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Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

Author

Listed:
  • Yan Hu

    (College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China)

  • Lijia Xu

    (College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China)

  • Peng Huang

    (College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China)

  • Xiong Luo

    (College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China)

  • Peng Wang

    (College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China)

  • Zhiliang Kang

    (College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China)

Abstract

A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology and machine learning to distinguish Oolong tea by analyzing the spectral features of tea in the wavelength ranging from 475 to 1100 nm. The spectral data are preprocessed by multivariate scattering correction (MSC) and standard normal variable (SNV), which can effectively reduce the impact of baseline drift and tilt. Then principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are adopted for feature dimensionality reduction and visual display. Random Forest-Recursive Feature Elimination (RF-RFE) is used for feature selection. Decision Tree (DT), Random Forest Classification (RFC), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used to establish the classification model. The results show that MSC-RF-RFE-SVM is the best model for the classification of Oolong tea in which the accuracy of the training set and test set is 100% and 98.73%, respectively. It can be concluded that fluorescence hyperspectral technology and machine learning are feasible to classify Oolong tea.

Suggested Citation

  • Yan Hu & Lijia Xu & Peng Huang & Xiong Luo & Peng Wang & Zhiliang Kang, 2021. "Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning," Agriculture, MDPI, vol. 11(11), pages 1-19, November.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:11:p:1106-:d:673496
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    References listed on IDEAS

    as
    1. Peng Wang & Jiang Liu & Lijia Xu & Peng Huang & Xiong Luo & Yan Hu & Zhiliang Kang, 2021. "Classification of Amanita Species Based on Bilinear Networks with Attention Mechanism," Agriculture, MDPI, vol. 11(5), pages 1-13, April.
    2. Xiong Luo & Lijia Xu & Peng Huang & Yuchao Wang & Jiang Liu & Yan Hu & Peng Wang & Zhiliang Kang, 2021. "Nondestructive Testing Model of Tea Polyphenols Based on Hyperspectral Technology Combined with Chemometric Methods," Agriculture, MDPI, vol. 11(7), pages 1-15, July.
    3. Unknown, 2006. "Front Materials," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 21(3), pages 1-4.
    4. Unknown, 2006. "Front Materials," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 21(1), pages 1-4.
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    Citations

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    Cited by:

    1. Zhiliang Kang & Jinping Geng & Rongsheng Fan & Yan Hu & Jie Sun & Youli Wu & Lijia Xu & Cheng Liu, 2022. "Nondestructive Testing Model of Mango Dry Matter Based on Fluorescence Hyperspectral Imaging Technology," Agriculture, MDPI, vol. 12(9), pages 1-21, August.

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