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.
Highlights
An electronic nose (e-nose) is a device intended to detect and discriminate odorants in the vapor phase [1,2,3,4,5,6]
We presented a reliable e-nose system using an appropriate feature extraction method based on the characteristics of e-nose data
Since the adjacent primitive variables are strongly correlated in e-nose data, the proposed method showed better performance than other methods
Summary
An electronic nose (e-nose) is a device intended to detect and discriminate odorants in the vapor phase [1,2,3,4,5,6]. Sensors 2012, 12 nose, calorimetric sensors were used to perform measurements on vapors, and the measurements were usually expressed in arrays of colors [7]. Such an e-nose system, which was used only in a laboratory environment, utilized complicated analytic procedures, including precise equipment such as gas chromatography (GC) systems or mass spectrometers (MS) combined with sophisticated machine intelligence [8,9]. Electronic noses are potentially useful for classifying and subphenotyping of patients with different respiratory diseases [23]
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