Micellar electrokinetic chromatography method development for simultaneous determination of thiabendazole, carbendazim, and fuberidazole
Bibliographic record
Abstract
Thiabendazole (TBz), carbendazim (CBz), and fuberidazole (FBz) are systemic benzimidazole-type fungicides used for pre- and post-harvest treatment to control various types of fungal diseases on a variety of crops. Significant levels of these fungicides could alter the composition or flavour of crops, and being possible carcinogens, they could also pose risks for humans and the environment. A mode of capillary electrophoresis called micellar electrokinetic chromatography (MEKC) was investigated for the determination of these three benzimidazole fungicides. The study involved two kinds of surfactants in which several experimental conditions were optimized, i.e., buffer concentration, pH, micelle concentration, and percent organic modifier (methanol). Using the optimum experimental conditions, the fungicides were successfully separated by MEKC. The limits of detection and quantification were in the range of 0.6-0.7 and 2.1-2.5 mg L(-1), respectively, and the calibration curves were linear over the range of 5-60 mg L(-1) for the three fungicides. The potential of the proposed MEKC method was demonstrated by analyzing water samples which were fortified with the fungicides. The proposed method enabled simultaneous determination of the three benzimidazole fungicides and method validation with spiked water samples yielded satisfactory quantitative recoveries for all the three fungicides.
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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 | 0.001 |
| 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 | 0.002 | 0.002 |
| 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".