Are commercial soy-sauce products naturally gluten-free? Preliminary evaluation of selected products sold in the USA
Bibliographic record
Abstract
Abstract Gluten present in wheat, rye, and barley has been linked to 3 types of immune mediated disorders: Celiac disease, non-celiac gluten sensitivity and wheat hypersensitivity (or wheat allergy). Consequently, detection of gluten in food products is routinely used to validate gluten-free products meant for gluten-sensitive subjects. Fermentation processing has been shown to be effective in degrading food allergens including gluten. Most commercial soy sauce products (SSPs) use wheat in addition to soybean as a major ingredient in their production, thus presence of wheat gluten is expected in such products. Here we tested the hypothesis that commercial SSPs sold in the retail market in the USA will test positive for gluten independent of the country of origin. Commercial SSPs were tested for gluten presence using gluten test kit (LOD, 5 ppm). The test kit was validated for positivity using purified gluten extracted from durum wheat flour and for negativity by screening 2 commercial gluten-free SSPs. The following products have been tested: 4 products from China, 3 from USA, 1 from Canada and Taiwan respectively. Commercial SSPs when used without dilution in the food extract buffer rendered the test kit system invalid for testing. Therefore, a dilution of 1/10 was used in testing. Despite identifying wheat as an ingredient on the product label, all SSPs from USA, Canada, and Taiwan tested negative for gluten. Among these products, only 1 from china tested slightly positive for gluten. These results demonstrate that most commercial SSPs sold in the united states, independent of country of origin, used in this study test negative for the gluten content despite identifying wheat as a major ingredient on the labels. Further product screening is in progress. USDA/NIFA
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".