| Summary: | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>c</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> and<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> 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.
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