School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Sensors (Basel). 2017 Jan 30;17(2):272. doi: 10.3390/s17020272.
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.
中国白酒是国际知名的发酵酒精饮料。它们具有独特的风味，这归因于使用了各种细菌和真菌、原料和生产工艺。开发一种新颖、快速和可靠的方法来识别多种中国白酒具有积极意义。本文提出了一种基于多维尺度（MDS）和支持向量机（SVM）算法的模式识别系统，用于对我们设计的基于石英晶体微天平（QCM）的电子鼻（e-nose）中的十种中国白酒品牌进行分类。我们评估了 MDS-SVM 分类器对十种中国白酒逐一预测的综合性能。预测准确率（98.3%）表明，MDS-SVM 分类器的性能优于反向传播人工神经网络（BP-ANN）分类器（93.3%）和移动平均线性判别分析（MA-LDA）分类器（87.6%）。MDS-SVM 分类器在对中国白酒进行分类时具有合理的可靠性、良好的拟合和预测（泛化）性能。考虑到电子鼻的应用和 MDS-SVM 分类器的验证，我们因此创建了一种用于多种中国白酒分类的有用方法。
