Gluten Contamination of Labelled Gluten-Free Food Products Marketed in China
Bibliographic record
Abstract
Gluten has a central role in the pathogenesis of several gluten-related disorders, with coeliac disease being one of the most well-known. A rigorous gluten-free diet is recognized as the only safe and efficient coeliac disease therapy. This study intended to use the enzyme-linked immunosorbent assay (ELISA) R5 Méndez method to determine gluten contamination in labelled gluten-free food products marketed in China and to evaluate the performance of two ELISA platforms (the Neogen Veratox® R5 and the Romer Labs AgraQuant® G12) for analyzing gluten, using R5 Méndez as the reference. In 2024, 119 prepackaged products labelled as gluten-free were purchased from internet suppliers. The results showed that among the 119 products, 13.4% contained gluten exceeding 20 mg/kg, and 5.0% contained over 100 mg/kg, ranging from 333.1 to 2737.4 mg/kg. When the threshold for gluten-free products was 20 mg/kg, the two ELISA platforms yielded results comparable to R5 Méndez. However, when the threshold was 10 mg/kg and 5 mg/kg, the McNemar x2 test showed significant differences between the proportions of positive results of the two ELISA platforms and R5 Méndez (p-value < 0.05); using R5 Méndez as the reference, the two ELISA platforms showed 53.1% to 70.3% positive predictive values, suffering the drawback of high false-positive results. When using stricter gluten limits of ≤10 mg/kg and ≤5 mg/kg, different ELISA platforms may produce different results for the same food product. These findings highlight the need for China to accelerate the development of national standards for gluten-free foods, and for food regulatory agencies to impose requirements of production that lead to the prevention of gluten contamination, confirmed by the possible monitoring of such compliance through routine sampling and testing. Such measures will no doubt contribute to protecting and improving the health of patients with gluten-related disorders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".