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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".