Applications of LC/ESI-MS/MS and UHPLC QqTOF MS for the Determination of 148 Pesticides in Berries<sup>†</sup>
Bibliographic record
Abstract
Applications of liquid chromatography electrospray ionization tandem mass spectrometry (LC/ESI-MS/MS) and ultrahigh-pressure liquid chromatography electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC QqTOF MS) for the determination of 148 pesticides in berry fruits are presented in this study. Pesticides were extracted from berries using a procedure known as QuEChERS (quick, easy, cheap, effective, rugged, and safe). Quantification, with an analytical range from 5 to 500 microg/kg, was achieved using matrix-matched standard calibration curves with isotopically labeled standards or a chemical analogue as internal standards. The method performance parameters, which included overall recovery, intermediate precision, and measurement uncertainty, were evaluated according to a designed experiment, that is, the nested design. For LC/ESI-MS/MS, 95% of the pesticides studied had recoveries between 81 and 110%, 98% of the pesticides had intermediate precision of <or=20%, and 95% of the pesticides showed measurement uncertainty of <or=40%. Compared to LC/ESI-MS/MS, UHPLC QqTOF MS showed a relatively poor repeatability and large measurement uncertainty. Ninety-five percent of the pesticides analyzed by UHPLC QqTOF MS had recoveries between 81 and 110%, 86% of the pesticides had intermediate precision of <or=20%, and 83% of the pesticides showed measurement uncertainty of <or=40%. LC/ESI-MS/MS proved to be the first choice for quantification or pretarget analysis due to its superior sensitivity and good repeatability. UHPLC QqTOF MS provided accurate mass measurement and was an ideal tool for post-target screening and confirmation.
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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 | 0.000 | 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".