A novel method to recognize and classify based on an E-nose

Abstract

The electronic nose (E-nose) has been widely reported as a new and convenient detection technology. While making continuous breakthroughs, the electronic nose is also facing a series of new problems and challenges. Especially in the actual application process, it faces high-dimensional and non-linear problems in the perception data of odor under non-ideal conditions. In this paper, a new method based on multiple optimal mapping is proposed, called superimposed mapping analysis (SMA). This method optimizes the loss function and mapping feature space of the model by making full use of the relationship between dependencies and mutual relationships within the class. The optimization method is used to construct the optimal loss function of the reconstruction matrix, which can achieve dimensionality reduction and feature extraction. The experimental results show that compared with the classical linear and non-linear dimensionality reduction methods, the performance index of SMA is more in line with practical application requirements.