Comparison of the Monte Carlo and guide to uncertainty in measurement methods in estimating measurement uncertainty: Indirect measurement of the CaMV35S promoter in mixed samples of genetically modified soybean

文献类型： 外文期刊

第一作者： Wang, Dong

作者： Wang, Dong;Zhang, Fuli;Niu, Bei

作者机构：

关键词： Genetically modified soybean; Quantitative assay; Measurement uncertainty; Monte Carlo method; Law of propagation of uncertainty

期刊名称：FOOD CONTROL （ 影响因子：5.548； 五年影响因子：5.498 ）

ISSN： 0956-7135

年卷期： 2018 年 90 卷

页码：

收录情况： SCI

摘要： The estimation of measurement uncertainty (MU) in the detection of genetically modified organisms is still not a systematic and common practice. Here, the Monte Carlo method (MCM) and the Guide to Uncertainty in Measurement (GUM) approach were simultaneously implemented to evaluate the MU for the relative content of the 35S promoter from cauliflower mosaic virus (CaMV35S) in a mixed sample of genetically modified soybean. The two methods gave a mean estimate of the measure that was very close to the theoretical content of CaMV35S in the mixed sample (3.00%). However, the mean value of CaMV35S estimated by the MCM (2.95%) was smaller than that obtained using the GUM method (3.09%). Moreover, the MCM concluded that the standard uncertainty (2 x 10(-4)) of CaMV35S was similar to 75% smaller than the value (8 x 10(-4)) estimated by the GUM approach. This suggests that the GUM method overestimated the uncertainty of the CaMV35S content. Additionally, the differences regarding the estimated coverage intervals between the MCM (2.91-3.00%) and GUM (2.93-3.25%) method were determined. The semi-width of the coverage interval (expanded uncertainty for 95% coverage probability) provided by the GUM method (0.16%) assuming a normal distribution was 72% greater than the value (0.045%) estimated by the MCM. Nevertheless, there was no significant difference between the two methods when their calculated uncertainties were rounded to two decimal places. This suggests that the normality assumption in the estimation of MU using the GUM approach is valid and satisfactory. (C) 2018 Elsevier Ltd. All rights reserved.

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