Thin film coated QCM-sensors and pattern recognition methods for discrimination of VOCs

Abstract

Discrimination and quantification of volatile organic compounds (VOCs) using a non-selective sensor requires a combination of sensors followed by pattern recognition methods. Based on this concept, this paper deals with the discrimination of gas from the responses of several gas sensors coated with different type of polymer. Quartz crystal microbalance (QCM) electrodes were coated from hexamethyldisiloxane (HMDSO), hexamethyldisilazane (HMDSN) and tetraethoxysilane (TEOS) for the elaboration of gas sensors with different chemical affinity towards VOC molecules. The sensitivity of the elaborated QCM-based sensors was evaluated by monitoring the frequency shifts of the quartz exposed to different concentrations of volatile organic compounds, such as; ethanol, benzene and chloroform. The sensors responses data have been used for the identification and quantification of VOCs. The principal component analysis (PCA) and the neural-network (NNs) pattern recognition analysis were used for the discrimination of gas species and concentrations. Good separation among gases has been obtained using the principal component analysis. The feed-forward multi-layer neural network (MLNNs) with a hidden layer and trained by Broyden Fletcher Goldfarb Shanno (BFGS) Quasi Newton algorithm has been implemented in order to identify and quantify the VOCs. By increasing the number of the neuron in the hidden layer, the precision of the estimate concentration increases. The approach is standard, however its application on the elaborated sensors have not been studied in depth so far.