Sensor Fusion of Electronic Nose and Tongue for Identification of Chinese Liquors
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Abstract:
Two different fusion systems of electronic nose and tongue were used to collect data on aroma and taste of Chinese liquors of different brands: (A) a fusion system of electronic tongue and nose, based on Taguchi gas sensors (TGS); (B) a fusion system of electronic tongue and nose, based on MQ-MP-type gas sensors. Clustering analysis of the aroma and taste was performed using principal component analysis (PCA) and the k-means algorithm. Next, prediction and classification of the different brands of Chinese liquors were performed using a support vector machine (SVM). After clustering analysis by PCA, fusion system A could distinguish three brands of Chinese liquors, while the rest of the brands showed overlapping results. Fusion system B could distinguish eight brands of Chinese liquors. The k-means algorithm was applied to the two fusion systems, and the wrong classification rates of fusion systems A and B were found to be 33.3% and 23.75%, respectively. The accuracy of prediction and classification of the Chinese liquors by the fusion systems A and B for was 93.75% and 98.75%, respectively. The study showed that the combination of information on aroma and taste could be used for identification of different brands of Chinese liquors. Additionally, identification by the fusion system B was better than that by the fusion system A.
