邓婷婷,黄文胜,张九凯,葛毅强,邢冉冉,于 宁,陈 颖.常见转基因大米数字聚合酶链式反应多重定量检测方法研究[J].食品安全质量检测学报,2022,13(21):6979-6986
常见转基因大米数字聚合酶链式反应多重定量检测方法研究
Multiple quantitative analysis of genetically modified rice ingredient by digital polymerase chain reaction
投稿时间:2022-09-21  修订日期:2022-10-31
DOI:
中文关键词:  转基因  大米  多重定量  数字聚合酶链式反应
英文关键词:genetically modified  rice  multiple quantitation  digital polymerase chain reaction
基金项目:中国检验检疫科学研究院基本科研业务费项目(2020JK004)
作者单位
邓婷婷 中国检验检疫科学研究院 
黄文胜 中国检验检疫科学研究院 
张九凯 中国检验检疫科学研究院 
葛毅强 中国农村技术开发中心 
邢冉冉 中国检验检疫科学研究院 
于 宁 中国检验检疫科学研究院 
陈 颖 中国检验检疫科学研究院 
AuthorInstitution
DENG Ting-Ting Chinese Academy of Inspection and Quarantine 
HUANG Wen-Sheng Chinese Academy of Inspection and Quarantine 
ZHANG Jiu-Kai Chinese Academy of Inspection and Quarantine 
GE Yi-Qiang China Rural Technology Development Center 
XING Ran-Ran Chinese Academy of Inspection and Quarantine 
YU Ning Chinese Academy of Inspection and Quarantine 
CHEN Ying Chinese Academy of Inspection and Quarantine 
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中文摘要:
      目的 建立4种转基因(genetically modified, GM)大米成分多重定量检测方法, 解决混合转基因产品和多品系杂交的多价转基因产品高通量精准定量难题。方法 采用Raindrop微滴式数字聚合酶链式反应(polymerase chain reaction, PCR)方法对4种常见转基因大米TT51-1、克螟稻、科丰6号、M12品系特异性基因和大米蔗糖磷酸合成酶基因逐一进行拷贝数分析, 并将其转化为含量百分比。结果 转基因含量在0.1%~100.0%范围内的4个品系转基因大米混合样品的定量体系线性关系良好, 标准曲线r2值均在0.99以上, 定量相对灵敏度可达0.1%, 相对误差和相对标准偏差值均小于25%, 定量准确性和精密度符合国际通用要求。结论 本研究成功建立了4种常见转基因大米成分的多重定量检测方法, 解决了常规方法一次只能定量分析单一样品的缺点, 为大米、稻谷及其初加工产品中多种转基因成分的高效定量提供了新的技术手段。
英文摘要:
      Objective To establish a multiple quantitative detection method for 4 events of genetically modified (GM) rices, and solve the problem of high-throughput quantification of mixed and hybrid GM products. Methods The copy numbers of event-specific genes in 4 kinds of GM rices, including TT51-1, KMD, Kefeng 6, M12 and rice sucrose phosphate synthase gene were analyzed using the Raindrop digital polymerase chain reaction (PCR) method, and then converted into mass percentage. Results The quantitative system of the mixed samples of 4 events of genetically GM rices with the GM rice content in the range of 0.1%-100.0% had good linear relationship and the r2 of the standard curve was above 0.99. Its relative sensitivity could reach 0.1%. The bias and relative standard deviation value were less than 25%. This indicated that the quantitative accuracy and precision met the international general requirements. Conclusions This study has successfully established a multiple quantitative detection method for 4 events of GM rice ingredients, and has solved the shortcoming that conventional methods can only quantitatively analyze a single ingredients at a time, which provides a new technical means for efficient quantitative determination of multiple transgenic ingredients in rice and its primary products.
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