Comparison of two digital PCR platforms for quantification of genetically modified soybean events
Bibliographic record
Abstract
In the European Union, the food and feed containing more than 0.9% of approved genetically modified organisms (GMOs) per ingredient must be labeled before placed on the market. In this legislative context, the official control laboratories have to perform validated PCR assays, according to the principles and requirements of ISO/IEC 17025 standard, regarding event-specific methods for the detection, identification and quantification of GMOs. In recent years, with the advent of digital PCR (dPCR) techniques, a growing number of laboratories have transferred the previously validated real-time PCR testings into a dPCR format. Compared to real-time PCR, the dPCR offers the advantage to provide accurate quantification without the need for external calibration samples, show less sensitivity to PCR inhibitors and is more suitable for multiplexing. In this study, an in-house validation of quantitative duplex dPCR methods was performed involving MON-04032–6 and MON89788 assays with the lectin reference gene, on the two different platforms Bio-Rad QX200 and Qiagen QIAcuity. All evaluated data and the validation parameters agree with the acceptance criteria validation performance parameters according to the JRC Guidance documents and technical reports in both platforms. The duplex PCR methods here investigated are equivalent in terms of performance to the singleplex real-time PCR method and suitable to perform a collaborative trial for a full validation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".