Research on electronic nose for compound malodor recognition combined with artificial neural network and linear discriminant analysis

Abstract

The foul odor of foul gas has many harmful effects on the environment and human health. In order to accurately assess this impact, it is necessary to identify specific malodorous components and levels. In order to meet the qualitative and quantitative identification of the components of malodorous gas, an electronic nose system is developed in this paper. Both principal component analysis (PCA) and linear discriminant analysis (LDA) were used to reduce the dimensionality of the collected data. The reduced-dimensional data are combined with a support vector machine (SVM) and backpropagation (BP) neural network for classification and recognition to compare the recognition results. Regarding qualitative recognition, this paper selects the method of LDA combined with the BP neural network after comparison. Experiments show that the qualitative recognition rate of this method in this study can reach 100%, and the amount of data after LDA dimensionality reduction is small, which speeds up the pattern speed of recognition. Regarding quantitative identification, this paper proposes a prediction experiment through Partial least squares (PLS) and BP neural networks. The experiment shows that the average relative error of the trained BP network is within 6%. Finally, the experiment of quantitative analysis of malodorous compound gas by this system shows that the maximum relative error of this method is only 4.238%. This system has higher accuracy and faster recognition speed than traditional methods.