Non-target analysis of antimicrobial residues and their transformation products in fish and shrimp
Bibliographic record
Abstract
Analytical techniques targeting specific analytes, i.e., targeted analysis, have long been established as the main methods in food safety and environmental analysis.On account of the large number of chemical contaminants with various physicochemical properties detected in the food and water, there is a need to adapt these methods to screen and identify a broader number of chemicals.Additionally, studies have shown a decrease in contaminant levels during cooking and with food, especially seafood, eaten following some sort of thermal processing, it is important to study the outcome of processing on food contaminants.The preferred technique to screen for, and study the fate of contaminants in food is non-targeted analysis (NTA) using high resolution mass spectrometry.The objective in this thesis was to develop a non-targeted method in food analysis, focusing on the determination of veterinary drugs and other pharmaceuticals (drugs used in human medicine) in fish and shrimp.Chapter 3 covers the first step in NTA, which is the selection of an extraction method.Malachite green (MG) exposed brook trout and pacific white shrimp were used as a case study to evaluate the impact of extraction methods on the determination of the veterinary drug.Based on the comparison parameters, e.g., matrix, ionization mode, a different extraction was considered more suitable.Although a compromise must be reached, based on specific research objectives, there is a need for a more harmonized approach on some aspects, like data filtering, e.g., blank subtraction, and data processing.Overall, QuEChERS extraction provided satisfactory results and was chosen to further study MG.In Chapter 4, a data analysis approach was validated for compound discovery from nontargeted data, focusing on the identification of antimicrobials and other pharmaceuticals in fish livers.The validated workflow was suitable, as it led to the identification of an antibiotic, azithromycin, and an anti-depressant metabolite, erythrohydrobupropion.Chapter 5 covered the for their time, advice and for helping me progress through my research.Many thanks for Dr.Céline Audet, without whom a large part of this project would have been very difficult, for setting up the trout exposure experiment.To my lab mates, thank you for making those busy days in the lab doing extractions and waiting for Profinder to finish a little easier, and especially to Lei and Annie for all the help with data analysis.A very big thank you to Pablo Elizondo for our Aylmer shrimp driving adventure and all the help through the shrimp experiments.Many thanks to all my friends and family for their encouragement and emotional support.Last but not least, I cannot express how grateful I am to my parents
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".