Simplified Liquid Chromatography–Mass Spectrometry Methods for Gestagen Analysis in Animal Fat and Liver
Bibliographic record
Abstract
Gestagens, a class of veterinary drugs also called progestogens, are synthetic hormones used to increase feed efficiency and rate of gain in heifers. The Canadian Food Inspection Agency analyzes progestogens melengestrol acetate (MGA), megestrol acetate, and chlormadinone acetate using liquid chromatography-mass spectrometry (LC-MS). Our conventional gestagen method for kidney fat has many time-consuming steps, including solid-phase extraction. A sample preparation procedure having fewer clean-up steps was developed for routine diagnostic analysis of kidney fat and provided similar results faster, and at lower cost. A confirmatory liver method for gestagens, developed using salt-assisted extraction, employed minimal clean-up steps that resulted in high chemical background at the desired lower limit of quantification (LLOQ). Differential ion mobility spectrometry, specifically high-field asymmetric waveform ion mobility spectrometry (FAIMS), was used to filter chemical background in the gas phase. The effect of the ionization probe position on FAIMS parameters, including sensitivity, is described. With LC-FAIMS-MS, chemical background for each gestagen was virtually eliminated, resulting in a quantitative liver method having the desired 0.6 ng/g LLOQ and estimated limits of detection (LODs) up to 140 times lower than LC-MS. Incurred MGA samples, analyzed using kidney fat and liver methods from the same animal, show levels within the quantitative ranges of both methods.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".