Evaluation of Measurement Uncertainty of Transformation Quantitative Detection Results Based on Joint Validation Measurement Data
With the continuous improvement of the quantitative identification system of transgenic organisms and products,it is urgent to establish a large number of quantitative detection methods for transformation.In order to evaluate the measurement uncertainty in time at the early stage of method establishment when experimental data accumulation is insufficient,it is urgent to establish a scientific and effective evaluation method.This study takes the quantitative detection method of herbicide-resistant soybean DBN9004 as an example.It introduces a method of using 8 laboratories to conduct standard establishment joint validation on 5 kinds of transgenic content samples before the detection laboratory accumulates enough detection data.It analyzes the reproducibility standard deviation of experimental data,and conducts a"top-down"pre-evaluation of uncertainty.The results showed that after Grubbs and Cochran tests of the data from 8 laboratories,the data from laboratory 5 had outliers and were excluded.The relative standard deviation of reproducibility of the 5 content samples from the remaining 7 laboratories was between 6.025％and 11.532％,which was less than the specified 35％,indicating that the method has good precision.Based on the above analysis data,the regression curve equation was sjR=0.0783 ×(c)j-0.010 4,and the relative standard uncertainty component ur=0.078 3,u0=0.012.Then,the average value of the sample detection results((c))was substituted into the formula to calculate the standard uncertainty of the quantitative results u=√u20+(ur ×(c))2=√0.0122+(0.078 3 ×(c))2.The experiment shows that the evaluation of measurement uncertainty of transgenic quantitative detection results based on joint validation data provides a scientific and effective self-evaluation method for the establishment of transgenic quantitative detection methods in the early stage,and provides technical support for the safety regulation of transgenic organisms and products.
万方数据
维普
