Simultaneous Determination of 118 Pesticides in Vegetables by Atmospheric Pressure Gas Chromatography–Tandem Mass Spectrometry and QuEChERs Based on Multiwalled Carbon Nanotubes
Bibliographic record
Abstract
Pesticides are useful in agriculture but excessive usage of pesticides is hazardous to human health. Therefore, the pesticide residues in food are strictly monitored worldwide. In this study, a rapid method was developed for the simultaneous determination of 118 pesticides in vegetables by using atmospheric pressure gas chromatography–tandem mass spectrometry (APGC–MS/MS) and purification by multiwalled carbon nanotubes (MWCNTs). We optimized the chromatography and mass spectrometric conditions and obtained the best APGC–MS/MS analytical conditions. The optimal outer diameter, length, and amount of MWCNTs were investigated. In addition, the copurification effects of MWCNTs combined with N-propylendiamine (PSA) and C18 were tested. The limits of quantitation (LOQs) of 118 pesticides by this method were 0.05–3.24 μg/kg, which were sensitive enough for detection of the maximum residue limits of target pesticides according to standards in China, Japan, the EU, Canada, New Zealand, Australia, and the U.S.A. The recoveries and RSDs of the 118 pesticides in the three vegetable matrices were 67.1–117.6% and 1.1–14.8%, respectively. We successfully used this method to detect pesticide residues in 93 vegetable samples. Together, our method would be suitable for the routine analysis of multiclass residues in vegetable samples, especially for food of international trade.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".