Matrix-induced sugaring-out liquid–liquid extraction for the determination of fumagillin and dicyclohexylamine residues in honey by LC–MS/MS
Bibliographic record
Abstract
The dicyclohexylamine salt of fumagillin is currently used in some countries to control nosema disease in honey bee colonies. Chemical residues in honey resulting from the use of chemotherapies in apiculture are a food safety concern. A method employing matrix-induced sugaring-out liquid–liquid extraction (SULLE) with LC–MS/MS detection was developed for the determination of fumagillin and dicyclohexylamine (DCH) residues. Honey was dissolved in water and extracted with acetonitrile where the high concentration of sugars in the sample matrix enabled phase separation of the two solvents and partitioning of the analytes into the organic layer. The working analytical range spanned 5 to 500 µg kg −1 with limits of detection (LOD) estimated to be 1.7 and 1.4 µg kg −1 for fumagillin and DCH, respectively. Method validation experiments using blank replicate honey samples fortified with 5, 25, 150, and 400 µg kg −1 of each analyte were carried out over multiple days. Average accuracies ranged from 86.6 to 106.3% and 95.2 to 108.8% for fumagillin and DCH, respectively. Intra- and inter-day precisions were less than 20% at the limit of quantitation (LOQ) concentration of 5 µg kg −1 and less than 10% at all other studied concentrations. The analytical method was applied to the determination of fumagillin and DCH residues in 464 honey samples. Fumagillin was found in 28 samples at concentrations from 5 to 74 µg kg −1 . Residues of DCH were observed more frequently (265 samples) at concentrations ranging from 5 to 219 µg kg −1 .
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".