Increasing ion selective electrodes performance using neural networks

Abstract

This paper reports the implementation of a neural processing structure as a component of an intelligent measuring system that uses ion selective electrodes (ISEs) as sensing elements of heavy metal ions (Pb/sup +2/, Cd/sup +2/) concentration. The neural network (NN), designed and implemented to reduce errors due to ion interference and to pH and temperature variations, is of the multiple-input multiple-output Multilayer Perception (MLP-NN) type The NN is a component of a virtual instrument that includes a PC laptop, a PCMCI data acquisition board with associated conditioning circuits and the specific ISE sensors. A practical approach concerning the optimal neural processing solution (number of NN structures, number of neurons, neuron transfer functions) to increase the performance of low cost ISEs is presented. Results are presented to evaluate the performance of the NN intelligent ISE system and to discuss the possibility of transferring the acquisition and processing task to a low cost acquisition and control unit such as a microcontroller.