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Classification of Odorants in the Vapor Phase Using Composite Features for a Portable E-Nose System
Department of Applied Computer Engineering, Dankook University, 126 Jukjeon-dong, Suji-gu, Yongin-si, 448-701 Gyeonggi-do, Korea
Electrical Engineering, Kookmin University, 2, 861-1, Jeongeung-dong, Songbuk-gu, 136-702 Seoul, Korea
Korea Institute of Industrial Technology, 1271-18, Sa-3-dong, Sangrok-gu, 426-791 Ansan, Korea
* Author to whom correspondence should be addressed.
Received: 30 August 2012; in revised form: 5 November 2012 / Accepted: 13 November 2012 / Published: 22 November 2012
Abstract: We present an effective portable e-nose system that performs well even in noisy environments. Considering the characteristics of the e-nose data, we use an image covariance matrix-based method for extracting discriminant features for vapor classification. To construct composite vectors, primitive variables of the data measured by a sensor array are rearranged. Then, composite features are extracted by utilizing the information about the statistical dependency among multiple primitive variables, and a classifier for vapor classification is designed with these composite features. Experimental results with different volatile organic compounds data show that the proposed system has better classification performance than other methods in a noisy environment.
Keywords: e-nose system; vapor classification; composite feature; discriminant features
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Cite This Article
MDPI and ACS Style
Choi, S.-I.; Jeong, G.-M.; Kim, C. Classification of Odorants in the Vapor Phase Using Composite Features for a Portable E-Nose System. Sensors 2012, 12, 16182-16193.
Choi S-I, Jeong G-M, Kim C. Classification of Odorants in the Vapor Phase Using Composite Features for a Portable E-Nose System. Sensors. 2012; 12(12):16182-16193.
Choi, Sang-Il; Jeong, Gu-Min; Kim, Chunghoon. 2012. "Classification of Odorants in the Vapor Phase Using Composite Features for a Portable E-Nose System." Sensors 12, no. 12: 16182-16193.