Manifold learning for dimension reduction of electronic nose data

Electronic nose is a system based on the sense of smell, which is consisted of several metal oxide semiconductor sensors based on the characteristic odor or volatile components in the air. Its structure includes gas acquisition system, sensor array, signal conditioning circuit and data processing. Compared with other systems, it has the advantages of sensitive detection, simple operation, and high cost performance, etc. However, the materials of sensors are non-linear metal-semiconductor, the obtained odor information must also include non-linear characteristic. Traditional linear feature extraction algorithm can't extract the nonlinear characteristics, so this work proposed four manifold learning algorithms for extracting odor information characteristics. In order to verify feature extraction results, electronic nose measurement data based on the living Chinese mitten crab was processed. The prediction model based on support vector machine (SVM) algorithm according to the guide of the above classification was presented. Total volatile basic nitrogen (TVB-N) of the samples was measured and its results acted as reference information. Experimental results illustrated that the system based on electronic nose with manifold learning can assess the freshness of the living crab.