Direct Immersion Solid-Phase Microextraction with Matrix-Compatible Fiber Coating for Multiresidue Pesticide Analysis of Grapes by Gas Chromatography–Time-of-Flight Mass Spectrometry (DI-SPME-GC-ToFMS)
Bibliographic record
Abstract
A fast and sensitive direct immersion-solid-phase microextraction-gas chromatography-time-of-flight mass spectrometry (DI-SPME-GC-ToFMS) method for the determination of multiresidue pesticides in grapes employing a PDMS-modified PDMS/DVB coating was developed utilizing multivariate approaches for optimization of the most important factors affecting SPME performance. A comprehensive investigation of appropriate internal standards using a bottom-up approach led to the selection of suitable compounds that adequately covered a range of 40 pesticides pertaining to various classes. The validated method yielded good accuracy, precision, and sensitivity and has been successfully applied to the analysis of commercial samples. With regard to the limitations of the proposed method, the DI-SPME method did not provide a satisfactory performance toward more polar pesticides (e.g., acephate, omethoate, dimethoate) and highly hydrophobic pesticides, such as pyrethroids. Despite the challenges and limitations encountered by this method, the practical aspects of the PDMS-modified coating demonstrated here create new opportunities for SPME applied in food analysis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".