Ultratrace Analysis of Nine Macrolides, Including Tulathromycin A (Draxxin), in Edible Animal Tissues with Minicolumn Liquid Chromatography Tandem Mass Spectrometry
Bibliographic record
Abstract
The analysis of nine macrolides is presented, including tulathromycin A (Draxxin), in beef, poultry, and pork muscle with a simple multiresidue extraction and analysis method using high-performance liquid chromatography coupled to electrospray ionization tandem mass spectrometry. The sample preparation method involves extraction with acetonitrile and defatting with hexane followed by dilution of the extracts for analysis. Separation of the nine macrolides was performed using an Atlantis dC 18, 3 mum, 3.9 mm x 20 mm minicolumn (guard column). Detection was carried out with two multiple reaction monitoring experiments per macrolide. The method detection limits (MDLs) were based on three times standard deviation of eight repeat spikes at 3.0 ng/g of a mix of the nine macrolides in the various tissues. The MDLs and retention times for the macrolides were as follows: lincomycin, 0.19 ng/g (t R = 5.00 min); tulathromycin, 0.46 ng/g (t R = 5.63 min); spiramycin, 0.21 ng/g (t R = 6.06 min); pirlimycin, 0.10 ng/g (t R = 6.04 min); clindamycin, 0.16 ng/g (t R = 6.20 min); tilmicosin, 0.29 ng/g (t R = 6.38 min); erythromycin, 0.19 ng/g (t R = 6.62 min); tylosin, 0.10 ng/g (t R = 6.72 min); and josamycin, 0.09 ng/g (t R = 6.98 min). Precision at 25 ng/g (n = 4) ranged from 2.3 to 9.4% for the compounds from beef muscle. Of interest is the detection of incurred residues of tulathromycin A in edible calf tissue at 0.10-7 mug/g, which is presented here for the first time.
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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) | 0.000 | 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 | 0.000 | 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".