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Proceedings Paper

Identifying chemicals from their Raman spectra using minimum description length
Author(s): Ryan D. Palkki; Aaron D. Lanterman
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Paper Abstract

Raman spectroscopy has been a powerful means of chemical identification in a variety of fields, partly because of its non-contact nature and the speed at which measurements can be taken. Given a library of known Raman spectra, a common detection approach is to first estimate the relative amount of each chemical present, and then compare the estimated mixing coefficients to an ad hoc threshold. We present a more rigorous detection scheme by formulating the problem as one of Multiple Hypothesis Detection (MHD) and using the maximum a posteriori (MAP) decision rule to minimize the probability of classification error. The probability that a specific target chemical is present is estimated by summing the estimated probabilities of all the hypotheses containing it. Alternatively, since we do not typically have reasonable priors for the hypotheses, it is perhaps preferable to interpret the result as an abstract score corresponding to the Minimum Description Length (MDL) approach. The resulting detection performance of this approach is compared to that of several other classification algorithms.

Paper Details

Date Published: 15 April 2010
PDF: 11 pages
Proc. SPIE 7698, Signal and Data Processing of Small Targets 2010, 769807 (15 April 2010); doi: 10.1117/12.850613
Show Author Affiliations
Ryan D. Palkki, Georgia Institute of Technology (United States)
Aaron D. Lanterman, Georgia Institute of Technology (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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