Determination of 142 Pesticides in Fruit- and Vegetable-Based Infant Foods by Liquid Chromatography/Electrospray Ionization-Tandem Mass Spectrometry and Estimation of Measurement Uncertainty
Bibliographic record
Abstract
A liquid chromatographic/electrospray ionization-tandem mass spectrometric method was developed and validated to determine 142 pesticides in fruit- and vegetable-based infant foods, including apples, apples and bananas, pears, bananas, apple juice, peas, sweet potatoes, creamed corn, squash, and carrots. Pesticides were extracted from infant foods by using the procedure known as the quick, easy, cheap, effective, rugged, and safe (QuEChERS) method. Quantification was based on matrix-matched standard calibration curves with the use of an isotopically labeled standard or a chemical analogue as the internal standard to obtain method accuracy. The method performance parameters, including overall recovery, intermediate precision, and measurement uncertainty, were evaluated on the basis of a nested design. The performance results were calculated by using a compiled SAS program that provided a semiautomated procedure for handling a large number of calculations in a few seconds. In general, the overall recoveries, for spiking levels of 10, 50, and 80 microg/kg, fell in a range of 81-110%, intermediate precisions were <20%, and measurement uncertainties were <40%. Chlorimuron-ethyl, ethofenprox, haloxyfop, naptalam, primisulfuron-methyl, pyridalyl, pyridate, quizalofop, and tebufenozide were the method problematic pesticides that had large measurement uncertainty (>40%) due to low recovery andlor poor repeatability. The method provided an analytical range of 1-100 microg/kg with the lowest concentration level at 1 microg/kg for all pesticides (signal-to-noise ratio of >10), except for aclonifen at 5 microg/kg.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".