Applications of LC/ESI-MS/MS and UHPLC/Qq-TOF-MS for the Determination of 141 Pesticides in Tea
Bibliographic record
Abstract
This paper presents the applications of LC-electrospray ionization (ESI)/MS/MS and ultra-HPLC (UHPLC)/ESI quadrupole (Qq)-time-of-flight (TOF) MS for the determination of 141 pesticides in tea. Pesticides were extracted and cleaned up from tea with a modified quick, easy, cheap, effective, rugged, and safe method using graphitized carbon black and primary-secondary amine sorbents. Quantification was achieved using matrix-matched standard calibration curves with isotopically labeled standards or a chemical analog as internal standards in an analytical range from 5 to 500 microg/kg. The LC/ESI-MS/MS served as a reliable tool to quantify the pesticides due to its superior sensitivity and good repeatability. Its method performance characteristics that include overall recovery, intermediate precision, and measurement uncertainty were evaluated according to a statistically designed experiment, i.e., a nested design. About 87% of the pesticides had recoveries between 81 and 110%; 94% had an intermediate precision < or = 20%; and 90% showed measurement uncertainty < or = 40%. About 92% of the pesticides were able to be detected at 5 microg/kg with an S/N > or = 3. The UHPLC/Qq-TOF-MS showed much less sensitivity and poorer repeatability compared to the LC/ESI-MS/MS, and, therefore, it was primarily used for confirmatory purposes based on the accurate mass measurement and isotopic patterns.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".