Fourier transform infrared (FT-IR) data of pure and mixture samples of benzene and nitrobenzene are used to investigate and improve methods for interferogram-based qualitative analyses. For use in dedicated monitoring applications, the methodology employed is based on the application of pattern recognition analysis to short, digitally filtered interferogram segments. In the work described here, the impact of the interferogram data sampling rate on the analysis is studied. The results of this study indicate that optimal pattern recognition prediction performance is achieved by use of linear discriminants developed from faster sampled interferogram data. These findings suggest that improved performance can be obtained in FT-IR monitoring applications through the use of spectrometer designs based on a decreased interferogram scan length, coupled with faster sampling electronics.

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