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Vol. 112, Issue 1, January 2010, pp. 47-63




Statistical Feature Extraction and Recognition of Beverages Using Electronic Tongue



Digital Systems Group

Central Electronics Engineering Research Institute (CEERI/CSIR), Pilani-333031, India

Tel.: +91-1596-252267, fax: +91-1596-242294

E-mail: pcp@ceeri.ernet.in, kiran@ceeri.ernet.in



Received: 3 November 2009   /Accepted: 22 January 2010   /Published: 29 January 2010


Abstract: This paper describes an approach for extraction of features from data generated from an electronic tongue based on large amplitude pulse voltammetry. In this approach statistical features of the meaningful selected variables from current response signals are extracted and used for recognition of beverage samples. The proposed feature extraction approach not only reduces the computational complexity but also reduces the computation time and requirement of storage of data for the development of E-tongue for field applications. With the reduced information, a probabilistic neural network (PNN) was trained for qualitative analysis of different beverages. Before the qualitative analysis of the beverages, the methodology has been tested for the basic artificial taste solutions i.e. sweet, sour, salt, bitter, and umami. The proposed procedure was compared with the more conventional and linear feature extraction technique employing principal component analysis combined with PNN. Using the extracted feature vectors, highly correct classification by PNN was achieved for eight types of juices and six types of soft drinks. The results indicated that the electronic tongue based on large amplitude pulse voltammetry with reduced feature was capable of discriminating not only basic artificial taste solutions but also the various sorts of the same type of natural beverages (fruit juices, vegetable juices, soft drinks, etc.).


Keywords: Electronic tongue, Probabilistic neural network, Principal components analysis, Pattern recognition


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