An automated voltammetric electronic tongue has been designed employing a biosensor array formed by three different enzymatic Glucose Oxidase (GOD) electrodes and the Sequential Injection Analysis principle. The system is used for its automated training and operation devised for determining glucose and one of its classical interferents, ascorbic acid. The three enzymatic biosensors contain GOD and different metallic catalysts in order to decrease the working potential and to differentiate the response of primary species and interferents. Linear sweep voltammetry has been the chosen technique for data generation and artificial neural networks have been used as the modeling tool. Different learning algorithms have been tried in order to obtaining the best architecture for the neural network. Glucose has been determined in different fruit juice samples by employing this system, correcting the ascorbic acid contents. © 2006 Wiley-VCH Verlag GmbH & Co. KGaA.
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