104 105<br />
E. coli ISO 16649-2 5 5 2 10 102<br />
S. aureus ISO 6888-1 8 5 1 102 103<br />
Cooked uncured meat<br />
(e.g., roast beef)<br />
C. perfringens ISO 7937 8 5 1 102 103<br />
Medium Routine sampling for pathogens is not recommended. If application of GHP or<br />
HACCP is in question, the following sampling plans are recommended (see text)<br />
Product Microorganism<br />
Analytical<br />
methoda Case<br />
Sampling plan &<br />
limits/25 gb<br />
n c m M<br />
Cooked meat Salmonella ISO 6579 11 10c 0 0 –<br />
Cooked meat: No growth L. monocytogenes ISO 11290-2 NAd 5 0 102 –<br />
Cooked meat: Supports<br />
growth<br />
L. monocytogenes ISO 11290-1 NA 5c 0 0 –<br />
aAlternative methods may be used when validated against ISO methods<br />
bRefer to Appendix A for performance of these sampling plans<br />
cIndividual 25 g analytical units (see Sect. 7.5.2 for compositing)<br />
dNA not applicable; used Codex criterion<br />
8.7 Cooked Meat Products 89<br />
areas of equipment should be collected during production. Samples can also be collected from<br />
nonproduct<br />
contact surfaces as an additional measure of control (Codex Alimentarius 2009a).<br />
Environmental sampling for products given a validated final in-package listericidal treatment is not<br />
recommended. Environmental monitoring for products that do not support growth depends on the<br />
products produced in the facility (e.g., some products support growth and others do not), historical<br />
trends and regulatory requirements.<br />
The principles for control and monitoring of Listeria can also be applied to spoilage microorganisms<br />
such as lactic acid bacteria. Swab or sponge samples can be collected before the start of operation<br />
to verify the effectiveness of cleaning and disinfecting. Analysis for aerobic colony count is a<br />
common analysis, but other tests (e.g., ATP-bioluminescence) may provide useful information.<br />
Typical aerobic colony counts on thoroughly cleaned, disinfected stainless steel are <500 CFU/cm2.<br />
Higher numbers may be encountered on other surfaces (e.g., nonmetal conveyor belts).<br />
8.7.2.4 Shelf Life<br />
Code dating practices can be validated by holding the product at a controlled temperature and performing<br />
sensory evaluation and microbiological analysis at selected intervals, including packages<br />
before, on and after the expected expiration date. Subsequent verification can be performed at a frequency<br />
that reflects confidence in whether the product will consistently meet the stated expiration<br />
date on the package. Shelf life testing of frozen cooked meat products is not necessary.<br />
Validating that growth of L. monocytogenes will not occur within the code date applied on the package<br />
may also be useful (EU Regulation 2073/2005/EC, Chap. 1, Sects. 1.1, 1.2 and 1.3). This regulation<br />
defines the food safety criteria for the validation of RTE products (including meat products) regarding<br />
presence or number of L. monocytogenes in the end product. The manufacturer should be able to demonstrate,<br />
to the satisfaction of the competent authority, that the product will not exceed the limit of<br />
102 CFU/g of L. monocytogenes throughout the shelf life. Therefore, the operator may establish intermediate<br />
limits during the production process that should be low enough to guarantee that the limit of<br />
102 CFU/g is not exceeded at the end of the shelf life and, for RTE products that are able to support the<br />
growth of L. monocytogenes, that absence of the pathogen in 25 g of sample at the end of the manufacturing<br />
process is assured. Guidelines for validation are available (Scott et al. 2005 and Chap. 2).<br />
8.7.2.5 End Product<br />
Recommended end product testing is summarized in Table 8.5. Testing for indicators such as aerobic<br />
colony count and E. coli is useful to evaluate ongoing process control and trend analysis. Aerobic<br />
colony counts typically encountered are <104 CFU/g and E. coli is typically <10 CFU/g. Indicator<br />
tests during distribution and retail display cannot be used to assess the conditions during time of<br />
manufacture. If high levels of E. coli are encountered at retail, investigational samples are necessary<br />
to determine the reason such as poor hygienic conditions during manufacture and/or storage at elevated<br />
temperatures (e.g., >7–8°C) that permit growth.<br />
The Salmonella sampling plan in Table 8.5 assumes that it will not grow under the normal conditions<br />
of distribution and storage and that the product will not receive a further cook step (i.e., case 11).<br />
Use of case 10 or 12 would be appropriate if the product would be subject to further cooking (e.g.,<br />
cooked meat used in a frozen entrée that is to be cooked prior to consumption) or if there is considerable<br />
potential for produce abuse prior to consumption, respectively. The sampling plans for L. monocytogenes<br />
are for ready-to-eat foods produced following the general principles of food hygiene for<br />
control of L. monocytogenes and with an appropriate environmental monitoring program (Codex<br />
Alimentarius 2009b).<br />
If the reliable application of GHP and HACCP is in question, sampling for Salmonella and/or<br />
L. monocytogenes may be appropriate. When evidence indicates a potential for contamination with<br />
90 8 Meat Products<br />
L. monocytogenes (e.g., positive food contact surface results or the effectiveness of corrective actions<br />
has yet to be verified) sampling the food should be considered. The stringency of sampling should<br />
reflect consumer risk (e.g., whether growth can occur in the food, intended consumers). Guidance on<br />
increasing the stringency of sampling by sub-lotting is discussed in Chap. 5.<br />
If the rate of chilling after cooking exceeds the critical limit in the HACCP plan, testing for<br />
C. perfringens may provide useful information to determine the disposition of the lot. The sample<br />
units should be taken from the center of the product or other region that is slowest to chill. Samples<br />
should be submitted to the laboratory as refrigerated, not frozen, samples. The decision to test for C.<br />
perfringens will depend on the available information (e.g., pH, aW, added inhibitors such as sodium<br />
nitrite, lactate or diacetate), the extent of the deviation and options that may be available for product<br />
disposition. A sampling plan is also provided for products in which temperature abuse is suspected<br />
and S. aureus is of concern.<br />
If there is a failure to meet the criteria for L. monocytogenes or Salmonella in Table 8.5, the typical<br />
actions to take include (1) prevent the affected lot from being released for human consumption, (2)<br />
recall the product if it has been released for human consumption, and (3) determine and correct the<br />
root cause of the failure.<br />
8.8 Fully Retorted Shelf-Stable Uncured Meats<br />
8.8.1 Significant Organisms<br />
The hazards and controls are the same as applied for other low-acid canned foods (see Chap. 24).<br />
Spoilage of canned uncured meat products is controllable and should rarely occur. Incipient spoilage<br />
may occur if the product is not retorted in a timely manner. This can occur when equipment breaks<br />
down and the food is held for an extended period of time before retorting.<br />
8.8.2 Microbial Data<br />
There are no critical nonmeat or meat ingredients for these products. Routine in-process, environmental,<br />
and end product testing are not recommended for either safety or quality. Current recommended<br />
procedures for commercial processing based on GHP and HACCP yield products that are<br />
commercially sterile and stable for the expected conditions of storage and distribution.<br />
8.9 Shelf-Stable Cooked Cured Meats<br />
8.9.1 Significant Organisms<br />
8.9.1.1 Hazards and Controls<br />
The hazards of significance in the raw meat ingredients used for these products are salmonellae,<br />
C. botulinum and, in the case of products containing beef, E. coli O157:H7 and other EHEC strains.<br />
The heat process used for shelf-stable canned cured meats destroys vegetative microorganisms, some<br />
spores and sublethally damages other spores. Safety and stability depends upon the combined effect<br />
of thermal destruction or injury of a low indigenous number of spores and inhibition of the survivors<br />
by an adequate amount of added salt and sodium nitrite.<br />
8.10 Snails 91<br />
For shelf-stable liver, blood and bologna-style sausages, important factors to control are initial<br />
spore load, heat treatment, pH, aw, and nitrite. For products like Italian mortadella and German<br />
bruhdauerwurst, stability is achieved by heating to >75°C to inactivate vegetative cells, reducing aw<br />
to <0.95 and heating in a sealed container to prevent recontamination.<br />
Brawns are made shelf-stable by adjusting the pH to 5.0 with acetic acid and protecting the<br />
product from recontamination after heating. Gelder smoked sausage (a traditional Dutch product) is<br />
made shelf-stable by adjusting the pH to 5.4–5.6 with GDL, reducing aw to 0.97, vacuum-packing,<br />
and heating for 1 h to a center temperature of 80°C.<br />
8.9.1.2 Spoilage and Controls<br />
These products are shelf-stable and generally do not undergo microbial spoilage during storage and<br />
distribution. Spoilage might occur due to postprocessing contamination through leaks in the container<br />
(e.g., in the seams of cans or through the clip-seals of plastic casings) or from growth of<br />
Bacillus spp. just under the casing. The extent of growth is determined mainly by product composition<br />
and the oxygen permeability of the casing or container.<br />
8.9.2 Microbial Data<br />
The ingredients added to these products are rarely a source of significant pathogens or spoilage<br />
microorganisms. However, the level of some ingredients, such as salt, sodium nitrite, and acidulants<br />
is critical for safety and spoilage control. Insufficient amounts of these ingredients can permit<br />
growth of surviving spores, including C. botulinum, if present.<br />
Routine in-process and environmental samples are not recommended. Products produced following<br />
recommended guidance and programs based on GHP and HACCP should not experience microbial<br />
spoilage. Routine sampling of these products is not recommended for either quality or safety.<br />
8.10 Snails<br />
8.10.1 Significant Organisms<br />
The hazards to consider include salmonellae, shigellae, EHEC and parasites. The conditions of growing<br />
and harvesting influence the potential presence of enteric pathogens. Snails should be cooked to inactivate<br />
enteric pathogens and parasites. Freezing is another means to inactivate parasites. Recontamination<br />
of the cooked snails should be prevented through GHP. Snails are also sold as a canned shelf-stable food<br />
(see Chap. 24). Freezing or canning prevents microbial spoilage. Time and temperature of storage of<br />
fresh snails and frozen snails after thawing will influence the rate of spoilage.<br />
8.10.2 Microbial Data<br />
There are no critical ingredients. Routine in-process and environmental samples are not normally<br />
collected. Code dating practices for fresh snails can be validated as described for most other raw<br />
foods. Enteric pathogens should be assumed to be present and cooking or canning will eliminate<br />
these pathogens before they are eaten. Routine sampling of fresh and frozen snails for pathogens is<br />
not recommended.<br />
92 8 Meat Products<br />
8.11 Frog Legs<br />
8.11.1 Significant Organisms<br />
Frog legs are typically distributed as a raw frozen product, which may be thawed during retail display.<br />
The hazard of significance is Salmonella. Shigella may be a concern if frogs are raised in insanitary<br />
ponds that may contain human waste. The time between capture and slaughter should be minimized.<br />
Care should be exercised in removal of the legs to avoid cutting the intestinal tract. Processing water<br />
should be chlorinated and equipment and contact surfaces should be cleaned and disinfected.<br />
Guidance for the hygienic processing of frog legs is available from the Codex Alimentarius<br />
Commission (Codex Alimentarius 1983). Freezing prevents microbial spoilage. Time and temperature<br />
of storage after thawing will influence the rate of spoilage.<br />
8.11.2 Microbial Data<br />
There are no critical ingredients. Routine in-process and environmental samples are not normally<br />
collected. See Sect. 8.3.2.3 for guidance assessing cleaning and disinfecting procedures. Microbial<br />
spoilage of frozen frog legs should not occur. The Codex Alimentarius Commission guidance for end<br />
product specifications is very general: “Frog legs should be free from microorganisms in amounts<br />
harmful to man, free from parasites harmful to man and should not contain any substances originating<br />
from microorganisms in amount which may represent a hazard to health” (Codex Alimentarius 1983).<br />
Salmonellae should be assumed to be present on raw frog legs. Routine sampling of frozen frog legs<br />
for salmonellae and other pathogens is not recommended.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-20019549106757299132011-09-05T17:22:00.000-07:002017-07-29T08:23:21.075-07:00Prospective use of Oryza longistaminata for rice breedingMorphological types, fertility, and outcrossing rates were studied in a population<br />
of 10 interspecific backcross progenies ( O. longistaminata/ O. sativa// O. sativa)<br />
left under open pollination conditions. By segregation analysis at eight electrophoretic<br />
loci, single-locus and multilocus estimates of the outcrossing rates were<br />
calculated. In the first generation, 75% of the seeds came from outcrossing; this<br />
rate decreased to 35% in the second generation, following pollen fertility restoration.<br />
Outcrossing rates appeared primarily related to plant sterility and secondarily<br />
to stigma length and exsertion. At the morphological level, an important diversity<br />
of plant types was observed in the first generation, but plants were characterized<br />
by various wild traits. The second generation spontaneously evolved toward a<br />
more cultivated type, and transgressive segregants were observed for different<br />
morphological traits. Allelic segregations at the F 1 level were normal, but the<br />
second generation exhibited highly significant distortions. A loss of alleles coming<br />
from the wild species was observed for 5 of the 8 loci and for all 10 families.<br />
Oryza longistaminata is a wild species of rice that grows widely throughout intertropical<br />
Africa. It covers a large range of ecological sites, from flooded plains to temporary<br />
ponds, and propagates itself by developing vigorous rhizomes (Ghesquiere 1985). This<br />
species is allogamous, with a self-incompatibility system, and shows the extreme<br />
maximum values of stigma and anther length and number of pollen grains within the<br />
Sativa species group (Oka and Morishima 1967).<br />
This species shows significant diversity at the isozyme level (Ghesquiere 1988) and<br />
appears to be among the most distant species from O. sativa within the Sativa group<br />
(Second 1985). O. longistaminata has not intervened during the domestication of O.<br />
sativa, nor in the latter’s diversification on the African continent since its introduction<br />
there, because of the strong reproductive barrier that isolates the former from all other<br />
species. This barrier is due to the action of two complementary lethal genes that cause<br />
abortion of the embryo (Chu and Oka 1970a, Ghesquiere 1988). In spite of this barrier,<br />
hybrid plants may be obtained, either by artificial crossing or, rarely, in seed sets<br />
collected from wild plants along the borders of ricefields.<br />
<br />
<a name='more'></a><br /><br />
The spontaneous hybrids were called Obake plants by Chu and Oka (1970b). They<br />
are characterized by different features shared with artificial hybrids: absence of<br />
rhizomes, high tillering habit, photoperiod insensitivity, low male fertility, and<br />
variable female fertility. When pollen fertility is not limiting, their inbred offspring are<br />
highly depressed. The genetic structure of these plants has been extensively studied by<br />
Ghesquiere (1988), who showed their possible origins from F 1 seeds to alternate<br />
backcrosses of O. sativa and O. longistaminata.<br />
The use of O. longistaminata in rice breeding has been envisaged for the introgression<br />
of some of its allogamous characteristics into O. sativa to produce hybrid varieties<br />
(Taillebois and Guimaraes 1987). O. longistaminata may also be useful for the<br />
introgression of special disease resistance genes. This study considered the wild<br />
species as a source of diversity in general, and as a source of allogamy to favor<br />
spontaneous intermating in a hybrid population with a wide genetic base. This<br />
experiment was conducted to show that it was possible to avoid the classical reduction<br />
of variability of the backcross technique. The morphological diversity of a population<br />
composed of backcross progenies between an Obake plant and 10 varieties of<br />
cultivated rice was described for two generations. Outcrossing rates were estimated<br />
and related to the fertility of the population. Intergenomic recombination was studied<br />
at the isozyme level. The possibility of rapidly restoring a cultivated and fertile type,<br />
and the efficiency of selection were estimated in a third trial.<br />
Materials and methods<br />
The maternal parent of all progenies was an Obake plant obtained from a seed collected<br />
in North Cameroons. Its isozyme pattern was similar to that of a hybrid between O.<br />
longistaminata and an indica variety of O. sativa. This plant was male sterile. It had<br />
been pollinated by 10 upland rice varieties well adapted to West African conditions.<br />
The 10 progenies (300 plants each) were observed in field trials during 2 generations.<br />
To enhance natural intermating, the completely randomized trials were left under open<br />
pollination conditions for two generations. Because the first (G 1 ) generation showed<br />
significant variation in plant fertility, two trials were carried out in the second (G 2 )<br />
generation. In one (the single seed descent [SSD] method), one seed of each G 1 plant<br />
was cultivated; in the other (the bulk method), all the seeds of each family were mixed,<br />
and a sample of 300 plants/family was cultivated. A third trial (G 3 ), composed of 20<br />
self-pollinated progenies of G 2 plants chosen for their fertility, was studied. Each<br />
progeny was composed of 60 plants. To compare the hybrids with their cultivated<br />
parents in the G 1 and G 3 , the parental varieties were studied, but they did not participate<br />
in the pollination. Measurements from single plants were taken to describe morphological<br />
characters including plant height, tillering, flowering date, panicle architecture<br />
(length, number of primary and secondary branches), pigmentation, awn development,<br />
and seed shedding. Fertility was described by pollen fertility and total number of seeds<br />
produced per plant. In the G 2 , the stigma and anther length and the rate of exserted<br />
stigma were measured on plants whose progenies were used for estimation of<br />
outcrossing rates.<br />
82 Causse and Ghesquiere<br />
Electrophoretic analyses, based on the technique described by Second and Trouslot<br />
(1980), were performed on samples of each generation. Seven enzymatic systems were<br />
studied, corresponding to nine polymorphic loci: Amp-1, Cat-1, Est-2, Est-5, Est-9,<br />
Enp-1, Pgd-1, Pgi-1, and Sdh-1. Distinguishing the parental species origin of the alleles<br />
was possible for five loci. Estimates of outcrossing rates in the G 1 were calculated from<br />
the genotypic frequencies observed in the G 2 populations, and estimates of outcrossing<br />
rates in the G 2 were calculated from the studies of open-pollinated progenies of G 2<br />
single-plant arrays. Sixty-four progenies of six plants were analyzed. Two maximum<br />
likelihood methods were used: one gives an estimate for each locus (method derived<br />
from Brown and Allard 1970) and the other, based on the simultaneous analysis of<br />
genotypes at different loci, gives a multilocus estimate (Shaw et al 1981). The maternal<br />
genotypic frequencies were known in both cases, but pollen pool allele frequencies<br />
were estimated on the same samples as outcrossing rates. Heterogeneity of the singlelocus<br />
estimates was tested by chi-square analysis.<br />
Results<br />
The evolution of the population is first presented according to its reproductive behavior<br />
and morphological traits. The extent of intergenomic recombination as shown at the<br />
isozyme level is then presented. Finally, the implications of these results for the use of<br />
O. longistuminata for plant breeding and genetic studies are discussed.<br />
Reproductive behavior of interspecific progenies<br />
The mean pollen fertility was very low in both generations: 18% in the G 1 and 37% in<br />
the G 2 . Nevertheless, there was high variability between plants. In each generation, a<br />
few fertile plants composed the efficient pollen pool. In the G 1 , 5% of the plants had<br />
a pollen fertility above 60%; in the G 2 , 20% of the plants had a fertility slightly higher.<br />
This low rate of pollinators is a source of drift. Nevertheless, the distributions of pollen<br />
fertility among families were not statistically different. Seed production was also very<br />
low: the mean was 20 seeds/plant in the G 1 and 47 in the G 2 . In both trials, 20% of the<br />
plants produced no seed and therefore did not contribute to the next generation.<br />
Although the SSD and bulk methods differed in seed contribution of G 1 plants to the<br />
G 2 , the distribution of fertility of the plants in these two methods exhibited no<br />
difference. The early selection, at low intensity, in the bulk method was inefficient in<br />
increasing the fertility of the progeny.<br />
In the third trial, selection of the most fertile plants in the G 2 was efficient, and strong<br />
correlations were found between the pollen and seed fertility of G 2 selected plants and<br />
the mean fertility of their offspring (0.68 and 0.75, respectively); yet large variability<br />
was found in each progeny. These results show that it is possible to rapidly restore the<br />
fertility of hybrid-derived plants.<br />
The evolution of apparent outcrossing rates in the G 1 and G 2 is presented in Table<br />
1. Significant differences between loci estimates were found in each generation.<br />
Estimation of outcrossing rates is usually applied to natural populations with<br />
homogeneous fertility. Differences between genotypic frequencies at the adult stage<br />
Use of Oryza longistaminata for rice breeding 83<br />
Table 1. Mean outcrossing rates (m) and standard deviation (SD) in G 1 (estimates based on<br />
genotypic frequencies observed in 2 G 2 experiments) and G 2 (estimates based on genotypic<br />
frequencies of 64 single G 2 plant progeny arrays). Estimates for each locus, means over<br />
loci, and multilocus estimates.<br />
Estimate G 1 G 2<br />
from<br />
locus G 2 :SSD (n=371) G 2 :bulk (n=304) Progenies (n=435)<br />
m SD m SD m SD<br />
Est-5<br />
Enp-1<br />
Amp-1<br />
Cat-1<br />
Sdh-1<br />
Pgd-1<br />
Mean over<br />
Mean, multilocus<br />
loci<br />
1.165<br />
1.124<br />
0.886<br />
0.472<br />
0.563<br />
0.891<br />
(0.154)<br />
(0.109)<br />
(0.098)<br />
(0.102)<br />
(0.087)<br />
(0.088)<br />
0.82<br />
0.74<br />
0.564<br />
1.040<br />
0.662<br />
0.184<br />
0.551<br />
0.579<br />
(0.200)<br />
(0.157)<br />
(0.120)<br />
(0.102)<br />
(0.118)<br />
(0.077)<br />
0.63<br />
0.54<br />
0.492<br />
0.519<br />
0.340<br />
0.582<br />
0.240<br />
0.031<br />
(0.122)<br />
(0.159)<br />
(0.121)<br />
(0.082)<br />
(0.141)<br />
(0.097)<br />
0.35<br />
0.35<br />
and pollen frequencies might be a source of deviation in estimates, the existence of<br />
selective factors usually being the cause of variations between estimates provided by<br />
different loci (Kesseli and Jain 1985).<br />
Here the artificial hybrid population was submitted to different selective pressures.<br />
Despite certain aberrant values, the mean estimates look coherent. A comparison of<br />
estimates provided by the two methods confirms this result, because the multilocus<br />
estimate is supposedly less affected by variations between pollination and estimation<br />
of genotypic frequencies (Shaw et al 1981). Actually, at least 75% of the plants<br />
observed in the G 2 came from a detectable outcrossing event in the G 1 . In the G 2 , the<br />
rate was reduced and involved 35% of the plants.<br />
A relationship between inbreeding rate and pollen fertility was found at the level of<br />
the individual: in the G 2 , plants whose pollen fertility was higher than 40% were<br />
preferentially inbred. This relationship might also be extended to the succession of<br />
generations. Lower values of the outcrossing rate estimates provided by the bulk<br />
experiment, compared with the SSD experiment, reflect the importance of inbreeding<br />
in the origin of bulk plants. In the G 3 progenies, outcrossing rates were estimated by<br />
comparing seed production with obligate inbreeding and with open pollination. At this<br />
level, the mean rate was 15%. The outcrossing rates were related to the stigma length<br />
of plants ( r = 0.39) and to their exsertion rates, but were independent of anther length.<br />
This result agrees with studies of floral characteristics influencing outcrossing in O.<br />
sativa (Xu and Shen 1988).<br />
Morphological changes along generations<br />
In the G 1 and G 2 , considerable diversity was found for all the morphological traits<br />
(Table 2). However, G 1 plants were characterized by very high and continuous tillering,<br />
partly due to high general vegetative vigor and to the ability to emit new tillers from<br />
84 Causse and Ghesquiere<br />
Table 2. Means (m) and standard deviations (SD) of one of the 10 parental<br />
varieties (P 6 ) and its progeny, in cross with an Obake plant, in G 1 and G 2 (SSD).<br />
P 6 (n=10) G 1 (n=62) G 2 (SSD)<br />
m SD m SD m SD<br />
Character<br />
Panicle length (cm)<br />
Primary branches (no.)<br />
Secondary branches (no.)<br />
Panicle density a<br />
Earliness (no. of days)<br />
Tillering b<br />
Height (cm)<br />
25.5<br />
15.5<br />
34.0<br />
94.2<br />
24.1<br />
92.5<br />
2.19<br />
1.05<br />
0.93<br />
4.71<br />
0.27<br />
3.62<br />
9.30<br />
7.1 6<br />
26.8<br />
11.4<br />
31.5<br />
102.7<br />
37.1<br />
87.7<br />
2.76<br />
3.71<br />
2.44<br />
10.35<br />
0.67<br />
7.15<br />
18.00<br />
13.16<br />
23.8<br />
9.9<br />
29.8<br />
2.36<br />
110.9<br />
24.7<br />
75.9<br />
4.70<br />
3.74<br />
21.72<br />
1.25<br />
12.39<br />
15.20<br />
15.41<br />
a The ratio of secondary to primary branches. b Number of panicles produced in 4 wk after the<br />
beginning of flowering.<br />
buds located on aerial nodes. This capacity, observed in every family, allowed the<br />
overlapping of flowering periods of plants with different growth durations. Panicles<br />
were usually longer but with fewer branches than those of the cultivated parents,<br />
although the ratio of secondary to primary branches appeared higher in G 1 plants than<br />
in the cultivated parental varieties. O. longistaminata plants and the mother Obake<br />
plant, observed in irrigated conditions, showed long panicles with few secondary<br />
branches. Other wild traits characterized hybrid progenies: they were frequently<br />
pigmented (60% of G 1 plants showed collar, spikelet, or stigma pigmentation) and long<br />
awned, and, where measurement was possible, shedding was high in almost every<br />
plant. Rhizomes never developed; this trait appeared to have been totally eliminated<br />
since the first hybridization.<br />
G 2 plants evolved toward a more cultivated type. A reduction in the rates of<br />
pigmented plants and awned spikelets was observed with lower tillering habit and<br />
reduced perenniality. The genetic load of O. longistaminata, a wild and allogamous<br />
species, was expressed through weakness and many panicle or spikelet abnormalities.<br />
At the same time, an increase of total variance was found for every trait, while the<br />
between-family variances decreased (Table 3). Interspecific hybridization does not<br />
seem to induce high hereditary modification of quantitative traits. Analysis of the<br />
regressions between the cultivated parents of the population and their progenies in the<br />
G 1 and G 2 shows intermediate values for the majority of characters, all significantly<br />
different from zero, except for height (Table 3). Higher coefficients were observed in<br />
regressions between G 2 plants and their inbred progenies, indicating the expected<br />
efficiency of selection within these progenies. Though the choice of G 3 mother plants<br />
had been based only on the fertility of the hybrids, comparison of the G 3 progenies with<br />
four cultivated rice varieties showed the existence of transgressive segregants for all<br />
traits studied, particularly for early developmental characteristics and panicle architecture.<br />
In the G 1 and G 2 , the majority of the plants were arrested, and seed shedding was<br />
important. Segregation for these traits was analyzed in the G 3 progenies in relation to<br />
the parental phenotypes. These two characters showed complex segregation and<br />
Use of Oryza longistaminata for rice breeding 85<br />
Table 3. Comparison of 1) ratios of between-family variance (Vb) to total<br />
variance (Vt) for the 10 parental varieties (Pi), and the corresponding families<br />
in G 1 and G 2 (based on 10 families of 62 individuals), and 2) coefficients of<br />
parent-offspring regressions for different generation associations. a<br />
Character<br />
Panicle length<br />
Primary branches<br />
Secondary branches<br />
Earliness<br />
Tillering<br />
Height<br />
Vb/Vt Regression coefficients<br />
Pi G 1 G 2 Pi/G 1 Pi/G 2 G 2 /G 3<br />
0.611<br />
0.797<br />
0.580<br />
0.762<br />
0.836<br />
0.334<br />
0.157<br />
0.176<br />
0.099<br />
0.271<br />
0.123<br />
0.095<br />
0.052<br />
0.010<br />
0.023<br />
0.064<br />
0.045<br />
0.009<br />
0.732<br />
0.378<br />
0.301<br />
0.337<br />
0.262<br />
0.018<br />
0.533<br />
0.2220<br />
0.344<br />
0.502<br />
0.129<br />
0<br />
0.674<br />
0.765<br />
0.462<br />
0.679<br />
0.117<br />
0.330<br />
a For parent to G 1 (Pi/G 1 ) and G 2 (Pi/G 2 ), the regressions are based on 10 families of 62 individuals;<br />
for G 2 to G 3 (G 2 /G 3 ), the regressions are based on 20 progenies of 30 individuals.<br />
appeared independent of each other. Some fertile and nonshedding offspring were<br />
observed. Segregation of seed shedding is independent of plant fertility. These results<br />
are consistent with those of Morishima (1985), who suggested that the “domestication”<br />
traits had been acquired by the accumulation of genes spread throughout the genome.<br />
Intergenomic recombination<br />
Linkage studies were carried out on 2 G 1 and 10 G 3 progenies. Except for Enp-1 and<br />
Cat-1, which were found tightly linked (with 0.04 and 0.13% recombinants in the 2<br />
progenies studied), all the other markers were independent. This result is consistent<br />
with previous studies of isozyme location (Pham et al 1990). Knowing the parental<br />
genotypes, allelic frequencies were compared with expected ones (Table 4). The<br />
gametic segregation of the Obake plant, tested through G 1 genotypes, appeared<br />
significantly skewed for only one ( Amp-1 ) of the seven loci studied, although others<br />
also showed slight losses of the alleles coming from the wild species; Sdh-1 was the<br />
only exception. In G 2 SSD, significant losses of wild alleles were found for five loci<br />
( Amp-1, Est-9, Enp-1, Pgd-1, Sdh-1 ). The importance of the loss varied between<br />
families and between loci. Differences between bulk and SSD frequencies were a<br />
consequence of differences in the contribution of G 1 plants to the next segregation. In<br />
bulk, losses were of the same or had increased intensity for all families for loci Amp-<br />
1, Enp-1, Pgd-1, and Pgi-1 ; they differed from one family to another for Sdh-1 and Est-<br />
9. Study of open-pollinated progenies of G 2 plants (used mainly to estimate outcrossing<br />
rates) allowed us to follow this evolution. Losses seemed to become stable for loci Pgi-<br />
1 and Enp-1 but still increased for Est-5, Amp-1, and Sdh-1.<br />
Segregation was also studied in inbred G 3 progenies, which presented different<br />
parental genotypes. Among 43 segregation patterns studied, 11 showed significant<br />
distortions, but their intensity and direction varied between loci and progenies. Amp-<br />
1 was the only locus to show high loss of the wild allele in both progenies where it<br />
segregated. Est-5 and Cat-1 were studied in five progenies; they showed loss of the wild<br />
86 Causse and Ghesquiere<br />
Table 4. Evolution of the frequencies of the alleles coming from O. longistaminata<br />
in the backcross population over generations. a<br />
Locus G 1 G 2 -SSD G 2 -bulk G 3 -op<br />
Est-5 0.200** 0.140** 0.090*** 0.095***<br />
Enp-1 0.230 0.163** 0.1 35*** 0.135***<br />
Amp-1 0.200** 0.140** 0.140** 0.125***<br />
Sdh-1 0.270 0.195* 0.230 0.140**<br />
Pgi-1 0.209 0.186* 0.090*** 0.095***<br />
a Level of significance of the test of comparison between observed and expected frequency =<br />
0.25. Estimates are based on the genotypes of 170 plants of the G 1 , 371 and 304 of the G 2<br />
observed in SSD (G 2 -SSD) or bulk (G 2 -bulk), and 435 open-pollinated offspring of G 2 plants (G 3 -<br />
op).<br />
allele in one, an excess in another, and no distortion in three others. Est-2, Pgi-1, and<br />
Sdh-1 studied in four, six, and five progenies, respectively, showed no distortion.<br />
Contrary to expectations from the comparison of allelic frequencies in SSD and bulk,<br />
a systematic analysis of the relationship between isozyme genotype and fertility<br />
showed no significance. Losses of wild alleles could be attributed not only to<br />
differences in fertility between genotypes but also to other sources of deviations, linked<br />
or not to the genetic load of the wild species, such as losses at germination, weakness,<br />
or selective assortment of gametes.<br />
Discussion and conclusions<br />
This experiment showed that it is possible to obtain spontaneous intermating in a wide<br />
experimental population despite a high level of sterility. The mixing of two distant<br />
species under two mating systems led to highly sterile plants; few fertile plants<br />
composed the efficient pollen pool in each generation. Long and well-exserted stigmas,<br />
contributed by the wild species, tended to enhance outcrossing rates. Perenniality,<br />
expressed by continuous tillering, also favored intermating between plants of different<br />
families. Because of this important outcrossing rate in the G 1 , variability remained high<br />
in the G 2 . However, the parallel evolution of pollen fertility and inbreeding showed the<br />
limits of the intermating system. Nevertheless, introgression of the long stigma in a<br />
male sterile variety should lead to increased outcrossing rates necessary for high<br />
production of hybrid seed.<br />
Spontaneous evolution of the population toward a more cultivated type was found<br />
at the morphological and isozyme level, and different origins were proposed for that<br />
evolution. Evidence for genetic exchanges between cultivated rices and O. longistaminata<br />
was suggested at the isozyme level, but because of its reproductive barrier, this<br />
species did not play a major role in the diversification of cultivated rices in Africa.<br />
Nevertheless, a very high morphological diversity could be expected in the offspring<br />
of such crosses. With distant phenotypes and complementary growth habits, introgressive<br />
hybridization may be useful for rice breeding. Though plants in the first generation<br />
were not attractive from a plant breeding point of view, it was possible to restore<br />
Use of Oryza longistaminata for rice breeding 87<br />
fertility and to eliminate unfavorable traits in a few generations. Interesting transgressive<br />
segregants were observed in G 3 progenies. Without major variation in the<br />
heritability of quantitative traits, selection among and within the progenies might be<br />
efficient. Even if introgressive lines derived from this cross are not directly exploitable<br />
as cultivated varieties, they might be an interesting source of new diversity for rice<br />
breeding.<br />
New technologies, such as restriction fragment length polymorphism (RFLP)<br />
marker-assisted selection, may facilitate the elimination of unfavorable wild traits.<br />
Shedding and awning are governed by a few genes spread throughout the genome,<br />
which should be easily located by RFLP studies. The detection and location of<br />
quantitative trait loci involved in other morphological traits that distinguish rice<br />
species might also help greatly in selecting hybrid derivatives. In an interspecific<br />
population showing a strong linkage disequilibrium, selection by target markers should<br />
be easy. An RFLP map of the rice genome is already available (McCouch et al 1988)<br />
and has been transferred to an interspecific backcross population involving O.<br />
longistaminata (Tanksley et al 1991), which will allow testing such a hypothesis.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-82316174205734236162011-09-05T01:18:00.000-07:002017-08-18T04:04:00.174-07:00Traditional highland rices originating from intersubspecific recombination in Madagascar Genetic divergence among traditional rices from Madagascar was investigated on<br />
the basis of 39 morphophysiological traits and 19 isozyme genes. Comparison<br />
with Asian and African rices revealed the existence of new varietal types that do<br />
not fit the existing classification schemes. These types are mainly lowland<br />
cultivars grown in the high plateau region at altitudes ranging from 1,000 to<br />
1,500 m. Based on morphophysiology, they are intermediate between indica and<br />
tropical japonica types for most traits, although they are the tallest. Isozyme data<br />
show a limited global gene diversity and a marked bipolar structure similar to the<br />
classical indica-japonica structure with, however, a peculiar predominance of<br />
allele 2 at locus Amp-1, forming multilocus types that are rare or absent in Asia.<br />
Classical associations between some isozymes and some morphological traits<br />
are almost nonexistent. The introduction of rices from Asia to Madagascar was<br />
thus probably accompanied by a strong founder effect and was followed by<br />
intensive intersubspecific recombination. Adaptation to new ecological niches<br />
took place without pronounced disruption of subspecific complexes of coadapted<br />
genes.<br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgWWxhlK-u6DQlXJziuE_hFaaKTBRXotqj5uqlVzoD4JphOVN-lMaQXs6L2GgRf2uwX5gTJaosv_b2ZKZgF3V4HrI5mSlQQsaW98SVvZPcDhVajaQbN8ZhadyozLhrI4J_yGoc_cMSYyg-A/s1600/20160324_121416.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1200" data-original-width="1600" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgWWxhlK-u6DQlXJziuE_hFaaKTBRXotqj5uqlVzoD4JphOVN-lMaQXs6L2GgRf2uwX5gTJaosv_b2ZKZgF3V4HrI5mSlQQsaW98SVvZPcDhVajaQbN8ZhadyozLhrI4J_yGoc_cMSYyg-A/s320/20160324_121416.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Sandy land</td></tr>
</tbody></table>
<br />
<br />
The indica-japonica differentiation is the main feature of varietal diversity in Asian<br />
cultivated rice (see Oka 1988 for a review). Such a pattern most probably arose from<br />
multiple domestications and the associated founder effects. Post-domestication varietal<br />
migrations were extensive, and the two types are now distributed over most Asian<br />
regions. There remains evidence of ecological specialization, leading indica varieties<br />
to be grown mainly under tropical lowland conditions, and japonica varieties mainly<br />
under temperate conditions and tropical upland conditions. In some environments,<br />
such as tropical highlands, both types are sympatric and are thus exposed to intersubspecific<br />
introgressions. An isozymic survey of Asian traditional varieties (Glaszmann<br />
1987, 1988) suggested that few indica-japonica intermediates exist, and that most<br />
intermediate-like varieties are more probably consequent to the contribution of local<br />
wild rices rather than to intervarietal recombination.<br />
<br />
<a name='more'></a><br />
<br />
The introduction of Asian rice to other continents has enabled the study of various<br />
aspects of rice evolution. Continental Africa has been extensively studied, and the<br />
genetic structure of local cultivated rice is considered the result of new opportunities<br />
for intra- and interspecific introgressions (Bezançon and Second 1984, Ghesquière<br />
1988, Kochko 1987b). Madagascar offers a simpler situation. Because of geographic<br />
isolation due to insularity, ancient rice introductions were limited in number. Historical<br />
and linguistic hints (Boiteau 1977, Dez 1965, Domenichini-Ramiaramanana 1988,<br />
Domenichini-Ramiaramanana and Domenichini 1983) suggest two main origins: from<br />
Indonesia with the Protomalagasy people, and from the Indian subcontinent. Recent<br />
studies showed that Madagascar exhibits a large proportion of varieties showing<br />
particular morphophysiological and biochemical character associations, beside the<br />
typical indica and japonica varieties (Ahmadi et al 1988, Kochko 1988, Rabary et al<br />
1989).<br />
In this paper, we review the peculiarities of the rices from Madagascar. We compare<br />
them with the Asian morphological types, and with the Asian and African isozymic<br />
types. Both sources of information lead to deductions about the evolutionary past of<br />
cultivated rice in Madagascar that are relevant to the interpretation of cultivated rice<br />
diversity in other parts of the world.<br />
Materials and methods<br />
Morphophysiological and isozymic evaluations were carried out on 179 and 182<br />
varieties, respectively, from the Madagascar national collection maintained by the<br />
Centre National de la Recherche Appliquée au Développement Rural (FOFIFA) and<br />
from recent field collections by FOFIFA and the International Board for Plant Genetic<br />
Resources; 145 varieties were common to the 2 samples.<br />
The morphophysiological evaluation was performed at Alaotra Lake, Madagascar,<br />
and involved 24 quantitative and 15 qualitative characters. The procedures followed<br />
were those described by Jacquot and Arnaud (1979). The data were subjected to a factor<br />
analysis of correspondences (FAC) (Benzecri 1973) and “nuées dynamiques” (Diday<br />
1971) after transformation of the quantitative data into qualitative data.<br />
The isozyme study involved 10 enzymes encoded by 19 polymorphic genes as<br />
described by Glaszmann et al (1988), namely catalase (CAT), esterase (EST), aminopeptidase<br />
(AMP), acid phosphatase (ACP), shikimate dehydrogenase (SDH), alcohol<br />
dehydrogenase (ADH), isocitrate dehydrogenase (ICD), phosphogluconate dehydrogenase<br />
(PGD), phosphoglucose isomerase (PGI), and glutamate oxaloacetate<br />
transaminase (GOT).<br />
Results<br />
Both morphophysiological and isozymic evaluations identified varietal types specific<br />
to Madagascar, besides types commonly found in Asia.<br />
68 Ahmadi et al<br />
Morphophysiological variability<br />
The FAC of the morphophysiological variability identified four groups, named G2,<br />
G4, G5, and G6 (Ahmadi et al 1988) for the sake of homology with a previous study<br />
(Jacquot and Arnaud 1979). The distribution of these groups on plane 1, 2 of the FAC<br />
is shown in Figure 1. Axis 1 positively attracts the varieties with high tillering capacity,<br />
thin organs, and short panicles with few branches. Axis 2 positively attracts the<br />
varieties with high stature; intermediate tillering; and big, heavy grains with a positive<br />
phenol reaction. The main traits of the morphological groups defined in Asia (Matsuo<br />
1952, as reported by Angladette 1966) and in Madagascar are presented in Table 1.<br />
The indica type in Asia, with high stature, high tillering capacity, thin organs, and<br />
fine and light grains with a positive phenol reaction, is represented in Madagascar by<br />
group G5, and is associated with lowland culture (with standing water) in regions<br />
having low altitude. In Madagascar, this group could be subdivided into two subgroups,<br />
G5A and G5B;G5A varieties display higher tiller numbers and thinner organs.<br />
The temperate japonica type in Asia, with short stature, short panicles with little<br />
branching, low grain shattering, and bold grains with negative phenol reaction, is<br />
represented in Madagascar by group G2. Since these are modem varieties introduced<br />
from Asia less than a century ago, they are not considered here.<br />
The tropical japonica type (often referred to as javanica) in Asia, with high stature,<br />
few tillers, long panicles with profuse branching, low grain shattering, and heavy, thick<br />
grains with negative phenol reaction, is widely represented in Madagascar by group<br />
G4. This type of rice has been grown under slash-and-bum culture in low-altitude forest<br />
areas on the eastern coast for more than 1,500 yr (Labatut and Raharinarivonirina<br />
1969).<br />
1. Distribution of 4 groups of varieties in plane 1, 2 of the factor analysis of correspondences among 39<br />
morphophysiological characters of rice in Madagascar (after Ahmadi et al 1988).<br />
Traditional highland rices from intersubspecific recombination 69<br />
Table 1. Main features of morphological groups defined in Asia (general description,<br />
Angladette 1966) and in Madagascar (average and range, Ahmadi et al 1988). a<br />
Varietal group<br />
Character Temperate japonica Tropical japonica Indica Atypical<br />
Asia Madagascar Asia Madagascar Asia Madagascar Madagascar<br />
(G2) (G4) (G5A/G5B) (G6)<br />
Length of<br />
under flag<br />
1st leaf<br />
leaf(cm)<br />
1st leaf<br />
under flag<br />
leaf(cm)<br />
Width of 1st leaf<br />
Tillering<br />
Plant height (cm)<br />
Culm diameter<br />
Panicle length<br />
Shattering (%)<br />
Panicle secondary<br />
Grain length<br />
branches<br />
Grain width<br />
(L, mm)<br />
Grain shape (L/W)<br />
(W, mm)<br />
100-grain<br />
Phenol reaction<br />
(mm)<br />
(cm)<br />
weight (g)<br />
Short<br />
Narrow<br />
Intermediate<br />
Short<br />
Intermediate<br />
Short<br />
Little<br />
Few<br />
Short<br />
Wide<br />
Bold<br />
Heavy<br />
Negative<br />
30.9<br />
(24.6–36.2)<br />
10.3<br />
(8.7–12.3)<br />
(9.5–18.1)<br />
14.8<br />
85<br />
4.3<br />
(3.6–4.8)<br />
16.8<br />
10.6<br />
25.5<br />
(74–101)<br />
(15.2–19.5)<br />
(4.7–24.6)<br />
(22.1–30.5)<br />
8.5<br />
(7.9–9.3)<br />
3.5<br />
(2.8–4.1)<br />
2.4<br />
(2.1–2.7)<br />
2.7<br />
(2.0–4.9)<br />
a Figures in parentheses are ranges.<br />
Long 46.3<br />
(39.4–51.9)<br />
Wide<br />
Low<br />
Tall<br />
Thick<br />
Long<br />
Intermediate<br />
Many<br />
Inter-<br />
Wide<br />
mediate<br />
Big<br />
Heavy<br />
Negative<br />
16.0<br />
(10.7–18.1)<br />
(5.5–16.6)<br />
8.7<br />
120<br />
(97–137)<br />
5.3<br />
(3.8–6.5)<br />
(18.8–29.3)<br />
24.4<br />
7.9<br />
(1.7–17.9)<br />
44.0<br />
(28.0–66.4)<br />
(7.1–11.3)<br />
9.4<br />
3.6<br />
2.6<br />
(2.2–4.4)<br />
3.0<br />
(3.1–4.2)<br />
(2.1–3.6)<br />
Long<br />
Narrow<br />
High<br />
Tall<br />
Intermediate<br />
Intermediate<br />
Much<br />
Intermediate<br />
Long<br />
Thin<br />
Fine<br />
Light<br />
Positive<br />
(23.0–40.1)<br />
32.8<br />
10.6<br />
(8.0–13.8)<br />
14.6<br />
10.9–23.2)<br />
111<br />
(73–135)<br />
4.3<br />
(3.3–5.6)<br />
20.7<br />
17.2–23.2)<br />
17.4<br />
(4.5-32.8)<br />
31.5<br />
21.5–39.5)<br />
10.0<br />
(7.8-11.8)<br />
(2.5–3.4)<br />
2.9<br />
3.4<br />
(2.7–4.2)<br />
2.7<br />
(2.2–3.5)<br />
+<br />
(29.8–57.7)<br />
39.1<br />
(9.7–17.3)<br />
12.4<br />
(8.2–19.7)<br />
11.3<br />
(104–145)<br />
122<br />
5.0<br />
(3.3–6.0)<br />
(17.7–26.6)<br />
22.1<br />
12.5<br />
(5.1–32.0)<br />
(23.3–50.4)<br />
35.9<br />
9.6<br />
(7.6–12.1)<br />
3.3<br />
(3.0–3.8)<br />
2.8<br />
(2.0–3.8)<br />
3.0<br />
+/-<br />
(2.7–3.8)<br />
The group specific for Madagascar, namely group G6, encompasses varieties grown<br />
under lowland conditions, having the highest stature, and whose other characters are<br />
intermediate between those of the tropical japonica group (G4) and the indica group<br />
(G5). Group G6 is numerically important in Madagascar and has no equivalent in Asia.<br />
Thus, the morphophysiological variability of rice is particular in Madagascar:<br />
beside varietal groups G2, G4, and G5, which fully correspond to the temperate<br />
japonica, tropical japonica, and indica Asian types, respectively, an additional group,<br />
G6, stands out with atypical character combinations. Its members, particularly those<br />
with the vernacular designations “Rojo” and “Latsika,” are grown mostly at altitudes<br />
higher than 1,000 m and have a high level of cold tolerance (Rasolofo et al 1986).<br />
Isozymic variability<br />
The FAC of the isozymic variability identified two main clusters close to the indica and<br />
japonica groups (groups I and VI of Glaszmann 1987; Fig. 2). Two representatives of<br />
70 Ahmadi et al<br />
–<br />
2. Distribution of<br />
isozyme data at 15<br />
from Madagascar,<br />
varieties from Madagascar in plane 1, 2 of the factor analysis of correspondences of<br />
loci. Delimitations of the 6 varietal groups found in Asia are shown. Among the varieties<br />
129 cluster in the shaded area close to group I, 48 cluster in the shaded area close to group<br />
VI, 2 fall into group V, and 3 are intermediate (from Rabary et al 1989).<br />
group V, otherwise characteristic of the Indian subcontinent, were also found. The<br />
main features of the genetic diversity in the main enzymatic groups in Asia (Glaszmann<br />
1987, 1988), in Africa (Kochko 1987a), and in Madagascar (Rabary et al 1989) are<br />
given in Table 2.<br />
Among the 19 loci surveyed, all polymorphic in Asia and Africa, only 13 exhibit<br />
some variation in Madagascar, and they display 30 alleles vs 46 in Asia and 33 in<br />
Africa. The missing alleles are those rare in Asia. Thus, for most polymorphic loci, the<br />
allele most frequent in Asia further gained importance in Africa and Madagascar. This<br />
tendency is reversed in Madagascar for several loci. The reversal is particularly<br />
pronounced for Amp-1, where allele 2, rare in Asia (13%) and in Africa (10%), has<br />
become frequent (61%).<br />
When the indica and japonica groups are compared with their Asian counterparts,<br />
the loss of alleles is less pronounced than when the overall allele frequencies are<br />
considered, because the alleles specific for the minor Asian groups are no longer taken<br />
into account. However, some of the reversals noted above become stronger.<br />
For the indica group, Madagascar is characterized by a reversal of allele frequencies<br />
at loci Amp-1, Est-2, and Sdh-1 as compared with Africa, and at those loci and Pgi-2<br />
as compared with Asia. The most significant peculiarity is the high frequency of allele<br />
2 at locus Amp-1, which is very rare in Asia and much less frequent in Africa.<br />
Traditional highland rices from intersubspecific recombination 71<br />
Table 2. Genetic diversity of the main enzymatic groups in Asia (Glaszmann 1988), in Africa (including Madagascar, Kochko 1987b),<br />
and in Madagascar (Rabary et al 1989); allele frequencies; and diversity indices (H) (Nei 1975). a<br />
Allele frequency (%)<br />
Locus Allele Whole sample Indica group Japonica group<br />
Asia Africa Mada- Asia Africa<br />
Madagascar Madagascar<br />
Asia Africa<br />
(n= (n= gascar (n=900) (n=359) Indica I I* (n= (n= Japonica J J*<br />
1688) 688) (n=181) (n=129) (n=32) (n=97) 451) 329) (n=47) (n=41) (n=6)<br />
Cat-1<br />
Sdh-1<br />
Pgi-1<br />
Pgi-2<br />
Est-1<br />
Est-2<br />
2<br />
1<br />
3<br />
H<br />
1<br />
3<br />
2<br />
4<br />
H<br />
2<br />
1<br />
H<br />
2<br />
1<br />
3<br />
4<br />
H<br />
0<br />
1<br />
H<br />
0<br />
2<br />
1<br />
H<br />
83<br />
17<br />
100 99<br />
1<br />
100<br />
96<br />
4<br />
tr<br />
0.08<br />
99<br />
1<br />
81<br />
19<br />
0.31<br />
100<br />
0<br />
98<br />
2<br />
0.04<br />
100<br />
71<br />
29<br />
0.41<br />
37<br />
62<br />
1<br />
1<br />
0.48<br />
49<br />
51<br />
0.50<br />
60<br />
29<br />
8<br />
3<br />
0.55<br />
9<br />
91<br />
0.17<br />
36<br />
42<br />
22<br />
0.65<br />
97<br />
3<br />
0.05<br />
13<br />
87<br />
97<br />
3<br />
0.05<br />
50<br />
50<br />
0.50<br />
100<br />
0<br />
97<br />
3<br />
0.06<br />
88<br />
12<br />
0.21<br />
19<br />
31<br />
50<br />
0.62<br />
65<br />
35<br />
93<br />
7<br />
100<br />
tr 100<br />
0<br />
100<br />
0<br />
100<br />
0<br />
100<br />
0<br />
100<br />
0<br />
100<br />
0<br />
0.28<br />
30<br />
67<br />
3<br />
0.46<br />
39<br />
61<br />
0.48<br />
92<br />
7<br />
tr<br />
tr<br />
0.15<br />
32<br />
68<br />
0.44<br />
36<br />
38<br />
26<br />
0.66<br />
0<br />
58<br />
40<br />
2<br />
0.02<br />
82<br />
18<br />
0<br />
7<br />
93<br />
0 0.46 0.13<br />
43<br />
54<br />
2<br />
0.52<br />
1<br />
87<br />
13<br />
0.23<br />
47<br />
53<br />
100 100<br />
0.22<br />
66<br />
34<br />
0.44<br />
98<br />
1<br />
1<br />
0.04<br />
12<br />
88<br />
0.21<br />
64<br />
22<br />
12<br />
0.52<br />
0.50<br />
76<br />
24<br />
0.36<br />
84<br />
14<br />
1<br />
1<br />
0.27<br />
0.30<br />
90<br />
10<br />
0.18<br />
98<br />
2<br />
0.13<br />
87<br />
13<br />
0.23<br />
98<br />
2<br />
0.04<br />
6<br />
94<br />
0.11<br />
0.02<br />
tr<br />
100<br />
tr<br />
100<br />
0 0<br />
98<br />
2<br />
0.04<br />
100<br />
100<br />
0<br />
100<br />
0.04<br />
93<br />
7<br />
0.13<br />
59<br />
24<br />
17<br />
0.57<br />
0<br />
25<br />
75<br />
0.38<br />
76<br />
24<br />
0<br />
29<br />
71<br />
0.41<br />
74<br />
24<br />
2<br />
0.39<br />
0.50<br />
3<br />
97<br />
0.06<br />
13<br />
47<br />
40<br />
0.60<br />
0<br />
67<br />
33<br />
0.44<br />
53<br />
47<br />
0<br />
25<br />
75<br />
0.38<br />
75<br />
23<br />
2<br />
0.36<br />
100<br />
0<br />
21<br />
30<br />
49<br />
0.63<br />
72<br />
22<br />
6<br />
0.43 0.37 0.50<br />
continued<br />
– – – – – – – – – – – –<br />
– – – –<br />
– – – – – – – –<br />
– – – – – – – – –<br />
– –<br />
–<br />
–<br />
– –<br />
– –<br />
–<br />
– –– – – – –– –– –– –– –– – – – – – – – –<br />
– –<br />
– – ––<br />
Table 2 continued.<br />
Allele frequency (%)<br />
Locus Allele Whole sample Indica group Japonica group<br />
Asia Africa Mada- Asia Africa<br />
(n= (n= gascar (n=900) (n=359) Indica I I* (n= (n= Japonica J J*<br />
1688) 688) (n= 181) (n=129) (n=32) (n=97) 451) 329) (n=47) (n=41) (n=6)<br />
Madagascar Madagascar<br />
Asia Africa<br />
Est-5 0 tr<br />
1 99<br />
3<br />
2<br />
97<br />
1<br />
H 0.02 0.06<br />
Est-9 1 40<br />
2 60<br />
43<br />
H 0.48 0.49<br />
57<br />
Cat-1 1 71 83<br />
Amp-1 1 78 88<br />
2 13 10<br />
3 4 2<br />
4 5<br />
5 tr<br />
H 0.37 0.22<br />
Amp-2 1 39 P<br />
2 61 P<br />
3 tr<br />
4 tr<br />
H 0.48 –<br />
100 100<br />
tr<br />
94<br />
6<br />
0.11<br />
43<br />
57<br />
0.29<br />
100<br />
78<br />
19<br />
3<br />
100<br />
0<br />
100 100 100<br />
0 0 0<br />
100<br />
0<br />
100<br />
0<br />
100<br />
0<br />
100<br />
0<br />
continued<br />
100 100 100<br />
0<br />
62<br />
38<br />
0.47<br />
97<br />
39<br />
61<br />
tr<br />
64<br />
34<br />
0.45<br />
100<br />
93<br />
tr<br />
6<br />
1<br />
0.13<br />
1<br />
99<br />
0<br />
81<br />
19<br />
0.23<br />
97<br />
100<br />
0<br />
87<br />
13<br />
0.28<br />
100<br />
100<br />
0<br />
tr<br />
100<br />
tr<br />
4<br />
90<br />
62<br />
38<br />
0.47<br />
99<br />
25<br />
75<br />
100<br />
0<br />
65<br />
100<br />
tr<br />
tr<br />
P<br />
100<br />
0<br />
81<br />
88<br />
12<br />
0.21<br />
100<br />
0<br />
100<br />
0<br />
93<br />
100<br />
0<br />
100<br />
0<br />
10<br />
tr<br />
0.47<br />
30<br />
70<br />
0.35<br />
P<br />
P<br />
0.38<br />
2<br />
98<br />
0.04<br />
0<br />
100<br />
0<br />
3<br />
97<br />
0.18<br />
99<br />
tr<br />
tr<br />
tr<br />
0.42 0.02 0 0.06 0.02<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
– –<br />
– –<br />
– –<br />
–<br />
–<br />
–<br />
–<br />
–<br />
Table 2 continued.<br />
Allele frequency (%)<br />
Locus Allele Whole sample Indica group Japonica group<br />
Madagascar Madagascar<br />
Asia Africa Mada- Asia Africa Asia Africa<br />
(n= (n= gascar (n=900) (n=359) Indica I I* (n= (n= Japonica J J*<br />
1688) 688) (n = 181) (n=129) (n=32) (n=97) 451) 329) (n=47) (n=41) (n=6)<br />
51<br />
47<br />
2<br />
67 62<br />
33 38<br />
0.44 0.47<br />
Amp-3<br />
Acp-1<br />
Adh-1<br />
Pgd-7<br />
All loci<br />
P<br />
P<br />
P<br />
1<br />
71<br />
28<br />
P<br />
P<br />
0<br />
1<br />
2<br />
3<br />
4<br />
5<br />
6<br />
H<br />
1<br />
2<br />
3<br />
H<br />
0<br />
1<br />
2<br />
3<br />
H<br />
1<br />
2<br />
3<br />
H<br />
4<br />
48<br />
43<br />
1<br />
3<br />
tr<br />
1<br />
0.58<br />
62<br />
67<br />
1<br />
0.48<br />
95<br />
tr<br />
4<br />
1<br />
0.09<br />
67<br />
6<br />
27<br />
0.47<br />
0.62<br />
P<br />
P<br />
73 100<br />
27<br />
63<br />
37<br />
76<br />
24<br />
73<br />
27<br />
tr<br />
0.39<br />
96<br />
4<br />
0.08<br />
99<br />
1<br />
0.02<br />
68<br />
32<br />
0.47<br />
91<br />
9<br />
0<br />
17<br />
83<br />
0.36<br />
98<br />
2<br />
0.39<br />
1<br />
99<br />
0.41<br />
71<br />
29<br />
0.52<br />
98<br />
2<br />
99<br />
1<br />
0.02<br />
P<br />
59<br />
17<br />
24<br />
0.56<br />
0.30<br />
52<br />
48<br />
0.50<br />
P<br />
P<br />
2<br />
100 98 100<br />
0.42 0.28<br />
99<br />
0.04 0.16 0.04 0.02<br />
tr<br />
88<br />
12<br />
tr<br />
0.21<br />
96<br />
0 0.04 0<br />
99 100 99 P 100 100 100<br />
1<br />
0.02<br />
76<br />
21<br />
3<br />
0.42<br />
0.29<br />
1<br />
0.02<br />
61<br />
15<br />
24<br />
0.55<br />
0.28<br />
1<br />
0.02<br />
63<br />
37<br />
0.47<br />
0.1 5<br />
0<br />
100<br />
0 0<br />
85 83<br />
15 17<br />
0.26 0.28<br />
0.05 0.12<br />
64 70<br />
9<br />
27<br />
0.51<br />
0.38<br />
84<br />
16<br />
4<br />
0.08<br />
0.1 0<br />
30<br />
0.42<br />
0.16<br />
0<br />
0.03<br />
0.44<br />
0.23<br />
0.27<br />
0.20<br />
a tr = traces, 0.5 > tr > 0; P = present, >0, but undetermined. * = presence of allele 2 at locus Am p-1.<br />
– – – – – – – – – –<br />
– – – – – – – – – – –<br />
– – – – – – – – – – – –<br />
– – – – – – – – – – –<br />
– – – – – – – – – – – –<br />
– – –<br />
– – –<br />
– – – – – – – – – – – –<br />
– – – – – – – – – – –<br />
– – – – – – – – – –<br />
– – – – – – –<br />
– – – –<br />
– – – – –<br />
– – – –<br />
For the japonica group, Madagascar differs from Africa by the reversal of allele<br />
frequencies at locus Est-1, and from Asia by the reversal at locus Cat-1. For this group,<br />
too, the frequency of allele 2 at locus Amp-1 is significant, whereas this allele is absent<br />
from the japonica group in Asia and Africa.<br />
On the basis of the allele at locus Amp-1, Rabary et al (1989) tentatively distinguished<br />
subgroups I and I* in the indica group, and J and J* in the japonica group,<br />
* indicating the presence of allele 2. The subgroups I and J display only minor<br />
differences from their African and Asian counterparts. The same is true for J*, besides<br />
the difference at locus Amp-1. For I*, more significant differences account for all the<br />
peculiarities of the indica group in Madagascar.<br />
Thus, as morphophysiological variability does, isozyme variability of rice in<br />
Madagascar identifies specific types beside the usual indica and japonica types<br />
observed in Asia.<br />
Congruence between the two classification schemes<br />
The distribution of the 144 varieties analyzed for both morphology and isozymes on<br />
plane 1, 2 of the FAC of the morphological data is shown in Figure 3. The isozyme<br />
groups and the culture type—upland vs lowland—are distinguished, and two classes<br />
of elevation are considered.<br />
Enzymatic group I contains all lowland varieties, distributed mostly in morphological<br />
group G5, but a few falling into group G6. All are grown at elevations lower than<br />
1,000 m.<br />
Varieties of enzymatic group I* are scattered in all morphological groups at all<br />
altitudes with, however, higher concentration in group G6 and at elevations above<br />
1,000m. Among the 78 I* varieties, 3 are grown under upland conditions.<br />
Varieties of enzymatic groups J and J* cluster around morphological groups G4 and<br />
G6. In G4, most are upland varieties (tropical japonica) grown at low elevations,<br />
whereas in G6, all are lowland varieties belonging to the vernacular families “Vary<br />
Lava” and “Latsika.” The former are well known for their very long and wide grains;<br />
the latter are grown only above 1,500 m.<br />
• About 75% of the morphologically atypical varieties (G6) are also atypical<br />
• A higher frequency of atypical varieties is observed above 1,000 m.<br />
• The cold-tolerant lowland japonica varieties do not morphologically resemble the<br />
• Some upland varieties possess both the typical morphology of tropical japonica<br />
These are the main noteworthy outputs of this comparison:<br />
regarding isozymes.<br />
temperate Asian japonica varieties.<br />
(G4) and basically indica isozymes (I*).<br />
Discussion<br />
The genetic diversity of rice in Madagascar is characterized by the presence both of<br />
varietal groups similar to the indica and japonica groups from Asia and Africa and of<br />
Traditional highland rices from intersubspecific recombination 75<br />
3. Varieties from Madagascar scattered in plane 1, 2 of the factor analysis of correspondences among 39 morphophysiological<br />
characters (see Figure 1) and differentiated according to enzymatic group and culture type<br />
for 2 classes of elevation. = I, lowland; = I*, lowland; = I*, upland; = J, lowland; = J, upland,<br />
= J*, lowland; =group V, lowland; * = intermediate, lowland (Rabary et al 1989).<br />
groups specific to the island, where morphophysiological and isozymic variations are<br />
not fully congruent.<br />
Comparison of isozyme allele frequencies among Asia, Africa, and Madagascar<br />
reveals that the introduction of rice to Africa and Madagascar was accompanied by the<br />
loss of minor Asian alleles. But in Madagascar, a few exceptions are noted, such as<br />
allele 2 at locus Amp-1, which is restricted to the Indian subcontinent in Asia and is<br />
predominant in Madagascar.<br />
76 Ahmadi et al<br />
Thus, rice introduction in Madagascar caused genetic drift linked to founder effects.<br />
Allele Amp-1 2 in Asia is found mainly among varieties that differ from indica and<br />
japonica types in several other specific alleles; in a survey of 1,688 Asian varieties<br />
(Glaszmann 1987,1988), only 2 indicas displayed this allele: one from southern India<br />
and one from Sri Lanka. In Madagascar, this allele is found in an array of genotypes,<br />
all very rare or absent in Asia. Its introgression from local wild rices can be excluded,<br />
for only Oryza longistaminata is present on the island, and it does not possess this allele<br />
(Ghesquière 1988, Second 1985). Therefore, these genotypes must have arisen from<br />
local genetic mixing within O. sativa. Moreover, since this allele is found in both indica<br />
and japonica isozymic backgrounds, mixing has involved both intrasubspecific and<br />
intersubspecific recombinations. The higher frequency of japonica-prone alleles<br />
Est-2 0 and Sdh-1 2 in group I * than in group I confirms that I * varieties probably arose<br />
from intersubspecific recombination. The situation is different in Africa, where the frequency<br />
of Amp-1 2 is higher than in Asia only among indicas, with a low frequency of<br />
alleles Est-2 0 and Sdh-1 2 .<br />
Evidence drawn from the isozyme data alone is reinforced by the morphological<br />
evidence of many intermediate forms. The environmental conditions of high plateaus<br />
in Madagascar certainly exerted particular selection pressures that favored certain<br />
recombinant forms, as shown by the high frequency of these forms at high elevations<br />
and their cold tolerance (Rasolofo et al 1986).<br />
The data are conclusive, thanks to the peculiarities of Madagascar, where varietal<br />
migrations and gene introgression from wild rices did not complicate the pattern of<br />
variation. Some firm conclusions are relevant to other, less favorable situations. In<br />
particular, it is striking that intersubspecific recombination has left so few intermediates<br />
as far as isozymes are concerned. This is to be compared with the tendency of<br />
parental gene combinations to increase among the progenies of indica/japonica crosses<br />
(see Oka 1988 for a review). Those genes that kept their initial assortment are certainly<br />
tightly linked to components of coadapted gene complexes involved in the maintenance<br />
of indica-japonica differentiation. These are Pgi-1 on chromosome 3, Cat-1 on<br />
chromosome 6, Est-9 on chromosome 7, Amp-2 on chromosome 8, and Acp-1 on chromosome<br />
12 (Wu et al 1988), using the new chromosome nomenclature. Therefore,<br />
other regions in Asia or Africa where these genes form multilocus associations should<br />
not be considered without indica-japonica gene introgression.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-51018517665877845842011-09-05T01:16:00.000-07:002017-07-29T14:43:40.350-07:00Applications and Use of Criteria and Other Tests2011<br />
7.1 Introduction<br />
As discussed in Chap. 1, it is widely recognized that application of prerequisite programs at<br />
preharvest,<br />
harvest and postharvest level (e.g., Good Agricultural Practices (GAP), Good Farming<br />
Practices (GFP), Good Veterinary Practices (GVP), Good Hygiene Practices (GHP), Good<br />
Manufacturing Practices (GMP), etc.) and Hazard Analysis Critical Control Point (HACCP) program<br />
is the most effective food safety management strategy. Effective control of undesirable microorganisms<br />
<a name='more'></a><br />
in foods is best addressed at appropriate steps in the food chain through targeted and synergistic<br />
application of these approaches. Microbiological testing of process hygiene can play an important<br />
role in verifying the effectiveness of food safety management programs (prerequisite programs and<br />
HACCP) when used in a thoughtful, well-planned manner. In some cases, microbiological testing of<br />
the end product may also be used if no prior history of the product is available (e.g., at port of entry).<br />
Consistent with previous ICMSF considerations (2002), testing should be required only when the<br />
following two conditions exist:<br />
1. The product group has been implicated in foodborne disease or may have an inadequate shelf life<br />
or other microbiological issues if effective controls are not applied.<br />
2. The application of testing will reduce the health risk or quality issues associated with a food or will<br />
effectively assess adherence to microbiological control measures or process controls.<br />
This chapter provides background on the considerations that the Commission used to propose microbiological<br />
criteria for some commodities and not others. It also indicates how the criteria should be<br />
interpreted and applied.<br />
The recommendations for end product testing in the following chapters replace those given in<br />
Microorganisms in Foods 2: Sampling for Microbiological Analysis: Principles and Specific<br />
Applications (ICMSF 1986). Significant advances in the understanding of food production and processing,<br />
risk management, and the statistics of sampling have made changes necessary. Additionally,<br />
the following chapters provide recommendations not only for end product testing, but also other tests<br />
that may provide useful information for microbiological safety and quality management.<br />
Although considerable effort was given to develop appropriate, risk-based criteria, ICMSF recommendations<br />
have no official status. Promulgation of official national microbiological standards is the<br />
responsibility of governments and articulation of international food safety guidelines is the province<br />
of intergovernmental standards setting bodies such as the Codex Alimentarius Commission, which is<br />
organized under the Food and Agriculture Organization (FAO) of the United Nations and the World<br />
Health Organization (WHO).<br />
7.2 Format of Product Chapters<br />
The product groupings generally follow those used in Microorganisms in Foods 6: Microbial Ecology<br />
of Food Commodities (ICMSF 2005), which provides details on the microbial ecology of specific<br />
commodities and products. The following chapters focus on practical application of testing in the<br />
production of microbiologically safe and wholesome foods rather than on the microbial ecology of the<br />
products. Each chapter briefly discusses the relevant microbial hazards and spoilage concerns for<br />
each sub-commodity and, based on their significance, may recommend tests and criteria for the various<br />
stages of production and distribution, as described below.<br />
7.2.1 Primary Production<br />
For some commodities, such as fruits, vegetables, spices, meat, poultry, and fish products, primary<br />
production practices can have a major influence on the microbiological quality of the product. Where<br />
appropriate and where information is available, recommendations for irrigation or seafood harvest<br />
waters, fertilizer, vaccination programs, feeding regime and other on-farm practices may be provided<br />
or referenced to national standards.<br />
7.2.2 Ingredients<br />
Many foods are composed of a number of different ingredients. The microbiological quality and<br />
safety of some ingredients may be critical to the safety and stability of the final product. Control of<br />
a microbiological concern at the ingredient level may be essential for products when there is no subsequent<br />
kill step (e.g., cocoa powder in chocolate that has no heat treatment, beef intended for production<br />
of unheated fermented salami). For other foods, ingredients may be subjected to a kill step<br />
during processing and therefore microbiological criteria are less important (e.g., cocoa powder in ice<br />
cream mix that is subsequently pasteurized, beef intended for production of cooked meat products).<br />
Anticipated initial levels or criteria for such ingredients discussed in other chapters may be crossreferenced,<br />
as appropriate. Testing is generally recommended for an ingredient if the answer to either<br />
of the following questions is “Yes” for the commodity under consideration:<br />
1. Is control at the ingredient step necessary for safety or quality?<br />
2. Is testing necessary to verify the acceptability of the ingredient?<br />
7.2.3 In-Process<br />
In this book, the term “in-process” testing is used to describe testing to (1) verify a kill step or (2)<br />
monitor whether the product is likely to become contaminated. The concept of HACCP emphasizes<br />
the importance of applying validated and verified process controls for the production of safe food.<br />
Certain tests may be used to verify that processes are performing as anticipated (e.g., initial in-plant<br />
validation to assess the performance of a control measure at certain production step). For example,<br />
testing for indicator organisms such as coliforms or Enterobacteriaceae on in-process product<br />
emerging from cooking equipment may be useful to verify adequacy of the cooking process.<br />
Sampling intermediate product (e.g., from conveyors, filler heads, holding tanks or vats, etc.) and<br />
processing line samples (e.g., process wash water, sifter tailings, fines, line residues, and scrapings)<br />
offers an alternative or complimentary approach to the use of swabs or sponge samples to monitor<br />
for contamination with microorganisms of concern to public health or spoilage. In-process product<br />
7.2 Format of Product Chapters 65<br />
or product residues that accumulate on equipment may represent a worst case when such materials<br />
accumulate under conditions that support microbial growth throughout a production period.<br />
In-process testing may provide more useful information about potential microbiological concerns<br />
than end product testing, particularly when the data are used in a process control system as discussed<br />
in Chap. 3 of this book and in Microorganisms in Foods 7: Microbiological Testing in Food Safety<br />
Management (ICMSF 2002).<br />
In-process testing is generally recommended if the answer to all of the following questions is<br />
“Yes” for the commodity under consideration:<br />
1. Does the process need to be controlled to prevent increase, ensure decrease, maintain current level,<br />
or prevent spread of a microbial concern?<br />
2. Is testing needed to verify (a) the process is functioning as intended or (b) contamination is not<br />
occurring in the process?<br />
3. Are there locations in the process where accumulated product residue may provide a representative<br />
or worst case sample that predicts the safety or quality of the final product?<br />
7.2.4 Processing Environment<br />
Maintenance of a hygienic processing environment is essential for the production of safe and wholesome<br />
food; however, microbiologically relevant considerations will vary for different food commodities.<br />
This section generally addresses the use of swabs or sponges for sampling sites on equipment or<br />
in the environment. This type of testing is very useful and effective for verifying that the environment<br />
is under appropriate hygienic control for the specific commodity. General guidance on environmental<br />
sampling can be found in Chap. 4 of this book and in Microorganisms in Foods 7: Microbiological<br />
Testing in Food Safety Management (ICMSF 2002). As with in-process sampling, a well designed<br />
environmental testing program based on a predetermined clear objective may provide more useful<br />
information about potential microbiological concerns than end product testing, particularly when the<br />
data are used in a process control system as discussed in Chap. 3 of this book and in Microorganisms<br />
in Foods 7 (ICMSF 2002).<br />
Environmental testing is generally recommended with potential tests to consider, if the answer to<br />
the following two questions is “Yes” for the commodity under consideration:<br />
1. Does the environment need to be controlled to prevent contamination of the product with a microbial<br />
concern?<br />
2. Will testing be beneficial to verify control of the microbial concern in the environment?<br />
7.2.5 Shelf Life<br />
The shelf life of food commodities is influenced by deleterious changes to product quality that<br />
develop over time, many of which are nonmicrobial, such as enzymatic activity, oxidation, structural<br />
changes, staleness etc. However, microbial activity can play an important role in the safety or spoilage<br />
of some food commodities. Shelf life testing is discussed only if microbial activity is relevant to<br />
a particular commodity. For certain products (e.g., bulk shipments) shelf life testing may not be feasible.<br />
Shelf life testing is generally recommended if the answer to the following two questions is<br />
“Yes” for the commodity under consideration:<br />
1. Is shelf life limited by a microbiological safety or quality concern?<br />
2. Is shelf life testing feasible?<br />
66 7 Applications and Use of Criteria and Other Tests<br />
7.2.6 End Product<br />
End product criteria are recommended if they can be used to demonstrate the successful application<br />
of food safety controls or to assess the microbiological status of a lot when insufficient information<br />
exists to assess its status. For a limited number of foods, available prerequisite programs and HACCP<br />
may be inadequate to provide consumer protection. For such foods end product testing may be a<br />
necessary step to provide additional protection to consumers.<br />
The determination of the relative importance of end product testing must be made on a product by<br />
product basis (see Sect. 7.2.7), and end product testing may be used for lot acceptance when there is<br />
insufficient access to other process or testing information. The suggested criteria for lot acceptance<br />
are based on baseline data, experience, industry practice, relative risk when ICMSF cases are considered,<br />
or existing microbiological criteria that have been developed internationally as a result of the<br />
risk analysis process established by The Codex Alimentarius Commission (see Sect. 7.4). Other<br />
sampling plans may be appropriate in certain situations. For example, reducing the number of samples<br />
may be acceptable for on-going surveillance activity; whereas increasing the number of samples<br />
may be prudent when investigating significant process deviations or outbreaks. Testing is generally<br />
recommended if the answer to one of the following questions is “Yes” for the commodity under<br />
consideration:<br />
1. Is end product testing necessary to verify control of the overall manufacturing process?<br />
2. Is end product testing relied upon for ensuring the safety or quality of the lot?<br />
7.2.7 Relative Importance of Tests Recommended<br />
Tables within each commodity chapter include a rating (i.e., low, medium, high) for the relative<br />
importance of the tests recommended. These ratings reflect the level of importance for routine testing<br />
during operation under GHP/GMP and HACCP using processes that have been validated to consistently<br />
deliver product that is acceptable from the perspective of safety and quality. In assigning ratings,<br />
the Commission attempted to identify the types of samples that would provide the most useful<br />
information to evaluate the microbiological status of the product being manufactured. It is important<br />
to note that the relative importance of a test must be evaluated in the context of the overall microbiological<br />
testing program. For example, if ingredient, in-process, and environmental monitoring are<br />
routinely conducted in a diligent manner, on a routine basis, in a stable processing environment, with<br />
the intent to use the information for trend analysis and process improvement, then the relative importance<br />
of finished product testing is likely to be low. However, if upstream testing is done occasionally<br />
or in a manner that would not provide confidence that the process is under control, then the relative<br />
importance of finished product sampling may increase.<br />
The relative importance and recommended sampling plans may change in nonroutine situations.<br />
For example, when validating a new process, qualifying a new ingredient or supplier, performing<br />
corrective action for a significant process deviation or investigating a foodborne illness outbreak,<br />
more extensive testing is generally warranted. Previous chapters on corrective action, process validation<br />
and customer–supplier relationships provide guidance in these areas.<br />
7.3 Choice of Microorganisms or Products Thereof<br />
Recommendations for tests are included for microbes or their products (e.g., mycotoxins) that are most<br />
important in respect to hazard/risks or noncompliance with GHP/GMP. This choice is based on a hazard<br />
analysis and risk categorization (i.e., a qualitative risk assessment) that considers epidemiologic<br />
evidence,<br />
public health impact, the scientific literature and expert opinion, in-process experimental<br />
7.4 Selection of Limits and Sampling Plans 67<br />
validation, and recognizes the limitations of current methodologies. Quality issues are also considered<br />
in recommending tests. Detailed discussion of microbial concerns for each commodity is provided in<br />
Microorganisms in Foods 6: Microbial Ecology of Food Commodities (ICMSF 2005).<br />
7.4 Selection of Limits and Sampling Plans<br />
Limits and sampling for in-process and environmental tests are greatly influenced by the site, process,<br />
geographic region and other factors, therefore it is not possible to specify limits that are universally<br />
applicable in all situations. Typical guidance levels or typical levels encountered may be provided for<br />
these tests, but these are not intended to be applied universally. Accordingly, no methods, number of<br />
samples, or sample size are specified in most instances. It is important to emphasize that a typical<br />
level encountered does not indicate a limit. It is expected that levels will periodically exceed a typical<br />
level encountered.<br />
For end product testing, the following questions were asked sequentially to help identify the<br />
appropriate basis for recommended sampling plans and criteria:<br />
1. Does a risk assessment exist?<br />
2. Has an appropriate level of protection (ALOP) been established that would enable determination<br />
of a Food Safety Objective or a Performance Objective?<br />
3. Are sufficient data available to define typical values likely to be encountered that are consistent<br />
with safe food, or food of good quality, and do data exist to estimate the variability in values<br />
encountered, e.g., within and between batches?<br />
The availability of a risk assessment, dose-response data, consumer exposure data and defined ALOP<br />
or FSO/PO, and data on microbial levels typically encountered in a food facilitates development of<br />
microbiological criteria that have a link to public health goals. ICMSF (2002) and van Schothorst<br />
et al. (2009) reviewed this process in some detail. However, the availability of formal risk assessments<br />
for many food types is limited (e.g., qualitative and quantitative). In the absence of a risk<br />
assessment, the Commission used the ICMSF cases (ICMSF 2002), generally accepted international<br />
regulations (e.g., Codex, national regulations, industry guidelines) or expert opinion to recommend<br />
sampling plans and limits.<br />
ICMSF cases, summarized in Table 7.1, consider both the severity of the hazard, the susceptibility<br />
of the intended consumer and the potential for the risk to decrease, remain the same, or increase<br />
between the time of sampling and when the food is consumed. Sampling plans become increasingly<br />
more stringent with increased severity. The following terms are used:<br />
n = the number of sample units to be analyzed<br />
c = the maximum number of sample units allowable with marginal but acceptable results (i.e., between<br />
m and M)<br />
m = concentration separating good quality or safety from marginally acceptable quality<br />
M = concentration separating marginally acceptable quality from unacceptable quality or safety<br />
Limits (m and M) recommended for utility, indicator, and moderate hazards (Cases 1–9) are typically<br />
reported on a per gram basis, and quantitative methods are generally used. The c criterion included<br />
in Cases 1–9 recognizes that statistical variation may occasionally contribute to results above m.<br />
Specifying a maximum limit M helps to prevent acceptance of product that may greatly exceed quality<br />
or safety indicators without further investigation or action.<br />
For serious and severe hazards (Cases 10–15), when c = 0, the maximum acceptable level is m = M.<br />
For Cases 10–15, test results are greatly influenced by sample size because they are typically reported<br />
as being present (positive) or absent (negative) in the sample tested. For this book,<br />
the analytical unit for each sample, n, for Cases 10–15 is 25 g unless otherwise specified. Thus, for<br />
68 7 Applications and Use of Criteria and Other Tests<br />
Table 7.1 Sampling plan stringency (case) in relation to degree of risk and conditions of use<br />
Degree of concern relative<br />
to utility and health hazard Examples<br />
Conditions under which food is expected to be<br />
handled and consumed after sampling in the usual<br />
course of eventsa<br />
Reduce risk No change in risk May increase risk<br />
Utility: General<br />
contamination, reduced<br />
shelf life, incipient<br />
spoilage<br />
Aerobic colony count, yeasts<br />
and molds<br />
Case 1 Case 2 Case 3<br />
n = 5, c = 3 n = 5, c = 2 n = 5, c = 1<br />
Indicator: Low,<br />
indirect hazard<br />
Enterobacteriaceae,<br />
generic E. coli<br />
Case 4 Case 5 Case 6<br />
n = 5, c = 3 n = 5, c = 2 n = 5, c = 1<br />
Moderate hazard: Not<br />
usually life threatening,<br />
usually no sequelae,<br />
normally of short<br />
duration, symptoms<br />
self-limiting, can be<br />
severe discomfort<br />
S. aureus, B. cereus,<br />
C. perfringens,<br />
V. parahaemolyticus<br />
Case 7 Case 8 Case 9<br />
n = 5, c = 2 n = 5, c = 1 n = 10, c = 1<br />
Serious hazard:<br />
Incapacitating but not<br />
usually life threatening,<br />
sequelae are rare,<br />
moderate duration<br />
Salmonella,<br />
L. monocytogenes<br />
Case 10 Case 11 Case 12<br />
n = 5, c = 0 n = 10, c = 0 n = 20, c = 0<br />
Severe hazard: For the<br />
general population or<br />
in foods targeted for<br />
susceptible populations,<br />
causing life threatening<br />
or substantial chronic<br />
sequelae or illness of<br />
long duration<br />
For the general<br />
population,<br />
E. coli O157:H7, C.<br />
botulinum neurotoxin;<br />
for restricted<br />
populations, Salmonella,<br />
Cronobacter spp.;<br />
L. monocytogenes<br />
Case 13 Case 14 Case 15<br />
n = 15, c = 0 n = 30, c = 0 n = 60, c = 0<br />
a More stringent sampling plans would generally be used for sensitive foods destined for susceptible populations<br />
Case 10, n = 5, five individual 25 g samples are analyzed. Statistical considerations underlying the<br />
sampling plans recommended in this book are discussed in Appendix A and explained in greater<br />
detail with examples by van Schothorst et al. (2009), Whiting et al. (2006) and ICMSF (2002).<br />
7.4.1 Comparing ICMSF Cases to Codex Criteria for L. monocytogenes<br />
The following example evaluates the relative stringency of ICMSF cases, which use a qualitative risk<br />
assessment approach for groups of microorganisms, to the Codex Alimentarius Commission criteria<br />
for L. monocytogenes in ready-to-eat (RTE) foods, which was based on quantitative risk<br />
assessments.<br />
7.4.1.1 Stringency of Sampling Plans Using ICMSF Cases<br />
The relative stringency of selected ICMSF cases is compared in Table 7.2, using various hypothetical<br />
values for m and M. The mean concentration that would be correctly rejected with a probability of 95%<br />
is provided using the calculations of van Schothorst et al. (2009). A standard deviation of 0.8 and a log<br />
7.4 Selection of Limits and Sampling Plans 69<br />
normal distribution is assumed. As the severity of hazard increases, the stringency of the cases increases<br />
and the mean concentration that can be reliably detected decreases (from top to bottom).<br />
The mean concentration also decreases as the potential for the hazard increases from left to right.<br />
7.4.1.2 Stringency of Codex L. monocytogenes Criteria<br />
The criteria for L. monocytogenes in RTE food recommended in this book where developed through<br />
the step-wise consensus process within the Codex Alimentarius Committee for Food Hygiene. FAO/<br />
WHO (2004) conducted a risk assessment on L. monocytogenes in RTE foods to address questions<br />
on the risk of serious illness in relation to the level of L. monocytogenes in food for different<br />
susceptible<br />
populations relative to the general population, as well as the risk of serious illness from<br />
L. monocytogenes in foods that support and do not support its growth at specific storage and shelf<br />
life. The risk assessment indicated that the vast majority of listeriosis cases were associated with the<br />
consumption of foods that do not meet current standards for L. monocytogenes (not detected in 25 g<br />
or <100 CFU/g) and that the greatest benefit to public health would be to effect a significant reduction<br />
in the number of servings contaminated with high numbers of L. monocytogenes (FAO/WHO 2004).<br />
Therefore, control measures that reduced the frequency of contamination would be expected to have<br />
a proportional reduction in the rates of illness.<br />
The risk assessment used a worst case scenario, where it was assumed that all servings had the<br />
maximum level being considered, as well as a more realistic approach that allowed for a distribution<br />
of the levels of L. monocytogenes to be considered. Both scenarios demonstrated that as the frequency<br />
or level of contamination increased the risk and the predicted number of cases also increased. It was<br />
assumed that ingestion of a single cell could potentially cause illness. According to the risk assessment<br />
and assuming a fixed serving size, if all RTE foods went from having 1 to 1,000 CFU/serving,<br />
the risk of listeriosis would increase 1,000-fold (see Table 7.3).<br />
In contrast, the risk associated with introducing 10,000 servings of food that were contaminated<br />
with 1,000 L. monocytogenes CFU/g into the food supply would, theoretically be compensated by<br />
removing a single serving contaminated at a level of 107 CFU/g from the food supply. In interpreting<br />
these results and the actual effect of a change in the regulatory limits for L. monocytogenes in RTE<br />
foods, one also has to take into account the extent to which noncompliance with established limits<br />
occurs. Based on data available for the US, where the limit for L. monocytogenes in RTE foods was<br />
Table 7.2 Relative performance of ICMSF cases in terms of the mean concentrations (in bold<br />
text) that will be rejected with at least 95% probability, assuming hypothetical criteria<br />
and a<br />
standard deviation of 0.8<br />
Type and likely change<br />
to level of hazard Reduce No change<br />
May<br />
increase<br />
Indicator, indirect hazard;<br />
m = 1,000/g, M = 10,000/g<br />
Case 4 Case 5 Case 6<br />
n = 5, c = 3 n = 5, c = 2 n = 5, c = 1<br />
5,100 CFU/g 3,300 CFU/g 1,800 CFU/g<br />
Moderate hazard; m = 100/g,<br />
M = 10,000/g<br />
Case 7 Case 8 Case 9<br />
n = 5, c = 2 n = 5, c = 1 n = 10, c = 1<br />
2,600 CFU/g 1,100 CFU/g 330 CFU/g<br />
Serious hazard; m = 0/25 g Case 10 Case 11 Case 12<br />
n = 5, c = 0 n = 10, c = 0 n = 20, c = 0<br />
1 CFU/55 g 1 CFU/100 g 1 CFU/490 g<br />
Severe hazard; m = 0/25 g Case 13 Case 14 Case 15<br />
n = 15, c = 0 n = 30, c = 0 n = 60, c = 0<br />
1 CFU/330 g 1 CFU/850 g 1 CFU/2,000 g<br />
70 7 Applications and Use of Criteria and Other Tests<br />
0.04 CFU/g, the estimated number of cases for listeriosis for that population was 2,130, using the<br />
baseline level in the US Listeria risk assessment (FDA-FSIS 2003). If a level of 0.04 CFU/g was<br />
consistently achieved, one could expect <1 case of listeriosis/year. This, in combination with available<br />
exposure data, suggested that a portion of RTE food in the US contains a substantially greater number<br />
of the pathogen than the limit of 0.04 CFU/g and that the public health impact of<br />
L. monocytogenes is almost exclusively a function of the foods that greatly exceed that limit.<br />
Therefore it could be asked if a less stringent microbiological limit for RTE foods could be beneficial<br />
in terms of public health if it simultaneously fostered the adoption of control measures that resulted<br />
in a substantial decrease in the number of servings that greatly exceeded the established limit. The<br />
results of the risk assessment illustrated that the potential for growth of L. monocytogenes strongly<br />
influences risk, though the extent to which growth occurs depends on the characteristics of the food<br />
and the conditions and duration of refrigerated storage. Using selected RTE foods, their ability to<br />
support the growth of L. monocytogenes appears to increase the risk of listeriosis 100- to 1,000-fold<br />
on a per-serving basis. In order to reflect the difference in relative risk different criteria were developed<br />
depending on whether the product will support the growth (Table 7.4).<br />
The criterion for products that do not support the growth of L. monocytogenes (i.e., 5 samples with<br />
a limit of 102 CFU/g) would reject a lot of food, with a probability of 95%, when the geometric mean<br />
concentration was 80 CFU/g, assuming a standard deviation of 0.8 (see Appendix A). This criterion<br />
reflects the conclusion from the risk assessment that the vast majority of listeriosis cases result from<br />
the consumption of high numbers of L. monocytogenes and also the desire to use a level that helps<br />
promote compliance within the industry. In contrast, the criterion for products that may support<br />
growth is much more stringent. This criterion also uses 5 samples but has a much more stringent limit<br />
of absence in 25 g for each analytical unit. This would be able to reject a lot with a geometric mean<br />
concentration of 1 CFU in 55 g with 95% confidence (assuming a standard deviation of 0.8). It should<br />
be noted that in this example a standard deviation of 0.8 was used to calculate the relative stringency<br />
of the ICMSF cases, whereas a standard deviation of 0.25 was used for calculations in the Codex<br />
Annex (Codex Alimentarius 2009). The effect of using different standard deviation values from 0.25<br />
to 1.2 on the relative performance of different criteria is given in Appendix A. The risk assessment<br />
estimated that products that support growth represent a 100- to 1,000-fold increase in risk per serving.<br />
This relative difference in stringency and also comparison to existing ICMSF cases is illustrated in<br />
the Fig. 7.1. This criterion provides a higher degree of confidence that L. monocytogenes will not be<br />
present in foods that represent the greatest risk from illness and is therefore approximately 1,000<br />
times more stringent than the criterion for products that do not support growth.<br />
In this book, the Codex criteria for L. monocytogenes are used in place of ICMSF cases.<br />
Table 7.3 Relative risk of illness and estimated number of cases/year in the United<br />
States if all RTE meals were contaminated at that level. Relative risk used the risk<br />
from a dose of 1 CFU (FAO/WHO 2004)<br />
Level (CFU/g) Dose (CFU) Relative risk<br />
Estimated number<br />
of cases/year<br />
<0.04 1 1 0.54<br />
0.1 3 2.5 1<br />
1 32 25 12<br />
10 316 250 118<br />
100 3,160 2,500 1,185<br />
1,000 31,600 25,000 11,850<br />
7.5 Limitations of Microbiological Tests 71<br />
Table 7.4 Codex criteria for L. monocytogenes in RTE foods (Codex Alimentarius 2009) and relative performance in<br />
terms of the log mean concentration (in bold text) that will be rejected with at least 95% probability, assuming a standard<br />
deviation of 0.8<br />
Product Microorganism Analytical methoda Case<br />
Sampling plan and limits/g<br />
n c m M<br />
Ready-to-eat foods that do<br />
not support growth<br />
L. monocytogenes ISO 11290-2 NAb 5 0 102 NA<br />
Log mean concentration rejected = 80 CFU/g<br />
Sampling plan and limits/25 g<br />
n c m M<br />
Ready-to-eat foods support<br />
growth<br />
L. monocytogenes ISO 11290-1 NA 5c 0 0 NA<br />
Log mean concentration rejected = 1 CFU in 55 g<br />
a Alternative methods may be used when validated against ISO methods<br />
b NA = not applicable as Codex criterion used in place of ICMSF cases<br />
c Individual 25 g analytical units (see Sect. 7.5.2 for compositing)<br />
7.5 Limitations of Microbiological Tests<br />
When used properly and combined with validated process controls, testing can provide actionable<br />
information that helps to assure the safety and stability of the products produced. However, testing<br />
cannot guarantee the safety of the product. Microbiological testing alone may convey a false sense of<br />
security due to the statistical limitations of sampling plans, particularly when the hazard presents an<br />
unacceptable risk at low concentrations and has a low and variable prevalence. This is because microorganisms<br />
are not homogeneously distributed throughout food and therefore, testing may fail to<br />
Increasing Plan Stringency<br />
2-Class Sampling Plans<br />
1/10kg 1/kg 1/100g 1/10g 1/g 10/g 102/g 103/g 104/g 105/g<br />
Concentration (CFU)<br />
ICMSF<br />
Cases<br />
13-15<br />
ICMSF<br />
Cases<br />
10-12<br />
ICMSF<br />
Cases<br />
7-9<br />
ICMSF<br />
Cases<br />
4-6<br />
Codex standard for<br />
L. monocytogenes<br />
products that do not<br />
support growth<br />
in<br />
Codex standard for<br />
L. monocytogenes in<br />
products that support<br />
growth<br />
3-Class Sampling Plans<br />
Fig. 7.1 Geometric mean concentrations of hazard rejected with at least 95% probability for Codex L. monocytogenes<br />
standards and ICMSF Cases 4–6 (m = 103/g, M = 104/g), Cases 7–9 (m = 102/g, M = 104/g), and Cases 10–15 (m = 0/25 g),<br />
assuming a standard deviation of 0.8<br />
72 7 Applications and Use of Criteria and Other Tests<br />
detect organisms present in the batch when the sample is taken from an acceptable portion of the<br />
batch. Food safety is always a result of several factors and it is ensured primarily through appropriate<br />
preventive, proactive measures applied along the food chain (e.g., primary production, ingredients,<br />
in-process and processing environment) and not through a microbiological testing alone. End product<br />
testing alone is reactive and deals only with consequences and not with the causes of the problems.<br />
7.5.1 Analytical Method<br />
To be complete, it is important to identify the analytical method that is associated with a microbiological<br />
criterion because variation can exist between the results generated by different methods.<br />
Considerations in assessing and assuring the performance of microbiological analytical methods are<br />
discussed in Appendix A, Sampling Considerations and Statistical Aspects of Sampling Plans.<br />
Estimates for the performance of sampling plans presented in this book do not take into account any<br />
errors that might occur from the microbiological methods used to determine either the presence or<br />
concentration of microorganisms in foods. For consistency, with the Codex Alimentarius Commission,<br />
International Standards Organization (ISO) methods are used for most of the criteria identified in this<br />
book. Appendix C provides a list of the ISO methods referenced in the product chapters. Other methods<br />
may be used if validated against the ISO methods identified.<br />
7.5.2 Analytical Units and Compositing<br />
For serious and severe hazards, enrichment methods are generally recommended to increase the likelihood<br />
that contamination can be detected. Enrichment methods rely on growth of the pathogen to a<br />
level that can be detected in the enrichment medium and the level of detection can vary dependingon<br />
the analytical method used. In most instances, this book recommends use of 25 g analytical units<br />
for enrichment methods. Each 25 g analytical unit should be selected individually. However, for<br />
analysis, multiple units (e.g., 5, 10, 15, 20 etc.) may be composited and run as one test if the method<br />
has been validated to detect growth of a single cell after the period of enrichment. Jarvis (2007)<br />
reviewed the practical considerations to ensure that testing composited samples is as sensitive<br />
as testing<br />
individual units.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-78852379134495507332011-09-03T15:47:00.000-07:002017-07-29T14:44:28.291-07:00Genetic diversity and intervarietal relationships in rice ( Oryza sativa L.) in AfricaTwelve African cultivars of Oryza sativa were sampled to examine the biological<br />
significance of varietal classification based on isozyme studies. Analyses of F 1<br />
hybrid sterility and of F 2 segregations showed good correspondence between<br />
biochemical markers and observed reproductive barriers. Genetic markers for<br />
some vegetative and reproductive traits were identified. Such linkages could be<br />
involved in the relationships between classifications based on various criteria.<br />
The variability in Africa of the Asian cultivated rice species Oryza sativa has been<br />
described and analyzed with regard to isozymic polymorphism (Ghesquière and<br />
Second 1983; de Kochko 1987, 1988) as well as agromorphological traits (Jacquot and<br />
Arnaud 1979, Miezan and Ghesquière 1986). These studies have shown that the genetic<br />
diversity of O. sativa in Africa is similar to that in Asia and that it is organized in two<br />
main groups corresponding to the indica and japonica subspecies.<br />
The indica-japonica distinction is partly based on the existence of reproductive<br />
barriers among Asian cultivars of O. sativa (Oka 1988). It is thus important to know<br />
if the indica-japonica distinction, maintained in Africa by enzymatic diversity, also<br />
reflects differences in the genetic pool, as in Asia.<br />
Materials and methods<br />
<a name='more'></a><br />
Observations were made at the agromorphological, fertility, and biochemical levels.<br />
Plant material<br />
Twelve African traditional cultivars that represent part of the isozyme variation within<br />
the species on that continent (ORSTOM collection, Table 1) were chosen for their<br />
enzymatic polymorphism. Three of them possess alleles that are, in Africa, characteristic<br />
of the wild species O. longistaminata.<br />
55<br />
Table 1. Varieties used in crossing design.<br />
Variety Origin Culture type Enzymatic structure reaction<br />
Phenol<br />
ES70-6<br />
YS138-3<br />
YS252-1<br />
YS45-1<br />
BS117<br />
BS125<br />
ES44<br />
ES79<br />
SS404<br />
YS309<br />
BS20<br />
2LS102<br />
Tanzania<br />
Guinea<br />
Guinea<br />
Guinea<br />
Guinea-Bissau<br />
Guinea-Bissau<br />
Tanzania<br />
Tanzania<br />
Senegal<br />
Guinea<br />
Guinea-Bissau<br />
Mali<br />
Phreatic<br />
Pluvial<br />
Phreatic, pluvial<br />
Pluvial<br />
Phreatic<br />
?<br />
Irrigated<br />
Pluvial<br />
Irrigated<br />
Phreatic<br />
Pluvial<br />
Irrigated<br />
Japonica<br />
Japonica<br />
Japonica<br />
Japonica<br />
Indica<br />
Indica<br />
Indica<br />
Indica<br />
Indica<br />
lntrogressed japonica a<br />
lntrogressed indica a<br />
lntrogressed indica a<br />
–<br />
–<br />
–<br />
–<br />
+<br />
+<br />
+<br />
+<br />
+<br />
+<br />
–<br />
+<br />
a See text.<br />
F 1 fertility<br />
A 12 × 11 crossing design was used. Variety 2LS102 was used as the female parent.<br />
Four plants of each genotype were studied. Their panicles were bagged at heading to<br />
avoid allopollination. Three panicles per plant were counted. Seed fertility was defined<br />
as number of grains over total number of spikelets. The data were treated by<br />
correspondence analysis and hierarchical ascending classification (HAC).<br />
F 2 segregation<br />
Numerous F 2 progenies were studied for segregation of isozyme markers by enzyme<br />
electrophoresis as described by Second (1982) and de Kochko (1987). The terminology<br />
used for isozyme loci and the correspondence with previous notations are given in<br />
Pham et al (1990).<br />
The conformity of F 2 segregation to Mendelian proportions and the sources of<br />
distortions were tested by chi-square analysis. In segregation involving a null allele,<br />
where heterozygous genotypes were indistinguishable, the allelic frequencies p and q<br />
were estimated based on the frequency of the double-recessive homozygote genotype<br />
(assuming an F 2 distribution according to p 2 :2pq:q 2 ).<br />
Identification of markers for quantitative traits<br />
Both agromorphological traits and genetic markers (isozyme and phenol reaction loci)<br />
were followed in the F 2 of the cross ES70-6/SS404 (321 plants). The methods and<br />
experimental design used to study the relationships between marker loci and quantitative<br />
traits were as described by Pham (1990). Following Tanksley et al (1982), a<br />
significant difference for a trait between F 2 genotypic classes of a marker locus was<br />
interpreted as the existence of linkage between the marker locus and at least one of the<br />
quantitative trait loci of the studied trait.<br />
56 J.-L. Pham<br />
Table 2. Seed fertility of F 1 hybrids and parental lines. a<br />
Seed fertility (%) with indicated male parent<br />
Female<br />
ES YS YS YS BS BS ES ES SS BS YS 2LS<br />
70-6 138-3 252-1 45-1 117 125 44 79 404 20 309 102<br />
ES70-6<br />
YS138-3<br />
YS252-1<br />
YS45-1<br />
BS117<br />
BS125<br />
ES44<br />
ES79<br />
SS404<br />
BS20<br />
YS309<br />
2LS102<br />
87<br />
84<br />
67<br />
74<br />
0<br />
7<br />
85<br />
0<br />
43<br />
57<br />
33<br />
44<br />
83<br />
76<br />
85<br />
0<br />
9<br />
11<br />
34<br />
50<br />
30<br />
15<br />
82<br />
87<br />
88<br />
89<br />
0.5<br />
41<br />
38<br />
0.5<br />
60<br />
79<br />
43<br />
27<br />
70<br />
84<br />
84<br />
88<br />
43<br />
17<br />
21<br />
9<br />
74<br />
83<br />
28<br />
29<br />
12<br />
32<br />
67<br />
73<br />
87<br />
88<br />
90<br />
84<br />
89<br />
64<br />
18<br />
71<br />
16<br />
34<br />
70<br />
51<br />
75<br />
84<br />
44<br />
74<br />
81<br />
85<br />
12<br />
78<br />
39<br />
29<br />
73<br />
67<br />
80<br />
f64<br />
75<br />
78<br />
90<br />
54<br />
68<br />
58<br />
48<br />
47<br />
83<br />
79<br />
74<br />
87<br />
73<br />
90<br />
57<br />
17<br />
65<br />
35<br />
34<br />
81<br />
65<br />
81<br />
66<br />
86<br />
85<br />
77<br />
60<br />
19<br />
65<br />
60<br />
87<br />
80<br />
79<br />
40<br />
53<br />
63<br />
64<br />
90<br />
74<br />
92<br />
26<br />
29<br />
70<br />
16<br />
0.2<br />
2<br />
20<br />
23<br />
9<br />
72<br />
72<br />
51 81<br />
a Parental lines in boldface, Significant differences between reciprocal crosses are indicated by italics. (-) =<br />
missing data.<br />
F 1 hybrids<br />
No genotype effect was detected in F 1 seed from crosses, germination rate (71%), or<br />
losses after sowing (<3%).<br />
Table 2 shows the seed fertility of F 1 hybrids and parental lines, which covered a<br />
uniform range (0–90%). The sample of varieties presents various situations, showing<br />
that a diversity of relationships between genotypes corresponds to the diversity of the<br />
genotypes.<br />
Differences occurred between reciprocal crosses for about one-third of the observed<br />
combinations (Table 2).<br />
Classification of varieties by F 1 fertility<br />
The data were subjected to correspondence analysis after joining Table 2 and its<br />
transposition (although the information given by the crosses with 2LS102 is thus lost,<br />
this genotype is less discriminating). The varieties were then classified by HAC using<br />
as variables the factorial coordinates on axes 1 to 4 (Fig. 1). The plane defined by the<br />
first two axes of the correspondence analysis in Table 2 allows a quick visualization<br />
of the results (Fig. 2).<br />
Two main clusters appear: 1) the japonica (as classed by their enzymatic genotype)<br />
varieties ES70-6, YS138-3, YS45-1, YS252-1, and YS309 and the sole indica BS20;<br />
and 2) the indica varieties SS404, ES44, ES79, BS117, BS125, and 2LS102. For all<br />
varieties, classification into either of these two groups is the same for both male and<br />
female parents.<br />
Genetic diversity in rice ( Oryza sativa L.) in Africa 57<br />
– –<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
–<br />
1. Hierarchical ascendant classification of 12 cultivars from fertility of their F 1 hybrids. Roman type =<br />
cultivar as male parent; italics = cultivar as female parent.<br />
The japonica cluster + BS20 includes three unequal groups:<br />
• The first consists of typical japonica varieties, which generate fertile withinjaponica<br />
hybrids and sterile hybrids when crossed with indicas. They are ES70-<br />
6 and YS138-3 (male and female), and YS45-1 and YS252-1 (male).<br />
• BS20 (male and female) and YS252-1 (female) are close, and YS45-1 (female)<br />
belongs to this group. These varieties could be called wide compatible (Ikehashi<br />
and Haraki 1984), but only BS20 was wide compatible in both reciprocal crosses.<br />
• YS309 is alone. This genotype generates mostly sterile or semisterile hybrids<br />
and could be called a narrow compatibility variety.<br />
The differences between male and female behavior play a clear part in the<br />
classification of indica varieties (Fig. 1,2). BS125, ES44, SS404, and BS117 form a<br />
group that is notably homogeneous in male behavior but is more scattered in female<br />
behavior, where two subgroups appear. The first female subgroup consists of varieties<br />
(ES79, BS125, and BS117) showing extreme reactions with japonicas, especially with<br />
ES70-6. The second consists of ES44, SS404, and 2LS102, which generate more<br />
intermediate reactions. These results suggest the existence of cytoplasmic variability<br />
in indica cultivars.<br />
Usefulness of biochemical markers<br />
Is there any correspondence between the information from F 1 analyses and that from<br />
biochemical markers?<br />
58 J.-L. Pham<br />
2. Twelve cultivars plotted in the plane defied by axes 1 and 2 of correspondence analysis of F 1 fertility table.<br />
Heavy frame = female parent, light frame = male parent, = japonica cultivar, = indica cultivar.<br />
Enzymatic polymorphism. The classification into two main groups based on F 1<br />
fertility corresponds to that based upon enzymatic criteria. The only exception is BS20,<br />
which is grouped with japonica varieties, though enzymatically it is classed as an<br />
indica.<br />
Furthermore, the enzymatic classification of japonica varieties (de Kochko 1988)<br />
separates YS45-1 and YS252-1 from YS138-3 and ES70-6. This separation is also<br />
apparent in fertility analysis. This correspondence is all the more interesting because<br />
YS45-1 and YS252-1 have “hybrid” genotypes (Second 1982) resulting from reciprocal<br />
introgressions between indica and japonica “ancestral” genotypes. These intermediate<br />
genotypes present a trend toward wide compatibility (as the female parent only).<br />
Our observations agree with those of Clément and Poisson (1986), who describe the<br />
wide compatibility of the japonica varieties of the G3 group (Jacquot and Arnaud<br />
1979); however, reciprocal crosses were not studied by Clément and Poisson (1986).<br />
On the other hand, no correspondence is found in indica varieties between<br />
enzymatic and fertility classification. This lower efficiency of enzymatic analysis<br />
confirms the possible importance of cytoplasmic variability for classifying these<br />
cultivars.<br />
Cytoplasmic variability. Polymorphism of chloroplast DNA (restriction fragment<br />
length polymorphism) of the parental lines (except ES70-6) was studied by Z.H. Shang<br />
(pers. comm.) at ORSTOM, Montpellier, following the method described by Dally and<br />
Second (1989). Using the restriction endonuclease Eco RI, the indica varieties showed<br />
Genetic diversity in rice ( Oryza sativa L.) in Africa 59<br />
three restriction patterns, while all the japonicas showed a common pattern. This<br />
difference in variability cannot be discussed because of the small size of the sample.<br />
Although there is no overlapping between groups of indica varieties obtained from<br />
fertility analysis and the observed restriction patterns, these results show the convergence<br />
of different methods to demonstrate the cytoplasmic diversity of indica varieties.<br />
This conclusion is supported by an analysis of agromorphological traits (data not<br />
shown), which shows numerous differences between reciprocal crosses. Cytoplasmic<br />
variability must be considered in germplasm management and in breeding programs,<br />
especially those using multi-origin populations.<br />
F 2 progenies<br />
Thirty-two F 2 progenies were studied for marker loci segregation, including isozyme<br />
loci and phenol reaction locus.<br />
Analysis of marker loci segregation<br />
Table 3 shows the conformity of F 2 segregations to Mendelian proportions for each<br />
progeny and chromosome. All the abnormal segregations are analyzed in Table 4.<br />
Crosses within subspecies. Two progenies of japonica/japonica crosses and three of<br />
indica/indica crosses were examined. Five chromosomes were marked. All segregations<br />
were normal.<br />
Intersubspecific crosses. Of 15 F 2 progenies studied, 8 showed at least 1 distorted<br />
segregation. Among the seven marked chromosomes, six carried loci that are subject<br />
to distortion. The most common chromosomes with abnormal segregations were<br />
chromosome 6 (loci Est-2 and Pgi-2 ) and chromosome 12 ( Sdh-1 and Acp-1 ). For the<br />
other loci, distortions seemed to be relevant to particular cases. Most of the abnormal<br />
segregations (Table 4) showed unequal allelic frequencies. Random assortment of<br />
gametes was generally observed. Thus, as noted by Pham et al (1990) for other<br />
progenies, deviations from Mendelian ratios result from gametic selection rather than<br />
from zygotic selection.<br />
All distortions did not have the same range (Table 4). The F 2 progenies BS125/<br />
ES70-6, ES70-6/ES79, and YS138-3/ES79 presented extreme distortions at locus Est-<br />
2 (chromosome 6), since only one recessive homozygote plant was observed instead<br />
of the 70 theoretical plants. In the F 2 of ES70-6/SS404, the ratio of allelic frequencies<br />
was nearly 1:3. Similar distortions were observed at locus Cat-1 (chromosome 6) in the<br />
F 2 of BS125/ES70-6 and on chromosome 12 (locus Sdh-1 and Acp-1 ) in the F 2 of ES70-<br />
6/SS404. Other distortions presented an allelic frequency ratio of about 2:3. All allelic<br />
excesses were in favor of the indica allele.<br />
Crosses involving BS20 and YS309. Of 13 F 2 progenies observed, 6 showed at least<br />
one skewed segregation. Seven of the nine marked chromosomes carried loci subject<br />
to distortions. Chromosome 6 was the most susceptible, but the allelic frequency did<br />
not exceed 60:40.<br />
60 J.-L. Pham<br />
Table 3. Conformity of marker loci segregations for each F 2 progeny and marked chromosome. a<br />
Cross<br />
Chromosome and locus (loci)<br />
12<br />
Acp-1<br />
Sdh-1<br />
1<br />
Got-1<br />
Est-5<br />
2<br />
Amp-1<br />
3<br />
Pgi-1 Ph<br />
4 6<br />
Pgi-2<br />
Est-2<br />
6<br />
Cat-1<br />
7 11<br />
Est-9 Adh-1<br />
Pgd-1<br />
Est-1<br />
?<br />
Got-3<br />
?<br />
Chromosomes<br />
(no.) with<br />
distorted loci<br />
Marked<br />
chromosomes<br />
(no.)<br />
Japonica/japonica<br />
YS252-1/YS45-1<br />
ES70-6/YS45-1<br />
BS125/SS404<br />
lndica/indica<br />
SS404/BS125<br />
ES79/BS117<br />
Indica/japonica or<br />
japonica/indica<br />
BS117/YS138-3<br />
BS125/ES70-6<br />
BS125/YS45-1<br />
ES70-6/ES79<br />
ES70-6/SS404<br />
ES79/YS45-1<br />
SS404/ES70-6<br />
SS404/YS45-1<br />
SS404/YS252-1<br />
YS138-3/ES79<br />
YS252-1/ES79<br />
YS252-1/SS404<br />
YS45-1/ES44<br />
YS45-1/ES79<br />
YS45-1/SS404<br />
0<br />
+<br />
0<br />
0<br />
0<br />
0<br />
BS117/BS20<br />
With introgressed varieties<br />
BS20/SS404<br />
BS20/YS309<br />
BS20/YS45-1<br />
ES70-6/BS20<br />
ES70-6/YS309<br />
SS404/BS20<br />
YS138-3/BS20<br />
YS309/BS125<br />
YS138-3/YS309<br />
YS309/SS404<br />
YS45-1NS309<br />
0<br />
0<br />
0<br />
+<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
+<br />
+<br />
0<br />
0<br />
+<br />
0<br />
0<br />
0<br />
+<br />
0<br />
0<br />
0<br />
0<br />
+<br />
0<br />
+<br />
+<br />
0<br />
+<br />
+<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
+<br />
0<br />
+<br />
+<br />
0<br />
0<br />
0<br />
0<br />
0<br />
+<br />
+<br />
+<br />
0<br />
0<br />
+<br />
+<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
+<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
+<br />
0<br />
+<br />
0<br />
0<br />
0<br />
0<br />
+<br />
0<br />
+<br />
+<br />
+<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
+<br />
+<br />
+<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
+<br />
0<br />
+<br />
+<br />
0<br />
+<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
0<br />
2<br />
0<br />
4<br />
3<br />
0<br />
2<br />
2<br />
0<br />
0<br />
1<br />
0<br />
0<br />
1<br />
1<br />
0<br />
2<br />
4<br />
1<br />
0<br />
3<br />
0<br />
2<br />
0<br />
2<br />
0<br />
0<br />
2<br />
2<br />
5<br />
4<br />
5<br />
4<br />
5<br />
3<br />
5<br />
6<br />
2<br />
3<br />
2<br />
2<br />
5<br />
2<br />
3<br />
3<br />
3<br />
3<br />
2<br />
6<br />
7<br />
4<br />
5<br />
7<br />
2<br />
5<br />
2<br />
2<br />
2<br />
3<br />
a 0 = conformity, + = distortion.<br />
Genetic diversity in rice ( Oryza sativa L.) in Africa 61<br />
Table 4. Distorted F 2 segregations.<br />
Allelic c 2 test a<br />
F 2 plants frequency<br />
Chromosome Cross (P 1 /P 2 ) (no.) Locus Homogeneity F 2 distribution<br />
P 1 P 2 of allelic (p 2 :2pq:q 2 )<br />
frequency<br />
1<br />
1<br />
3<br />
4<br />
4<br />
6<br />
6<br />
6<br />
6<br />
6<br />
6<br />
6<br />
6<br />
6<br />
6<br />
6<br />
6<br />
6<br />
6<br />
6<br />
7<br />
11<br />
11<br />
12<br />
12<br />
12<br />
12<br />
12<br />
12<br />
12<br />
12<br />
12<br />
?<br />
?<br />
?<br />
?<br />
?<br />
ES70-6/ES79 120<br />
BS20/YS45-1 258<br />
YS45-1/ES44 52<br />
BS20/YS309 79<br />
ES70-6/SS404 281<br />
BS125/ES70-6 48<br />
ES70-6/ES79 115<br />
BS125/ES70-6 65<br />
BS20/SS404 100<br />
ES70-6/ES79 120<br />
ES70-6/SS404 398<br />
ES70-6/YS309 243<br />
SS404-ES70-6 60<br />
SS404/YS252-1 80<br />
YS138-3/ES79 96<br />
YS309/BS125 80<br />
BS20/YS309 80<br />
ES70-6/SS404 398<br />
SS404/ES70-6 97<br />
YS138-3/BS20 100<br />
ES70-6/ES79 117<br />
BS20/YS309 76<br />
ES70-6/YS309 309<br />
ES70-6/SS404 394<br />
BS125ES70-6 64<br />
BS20/SS404 100<br />
ES70-6/SS404 387<br />
SS404/ES70-6 99<br />
SS404/YS252-1 80<br />
YS309/SS404 59<br />
YS309/BS125 80<br />
YS45-1/SS404 52<br />
BS20/YS309 79<br />
BS125/ES70-6 60<br />
ES70-6/ES79 46<br />
ES70-6/YS309 243<br />
YS138-3/BS20 60<br />
Est5<br />
Got-1<br />
Pgi-1<br />
Ph<br />
Ph<br />
Cat-1<br />
Cat-1<br />
Est-2<br />
Est-2<br />
Est-2<br />
Est-2<br />
Est-2<br />
Est-2<br />
Est-2<br />
Est-2<br />
Est-2<br />
Pgi-2<br />
Pgi-2<br />
Pgi-2<br />
Pgi-2<br />
Est-8<br />
Adh-1<br />
Pgd-1<br />
Acp-1<br />
Sdh-1<br />
Sdh-1<br />
Sdh- 1<br />
Sdh- 1<br />
Sdh- 1<br />
Sdh- 1<br />
Sdh- 1<br />
Sdh- 1<br />
Got-3<br />
Pox-3<br />
Pox-4<br />
Pox-4<br />
Est- 1<br />
.42<br />
.56<br />
.38<br />
.61<br />
.44<br />
.75<br />
.40<br />
1 .00<br />
.40<br />
.00<br />
.24<br />
.44<br />
.78<br />
.59<br />
.10<br />
.47<br />
.34<br />
.40<br />
.58<br />
.52<br />
.40<br />
.38<br />
.62<br />
.36<br />
.61<br />
.39<br />
.36<br />
.65<br />
.69<br />
.19<br />
.30<br />
.48<br />
.58<br />
.59<br />
.33<br />
.48<br />
.61<br />
.58<br />
.44<br />
.62<br />
.39<br />
.56<br />
.25<br />
.60<br />
.00<br />
.60<br />
1 .00<br />
.76<br />
.56<br />
.22<br />
.41<br />
.90<br />
.53<br />
.66<br />
.60<br />
.42<br />
.48<br />
.60<br />
.63<br />
.38<br />
.64<br />
.39<br />
.61<br />
.64<br />
.35<br />
.31<br />
.81<br />
.70<br />
.52<br />
.42<br />
.41<br />
.67<br />
.52<br />
.39<br />
6.52*<br />
2.77 ns<br />
0.93 ns<br />
6.83**<br />
24.00***<br />
8.42**<br />
8.00**<br />
4.90*<br />
0.63 ns<br />
15.63***<br />
32.16***<br />
4.64*<br />
0.32 ns<br />
9.85**<br />
9.50**<br />
37.39***<br />
62.54***<br />
6.1 3*<br />
9.68**<br />
64.83***<br />
16.99***<br />
22.50***<br />
43.93***<br />
25.60***<br />
0.15 ns<br />
5.94*<br />
4.03*<br />
11.13**<br />
1.00 ns<br />
0.59 ns<br />
0.10 ns<br />
2.78 ns<br />
0.41 ns<br />
6.27*<br />
1.48 ns<br />
2.47ns<br />
3.25 ns<br />
5.86*<br />
0.92 ns<br />
1.28 ns<br />
0.07 ns<br />
0.38 ns<br />
0.02 ns<br />
5.92*<br />
0.73 ns<br />
0.36 ns<br />
0.18 ns<br />
3.46 ns<br />
2.90 ns<br />
4.99*<br />
0.1 6 ns<br />
2.58 ns<br />
0.01 ns<br />
6.1 3*<br />
a ns = nonsignificant; significance at the 5% (*), 1% (**), and 0.1% (***) levels.<br />
62 J.-L. Pham<br />
The case of chromosome 6<br />
The region of chromosome 6 marked by loci Pgi-2 and Est-2 is apparently very<br />
susceptible to distortions. Several researchers have noted distortions on loci wx and C,<br />
which are located on the same chromosome (see Nakagahra et al 1974). Figure 3 shows<br />
the intervarietal relationships of these loci. Some results were added involving parent<br />
108 used by Oka (1958) as an indica tester. The results support the classification<br />
obtained from studying F 1 hybrids. Japonicas ES70-6 and YS138-3 are opposed to<br />
indica varieties. The wide compatibility of YS252-1, YS45-1, and BS20 was confirmed,<br />
although a light reciprocal effect was observed with SS404. The uniqueness of<br />
YS309 was also confirmed.<br />
The rate of distortion in the F 2 was not proportional to F 1 sterility. One explanation<br />
is that the genetic systems involved in hybrid sterility are not all located near loci Est-<br />
2 and Pgi-2, and that other mechanisms are involved in distortions (Nakagahra et al<br />
1974).<br />
Relationships between agromorphological traits and genetic markers<br />
We will discuss here some of the results obtained from studying the japonica/indica F 2<br />
progeny of ES70-6/SS404 (Pham 1990).<br />
Table 5 presents the significant effects obtained for three loci that were used for<br />
classifying cultivated rice: Pgi-2 (Second 1982), Acp-1 (Inouye and Hagiwara 1980,<br />
Shahi et al 1969) and Ph (Oka 1958). Our detection of quantitative trait loci indicates<br />
that these loci have different allelic states in ES70-6 and SS404. Polymorphism among<br />
quantitative trait loci seems therefore to correspond to enzymatic polymorphism.<br />
Assuming that these results could be extended to other varieties, the linkage of these<br />
markers with traits that were often considered useful in classifying cultivated rice<br />
(tillering, shape of the grain, size of the flag leaf, length of the panicle) could partially<br />
3. Intervarietal relationships of loci Est-2 and Pgi-2 on chromosome 3. Broken lines indicate normal F 2<br />
segregations; solid lines correspond to skewed F 2 segregations. Arrows show parent whose allele is in excess.<br />
Genetic diversity in rice ( Oryza sativa L.) in Africa 63<br />
explain the correspondence between the classifications obtained using enzymatic and<br />
morphological criteria.<br />
Locus Pgi-2 appears to be a marker common to morphological and reproductive<br />
traits, since linkage was also demonstrated with fertility. This result agrees with the<br />
genetic map of rice, which locates numerous genetic sterility systems on chromosome<br />
6. The existence of reproductive barriers contributing to the isolation of indica and<br />
japonica subspecies could therefore be correlated with the preservation of a morphological<br />
identity.<br />
Conclusion<br />
The genetic structure of the sample of African varieties may be clearly revealed by<br />
studying the diversity of biochemical markers. The indica-japonica distinction revealed<br />
by isozyme studies corresponds to a distinction based on reproductive barriers<br />
like hybrid sterility and abnormal transmission of genetic information in F 2 progeny.<br />
Although our study was limited by the small number of varieties, the results favor using<br />
biochemical markers for evaluating rice germplasm collections, since the resulting<br />
classifications have biological significance.<br />
Table 5. F 2 progeny of ES70-6/SS404. Tests for difference between genotypes for 3 marker<br />
loci with respect to some quantitative traits. a<br />
Locus<br />
Trait ACp-1 Pgi-2 Ph<br />
ES/ ES/ SS/ Test ES/ ES/ SS/ Test ES/ ?/ Test<br />
ES SS SS ES SS SS ES SS<br />
No. of tillers at 50 d<br />
after sowing<br />
Heading date (no. of<br />
days from sowing)<br />
Plant height (cm)<br />
Length-width ratio<br />
of flag leaf<br />
No. of primary<br />
branches of panicle<br />
Length-width ratio<br />
of grain<br />
Seed fertility (%)<br />
107 100 104<br />
24.8 26.1 27.2<br />
12.2 11.3 1.7<br />
2.76 2.88 2.93<br />
ns<br />
ns<br />
**<br />
**<br />
**<br />
ns<br />
4.72 5.70 6.11<br />
99.0 92.8 94.5<br />
110 102 102<br />
11.6 11.3 11.9<br />
31.2 24.4 34.0<br />
***<br />
**<br />
ns<br />
ns<br />
**<br />
26.0<br />
12.7<br />
26.5<br />
11.2<br />
ns<br />
ns<br />
ns<br />
***<br />
ns<br />
ns<br />
a 321 plants were studied. ES/ES = homozygous for ES70-6 allele, SS/SS = homozygous for SS404 allele, ES/<br />
SS = heterozygous, ?/SS = ES/SS and SS/SS are indistinguishable. ns = nonsignificant, * = significant at the<br />
5% level, ** = 1%, *** = 0.1%.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-77022242904998260242011-09-03T15:42:00.000-07:002017-07-29T14:45:07.705-07:00Microbiological Testing in Customer–Supplier Relations6.1 Introduction<br />
The complete food chain from farm to fork is characterized by a sequence of supplier–customer<br />
interfaces. These interfaces imply the establishment of contracts defining the requirements of the<br />
customers with respect to their suppliers. These contracts also reflect the commitment of the supplier<br />
to guarantee the delivery of goods in compliance with the agreed-upon requirements.<br />
This sequence of interfaces plays an important role in fulfilling a Food Safety Objective (FSO) at<br />
the level of the final consumer as defined by public health authorities. As shown in Fig. 6.1, individual<br />
<a name='more'></a><br />
performance objectives (PO) can be established along the whole food chain at these interfaces.<br />
These POs should be identical to FSOs if no changes in the level of the pathogen of concern occur<br />
in the food chain up to the consumer. Different POs need to be defined to meet the final FSO if either<br />
a decrease or an increase in the level of a hazard is anticipated in the food chain (ICMSF 2002).<br />
If not done by authorities, customers or manufacturers have to define a PO that is suitable, considering<br />
the impact of processing steps and conditions on the relevant pathogen, as well as the impact of<br />
distribution and preparation by the consumer. While FSOs and POs are related to a single pathogen,<br />
all significant hazards as well as other parameters such as indicators and spoilage microorganisms<br />
need to be considered in customer–supplier relationships.<br />
Formal articulation of FSOs by public health authorities is anticipated. Absence in 1, 10, 100 kg<br />
have been proposed in the European Union for Cronobacter spp. and Salmonella in powdered infant<br />
formula (EFSA 2004). Thus contracts between suppliers and customers are based on established<br />
microbiological criteria, typically applying the worst case scenario established by commercial or<br />
administrative people. This chapter discusses the relations between suppliers and customers and the<br />
role of microbiological testing in these commercial interactions.<br />
Requirements in contracts established between a supplier and a customer may apply to raw materials<br />
or ingredients, semifinished products or finished products. These requirements may include<br />
microbiological specifications with relevant parameters such as significant pathogens and indicator<br />
microorganism or even spoilage microorganisms. Examples of such requirements can be found in the<br />
different chapters in the book. The requirements may also include other elements related to the microbiological<br />
conditions or status of the goods in question such as:<br />
• Physico-chemical parameters that may have an impact on growth:<br />
–– Gassing conditions and limits of residual oxygen<br />
–– pH or acidity<br />
–– Temperature maximum during transport and at reception<br />
Chapter 6<br />
Microbiological Testing in Customer–Supplier Relations<br />
56 6 Microbiological Testing in Customer–Supplier Relations<br />
–– Time lapse for transportation between supplier and customer<br />
–– Requirement for intermediate pasteurization (e.g., liquid whey)<br />
• Parameters related to hygiene:<br />
–– Separation of goods during transport; e.g., according to the risk of contamination, formation<br />
and transfer of off-odors etc.<br />
–– Location of containers in a ship to avoid the formation of condensation due to temperature<br />
differences<br />
–– Type of packaging material used; e.g., the requirement of strippable bags to avoid contamination<br />
during handling and tipping of critical ingredients (e.g., dry mixing)<br />
–– Specific protection of packaging material; e.g., plasticized intermediate cardboard layers<br />
between glass jars to avoid the presence of dust in normal cardboard<br />
–– Cleaning procedures for containers and tanks used to transport raw materials or semifinished<br />
product<br />
6.1.1 Raw Materials and Ingredients Used by Manufacturers<br />
The choice of parameters included in specifications for raw materials and ingredients depends on<br />
several elements such as the point in the food chain, the impact of subsequent processing steps and<br />
the regulatory environment.<br />
6.1.1.1 Raw Agricultural Commodities<br />
For unprocessed agricultural raw materials, visual qualitative or quantitative parameters play an<br />
important role. Examples are:<br />
• Absence or maximum percentage of moldy pieces in a bulk delivery (e.g., cocoa beans, peanuts,<br />
grain or maize)<br />
• Absence or maximum percentage level of rotten or unripe fruits or vegetables in a bulk delivery<br />
from the field<br />
• Defined characteristics of color or odor (absence of off-odors) for fresh meat or fish<br />
Quantitative microbiological specifications for unprocessed agricultural raw materials that will be<br />
further processed may also be included. They may, however, be expressed as percentage of positive<br />
findings or as maximum levels of counts; e.g., for Salmonella in meat used to manufacture products<br />
<br />
6.1 Introduction 57<br />
such as salami or a maximal level of viable counts in fresh milk beyond which the raw material will<br />
be downgraded, respectively. These limits are not necessarily used as acceptance criteria for delivered<br />
materials. Rather they may be used to drive improvements by the supplying party through rewarding<br />
good quality with a bonus and penalizing poor quality by deduction at payment.<br />
6.1.1.2 Processed Ingredients<br />
For processed ingredients, microbiological specifications are established according to their further<br />
use. Skimmed milk powder, for example, is an ingredient that is widely used in the manufacture of<br />
many different products such as:<br />
• Dry-mixing operations without any further heat-treatment:<br />
–– Chocolate and confectionery<br />
–– Infant formulae and infant cereals<br />
–– Instant beverages<br />
–– Dehydrated culinary products<br />
• Wet mixing operations with subsequent heat-treatment:<br />
–– Recombined liquid milks (pasteurized or UHT)<br />
–– Fermented dairy products<br />
–– Ice cream<br />
–– Heat-processed refrigerated culinary products<br />
–– Bakery products<br />
The specifications for the skimmed milk powder thus depend very much on use, and they vary from<br />
very stringent specifications (e.g., for critical products such as infant formulae) to less stringent ones<br />
(e.g., for manufacture of UHT-milk). For example, when used in infant formula, specifications are<br />
typically based on standards for finished products established by authorities. Conversely, for use in<br />
UHT milk, more lenient specifications may be used for Salmonella and Enterobacteriaceae, but limits<br />
for process-relevant spore formers are typically included by the customer to minimize the risks of<br />
failure of the thermal process (see Chap. 24).<br />
While the adherence to established microbiological requirements can be verified through sampling<br />
and testing, limitations of sampling plans need to be considered (see Appendix A). Therefore, it is<br />
important for a customer to assess the microbiological hazards and associated risks when using and<br />
purchasing a given ingredient. This will allow categorization of the different ingredients according to<br />
risk and defining the approach taken in handling ingredients after delivery.<br />
For high risk ingredients used for sensitive products (e.g., skimmed milk for infant formulae) an<br />
assessment of the confidence level in the suppliers is also needed. This assessment should be based<br />
on audits against key parameters to ensure the manufacture of safe ingredients and may include, but<br />
is not limited to, the following:<br />
• Implementation of appropriate preventive prerequisite measures such as GHP<br />
• Implementation of HACCP<br />
• Validation of control measures including critical limits<br />
• Implementation of verification measures such as environmental pathogen monitoring<br />
• Historical data<br />
• Trend analyses techniques<br />
• Release procedures<br />
• Appropriate sampling methods<br />
• Analytical procedures such as the use of validated methods and participation in proficiency tests<br />
58 6 Microbiological Testing in Customer–Supplier Relations<br />
6.1.2 Interactions with Retailers<br />
Microbiological specifications between manufacturers and retailers and food service are frequently<br />
based on national or international criteria established by public health authorities. However, additional<br />
or more specific requirements may be established by the retailer. Retailer requirements for raw<br />
agricultural commodities, such as fresh fruits or vegetables, or for manufactured products may be<br />
similar or identical to those outlined under Sect. 6.1.1. Additional elements may include:<br />
• Elements related to the shelf life of refrigerated products, such as dairy or culinary products, to<br />
meet their distribution channel needs<br />
• Elements related to the composition of the products, such as salt or sugar content, or the heattreatments<br />
used to manufacture the product<br />
• Elements related to certification and auditing of the manufacturer<br />
Such requirements may require the manufacturer to conduct challenge and storage tests to demonstrate<br />
the stability and safety of the products with the specified recipe modification or the required shelf<br />
life. A further requirement may also include monitoring retention samples.<br />
6.1.3 Contract Manufacturers<br />
Food manufacturers may subcontract the production of some products for several reasons:<br />
• Small volumes which may benefit from existing production lines dedicated to the same or similar<br />
products (cost reasons)<br />
• Proprietary technologies used by contracted manufacturers that are not available at the factory of<br />
the contracting party<br />
• Temporary production of new products until it becomes clear that the product will be successful<br />
and thus justify the investment for a new processing line<br />
• Insufficient capacity in the manufacturer’s own factory thus requiring a contract manufacturer to<br />
increase capacity<br />
The main issue related to contracting production is the control over the quality and safety of the<br />
product. The required quality can be achieved through the definition of the product characteristics<br />
based on the recipe and processing conditions or though use of a contract manufacturer chosen<br />
because of the quality attributes of the products they produce. However, ensuring the microbiological<br />
safety of the products may not be easy to control. This is particularly true if the standards applied by<br />
the manufacturer are different from those of the contracted organization. These differences must be<br />
addressed to assure that the level of understanding and implementation of GHP and HACCP are<br />
consistent to avoid the potential for increased microbiological risk.<br />
While implementation of the appropriate preventive measures, sampling and testing procedures is<br />
usually negotiated as part of the contract, it may not always be possible to impose the requirements<br />
of the contracting party. This may be the case if the volumes subcontracted are small in comparison<br />
to the total volume produced by the chosen manufacturer. In such cases the contracting party may not<br />
be in a position to implement or impose its own quality system and associated standards, and it may<br />
be advisable to look for an appropriate alternative. However, different options may be possible and<br />
depend on the type of product and its sensitivity in terms of risks for the consumer and risk for the<br />
contracting manufacturer. Potential approaches include:<br />
• The contract manufacturer agrees to manufacture and release product according to the specifications<br />
and the implemented control measures are approved by the contracting party<br />
6.3 Microbiological Data 59<br />
• Production lines on which the subcontracted production takes place are under the direct supervision<br />
of personnel from the contracting party<br />
• Release is performed by the contracting party’s own quality assurance people, who are either<br />
located at the contractors site or visit the contracted location during production<br />
• Regular audits conducted by the contracting party (see Sect. 6.2)<br />
6.2 Auditing<br />
Auditing suppliers in a supplier–customer relationship plays an important role in assessing whether<br />
the agreed-upon requirements will be met consistently and thus the confidence level in a particular<br />
supplier. Audits of HACCP and of prerequisite measures such as GHP can be very different in their<br />
nature and may range from a simple system audit to a full technical audit. In the first case, audits<br />
focus on whether or not a HACCP plan has been established and whether the different steps of a<br />
HACCP study have been addressed. In the second case, attention is given not only to the formal<br />
aspects, but also to the technical and scientific content, such as the validity of the hazard identification,<br />
and the appropriateness of control measures and derived critical limits. This will also include,<br />
assessing validation information, the effectiveness of the proposed corrective actions, appropriate<br />
verification procedures and improvement of the HACCP plan where necessary. These technical audits<br />
require deep knowledge and understanding of the product, possible associated microorganisms, the<br />
process and the processing conditions to determine whether the right decisions have been made.<br />
These technical audits usually require multidisciplinary teams, including, at a minimum, process or<br />
manufacturing specialists and hygienists or industrial microbiologists. This is important because<br />
these audits go beyond the sole assessment of the HACCP plan, and also focus on the degree of<br />
implementation and effectiveness of GHP, which provide the necessary foundation for a sound<br />
HACCP plan. In addition, it is also necessary to audit the procedures designed to verify the effectiveness<br />
of the measures. This may include environmental monitoring, verification of end product and<br />
review of methods to ascertain if they are appropriate for the particular matrix and for environmental<br />
samples. For details on process control, refer to Chap. 3.<br />
Individuals conducting audits need to be qualified and trained to be effective in this role. Two<br />
issues are relevant to consider; i.e., training to gain specific competencies and secondly registration<br />
of auditors with an appropriate body according to the sector. This is important to avoid an auditor<br />
with competence in, for example plastic packaging, from auditing a poultry factory. Ongoing verification<br />
of auditor competence also needs to be considered. The auditor training course should be<br />
registered with an appropriate training body and if an auditor needs to audit a facility for which they<br />
are not competent, then a technical expert should accompany them on the audit. These are all especially<br />
applicable where third party audits are used.<br />
6.3 Microbiological Data<br />
Usually the only microbiological data provided in supplier–customer relationships are limited to<br />
results of the purchased goods and communicated, depending on the agreements or level of confidence<br />
in the form of certificates of analysis (CoA) or certificates of conformance or compliance<br />
(CoC). The first provide detailed analytical results of the parameters included in the specifications,<br />
the latter represent a confirmation or guarantee that based on the implemented control measures and<br />
verifications, the products are in compliance with the specification. This provides information on the<br />
delivered lots and, since they have been released and shipped, indicates that they comply with the<br />
60 6 Microbiological Testing in Customer–Supplier Relations<br />
agreed-upon requirements. However, results of the CoA, will only provide information on the specific<br />
lot and not on the overall performance or process capability of the supplier.<br />
A much more useful approach would be for suppliers to share not only results for finished products,<br />
but also data on line samples, historical data on lots manufactured on the same processing line<br />
or during a time frame around the time delivered lots were manufactured, environmental data or other<br />
relevant parameters. These data are more useful for the customer to confirm or modify their confidence<br />
level in a particular supplier and could be considered a certificate of conformance and compliance<br />
rather than a certificate of analysis.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-47278240710887302672011-09-02T17:59:00.000-07:002017-07-29T14:45:45.546-07:00How was rice differentiated into indica and japonica?The evolutionary dynamics of the indica-japonica differentiation was studied from<br />
the viewpoint of population genetics. Indica and japonica are distinguished by<br />
genes and characters associated with each other nonrandomly. In indica/japonica<br />
hybrid progenies, the same direction of gene and character association found<br />
among the cultivars was generally observed. This means a trend toward the<br />
restriction of recombination among several independent loci. Accordingly, intermediate<br />
types between indica and japonica are relatively infrequent, even if natural<br />
hybridization occurs frequently between them. lndicas and japonicas are isolated<br />
by the restriction of recombination in the hybrids.<br />
<a name='more'></a><br />
Two subspecies or ecogeographic races of common rice, indica and japonica, are<br />
represented by genes and characters associated nonrandomly among many cultivars<br />
(gene and character association). Oka (1958) defined the indica and japonica types as<br />
two varietal groups having associations of genes or phenotypes in contrasting states of<br />
phenol reaction ( Ph/ph ), apiculus hair length, KClO 3 susceptibility, and tolerance for<br />
cold and drought. Glaszmann (1987) reported that two major varietal groups represented<br />
by associations of alleles at 15 isozyme loci largely corresponded to the indica<br />
and japonica types defined by Oka (1958).<br />
Yet the causal factor of the indica-japonica differentiation among rice cultivars<br />
remains unknown. To elucidate the factors causing nonrandom association of genes<br />
and characters that result in the indica-japonica differentiation among cultivars, the<br />
pattern of their associations was studied in hybrid populations. Here I describe patterns<br />
of association in 12 genes and characters among many cultivars, and also in F 2 and F 5<br />
populations derived from an indica/japonica cross, and discuss the factors causing such<br />
nonrandom associations.<br />
Materials and methods<br />
Two hundred cultivars and 4 single seed descent populations derived from an indica/<br />
japonica cross were tested to examine 12 characters and genes.<br />
45<br />
Plant materials<br />
A sample of 200 native cultivars collected from various localities in Asia was used to<br />
represent varietal variation occurring in nature. The cultivars were classified into<br />
indica and japonica by the method described here.<br />
Hybrid populations used in this study were derived from the cross Acc. 419 (indica)/<br />
Acc. 504 (japonica). Both parents were included in the varietal sample. Acc. 419 was<br />
developed by pureline selection in India. Acc. 504 is the Taiwanese cultivar Taichung<br />
65 (T65), from a cross between two Japanese native cultivars. Acc. 419 is a typical<br />
indica, and T65 a typical japonica. They have different alleles at a number of loci, and<br />
different phenotypes.<br />
The F 2 population consisted of 200 individuals. The F 3 and F 4 populations were<br />
raised by the single seed descent method in which seed for the next generation is<br />
prepared as a bulk of a single seed from each plant of the previous generation. The F 3<br />
and F 4 populations consisted of 188 and 172 plants, respectively. The F 5 population was<br />
raised by bulking 2 seeds from each F 4 individual, and 300 plants were randomly<br />
chosen for analysis.<br />
Genes and characters examined<br />
All F 2 and F 5 plants were examined on a single-plant basis for phenol reaction ( Ph/ph,<br />
chromosome 4), susceptibility to KClO 3 at the two- or three-leaf stage (genes<br />
unknown), and apiculus hair length (in millimeters) to classify them as indica or<br />
japonica. The same characters were also recorded in the 200 cultivars. Measurement<br />
methods for these characters are described by Sato et al (1986). For quantitative<br />
numerical evaluation of the indica-japonica differentiation, a discriminant score (Z)<br />
was calculated for each cultivar by combining three characters as follows:<br />
Z=Ph + 1.313 K – 0.82 Hr – 1.251<br />
where Ph, K, and Hr indicate phenol reaction, KClO 3 susceptibility, and apiculus hair<br />
length, respectively. Ph is 1 if positive or 0 if negative. K varies from 0.0 (most<br />
resistant) to 2.0 (most susceptible). Hr value is given in millimeters.<br />
The hybrids and cultivars were also examined for pericarp color ( Rc/rc, chromosome<br />
7), apiculus color ( C/c, chromosome 6), hull color (black or straw, complementary<br />
action of Ph, Bh-a, and Bh-b ), and awn (gene[s] unknown), which segregated in<br />
the present cross. Furthermore, they were examined for five enzyme-encoding loci that<br />
also segregated in the hybrid populations: Est-2 (chromosome 6), Pgi-2 (chromosome<br />
6), Amp-2 (chromosome 8), Cat-1 (chromosome 6, but independent of Est-2 and Pgi-<br />
2 ), and Acp-1 (chromosome 12). Detailed descriptions of the methods of isozyme assay<br />
are given in Ishikawa et al (1987).<br />
Results<br />
Character and gene associations in the varietal sample<br />
Correlations among Ph, K, and Hr among the cultivars are indicated in Figure 1. K<br />
values showed continuous variation, but seemed to be divided into susceptible (W) and<br />
46 Y. -I. Sato<br />
1. Relations among phenol reaction, KClO 3 susceptibility, and apiculus hair length. R = resistant, W =<br />
susceptible to KClO 3 , L = long, S = short apiculus hair length, o = negative phenol reaction, = positive.<br />
How was rice differentiated into indica and japonica? 47<br />
resistant (R) types. Hr values showed continuous and unimodal variation. However,<br />
this character is controlled by the Aph locus; Aph carriers mostly have apiculus hairs<br />
longer than 0.7 mm, while aph carriers have ones shorter than 0.7 mm (Sato 1985).<br />
Cultivars used here were divided into long (L) and short (S) hair types, taking 0.7 mm<br />
as the dividing line.<br />
K and Hr showed a negative correlation. Cultivars of WL type were less frequent<br />
than WS, RL, or RS types. Cultivars of RL type frequently had the ph allele, while those<br />
of WS type had the Ph allele. Thus, the cultivars tended to be divided into Ph WS and<br />
ph RL types. These two types correspond to indica and japonica.<br />
The distribution pattern of Z values is shown in Figure 2. Typical indicas had<br />
positive Z values, and typical japonicas had negative Z values. The frequency of Z<br />
values for all cultivars showed a continuous but bimodal distribution, indicating that<br />
the cultivars used here showed a fair tendency to differentiate into indica and japonica<br />
groups.<br />
The pattern of association among nine genes and characters other than the three<br />
discriminant characters is illustrated in Figure 3. Solid and dotted lines indicate<br />
nonrandom associations significant at the 1% and 5% levels, respectively, which are<br />
based on correlation coefficients (between quantitative characters), chi-square values<br />
(between qualitative characters), or t values (between qualitative and quantitative<br />
characters). Of the 36 possible combinations of genes and characters, 25 (69.4%)<br />
showed nonrandom association.<br />
Of these nine genes and characters, five genes (other than pericarp color, hull color,<br />
awn, and Est-2 ) were used for varietal classification. The cultivars were classified by<br />
allelic association at these five loci. Fourteen of 32 possible combinations (25=32,<br />
Table 1) were found. Many cultivars were classified into a few representative<br />
2. Frequency distribution of Z values showing indica-japonica variation among 200 cultivars.<br />
48 Y .-I. Sato<br />
3. Association of 9 genes and characters among 200 cultivars. Significance at the 1% (solid lines) and 5%<br />
(dotted lines) levels.<br />
Table 1. Classification of 200 cultivars into 32 genotypes based on nonrandom<br />
association among 5 loci.<br />
Allele at<br />
Cultivars Coefficient of<br />
C Pgi-2 Cat-1 Acp-1 Amp-2 (no.) estrangement a<br />
C<br />
C<br />
C<br />
C<br />
C<br />
C<br />
C<br />
C<br />
C<br />
C<br />
C<br />
C<br />
C<br />
C<br />
1<br />
1<br />
1<br />
1<br />
1<br />
2<br />
1<br />
1<br />
2<br />
1<br />
2<br />
1<br />
1<br />
2<br />
2<br />
2<br />
1<br />
2<br />
1<br />
1<br />
1<br />
1<br />
1<br />
1<br />
1<br />
2<br />
1<br />
1<br />
Total<br />
2<br />
2<br />
2<br />
2<br />
2<br />
1<br />
1<br />
1<br />
1<br />
1<br />
2<br />
1<br />
1<br />
1<br />
1<br />
1<br />
1<br />
2<br />
2<br />
1<br />
1<br />
2<br />
1<br />
2<br />
2<br />
2<br />
2<br />
2<br />
61<br />
27<br />
2<br />
1<br />
2<br />
2<br />
1<br />
3<br />
2<br />
4<br />
5<br />
11<br />
16<br />
63<br />
200<br />
0<br />
1<br />
1<br />
2<br />
2<br />
3<br />
3<br />
3<br />
4<br />
4<br />
4<br />
4<br />
4<br />
5<br />
a Number of loci having alleles different from C Pgi-2 1 Cat-1 2 Acp-1 2 and Amp-2 1 type.<br />
How was rice differentiated into indica and japonica? 49<br />
genotypes, such as C Pgi-2 1 Cat-1 2 Acp-1 2 Amp-2 1 , c Pgi-2 1 Cat-1 2 Acp-1 2 Amp-2 1 , and<br />
c Pgi-2 2 Cat-1 1 Acp-1 1 Amp-2 2 . The observed frequency was different from the<br />
expected frequency calculated from random association of the genes. Genotype c Pgi-<br />
2 2 Cat-1 1 Acp-1 1 Amp-2 2 was the most frequent. Its reverse genotype, C Pgi-2 1 Cat-1 2<br />
Acp-1 2 Amp-2 1 and a similar one, c Pgi-2 1 Cat-1 2 Acp-1 2 Amp-2 1 , were also frequent.<br />
This indicates that the cultivars used tended to be differentiated into c Pgi-2 2 Cat-1 1<br />
Acp-1 1 Amp-2 2 and C Pgi-2 1 Cat-1 2 Acp-1 2 Amp-2 1 types.<br />
The Z values of the cultivars belonging to these three genotypes are indicated in<br />
Figure 2. All cultivars belonging to c Pgi-2 2 Cat-1 1 Acp-1 1 Amp-2 2 had positive Z<br />
values, while those belonging to C Pgi-2 1 Cat-1 2 Acp-1 2 Amp-2 1 and c Pgi-2 1 Cat-1 2<br />
Acp-1 2 Amp-2 1 had negative Z values. This means that the two most frequent<br />
genotypes, C Pgi-2 1 Cat-1 2 Acp-1 2 Amp-2 1 and c Pgi-2 2 Cat-1 2 Acp-1 1 Amp-2 2 ,<br />
correspond to indica and japonica, respectively.<br />
Character and gene association in hybrid populations<br />
The frequency distributions of Z values in the hybrid populations are shown in Figure<br />
4. In the F 2 , the Z values showed a continuous and unimodal distribution, indicating that<br />
indica-japonica differentiation did not occur. The mean of Z (1.27) was much higher<br />
than zero, the value separating indicas and japonicas. This may be because the alleles<br />
carried by indicas are largely dominant over japonica alleles. The mean of Z shifted<br />
toward the negative from the F 2 to the F 5 because more segregants showed negative<br />
values. The standard deviation of the Z value was 0.97 in the F 2 and became greater with<br />
each generation, reaching 1.28 in the F 5 —still lower than that among the cultivars<br />
(1.66). However, in the F 5 , the range of variation was as wide as that in the cultivars.<br />
4. Z values in hybrid populations. Standard deviations in F 2 , F 3 , F 4 , and F 5 populations were 0.97, 1.06, 1.16,<br />
and 1.28, respectively. Arrows indicate means.<br />
50 Y.-I. Sato<br />
The pattern of association among the nine genes and characters in the F 2 and F 5<br />
populations is shown in Figure 5. Of 36 possible combinations, 4 in the F 2 and 6 in the<br />
F 5 showed nonrandom associations. The associations found in the F 2 are probably due<br />
to linkage (e.g., c, Est-2 1 , and Pgi-2 1 ).<br />
In the F 5 population, three associations that did not appear in the F 2 were recovered.<br />
They are the associations between black hull and Rc (red pericarp), between Rc and<br />
Amp-2 2 , and between Est-2 1 and Acp-1 1 . In the associations between black hull and Rc,<br />
and between Est-2 1 and Acp-1 1 , parental combinations of alleles were more frequent<br />
than their recombinant types.<br />
Discussion<br />
Indicas and japonicas differ in genes and characters that are associated nonrandomly,<br />
as indicated by many authors (Glaszmann 1987, Oka 1958, Sato et al 1986), who<br />
concluded that no single gene representative of indica-japonica differentiation can be<br />
pointed out. The genetic mechanisms responsible for such nonrandom association<br />
among cultivars should be studied to elucidate factors causing indica-japonica differentiation.<br />
The bimodality of Z values and the nonrandom association among nine genes and<br />
characters recovered in the F 5 suggest that these gene and character associations could<br />
be constructed against randomizing forces. Nonrandom associations between genes<br />
and characters are not completely understood, even though considerable outcrossing<br />
occurs. This may indicate that indicas and japonicas are genetically distant, because<br />
recombinant or intermediate types decreased in the hybrids.<br />
Nonrandom association between alleles is caused by various evolutionary forces<br />
such as gametic selection, zygotic selection, random drift, linkage, and nonrandom<br />
mating in higher plants (Hedrick et al 1978). Artificial selection also plays an important<br />
role in cultivated species.<br />
In the F 2 of the indica-japonica hybrid population, Z values showed continuous<br />
variation. Moreover, the associations observed among the cultivars largely disappeared.<br />
These facts indicate that the gene and character associations found among<br />
5. Association among 9 genes and characters in F 2 and F 5 populations.<br />
How was rice differentiated into indica and japonica? 51<br />
cultivars are caused largely by natural or artificial selection. Random drift may not be<br />
a causal factor of the indica-japonica differentiation, because it has been repeatedly<br />
reported in Asian and African cultivars (e.g., De Kochko 1987).<br />
Associations found in the F 2 population are likely to be caused by linkage (e.g., C ,<br />
Est-2, and Pgi-2 ). However, three associations found in the F 5 but absent in the F 2 could<br />
not be explained by linkage. In fact, in those three associations, the relevant loci are<br />
known to be carried by different chromosomes ( Acp-1 and Est-2, Rc and Amp-2, and<br />
Rc and Bh-a, Bh-b ). In addition, Ph and Rc, which are located on different chromosomes,<br />
are also nonrandomly associated in the F 5 . Since the F 5 population was raised<br />
by the single seed descent method, the effect of zygotic selection must have been<br />
eliminated. Thus, these associations are considered to be caused by gametic selection.<br />
Gametic selection is caused by various mechanisms. Hybrid sterility, in which<br />
pollen having particular genes becomes abortive, is frequently observed in indica/<br />
japonica crosses (Ikehashi and Araki 1986; Oka 1953, 1974; Oka and Doida 1962;<br />
Yokoo 1984) and results in distorted segregation in the hybrids. Distorted segregation<br />
also occurs in distant crosses by certation, which bring about differential fertilization<br />
among normal pollen grains (e.g., Nakagahra 1972). However, it has been proven from<br />
a computer simulation study that hybrid sterility and certation do not cause the<br />
nonrandom associations found in hybrid populations (Nomura et al 1991). Perhaps the<br />
gene and character associations found in the F 5 were due to gametic selection caused<br />
by the differential fertilizing abilities of gametes with different genotypes. This trend<br />
of gametic selection may act as an internal mechanism for indica-japonica differentiation.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-58325652424403658112011-09-02T17:55:00.000-07:002017-07-29T14:46:39.854-07:00Corrective Actions to Reestablish Control5.1 Introduction<br />
The primary goal of a food safety system is to prevent, eliminate or reduce hazards to the extent<br />
feasible by existing technology. Food safety systems are based on knowledge of the potential hazards<br />
that can occur in food operations, through the process of hazard analysis. Control measures are then<br />
selected and applied to ensure the food will comply with requirements established by the manufacturer,<br />
customers and control authorities. It is in the interest of manufacturers to produce foods that<br />
consumers can rely upon as being safe.<br />
Many countries require food safety systems that incorporate the principles of Good Hygiene<br />
Practices (GHP) and Hazard Analysis Critical Control Point (HACCP) programs (Codex Alimentarius<br />
<a name='more'></a><br />
1997a, b). Evidence may reveal that the food operation is not or has not been in control and that corrective<br />
action is needed. This evidence may be from an on-site inspection, monitoring GHP, monitoring<br />
or verifying a critical control point (CCP), sample analysis, consumer complaints or epidemiologic<br />
information implicating the food operation.<br />
In the context of HACCP, corrective action is “any action to be taken when the results of monitoring<br />
at the CCP indicate a loss of control” (Codex Alimentarius 1997a). Furthermore, principle 5 of the<br />
Codex document on HACCP states:<br />
Specific corrective actions must be developed for each CCP in the HACCP system in order to deal with deviations<br />
when they occur. The actions must ensure that the CCP has been brought under control. Actions taken<br />
must also include proper disposition of the affected product. Deviation and product disposition procedures must<br />
be documented in the HACCP record keeping.<br />
In this chapter the focus is on microbiological hazards and corrective actions for deficiencies in<br />
GHP and from the marketplace are also considered.<br />
5.2 Good Hygiene Practices<br />
GHP can be viewed as the basic hygienic conditions and practices that must be maintained to produce<br />
safe foods. Effective application of GHP provides the foundation upon which a HACCP plan<br />
can be developed and implemented. Collectively, GHP and the HACCP plan constitute the food<br />
safety system for a food operation. Failure to maintain and implement effective pathogen controls<br />
Chapter 5<br />
Corrective Actions to Reestablish Control<br />
48 5 Corrective Actions to Reestablish Control<br />
through implementation of GHP can result in production of unsafe food and invalidate the HACCP<br />
plan. Spoilage and quality defects may also be more prevalent when GHP is not effectively<br />
applied.<br />
The General Principles of Food Hygiene (Codex Alimentarius 1997b) describe the major components<br />
of GHP as:<br />
• Design and facilities (location, premises and rooms, equipment facilities)<br />
• Control of operation (control of food hazards, key aspects of food hygiene control, incoming material<br />
requirements, packaging, water, management and supervision, documentation and records)<br />
• Maintenance and cleaning (maintenance and cleaning, cleaning programs, pest control systems,<br />
waste management, monitoring effectiveness)<br />
• Personal hygiene (health status, illness and injuries, personal cleanliness and behavior, visitors)<br />
• Transportation (general, requirements, use and maintenance)<br />
• Product information and consumer awareness (lot identification, product information, labeling,<br />
consumer education, handling/storage instructions)<br />
• Training (awareness and responsibilities, training programs, instruction and supervision, refresher<br />
training)<br />
The components of GHP do not carry equal weight for pathogen control. It is necessary to consider<br />
the microbial hazards that are most likely to occur in each facility and identify those elements of<br />
GHP that contribute most to controlling the pathogens and spoilage microorganisms of concern.<br />
Certain elements of GHP may require modification from traditional practice to increase their effectiveness<br />
for controlling a specific pathogen. The principles of GHP are intended to provide a certain<br />
level of control for a wide variety of microbiological quality and safety concerns. Application of<br />
HACCP is targeted towards specific microbial hazards which, if not controlled, can lead to foodborne<br />
disease.<br />
The result of verification activities may also indicate a deviation occurred in the implementation<br />
or application of GHP requiring the application of corrective actions.<br />
5.3 HACCP<br />
HACCP plans are developed following a stepwise process in which:<br />
1. A team of individuals knowledgeable about the food operation is assembled.<br />
2. The food being produced is described.<br />
3. The intended use of the food is described.<br />
4. A flow diagram that describes the steps in the process that are under the manufacturer’s control is<br />
prepared.<br />
5. An on-site confirmation of the flow diagram is conducted.<br />
6. All potential hazards are listed and a hazard analysis is conducted.<br />
7. CCPs are determined.<br />
8. Critical limits are established for each CCP.<br />
9. A monitoring system is established for each CCP.<br />
10. Corrective actions are established.<br />
11. Verification procedures are established.<br />
12. Documentation and record keeping procedures are established.<br />
The results of monitoring (step 9) may indicate a deviation occurred at a CCP and corrective actions<br />
(step 10) are necessary (Codex Alimentarius 1997a).<br />
5.4 Assessing Control of GHP and the HACCP Plan 49<br />
5.4 Assessing Control of GHP and the HACCP Plan<br />
Control means “the state wherein correct procedures are being followed and criteria are being met” and<br />
“to take all necessary actions to ensure and maintain compliance with criteria established in the HACCP<br />
plan” (Codex Alimentarius 1997a). The latter definition incorporates several aspects of the food safety<br />
system: establishing critical limits, monitoring to ensure compliance and making adjustments to maintain<br />
compliance with the criteria. Chap. 3 addresses verifying compliance with GHP and HACCP plans. This<br />
chapter addresses corrective actions to reestablish control. In an ideal food operation:<br />
• Criteria are supported by research and technical literature.<br />
• Criteria are specific, quantifiable and provide a yes/no response.<br />
• The technology for controlling microbial hazards is readily available and at reasonable cost.<br />
• Monitoring is continuous and provides immediate results, while the operation is automatically<br />
adjusted to maintain control.<br />
• There is a favorable history of control.<br />
• The potential hazard is prevented or eliminated completely.<br />
Ideal food operations, however, do not exist in the real world. Unfortunately, criteria cannot always<br />
be clearly defined and assessments of whether the food operation is in compliance with criteria must<br />
be based on the judgment and experience of an observer. In many cases, it may be possible to reduce<br />
but not prevent a hazard (e.g., enteric pathogens on raw seafood and agricultural commodities).<br />
Control frequently does not rely on a single measure but on a set of measures embedded in GHP and/<br />
or HACCP that all need to be functioning as designed during the course of operation. In some cases<br />
small changes to the product or processing may impact the effectiveness of control measures. Also,<br />
the effectiveness of control measures can range from partial reduction of certain hazards (e.g., salmonellae<br />
on raw poultry) to significant reductions of highly resistant hazards (e.g., Clostridium botulinum<br />
in low acid canned foods). Assessment of whether an operation is under control may vary among<br />
individuals with different backgrounds unless there is a common understanding (e.g., guideline, regulation)<br />
that clearly defines how to assess control.<br />
5.4.1 Assessing Control of GHP<br />
Many food operations establish written procedures to assess control of the GHP factors listed in Sect. 5.2.<br />
The two most common methods to assess control are visual inspection and microbiological sampling.<br />
Visual inspection is normally assigned to one or more trained experienced employees in the food operation.<br />
Inspections can also be performed by control authorities or third party auditors (ICMSF 2002).<br />
The time of at which inspections are carried out is important and depends on their purpose.<br />
Preoperational inspection is performed after the facility and equipment have been cleaned and sanitized<br />
to determine whether the equipment and processing environment are acceptable for the subsequent<br />
production. Attention may also be given to maintenance activities to be certain personnel<br />
follow procedures and do not contaminate the equipment during equipment maintenance, reassembly<br />
and start-up. Inspections during production should cover activities that can lead to product contamination,<br />
such as employee practices, product flow, build-up of residues, etc. Inspections that address<br />
plant construction and layout are less frequent, but are also important.<br />
Results from inspections are recorded and made available for review by those who need the information<br />
to respond appropriately. Organizing and evaluating the data for trend analysis can identify<br />
situations of improved or reduced control (ICMSF 2002). Timely review is essential so adjustments<br />
can be made in a timely manner and a deviation can be avoided.<br />
50 5 Corrective Actions to Reestablish Control<br />
Visual inspections provide one means of assessing GHP control but in many instances<br />
microbiological<br />
sampling can provide greater insight and a more accurate assessment of microbial<br />
control. For many facilities, it may be relevant to maintain a program of sampling equipment before<br />
production commences, as well as collecting samples from the equipment or the food during<br />
production.<br />
The samples may be tested for indicators (e.g., aerobic colony count, coliforms,<br />
Enterobacteriaceae)<br />
that reflect the hygienic conditions during processing. Additional tests for pathogens may be performed<br />
for certain products. Extensive guidance on microbiologicalsampling<br />
of the processing<br />
environment and food has been provided (ICMSF 2002), as well as in this book (see Chap. 4, and<br />
product chapters).<br />
For certain food operations the likelihood of resident pathogens and harborage sites must be considered<br />
(ICMSF 2002). If this is likely to occur, it may be necessary to establish an environmental<br />
sampling program to verify the effectiveness of the GHP procedures (ICMSF 2002). This information<br />
could be used to make adjustments in GHP to control one, or more, target pathogens that could<br />
become established in the food production environment and lead to contamination of the food.<br />
The basic components of a monitoring program to assess control of persistent pathogens in the<br />
processing environment include the following strategies:<br />
1. Preventing the establishment and growth of pathogens in harborage sites that can lead to the contamination<br />
of food.<br />
2. Implementing a sampling program that can assess in a timely manner whether the environment<br />
where the food is exposed is under control.<br />
3. Detecting the source or route of pathogen transfer that leads to contaminated food.<br />
4. Applying appropriate corrective actions in response to each positive finding of a target pathogen.<br />
5. Verifying, by follow-up sampling, that the source has been detected and corrected.<br />
6. Providing a short-term assessment (e.g., involving the last four to eight samplings) to facilitate the<br />
detection of problems and trends.<br />
7. Providing a longer-term assessment (e.g., quarterly, annually) to detect widely scattered incidents<br />
of pathogen detection and to measure overall progress toward continuous improvement.<br />
An inherent weakness in industry’s ability to detect and respond to pathogens in harborage sites is<br />
the difficulty and time needed to collect the samples and perform the analytical tests needed to detect<br />
the source(s) of contamination. A common issue is that all the investigational samples may test negative<br />
for the target pathogen and a clear direction for appropriate corrective actions is lacking.<br />
Furthermore, the pathogen may be detected again at some later date after the routine monitoring<br />
program has been resumed.<br />
Microbiological data should be recorded and made available for review by others who need to<br />
know the results so they can respond appropriately. In addition, the data should be organized and<br />
evaluated for trends toward improved or reduced control (ICMSF 2002). As with visual inspections,<br />
this information is essential so appropriate corrective actions can occur in a timely manner.<br />
5.4.2 Assessing Control of the HACCP Plan<br />
HACCP plans are formal, structured documents that are based on the seven principles of HACCP<br />
(Codex Alimentarius 1997a). The size and type of food operation will influence the content of the<br />
HACCP plan. Food operations that do not have a CCP that can prevent, eliminate or reduce the hazards<br />
of concern may not have a HACCP plan. Smaller operations, such as street food vendors, may<br />
rely more on regulations or guidelines from health authorities that emphasize GHP.<br />
For larger operations that have HACCP plans, control is assessed through the monitoring and verification<br />
activities stated in the HACCP plan. The HACCP plan should include corrective actions for<br />
the deviations that are likely to occur (step 10 in Sect. 5.3).<br />
5.5 Corrective Actions 51<br />
5.5 Corrective Actions<br />
5.5.1 Corrective Actions for GHP<br />
Information about how microbial hazards can be introduced is necessary to design a food operation<br />
and implement appropriate control procedures. It is not unusual to occasionally detect weaknesses in<br />
the design and implementation of GHP, which requires corrective action. Typical corrective actions<br />
associated with GHP involve the factors listed in Sect. 5.2. For example, microbiological data might<br />
indicate improvements are needed in how processing rooms or equipment are cleaned and sanitized.<br />
This could involve training individuals on the correct procedures, changing the method or frequency<br />
of cleaning and sanitizing, or performing maintenance and repair on equipment. When food operations<br />
increase production or add new products, this may result in an unacceptable increase in risk that<br />
the food may become contaminated and may require a change in the plant layout. Another common<br />
corrective action for GHP is retraining employees who have not followed established procedures for<br />
personal hygiene, food handling or following the traffic pattern that separates raw ingredient processing<br />
and areas where ready-to-eat foods are handled.<br />
When equipment is suspected to be a persistent source of contamination, corrective action may<br />
include complete dismantling of the equipment to allow more thorough cleaning and sanitizing of the<br />
parts before reassembling. For small equipment with many parts, cleaning in a recirculating bath of<br />
hot water with detergent (e.g., Clean Out of Place (COP) tank) is effective. COP cleaning requires<br />
placement of parts in a way that assures adequate circulation of the cleaning solution for optimum<br />
results. These procedures are normally adequate and the preferred corrective action. As equipment is<br />
being dismantled, sampling sites suspected of harboring microbial contaminants can provide useful<br />
information that can be used to change maintenance and cleaning procedures. For example, the<br />
equipment may need to be modified for more effective cleanability. In some situations, lubricants<br />
may be a potential harborage site for contamination, and use of food-grade antimicrobial lubricants<br />
may be an appropriate corrective action.<br />
Occasionally, even extensive dismantling and cleaning will prove ineffective. For equipment that<br />
can be moved, heating with moist heat in a chamber, after sensitive electronics, oil, and grease are<br />
removed, can be effective. If this is not possible, the equipment can be covered with a heat-resistant<br />
tarpaulin and steam can be introduced from the bottom. When these moist heating techniques are<br />
used, an internal temperature of 71°C for 20–30 min is recommended to eliminate vegetative cells.<br />
The temperature can be monitored with thermocouples placed within the equipment or thermometers<br />
that pierce through the tarpaulin. Of course, equipment such as drying towers for dried milk products<br />
and many closed systems must be cleaned and sanitized in-place.<br />
To regain control, it is helpful to determine the source of the contamination so that appropriate<br />
corrective actions can be taken. Investigational samples are analyzed individually rather than as composites,<br />
samples are collected more frequently (e.g., every four hours) and additional sites are<br />
included. A simple map showing the layout of the rooms and the equipment can be beneficial.<br />
Positive sites are marked on the map with the dates and times of collection. A very simple schematic<br />
drawing or a blueprint of the facility can be used. By organizing the results to show which sites test<br />
positive more frequently and where the positive samples first occur, the source of contamination can<br />
be more easily located. In an environment that has been in control, this will often identify specific<br />
equipment that is a harborage for the contaminant. In general, contamination flows down along or<br />
through processing equipment with the flow of product. Fingerprinting isolates can be a very useful<br />
tool for identifying the source and pathways of contamination.<br />
Exposed surfaces of equipment may be transfer points but generally are not sources of contaminants<br />
due to their ease of cleaning and sanitizing. Of greater concern are enclosed areas (e.g., within<br />
a hollow roller for a conveyor) where food deposits and moisture accumulate and cannot be removed<br />
52 5 Corrective Actions to Reestablish Control<br />
by normal cleaning, scrubbing, and sanitizing. These harborage sites are not necessarily biofilms per<br />
se, but rather sites in which a variety of bacteria become established and multiply.<br />
To achieve continuous improvement and long-term control, corrective actions may involve<br />
changes in the plant layout, equipment design or maintenance, replacing floors or walls, or changing<br />
the procedures for cleaning and sanitizing. In the event construction is required, extra precautions<br />
must be taken to control the pathogen and prevent food from becoming contaminated during the<br />
construction process.<br />
5.5.2 Corrective Actions for HACCP<br />
Seven possible corrective actions are appropriate to consider when a deviation occurs at a CCP within<br />
the HACCP plan:<br />
1. If necessary, stop the operation<br />
2. Place all suspect product on hold<br />
3. Provide a short-term resolution or “fix” so that production can be safely resumed and additional<br />
deviations will not occur<br />
4. Verify that the short-term fix has been effective and recurrences do not occur<br />
5. Identify and correct the root cause for failure to prevent future deviations<br />
6. Collect the necessary information to decide what to do with suspect product<br />
7. Record what happened and the actions taken<br />
8. If necessary, review and improve the HACCP Plan<br />
The corrective actions must bring the food operation into compliance with established criteria and<br />
ensure safe disposition of the product involved. Corrective actions should be considered in advance<br />
for each CCP in the HACCP plan; however, it is unrealistic to anticipate and prepare for all the possible<br />
deviations that can occur.<br />
5.5.3 Response to Epidemiologic Evidence and Complaints<br />
When an epidemiologic investigation implicates a specific food as the likely cause of illness or when<br />
consumer complaints provide such an implication, the root cause(s) leading to disease may not be<br />
immediately apparent. While removal of the implicated food may prevent additional consumer exposure,<br />
the corrective actions necessary to prevent future cases of disease may be unclear. Detailed<br />
review of the relevant operations before and during the period of likely contamination along with<br />
extensive microbiological evaluation of the environment, ingredients and finished foods may reveal<br />
information about the root cause(s). Food and environmental isolates should be compared with<br />
human clinical isolates to confirm, as clearly as possible, the source(s) of the pathogen and root<br />
causes. When the location within the food chain is identified as the likely source, every effort should<br />
be made to determine the important factors involved so adjustments can be made to existing control<br />
measures (i.e., GHP, HACCP) to prevent additional outbreaks.<br />
It is possible that a thorough evaluation of the food implicated by the epidemiologic investigation<br />
will correctly reveal a food system under good control without obvious defects in GHP and HACCP<br />
plans or their implementation, despite the presence of pathogens at a frequency and concentration<br />
sufficient to cause disease. This scenario is more likely to occur when raw agricultural commodities<br />
are involved and existing technology and food safety controls can reduce but not prevent or eliminate<br />
the hazard. While it remains appropriate to prevent additional exposures to the implicated food, this<br />
situation may require issuance of a consumer advisory for persons at risk. A consumer advisory on<br />
5.6 Options for Disposition of Questionable Product 53<br />
the retail package to properly store, prepare and cook raw meat and poultry products is one such<br />
example. Information from public health agencies to physicians and other health care providers who<br />
advise high risk patients is another example.<br />
5.6 Options for Disposition of Questionable Product<br />
If control is lost and a deviation occurs, several options may be considered for disposition of suspect<br />
product:<br />
1. Determine whether the suspect product complies with existing criteria for safety and can be used<br />
as intended. To assess the acceptability, a sampling plan can be applied, keeping in mind the limitations<br />
of the sampling plan to detect lots with defects that are of very low prevalence (Appendix A<br />
and ICMSF 2002). In some situations dividing the lot(s) into smaller portions (e.g., pallet, hourly)<br />
may be considered, with sampling and testing of each portion or sub-lot as separate entities. This<br />
increases the number of samples across the total production and also provides information about<br />
distribution of the defect. Testing sub-lots should be evaluated carefully. See Sect. 5.6.1 for further<br />
considerations.<br />
2. The suspect product can be diverted to a safe use. For example, eggs or cooked chicken contaminated<br />
with salmonellae could be used as ingredients in the manufacture of a commercial product<br />
that will receive a kill step that can ensure the food will be safe.<br />
3. The suspect food could be reprocessed, if reprocessing will destroy the hazard.<br />
4. The suspect food could be destroyed.<br />
Reaching a decision on the appropriate disposition of non-compliant product is influenced by a number<br />
of factors. First is the severity or the seriousness of the hazard. For example, does the potential<br />
defect consist of spoilage or could it be a severe hazard such as botulinum toxin? Second is the type<br />
of microbial hazard. For example, staphylococcal enterotoxin is very heat stable and its presence in<br />
a food would render the food unacceptable for human consumption in any manner. Third is the likelihood<br />
of the hazard being present in the food. Is it one chance in a million or is it likely to occur every<br />
time the deviation occurs? Fourth is how the food will be stored, shipped, and prepared. Fifth is who<br />
will prepare the food. Sixth is whether the intended consumers include highly susceptible individuals.<br />
Each of these factors and, perhaps, others should be considered before reaching a recommendation<br />
on the disposition of the product.<br />
5.6.1 Sub-Lot Testing Considerations<br />
No sampling plan, other than one that tests the entire lot, can prove that the lot is not contaminated.<br />
Thus, while the term “zero tolerance” is often used, in actuality sampling to assess compliance can only<br />
provide a certain level of confidence that the contamination level is below some mean concentration.<br />
That concentration depends on the size and number of analytical units tested, and the variance<br />
in the<br />
concentration of the pathogen in the lot. From statistical standpoint the size of a lot does not influence<br />
the performance of a sampling plan. An example from probability statistics can help explain why this<br />
is true. If a die is tossed 100 times and the numbers are recorded and then 3 random numbers from 1 to<br />
6 are determined, there is a certain probability of having a “1” in the set of 3. If the die is tossed 1,000<br />
times, the probability of having a “1” in the set of three random numbers is the same as that for tossing<br />
the die 100 times.<br />
If contaminating cells are distributed randomly throughout a lot, creating five sub-lots is equal to<br />
taking 5 times the number of samples from the lot, and the average concentration of a microbial population<br />
would remain valid for the whole lot and not just the sub-lots. However, in many instances<br />
54 5 Corrective Actions to Reestablish Control<br />
microorganisms are not randomly distributed. Examples of situations that may alter distribution of<br />
contamination during processing include introduction of water from a leaking roof or a drain back-up<br />
at one point in time, a change in raw materials, equipment being inserted into the process, mechanical<br />
breakdown of equipment, production interruptions for cleaning, a function of production time, and<br />
other specific events. In such cases, it is not a good assumption to define this as a uniform lot, and<br />
sub-lotting may assist in identifying trends and defective portions of the lot.<br />
The application of testing sub-lots should be evaluated very carefully. Elements to consider are:<br />
• Readily available microbiological data on pathogens as well as indicator organisms from the lot<br />
in question<br />
• Data for lots manufactured before and after as well as in-process and/or environmental samples<br />
• Data on processing parameters<br />
• The type of microbiological hazard, its severity and its fate during further handling, i.e., the likelihood<br />
that it could increase or decrease prior to consumption, as well as the sensitivity of the<br />
consumer, etc.<br />
5.7 Repetitive Loss of Control<br />
The HACCP concept has gained wide acceptance because it provides a logical, structured approach<br />
to prevent, eliminate or reduce hazards in foods. The system is designed to detect loss of control and,<br />
thereby prevent suspect food from reaching consumers. This is an essential component of the food<br />
safety system because deviations can and will occur during the normal course of operation. Preventing<br />
repetitive deviations for GHPs and CCPs is a desirable goal but may be difficult to achieve in some<br />
food operations. Each food operation should strive to prevent repetitive loss of control by implementing<br />
a continuous improvement program to achieve greater reliability for controlling GHPs and CCPs<br />
within the food safety system.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-68788966897571831912011-09-01T15:46:00.000-07:002017-07-29T14:47:16.796-07:00Screening and analysis of wide compatibility loci in wide crosses of riceWith indica and japonica testers to screen out wide compatibility types, a number<br />
of varieties seemed to be indicas but differed from them by showing semisterility<br />
in crosses with Ketan Nangka, a donor of the wide compatibility allele (neutral<br />
allele). Another varietal group showed good fertility with indica and japonica<br />
testers, but revealed sterility in crosses with Ketan Nangka. Thus, Ketan Nangka<br />
is suggested as a standard variety, along with the aus varieties, which show semisterility<br />
in crosses with indica and japonica testers but normal fertility with most<br />
aus varieties. A set of four varieties—Achar Bhog, Ketan Nangka, IR36, and a<br />
japonica type—is proposed as standard testers for hybrid sterility. F 1 hybrid<br />
sterility in rice is understood with allelic interactions at the S-5 locus. With the<br />
identification system for S-5, a large number of crosses were made to test the<br />
extent to which the neutral allele at the S-5 locus is effective. Hybrid sterility in<br />
Penuh Baru II and aus varieties, which is not explained by the testers for S-5, was<br />
found to be due to an additional locus rather than to a new allele. The neutral<br />
allele at the S-5 locus can now be effectively used, but a new neutral allele<br />
indicated by Dular would also be important in rice breeding.<br />
<a name='more'></a><br />
Hybrid sterility limits the application of wide crossing in rice breeding and lowers the<br />
productivity of hybrid rices. Its genetic basis was not understood for a long time.<br />
Recently, it was found that the F 1 sterility of hybrids between indica and japonica<br />
varieties is caused by an allelic interaction at a locus at which the indicas have S-5 i , the<br />
japonicas have S-5 j , and some javanicas have a neutral allele, S-5 n . The donor of S-5 n<br />
is termed a wide compatibility variety (WCV). The S-5 i /S-5 j genotype is sterile because<br />
of the abortion of gametes carrying the S-5 j allele, but S-5 n /S-5 i and S-5 n /S-5 j are fertile<br />
(Ikehashi and Araki 1986). Allele S-5 n has been incorporated as a wide compatibility<br />
gene into indica and japonica backgrounds to overcome sterility problems in wide cross<br />
and hybrid rice breeding (Araki et al 1988). This approach has been promising for<br />
breeding commercial hybrids between indicas and japonicas. A record of very high<br />
yield is reported in indica/japonica hybrids using the wide compatibility allele<br />
(Maruyama 1988).<br />
Following initial successes in utilizing S-5 n , a number of problems emerged: the<br />
extent to which the locus is effective, the possibility of an additional locus, and systems<br />
33<br />
to identify the wide compatibility alleles. So far, the S-5 n allele seems to be effective<br />
for most typical indicas and japonicas. However, in the expansion of our screening of<br />
WCVs, many varieties were found for which S-5 n was not effective. Hybrids between<br />
some aus varieties and javanicas including WCVs show clear semisterility, while some<br />
aus varieties such as Dular show fertility when crossed with javanica WCVs as well as<br />
with indicas or japonicas. As hybrid sterility caused at the S-5 locus can be identified<br />
with tester varieties and marker genes, it was possible to know whether the hybrid<br />
sterility between javanica and aus varieties is caused at the S-5 locus or not. Contrasting<br />
results between the javanica WCV and Dular in their crosses with aus varieties<br />
appeared to be caused at a new locus at which Dular has another neutral allele and the<br />
javanicas and aus varieties each have interacting alleles. Genetic analyses suggested<br />
that a new locus is linked with Rc (red pericarp) in linkage group IV. Simultaneous use<br />
of neutral alleles at the two loci may solve the F 1 sterility problem in practically all kinds<br />
of crosses in rice.<br />
A set of indica and japonica varieties has been used for screening WCVs. With our<br />
expanded knowledge of the types of hybrid sterility, it is now necessary to use more<br />
varieties as standards for screening WCVs. Several varieties are suggested.<br />
Male sterility was recorded for individual plants in all the experiments, but no<br />
relationship was found between male sterility and the marker genes. Information is<br />
necessary to identify the genetic basis of male sterility; however, spikelet sterility is<br />
discussed here for simplicity. So far, pollen semisterility does not seem to lower the<br />
seed fertility of indica/japonica hybrids.<br />
Hybrid sterility within and between varietal groups<br />
To identify WCVs, more than 80 varieties, most of which were aus or javanicas, were<br />
crossed with indica and japonica tester varieties, and the pollen and spikelet fertility of<br />
the F 1 hybrids was examined (Ikehashi and Araki 1984). Along with the screening, a<br />
large number of crosses were made to determine compatibility types in each varietal<br />
group. The compatibility types of a given variety are here defined by hybrid sterilities<br />
shown in a set of crosses between the variety and testers. In the initial tests, several<br />
compatibility types in the javanica as well as in the aus varieties were found with the<br />
use of indica and japonica tester varieties. Additional crosses were made between the<br />
different types within each varietal group, and the F 1 hybrids were tested for fertility<br />
from 1983 to 1985. Many crosses were also made between javanica and aus varieties<br />
in the same period. All the tests were conducted at the Okinawa Branch of the Tropical<br />
Agriculture Research Center, where spikelet fertility is least affected by cold temperature<br />
due to the subtropical climate. For all the crosses, pollen and spikelet fertilities of<br />
the F 1 hybrids were determined by a standard method (Ikehashi and Araki 1984).<br />
Compatibility types in javanicas<br />
Following the identification of WCVs such as Ketan Nangka, Calotoc, and CPSLO-<br />
17, other compatibility types in the javanica group were found. Of 24 javanicas, 15<br />
34 Ikehashi et ai<br />
were classified into 1 group based on their high pollen fertility with both the indica and<br />
japonica testers, normal spikelet fertility with the japonica tester, and clear semisterility<br />
with the indica tester. These 15 varieties were designated Banten types. Six<br />
varieties showed semisterility in their crosses with indicas as well as japonicas. A<br />
representative variety, Penuh Baru II, was selected from this group. Only one variety,<br />
Padi Bujang Pendek, was identified as a WCV. The rest seemed to be exceptional.<br />
Hybrids between types of javanicas<br />
The six javanicas that showed semisterility in their crosses with the indica and japonica<br />
testers showed normal fertility in their cross with Ketan Nangka, a javanica WCV, and<br />
with varieties of the Banten group. It was thus concluded that, with a few exceptions,<br />
F 1 hybrids between different compatibility types in the javanica group show normal<br />
fertility.<br />
Compatibility types in aus varieties<br />
Forty-one aus varieties were tested; all the data are presented by Ikehashi and Araki<br />
(1987). The fertilities of some hybrids between aus varieties and indica or japonica<br />
testers are shown in Table 1. Aus 373 and Dular seemed to be widely compatible,<br />
although the pollen fertility in their crosses with IR varieties was marginal. Next to<br />
these two, five varieties including Panbira showed good fertility with the testers. A<br />
majority of 18 varieties were not classified into any definite category. Of them, five<br />
including Achar Bhog showed semisterility with indica and japonica testers. In many<br />
cases, the hybrid sterility exhibited by these varieties was marginal, leaving some<br />
possibility of reclassification.<br />
Table 1. Spikelet fertility (%) of F 1 hybrids between testers and some aus varieties, 1984-85. a<br />
Spikelet fertility (%)<br />
Variety Source Testers Javanicas<br />
Japonicas IR36 Ketan Banten Penuh<br />
Nangka Baru II<br />
Achar Bhog<br />
Aus 373<br />
CH972<br />
D1123<br />
Dular<br />
lngra<br />
Kaladumai<br />
Kele<br />
Panbira<br />
Prambu Vattan<br />
Satika<br />
Acc. 25826<br />
Acc. 29158<br />
200011<br />
Acc. 8455<br />
200041<br />
Acc. 27552<br />
200040<br />
210013<br />
VT. 64<br />
200049<br />
210003<br />
71.1<br />
88.0<br />
30.5<br />
87.2<br />
83.7<br />
61.4<br />
94.7<br />
69.4<br />
84.8<br />
89.1<br />
81.8<br />
62.0<br />
91.3<br />
70.9<br />
86.7<br />
68.3<br />
81.1<br />
88.2<br />
92.1<br />
34.0<br />
54.4<br />
84.8<br />
59.2<br />
50.1<br />
65.3<br />
57.9<br />
90.9<br />
48.8<br />
97.5<br />
22.6<br />
18.1<br />
92.4<br />
60.1<br />
39.1<br />
24.0<br />
64.4<br />
89.6<br />
85.5<br />
50.0<br />
25.0<br />
81.4<br />
48.6<br />
50.0<br />
58.6<br />
73.1<br />
89.2<br />
44.6<br />
54.7<br />
59.4<br />
a Details in lkehashi and Araki (1987).<br />
Wide compatibility loci in wide crosses of rice 35<br />
–<br />
–<br />
–<br />
–<br />
Hybrids between aus varieties<br />
Because there were different compatibility types in the aus varieties—some were like<br />
indicas and others like japonicas—many varieties from different types were chosen<br />
and crossed with each other to test the fertility of the F 1 hybrids. The fertility of such<br />
hybrids was normal regardless of the compatibility type of the parent variety.<br />
Exceptionally low fertility was found only in the crosses of Prambu Vattan, which<br />
seemed to be a japonica in various aspects.<br />
Crosses between aus varieties and javanica WCVs<br />
Some aus varieties were crossed with WCVs, and the fertility of the F 1 hybrids was<br />
examined. The fertilities of some hybrids between aus varieties and Ketan Nangka are<br />
shown in Table 1. Many aus varieties showed hybrid sterility in their crosses with Ketan<br />
Nangka. Two japonica-like varieties—Kaladumai and Prambu Vattan—and Dular<br />
showed normal fertility in the cross with Ketan Nangka (Table 1).<br />
To determine whether semisterility is a common fact in F 1 hybrids between WCVs<br />
and aus varieties, additional aus varieties were crossed with other WCVs, viz., Calotoc<br />
and CPSLO, and with other javanicas. With some exceptions, the F 1 s between WCVs<br />
and aus varieties showed semisterility. Earlier, it was indicated that the Penuh Baru<br />
group of javanicas and some aus varieties such as Achar Bhog were similar in their<br />
semisterility both with indica and japonica testers; however, F 1 hybrids between<br />
Penuh Baru II and some aus varieties such as Achar Bhog showed clear semisterility<br />
(Table 1).<br />
Tests of South Indian varieties<br />
Varieties from South India or Sri Lanka were tested together with additional varieties<br />
in the aus group (Table 2). Karalath, Pusur, and Eat Samba showed good fertility in<br />
their cross with the indica or japonica testers, but not so with Ketan Nangka. Only<br />
Table 2. Spikelet fertility (%) of F 1 hybrids between testers and aus or South Indian varieties,<br />
1985.<br />
Spikelet fertility (%)<br />
Tester Aus varieties South Indian varieties<br />
Karalath Pusur Surjamukhi Triveni Eat Dahanala<br />
Samba<br />
Japonicas<br />
IR36<br />
Ketan Nangka<br />
Panbira<br />
Achar Bhog<br />
91.7<br />
77.2<br />
58.8<br />
91.6<br />
85.1<br />
91.1<br />
47.2<br />
95.0<br />
96.2 a<br />
41.4 a<br />
91.2 a<br />
83.1 a<br />
38.7 a<br />
85.7 a<br />
37.9<br />
98.6<br />
90.7 a<br />
87.5 a<br />
49.9 a<br />
45.0 a<br />
74.3 a<br />
67.6 a<br />
a Used as pollinator.<br />
36 lkehashi et al<br />
– –<br />
– – –<br />
–<br />
–<br />
–<br />
Surjamukhi showed high fertility with Ketan Nangka. Triveni and Dahanala were<br />
definitely not classified into indicas or japonicas. These varieties from South India or<br />
Sri Lanka seemed to be similar to most aus varieties in that they showed semisterility<br />
when crossed with Ketan Nangka.<br />
Tests of varieties from Bhutan, China, and Korea<br />
Crosses between some testers and Asian varieties gave the results shown in Table 3.<br />
Two improved Korean lines and Nanjing 11 seemed to be typical indica types. Three<br />
native varieties from China were similar to indicas but differed in their lower fertility<br />
with Ketan Nangka. They were similar to Triveni and Dahanala. The Bhutan varieties<br />
Jyakuchem and Kuchem showed good fertility both with indica and japonica testers,<br />
suggesting their wide compatibility. Jyakuchem was found to possess S-5 n .<br />
Standard varieties for identifying compatibility types<br />
The compatibility tests revealed a number of varieties in China and India that can be<br />
identified as indicas with the use of the indica-japonica testers. But they differ from<br />
indica testers such as IR36 and IR50 in their low compatibility with Ketan Nangka.<br />
Examples of such varieties are CH972, Triveni, Dahanala, Pe-Bi-Hun, and Tao-Jen-<br />
Chiao. Another type shows good fertility with both indica and japonica testers but<br />
significantly lower fertility in crosses with Ketan Nangka. To this type belong Aus 373,<br />
Panbira, Pusur, and Eat Samba. Ketan Nangka can thus be considered a standard<br />
variety. Whether or not a hybrid between a given variety and Ketan Nangka shows<br />
semisterility can be a criterion for classifying compatibility types. As most aus varieties<br />
show good fertility in crosses with each other, aus variety Achar Bhog was also selected<br />
as a standard variety. This variety shows semisterility in its cross with indica and<br />
japonica testers, so that any variety showing good fertility when crossed with it may<br />
be a kind of aus. Thus, a set of four varieties—Achar Bhog, Ketan Nangka, IR36, and<br />
japonica variety Taichung 65 or Akihikari—would be useful to identify compatibility<br />
types.<br />
Table 3. Spikelet fertility (%) of F 1 hybrids between testers and some Asian varieties<br />
showing atypical performance.<br />
Spikelet fertility (%)<br />
Tester Korea (1984) China (1984) Bhutan (1985)<br />
Milyang Suweon Pi-bi-hun Tuan-ku- Tao-jen- Nanjing Jyaku- Kuchem<br />
23 258 chao chiao 11 chem<br />
Japonicas<br />
IR36<br />
Ketan Nangka<br />
Achar Bhog<br />
Aus 373<br />
38.5<br />
85.5<br />
96.8<br />
64.6<br />
44.8<br />
86.5<br />
92.8<br />
87.8<br />
93.6<br />
41.7<br />
89.2<br />
44.8<br />
74.2<br />
86.5<br />
37.9<br />
94.9<br />
83.3<br />
98.4<br />
12.5<br />
82.1<br />
44.8<br />
57.8<br />
89.5<br />
48.5<br />
94.3<br />
94.8<br />
89.7<br />
93.6<br />
90.2<br />
90.5<br />
84.1<br />
89.7<br />
71.4<br />
Wide compatibility loci in wide crosses of rice 37<br />
–<br />
–<br />
–<br />
– –<br />
– –<br />
Genetic analysis of hybrid sterility between varietal groups<br />
A large number of crosses were made to test compatibility types of varieties, and Penuh<br />
Baru II was found to show clear semisterility in its cross with indica as well as japonica<br />
testers, suggesting that the tester system for S-5 is not applicable to this variety. Also,<br />
many aus varieties showed clear semisterility with javanica WCVs such as Ketan<br />
Nangka and Calotoc. Some aus varieties were crossed for genetic analysis.<br />
New locus suggested by a javanica variety<br />
Among the compatibility types in the javanica group, Penuh Baru II was found to<br />
produce semisterile F 1 hybrids when crossed with japonica and indica testers, while<br />
producing fertile F 1 s in its cross with Ketan Nangka. In the three-variety cross Ketan<br />
Nangka/Penuh Baru II//IR50, close linkages were found between spikelet fertility and<br />
the wx or C gene from Ketan Nangka (Table 4). In the complementary cross, Ketan<br />
Nangka/IR36//Penuh Baru II, the same linkage relationship was shown (Table 4).<br />
Therefore, Ketan Nangka’s S-5 n allele, which is closely linked with the wx and C genes,<br />
must be allelic to the sterility-causing allele in the F 1 between the indicas and Penuh<br />
Baru II. Since the F 1 hybrids from Penuh Baru II and indicas are semisterile, the allele<br />
possessed by Penuh Baru II must be different from S-5 i . On the other hand, the sterility<br />
of the F 1 of Penuh Baru II and japonica variety Akihikari was not affected by the locus<br />
near C and wx in the related cross of Ketan Nangka/Akihikari//Penuh Baru II (Table<br />
4). The compatibility relation among indica, javanica, and japonica varieties can thus<br />
be ascribed to an allelic interaction at the S-5 locus. However, the sterility of the F 1 s of<br />
Table 4. Distribution among 8 fertility classes (0–30 to 91–100%) of spikelet fertility in 3-<br />
variety crosses with Ketan Nangka and Penuh Baru II.<br />
Plants (no.) in each fertility class<br />
Marker Total Mean<br />
0-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 (no.) (%)<br />
C/C +<br />
C + /C +<br />
wx/+<br />
+/+<br />
C/C +<br />
C + /C +<br />
wx/+<br />
+/+<br />
C/C +<br />
C + /C +<br />
wx/+<br />
+I+<br />
1<br />
1<br />
3<br />
3<br />
2<br />
2<br />
3<br />
3<br />
Ketan Nangka/Penuh Baru ll/lR50 (1984)<br />
1 7 16<br />
7 14 1<br />
2 3 6 13<br />
6 11 1 4<br />
Ketan Nangkal/lR36//Penuh Baru II (1985)<br />
1<br />
6<br />
2<br />
5<br />
26<br />
2<br />
24<br />
2<br />
21<br />
9<br />
14<br />
2<br />
1<br />
1<br />
12<br />
9<br />
3<br />
1<br />
5<br />
3<br />
3<br />
3<br />
2<br />
3<br />
2<br />
Ketan Nangka/Akihikari//Penuh Baru II (1985)<br />
14 15 23<br />
9 11 15<br />
15 16 21<br />
8 10 17<br />
3<br />
1<br />
2<br />
42<br />
29<br />
13<br />
4<br />
6<br />
5<br />
5<br />
27<br />
25<br />
25<br />
27<br />
57<br />
58<br />
52<br />
63<br />
63<br />
48<br />
66<br />
45<br />
82.1<br />
50.9<br />
76.5<br />
58.5<br />
91.7<br />
52.1<br />
83.3<br />
61.4<br />
73.7<br />
74.2<br />
73.4<br />
76.2<br />
38 lkehashi et al<br />
Penuh Baru II and the japonicas must be caused at a locus other than S-5, where Ketan<br />
Nangka and indicas may have a neutral allele, since the sterility of the two varieties is<br />
caused only at the S-5 locus (Fig. 1).<br />
Detection of S-5 n in wide compatible aus varieties<br />
It has been indicated that aus varieties include various compatibility types in terms of<br />
F 1 sterility with indica and japonica testers, and that the types of compatibility are likely<br />
to differ from those of other groups. Therefore, the same method that applied to the<br />
analysis of WCVs was attempted for hybrids between aus varieties and the other types.<br />
Aus 373 seemed to be a WCV and was tested in a three-variety cross using indica<br />
and japonica testers (Table 5). Spikelet fertility in the cross Aus 373/IR50//Akihikari<br />
was related to that of the genotype of C/C + , suggesting that an allelic interaction<br />
between an allele from IR50 and another from Akihikari was responsible for spikelet<br />
sterility. Therefore, allelic interaction at the S-5 locus was indicated. Similarly, Dular<br />
and Pusur were found to show normal fertility in their crosses with indica and japonica<br />
1. Loci for hybrid sterility in Penuh Baru II. In its cross with IR36, Penuh Baru II reveals sterility due to<br />
allelic interaction at the S-5 locus. Its sterility in crosses with japonicas is due to another locus, where both<br />
Ketan Nangka and IR36 have a neutral allele.<br />
Wide compatibility loci in wide crosses of rice 39<br />
Table 5. Detection of wide compatibility allele near C locus by differentiating spikelet<br />
sterility in crosses to indica and japonica testers.<br />
C/C +<br />
C + /C +<br />
C/C +<br />
C + /C +<br />
C/C +<br />
C + /C +<br />
alk/ +<br />
+I+<br />
1<br />
5<br />
1<br />
4<br />
2<br />
3<br />
1<br />
2<br />
1<br />
4<br />
6<br />
4<br />
2<br />
8<br />
3<br />
7<br />
2<br />
10<br />
4<br />
7<br />
2<br />
8<br />
Total<br />
(no.)<br />
Plants (no.) in each fertility class<br />
Marker<br />
genes 0-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100<br />
Aus 373/lR50//Akihikari (1983)<br />
8 6 9 4 2 33<br />
5 3 7 1 26<br />
Pusur/lR36//Akihikari (1985)<br />
1 5 4 22<br />
12 11 3 1<br />
Akihikari/Dular//Tae baekb (1988)<br />
6 8 12 30 25<br />
13 15 18 18 5<br />
7 6 10 30 25<br />
12 17 20 18 4<br />
12<br />
7<br />
7<br />
46<br />
46<br />
99<br />
84<br />
91<br />
90<br />
** = significant at the 1% level. Wigh-yielding indica variety from Korea.<br />
Mean<br />
(%) test a<br />
t-<br />
57.5<br />
47.5<br />
80.9<br />
51.4<br />
68.7<br />
57.0<br />
71.4<br />
55.4<br />
**<br />
**<br />
**<br />
**<br />
testers. Three-variety crosses with the two varieties indicated the existence of an S-5 n<br />
allele (Table 5). But Pusur is different from Dular in respect to its semisterility when<br />
crossed with Ketan Nangka.<br />
Locus suggested by javanica WCVs and aus varieties<br />
Many crosses between aus varieties and indica or japonica varieties were tested, but no<br />
clear relationship was found between fertility level and marker genotype. Allelic<br />
interactions can probably not be detected with the limited number of marker genotypes.<br />
After such tests, a different kind of three-variety cross was made, in which one aus<br />
variety and two WCVs were crossed to detect allelic interaction between the aus<br />
varieties and the javanica WCVs. An aus variety with red pericarp, Ingra, was used in<br />
Ingra/Ketan Nangka//CPSLO 17, where the cross between Ketan Nangka and CPSLO<br />
17 did not show hybrid sterility and the sterility could be due only to Ingra and CPSLO<br />
17. In this cross, the level of spikelet fertility was related to the Rc locus in linkage group<br />
IV (Table 6). Therefore, the pronounced hybrid sterility between javanica WCVs and<br />
aus varieties may be caused at this locus near Rc. To analyze the new locus, several<br />
crosses were tested. But in similar crosses using aus varieties with red pericarp, such<br />
as Kele and Chakila, the effect of the locus near Rc was not found (Table 6). It is likely<br />
that the locus near Rc can function in only some aus varieties.<br />
Dular and Ketan Nangka have the same neutral allele at the S-5 locus but differ in<br />
their cross with aus varieties. Dular also has a neutral allele at the newly suggested<br />
locus, where Ketan Nangka shows allelic interaction with some aus varieties (Fig. 2).<br />
40 lkehashi et al<br />
Table 6. Three-variety crosses with aus, javanica, or japonica varieties.<br />
Plants (no.) in each fertility class<br />
Marker Total Mean tgenes<br />
0-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 (no.) (%) test a<br />
+/+<br />
wx/ +<br />
Rc/Rc +<br />
Rc + /Rc +<br />
Ingra/Ketan nangka//CPSLO 17<br />
3 10 3 5 12<br />
9 9 5 1 14<br />
11 14 5 2 2<br />
1 5 3 3 19<br />
Rc/Rc +<br />
Rc + /Rc +<br />
Rc/Rc +<br />
Rc + /Rc +<br />
1<br />
1<br />
0<br />
2<br />
2<br />
3<br />
1<br />
1<br />
1<br />
1<br />
2<br />
a ** = significant at the 1% level.<br />
Tatsumimochi/Chakila//Banten (1989)<br />
1 2 6 12 23<br />
0 7 13 4 17<br />
Tatsumimochi/Kele//Banten (1989)<br />
4 5 6 8 11<br />
3 4 7 7 20<br />
11 46 73.8<br />
12 52 71.8<br />
1 38 55.9<br />
28 60 83.7 **<br />
22 68 82.2<br />
21 63 79.0<br />
20 56 81.1<br />
26 69 77.9<br />
2. Loci for hybrid sterility between aus varieties and javanica WCVs. Dular and Ketan Nangka have neutral<br />
alleles at the S-5 locus. But Dular has another neutral allele at a locus near Rc, where an aus variety and<br />
javanica WCVs have two interacting alleles for gamete abortion.<br />
Wide compatibility loci in wide crosses of rice 41<br />
Discussion<br />
The system of F 1 hybrid sterility in rice is now better understood in the light of allelic<br />
interactions at a locus. The basic structure of allelic interactions can be shown as a<br />
triangular relationship between three alleles, i.e., one neutral and two interacting<br />
alleles. Gametes possessing one of the interacting alleles are eliminated in heterozygotes<br />
of such alleles, while in heterozygotes of a neutral allele and another allele no<br />
gamete abortion occurs.<br />
The identification system for S-5 is well constructed, with standard testers from<br />
indica and japonica types as well as such marker genes as C and alk in linkage group<br />
I. Thus, the neutral allele known as the wide compatibility gene has been used in<br />
breeding hybrid varieties.<br />
Since this gene mechanism has been understood, a large number of crosses have<br />
been made to test the extent to which the neutral allele is effective. Then two varietal<br />
groups were found for which the identification system for S-5 is not adequate. First, an<br />
exception was demonstrated by Penuh Baru II, which showed clear semisterility in its<br />
crosses with indica as well as with japonica testers, suggesting that the testers for S-5<br />
are not applicable. Second, many aus varieties showed clear semisterility in their<br />
crosses with javanica WCVs such as Ketan Nangka and Calotoc.<br />
The limitation of the initial identification system for alleles at the S-5 locus implies<br />
that there are more alleles at the S-5 locus or that additional loci function independently<br />
of the S-5. One of the clues for determining the genetic basis was obtained by marker<br />
genes. Because any allelic action at the S-5 locus can be definitely traced by such<br />
markers as C or alk, a sterility reaction without any relation to the markers may be due<br />
to another locus.<br />
Hybrid sterility in Penuh Baru II and aus varieties, which is not explained by the<br />
standard system for S-5 alleles, is found to be caused at an additional locus rather than<br />
by a new allele. In the case of Penuh Baru II, a new locus is suggested, where the indica<br />
tester and Ketan Nangka have a neutral allele, while japonicas and Penuh Baru II have<br />
interacting alleles. In the hybrid sterility between WCVs and some aus varieties, they<br />
are assumed to possess interacting alleles, with Dular possessing a neutral allele at this<br />
locus. Dular showed an exceptionally good compatibility with indica, javanica, and<br />
japonica varieties.<br />
Although the neutral allele at the S-5 locus is effectively incorporated into indica and<br />
japonica varieties, the use of a new neutral allele indicated by Dular would be important<br />
to breeding work on the Indian Subcontinent. Further progress in the study of hybrid<br />
sterility will depend on the availability of marker genes. The use of isozymes provides<br />
a partial solution. But biochemical markers may be more useful. Standardization of<br />
tester varieties is also necessary. Such a system would be useful in classifying varieties,<br />
inasmuch as such terms as indicas and japonicas are as confusing in studies of hybrid<br />
sterility as in other research areas.<br />
42 lkehashi et alUnknownnoreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-91791401991523254692011-09-01T15:38:00.000-07:002017-07-29T14:48:25.521-07:00Verification of Environmental Control 4.1 Introduction<br />
The microbiological safety of industrially manufactured foods is based on the effective design and<br />
implementation of Good Hygienic Practices (GHP) and Hazard Analysis and Critical Control Points<br />
(HACCP).<br />
Published case studies demonstrate the impact of postprocess contamination (ICMSF 2002). Even<br />
when strict control at all CCPs ensures destruction or reduction of pathogens to acceptable levels<br />
during processing, foods may become contaminated during subsequent processing and handling. Such<br />
<a name='more'></a><br />
contamination typically results from two general circumstances:<br />
1. Addition of contaminated ingredients after the kill step<br />
2. Contamination from the processing environment<br />
The basic elements of GHP are described in the Codex Alimentarius Commissions document<br />
“General Principles of Food Hygiene” (Codex Alimentarius 1997). These general principles are supported<br />
by numerous product-specific guidelines issued by Codex Alimentarius or organizations.<br />
These elements of GHP are defined to minimize or prevent introduction of a pathogen to a product<br />
during its manufacture. This is achieved through the implementation of combined measures and<br />
multiple protective barriers, which can be described as follows:<br />
1. Prevention of entry of pathogens into areas close to the processing lines.<br />
2. In the event of entry, prevention of establishment in the premises.<br />
3. In the event of establishment, prevention or limitation of microbial multiplication, which would<br />
favor persistence and dissemination throughout the plant.<br />
4. In the event of presence, implementation of corrective actions to ensure control of microbial concerns<br />
at low levels or eradication where feasible.<br />
4.2 Establishing an Environmental Control Program<br />
Elements that contribute to postprocess contamination and measures to control pathogens in food<br />
processing environments are extensively discussed and illustrated in ICMSF (2002) and GMA (2009)<br />
for Salmonella in low moisture food. Testing of in-process and processing environment samples<br />
demonstrates that the GHP measures implemented are effective in achieving the desired prevention<br />
of contamination. The test results can be used to (1) assess the risk of product contamination, (2) establish<br />
Chapter 4<br />
Verification of Environmental Control<br />
42 4 Verification of Environmental Control<br />
a baseline for when the facility is considered under control, (3) assess whether control is maintained<br />
over time and (4) investigate sources of contamination in order to apply appropriate corrective<br />
actions.<br />
While sampling plans applied to verify environmental control are typically not based on statistical<br />
considerations, it is important to consider evaluating results using appropriate statistical tools such as<br />
trend analyses. These elements are discussed in detail in ICMSF (2002) and an approach for establishing<br />
a testing program is illustrated in Fig. 4.1. This approach can be applied for control of pathogens,<br />
hygiene indicators or spoilage organisms.<br />
4.2.1 Step A: Determine the Microorganisms of Concern<br />
Determine the relevant microorganism for the manufacturing process based on a HACCP study, guidance<br />
provided in this book or ICMSF (2005). In many cases, a program is established for a single<br />
A. Determine the organism(s) of<br />
concern<br />
B. Determine the relevant test<br />
organism<br />
C. Review implemented measures to<br />
prevent ingress<br />
J. Establish a plan of action<br />
according to findings<br />
H. Define sampling frequencies<br />
K. Periodic review of sampling<br />
programs<br />
I. Establish a plan for evaluation of<br />
data<br />
D. Review other hygiene control<br />
measures and their impact<br />
F. Perform investigative sampling<br />
G. Develop sampling programs for<br />
(a) in-process<br />
(b) environment<br />
E. Review historical data<br />
Fig. 4.1 Proposed approach for<br />
establishing an environmental<br />
sampling program<br />
4.2 Establishing an Environmental Control Program 43<br />
pathogen; however, it may be done for more than one microorganism if it is deemed necessary for the<br />
product under consideration.<br />
4.2.2 Step B: Determine the Relevant Test Microorganism<br />
Determine if testing should involve an indicator or the organism of concern. Examples of indicators<br />
include Enterobacteriaceae for Salmonella or Cronobacter spp. and Listeria spp. for L. monocytogenes.<br />
In most of the cases to obtain a full picture of the status, testing for the both the indicator and<br />
the pathogen is necessary albeit number of sampling points and frequencies may be different.<br />
4.2.3 Step C: Review Measures to Prevent Ingress<br />
Review the existing preventive measures such as zoning within the premises, the layout of different<br />
processing lines, interfaces between different parts of the factory, elements such as flow of personnel,<br />
equipment and goods (e.g., raw materials, packaging materials, finished products, containers, fork-lift<br />
trucks, pallets, waste, rework etc.), as well as the flow of air and water. This is best done using a<br />
master plan and having detailed discussions on parameters affecting the preventive measures to avoid<br />
the ingress of pathogens in specific areas of the factory, in particular high hygiene areas as described<br />
in ICMSF (2002, Chap. 11).<br />
4.2.4 Step D: Review Other Hygiene Control Measures and Their Impact<br />
Review other factors that may contribute to the establishment or dissemination of the microbiological<br />
concern in the processing areas. This includes reviewing the layout of processing lines, the type of<br />
equipment including hygienic design and interfaces with the environment, cleaning procedures used<br />
for the environment and equipment (e.g., wet versus dry), cleaning schedules etc. Based on the design<br />
of the processing lines, equipment and processing conditions, determine whether the build up of<br />
product residues on food contact surfaces may also lead to microbial growth – e.g., at points where<br />
condensation is more likely to occur or growth temperatures may be experienced for extended periods<br />
of time.<br />
4.2.5 Step E: Review Historical Data<br />
Determine whether historical data on environmental sampling and testing of pathogens or indicator<br />
microorganisms exist and if the data still apply to the current environment. For example, if construction<br />
events occurred after data were collected, investigative sampling may be appropriate.<br />
4.2.6 Step F: Perform Investigative Sampling<br />
If no historical data exist, investigative sampling is recommended to establish a base line that can be<br />
used for the development of the sampling program. It may be useful to initially focus this investigative<br />
sampling on indicator microorganisms (e.g., aerobic colony counts, Enterobacteriaceae) to evaluate<br />
trends that can be used to establish sampling times during production and frequencies for sampling.<br />
44 4 Verification of Environmental Control<br />
4.2.7 Step G: Develop Sampling Programs<br />
With historical or investigative sampling data available and considering critical ingredients that may<br />
impact the quality and safety of the finished product, an environmental sampling and testing program<br />
can be developed. The terminology used to describe in-process and environmental samples may vary<br />
depending on the manufacturer. The following definitions have been used in this book.<br />
• In-process samples: These samples provide a representative sampling for an entire line and sometimes<br />
represent the “worst case.” In-process samples include:<br />
–– Intermediate product collected from different process steps that would end up in a container as<br />
finished product, such as samples of sauces that would top a pizza or grab samples from a<br />
depositor.<br />
–– Samples from equipment or product contact surfaces that could lead to a contamination of<br />
product such as process wash water, sifter tailings, fines, line residues or scrapings.<br />
• Processing environment samples: The most common method of sampling for the processing environment<br />
is with sponges or swabs but it is important to adapt sampling tools to the situation. If air<br />
sampling is performed then air collector devices are preferred. These are used to verify that the<br />
environment is under control, i.e., free of pathogens or the indicator microorganisms of choice do<br />
not exceed target levels. Samples from food contact surfaces taken prior to production and after<br />
wet cleaning as part of the preoperational inspection are included in this category.<br />
The sampling sites for both in-process and environmental testing should be based on a thorough<br />
knowledge of the premises, processing lines and equipment and the outcome of the HACCP study.<br />
Guidance on the relative importance of such sampling programs is provided in individual chapters of<br />
this book. Practical details on sampling tools, sampling techniques, routine and investigative samples<br />
are provided in ICMSF (2002).<br />
4.2.8 Step H: Define Sampling Frequencies<br />
After establishing the sampling plans it is important to determine the sampling frequency. The frequency<br />
may vary depending on the type of product manufactured and the duration of production runs.<br />
For example, daily sampling may be appropriate for sensitive products such as infant formulae, while<br />
weekly or monthly sampling may be appropriate for other product categories. Rotation between different<br />
sampling points in the same area may also be appropriate because conditions in manufacturing<br />
facilities can change.<br />
It is also important to determine whether the sampling frequencies for indicators and pathogens<br />
should differ. Testing for Enterobacteriaceae, for example, provides results within 1–2 days and may<br />
therefore be used as a management tool with a higher frequency than Salmonella in some facilities.<br />
4.2.9 Step I: Establish a Plan for Data Evaluation<br />
To maximize the benefit of an environmental sampling program, it is very important to analyze the<br />
data generated in the most effective and proactive way. Different options such as statistical trend<br />
analyses, mapping or charting of data and findings etc. exist. The most familiar and convenient<br />
method for the establishment should be used. It is important to review the data in a timely manner to<br />
allow for corrective action, if necessary.<br />
References 45<br />
4.2.10 Step J: Establish a Plan of Action to Respond to Findings<br />
When results deviate from standards, guidelines or specifications (e.g., the presence of Salmonella in<br />
a sample or levels of indicators exceed established internal limits), it is important to take appropriate<br />
actions. This is best done according to a preestablished action plan that is “activated” only when a<br />
deviation is detected.<br />
Depending on the findings, the action plan may consider the following options: (1) thorough<br />
investigational sampling to identify root causes of the deviation and source(s) of the pathogen or<br />
indicator, (2) increased sampling frequency over a certain period to demonstrate that control is reestablished,<br />
(3) adjustment of the sampling regime for end products; e.g., change from verification to<br />
acceptance.<br />
4.2.11 Step K: Periodic Review of Sampling Programs<br />
A periodical review (e.g., once per year or when important changes occur) of sampling programs<br />
should be performed. This review should consider changes in premises, layout and type of equipment.<br />
Historical results should also be considered to optimize sampling plan. For example, sampling points<br />
that have not proven to be very useful might be eliminated and new sampling points might be added<br />
in areas where more issues have been detected. Changes in sampling frequencies may also be made<br />
during such reviews.<br />
Such reviews should be combined with a review of the skills and training level of personnel<br />
involved in sampling, as well as a review of the adequacy of sampling tools and techniques.<br />
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8582264228705147635.post-17347893528486936032011-08-31T18:18:00.000-07:002017-07-29T14:50:22.159-07:00Verification of Process Control<br />
Many food microbiologists are familiar with sampling plans that use microbiological data to make<br />
decisions regarding the quality or safety of a specific lot of food. Ideally, the statistical basis for this<br />
type of testing is that analyses are performed on a sufficient number of samples from a single lot such<br />
that there is a high degree of confidence that the lot does not have an unacceptable level of microorganisms<br />
that affect the quality or suitability of the food.<br />
An important concept in understanding the statistical basis for such lot-by-lot or within-lot testing<br />
is that of defect rates, i.e., the portion of servings or containers that do not satisfy some attribute, such<br />
as absence in a defined quantity of product, or below a specified concentration (ICMSF 2002). Such<br />
sampling programs become increasingly more resource intensive as the acceptable defect rate<br />
becomes smaller. Once a standard method with the appropriate sensitivity has been selected for analyzing<br />
samples, achieving the desired test stringency as the defect rate decreases is typically accomplished<br />
by analyzing more samples from the lot or by increasing the size of the analytical units<br />
examined. When the acceptable defect rate is low (e.g., <5%), the number of samples that need to be<br />
analyzed can be a severe practical impediment to using microbiological testing. For example, consider<br />
two lots of ready-to-eat food that are required to be free of Salmonella, one with 50% of the<br />
servings contaminated and a second where 1% of the servings are defective. In the first lot, examining<br />
three servings would have a high probability (87.5%) of identifying the lot as contaminated, whereas<br />
the probability of identifying the second lot as containing Salmonella would only be 63% if 100 servings<br />
were examined.<br />
Another important concept associated with within-lot testing is the underlying assumption that<br />
there is little or no knowledge about the product and the processes and conditions under which it was<br />
manufactured and distributed. In such instances, microbiological testing is used as a control measure<br />
to segregate sound and unsound lots. An important consequence of this assumption is that since no<br />
prior knowledge of the lot is assumed, the results from testing one lot cannot be considered predictive<br />
of the status of other lots.<br />
While within-lot testing plays an important role in food safety particularly for examination of foods<br />
at ports of entry for regulatory actions, typically microbiological data collected is not based on traditional<br />
within-lot sampling plans and statistics. Instead, sampling is often conducted periodically and<br />
on only a portion of the lots. Furthermore, the extent of testing (i.e., number and size of samples analyzed)<br />
is typically at a level that it does not provide a high level of confidence that a lot contaminated<br />
at a low rate would be detected. This is not to imply that this type of testing does not provide manufacturers<br />
or control authorities with important microbiological data; however, too often such testing<br />
programs are conducted in a manner that does not provide the best use of the data acquired.<br />
Chapter 3<br />
Verification of Process Control<br />
34 3 Verification of Process Control<br />
These testing programs are referred to as process control testing or between-lot testing, and their<br />
usefulness can be enhanced significantly if they are appropriately designed, including appropriate<br />
analysis, interpretation and review of the data. When this is done testing programs provide a powerful<br />
tool for evaluating and correcting the systems used to control microbiological safety and quality<br />
before the system crosses the threshold where the product is no longer suitable for commerce. This<br />
chapter provides a brief introduction to the concepts and application of this type of microbiological<br />
data acquisition. Detailed requirements for establishing such a testing program are found in other<br />
standard references (Does et al. 1996; Roes et al. 1999; ICMSF 2002; Hubbard 2003; NAS US<br />
National Academy of Sciences 2003; ECF 2004; NIST/SEMATECH 2006).<br />
Understanding the differences in the goals and assumptions associated with within-lot and<br />
between-lot testing is important for successful process control testing. Within-lot testing is used to<br />
establish the safety or quality of a specific lot of product, presumably because of a lack of knowledge<br />
about the effectiveness of the means for controlling contamination and ensuring safe production, processing<br />
and marketing. The purpose of between-lot testing is not to establish the safety of a specific<br />
lot; rather safety is assumed to have been achieved by establishing and validating processes and practices<br />
that control significant hazards including the variability of ingredients, processes and products.<br />
The purpose of between-lot testing is to verify that the process and practices for ensuring safety are<br />
still performing as intended. The underlying assumption in this case is that there is detailed knowledge<br />
of how the food was manufactured. Thus, process control sampling is most effectively implemented<br />
as part of an overall food safety risk management program such as HACCP (ICMSF 1988).<br />
To reiterate the different applications of within-lot and between-lot testing – if the testing of all lots<br />
using within-lot sampling plans was implemented in a HACCP program, that sampling would be both<br />
a control measure (that would likely be a critical control point) and part of monitoring activities.<br />
Conversely, between-lot testing would be used as part of the verification phase of HACCP. Thus,<br />
failure to meet a within-lot sampling plan would indicate a potentially unacceptable lot whereas<br />
failure of a between-lot sampling plan would signal a potential loss of control of a HACCP<br />
program.<br />
As indicated above, the purpose of process control testing is to determine whether a control system<br />
is functioning as designed; i.e., producing servings that have a defect rate below a specified value or<br />
within a specified range. An inherent assumption made in conducting between-lot microbiological<br />
testing is that actions have been taken to reduce the variability among lots so that the variability<br />
between lots is minimized or that the system is consistently operating at a level of control such that<br />
the products are substantially better than the specified acceptable level. It is questionable whether a<br />
HACCP program could be truly considered under control if there is a large between-lot variation.<br />
Thus, between-lot testing is most effective when there is little variation in the mean and standard<br />
deviation of the log concentrations of a hazard among lots under normal operation. A small betweenlot<br />
variance allows a loss of control of the food safety or quality system to be more readily identified<br />
with the least amount of microbiological sample analysis.<br />
As a simple example of the difference between within-lot and between-lot sampling, consider a<br />
company that has two processing lines, one old and less reliable, and one new and highly reliable, for<br />
the same product. The company wants to ensure a defect rate of <1% of that product from either line.<br />
For product from the old line, where there is less confidence in the reliability of the process, the<br />
company may opt to test each lot. In this case, end product testing is used as a critical control point.<br />
Given that the within lot variability of product from the old line is higher, the manufacturer might<br />
even choose to use a sampling plan that involves a greater number of samples so as to have more<br />
confidence that the results of the sampling plan are representative of the entire lot. Conversely, for<br />
the new line, the company could apply the same sampling plan but draw the samples from a greater<br />
number of lots; i.e., effectively considering the process as a continuous lot, or a series of large lots,<br />
with the lot being defined by a period of time and lots overlapping in time. This is the basis of<br />
the moving window approach, exemplified in Sect. 3.4. In the moving window approach,<br />
3.2 How to Verify that a Process is Under Control 35<br />
an increase in the number of positive results over time indicates a trend toward loss of control.<br />
In this case the same sampling plan is used to verify the process.<br />
Appropriate statistical analysis can identify when the incidence of defective units significantly<br />
exceeds the tolerable defect rate. If the incidence exceeds that level, the manufacturer should investigate<br />
the cause of the elevated defect rate to determine why the process is no longer functioning as<br />
intended and should take corrective action. Examination of the system’s performance over time also<br />
provides useful information and insights into the type of failures that occur (ICMSF 2002). Process<br />
control testing is most effective when it can detect an issue at a level or frequency below that which<br />
would be considered unacceptable for safety or quality if it were to enter the marketplace. In this way<br />
corrective actions can be taken before a critical limit is exceeded.<br />
3.2 How to Verify that a Process is Under Control<br />
The actual microbiological methods used to detect, identify and enumerate microorganisms of concern<br />
for process control verification are essentially the same as those used for within-lot testing. These<br />
methods are available in a variety of standard references (e.g., ISO, AOAC, FDA Bacteriological<br />
Analytical Manual, American Public Health Association etc.) and are not discussed further.<br />
Like within-lot testing, microbiological criteria established for a process control testing program<br />
can be based on either 2 or 3 class attributes testing plans; i.e., presence/absence or a numerical limit<br />
(or limits in the case of three class plans) or variables testing (i.e., full range of quantitative data).<br />
Similarly, attribute testing can be based on a 2-class or 3-class sampling plan. Process control sampling<br />
plans can be applied to finished products, in-process samples or ingredients. Ideally a decision<br />
on the analytical approach used is reached early in the development of the process control sampling<br />
program. The approach selected strongly influences the types of data needed during the initial phases<br />
of establishing the program. A decision on the approach used should be determined before establishing<br />
the microbiological criteria (i.e., decision criteria) for the program.<br />
3.2.1 Information Required to Establish a Process Control Testing Program<br />
As indicated above, use of process control testing is based on detailed knowledge of the product<br />
and process. A meaningful process control testing program requires detailed knowledge of the<br />
levels or frequency at which the microorganism of concern can be expected in a product when it is<br />
produced and handled properly. This includes information on the variation in those levels both<br />
between lots and within lots. Thus, the first step in establishing a process control testing program<br />
to verify continued successful operation of food safety or quality system is to gather baseline data<br />
on the performance of the food safety system when it is functioning as intended. This is commonly<br />
referred to as a process capability study. During this period, intensive acquisition of data that characterizes<br />
the performance of the system is undertaken, either by generating new data from tests on<br />
the system or by collating existing data. The data collected are specific to the system being evaluated.<br />
This can be as specific as the performance of a single line within a manufacturing plant or as<br />
broad as a commodity class for an industry. However, the latter requires a great deal of forethought<br />
and effort to ensure that the acquisition of data is not biased and adequately represents an entire<br />
industry. On a national basis, this is typically done through a series of national baseline studies; a<br />
major undertaking that is typically done by a national government or industry representative body.<br />
The sensitivity of the methods and sampling plans selected should be adequate to provide sufficient<br />
data on the true incidence of defects within a lot as well as prevalence (the average rate of defects<br />
over time) of the microbiological hazard in the food. Ideally the sensitivity will be set at a level<br />
36 3 Verification of Process Control<br />
that is sufficient to detect the pathogen or quality defect at least a portion of the time. Historical<br />
within-lot testing results can be highly useful for determining the system’s performance and<br />
variability.<br />
When conducting a process capability study, care must be taken to ensure that the data collected<br />
represent product manufactured when the food safety system is under control. If not, it is likely to<br />
increase the variability of the levels (or frequencies) of the microbiological hazard that will form the<br />
basis of the reference level against which ongoing performance will be assessed. This could decrease<br />
the ability of the process control program to identify when the system is not functioning as intended.<br />
The duration of a process capability study will vary with product, pathogen and purpose, but it should<br />
be long enough to generate sufficient data to ensure that the variability in the process has been characterized<br />
accurately. At a minimum, 30 lots should be examined so that the influence of sampling<br />
error is acceptably small and that the performance characterization is reasonably robust. There are<br />
instances where the process control study may need to be conducted for longer periods or in phases.<br />
For example, if raw ingredient contamination varies substantially over the course of a year, then the<br />
process capability study may need to consider seasonality as a factor, thereby extending the duration<br />
of the study for a full year. In such instances, it is possible to conduct the process capability study for<br />
30 days, perform initial analyses and set initial control limits; and then review and revise the analysis<br />
and control limits, if necessary, as additional data are accumulated. The inclusion of such data in the<br />
process control study depends, in part, on a value judgment related to whether the product is deemed<br />
under control during those periods when high levels are observed due to season or supplier. If the<br />
process is not deemed as being under control, then the data derived from it should not be included in<br />
the reference level data set. It also implies that means for preventing the increased defect rates associated<br />
with seasonality or supplier will need to be immediately identified since, once implemented, the<br />
process control testing program based on the process control study that does not include the period<br />
higher defect rate will appropriately identify the process as being out of control during those<br />
periods.<br />
As indicated above, process control testing programs are most effective when they detect loss of<br />
control before a critical limit is exceeded. For that reason, the microbiological limits for process<br />
control testing programs employed by companies are frequently established to effectively detect<br />
changes before a regulatory limit is exceeded. This allows corrective actions to be taken proactively.<br />
However, this proactive approach can be difficult to implement if competent authorities establish<br />
limits based on “zero tolerance” instead of specifying a specific microbiological criterion based on<br />
risk or on specific testing protocols.<br />
Process control testing can be used for assessing both food safety and food quality, and is not<br />
restricted to microbiological testing. Simple, easily performed physical and/or chemical measures of<br />
the impact of microbial contamination can offer distinct advantages over more sophisticated testing<br />
methods. For example, sterility testing of UHT milk products is amenable to process control testing<br />
based on sensory evaluation combined with a pH determination (von Bockelmann 1989).<br />
3.2.2 Setting Microbiological Criteria, Limits and Sampling Plans<br />
The concentration of microorganisms varies in lots of food and is often described by a log normal<br />
distribution. Such distributions are open-ended functions and high values can potentially occur even<br />
when the system is in control. However, such events should be rare and a high frequency of such<br />
occurrences is evidence that the system is no longer under control. A microbiological criterion establishes<br />
the decision criterion to assess whether a microbiological testing result could have occurred by<br />
chance alone or whether the food safety or quality system has undergone some significant change<br />
such that it is no longer functioning as intended.<br />
3.3 Routine Data Collection and Review 37<br />
The microbiological limit associated with a process that is under control effectively establishes<br />
that decision criterion, based on the results of the initial process capability study. Assuming that the<br />
current level of control within a plant or an industry is deemed acceptable, a limit can be established<br />
in combination with an appropriate sampling plan so that the frequency of detecting a positive result<br />
or a specific concentration would be unlikely to occur by chance alone. For example, a result that<br />
exceeds the 95% probability value would only be expected to occur, on average, about once in 20<br />
samples. If the frequency were higher, it would be indicative that the system is out of control. An<br />
increase in the number and size of analytical units examined increases the likelihood of detecting a<br />
positive result so that the decision criteria are specific to the microbiological criterion and sampling<br />
plan established. Establishing the stringency of a microbiological criterion is a risk management<br />
activity. Thus, the specific sampling plan thresholds selected (e.g., 95 or 99% confidence) may take<br />
into account a range of scientific and other parameters such as assessed risk, severity of the hazard,<br />
technological capability, public health goals, cost of taking action when the process is actually in<br />
control, or consumer preferences and expectations. Because this is a risk management issue and not<br />
a risk assessment, no specific value of probability of detection serves as a standard criterion. For<br />
example, consider two situations that a country or company might assess in establishing a microbiological<br />
limit for a food product. First, consider a product where the industry’s food safety or quality<br />
systems is based on a single, well established technology that is operating with a substantial safety<br />
margin to control a relatively mild hazard and has both a low between-lot and within-lot variance.<br />
In that instance a microbiological limit based on 99.99% of the baseline distribution (i.e., £0.001%<br />
of the test values from the program operating as intended would exceed the microbiological limit)<br />
might be deemed sufficient to protect public health and the microbiological criterion would be<br />
established accordingly. In such a situation, the microbiological limit established would result in the<br />
appropriate acceptance of the vast majority of this product. Such a process control standard would<br />
have little impact on the industry’s current performance. In contrast, consider an industry where<br />
there is substantial variability among the technologies, practices and standards of care used by individual<br />
companies, leading to substantial between-lot (and in some instances within-lot) variability.<br />
In this case, the country or company might establish a microbiological limit at 80% of the current<br />
baseline distribution (i.e., 1 in 5 of samples as currently produced would be deemed unacceptable).<br />
Over time a process control microbiological limit of such a magnitude would be likely have a large<br />
impact on the companies that are poorer performers; i.e., their food systems would be considered as<br />
not functioning as intended. Conversely, the limit would have minimal impact on companies that are<br />
good performers. The end result would be to decrease both the mean and variance of the log concentration<br />
of the hazard in servings of the product entering commerce. A similar outcome would<br />
occur over time if the stringency of a within-lot testing program was increased.<br />
3.3 Routine Data Collection and Review<br />
Once established, process control testing requires routine testing of only a small number of samples.<br />
The number of lots that need to be tested, the frequency of testing and the number of samples from<br />
each lot depends on the inherent defect rate when the food safety or quality system is functioning as<br />
intended and the degree of confidence that the microbiological limit is not being exceeded by the<br />
manufacturer or country. The specific testing requirements of the process control sampling plan<br />
depend on the type of process control analysis approach being employed (e.g., CUSUM, Moving<br />
Window) (ICMSF 2002). Process control testing programs can also include variations in testing frequency<br />
based on process performance; e.g., to increase testing when increased defects are detected<br />
or to decrease the frequency of testing when results are consistently acceptable over time. However,<br />
rules for variable sampling frequencies should be formulated with a clear understanding of the effect<br />
38 3 Verification of Process Control<br />
that the alternate sampling frequencies have on the ability of the testing program to detect an emerging<br />
loss of process control and to be able to respond in time to prevent unacceptable product from<br />
entering commerce.<br />
Implementation of process control testing programs requires effective data management systems<br />
and the ongoing evaluation of collected data over time. This is usually done through control charting<br />
where the data are arrayed over time (Fig. 3.1). Graphical representation is often a useful tool as an<br />
initial evaluation of the data. Comparing these data with the data collected in the routine monitoring<br />
of critical control points in HACCP plans and other verification data can be useful for interpreting<br />
the results of the process control testing and enhancing the identification of the underlying causes of<br />
process deviations For most food microbiology concerns, the lower limit would not typically be<br />
considered a decision criterion, with the possible exception of fermented foods or probiotic-containing<br />
foods; however, the lower limit may reflect the limit of detection of the test. In the hypothetical<br />
example in Fig. 3.1, a loss of control is apparent at weeks 50 and 51 that should have elicited investigation<br />
to restore control. Additionally, a general increasing trend began at week 42 and became<br />
apparent by week 46–47. This could have stimulated corrective action investigations even before a<br />
loss of control occurred.<br />
3.4 Competent Authority Process Control Program Examples<br />
The use of process control testing for regulatory verification of food safety programs began in the<br />
1990s as competent authorities began to incorporate HACCP into their regulatory programs. The use<br />
of process control analysis techniques provided them with a statistically sound means of establishing<br />
microbiological testing as a HACCP verification tool, while minimizing the economic impact of testing<br />
on both business operators and the competent authority. While the techniques are increasingly<br />
being used by industry and governments, the greatest adoption of this approach has been in North<br />
America. Examples of early use of this approach follow.<br />
3.4.1 Meat and Poultry<br />
One of the first uses of process control programs by competent authorities was in the Pathogen<br />
Reduction/Hazard Analysis and Critical Control Point (HACCP) Systems rule (USDA 1996).<br />
This regulation established two microbiological criteria as a means of verifying HACCP plans for<br />
meat and poultry products:<br />
2<br />
3<br />
4<br />
5<br />
6<br />
0 10 20 30 40 50 60<br />
Time (weeks)<br />
Log CFU/g<br />
Fig. 3.1 Hypothetical<br />
control chart for a microbial<br />
indicator assay conducted<br />
weekly. The center horizontal<br />
line (—) represents the<br />
hypothetical microbiological<br />
criterion and the two flanking<br />
lines (− −) represent 95%<br />
upper and lower confidence<br />
limits<br />
3.4 Competent Authority Process Control Program Examples 39<br />
1. Testing for Escherichia coli as an indicator of fecal contamination and adequate chilling<br />
performed<br />
by individual business operators.<br />
2. Salmonella enterica testing performed by USDA Food Safety and Inspection Service (FSIS).<br />
The microbiological limits established by FSIS were based on extensive review of baseline studies,<br />
regulatory testing and industry data for various classes of meat and poultry products (USDA 1995).<br />
Built into these standards was a goal of decreasing the incidence of foodborne disease attributable to<br />
meat and poultry. The program employed a between-lot moving window approach (i.e., as each new<br />
test result is obtained the window moves and the oldest result are discarded), where the results of<br />
single samples taken on individual production days are examined over the course of a specified number<br />
of days. The frequency of positive samples over that moving time frame is then related to the<br />
defect rate that is expected for the specific meat or poultry product. The testing required of manufacturers;<br />
i.e., the presence of biotype I E. coli as an indicator of fecal contamination, is based on a<br />
3-class attribute sampling plan. The testing by FSIS for S. enterica is based on a 2-class plan in conjunction<br />
with samples taken periodically by regulatory personnel over a specified number of days.<br />
Failure to meet the microbiological limit is considered indicative that the probability that the facility<br />
is not achieving the level of control required was >99% (USDA 1996). The Salmonella performance<br />
standards are not lot acceptance/rejection standards. The detection of Salmonella in a specific lot of<br />
carcasses or ground product does not, by itself, result in condemnation of the lot. Instead, the standards<br />
are intended to ensure that each establishment is consistently achieving an acceptable level of<br />
performance with regard to controlling and reducing enteric pathogens on raw meat and poultry<br />
products (USDA 1996).<br />
The FSIS regulation and requirements are intended to evolve to address new risks and availability<br />
of new data. Development of process control microbiological criteria is being considered by other<br />
national governments and intergovernmental organizations. For example, the EU has established<br />
process control-based hygiene criteria for controlling Salmonella in raw poultry (EFSA 2010), and<br />
the Codex Committee on Food Hygiene is considering a process control approach.<br />
3.4.2 Juice<br />
A more limited use of microbiological testing for process control is employed in the US FDA’s<br />
Hazard Analysis and Critical Control Point (HACCP); Procedures for the Safe and Sanitary<br />
Processing and Importing of Juice; Final Rule (FDA 2001). In this example the competent authority<br />
was concerned about the underlying scientific assumption that enteric pathogens would not become<br />
internalized in citrus fruit. The regulation has an exemption for citrus fruit juice producers enabling<br />
them to fulfill the required 5-D pathogen reduction by treating the surface of the fruit prior to the<br />
juice being expressed. This exemption was based on data that suggest enteric bacteria are limited to<br />
the surface of the fruit. This prompted a requirement that manufacturers choosing to use only surface<br />
treatments must analyze a 20-mL sample for every 1,000 gallons (~4,000 L) produced per day<br />
for generic E. coli, using a moving window analysis based on a 7-day window, where two positive<br />
samples in a 7-day window are deemed to indicate the process is no longer in control. This requires<br />
the manufacturer to investigate the cause of the deviation and divert juice to pasteurization after the<br />
juice is expressed. Based on extensive baseline studies of commercial juice operations indicating the<br />
range of initial contamination levels, juice that is successfully treated to achieve a 5-D reduction<br />
(99.999%) is likely to have <0.5% probability of having two positives in a 7-day window after 20<br />
samples. Conversely, a reduction that yields only 3-D inactivation is calculated to result in a 34%<br />
frequency of 2 positive E. coli findings within the 7-day window with 20 samples, which would<br />
detect the process failure (Garthright et al. 2000; FDA 2001).<br />
<br />Unknownnoreply@blogger.com0