30 October 2012 Variable basis function for least squares for chemical classification in surface enhance Raman spectroscopy (SERS)
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Abstract
Surface enhanced Raman Scattering spectroscopy is a valuable tool for detecting and identifying chemical threats. One difficulty, however, in utilizing its full capabilities is that the spectrum is dependent upon the chemical orientation, and to a lesser extent, concentration. Spectral peaks can shift and even disappear as the concentration of the chemical present varies. A potential solution to this problem is to model the spectrum as a set of random basis functions, with each basis function depending upon a random unobserved parameter. Relating these parameters to the concentration an expected least squares fitting procedure can be implemented. It is shown through computer simulation and some limited testing that the detection and classification performance can be improved over standard approaches that do not take into account this basis variation. The method proposed, however, is completely general. It is a viable alternative to standard least squares procedures whenever the goal is robustness of the procedure.
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Darren K. Emge, Darren K. Emge, Jason A. Guicheteau, Jason A. Guicheteau, } "Variable basis function for least squares for chemical classification in surface enhance Raman spectroscopy (SERS)", Proc. SPIE 8546, Optics and Photonics for Counterterrorism, Crime Fighting, and Defence VIII, 854605 (30 October 2012); doi: 10.1117/12.978939; https://doi.org/10.1117/12.978939
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