Extending the range of an Optical Vanadium (V) Sensor Based on Immobilized Fatty Hydroxamic Acid in Poly (methyl Methacrylate) using Artifical Neural Network

Abstract

An artifical neural network (ANN) was applied for the determination of V(V) based on immobilized fatty hydroxamic acid (FHA) in Poly (methyl Methacrylate)(PMMA). Spectraobtained from the V(V)-FHA complex at single wavelengths was used as the input data for the ANN. The V(V)-FHA complex shows a limited linear dynamic range was extended with low calibration error.A three layer feed forward neural network using backpropagation (BP) algorithm was employed in this study. The input layer consisted of single neurons,30 neurons in hidden a layer and one output neuron was found appropriate for the multivariate calibration used. The network were trained up to 10 000 epochs with 0.003% learning rate. This reagent also provided a good analytical performance with reproducibility characters of the method yeilding relative standard deviation (RSD) of 9.29% and 7.09% for V(V) at concentrations of 50mg/L and 200mg/L, respectively. The limit of detection of the method wa s 8.4mg/L.