Data Compression for a Voltammetric Electronic Tongue Modelled with Artificial Neural Networks

In the study of voltammetric electronic tongues, a key point is the preprocessing of the departure information, the voltammograms which form the response of the sensor array, prior to classification or modeling with advanced chemometric tools. This work demonstrates the use of the discrete wavelet transform (DWT) for compacting these voltammograms prior to modeling. After compression, a system based on artificial neural networks (ANNs) was used for the quantification of the electroactive substances present, using the obtained wavelet decomposition coefficients as their inputs. The Daubechies wavelet of fourth order permitted an effective compression Received 17 June 2005; accepted 22 June 2005 Financial support for this work was provided by the MECD (Madrid, Spain) through project CTQ2004-08134, by CONACYT (Mexico) through project 43553, and by the Department of Universities and the Information Society (DURSI) from the Generalitat