Multiresidue Pesticide Analysis of Agricultural Commodities Using Acetonitrile Salt-Out Extraction, Dispersive Solid-Phase Sample Clean-Up, and High-Performance Liquid Chromatography–Tandem Mass Spectrometry
Bibliographic record
Abstract
A multiresidue method analyzing 209 pesticides in 24 agricultural commodities has been developed and validated using the original Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS) procedure and high performance liquid chromatography-positive electrospray ionization-tandem mass spectrometry (LC-MS/MS) analysis. Using solvent-only calibration standards (SOCSs) and matrix-matched calibration standards (MMCSs), it was demonstrated that a minimal concentration of 5-10 μg/kg (part per billion, ppb) of analytes in matrix is required for the consistent identification of targeted pesticides with two MRM transitions. Method performance was validated by the precision and accuracy results obtained from fortification studies at 10, 25, 100, and 500 ppb and MMCSs. The method was demonstrated to achieve an average recovery of 100 ± 20% (n = 4) for >75% of evaluated pesticides at the low fortification level (10 ppb) and improved to >84% at the higher fortification concentrations in all 24 matrices. Matrix effects in LC-MS/MS analysis were studied by evaluating the slope ratios of calibration curves (1.0-100 ng/mL) obtained from the SOCSs and MMCSs. Principal component analysis (PCA) of LC-MS/MS and method validation data confirmed that each matrix exerts its specific effect during the sample preparation and LC-MS/MS analysis. The matrix effect is primarily dependent on the matrix type, pesticide type and concentration. Some caution is warranted when using matrix matched calibration curves for the quantitation of pesticides to alleviate concerns on matrix effects. The QuEChERS method with LC-MS/MS was used to identify and quantitate pesticides residues, with concentrations ranging from 2.5 to >1000 ppb in a variety of agricultural samples, demonstrating fitness for screening and surveillance applications.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".