Calibration transfer and maintenance in the pharmaceutical industry: a systematic review
Bibliographic record
Abstract
Effective calibration transfer is essential for ensuring accurate and reliable measurements on altering one or more components of spectroscopic measurements, including the spectrometers, sample characteristics, environmental conditions, and measurement settings. Taking a unique perspective, this review aims to provide a guide for pharmaceutical researchers, offering insights into calibration transfer and maintenance applications. The systematic review lists all documented applications of calibration transfer and maintenance algorithms in the pharmaceutical industry up until the time of manuscript preparation. These studies covered various types of calibration transfer scenarios, including intravendor, intervendor, different spectral technologies, and transfers from benchtop to miniaturized instruments. Calibration maintenance cases revealed sources of variation like production scale, temperature changes, sample physical properties, and varied dynamic nature of processes. The review links algorithms to practice while highlighting research gaps. These gaps include limited applications on semi-solid or liquid pharmaceutical products, limited inline applications, and a lack of consensus on best practices. By addressing these shortcomings, this review contributes to advancing calibration transfer in the pharmaceutical industry, supporting precise measurements, improved process control, and the development of high-quality pharmaceutical products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".