Applications of nDATA for screening, quantitation, and identification of pesticide residues in fruits and vegetables using UHPLC/ESI Q‐Orbitrap all ion fragmentation and data independent acquisition
Bibliographic record
Abstract
High sample throughput and effective multiresidue methods for screening, quantitation, and identification are desired for the analysis of a large number of pesticides in routine monitoring programs for food safety. This study was designed to explore the use of an UHPLC/ESI Q-Orbitrap nontarget data acquisition for target analysis (nDATA) workflow for screening 655 pesticides and quantifying a small group of 46 most likely incurred pesticide residues in fruits and vegetables in a single analysis. High-resolution mass spectrometers such as the Q-Orbitrap offer unique applications for pesticide analysis using full MS scan with data independent acquisition (DIA) or all ion fragmentation (AIF) scan. The experiments were designed to achieve a balance between selectivity and cycle time by considering parameter settings such as mass resolution and the number of mass isolation windows or isolation window widths. Coupled with ultra-high performance liquid chromatography (UHPLC), both full MS/DIA and full MS/AIF nDATA workflows were evaluated for screening, quantification, and identification in a single analysis. In general, UHPLC/ESI full MS/vDIA detected more fragment ions per pesticide than AIF when one to four fragments were compared. UHPLC/ESI full MS/vDIA and AIF generated comparable quantitative results, but the latter provided slightly better repeatability likely due to its shorter cycle time and more scans across a chromatographic peak. UHPLC/ESI full MS/vDIA may be preferable for screening, quantitation and identification when the testing scope covers a few hundreds of pesticides in a single analysis.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".