Zou Hanting, Li Ranyang, Xuan Xuan, Jiang Yongwen, Yuan Haibo, An Ting
State Key Laboratory of Tea Plant Germplasm Innovation and Resource Utilization, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China.
Foods. 2025 Aug 15;14(16):2829. doi: 10.3390/foods14162829.
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.
高效便捷的智能在线检测方法可为茶叶行业加工流程的标准化提供重要技术支持。因此，本研究聚焦于红茶揉捻过程中的关键化学指标——茶色素为研究对象，采用多源信息融合方法预测茶色素含量的变化。首先，通过系统分析方法测定红茶不同揉捻时间序列下样品的茶色素含量。其次，利用便携式近红外光谱仪和机器视觉系统同时获取不同揉捻时间序列下相应样品的光谱和图像。然后，通过提取图像特征信息的主成分并从光谱信息中筛选特征波长，选择低层次和中层次数据融合策略有效整合来自不同源的传感器数据。最后，基于不同特征数据信息分别建立线性（偏最小二乘回归，PLSR）和非线性（支持向量回归，SVR和最小二乘支持向量回归，LSSVR）模型。研究结果表明，基于中层次数据融合策略的LSSVR效果最佳。在茶黄素、茶红素和茶褐素的预测结果中，测试集的相关系数均大于0.98，相对百分偏差均大于5。光谱和图像信息的互补融合有效弥补了使用单模态数据信息对茶色素含量进行定量分析时信息冗余和特征缺失的问题。
