Multilaboratory Study of a Nontarget Data Acquisition for Target Analysis (nDATA) Workflow Using Ultrahigh-Performance Liquid Chromatography-High-Resolution Mass Spectrometry for the Screening of 1087 Pesticides in Fresh Fruits and Vegetables
Bibliographic record
Abstract
A nontarget Data Acquisition for Target Analysis (nDATA) workflow was developed to screen pesticides in fresh produce based on ultrahigh-performance liquid chromatography-high-resolution full scan mass spectrometry/variable data-independent tandem mass spectrometry acquisition (LC-FS MS/vDIA MSMS) and a pesticide database. The MSMS spectral library was generated to create a database consisting of 1087 pesticides based on authentic pesticide standards. The retention time (±0.5 min), precursor ion (≤± 5 ppm), and product ions (≤± 5 ppm) were extracted for each pesticide from LC-FS MS/data-dependent MSMS acquisition (LC-FS MS/DDA MSMS). Mass accuracy criteria, along with ±0.1 min retention time tolerance, were used for the identification of pesticides. Three laboratories evaluated and validated the nDATA workflow to screen and identify pesticides from produce extracts (apples, bananas, broccoli, carrots, grapes, lettuce, oranges, potatoes, strawberry, and tomatoes) prepared by the Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) procedure. Of the 1087 pesticides evaluated, false-positive rates were ≤5% for 977 pesticides in blank matrices and false-negative rates were ≤5% for 921 and 985 pesticides in fortified matrices at 10 and 100 μg/kg, respectively. False positives detected were misidentified pesticides, incurred residues, or contaminants possibly resulting from process or system contamination detected below the threshold level of 10 μg/kg. False negatives were attributed to pesticides that did not sufficiently ionize or fragment or had poor stabilities and QuEChERS extraction efficiencies. Incurred residues in archived produce samples (apple, Chinese broccoli, grape, kale, kohlrabi, orange, pepper, strawberry, tomato, and turnip green) were prepared using QuEChERS, evaluated by the nDATA workflow, and the results were compared and confirmed, if possible, to targeted GC-MS/MS, LC-MS/MS, and LC-FS MS/DDA MSMS methods. The three laboratories identified 25 parent pesticides at levels >10 μg/kg that were consistent with findings from targeted procedures and discovered 10 different metabolites that were not provided in the multiple reaction monitoring method or inclusion list of the targeted procedures. GC-MS/MS identified two pesticides, chlorothalonil and dacthal, and a possible chlorothalonil metabolite, pentachlorobenzonitrile, that were not amenable to LC-low or LC-high-resolution mass spectrometry analysis in produce samples. To improve the identification quality, the nDATA workflow further implemented quality control, operational, and processing measures to reduce the number of false detects, and the data evaluation workload. As demonstrated in this study, the validated nDATA workflow creates new opportunities for chemical residues analysis, offering a potential screening complement to targeted LC-MS/MS, GC-MS/MS, and nontargeted methods for pesticides and other contaminants of interest.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".