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
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".