The challenges of developing a generic extraction procedure to analyze multi‐class veterinary drug residues in milk and honey using ultra‐high pressure liquid chromatography quadrupole time‐of‐flight mass spectrometry
Bibliographic record
Abstract
This paper discusses the analytical challenges to develop a generic extraction procedure to analyze or screen multi-class veterinary drugs in milk and honey using ultra-high pressure liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC QqTOF MS). The veterinary drugs in this study included aminoglycosides, endectocides, fluoroquinolones, ionophores, β-lactams or penicillins, macrolides, NSAIDs, phenicols, sulfonamides and tetracyclines. Veterinary drugs were extracted using a QuEChERS (quick, easy, cheap, effective, rugged, and safe) method, which entailed the use of acetonitrile containing 1% acetic acid, sodium acetate, ethylenediaminetetra acetic acid disodium (EDTA) and magnesium sulfate, and no clean-up was performed. Chromatographic separation was achieved on a reversed-phase Acquity UPLC BEH C(18) , 100 × 2.1 mm, 1.7 µm column with 0.1% formic acid and 10 mM ammonium formate in water, and acetonitrile as mobile phases. Due to poor chromatographic retention, aminoglycosides were first dropped from the list, and because of poor extractability, β-lactams and tetracyclines were also excluded from the method. The method was able to quantify 31 or screen up to 54 drugs (unbound) in honey, and to quantify 34 or screen up to 59 drugs in milk. UHPLC QqTOF data were acquired in TOF MS full-scan mode that allowed both quantification and confirmation of veterinary drugs and identification of their degradation products in samples. The method could achieve detection limits as low as 1 µg/kg with analytical range from 1 to 100 µg/kg. The developed method was intended to be used for screening of as many analytes as possible in one single analysis, or unequivocal confirmation of positive findings and degradation product identification based on accurate mass measurement and isotopic patterns.
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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 | 0.007 | 0.005 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".