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Proceedings Paper

Infrared spectral classification with artificial neural networks and classical pattern recognition
Author(s): Howard T. Mayfield; DeLyle Eastwood; Larry W. Burggraf
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Paper Abstract

Infrared spectroscopy is an important technique for measuring airborne chemicals, for pollution monitoring and to warn of toxic compound releases. Infrared spectroscopy provides both detection and identification of airborne components. Computer-assisted classification tools, including pattern recognition and artificial neural network techniques, have been applied to a collection of infrared spectra of organophosphorus compounds, and these have successfully discriminated commercial pesticide compounds from military nerve agents, precursors, and hydrolysis products. Infrared spectra for previous tests came from a commercial infrared library, with permission, from military laboratories, and from defense contractors. In order to further test such classification tools, additional infrared spectra from the NIST gas-phase infrared library were added to the data set. These additional spectra probed the tendency of the trained classifiers to misidentify unrelated spectra into the trained classes.

Paper Details

Date Published: 28 July 2000
PDF: 12 pages
Proc. SPIE 4036, Chemical and Biological Sensing, (28 July 2000); doi: 10.1117/12.394079
Show Author Affiliations
Howard T. Mayfield, Air Force Research Lab. (United States)
DeLyle Eastwood, Air Force Institute of Technology (United States)
Larry W. Burggraf, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 4036:
Chemical and Biological Sensing
Patrick J. Gardner, Editor(s)

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