TRIO Method for Detection of Beta-Lactams, Sulfonamides, and Tetracyclines in Raw Commingled Cows' Milk
Bibliographic record
Abstract
A qualitative 3 min one-step assay for detecting beta-lactam, sulfonamide, and tetracycline antibiotics was validated following milk screening test guidelines developed by FDA-CVM, AOAC-RI, and IDF. The validated 90% detection levels with 95% confidence were: penicillin G 2 part per billion (ppb); amoxicillin 4 ppb; ampicillin 9 ppb; ceftiofur plus metabolites 50 ppb; cloxacillin 9 ppb; cephapirin 15 ppb; sulfadimethoxine 8 ppb; sulfamethazine 9 ppb; chlortetracycline 34 ppb; oxytetracycline 53 ppb; and tetracycline 42 ppb. Detection levels were lower than U.S. and Canadian allowable limits for milk and were consistent with most European Maximum Residue Limits. Tests of raw commingled cows' milk indicated a low positive error rate of <0.3% with no interferences demonstrated by 1.08 MM/mL somatic cells, Gram-positive or Gram-negative bacteria < 300 K/mL, freeze/thawing, or non-targeted drugs. Detection of incurred residues were similar to, or more sensitive to, fortified samples. Some cross reactivity across drug families occurred in interference studies and therefore initial positive samples should be confirmed with drug family specific screening methods. The National Conference of Interstate Milk Shipments approval as a bulk tank/tanker screening test was completed in three stages for each drug family, including a tetracycline confirmation procedure to target U.S. tolerance levels. Detection and robustness were found to be appropriate for multiple countries' regulatory requirements for screening tests. The method development, validation, and approval was intended to diversify and increase the verification tools for the control of the major antibiotic drug families used in managing cows' health and welfare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".