Food allergen recalls in the United Kingdom: A critical analysis of reported recalls from 2016 to 2021
Bibliographic record
Abstract
Allergen food recalls issued by food regulatory bodies in the UK from 2016 to 2021 are analysed herein by food type, allergenic food group, reasons for recall, and food expiry status. Trends and relationships have been assessed. Food allergen-related recalls in the UK have tended to increase annually despite increased awareness and regulations until 2019, peaking at 118 recalls before decreasing to 82 and 84 in 2020 and 2021 respectively. Recalls due to allergens were the main reason for food recalls at 57.6% (n = 597), with milk (25.2%) being the most recalled allergenic food group. Most recalls (40.0%) were issued due to the omission of priority allergens from the list of ingredients. The supermarket Lidl issued the most recalls with 37 recalls involving 62 products. 6.0% of recalls with expiry dates (n = 480) passed their best-before or use-by dates, of which 14 products (48.3%) had use-by dates which were microbiologically unsafe to be consumed once past the relevant dates, and cereal & bakery products accounted for 30.4% of all recalls with expiry dates. These analyses suggest that allergen-related recalls still present risks to consumers and the food industry, with larger retailers recalling the most despite modern facilities. More attention must be focused on all food allergen recalls, particularly the omission of intentionally added foods containing priority allergens from the list of ingredients. In addition, allergen-based recalls of food with expired or shortly to expire dates, which pose uncontrolled risks to consumers with food allergies, have been identified.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".