ArticleSensors (Basel, Switzerland)2019
Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Fluorescence-Based Sensor Combined with Chemometric and Deep Learning Approaches for Detecting and Quantifying Coconut Milk Fraud in Bovine Milk.Sensors (Basel, Switzerland) · 2026Article
- Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Transient 2D Junction Temperature Distribution Measurement by Short Pulse Driving and Gated Integration with Ordinary CCD Camera.Sensors (Basel, Switzerland) · 2022Article
- Quantitative Detection of Extra Virgin Olive Oil Adulteration, as Opposed to Peanut and Soybean Oil, Employing LED-Induced Fluorescence Spectroscopy.Sensors (Basel, Switzerland) · 2022Article
- An Online Tea Fixation State Monitoring Algorithm Based on Image Energy Attention Mechanism and Supervised Clustering (IEAMSC).Sensors (Basel, Switzerland) · 2020Article
- Processing Fluorescence Spectra for Pollutants Detection Systems in Inland Waters.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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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Registered trials
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