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Sensors 2015, 15(7), 16027-16039; doi:10.3390/s150716027

Optimal Sensor Selection for Classifying a Set of Ginsengs Using Metal-Oxide Sensors

State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou 310027, Zhejiang, China
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Academic Editor: W. Rudolf Seitz
Received: 27 May 2015 / Revised: 26 June 2015 / Accepted: 26 June 2015 / Published: 3 July 2015
(This article belongs to the Section Chemical Sensors)
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Abstract

The sensor selection problem was investigated for the application of classification of a set of ginsengs using a metal-oxide sensor-based homemade electronic nose with linear discriminant analysis. Samples (315) were measured for nine kinds of ginsengs using 12 sensors. We investigated the classification performances of combinations of 12 sensors for the overall discrimination of combinations of nine ginsengs. The minimum numbers of sensors for discriminating each sample set to obtain an optimal classification performance were defined. The relation of the minimum numbers of sensors with number of samples in the sample set was revealed. The results showed that as the number of samples increased, the average minimum number of sensors increased, while the increment decreased gradually and the average optimal classification rate decreased gradually. Moreover, a new approach of sensor selection was proposed to estimate and compare the effective information capacity of each sensor. View Full-Text
Keywords: sensor selection; metal-oxide sensors; classification; electronic nose; linear discriminant analysis sensor selection; metal-oxide sensors; classification; electronic nose; linear discriminant analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Miao, J.; Zhang, T.; Wang, Y.; Li, G. Optimal Sensor Selection for Classifying a Set of Ginsengs Using Metal-Oxide Sensors. Sensors 2015, 15, 16027-16039.

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