Quantification of Absorber through a Scattering Medium of Different Thickness Using Evanescent Light Piping
Bibliographic record
Abstract
A non-invasive method has been developed for analyte quantification in fluids surrounded by optically-scattering, opaque walls. This method is based on steady state, visible wavelength reflectance measurements made simultaneously at multiple positions on the surface of a sample. Previous work has shown that reflectance measurements contain information about underlying scattering layers in layered scattering samples. We hypothesise that similar information about an absorbing layer below a scattering layer can be obtained from evanescent wave effects. Principal component analysis showed the data to be composed of three components, which were refined by a multivariate curve resolution alternating least squares (MCR-ALS) approach with non-negativity constraints. The first component is related to the scattering layer thickness, the second is associated with analyte concentration and the third is due to a minor back reflection within the sample cell. Both MCR and stagewise multi-linear regression (SMLR) approaches were taken to estimate analyte concentration and scattering layer thickness, for samples having thicknesses between 1 mm and 8 mm. Results demonstrate that a simple experimental configuration can easily predict optical properties of unknown samples. With the adoption of a multi-wavelength approach to this method, it is expected that improved absorption coefficient (μ a ) estimation accuracy can be realised in a variety of application areas such as in analysis through opaque containers, in vivo measurements and in-line monitoring of reactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".