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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".