Quantitative Macro-Raman Spectroscopy on Microparticle-Based Pharmaceutical Dosage Forms
Bibliographic record
Abstract
Quantitative macro-Raman spectroscopy was applied to the analysis of the bulk composition of pharmaceutical drug powders. Powders were extracted from seven commercial lactose-carrier-based dry-powder inhalers: Flixotide 50, 100, 250, and 500 μg/dose (four concentrations of fluticasone propionate) and Seretide 100, 250, and 500 μg/dose (three concentrations of fluticasone propionate, each with 50 μg/dose salmeterol xinafoate ). Also, a carrier-free pressurized metered-dose inhaler of the same combination product, Seretide 50 (50 μg fluticasone propionate and 25 μg salmeterol xinafoate per dose) was tested. The applicability of a custom-designed dispersive macro-Raman instrument with a large sample volume of 0.16 μL was tested to determine the composition of the multicomponent powder samples. To quantify the error caused by sample heterogeneity, a Monte Carlo model was developed to predict the minimum sample volume required for representative sampling of potentially heterogeneous samples at the microscopic level, characterized by different particle-size distributions and compositions. Typical carrier-free respirable powder samples required a minimum sample volume on the order of 10(-4) μL to achieve representative sampling with less than 3% relative error. In contrast, dosage forms containing non-respirable carriers (e.g., lactose) required a sample volume on the order of 0.1 μL for representative measurements. Error analysis of the experimental results showed good agreement with the error predicted by the simulation.
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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) | 0.000 | 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 | 0.001 |
| 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".