Addressing temperature variations of miniaturized NIR spectrometers: Advancing quantitative models for pharmaceutical analysis
Bibliographic record
Abstract
Miniaturized near-infrared (NIR) spectrometers have gained wide popularity in various pharmaceutical applications, particularly for inline processes. However, their quantification capabilities still lag behind those of mature benchtop spectrometers, with miniaturized NIR spectrometers often lacking the accuracy required for pharmaceutical analysis without calibration transfer to a robust benchtop device. Therefore, a comprehensive investigation of their inherent error sources is imperative to enhance their analytical performance. Factors such as compact size, lack of thermal management systems, and operational conditions render miniaturized devices more susceptible to temperature fluctuations. These fluctuations lead to measurement errors, especially when the duration of inline processes limits the frequency of updating background scans. While previous research investigated the impact of sample and ambient temperature variations, we investigated the impact of temperature variations of miniaturized NIR spectrometers itself during sample and background acquisitions. These variations led to emergence of distinct spectral subsets, posing a risk to accuracy when combined in one model and making prediction of one subset by another challenging. We explored calibration transfer (CT) methods to enhance model robustness and maintain prediction accuracy across temperature subsets, with Ridge and LASSO regressions showing superior results. By addressing this error source, it is possible to enhance the accuracy and robustness of miniaturized NIR spectrometers, particularly in prolonged inline measurements.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".