High-performance dispersive Raman and absorption spectroscopy as tools for drug identification
Bibliographic record
Abstract
Due to increasing availability of pharmaceuticals from many sources, a need is growing to quickly and efficiently analyze substances in terms of the consistency and accuracy of their chemical composition. Differences in chemical composition occur at very low concentrations, so that highly sensitive analytical methods become crucial. Recent progress in dispersive spectroscopy with the use of 2-dimensional detector arrays, permits for signal integration along a long (up to 12 mm long) entrance slit of a spectrometer, thereby increasing signal to noise ratio and improving the ability to detect small concentration changes. This is achieved with a non-scanning, non-destructive system. Two different methods using P&P Optica high performance spectrometers were used. High performance optical dispersion Raman and high performance optical absorption spectroscopy were employed to differentiate various acetaminophen-containing drugs, such as Tylenol™ and other generic brands, which differ in their ingredients. A 785 nm excitation wavelength was used in Raman measurements and strong Raman signals were observed in the spectral range 300-1800 cm-1. Measurements with the absorption spectrometer were performed in the wavelength range 620-1020 nm. Both Raman and absorption techniques used transmission light spectrometers with volume phase holographic gratings and provided sufficient spectral differences, often structural, allowing for drug differentiation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".