Development and Validation of a Hybrid Screening and Quantitative Method for the Analysis of Eight Classes of Therapeutants in Aquaculture Products by Liquid Chromatography–Tandem Mass Spectrometry
Bibliographic record
Abstract
A method using reverse-phase ultra-high-performance liquid chromatography coupled with tandem mass spectrometry is described for eight classes of therapeutants that are used in marine aquaculture products. Validation studies to evaluate recovery, precision, method detection limits, and measurement uncertainty were performed at three levels, using three representative matrices [salmon (fatty fish), tilapia (lean fish), and shrimp (crustaceans)] to assess the method performance for use as a screening or determinative (quantitative and confirmatory) method. A total of 16 sulfonamides (plus 2 potentiators), 2 tetracyclines, 11 (fluoro)quinolones, 7 nitroimidazoles, 3 amphenicols, 5 steroids, and 3 stilbenes met the quantitative criteria for method validation. An additional 5 triphenylmethane dyes, 2 sulfonamides, 2 tetracyclines, and 1 amphenicol met the required performance for use as a screening method. Limits of detection (LODs) for the compounds that met the quantitative criteria ranged from 0.1 to 5 μg/kg, while LODs for compounds from the screening group ranged from 0.1 to 30 μg/kg. This method provides a comprehensive approach to the determination of different classes of compounds in aquaculture products.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 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".