Standard Article

Spectral Data, Modern Classification Methods for

Infrared Spectroscopy

  1. Yvette L. Mallet1,
  2. Danny H. Coomans1,
  3. Olivier de Vel2

Published Online: 15 SEP 2006

DOI: 10.1002/9780470027318.a5602

Encyclopedia of Analytical Chemistry

Encyclopedia of Analytical Chemistry

How to Cite

Mallet, Y. L., Coomans, D. H. and de Vel, O. 2006. Spectral Data, Modern Classification Methods for. Encyclopedia of Analytical Chemistry. .

Author Information

  1. 1

    James Cook University, Townsville, Australia

  2. 2

    DSTO, Salisbury, Australia

Publication History

  1. Published Online: 15 SEP 2006

Abstract

A major concern which arises from the classification of spectral data, such as near-infrared (NIR) spectra, is that the number of variables often exceeds the number of cases. In this instance each spectrum represents a case or observation, and each variable corresponds to the wavelengths at which a response (e.g. reflectance) is measured. Classification refers to the process of assigning a spectrum whose class identity is unknown into one of several predefined classes. There are two main strategies for combating the high-dimensional (i.e. more variables than cases) scenario. One strategy is to use a high-dimensional classifier, that is, one which is suited to the ill-posed nature of the data. The second strategy is to reduce the number of variables by a feature extraction method and supply the variables to a low-dimensional classifier, one which is suited to high observation-to-variable ratios. This article presents two high-dimensional classification methods – regularized discriminant analysis (RDA) and penalized discriminant analysis (PDA). The low-dimensional classifiers used include Bayesian linear discriminant analysis (BLDA), Bayesian quadratic discriminant analysis (BQDA), RDA and flexible discriminant analysis (FDA). Common stepwise selection routines are employed to investigate the effectiveness of the wavelet transform as a feature extraction technique. This includes implementation of the local discriminant bases (LDBs) algorithm and the adaptive wavelet algorithm (AWA).