Quantitative Analysis of Near-Infrared Spectroscopy by Combined Stationary Wavelet Transform–Support Vector Machine

Abstract

ABSTRACT Data preprocessing and multivariate regression methods are two key factors influencing the model prediction ability of near-infrared (NIR) spectroscopy. The present paper evaluated the application of the combined stationary wavelet transform–support vector machine method for developing juice NIR models. The performance of this method has been compared with other methods, such as stand normal variate–partial least squares, stationary wavelet transform–partial least squares, and stand normal variate–stationary wavelet transform methods. The result showed that compared with other methods, the stationary wavelet transform–support vector machine method can provide good quantitative analysis on saccharose concentration in juice.