Quantification of genetically modified organisms (GMOs) in food and feed products is often required for their labelling or tolerance thresholds. Today the golden standard for testing of specific nucleic-acid sequences derived from genetically modified organisms (GMOs) is real-time quantitative PCR (qPCR). However, with the increasing number of GMOs, qPCR is becoming barely time and cost-effective. Furthermore, qPCR can be limited by its sensitivity to the inhibitors that can be frequently co-extracted with the nucleic acids from complex matrices and by a significant bias when the target is present at low concentrations in a background of high levels of non-target nucleic acids.

To tackle this issues, four droplet digital PCR (ddPCR) multiplex assays, two quantifying 12 in EU authorised GM maize lines [1] and two quantifying 11 in EU authorised GM soybean lines [2], have been developed. Enabling direct quantification of 12 maize and 11 soybean lines in just four reactions. Performance was assessed for the critical parameters, including limits of detection and quantification, trueness, repeatability, and robustness. Trueness was determined on a number of proficiency programme and real-life samples. Moreover, potentially significant improvement in cost efficiency was demonstrated.

With the increased use of authorized genetically modified organisms (GMOs), especially in feed products, the possibility of intentional or unintentional presence of unauthorized or unknown GMOs (UGMOs) is also on the rise. Thus, a novel GW technology coupled with NGS called amplification of linear-enriched fragments (ALF) [3], has been developed. The approach was tested on a complex sample, containing four GMOs of different concentrations, allowing the identification of GMOs even when present in a low concentration. ALF eliminates drawbacks, such as random start of DNA amplification and semi-nested PCR, of previous GW strategies. NGS is ideally suited for sequencing all amplified fragments in a mixture. Furthermore, a first outline of an automated, web-based analysis pipeline for identification of UGMOs containing known screening elements has been developed. To prove the power of the designed pipeline to identify UGMOs, a complete sequence of one GMO in the sample was unknown, mimicking a UGMO. All four GMOs in the sample were identified, thus proving the detection of UGMO.