Determination of Sulfonamide Residues in the Tissues of Food Animals Using Automated Precolumn Derivatization and Liquid Chromatography with Fluorescence Detection
Bibliographic record
Abstract
A liquid chromatographic method for the determination of sulfachloropyridazine, sulfadiazine, sulfadimethoxine, sulfadoxine, sulfaethoxypyridazine, sulfamethazine, sulfaquinoxaline, and sulfathiazole residues in the muscle, liver, and kidney of food animals using sulfapyridine as internal standard is reported. Tissues are extracted using a modified version of AOAC Official Method 983.31 (Sulfonamide Residues in Animal Tissues). The sample extract is reconstituted in pH 3.0 buffer-acetonitrile (60 + 40) and filtered into an autosampler vial. Using a programmable autosampler of a liquid chromatograph, a portion of the sample is derivatized precolumn with fluorescamine. The sulfonamide derivatives are separated by liquid chromatography using a C18 column with a mobile phase of 0.02M phosphoric acid-acetonitrile (60.5 + 39.5) and detected by fluorescence (excitation, 405 nm; emission, 495 nm). The method was applied to swine and cattle muscle, liver, and kidney; sheep and horse muscle and kidney; and chicken muscle and liver. The mean values for samples fortified with sulfonamides at levels between 0.05 and 0.2 microg/g agreed within 96-99% of spiked levels, with coefficients of variation ranging from 4-10%. The limit of detection (LOD) for all sulfonamides was 0.01 microg/g, with the exception of sulfaquinoxaline, for which the LOD was 0.015 microg/g.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".