Surveillance study of novobiocin and phenylbutazone residues in raw bovine milk using liquid chromatography-tandem mass spectrometry
Bibliographic record
Abstract
A simple method permitting the simultaneous determination of trace residues of novobiocin and phenylbutazone in raw milk samples using liquid chromatography-tandem mass spectrometry was developed. Raw milk samples were mixed with acetonitrile to facilitate the concurrent precipitation of milk proteins and extraction of both veterinary drugs. Without additional clean-up or concentration of the resulting extract, the analytes could be quantified at concentrations as low as 0.0025 and 0.001 microg ml(-1) for phenylbutazone and novobiocin, respectively. The analysis of a series of fortified raw milk samples at analyte concentrations ranging from 0.005 to 0.1 microg ml(-1) and from 0.01 to 0.2 microg ml(-1) for phenylbutazone and novobiocin, respectively, yielded average recoveries ranging from 89.2% to 104.3% with standard deviations below 7%. The analytical method was applied to the analysis of raw milk samples collected from transport trucks upon delivery at dairy-processing plants throughout Alberta, Canada. Novobiocin was detected in 13 of 1072 samples tested at concentrations ranging from 0.001 to 0.007 microg ml(-1). Phenylbutazone was not detected in any of the samples tested.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".