TNT detection using a voltammetric electronic tongue based on neural networks

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. © 2012 Elsevier B.V.We gratefully acknowledge financial support from the Spanish Government project MAT2009-14564-C04-02.García Breijo, E.; Olguín Pinatti, CA.; Masot Peris, R.; Alcañiz Fillol, M.; Martínez Mañez, R.; Soto Camino, J. (2013). TNT detection using a voltammetric electronic tongue based on neural networks. Sensors and Actuators A: Physical. 192:1-8. https://doi.org/10.1016/j.sna.2012.11.0381819