Direct injection high performance liquid chromatography coupled to data independent acquisition mass spectrometry for the screening of antibiotics in honey
Bibliographic record
Abstract
The targeted analysis of veterinary drug residues in honey traditionally involves a series of extraction and purification steps prior to quantification with high performance liquid chromatography coupled to high resolution or tandem mass spectrometry. These steps, designed to separate the target analytes from interferences, are generally time-consuming and costly. In addition, traditional cleanup steps are likely to eliminate other compounds whose analysis could prove decisive in current or future assessment of the honey sample. Alternatively, direct injection without complex sample preparation steps has been introduced for the fast analysis of trace compounds in environmental and food matrices. The aim of this study was to develop a rapid method for the targeted analysis of 7 key veterinary drug residues in honey based on direct injection high performance liquid chromatography coupled to quadrupole time-of-flight, while simultaneously recording data-independent MS/MS (e.g. All Ions MS/MS data) for future re-examination of the data for other purposes. The new method allowed for the detection of the target residues at levels approximately 20-100 times lower than current regulatory limits, for a total analysis time of about 45 min. The recoveries (103-119%), the linearity (R ≥ 0.996) and the repeatability (RSD ≤ 7%) were satisfactory. The method was then applied to 35 honey samples from the Canadian market. Residues of tylosin A, tylosin B, sulfamethazine and sulfadimethoxine were detected in 6, 9, 6 and 23% of the samples respectively, at levels below the regulatory limits in Canada. The possibility of adding a hydrolysis step to study sulfonamides in honey was tested, which provided good results for this family of compounds but lead to degradation of some of the other analytes. Finally, the non-targeted identification of several compounds was demonstrated as a proof of concept of future re-examination of All Ions MS/MS data. This paper illustrates the capacity of this novel method to combine targeted and non-targeted screening of chemical residues in honey.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".