Automated Detection of Trichloroethylene by Fourier Transform Infrared Remote Sensing Measurements

Abstract

Passive Fourier transform infrared (FT-IR) remote sensing measurements are used to implement the automated detection of trichloroethylene (TCE) vapor in the presence of a variety of infrared background signatures. Through the use of a combination of bandpass digital filtering and piecewise linear discriminant analysis, this detection procedure is applied directly to short segments of the interferogram data collected by the FT-IR spectrometer. Data employed in this work were collected during open-air/passive cell terrestrial and passive cell laboratory measurements. Infrared backgrounds employed included terrain, low-angle sky, and water backgrounds, in addition to laboratory blackbody measurements. Other potentially interfering chemical species present were carbon tetrachloride, acetone, methyl ethyl ketone, and sulfur hexafluoride (SF6). These data are used to assemble two data sets of differing complexity. Optimization studies are performed separately with each data set to study the influence of filter bandpass position, bandpass width, interferogram segment location, and segment size on the ability to detect TCE. The optimal parameters found consist of a Gaussian-shaped filter positioned at 939.5 cm-1, with a width at half-height of 123.4 cm-1. This filter is applied to interferogram points 111−220 (relative to the centerburst). When applied to a prediction set of 60 000 interferograms, the piecewise linear discriminant developed on the basis of these optimal parameters is found to detect TCE successfully in 96.2% of the cases in which it is present. The overall rate of false detections is 0.5%. The limit of detection of TCE is found to be 102 ppm-m at a temperature difference of 10.5 °C between the infrared background and the analyte. SF6 is observed to provide the greatest spectral interference among the compounds tested, producing a false detection rate of 8.6%. It is found that this false detection rate can be reduced to 1.5% through the development of a probability-based interpretation of the piecewise linear discriminant results. These results are observed to compare favorably with those obtained in a separate analysis of filtered single-beam spectra.