GMO testing laboratories operating within the current European Union (EU) regulatory framework governing the presence of genetically modified organisms (GMOs) in food and feed (EC Reg. 1829/2003 and EC Reg. 1830/2003) perform a stepwise workflow from DNA extraction to quantification of genetically modified events. A very valuable intermediate step in guiding and optimizing this workflow is the screening phase, where any positives require the laboratory to proceed to the subsequent identification and quantification steps. Very often, however, samples with low GM content result positive at screening but then the GM component is not quantifiable, wasting time and resources. In order to overcome this issue, in this study a statistical framework was developed to predict the presence of soybean GM events based on the difference between the quantification cycle (Cq, also known as threshold cycle (Ct) of the Real time PCR technique) values observed from screening elements as cauliflower mosaic virus 35S promoter (P35S), the nopaline synthase terminator (T-nos), the 5-enolpyruvylshikimate -3-phosphate synthase gene (CP4 epsps) and lectin reference gene (Lec) (ΔCq values). The feasibility of this approach was successfully in-house verified on real life and spiked samples. This approach can be seen as a proof of concept to suggest how to optimize, on a statistical basis, the workflow of GMO testing laboratories that need to evaluate sample compliance with quantitative tests.
Rocca et al. (Sun,) studied this question.