Journal
SENSORS
Volume 19, Issue 21, Pages -Publisher
MDPI
DOI: 10.3390/s19214687
Keywords
tea; classification; fluorescence; LED; convolutional neural network; variety; EEM
Funding
- National Training Programs of Innovation and Entrepreneurship for Undergraduates, China [201810336031]
- National Natural Science Foundation of China [U1609218, 61705056]
- Public Projects of Zhejiang Province [LGF19H180005]
- Scientific Plan Project of Zhejiang Province [2019C03137]
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A multi-channel light emitting diode (LED)-induced fluorescence system combined with a convolutional neural network (CNN) analytical method was proposed to classify the varieties of tea leaves. The fluorescence system was developed employing seven LEDs with spectra ranging from ultra-violet (UV) to blue as excitation light sources. The LEDs were lit up sequentially to induce a respective fluorescence spectrum, and their ability to excite fluorescence from components in tea leaves were investigated. All the spectral data were merged together to form a two-dimensional matrix and processed by a CNN model, which is famous for its strong ability in pattern recognition. Principal component analysis combined with k-nearest-neighbor classification was also employed as a baseline for comparison. Six grades of green tea, two types of black tea and one kind of white tea were verified. The result proved a significant improvement in accuracy and showed that the proposed system and methodology provides a fast, compact and robust approach for tea classification.
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