Abstract

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.

Correction of interferogram data acquired using a focal plane FT-IR spectrometer system

Cong Gao, Jianhua Mao, and Ren Chen
Appl. Opt. 57(10) 2434-2440 (2018)

Spectral pattern recognition in IR remote sensing

Alexander S. Zachor
Appl. Opt. 22(17) 2699-2703 (1983)

Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network

Haiyan Cen, Yidan Bao, and Yong He
Appl. Opt. 45(29) 7679-7683 (2006)

Disease pattern recognition in infrared spectra of human sera with diabetes mellitus as an example

Wolfgang Petrich, Brion Dolenko, Johanna Früh, Manfred Ganz, Helmut Greger, Stephan Jacob, Franz Keller, Alexander E. Nikulin, Matthias Otto, Ortrud Quarder, Ray L. Somorjai, Arnulf Staib, Gerhard Werner, and Hans Wielinger
Appl. Opt. 39(19) 3372-3379 (2000)

Discriminant analysis for recognition of human face images

Kamran Etemad and Rama Chellappa
J. Opt. Soc. Am. A 14(8) 1724-1733 (1997)