Quantitative HPLC Analysis of Active Pharmaceutical Ingredients in Syrup Vehicle Using Centrifugal Filter Devices and Determination of Xanthan Gum in Syrup Vehicle Using Rheometry
Bibliographic record
Abstract
Using rapid centrifugal filtration (≤ 30 min) of diluted samples, the filter membrane prevented compounds with molecular weight higher than the nominal molecular weight limit (NMWL) from transporting through the membrane, thus separating them from compounds with molecular weight smaller than NMWL, which would pass through the membrane. The purpose of this study aims to remove high molecular weight matrix (such as xanthan gum) interferences while achieving a quantitative analysis of the active pharmaceutical ingradients of interest. Two model active pharmaceutical ingredients, L-arginine and amphotericin B, were quantitatively recovered from the diluted syrup vehicle after centrifugation with the filter devices. The reproducibility [% relative standard deviation (RSD), peak area] of the filtered samples was less than 0.5%. For amphotericin B samples. The linear range was 0.28 µg/mL to 28.2 µg/mL. The limit of detection was 0.06 µg/mL. The limit of quantification was 0.28 µg/mL. The viscosity of a syrup vehicle changed linearly with the concentration of xanthan gum. A method was thus developed to determine xanthan gum in the syrup vehicle. The accuracy was within 95.0% to 105.0% at different concentration levels.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".