Modeling reduced spectra of diffuse reflectance for NIR-SWIR absorbing-dye formulations

This study describes a parametric model of diffuse reflectance based on the reduction of absorption spectra for Near Infrared (NIR) and Short-Wave Infrared (SWIR) absorbing dyes on substrates using critical feature isolation and projection, which is structured for measurement-driven parameter optimization. The critical features are identified through a structural analysis of the peaks, troughs, and points of inflection within the Kubelka–Munk absorption spectra, which are calculated from diffuse reflectance measurements. These critical features are then parameterized and projected into a reduced feature subspace using Lorentzian decompositions to effectively capture the fundamental characteristics of the absorbing dyes, while removing processing and measurement noise. A Kramers–Kronig analysis then characterizes the dielectric responses and provides an estimation of the reduced spectra reflectance. The model parameters for the analytical reduction are further refined using a nonlinear multivariable optimization function between the analytically predicted reflectance and the measured reflectance.

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Publication Details

Journal
Journal of Electromagnetic Waves and Applications
Published
2025-02-20
DOI
https://doi.org/10.1080/09205071.2025.2465512
Primary Topic
Water Quality Monitoring and Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Modeling reduced spectra of diffuse reflectance for NIR-SWIR absorbing-dye formulations

Troy Mayo, Rachel Viger, Scott Ramsey, Samuel G. Lambrakos
Journal of Electromagnetic Waves and Applications
Water Quality Monitoring and Analysis
article

Modeling reduced spectra of diffuse reflectance for NIR-SWIR absorbing-dye formulations

Troy Mayo, Rachel Viger, Scott Ramsey, Samuel G. Lambrakos
article en

Abstract

This study describes a parametric model of diffuse reflectance based on the reduction of absorption spectra for Near Infrared (NIR) and Short-Wave Infrared (SWIR) absorbing dyes on substrates using critical feature isolation and projection, which is structured for measurement-driven parameter optimization. The critical features are identified through a structural analysis of the peaks, troughs, and points of inflection within the Kubelka–Munk absorption spectra, which are calculated from diffuse reflectance measurements. These critical features are then parameterized and projected into a reduced feature subspace using Lorentzian decompositions to effectively capture the fundamental characteristics of the absorbing dyes, while removing processing and measurement noise. A Kramers–Kronig analysis then characterizes the dielectric responses and provides an estimation of the reduced spectra reflectance. The model parameters for the analytical reduction are further refined using a nonlinear multivariable optimization function between the analytically predicted reflectance and the measured reflectance.

Journal of Electromagnetic Waves and ApplicationsVol. 39(6)
United States Naval Research Laboratory (US), United States Navy (US)
Defense Threat Reduction Agency
Openalex Percentile: Top 96%
Water Quality Monitoring and Analysis
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