Infrared spectral classification with artificial neural networks and classical pattern recognition

Howard T. Mayfield; DeLyle Eastwood; Larry W. Burggraf

doi:10.1117/12.394079

28 July 2000 Infrared spectral classification with artificial neural networks and classical pattern recognition

Howard T. Mayfield, DeLyle Eastwood, Larry W. Burggraf

Proceedings Volume 4036, Chemical and Biological Sensing; (2000) https://doi.org/10.1117/12.394079
Event: AeroSense 2000, 2000, Orlando, FL, United States

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.

Citation Download Citation

Howard T. Mayfield, DeLyle Eastwood, and Larry W. Burggraf "Infrared spectral classification with artificial neural networks and classical pattern recognition", Proc. SPIE 4036, Chemical and Biological Sensing, (28 July 2000); https://doi.org/10.1117/12.394079

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