Multivariate calibration model for a voltammetric electronic tongue based on a multiple output Wavelet Neural Network

Electronic tongues are bioinspired sensing schemes that employ an array of sensors for analysis, recognition or identification in liquid media. An especially complex case happens when the sensors used are of the voltammetric type, as each sensor in the array yields a 1-dimensional data vector. This work presents the use of a Wavelet Neural Network (WNN) with multiple outputs to model multianalyte quantification from an overlapped voltammetric signal. WNN is implemented with a feedforward multilayer perceptron architecture, whose activation functions in its hidden layer neurons are wavelet functions, in our case, the first derivative of a Gaussian function. The neural network is trained using a backpropagation algorithm, adjusting the connection weights along with the network parameters. The principle is applied to the simultaneous quantification of the oxidizable aminoacids tryptophan, cysteine and tyrosine, from its differential-pulse voltammetric signal. WNN generalization ability was validated with training processes of k-fold cross validation with random selection of the testing set. © 2009 Springer-Verlag Berlin Heidelberg.