Optimization of Capillary Electrophoresis by Central Composite Design for Separation of Pharmaceutical Contaminants in Water Quality Testing
Bibliographic record
Abstract
Many pharmaceutical active compounds are prepared as hydrochlorides for quick release in the gastrointestinal tract upon oral administration. Their inadvertent escape into the water environment requires efficient analytical separation for accurate quantitation to monitor their environmental fate. The purpose of this study is to demonstrate how best to optimize a capillary electrophoresis method for the separation of four model pharmaceutical hydrochlorides. Concentration of sodium dibasic phosphate in the background electrolyte solution, pH adjustment with HCl or NaOH, and applied voltage across the capillary were the three key factors chosen for optimization. The peak resolutions and total migration time were examined as the response indicators to complete a central composite design in response surface methodology. The examination revealed that CE separation was driven significantly by a linear regression model and minimally by a quadratic regression model, based on the coefficient of determination, the lack of fit, the total sum of squares, and the p values. Under optimal conditions of the background electrolyte concentration of 75 mM, pH 9, and the applied voltage of 10 kV, the model hydrochlorides were separated within five minutes in the migration order of metformin (first) > phenformin > mexiletine > ranitidine (last). The limits of UV detection/quantification attained under optimal CE conditions were 0.015/0.045, 0.020/0.060, 0.142/0.426, and 0.017/0.051, respectively.
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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 | 0.005 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".