Abstract：In using Real-time quantitative PCR (qRT-PCR) detection of genetically modified (GM) ingredients, the standardization of data analysis is of great significance for the accuracy of the experimental data and the comparability of inter-laboratory data. This research was designed to promote the standardization of data analysis in GM ingredients quantitative detection. Taking the GM maize (Zea mays) NK603 as an example in qRT-PCR analysis, maize endogenous reference gene alcohol dehydrogenase 1 (Adh1) and NK603 event-specific sequence were set as amplified targets. Two certified reference materials (GM content of 0.98% and 4.91%, respectively) were used as blind samples and the GM content were measured. In qRT-PCR assays, standard curves were generated based on the least-square method, and GM content of blind samples was measured through the approach of absolute quantification. The construction of standard curves and analysis of blind samples were repeated 3 times, and 3 parallel reactions were included in one plate for each time. Calibration curves were produced using the mean Ct values of three parallels or using the individual Ct values of three parallels. Corresponding, the GM content of blind samples should be calculated firstly based on mean of Ct values and then transformed to copies, or calculated firstly based on individual copies which was transformed from individual Ct values and then taking the geometric mean or arithmetic mean of copies. It was considered that different treatments on data lead to varied accuracy of the results. In order to enhance the comparability of inter-laboratory data, our results suggested that standard curves should be produced using the original Ct values rather than the mean of the Ct values, and the GM content of blind samples should be based on arithmetic mean of Ct values or geometric mean of copies. The accuracy of the test should be judged through comparing the measurement results with the certified value (UΔ＞Δm, which indicating there was no significant difference between the measurement result and the certified value). This work provides useful information for the standardization of qRT-PCR data analysis in GMO absolute quantification.
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