Global survey of bisphenol color developers in thermal food labels and a study of the role of food packaging materials in preventing color developer migration into food
Bibliographic record
Abstract
In this study, 247 food thermal labels, collected from 15 countries across five continents in 2021–2023, were assessed for the occurrence of bisphenol and alternative color developers. Analysis with LC-qTOF-MS showed that BPS was the most frequently detected color developer, present in 48 % of the thermal labels. Some relatively new alternatives, such as NKK-1304 (15 %) and DBSP (11 %), were identified for the first time, indicating the growing use of new compounds in the food packaging industry. Despite regulatory restrictions in some countries on its usage, bisphenol A (BPA) was still detected in 2 % of the food thermal label samples from certain countries. 80 % of the thermal labels collected were appended to food packaging made of polyvinyl chloride (PVC). In a controlled food simulant (10–95 % ethanol, 10 days) experiment, approximately 63 % of BPS and 31 % of D-90 in the labels were able to migrate across PVC cling films. In contrast, polyethylene (PE) films and paper-based materials exhibited a significant barrier to both BPS and D-90 migration. These results highlight the diversity of color developers used in food thermal labels globally, the need for comprehensive risk assessments of novel color developers and the importance of choosing packaging materials that can act as barriers to their migration into food. • 247 food thermal labels were sampled from 15 countries. • BPS was the most frequently detected color developer in the food thermal labels. • Relatively new alternatives, such as NKK-1304 and DBSP, were identified in labels. • The film material influence the migration of color developers from the label into the food.
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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 | 0.001 | 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 | 0.001 | 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".