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 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 | 0.000 |
| 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".