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
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it