A Complementary, Quantitative, and Confirmatory Method to UHPLC/ESI Q-Orbitrap Screening Based on UPHLC/ESI-MS/MS for Analysis of 416 Pesticides in Fruits and Vegetables
Bibliographic record
Abstract
BACKGROUND: Triple quadrupole (MS/MS) and high-resolution mass spectrometry (HRMS), coupled with ultra-high performance (UHP) liquid chromatography (LC) or gas chromatography (GC), are technologies used to analyze pesticide residues in fruits and vegetables. LC-MS/MS has been the gold standard for analysis of pesticides, offering reliable performance and sensitivity, while LC-HRMS is expanding in application to serve as another benchmark. Method development and testing scope expansion are constantly required with new generation mass spectrometers. OBJECTIVE: This article discusses the development and validation of a quantitative and confirmatory method that can analyze over 400 pesticide residues using a state-of-the-art UHPLC/electrospray ionization (ESI)-MS/MS system. METHODS: Homogenized fruit and vegetable samples were fortified with pesticides and were extracted using a modified quick, easy, cheap, effective, rugged, and safe method. Samples were injected onto a UHPLC/ESI-MS/MS system, and data were acquired in multiple reaction monitoring (MRM) mode. The method was validated using a nested experimental design, and was able to quantify and confirm 416 pesticides in fruits and vegetables. It was also complimentary to the UHPLC/ESI Q-Orbitrap quantitative and screening methods previously developed in the authors' laboratory. RESULTS: The method demonstrated good performance. In all matrixes, 92% of pesticides yielded recoveries between 81-110%, more than 95% of pesticides yielded intermediate precision ≤20%, about 65% of pesticides yielded measurement uncertainties ≤20%, and 96% of pesticides yielded measurement uncertainties ≤50%. CONCLUSION: This method shows great potential to be a standalone method or as part of a laboratory workflow for quantitative and confirmatory analysis of pesticide residues in fruits and vegetables. HIGHLIGHTS: This method was developed using the same mobile phases, analytical columns, and extraction procedure, as UHPLC/ESI Q-Orbitrap methods. Extracts can be run on either system, streamlining monitoring programs and offering high sample throughput.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".