Determination of fluoroquinolones in aquaculture products by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS)
Bibliographic record
Abstract
An ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed for the determination of fluoroquinolones-ciprofloxacin (CIPRO), danofloxacin (DANO), enrofloxacin (ENRO) and sarafloxacin (SARA)-in aquaculture products, specifically salmon, shrimp and tilapia. After initial sample extraction with an acidic acetonitrile solution, the extract was diluted with dichloromethane and centrifuged, then an aliquot was concentrated and applied to a C18 solid-phase extraction cartridge and concentrated for a second time. The resultant residue was dissolved in acetonitrile, diluted with water, and then further defatted with hexane. The fluoroquinolone residues were determined by UPLC with an HSS T3 C18 reverse-phase column using an ammonium hydroxide-formic acid buffer in an acetonitrile gradient with MS/MS detection using multiple reaction monitoring. Average recoveries for salmon tissue ranged from 73% for DANO to 95% for SARA, for shrimp from 71% for DANO to 109% for SARA, and from 62% for DANO to 111% for SARA in tilapia, fortified at the 1.0 ng g(-1) level. Standard curves were linear between 0.002 and 0.5 ng injected for all compounds. Detection limits of 0.2 ng g(-1) for CIPRO, DANO, ENRO, and SARA were easily obtainable. The operational errors, interferences, and recoveries for fortified samples demonstrate that this described method is suitable for routine use in a regulatory programme. The recommended method is simple, rapid, specific and reliable for the routine monitoring of fluoroquinolone residues in aquatic species such as salmon, tilapia and shrimp.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".