Compression of electronic tongue data based on voltammetry - a comparativestudy

In this paper, three data compression methods are investigated to determine their ability to reduce large data sets obtained by a voltammetric electro nic tongue without loss of information, since compressed data sets will sav e data storage and computational time. The electronic tongue is based on a combination of non-specific sensors and pattern recognition tools, such as principal component analysis (PCA). A series of potential pulses of decreas ing amplitude are applied to one working electrode at a time and resulting current transients are collected at each potential step. Voltammograms cont aining up to 8000 variables are subsequently obtained. The methods investig ated are wavelet transformation (WT) and hierarchical principal component a nalysis (HPCA). Also, a new chemical/physical model based on voltammetric t heory is developed in order to extract interesting features of the current transients, revealing different information about species in solutions. Two model experiments are performed, one containing solutions of different ele ctroactive compounds and the other containing complex samples, such as juic es from fruits and tomatoes. It is shown that WT and HPCA compress the data sets without loss of information, and the chemical/physical model improves the separations slightly. HPCA is able to compress the two data sets to th e largest extent, from 8000 to 16 variables. When data sets are scaled to u nit variance, the separation ability improves even further for HPCA and the chemical/physical model. (C) 2001 Elsevier Science B.V. All rights reserve d.