Simultaneous Determination of Residues of Chloramphenicol, Thiamphenicol, Florfenicol, and Florfenicol Amine in Farmed Aquatic Species by Liquid Chromatography/Mass Spectrometry
Bibliographic record
Abstract
A liquid chromatographic (LC)/mass spectrometric (MS) method was developed for determining the residues of chloramphenicol, thiamphenicol, florfenicol, and florfenicol amine in a number of aquatic species. The phenicols are extracted with acetone, the extracts are partitioned with dichloromethane, the aqueous layer is removed, and the organic layer is evaporated to dryness. The residue is dissolved in dilute acid and defatted with hexane, and the aqueous layer is prepared for analysis by LC. The phenicols are determined by reversed-phase LC by using a Hypersil C18-BD column with a water-acetonitrile gradient and MS detection using selected-ion recording. Calibration curves were linear for all analytes between 0.015 and 0.425 ng injected. The relative standard deviations for measurements by the proposed method were < 10% for all of the analytes studied, with recoveries ranging from 71% for florfenicol amine to 107% for florfenicol in salmon tissue spiked at the 2 ng/g level. Detection limits of 0.1 ng/g for florfenicol and chloramphenicol, 0.3 ng/g for thiamphenicol, and 1.0 ng/g for florfenicol amine are easily obtainable. The operational errors, interferences, and recoveries for spiked samples compare favorably with those obtained by established LC methodology. The proposed method is simple, rapid, and specific for monitoring residues of chloramphenicol, thiamphenicol, florfenicol, and florfenicol amine in a number of aquatic species.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".