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Sensors 2012, 12(12), 16182-16193; doi:10.3390/s121216182

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 / Revised: 5 November 2012 / Accepted: 13 November 2012 / Published: 22 November 2012
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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. View Full-Text
Keywords: e-nose system; vapor classification; composite feature; discriminant features e-nose system; vapor classification; composite feature; discriminant features

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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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.

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