Evaluation of an optical phenolic biosensor signal employing artificial neural networks

This paper presents artificial neural network (ANN)-based evaluation in signal processing of an optical phenolic biosensor. The biosensor was developed based on stacked immobilization of 3-methyl-2-benzothiazolinone hydrazone (MBTH) in hybrid Nafion/sol–gel silicate and tyrosinase in chitosan. The biosensor signal was simulated employing a feed-forward neural network with three layers and trained using back-propagation (BP) algorithm. Spectra generated from an optical phenolic biosensor at selected wavelengths were used as input data for ANN. The network architecture of 5 inputs neurons, 21 hidden neurons and 1 output neuron was found suitable for this application. The results show very good agreement between phenol concentration values obtained by using the developed biosensor and those predicted by ANN.