Smell classification electronic nose system design based on pattern recognition algorithm

Abstract

The invention belongs to the technical field of gas detection and mode recognition, and particularly provides a smell classification electronic nose system based on a multi-sensor array and mode recognition, which is used for solving the problems of poor generalization and low classification precision of a traditional gas sensor on complex smells. The method comprises the following steps that (1) an embedded electronic nose system platform is built, data of five smells of mint sugar, coffee, chrysanthemum tea leaves, orange peel and apples are collected, and the platform comprises (1) a Raspberry Pi 4B main controller used for running of a main program; (2) four BME680 sensor arrays used for collecting multiple non-specific responses of the gas to be detected; (3) the speed-adjustable gas circuit module is used for driving a micro diaphragm gas pump through PWM (Pulse-Width Modulation), and the extraction rate of 0-0.8 L / min can be realized; (2) carrying out a data sampling experiment, and constructing a multi-scene data set; (3) establishment of a pattern recognition model: (1) data processing: performing data enhancement on originally acquired data, fitting a response curve through a nonlinear least square method, extracting characteristics such as slope, curvature and the like, and expanding characteristic dimensions of the original data; (2) carrying out principal component analysis on dimension-reduced data; and (3) model training: establishing a classifier model through various machine learning algorithms to obtain an optimal model. And the accuracy rate can reach 81.7%.