Safe food through better labelling; a robust method for the rapid determination of caprine and bovine milk allergens
Bibliographic record
Abstract
Accidental milk cross-contamination is one of the most common causes for costly food recalls. Yet, quantifying trace-levels of allergen is time-consuming and current methods are not adapted for routine analyses making quality control for trace-level allergen content impractical. This perpetuates voluntary “may-contain” statements that are unhelpful for people suffering from food allergies. Here, we developed a rapid LC-MS method enabling milk allergen quantification by comparing all tryptic-peptides of major milk allergens. The bovine-specific αS-2 casein peptide and allergen-epitope NAVPITPTLNR provided excellent performance in sensitivity (LOD 1 mg.kg−1; LOQ 2 mg.kg−1) across various dairy products, good recovery rates in baked croissants (77% with a 10% inter-day RSD) and a linear range of 2–2,000 mg.kg−1. The method can be used for routine determination of trace-contamination with bovine milk allergen and the adulteration of high-value caprine dairy products with lower-value bovine milk products, protecting consumer trust and the growing population suffering from food allergies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".