Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier
AbstractChinese 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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
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.
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(2):272.Chicago/Turabian Style
Li, Qiang; Gu, Yu; Jia, Jing. 2017. "Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier." Sensors 17, no. 2: 272.