Journal
SENSORS
Volume 17, Issue 5, Pages -Publisher
MDPI
DOI: 10.3390/s17051007
Keywords
multi-level fusion; feature fusion; decision fusion; electronic nose; electronic tongue; tea quality assessment
Funding
- National Natural Science Foundation of China [61673052, 31201358]
- Fundamental Research Fund for the Central Universities of China [06116070]
- National Research and Development Major Project [SQ2017YFNC010027]
- National High Technology Research and Development Program 863 [2011AA1008047]
- Chinese Scholarship Council (CSC)
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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.
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