Determination of Pesticides in Apples by Liquid Chromatography with Electrospray Ionization Tandem Mass Spectrometry and Estimation of Measurement Uncertainty
Bibliographic record
Abstract
A liquid chromatography/electrospray ionization-tandem mass spectrometry (LC/ESI-MS/MS) method was developed and validated to determine 90 pesticides in apple samples. MS/MS data acquisition was achieved by applying multiple-reaction monitoring of 2 fragment ion transitions to provide a high degree of sensitivity and selectivity for both quantitation and confirmation. Matrix-matched standard calibration curves with the use of isotopically labeled standards (or a chemical analog) as internal standards were used to achieve the best accuracy for the method. Both a conventional method validation procedure and a designed experiment were applied to study the accuracy and precision of the method. A compiled computer program that provided a semiautomated procedure for handling a large number of calculations was used to calculate the overall recovery, intermediate precision, and measurement uncertainty (MU). In general, the overall recoveries from samples spiked at levels of 10, 50, and 80 microg/kg, ranged from 60.8 to 121.1%, intermediate precision was <10%, and MUs were <30%. Poor accuracy and/or repeatability was observed for pyridate and etofenprox. The method limits of detection based on a signal-to-noise ratio of >3 were usually <1 microg/kg (ppb), except those for aldicarb sulfoxide and pyridaphenthion, which were about 5 microg/kg.
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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 | 0.000 |
| 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".