TNT detection using a voltammetric electronic tongue based on neural networks

Abstract

We report here the use of a voltammetric electronic tongue based on simple metallic electrodes for the detection and discrimination of different concentrations of 2,4,6-trinitrotoluene (TNT) in acetonitrile:water 1:1 (v/v) mixtures. The tongue consisted of noble working electrodes made of iridium, rhodium, platinum and gold and non-noble electrodes including silver, copper, cobalt and nickel. Both the self-organizing map (SOM) and multi-layer feed-forward network (MLFN) neural networks were applied to the data obtained from the electronic tongue and TNT solutions. From SOM analysis it was established that a suitable response in terms of a correct classification of the TNT concentration was observed when using only noble metal electrodes and only 5 selected pulses. Similar good classifications were found when using MLFN. Moreover, the algorithm of neural network MLFN was embedded in a microcontroller in order to obtain a smart portable system for discrimination of TNT. In this case an R2 of 0.993 was obtained for predicted vs observed graphs of concentrations of TNT concentrations.