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 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".