Simultaneous Determination of 147 Pesticide Residues in Traditional Chinese Medicines by GC–MS/MS
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Determination of pesticide residues remains a challenge in traditional Chinese medicines in which complex compounds may interfere with analysis signals. This study reports the development of a simple, effective, and high-throughput method combining gas chromatography–tandem mass spectrometry (GC–MS/MS) with either QuEChERS or solid phase extraction (SPE) to determine 147 pesticide residues in traditional Chinese medicines simultaneously. In SPE, the mixture of n -hexane and ethyl acetate (1:1, v/v) was selected to extract 147 pesticides in honeysuckle, and the extracted pesticides were determined by GC–MS/MS. The limits of detection for all pesticides were within 0.01–0.05 mg/kg. The recoveries were within 70–120% and the relative standard deviations were below 20% for over 90% pesticides. The coefficients of determination were up to 0.999 for the linearity between MS signals and different concentrations of pesticides (20–200 ng/mL). The analytical performance was confirmed in determining pesticide residues in dried tangerine peel. SPE achieved comparable recoveries for all pesticides compared to the QuEChERS method.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".