Multiresidue Pesticide Analysis in Ginseng and Spinach by Nontargeted and Targeted Screening Procedures
Bibliographic record
Abstract
Five different mass spectrometers interfaced to GC or LC were evaluated for their application to targeted and nontargeted screening of pesticides in two foods, spinach and ginseng. The five MS systems were capillary GC/MS/MS, GC-high resolution time-of-flight (GC/HR-TOF)-MS, TOF-MS interfaced with a comprehensive multidimensional GC (GCxGC/TOF-MS), an MS/MS ion trap hybrid mass (qTrap) system interfaced with an ultra-performance liquid chromatograph (UPLC-qTrap), and UPLC interfaced to an orbital trap high resolution mass spectrometer (UPLC/Orbitrap HR-MS). Each MS system was tested with spinach and ginseng extracts prepared through a modified quick, easy, cheap, effective, rugged, and safe (QuEChERS) procedure. Each matrix was fortified at 10 and 50 ng/g for spinach or 25 and 100 ng/g for ginseng with subsets of 486 pesticides, isomers, and metabolites representing most pesticide classes. HR-TOF-MS was effective in a targeted search for characteristic accurate mass ions and identified 97% of 170 pesticides in ginseng at 25 ng/g. A targeted screen of either ginseng or spinach found 94-95% of pesticides fortified for analysis at 10 ng/g with GC/MS/MS or LC/MS/MS using multiple reaction monitoring (MRM) procedures. Orbitrap-MS successfully found 89% of 177 fortified pesticides in spinach at 25 ng/g using a targeted search of accurate mass pseudomolecular ions in the positive electrospray ionization mode. A comprehensive GCxGC/TOF-MS system provided separation and identification of 342 pesticides and metabolites in a single 32 min acquisition with standards. Only 67 or 81% of the pesticides were identified in ginseng and spinach matrixes at 25 ng/g or 10 ng/g, respectively. MS/MS or qTrap-MS operated in the MRM mode produced the lowest false-negative rates, at 10 ng/g. Improvements to instrumentation, methods, and software are needed for efficient use of nontargeted screens in parallel with triple quadrupole MS.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".