A novel electronic nose for simultaneous quantitative determination of concentrations and odor intensity analysis of benzene, toluene and ethylbenzene mixtures

Abstract

Aromatic hydrocarbons (benzene, toluene, ethylbenzene etc.) are part of the main components of air pollution and odor nuisance. However, previous studies on simultaneous detection of aromatic mixtures and odor intensity analysis by an electronic nose (E-nose) are limited. The aim of this study is to develop a novel E-nose system to simultaneously determine chemical concentrations and odor intensity of benzene, toluene and ethylbenzene mixtures. The system consists of a sensor array with 5 gas sensors, a signal converter and a pattern recognition system which is based on a Back Propagation (BP) neural network. 300 groups of aromatic hydrocarbon mixtures (benzene, toluene and ethylbenzene) with different concentrations were determined by sensor array and gas chromatography (GC) to build, test and optimize the BP neural network. Then the optimum structure and functions of the BP network were verified by about 50 runs of contrast tests. The results showed that the average relative error of concentrations measured by the E-nose system was 9.71% relative to the results of GC. Furthermore, six odor intensity prediction models were used to convert the concentrations of the aromatic mixtures to their odor intensity. Based on the comparison with sensory analysis, the Weber–Fechner law model, the vector model and the simplified extended vectorial model were adopted to predict the odor intensity of single, binary and ternary compounds respectively.