Application of nDATA Workflow for Semi-Quantitative Screening of 1094 Pesticide Residues in Fruits and Vegetables Using UHPLC/ESI Q-Orbitrap Full MS/vDIA
Bibliographic record
Abstract
BACKGROUND: Cost-effective multi-residue pesticide methods with a broad detection scope are desired for risk-based monitoring programs. OBJECTIVE: The aims were to evaluate the nDATA (non-target data acquisition for target analysis) workflow using ultra high performance liquid chromatography/electrospray ionization quadrupole-Orbitrap mass spectrometry (UHPLC/ESI quadrupole-Orbitrap (Q-Orbitrap)) and semi-quantitate 1094 pesticides in fruits and vegetables. METHODS: Pesticide extracts from fresh produce were prepared using the Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS) procedure. nDATA was carried out by utilizing UHPLC/ESI Q-Orbitrap full MS scan and variable data independent acquisition (UHPLC/ESI Q-Orbitrap full MS/vDIA MSMS). Data were processed using a compound database (CDB, 1094 pesticides) and one-point standard calibration with internal standards for semi-quantitation. Data processing criteria were based on Retention Time (±0.5 min) and mass accuracy of a Precursor ion (±5 ppm; RTP by full MS), or Retention Time (±0.5 min) and mass accuracy of a precursor ion (±5 ppm) and that of its Fragment Ion (±10 ppm; RTFI by full MS/vDIA). RESULTS: RTP found 1010 and 1094 pesticides, while RTFI identified 906 and 1029 pesticides at 10 and 100 μg/kg, respectively. RTF detected all 30 LC-amenable pesticides and RTFI identified 29 of 30 LC-amenable pesticides in eight proficiency testing samples. There were 42 pairs of co-eluting isomeric pesticides and 5 pairs of isobaric pesticides that were not separated by mass resolving power and/or chromatographic separation (retention time difference ΔtR <0.12 min) with the current instrument parameter settings. CONCLUSION: The validated nDATA workflow using UHPLC/ESI Q-Orbitrap full MS/vDIA MSMS proved to be a comprehensive detection method for semi-quantitative screening of 1094 pesticides in fruits and vegetables. HIGHLIGHTS: nDATA combines both non-target data acquisition and target analysis. The non-target data acquisition generates data for retrospective analysis of a large number of pesticides (over 1000). The target analysis using a CDB and a one-point standard calibration affords confidence in semi-quantitative screening results.
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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".