Application of an artificial neural network and QCM sensor coated with γ-Fe2O3 nanoparticles for estimation of SO2 gas sensing characteristics

γ-Fe2O3 nanoparticles (NPs) were synthesized by co-precipitation method and a following annealing treatment at 200 °C in ambient air for 6 hours. A mass-type sensor was prepared by coating γ-Fe2O3 NPs on the active electrode of quartz crystal microbalance (QCM). The obtained results of the γ-Fe2O3 NPs based QCM sensor indicate the high response and good repeatability toward SO2 gas in the range of 2.5 – 20 ppm at room temperature. Moreover, the frequency shift (DF) and change in mass of SO2 adsorption per unit area (Dm) of the γ-Fe2O3 NPs coated QCM sensor have a relationship with the mass density of γ-Fe2O3 NPs and SO2 concentrations. The artificial neural network (ANN) model using Levenberg-Marquardt optimization was used to handle the DF and Dm of the γ-Fe2O3 NPs coated QCM sensor. The results of the model validation proved to be a reliable way between the experiment and prediction values.