Development and Validation of a Qualitative Method for Target Screening of 448 Pesticide Residues in Fruits and Vegetables Using UHPLC/ESI Q-Orbitrap Based on Data-Independent Acquisition and Compound Database
Bibliographic record
Abstract
A semiautomated qualitative method for target screening of 448 pesticide residues in fruits and vegetables was developed and validated using ultrahigh-performance liquid chromatography coupled with electrospray ionization quadrupole Orbitrap high-resolution mass spectrometry (UHPLC/ESI Q-Orbitrap). The Q-Orbitrap Full MS/dd-MS 2 (data dependent acquisition) was used to acquire product-ion spectra of individual pesticides to build a compound database or an MS library, while its Full MS/DIA (data independent acquisition) was utilized for sample data acquisition from fruit and vegetable matrices fortified with pesticides at 10 and 100 μg/kg for target screening purpose. Accurate mass, retention time and response threshold were three key parameters in a compound database that were used to detect incurred pesticide residues in samples. The concepts and practical aspects of in-spectrum mass correction or solvent background lock-mass correction, retention time alignment and response threshold adjustment are discussed while building a functional and working compound database for target screening. The validated target screening method is capable of screening at least 94% and 99% of 448 pesticides at 10 and 100 μg/kg, respectively, in fruits and vegetables without having to evaluate every compound manually during data processing, which significantly reduced the workload in routine practice.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".