Digital PCR as an effective tool for GMO quantification in complex matrices
Bibliographic record
Abstract
The increased use of genetically modified organisms (GMOs) is accompanied by increased complexity of the matrices that contain GMOs. The most common DNA-based approach for GMO detection and quantification is real-time quantitative polymerase chain reaction (qPCR). However, as qPCR is sensitive to inhibitors and relies on standard curves for quantification, it has limited application in GMO quantification for complex matrices. To overcome this hurdle in DNA quantification, we present droplet digital PCR (ddPCR) assays that were designed to target 'Roundup Ready' soybean and the soybean reference gene. Three ddPCR assays were transferred from qPCR to QX100/QX200 ddPCR platforms and characterised. Together, the fitness-for-purpose study on four real-life samples and the use of a chamber-based PCR system, showed that dPCR has great potential to improve such measurements in GMO testing and monitoring of food authenticity.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".