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Proceedings Paper

Comparison of swarm intelligence optimization with nonnegative weighted least squares for Raman spectra estimation
Author(s): Nisha Srinivas; Mahendra Mallick; Lisa Ann Osadciw
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Paper Abstract

Raman spectroscopy is a powerful technique for determining the chemical composition of a substance. Our objective is to determine the chemical composition of an unknown substance given a reference library of Raman spectra. The unknown spectrum is expressed as a linear combination of the reference library spectra and the non-zero mixing coefficients represent the presence of individual substances, which are not known. This approach is known as the supervised learning method. The mixing coefficients are usually estimated using the nonnegative least squares (NNLS) or nonnegative weighted least squares (NNWLS). This problem is a constrained estimation problem due to the presence of the nonnegativity constraint. In this paper, we present a swarm based algorithm, the particle swarm optimization (PSO), to estimate the mixing coefficients and Raman spectra. The PSO is used to determine the mixing coefficients. PSO efficiently finds an optimum solution. Results are presented for simulated data obtained from the Jennifer Kelly Raman spectra library. The reference library consists of Raman spectra for nine minerals and the measured spectrum is simulated by using spectrum/spectra of single/multiple minerals. We compare the root mean square error (RMSE) for parameter estimation and measurement residual and computational time of the NNWLS and nonnegative weighted PSO (NNWPSO) algorithms.

Paper Details

Date Published: 15 April 2010
PDF: 12 pages
Proc. SPIE 7698, Signal and Data Processing of Small Targets 2010, 769817 (15 April 2010); doi: 10.1117/12.850587
Show Author Affiliations
Nisha Srinivas, Syracuse Univ. (United States)
Mahendra Mallick, Georgia Tech Research Institute (United States)
Lisa Ann Osadciw, Syracuse Univ. (United States)

Published in SPIE Proceedings Vol. 7698:
Signal and Data Processing of Small Targets 2010
Oliver E. Drummond, Editor(s)

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