Improved quantification accuracy for duplex real-time PCR detection of genetically modified soybean and maize in heat processed foods
Real-time PCR technique has been widely used in quantitative GMO detection in recent years.The accuracy of GMOs quantification based on the real-time PCR methods is still a difficult problem,especially for the quantification of high processed samples.To develop the suitable and accurate real-time PC...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
Published: |
Academic Journals Center of Shanghai Normal University
2013-04-01
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Series: | Journal of Shanghai Normal University (Natural Sciences) |
Subjects: | |
Online Access: | http://qktg.shnu.edu.cn/zrb/shsfqkszrb/ch/reader/create_pdf.aspx?file_no=201302016&year_id=2013&quarter_id=2&falg=1 |
Summary: | Real-time PCR technique has been widely used in quantitative GMO detection in recent years.The accuracy of GMOs quantification based on the real-time PCR methods is still a difficult problem,especially for the quantification of high processed samples.To develop the suitable and accurate real-time PCR system for high processed GM samples,we made ameliorations to several real-time PCR parameters,including re-designed shorter target DNA fragment,similar lengths of amplified endogenous and exogenous gene targets,similar GC contents and melting temperatures of PCR primers and TaqMan probes.Also,one Heat-Treatment Processing Model (HTPM) was established using soybean flour samples containing GM soybean GTS 40-3-2 to validate the effectiveness of the improved real-time PCR system.Tested results showed that the quantitative bias of GM content in heat processed samples were lowered using the new PCR system.The improved duplex real-time PCR was further validated using processed foods derived from GM soybean,and more accurate GM content values in these foods was also achieved.These results demonstrated that the improved duplex real-time PCR would be quite suitable in quantitative detection of high processed food products. |
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ISSN: | 1000-5137 1000-5137 |