Abstract. Cyclic voltammetry is an electroanalytical technique for obtaining information about substances under analysis without the need for complex flow systems. However, classifying the information in voltammograms obtained using this technique is difficult. In this paper, we propose the use of fixed kernel regression as a method for extracting features from these voltammograms, reducing the information to a few coefficients. The proposed approach has been applied to a wine classification problem with accuracy rates of over 98%. Although the method is described here for extracting voltammogram information, it can be used for other types of signals.

Keywords: cyclic voltammetry, electronic tongue, regression, pattern classification

Print publication: Issue 12 (December 2009)
Received 14 July 2009, in final form 24 September 2009
Published 6 November 2009