数字农科院2.0

Rapid and Quantitative Prediction of Tea Pigments Content During the Rolling of Black Tea by Multi-Source Information Fusion and System Analysis Methods

文献类型： 外文期刊

作者： Zou, Hanting;Li, Ranyang;Xuan, Xuan;Jiang, Yongwen;Yuan, Haibo;An, Ting

作者机构：

关键词： black tea;tea pigments;machine vision;near-infrared spectroscopy

期刊名称： FOODS

ISSN：

年卷期： 2025 年 14 卷 16 期

页码：

收录情况： SCIE（2025版）

摘要： Efficient and convenient intelligent online detection methods can provide important technical support for the standardization of processing flow in the tea industry. Hence, this study focuses on the key chemical indicators-tea pigments in the rolling process of black tea as the research object, and uses multi-source information fusion methods to predict the changes of tea pigments content. Firstly, the tea pigments content of the samples under different rolling time series of black tea is determined by system analysis methods. Secondly, the spectra and images of the corresponding samples under different rolling time series are simultaneously obtained through the portable near-infrared spectrometer and the machine vision system. Then, by extracting the principal components of the image feature information and screening characteristic wavelengths from the spectral information, low-level and middle-level data fusion strategies are chosen to effectively integrate sensor data from different sources. At last, the linear (PLSR) and nonlinear (SVR and LSSVR) models are established respectively based on the different characteristic data information. The research results show that the LSSVR based on middle-level data fusion strategy have the best effect. In the prediction results of theaflavins, thearubigins, and theabrownins, the correlation coefficients of the testing sets are all greater than 0.98, and the relative percentage deviations are all greater than 5. The complementary fusion of the spectrum and image information effectively compensates for the problems of information redundancy and feature missing in the quantitative analysis of tea pigments content using the single-modal data information.

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