Development of a suspect and non‐target screening approach to detect veterinary antibiotic residues in a complex biological matrix using liquid chromatography/high‐resolution mass spectrometry
Bibliographic record
Abstract
RATIONALE: Swine manure can contain a wide range of veterinary antibiotics, which could enter the environment via manure spreading on agricultural fields. A suspect and non-target screening method was applied to swine manure samples to attempt to identify veterinary antibiotics and pharmaceutical compounds for a future targeted analysis method. METHODS: A combination of suspect and non-target screening method was developed to identify various veterinary antibiotic families using liquid chromatography coupled with high-resolution mass spectrometry (LC/HRMS). The sample preparation was based on the physicochemical parameters of antibiotics for the wide scope extraction of polar compounds prior to LC/HRMS analysis. The amount of data produced was processed by applying restrictive thresholds and filters to significantly reduce the number of compounds found and eliminate matrix components. RESULTS: The suspect and non-target screening was applied on swine manure samples and revealed the presence of seven common veterinary antibiotics and some of their relative metabolites, including tetracyclines, β-lactams, sulfonamides and lincosamides. However, one steroid and one analgesic were also identified. The occurrence of the identified compounds was validated by comparing their retention times, isotopic abundance patterns and fragmentation patterns with certified standards. CONCLUSIONS: This identification method could be very useful as an initial step to screen for and identify emerging contaminants such as veterinary antibiotics and pharmaceuticals in environmental and biological matrices prior to quantification.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".