Separation and determination of ephedra alkaloids in traditional Chinese medicine and human urines by capillary electrophoresis coupled with electrochemiluminescence detection
Bibliographic record
Abstract
A new approach for the simultaneous determination of ephedrine, methylephedrine, and pseudoephedrine is developed, using capillary electrophoresis coupled with electrochemiluminescence detection with internal standard method. Separation efficiency and sensitivity were improved by use of an ionic liquid. Parameters affecting separation and detection were investigated in detail. Under optimum conditions, the three ephedra alkaloids were well separated and detected. The limits of detection (S/N = 3) of ephedrine, methylephedrine and pseudoephedrine are 4.0 × 10−8, 6.5 × 10−8, and 4.6 × 10−8 mol/L, respectively. The limits of quantitation (S/N = 10) in human urine are 5.3 × 10−7 mol/L for ephedrine, 9.1 × 10−7 mol/L for methylephedrine, and 6.9 × 10−7 mol/L for pseudoephedrine, respectively. The precision (RSD%) of the peak area and the migration time were from 2.2% to 2.5% and from 0.1% to 0.2% within a day and from 2.7% to 3.9% and from 0.4% to 0.8% in three days. The proposed method was successfully applied to the determination of three analytes in traditional Chinese medicine and human urine, and the monitoring of pharmacokinetics of pseudoephedrine in human body.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".