A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment
AbstractElectronic 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
- Supplementary File 1:
PDF-Document (PDF, 737 KB)
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
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.
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(5):1007.Chicago/Turabian Style
Zhi, Ruicong; Zhao, Lei; Zhang, Dezheng. 2017. "A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment." Sensors 17, no. 5: 1007.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.