Targeted and non-targeted analysis of contaminants from food contact materials by LC-QTOF-MS coupled with QSRR modeling
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».