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Sensors 2017, 17(5), 1007; doi:10.3390/s17051007

A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment

1
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
3
China National Institute of Standardization, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Academic Editor: Manel del Valle
Received: 2 March 2017 / Revised: 10 April 2017 / Accepted: 20 April 2017 / Published: 3 May 2017
(This article belongs to the Special Issue Electronic Tongues and Electronic Noses)
View Full-Text   |   Download PDF [1551 KB, uploaded 3 May 2017]   |  

Abstract

Electronic nose (E-nose) and electronic tongue (E-tongue) can mimic the sensory perception of human smell and taste, and they are widely applied in tea quality evaluation by utilizing the fingerprints of response signals representing the overall information of tea samples. The intrinsic part of human perception is the fusion of sensors, as more information is provided comparing to the information from a single sensory organ. In this study, a framework for a multi-level fusion strategy of electronic nose and electronic tongue was proposed to enhance the tea quality prediction accuracies, by simultaneously modeling feature fusion and decision fusion. The procedure included feature-level fusion (fuse the time-domain based feature and frequency-domain based feature) and decision-level fusion (D-S evidence to combine the classification results from multiple classifiers). The experiments were conducted on tea samples collected from various tea providers with four grades. The large quantity made the quality assessment task very difficult, and the experimental results showed much better classification ability for the multi-level fusion system. The proposed algorithm could better represent the overall characteristics of tea samples for both odor and taste. View Full-Text
Keywords: multi-level fusion; feature fusion; decision fusion; electronic nose; electronic tongue; tea quality assessment multi-level fusion; feature fusion; decision fusion; electronic nose; electronic tongue; tea quality assessment
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Zhi, R.; Zhao, L.; Zhang, D. A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment. Sensors 2017, 17, 1007.

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