Multiresidue Pesticide Analysis in Fresh Produce by Capillary Gas Chromatography−Mass Spectrometry/Selective Ion Monitoring (GC-MS/SIM) and −Tandem Mass Spectrometry (GC-MS/MS)
Bibliographic record
Abstract
A multiresidue method for the analysis of pesticides in fresh produce has been developed using salt-out acetonitrile extraction, solid-phase dispersive cleanup with octadecyl-bonded silica (C<sub>18</sub>), and graphitized carbon black/primary−secondary amine (GCB/PSA) sorbents and toluene, followed by capillary gas chromatography−mass spectrometry in selected ion monitoring mode (GC-MS/SIM) or −tandem mass spectrometry (GC-MS/MS). Quantitation was determined from calibration curves using matrix-matched standards ranging from 3.3 to 6667 ng/mL with <i>r</i><sup>2</sup> > 0.99, and geometric mean limits of quantitation were typically 8.4 and 3.4 μg/kg for GC-MS/SIM and GC-MS/MS, respectively. Identification was determined by using target and qualifier ions and qualifier-to-target ratios for GC-MS/SIM and two ion transitions for GC-MS/MS. Fortification studies (10, 25, 100, and 500 μg/kg) were performed on 167 organohalogen, organophosphorus, and pyrethroid pesticides in 10 different commodities (apple, broccoli, carrot, onion, orange, pea, peach, potato, spinach, and tomato). The mean percent recoveries were 90 ± 14, 87 ± 14, 89 ± 14, and 92 ± 14% for GC-MS/SIM and 95 ± 22, 93 ± 14, 93 ± 13, and 97 ± 13% for GC-MS/MS at 10, 25, 100, and 500 μg/kg, respectively. GC-MS/MS was shown to be more effective than GC-MS/SIM due to its specificity and sensitivity in detecting pesticides in fresh produce samples. The method, based on concepts from the multiresidue procedure used by the Canadian Food Inspection Agency and QuEChERS (<i>Qu</i>ick, <i>E</i>asy, <i>Ch</i>eap, <i>E</i>ffective, <i>R</i>ugged, and <i>S</i>afe), was shown to be efficient in screening, identifying, and quantitating pesticides in fresh produce samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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; both teacher heads agree on what is shown here.
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".