A Model for Yellow Tea Polyphenols Content Estimation Based on Multi-Feature Fusion

Polyphenols are one of the important ingredients determining the quality of tea, which play an important role in affecting tea quality standards and quality control. At present, NIR spectroscopy technology has been widely used in tea quality detection to achieve good results. However, due to the lack of spatial features, it is difficult to meet the accuracy requirements to comprehensively judge the quality of both inside and outside of tea. With the development of hyperspectral imaging system (HIS), although progress has been made in the estimation of tea texture based on gray level co-occurrence matrix (GLCM) extraction, there are still some obstacles in practical applications. When the resolution is low, the texture features of the image are not significantly different, and a small number of features cannot more fully represent the image features, resulting in lower model accuracy. When the resolution is higher, the increase in the dimension of the feature leads to a more complex model. Therefore, it is necessary to extract multi-scale features from the hyperspectral image while preserving the original information of the hyperspectral image. In this study, a multi-feature fusion method is proposed to improve the accuracy of tea polyphenols content estimation. The process includes multi-scale wavelet decomposition, and feature fusion based on wavelet coefficient features, GLCM texture features and wavelet texture features. Experiments were carried out on five kinds of yellow tea by comparing different models based on different features, including partial least squares regression (PLSR) and support vector regression (SVR). Results indicated that the model based on multi-feature fusion is more accurate than the individual indicators, and models based on SVR perform better than PLSR.