A correlated information removing based interference suppression technique in electronic nose for detection of bacteria

Abstract

A sensor array with 30 gas sensors is used in the electronic nose (e-nose) for bacteria detection in wound infection. However, the interference is an urgent problem in e-nose, since it would impact on the detection of target due to the cross-sensitivity of gas sensors, especially the background interference caused by carrier gas. The related methods to suppress the background interference are independent component analysis and orthogonal signal correction algorithm which are unreasonable, because it is difficult to obtain the so-called reference vector in complex real-world scenario. Consider that the sampling process of pump suction is divided into three parts: baseline collecting, sample collecting and system purging. In the case of stabilized carrier gas, the information in baseline can be fully used to suppress the interference in sampling stage. Thus a novel and effective correlated information removing based interference suppression (CIRIS) method is proposed. Specifically, the principle of this method is to suppress the interference of the sampling stage by removing the information correlated with baseline samples. Experimental results show that the proposed method (CIRIS with principal component analysis used to calculate the projection matrix) is significantly effective for interference suppression in e-nose.