Bibliographic record
Abstract
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".