Concentration monitoring with near infrared chemical imaging in a tableting press
Bibliographic record
Abstract
Monitoring powder potency and homogeneity is important in achieving real-time release testing in a continuous tablet manufacturing operation. If quality related issues are encountered, monitoring powder potency inside a feed frame offers a last opportunity to intervene in the process before tablet compression. Feed frame monitoring methods based on near infrared (NIR) spectroscopy have been increasingly reported in recent years. New process analytical tools with the potential of being deployed alone or in combination with NIR spectroscopy for feed frame monitoring are now available commercially. The present study evaluated the potential of near infrared chemical imaging (NIR CI) for in-line monitoring of a prototype pharmaceutical composition containing ascorbic acid (AA), microcrystalline cellulose and dicalcium phosphate. NIR spectroscopy was the reference method. In-line calibration models based on partial least square regression were developed and validated with a range of AA concentrations. The ability of NIR spectroscopy and NIR CI to predict concentrations in test runs was ascertained both independently and in combination. NIR CI, with a single bandpass filter, predicted AA concentrations—present at commercially relevant concentrations—with acceptable accuracy. Comparative results showed that NIR CI has the potential for in-line monitoring of blend concentrations inside feed frames. In addition to the advantage of increased sample size, it also has the potential to detect segregation inside feed frames.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".