Abstract
The European Committee for Standardization (CEN) and the European Network of GMO Working Laboratories have proposed development of a modular strategy for stepwise validation of complex analytical techniques. When applied to the quantitation of genetically modified organisms (GMOs) in food products, the instrumental quantitation step of the technique is separately validated from the DNA extraction step to better control the sources of uncertainty and facilitate the validation of GMO-specific polymerase chain reaction (PCR) tests. This paper presents the results of an interlaboratory study on the quantitation step of the method standardized by CEN for the detection of a regulatory element commonly inserted in GMO maize-based foods. This is focused on the quantitation of P35S promoter through using the quantitative real-time PCR (QRT-PCR). Fifteen French laboratories participated in the interlaboratory study of the P35S quantitation operating procedure on DNA extract samples using either the thermal cycler ABI Prism® 7700 (Applied Biosystems, Foster City, CA) or Light Cycler® (Roche Diagnostics, Indianapolis, IN). Attention was focused on DNA extract samples used to calibrate the method and unknown extract samples. Data were processed according to the recommendations of ISO 5725 standard. Performance criteria, obtained using the robust algorithm, were compared to the classic data processing after rejection of outliers by the Cochran and Grubbs tests. Two laboratories were detected as outliers by the Grubbs test. The robust precision criteria gave values between the classical values estimated before and after rejection of the outliers. Using the robust method, the relative expanded uncertainty by the quantitation method is about 20% for a 1% Bt176 content, whereas it can reach 40% for a 0.1% Bt176. The performances of the quantitation assay are relevant to the application of the European regulation, which has an accepted tolerance interval of about ±50%. These data were fitted to a power model (r2 = 0.96). Thanks to this model, it is possible to propose an estimation of uncertainty of the QRT-PCR quantitation step and an uncertainty budget depending on the analytical conditions.
Product Moments of Sample Variances and Correlation for Variables with Bivariate Normal Distribution
