Classification and adulteration of mengding mountain green tea varieties based on fluorescence hyperspectral image method.

For rapid nondestructive detection of classification and adulteration of Mengding Mountain green tea, an experimental method combining Fluorescence hyperspectral imaging technology and machine learning modeling method was proposed. Spectral data were preprocessed with Median Filtering (MF) and Multiplicative Scatter Correction (MSC), which can effectively reduce the effects of baseline drift and tilt. Xgboost (XGB) and Logistic Regression (LGR) were used for feature band extraction. Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and CatBoost were used to build predictive models. The results show that the MF-LGR-RF algorithm model is the best model for the classification of Mengshan green tea, and its accuracy is 100%. The MF-XGB-CatBoost algorithm model is the best model for the quantitative analysis of the doping of Laochuan tea tree Ganlu. The R2 of the test set is 0.959 and the RMSE is 0.065. In order to further improve the model performance, a multi-model stacking ensemble learning model was designed to quantitatively analyze the adulteration of Laochuan tea tree Ganlu. The R2 of the test set was increased to 0.968 and the RMSE was reduced to 0.058. • Light scattering and light absorption characteristics of tea were revealed. • Various algorithms were selected for data noise reduction and feature extraction. • Design and build hot machine learning models of MF-LGR-RF and MF-XgBoost-CatBoost. • Optimize the stacking learning model to achieve more ideal model results. • The rapid non-destructive quality of the tea leaves of the detective was satisfactory. [ABSTRACT FROM AUTHOR]