Development and Interlaboratory Validation of a QuEChERS-Based Liquid Chromatography−Tandem Mass Spectrometry Method for Multiresidue Pesticide Analysis<sup>†</sup>
Bibliographic record
Abstract
A high-throughput, QuEChERS (Quick, Easy, Cheap, Effective, Rugged, Safe) sample preparation and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analytical method has been developed and validated for the determination of 191 pesticides in vegetation and fruit samples. Using identical LC analytical column and MS/MS instrumentation and operation parameters, this method was evaluated at the U.S. Food and Drug Administration (FDA), National Research Centre for Grapes (NRCG), India, and Ontario Ministry of the Environment (MOE) laboratories. Method validation results showed that all but 1 of these 191 pesticides can be analyzed by LC-MS/MS with instrument detection limits (IDL) in the parts per trillion (ppt) range. Matrix-dependent IDL studies showed that due to either the low ionization efficiency or matrix effect exerted, 14 of these 191 pesticides could not be analyzed by this method. Method recovery (%R) and method detection limits (MDLs) were determined by the three laboratories using four sample matrices in replicates (N = 4). With >79% of %R data from the fortification studies in the range from 80 to 120%, MDLs were determined in the low parts per billion range with >94% of MDLs in the range from 0.5 to 5 ppb. Applying this method to the analysis of incurred samples showed that two multiple reaction monitoring (MRM) transitions may not be enough to provide 100% true positive identification of target pesticides; however, quantitative results obtained from the three laboratories had an excellent match with only a few discrepancies in the low parts per billion levels. The %R data from the fortification studies were subjected to principal component analysis and showed the majority of %R fell into the cluster of 80% < %R < 120%. Due to the matrix effect exerted by ginseng and peach, outliers were observed at the lowest spiking levels of 10 and 25 ppb. The study also showed that QuEChERS samples should be analyzed as soon as prepared or stored in a freezer to avoid any adverse affect on the analytes evaluated.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".