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采用蒙特卡洛法评定转基因水稻样品中NOS终止子的测量不确定度 被引量:8

Measurement Uncertainty in NOS Terminator from Genetically Modified Rice Estimated by Monte Carlo Method
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摘要 采用蒙特卡洛方法(MCM)实施"概率分布传播"来评定转基因水稻样品中NOS终止子的测量不确定度,解析转基因成分测量不确定度的概率分布。评定结果表明:NOS终止子的相对含量为2. 95%,非常接近理论含量(3%),而且其标准不确定度为2. 13×10-4,远小于1. 00×10-2。在95%的包含概率下,NOS终止子的相对含量分布在2. 91%~3. 00%非常窄的包含区间内,充分说明测量质量好,测量结果可靠; NOS终止子相对含量的概率分布呈标准正态分布,揭示转基因成分测量条件满足GUM法的假设,MCM和GUM法都可以应用于转基因成分测量不确定度评定。 Owing to the complex process of GMO testing such as multi-stages with several intermediate inputs/outputs and their joint probability distribution,the propagation of probability distribution was employed here for the first time to evaluate the MU in NOS terminator from genetically modified rice using Monte Carlo Method. The results showed that the relative content of NOS termination was 2. 95%,very close to the theoretical content( 3%),and the standard uncertainty was 2. 13 × 10-4,which was much less than 1. 00 × 10-2. In the coverage probability 95%,the relative content of NOS terminator is within a very narrow range of 2. 91% ~ 3. 00%,indicating the measurement quality in this work is very good,and the measurement results are very reliable. Moreover,the relative contents of NOS terminator followed standard normal distribution,revealing the measuring conditions in the testing of GMOs are agreement with the assumption in GUM approach. Consequently,both MCM and GUM method can be applied to evaluation of measurement uncertainty in the detection of GMOs.
作者 宋君 常丽娟 张富丽 王东 李洁 SONG Jun;CHANG Li-juan;ZHANG Fu-li;WANG Dong;LI Jie(Analysis and test center,Sichuan Academy of Agricultural Science,Chengdu,Sichuan 610066,China;Agricultural information and rural economy institute,Sichuan Academy of Agricultural Science,Chengdu,Sichuan 610066,China)
出处 《计量学报》 CSCD 北大核心 2019年第1期164-171,共8页 Acta Metrologica Sinica
基金 四川省财政创新能力提升工程(2016GXTZ-010) 四川省农业科学院优秀论文基金(2017LWJJ-012 2018LWJJ-010)
关键词 计量学 转基因测量 概率分布传播 测量不确定度评定 蒙特卡洛法 metrology transgene measurement propagation of probability distribution evaluation of measurement uncertainty MCM
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