Determination of lincomycin and tylosin residues in honey by liquid chromatography/tandem mass spectrometry
Bibliographic record
Abstract
A simple and rapid analytical method was developed for the determination of lincomycin and tylosin residues in honey as part of field studies examining the efficacy and target animal safety of these antibiotics to control American foulbrood disease in honey bees. Residues of the antibiotics were determined using liquid chromatography/electrospray ionization tandem mass spectrometry (LC/ESI-MS/MS). Honey samples were diluted and injected directly into the LC/MS/MS system without additional cleanup by solid-phase extraction or liquid-liquid partitioning. A six-port valve system was utilized to selectively route eluant from the LC column into the mass spectrometer only during a relatively short portion of the chromatographic run corresponding to the elution of the analytes of interest. Minimal contamination of the MS source chamber was observed despite the analysis of large numbers of samples. Using internal standard quantitation, excellent accuracy and precision were obtained with no apparent matrix-to-matrix variation. Based on the analysis of fortified replicates, the mean percent deviation from the theoretical concentration and the percent relative standard deviation were both less than 10% for tylosin over an analytical range of 10-1000 microg/kg. Slightly higher mean percent deviations and relative standard deviations were observed for the analysis of lincomycin in fortified replicate samples. The method detection limits were determined to be 5 and 2 microg/kg for lincomycin and tylosin, respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".