Ultrahigh-Performance Liquid Chromatography Electrospray Ionization Q-Orbitrap Mass Spectrometry for the Analysis of 451 Pesticide Residues in Fruits and Vegetables: Method Development and Validation
Bibliographic record
Abstract
This paper presents an application of ultrahigh-performance liquid chromatography electrospray ionization quadrupole Orbitrap high-resolution mass spectrometry (UHPLC/ESI Q-Orbitrap MS) for the determination of 451 pesticide residues in fruits and vegetables. Pesticides were extracted from samples using the QuEChERS (quick, easy, cheap, effective, rugged, and safe) procedure. UHPLC/ESI Q-Orbitrap MS in full MS scan mode acquired full MS data for quantification, and UHPLC/ESI Q-Orbitrap Full MS/dd-MS(2) (i.e., data-dependent scan mode) obtained product ion spectra for identification. UHPLC/ESI Q-Orbitrap MS quantification was achieved using matrix-matched standard calibration curves along with the use of isotopically labeled standards or a chemical analogue as internal standards to achieve optimal method accuracy. The method performance characteristics include overall recovery, intermediate precision, and measurement uncertainty evaluated according to a nested experimental design. For the 10 matrices studied, 94.5% of the pesticides in fruits and 90.7% in vegetables had recoveries between 81 and 110%; 99.3% of the pesticides in fruits and 99.1% of the pesticides in vegetables had an intermediate precision of ≤20%; and 97.8% of the pesticides in fruits and 96.4% of the pesticides in vegetables showed measurement uncertainty of ≤50%. Overall, the UHPLC/ESI Q-Orbitrap MS demonstrated acceptable performance for the quantification of pesticide residues in fruits and vegetables. The UHPLC/ESI Q-Orbitrap Full MS/dd-MS(2) along with library matching showed great potential for identification and is being investigated further for routine practice.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.001 | 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".