Neural Networks for an Analysis of the Hemometabolites Biosensor Response

In this work, the concentration dependent response of amperometric biosensor array for the biomarkers glucose, cholesterol and urease was explored, using artificial neural nets (ANN). The aim was to explore an array of amperometric biosensors for the discrimination of the biomarkers glucose, cholesterol and urea in blood. Seven out of eight platinum electrodes on the array were modified with four different enzymes; glucose oxidase, cholesterol, urease and peroxidase. The dynamic biosensor response curves from the eight sensors were used for ANN analysis. The ANN were applied to an analysis of the biosensor response to multi-biomarkers mixtures the ANN was able to detect the conditions with an accuracy up to 90%. The results obtained by using ANN to interpret the electrical signal of the developed biosensor arrays leads to the conclusion that: i) after training the ANN, the evaluation of recorded data are on-line, ii) microelectrode sites which are highly correlated to the information about the concentrations within the recorded signals was identified, iii) the recognition of blood biomarkers is improved by using the ANN.