Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network
Abstract
:1. Introduction
2. Materials and Methods
2.1. Samples
2.2. Apparatus Design and Procedure
2.3. Data Pre-Processing
2.4. Principal Component Analysis-K-Nearest Neighbors
2.5. Convolutional Neural Network
3. Results
3.1. Exploratory Analysis of the Data
3.2. PCA Classification with Individual Fluorescence Spectrum
3.3. CNN Classification with Fluorescence Matrix
4. Discussion and Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Method | PCA+kNN | ||||||||
---|---|---|---|---|---|---|---|---|---|
Spectra with excitation LED No. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 1~7 | 1+…+7 |
PC number | 11 | 6 | 3 | 3 | 3 | 4 | 11 | 9 | 41 |
Accuracy | 0.826 | 0.802 | 0.796 | 0.839 | 0.790 | 0.754 | 0.828 | 0.828 | 0.826 |
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Lin, H.; Li, Z.; Lu, H.; Sun, S.; Chen, F.; Wei, K.; Ming, D. Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network. Sensors 2019, 19, 4687. https://doi.org/10.3390/s19214687
Lin H, Li Z, Lu H, Sun S, Chen F, Wei K, Ming D. Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network. Sensors. 2019; 19(21):4687. https://doi.org/10.3390/s19214687
Chicago/Turabian StyleLin, Hongze, Zejian Li, Huajin Lu, Shujuan Sun, Fengnong Chen, Kaihua Wei, and Dazhou Ming. 2019. "Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network" Sensors 19, no. 21: 4687. https://doi.org/10.3390/s19214687
APA StyleLin, H., Li, Z., Lu, H., Sun, S., Chen, F., Wei, K., & Ming, D. (2019). Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network. Sensors, 19(21), 4687. https://doi.org/10.3390/s19214687