Non-Targeted Analysis of PFAS in Food Contact Materials: Unveiling Hidden Dangers with LC-QTOF MS and FluoroMatch
Bibliographic record
Abstract
Per-and Polyfluorinated Substances (PFAS) are diverse synthetic chemicals widely used in industry because of their thermal stability, hydrophobicity, and relative inertness. These “forever chemicals” have been detected in food and food packaging and may cause harmful health effects. With thousands of PFAS compounds identified, processing non-targeted PFAS data poses a significant challenge. In this study, 40 paper-based food contact materials (FCMs) from shops in Montreal, Canada -- including to-go boxes, microwave popcorn bags, wrappers, paper straws, and baking liners -- were analyzed for the occurrence of PFAS using LC-QTOF MS. The highest PFAS concentrations were detected in clamshell to-go boxes, with levels reaching up to 356.6 ng/g. PFOA (up to 187.2 ng/g) and PFDA (up to 92.2 ng/g) were major contributors to the overall PFAS content. FluoroMatch Modular was used to annotate previously unidentified PFAS, confirming a homologous series (C3 to C14) of perfluoroalkyl carboxylic acids (PFCA) and C6 and C8 perfluoroalkyl sulfonic acids (PFSA). The study also demonstrated that the migration of both short-chain (PFHxA and PFHpA) and long-chain (PFOA, PFDA, PFNA) PFAS into food simulants increased upon heating (65 ℃), indicating that higher temperatures facilitate the release of these substances from packaging into food. These findings raise concerns about potential for PFAS exposure through food consumption, particularly under heated conditions such as hot meals and microwave heating. The widespread detection of PFAS in clamshell to-go boxes and other FCMs in this study highlights the need for more stringent regulations and oversight to minimize PFAS use in FCMs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".