IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v11y2021i7p673-d595243.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/11/7/673/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/11/7/673/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:11:y:2021:i:7:p:673-:d:595243. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.