Ultrahigh-Performance Liquid Chromatography Electrospray Ionization Q-Orbitrap Mass Spectrometry for the Analysis of 451 Pesticide Residues in Fruits and Vegetables: Method Development and Validation
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents an application of ultrahigh-performance liquid chromatography electrospray ionization quadrupole Orbitrap high-resolution mass spectrometry (UHPLC/ESI Q-Orbitrap MS) for the determination of 451 pesticide residues in fruits and vegetables. Pesticides were extracted from samples using the QuEChERS (quick, easy, cheap, effective, rugged, and safe) procedure. UHPLC/ESI Q-Orbitrap MS in full MS scan mode acquired full MS data for quantification, and UHPLC/ESI Q-Orbitrap Full MS/dd-MS(2) (i.e., data-dependent scan mode) obtained product ion spectra for identification. UHPLC/ESI Q-Orbitrap MS quantification was achieved using matrix-matched standard calibration curves along with the use of isotopically labeled standards or a chemical analogue as internal standards to achieve optimal method accuracy. The method performance characteristics include overall recovery, intermediate precision, and measurement uncertainty evaluated according to a nested experimental design. For the 10 matrices studied, 94.5% of the pesticides in fruits and 90.7% in vegetables had recoveries between 81 and 110%; 99.3% of the pesticides in fruits and 99.1% of the pesticides in vegetables had an intermediate precision of ≤20%; and 97.8% of the pesticides in fruits and 96.4% of the pesticides in vegetables showed measurement uncertainty of ≤50%. Overall, the UHPLC/ESI Q-Orbitrap MS demonstrated acceptable performance for the quantification of pesticide residues in fruits and vegetables. The UHPLC/ESI Q-Orbitrap Full MS/dd-MS(2) along with library matching showed great potential for identification and is being investigated further for routine practice.
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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 | 0.001 |
| 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 it