Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier.
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.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China. tslee@xs.ustb.edu.cn.
Qiang
Q
Li
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China. guyu@ustb.edu.cn.
Yu
Y
Gu
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China. s20160598@xs.ustb.edu.cn.
Jing
J
Jia
D000465:Algorithms; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D060388:Support Vector Machine
Chinese liquor classification; Multidimensional scaling (MDS); QCM-based e-nose; Support Vector Machine (SVM)