Occurrence and risk assessment of sesame as an allergen in selected Middle Eastern foods available in Montreal, Canada
Bibliographic record
Abstract
Sesame allergy is a public health problem in many countries around the world. The purpose of this study is to determine the occurrence of sesame allergen in unlabelled or labelled free-sesame Middle Eastern foods with or without Precautionary Allergen Labelling (PAL) ‘may contain’ and estimate the risk incurred by the Canadian population allergic to sesame with a focus on products purchased in Middle Eastern grocery stores and bakeries in Montreal, Canada. A total of 571 samples were analysed to determine the level of sesame protein. Of the 571 samples analysed, 19% (109/571) contained sesame (results >LOQ) with concentrations of sesame proteins varying between 0.5 and 1,875 mg kg−1 and 35% (199/571) contained traces (a value between LOD and LOQ). Unpackaged products were found to present the highest proportion of sesame containing samples (36%). For packaged products, 16% (27/173) of samples with PAL and 3% (5/173) without PAL were found to contain sesame. A probabilistic approach was used to estimate the risk incurred by the Canadian consumers allergic to sesame. Our evaluation estimated that 33 to 308 allergic reactions may occur out of 10 000 individuals ingesting one type of bakery product contaminated at a level of 0.6–74 mg kg−1 sesame proteins. The incidence and level of sesame cross-contact reported in this study demonstrate that sesame allergic consumers could react if they ignore the precautionary allergen statements on product labels. Attention to sesame as a potential cross-contact agent and as a priority allergen calls for better management, given the growing interest in this ingredient to be included in food formulations. Enhanced risk management efforts must be coupled with targeted risk communication covering both producers and consumers as to the need to adopt and an approach for the application of precautionary allergen labelling based on risk.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".