Identification of Wine According to Grape Variety Using Near-Infrared Spectroscopy Based on Radial Basis Function Neural Networks and Least-Squares Support Vector Machines

This paper describes how near-infrared (NIR) spectroscopy combined with radial basis function neural networks (RBFNN) and least-squares support vector machines (LS-SVMs) based on principal component analysis (PCA) can be used to classify wines from grape varieties. The effects of different preprocessing methods (standard normal variate (SNV) and multiplicative scattering correction (MSC)) on classification results were also compared. The results show that the use of NIR preprocessing spectral data with optimum RBFNN parameters produced a very high level of correct classification rate, 90.16-98.36%. For RBF LS-SVM, identification rates were from 91.80 to 98.36%. The results demonstrate that, combined with chemometrics with appropriate spectral data pretreatment, NIR spectroscopy has potential to rapidly and nondestructively differentiate wine according to grape variety. The results of this study are helpful to develop a more rapid and nondestructive detection method of wine.