Rapid Determination of Chloramphenicol in Honey by Laser Diode Thermal Desorption Using Atmospheric Pressure Chemical Ionization-Tandem Mass Spectrometry
Bibliographic record
Abstract
Abstract A high-throughput, rapid, and reliable method based on laser diode thermal desorption (LDTD) and atmospheric pressure chemical ionization coupled to tandem MS (APCI-MS/MS) was used to identify and quantify chloramphenicol (CAP) residues in honey. Sample pretreatment consisted of a liquid–liquid extraction of diluted honey in water with ethyl acetate containing stearic acid. After extraction, a 2 μL aliquot of the organic phase was deposited into a 96-well plate prior to detection by LDTD-APCI-MS/MS. The total analysis time was less than 6 s compared to several minutes for traditional chromatographic methods used for CAP detection, since no chromatographic separation was necessary. The extraction and analysis were made with honey samples of different color grading ranging from extra white to dark amber. The method showed good linearity (R2 = 0.99995) within a concentration range of 0.1 to 500 ng/g for CAP with D5-CAP as an internal standard. The RSD varied between 8 and 24% over the calibration range (n = 4). The method LOD for CAP in honey was 0.19 ng/g. This work demonstrates that LDTD-APCI-MS/MS could be used for fast and effective quantification of CAP in honey samples.
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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".