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Nondestructive Testing Model of Tea Polyphenols Based on Hyperspectral Technology Combined with Chemometric Methods

Author

Listed:
  • Xiong Luo

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

  • Lijia Xu

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

  • Peng Huang

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

  • Yuchao Wang

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

  • Jiang Liu

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

  • Yan Hu

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

  • Peng Wang

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

  • Zhiliang Kang

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

Abstract

Nondestructive detection of tea’s internal quality is of great significance for the processing and storage of tea. In this study, hyperspectral imaging technology is adopted to quantitatively detect the content of tea polyphenols in Tibetan teas by analyzing the features of the tea spectrum in the wavelength ranging from 420 to 1010 nm. The samples are divided with joint x-y distances (SPXY) and Kennard-Stone (KS) algorithms, while six algorithms are used to preprocess the spectral data. Six other algorithms, Random Forest (RF), Gradient Boosting (GB), Adaptive boost (AdaBoost), Categorical Boosting (CatBoost), LightGBM, and XGBoost, are used to carry out feature extractions. Then based on a stacking combination strategy, a new two-layer combination prediction model is constructed, which is used to compare with the four individual regressor prediction models: RF Regressor (RFR), CatBoost Regressor (CatBoostR), LightGBM Regressor (LightGBMR) and XGBoost Regressor (XGBoostR). The experimental results show that the newly-built Stacking model predicts more accurately than the individual regressor prediction models. The coefficients of determination R c 2 and R p 2 for the prediction of Tibetan tea polyphenols are 0.9709 and 0.9625, and the root mean square error RMSEC and RMSEP are 0.2766 and 0.3852 for the new model, respectively, which shows that the content of Tibetan tea polyphenols can be determined with precision.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:7:p:673-:d:595243
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Junyao Gong & Gang Chen & Yuezhao Deng & Cheng Li & Kui Fang, 2024. "Non-Destructive Detection of Tea Polyphenols in Fu Brick Tea Based on Hyperspectral Imaging and Improved PKO-SVR Method," Agriculture, MDPI, vol. 14(10), pages 1-23, September.
    2. 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.
    3. 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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    1. 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.

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