Targeted and non-targeted analysis of contaminants from food contact materials by LC-QTOF-MS coupled with QSRR modeling
Bibliographic record
Abstract
Growing concerns about food contact materials (FCMs) due to the migration of harmful substances, including bisphenols, per- and polyfluoroalkyl substances (PFAS), and various unknown contaminants into food. The conventional method for detecting known food contaminants is "targeted analysis," which relies on predefined information (e.g., exact mass or structural data) to detect and quantify analytes. However, non-intentionally added substances (NIAS), including both unexpected and unknown contaminants, require a novel approach. Non-targeted analysis, notably based on high-performance liquid chromatography-mass spectrometry (HPLC-MS) coupled with advanced data mining techniques, has the potential to identify unknown contaminants. The overall objective of this research was to improve the surveillance and safety of food contact materials through the development and application of novel LC-QTOF-MS strategies to analyze known and unknown migrants (NIAS). Chapter 3 of the study involved the analysis of 140 packaging materials from fresh food in North America, with a particular focus on the occurrence and migration of bisphenol A (BPA), bisphenol S (BPS), and other color developers in thermal labels through targeted analysis. While no detectable BPA was found in the samples, significant levels of BPS and other developers were identified. Controlled experiments demonstrated that BPS, along with other developers such as D-8, D-90, and Pergafast-201, could migrate into food. Notably, BPS levels exceeded the European Union's Specific Migration Limit (SML) of 50 ng/g wet weight (ww). In Chapter 4, 246 food thermal labels from 15 countries were analyzed to determine the occurrence of color developers in global markets. BPS, the most frequently detected, was found in 48% of the samples, but other compounds like benzenesulfonamide (NKK-1304) detected at 15%. A controlled migration study showed that PVC films, widely used for plastic wraps, had relatively higher BPS migration (up to 130.7 µg/cm²) compared to polyethylene (PE) films (<0.01 µg/cm²). These results underscore the need to select the proper packaging materials to reduce BPS migration and establish relevant regulations to minimize potential exposure risks. Chapter 5 focused on the occurrence and migration of 18 PFAS in paper-based FCMs. PFAS were detected in a quarter of samples collected in Montreal, primarily in clamshell to-go boxes (100%), popcorn bags (50%), and wrappers (16.7%). PFAS migration into ethanol-based food simulants was influenced by temperature and exposure duration, with increased migration levels under conditions simulating typical food consumption, such as hot meals or microwave heating. The widespread detection of PFAS in clamshell to-go boxes and other FCMs highlights the need for stricter regulations and the development of safer alternatives to reduce PFAS exposure. A large number of unexpected compounds found in food and simulants highlight the importance of developing non-targeted methods for identifying unknown contaminants in FCMs. In Chapter 6, the study developed and validated Quantitative Structure-Retention Relationship (QSRR) models, which predict the retention times of chemical standards across various analytical columns. These models demonstrated great predictive accuracy and effectively help remove false-positives candidates to enhance identification confidence. Overall, this research provides clear evidence of the migration of BPS, alternative color developers, and PFAS from FCMs into food, raising concerns about potential consumer exposure. Findings support the need for ongoing research, reinforced regulatory oversight, and the development of safer packaging materials to protect public health. The use of predictive QSRR models highlights their potential as valuable tools in identifying unexpected contaminants through non-targeted analysis, contributing to broader efforts to enhance food safety and promote environmental sustainability
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".