High-Throughput Quantitation of Seven Sulfonamide Residues in Dairy Milk using Laser Diode Thermal Desorption-Negative Mode Atmospheric Pressure Chemical Ionization Tandem Mass Spectrometry
Bibliographic record
Abstract
Sulfonamides are antibiotic compounds widely used in the dairy industry. Their presence in diary milk poses a risk to public health and may also contribute to the spread of antibiotic resistance in bacteria. Sulfonamide residues in dairy milk were quantified by tandem mass spectrometry (MS/MS) using a novel ionization source based on laser diode thermal desorption-negative mode atmospheric pressure chemical ionization (LDTD-APCI(-)). Seven sulfonamides spiked in milk were extracted with acetonitrile, which yielded high recoveries (77.5-101.5%). Calibration curves in the matrix showed good linearity (0.9977 >or= R(2) >or= 0.9658) over the dynamic range (1.6-500 microg L(-1)), and limits of quantitation were between 2 and 14 microg L(-1), lower than or of the same magnitude as maximum residue criteria set by several regulatory agencies (10-100 ng L(-1)). In addition, the run time using the LDTD-MS/MS system was 30 s per sample, as compared to actual methods running from 7 to 84 min for the same sulfonamide residue compounds, which gave the method the high screening throughput capacity necessary for monitoring milk production.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".