An optimized <scp>SPE</scp> ‐ <scp>UPLC</scp> – <scp>MS</scp> / <scp>MS</scp> method for simultaneous quantification of 11 tetracyclines in dairy products
Bibliographic record
Abstract
Abstract BACKGROUND The global rise in antibiotic use, particularly tetracyclines (TCs) in animal husbandry, poses significant risks to food safety and public health due to residual accumulation and bacterial resistance. Existing detection methods for TCs in dairy products often lack efficiency, sensitivity, or the capability for simultaneous multi‐residue analysis. Developing a rapid, precise, and cost‐effective method to monitor TCs in compliance with international maximum residue limits remains an urgent need. RESULTS We developed a streamlined approach integrating a three‐step purification process (PRiME HLB cartridges) with ultra‐performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS). The method utilizes 0.1 mol L −1 Na 2 EDTA–McIlvaine buffer for extraction, achieving 83.11–107.61% recovery rates with < 5.43% relative standard deviation (RSD). Optimized chromatographic separation on a bridged ethyl‐siloxane and silica hybrid (BEH) C 18 column enabled simultaneous detection of 11 TCs (including parent compounds and metabolites) within 9 min. Sensitivity was exceptional, with limits of detection and limits of quantification of 0.02–0.83 μg kg −1 and 0.07–2.78 μg kg −1 , respectively. Intra‐/inter‐day precision (RSD 3.03–9.33%) and compliance with European Union/Food and Drug Administration (EU/FDA) were validated using real milk and milk powder samples. CONCLUSIONS This method combines rapid sample preparation with UPLC–MS/MS for simultaneous quantification of 11 TCs in dairy products. Its high accuracy, sensitivity, and compliance with global regulatory standards make it indispensable for large‐scale food safety monitoring, effectively addressing antibiotic residue risks and supporting international trade harmonization. © 2025 Society of Chemical Industry.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".