Predicting granule size via in-line NIR spectroscopy during fluidized bed foam granulation and drying
Bibliographic record
Abstract
Wet granulation-a unit operation involving mixing polymeric binders with powdered formulations-is well established in the pharmaceutical industry, playing a major role in the manufacturing of oral solid dosage forms and improving the physical properties of granules (size, density, shape factor, etc.) before tableting. The foaming properties of aqueous polymeric binders prove useful for binder delivery within the mixing vessel, with foamed binders leading to enhanced process efficiency (binder distribution, drying time, and temperature) and product quality (heat-sensitive components) during granulation. Given the importance of this stage in producing oral solid dosage forms, understanding the relationship between critical process parameters and critical quality attributes is essential. The process analytical technology (PAT) framework enables process design, analysis, and control and facilitates process development via in-line spectroscopy combined with multivariate data analysis to yield critical product information during the unit operation. Herein, we used in-line NIR spectroscopy to monitor granule size in foam granulations of a pharmaceutical compound. The mean granule diameter was predicted using a partial least squares regression (PLSR) model (with a prediction error of 11.8 μm) and combined with a batch statistical process control (BSPC) approach for the temporal monitoring of granule size during three foam granulations.
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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) | 0.000 | 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 | 0.000 |
| 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".