Trace chemical detection and classification in stand-off reflection-based spectro- scopic data is challenging due to the variability of measured data and the lack of physics-based models that can accurately predict spectra. Most available models assume that the chemical takes the form of spherical particles or uniform thin films. A more realistic chemical presentation that could be encountered is that of a nonuniform chemical film that is deposited after evaporation of the solvent that contained the chemical. We present an improved signature model for this type of solid film. The proposed model, called sparse transfer matrix, includes a log-normal distribution of film thicknesses and is found to reduce the root mean square error between simulated and measured data by about 25% when compared with either the particle or uniform thin film models. When applied to measured data, the sparse transfer matrix model provides a 10% to 28% increase in classification accuracy over traditional models.
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Chester F. Carlson Center for Imaging Science (COS)
Cara P. Murphy, John P. Kerekes, Derek A. Wood, and Anish K. Goyal "Practical model for improved classification of trace chemical residues on surfaces in active spectroscopic measurements," Optical Engineering 59(9), 092012 (7 September 2020). https://doi.org/10.1117/1.OE.59.9.092012
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