1. ¹ University of Tennessee, Knoxville, USA
2. ²Texas Tech University, Lubbock, USA
3. ³The State University of New Jersey, Piscataway, USA

Correspondence: MK Jeong, Rutgers, The State University of New Jersey, 40 Bartholomew Road, Piscataway, NJ 08854, USA E-mail: mkjeong@rutcor.rutgers.edu

Received October 2007; Accepted November 2008; Published online 8 April 2009.

Abstract

Near infrared (NIR) spectroscopy has been extensively used in classification problems because it is fast, reliable, cost-effective, and non-destructive. However, NIR data often have several hundred or thousand variables (wavelengths) that are highly correlated with each other. Thus, it is critical to select a few important features or wavelengths that better explain NIR data. Wavelets are popular as preprocessing tools for spectra data. Many applications perform feature selection directly, based on high-dimensional wavelet coefficients, and this can be computationally expensive. This paper proposes a two-stage scheme for the classification of NIR spectra data. In the first stage, the proposed multi-scale vertical energy thresholding procedure is used to reduce the dimension of the high-dimensional spectral data. In the second stage, a few important wavelet coefficients are selected using the proposed support vector machines gradient-recursive feature elimination. The proposed two-stage method has produced better classification performance, with higher computational efficiency, when tested on four NIR data sets.