Targeted and non-targeted analysis of plastic-related chemicals in food
Notice bibliographique
Résumé
Plastic-related chemicals (PRCs) are substances related to plastics including the initial components of the plastics (e.g. monomers, antioxidants, additives) and the degradation products of plastics. The occurrence of PRCs in food and their potential adverse health effects have raised concerns about the health of consumers. To date, the surveillance of PRCs in food has mostly focused on the targeted screening and quantification of specific residues using tools such as high performance liquid chromatography (HPLC) or gas chromatography (GC) coupled with mass spectrometry (MS). For example, bisphenol A (BPA) and several phthalates have been detected in different types of food. To ensure food safety though, it is now acknowledged there is a need for analytical tools able to screen and identify not only “known” PRCs in food, but also the new “unknown” compounds. The main objective of my research is to develop and optimize a non-targeted method to investigate PRCs in food with an emphasis on the investigation of the influence of data processing parameters on the identification of trace residues in food. In Chapter 3, a non-targeted workflow was optimized based on the HPLC hyphenated to quadruple time-of-flight MS (HPLC-QTOF-MS) analysis to investigate leachable residues from reusable bottles.Results indicated that all tested bottles are free of BPA, and the bisphenol analogues were not applied as BPA replacement in these bottle manufacture. The effect of data post-processing parameters on the feature extraction in non-targeted analysis was also systematically investigated, and results confirmed that these parameters need to be carefully optimized to extract all the features and identify them accurately. The optimized method was effectively applied to identify monomethyl terephthalate at trace levels in food simulants in contact with TritanTM bottles. In Chapter 4, the non-targeted workflow was developed and optimized for the analysis of PRCs as well as other environmental contaminants in a complex food matrix (pike fish fillets). None of the bisphenol analogues used for targeted method validation were detected in pike samples suggesting that these chemicals do not accumulate at detectable concentrations in muscle of pike naturally-exposed in the St. Lawrence River at two sampling sites. The non-targeted workflow was shown to accurately identify chemicals of high environmental and health concern in pike muscle extracts. In Chapter 5, the optimized non-targeted workflow was applied to screen PRCs in different types of food (namely fish, chicken, canned tuna, leafy vegetables, bread and butter). A range of contaminants in different food matrices were detected and identified, including BPA, bisphenol S (BPS), bis(2-ethylhexyl) adipate, dibutyl adipate, hexadecyl methacrylate and Irganox1076. BPS was first reported in Canadian fresh fish and chicken breast samples. In Chapter 6, the optimized non-targeted workflow was applied to study the thermal degradation of BPA and BPS in water (model matrix) and fish muscles (real food). BPA and BPS did not degrade in water (less than 0.1% degradation) but degraded in fish matrix (about 35% degradation in fish for both BPA and BPS). The degradation products in spiked fish samples are different from those in incurred group. Overall, this research demonstrated that non-targeted analysis is crucial in understanding the occurrence and the fate of PRCs in food, and the results of the present research will contribute to refining current food safety risk assessments
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) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».