Multiple feedforward classifiers by bagging for ion-sensitive field effect transistor sensor response

Abstract

This paper presents the classification of ionic concentration using ion-sensitive field-effect transistor (ISFET) sensors with post-processing neural network ensemble. ISFETs are electrochemical potentiometric sensors that produce voltage response indicative of ionic concentration change. However, in the presence of ions of similar charge, the voltage levels tend to be influenced by the interfering ions. The training dataset is based on actual measured data and are collected from dc response of sensors from titration. The multiple classifier system consists of feedforward multilayer perceptron weak learners constructed by bagging algorithm. Diversity is achieved by randomness of free parameters and resampling techniques of datasets by bootstrapping. Results demonstrate that multiple classifier system improves the ionic concentration classification of the main ion by additional 5% as compared to the average of the individual classifier performance.