This study aimed to develop reliable models for predicting tea polyphenols and epigallocatechin gallate (EGCG) content in tea leaves using Fourier Transform-near-infrared (FT-NIR) spectroscopy and machine learning. Various spectral preprocessing methods were applied, and partial least squares regression (PLSR) and least squares support vector regression (LS-SVR) were used to build predictive models. Variable selection algorithms (CARS and RF) further improved model efficiency. The optimal model for tea polyphenols was LS-SVR with R<sub>p</sub> = 0.975 and RPD = 4.540, while for EGCG, it was LS-SVR with R<sub>p</sub> = 0.936 and RPD = 2.841. Variable selection enhanced prediction, reaching R<sub>p</sub> = 0.978 and RPD = 4.833 for tea polyphenols and R<sub>p</sub> = 0.944 and RPD = 3.049 for EGCG. The results demonstrate the potential of FT-NIR spectroscopy combined with machine learning for rapid tea polyphenol and EGCG content screening.
Publisher
Molecules
Published On
Jul 13, 2023
Authors
Sitan Ye, Haiyong Weng, Lirong Xiang, Liangquan Jia, Jinchai Xu
Tags
tea polyphenols
EGCG
FT-NIR spectroscopy
machine learning
predictive models
partial least squares regression
variable selection
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