UHPLC/ESI Q-Orbitrap Quantitation of 655 Pesticide Residues in Fruits and Vegetables—A Companion to an nDATA Working Flow
Bibliographic record
Abstract
BACKGROUND: Effective and expansive methods for multiresidue pesticide analysis are desired for routine monitoring programs. These methods are complex, especially when several hundred pesticides are involved. OBJECTIVE: Two approaches to sort data and identify isomers and isobaric ions in pesticide mixtures were evaluated to determine whether they could be differentiated by mass resolving power and/or chromatographic resolution. METHOD: This study presents an application of ultra-high performance liquid chromatography electrospray Q-Orbitrap mass spectrometry (UHPLC/ESI Q-Orbitrap) along with QuEChERS for the quantitation of 655 pesticide residues in fruits and vegetables. RESULTS: From the developed method, 94.7% of the 655 pesticides in fruits and 93.9% of those in vegetables had recoveries between 81% and 110%; 98.3% in both fruits and vegetables had an intermediate precision of ≤20%; and 97.7% in fruits or 97.4% in vegetables showed measurement uncertainty of ≤50%. When the retention time difference (ΔtR) of two isomers was ≥0.12 min, they were chromatographically resolved. Twenty five out of 35 pairs or groups of isomers were chromatographically separated (ΔtR ≥ 0.12 min), but 14 pairs were not resolved (ΔtR < 0.12 min). There were 493 pairs of pesticides with a mass-to-charge difference of <1 Da. Only one pair of isobaric ions could not be separated by mass and chromatographic resolution. HIGHLIGHTS: UHPLC/ESI Q-Orbitrap along with QuEChERS sample preparation offers a practical quantitative companion method to a non-target data acquisition for target analysis workflow for pesticide residue analysis in routine monitoring programs for food safety.
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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".