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Sensors 2017, 17(2), 272; doi:10.3390/s17020272

Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier

School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
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Received: 8 November 2016 / Accepted: 25 January 2017 / Published: 30 January 2017
(This article belongs to the Section Physical Sensors)
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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. View Full-Text
Keywords: Chinese liquor classification; Multidimensional scaling (MDS); Support Vector Machine (SVM); QCM-based e-nose Chinese liquor classification; Multidimensional scaling (MDS); Support Vector Machine (SVM); QCM-based e-nose
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Li, Q.; Gu, Y.; Jia, J. Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier. Sensors 2017, 17, 272.

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