A Predictive Study on the Content of Epigallocatechin Gallate (EGCG) in Yunnan Large Leaf Tea Trees Based on the Nomogram Model

Abstract

To explore the changes in epigallocatechin gallate (EGCG) content in tea under abiotic stress conditions, we collected tea samples, along with corresponding soil and altitude data, and utilized the measured data for single-factor analysis. At the same time, the LASSO regression method, which is rarely used in agriculture, was employed to screen modeling factors, a prediction model was established, and the Akaike information criterion (AIC) was introduced to compare the goodness of fit. The results show that LASSO screening reduced the AIC value of the model by 13.8%. The average area under the curve of the training set and the validation set was 0.81 and 0.76, respectively, and the calibration curve also showed good consistency. Based on the nomogram model, a visual prediction system was developed, and the content prediction curve was introduced for detailed soil evaluation. The accuracy rate reached 75% after external verification. This study provides a theoretical basis for elucidating the prediction and intervention of Pu’er tea quality under abiotic stress conditions.