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Proceedings Paper

A novel approach for spectrum decomposition for Raman spectroscopy
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Paper Abstract

As a fundamental study for improving the detection accuracy of Raman spectroscopy under noisy conditions, this paper proposes a novel spectrum decomposition method, where the observed spectrum from an unknown substance is decomposed into some known spectra. Raman spectroscopy can be used for a remote sensing method, where a laser is irradiated to the target and then the Raman scattering light is analyzed to detect the target constituents. The spectrum decomposition is the method to analyze the observed spectrum, that is the Raman scattering light, with some known spectra, which are previously developed as a database. The purpose of the decomposition is to find a linear combination of the known spectra so that the linear combination appropriately represents the observed spectrum. The coefficients of the linear combination show the density of molecules contained in the target. The coefficients can be found with multiple linear regression method. However, the coefficients can contain large errors under low signal-noise-ratio conditions. The proposed method tries to overcome the noise problem by using three techniques. The first technique is to employ the nonnegative least squares method, which is the least squares method with non-negative constraints for the coefficients. The second technique is to select the wavelengths of the observed and known spectra for the spectrum decomposition. The third technique is to select the wavelength of the laser irradiated to the target. This paper conducts numerical experiments to show the effectiveness of the proposed method.

Paper Details

Date Published: 19 September 2019
PDF: 9 pages
Proc. SPIE 11169, Artificial Intelligence and Machine Learning in Defense Applications, 111690N (19 September 2019); doi: 10.1117/12.2533073
Show Author Affiliations
Takayuki Higo, Central Research Institute of Electric Power Industry (Japan)
Shuzo Eto, Central Research Institute of Electric Power Industry (Japan)
Yuji Ichikawa, Shikoku Research Institute Inc. (Japan)
Hiromi Kodama, Shikoku Research Institute Inc. (Japan)
Ippei Asahi, Shikoku Research Institute Inc. (Japan)

Published in SPIE Proceedings Vol. 11169:
Artificial Intelligence and Machine Learning in Defense Applications
Judith Dijk, Editor(s)

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