Quantitative molecular analysis with molecular bands emission using laser-induced breakdown spectroscopy and chemometrics
Bibliographic record
Abstract
The present work describes the first quantitative molecular prediction using laser-induced molecular bands along with chemometrics. In addition, this spectroscopic procedure has demonstrated the first complete quantitative analysis utilizing traditionally insensitive elements for pharmaceutical formulations. Atomic LIBS requires certain sensitive elements, such as Cl, F, Br, S and P, in order to quantitate a specific organic compound in a complex matrix. Molecular LIBS has been demonstrated to be the first successful approach using atomic spectroscopy to evaluate a complex organic matrix. This procedure is also the first quantitative analysis using laser-induced molecular bands and chemometrics. We have successfully applied chemometrics to predict the formulation excipients and active pharmaceutical ingredient (API) in a complex pharmaceutical formulation. Using such an approach, we demonstrate that the accuracy for the API and a formulation lubricant, magnesium stearate, have less than 4% relative bias. The other formulation excipients such as Avicel® and lactose have been accurately predicted to have less than a 15% relative bias. Molecular LIBS and chemometrics have provided a novel approach for the quantitative analysis of several molecules that was not technically possible with the traditional atomic LIBS procedure, that required sensitive elements to be present in both API and formulation excipients.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".