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

Xiong Luo, Lijia Xu, Peng Huang, Yuchao Wang, Jiang Liu, Yan Hu, Peng Wang and Zhiliang Kang
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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

Agriculture, 2021, vol. 11, issue 7, 1-15

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.

Keywords: hyperspectral; tea polyphenols; sample division; feature selection; regression model; nondestructive detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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