Abstract
Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3%) showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN) classifier (93.3%) and moving average-linear discriminant analysis (MA-LDA) classifier (87.6%). The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization) performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.
Highlights
Chinese liquor is one of the oldest distillates in the world, dating back thousands of years [1].Some four million kiloliters of Chinese liquor are consumed annually, worth 500 billion Chinese Yuan [2]
Zhang et al [15] used principal component analysis (PCA) incorporated with discriminant analysis (PCA-DA), a back propagation artificial neural network (BP-ANN), and learning vector quantization (LVQ) for the recognition of five Chinese liquors; the recognition accuracies of PCA-DA, back-propagation artificial neural network (BP-ANN), and LVQ were 76.8, 71.4, and 89.3%, respectively
We present the used in a quartz crystal microbalance (QCM)-based e-nose we have designed of an algorithm based on Multidimensional Scaling (MDS) and Support Vector Machine (SVM)
Summary
Chinese liquor is one of the oldest distillates in the world, dating back thousands of years [1]. A quartz crystal microbalance (QCM)-based electronic nose (e-nose) has been successfully utilized to detect characteristics of Chinese liquors by imitating the human senses using sensor arrays and a pattern recognition system [10]. Our group has reported the design and application of a novel and simple QCM-based e-nose [13,14] for quickly and summarizing Chinese liquor characteristics. Zhang et al [15] used PCA incorporated with discriminant analysis (PCA-DA), a back propagation artificial neural network (BP-ANN), and learning vector quantization (LVQ) for the recognition of five Chinese liquors; the recognition accuracies of PCA-DA, BP-ANN, and LVQ were 76.8, 71.4, and 89.3%, respectively. Ema et al [16] presented an odor-sensing system to identify eleven brands of liquors using six QCM resonators with different coating materials and neural network pattern recognition. Performance was assessed through classifying ten brands of Chinese liquor samples
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