Detection of adventitious bread wheat and soybean presence in durum wheat semolina and pasta by droplet digital PCR
Bibliographic record
Abstract
Durum wheat is an important grain for ensuring food security and is processed to make many different foods worldwide, including pasta and couscous. Tools for quantifying contaminants in durum grain and grain products are needed for quality assurance during grain handling, grain transactions, and food processing. Bread wheat and soybean are the two major contaminants that can appear in durum grain and grain products that could affect quality. We developed and validated droplet digital PCR (ddPCR) methods that detect and quantify bread wheat and soybean contamination in processed durum semolina and pasta. We generated correction constants and formulas that use the dual dye frequencies from ddPCR to quantify these contaminants in the ranges of 0.5%–55% for bread wheat and 0.01%–5% for soybean. The methods provide insights into the interplay between DNA extraction yield, genome size, and quantitative DNA-based methods for the detection of contaminants. The results and methods also equip industry, regulators, and policy makers with new tools and knowledge that can be used to monitor for contaminants in durum wheat grain and durum wheat products for quality assurance purposes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".