Classification of Chinese Soybean Paste by Fourier Transform Near-Infrared (FT-NIR) Spectroscopy and Different Supervised Pattern Recognition

Abstract

Fourier transformation near-infrared (FT-NIR) spectroscopy as an analytical tool combined with supervised pattern recognition was attempted to classify four different brands of Chinese soybean paste in this work. Three supervised pattern recognition methods, which were K-nearest neighbors (KNN), error back-propagation neural network (BP-NN), and support vector machines (SVM), were used to develop the identification models based on principal component analysis. Some parameters of the algorithms and also the number of principal components (PCs) were optimized by cross-validation in developing models. The performances of three identification models were compared. Experimental results showed that the performance of SVM model was superior to KNN and BP-NN models. The optimal SVM model achieved when the 5 PCs were included, and the identification rates both were 100% in the training and validation sets. This work demonstrated that FT-NIR spectroscopy technique coupled with SVM algorithm could be successfully used to discriminate different brands of Chinese soybean paste.