Multiple Filtering Strategy for the Automated Detection of Ethanol by Passive Fourier Transform Infrared Spectrometry

Abstract

Digital filtering methods are evaluated for use in the automated detection of ethanol from passive Fourier transform infrared (FT-IR) data collected during laboratory and open-air remote sensing experiments. In applications in which analyte signals are overwhelmed by the overlapping signals of an interference, the use of multiple digital filters is observed to improve the sensitivity of the analyte detection. The detection strategy is based on the application of bandpass digital filters to short segments of the interferogram data collected by the FT-IR spectrometer. To implement the automated detection of a target analyte, the filtered interferogram segments are supplied as input to piecewise linear discriminant analysis. Through the use of a set of training data, discriminants are computed that can subsequently be applied to detect the presence of the analyte in an automated manner. This research focuses on the detection of ethanol vapor in the presence of an ammonia interference. A two-filter detection strategy based on the use of separate ethanol and ammonia filters is compared to an approach based on a single ethanol filter. Bandpass parameters of the digital filters and the interferogram segment location are optimized through the use of laboratory data in which ethanol and ammonia vapors are generated in a gas cell and viewed against various infrared background radiances. The filter and segment parameters obtained through this optimization are subsequently tested with field remote sensing data collected when the spectrometer is allowed to view ethanol and ammonia plumes generated from a heated stack. The two-filter strategy is found to outperform the single-filter approach with both the laboratory and field data in situations in which the ammonia interference dominates the ethanol signature.