Applications of Ultra-performance Liquid Chromatography Electrospray Ionization Quadrupole Time-of-Flight Mass Spectrometry on Analysis of 138 Pesticides in Fruit- and Vegetable-Based Infant Foods
Bibliographic record
Abstract
The applications of ultra-performance liquid chromatography electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC QqTOF) in the determination of 138 pesticides in fruit- and vegetable-based infant foods were investigated. Pesticides were extracted from infant foods using a procedure known as the quick, easy, cheap, effective, rugged, and safe (QuEChERS) method. UPLC QqTOF MS full-scan with a relatively high sensitivity proved to be an ideal tool for screening of a large number of pesticides in a single analysis. UPLC QqTOF MS/MS provided product ion spectra that allowed for unequivocal confirmation of pesticides. Quantification was achieved using matrix-matched standard calibration curves with isotopically labeled standards or a chemical analogue as internal standards. The method performance parameters that included overall recovery, intermediate precision, and measurement uncertainty were evaluated according to a designed experiment, that is, the nested design. Generally, about 90% of the pesticides studied had recoveries between 81 and 110%, 90% had intermediate precision of ≤25%, and 85% had measurement uncertainty of ≤50%. Compared to LC-ESI-MS/MS, UPLC QqTOF MS showed a relatively poor repeatability and large measurement uncertainty for quantification. In general, UPLC QqTOF can be used for screening, quantifying, and confirming pesticides in infant foods at 10 μg/kg.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".