DISCRIMINANT-ANALYSIS TECHNIQUES FOR THE IDENTIFICATION OF ATMOSPHERIC POLLUTANTS FROM PASSIVE FOURIER-TRANSFORM INFRARED INTERFEROGRAMS

Discriminant analysis techniques are developed for the detection of analyte signals directly from passive Fourier transform infrared (FT-IR) interferograms. The interferograms are preprocessed through the use of digital filters to isolate the spectral frequencies associated with a targeted analyte band. Subsequent application of the discriminant results in a yes/no decision regarding the presence of the analyte. Interferograms collected by a mobile FT-IR remote sensor are used in developing this methodology. Simplex optimization, coupled with a novel objective function, is found to produce the optimum discriminant for the prediction of the presence of the test analyte, SF6. The developed discriminant is able to detect low levels of SF6, while exhibiting a false alarm rate of less than 1%. This discriminant-based detection scheme can be implemented through the use of only a short interferogram segment, thereby decreasing the size of the interferogram that must be collected. This reduction in data collection requirements has great potential impact in the design of future FT-IR remote sensors.