Maximum Discrimination Approach for Classification of Nearly Identical Signatures
Document Type
Conference Proceeding
Date of Original Version
12-20-2017
Abstract
Spectroscopic analysis is used throughout industry, academia, and other areas to differentiate and identify compounds. In many cases the compounds have highly similar spectral structures, i.e., spectral overlap and may only readily be identified as belonging to a class of materials. Current analytical methods perform well when there are clearly discernible peaks within the spectra but are known to lose discrimination power as the spectra of interest become more and more similar. To overcome this loss in detection power we propose a novel method for determining the maximum discrimination spectral bands, known as the maximum discrimination approach (MDA). MDA is based upon determining the statistical distance between two spectra for each band, and is derived by assuming each spectrum is the result of estimating the power spectral density of Gaussian noise. We demonstrate the ability of MDA to find maximum discrimination spectral bands using the spectral data of gasoline and kerosoene, two related mixtures with similar spectral content.
Publication Title, e.g., Journal
2017 Sensor Signal Processing for Defence Conference, SSPD 2017
Volume
2017-January
Citation/Publisher Attribution
Emge, Darren, and Steven Kay. "Maximum Discrimination Approach for Classification of Nearly Identical Signatures." 2017 Sensor Signal Processing for Defence Conference, SSPD 2017 2017-January, (2017): 1-4. doi: 10.1109/SSPD.2017.8233242.