Determination of Chlormequat and Mepiquat Residues in Tomato Plants Using Accelerated Solvent Extraction-Ultra-Performance Liquid Chromatography- Tandem Mass Spectrometry
Bibliographic record
Abstract
An Accelerated-Solvent Extraction-Ultra performance Liquid Chromatography-Tandem Mass Spectrometry (ASE-UPLC-MS/MS) method using purified water as extraction solvent for quantitative analysis of chromequat (CQ) and mepiquat (MQ) in samples of tomato plants with higher sensibility and shorter extraction time was developed. The CQ and MQ residues and their dissipation rate were both covered in this paper. The limits of detection (S/N>3) and limits of quantitation (S/N>10) for CQ and MQ were 0.02 μg/kg and 0.1 μg/kg respectively. The linear range was 0.2~10 μg/kg and the correlation coefficients (r2) was no less than 0.9990, The average recoveries of CQ and MQ from tomato root, stem and leaf in the three spiked range of 1.0, 2.0 and 5.0 μg/kg were in the range of 100.0%~118.8% and 93.2%~110.7% respectively. The dissipation experiment showed that, on average, 98.8% of CQ residues and 99.7% of MQ residues had dissipated after 33 days, with a half-life of 3.67d and 3.66d, which can provide with guideline for using CQ and MQ on tomato in safe range. Key words: Tomato plants; Accelerated solvent extraction; Ultra-performance liquid chromatography-tandem mass spectrometry; Chlormequat; Mepiquat
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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 | 0.000 | 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) | 0.001 | 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".