Development and Validation of a Multiclass Method for Analysis of Veterinary Drug Residues in Milk Using Ultrahigh Performance Liquid Chromatography Electrospray Ionization Quadrupole Orbitrap Mass Spectrometry
Bibliographic record
Abstract
This paper presents the development and validation of a multiclass method for the analysis of veterinary drug residues in milk using ultrahigh performance liquid chromatography electrospray ionization quadrupole Orbitrap mass spectrometry (UHPLC/ESI Q-Orbitrap). The 12 classes of veterinary drugs (a total of 125) included in this study were endectocides, fluoroquinolones, ionophores, macrolides, nitroimidazole, NSAIDs, β-lactams, penicillins, phenicols, sulfonamides, tetracyclines, and aminoglycosides. Veterinary drug residues in milk were extracted using a modified salting-out supported liquid extraction (SOSLE) method, which entailed the precipitation of milk proteins using an extraction buffer (oxalic acid and EDTA, pH 3) and acetonitrile, a salting-out acetonitrile/water phase separation using ammonium sulfate, and solid-phase extraction (SPE) using polymeric reversed-phase sorbent cartridges. The final extracts were concentrated and reconstituted into a buffer solution and analyzed using UHPLC/ESI Q-Orbitrap mass spectrometry. The developed method was validated using a nested experimental design to evaluate the method performance characteristics, such as overall recovery, intermediate precision, and measurement uncertainty. The method was able to quantify or screen up to 105 veterinary drugs from 11 different classes, except aminoglycosides. The limits of quantification were as low as 1.0 μg/kg, with an analytical range from 1.0 to 100.0 μg/kg in milk.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".