Determination of Pesticide Residue Transfer Rates (Percent) from Dried Tea Leaves to Brewed Tea
Bibliographic record
Abstract
This paper presents a study on pesticide residue transfer rates (%) from dried tea leaves to brewed tea. In the study, a brewing procedure simulated the preparation of a hot tea drink as in routine. After brewing, pesticide residues were extracted from brewed tea using a method known as QuEChERS (quick, easy, cheap, effective, rugged, and safe). An UHPLC/ESI-MS/MS method was developed and validated to identify and quantify up to 172 pesticides in both tea leaves and brewed tea samples. Quantification was achieved using matrix-matched standard calibration curves with isotopically labeled standards or a chemical analogue as internal standards, and the calibration curves consisted of six points (0.4, 2.0, 8.0, 16.0, 24.0, and 40.0 μg/L equivalent in sample). The method was validated at four concentration levels (4.0, 12, 20.0, and 32.0 μg/L equivalent in sample) using five different brewed tea matrices on two separate days per matrix. Method performance parameters included overall recovery, intermediate precision, and measurement uncertainty, which were evaluated according to a nested experimental design. Approximately, 95% of the pesticides studied had recoveries between 81 and 110%, intermediate precision ≤20%, and measurement uncertainty ≤40%. From a pilot study of 44 incurred tea samples, pesticide residues were examined for their ability to transfer from dried tea leaves to brewed tea. Each sample, both tea leaves and brewed tea, was analyzed in duplicate. Pesticides were found to have different transfer rates (%). For example, imidacloprid, methomyl, and carbendazim had transfer rates of 84.9, 83.4, and 92.4%, respectively.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".