Abstract
Light absorption gas sensing technology has the characteristics of massive parallelism, cross-sensitivity and extensive responsiveness, which make it suitable for the sensing task of an electronic nose (e-nose). With the performance of hyperspectral resolution, spatial heterodyne spectrometer (SHS) can present absorption spectra of the gas in the form of a two dimensional (2D) interferogram which facilitates the analysis of gases with mature image processing techniques. Therefore, a visual e-nose system based on SHS was proposed. Firstly, a theoretical model of the visual e-nose system was constructed and its visual maps were obtained by an experiment. Then the local binary pattern (LBP) and Gray-Level Co-occurrence Matrix (GLCM) were used for feature extraction. Finally, classification algorithms based on distance similarity (Correlation coefficient (CC); Euclidean distance to centroids (EDC)) were chosen to carry on pattern recognition analysis to verify the feasibility of the visual e-nose system.
Highlights
Based on the theory of visual e-nose, the sensing data acquired by the system are images which are formed by the superposition of interferograms of different diffraction orders
An innovative visual e-nose system based on spatial heterodyne spectrometer (SHS) was proposed
The core of the article was to introduce a wide spectral SHS (WS-SHS) into the e-nose system taking its core gas sensing tasks
Summary
As a representative of artificial olfactory technology, e-nose can provide an objective assessment of smell which is widely used in food safety [1,2,3,4,5], disease diagnosis [6,7,8,9,10], environmental monitoring [11,12,13,14,15,16], public safety [17,18], etc. Light absorption gas sensing technology [24,25] has the characteristics of massive parallelism, cross-sensitivity, extensive and fast responsiveness, which make it suitable for the sensing task of an e-nose. If light absorption gas sensing technology is applied to e-nose system, the problems of fewer units, long response time, short life, poor repeatability and harsh environmental requirements of typical e-nose (such as PEN3, Alpha MOS, etc.) will be solved. Sensors 2018, 18, 1188 the characteristics of the interferogram to replace the original gas information as the sensor response of the e-nose can apply the mature image processing technology to the data processing of the e-nose, and reduce the complexity of data processing and improve the efficiency of the e-nose. Image feature extraction algorithms, principal component analysis (PCA) and classifiers were used for processing of the interferogram
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