Computational neural networks for the evaluation of biosensor FIA measurements

A computational neural network based evaluation method is presented, which enables a reliable quantification of enzyme field effect transistor (EnFET) flow injection analysis (FIA) signals from samples with changing pH values. Two FIA systems, one for glucose and the other for urea determination, are employed to test the evaluation method. Measurement signals were obtained from samples with different glucose concentrations (3, 4, 5, 6 and 7 g/l) and urea concentrations (1, 1.25, 1.5, 1.75 and 2.0 g/l) at various pH values (5.5, 5.75, 6.0, 6.25 and 6.5). These signals cannot be evaluated based on the peak height, width or integral. Using a large set of measuring signals for training the artificial neural network (12 samples, each measured fivefold (=60) signals) the error of analyte prediction from test signals are 3.2% and 2.5% for glucose and urea respectively. With a reduced training set of five measurement signals the error of prediction of the test set increases to 4.5% and 5.5% for glucose and urea respectively. In this investigation it will be demonstrated that computational neural networks are able to evaluate FIA signals, which cannot be evaluated reliably by FIA standard methods.