Extending the range of an optical vanadium (V) sensor based on immobilized fatty hydroxamic acid in poly (methyl methacrylate) using artificial neural network

An artificial neural network (ANN) was applied for the determination of V(V) based on immobilized fatty hydroxamic acid (FHA) in poly(methyl methacrylate) (PMMA). Spectra obtained 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 of V(V) concentration of 10 - 100 mg/ L. After training with ANN, the linear dynamic range was extended with low calibration error. A three layer feed forward neural network using back-propagation (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 10000 epochs with 0.003 % learning rate. This reagent also provided a good analytical performance with reproducibility characters of the method yielding relative standard deviation (RSD) of 9.29% and 7.09% for V(V) at concentrations of 50 mg/ L and 200 mg/ L, respectively. The limit of detection of the method was 8.4 mg/ L.

Actions (login required)

View Item