茶多酚是红茶品质的重要评价指标。以工夫红茶发酵在制品为研究对象,利用电特性检测技术与化学计量学方法相结合,构建发酵中茶多酚含量的预测模型。探讨了发酵中电参数的变化规律,以及不同标准化预处理和变量筛选算法对模型的影响。结果表明,对茶多酚最敏感的电参数为并联等效电容（Cp）、损耗因子（D）和电抗（X）,且集中在低频范围（0.05~0.10 kHz）。在茶多酚预测模型构建中,Z标准化（Center and zero mean normalization,Zscore）预处理、迭代空间收缩算法混合迭代保留信息变量算法（Variables combination population analysis and iterative retained information variable algorithm,VCPA-IRIV）能有效提升模型性能。VCPA-IRIV算法将引入变量数由162降低到31,压缩率达80.86%;VCPA-IRIV模型的最低交互验证均方根误差（Root-mean-squares error of calibration,RMSECV）和预测均方根误差（Root-mean-square error of prediction,RMSEP）分别为0.630和1.116,预测集的相关系数（Correlation coefficient of predication set,Rp）和相对标准偏差（Relative percent deviation,RPD）为0.941和2.956,表明电特性检测技术对红茶发酵中茶多酚含量的快速无损检测是可行的。
Tea polyphenols are an important evaluation index for the quality of black tea. The quantitative prediction model of tea polyphenol content in the fermentation process was established by combining electrical characteristics detection technology with chemometric method. The changes of electrical parameters during the fermentation process and the influence of different standardized pretreatment methods and variable optimization algorithms on the model were discussed. The results show that the most sensitive electrical parameters to tea polyphenols were Cp, D and X, all of which were concentrated in the low frequency range (0.05-0.10 kHz). In the construction of tea polyphenol prediction model, normalization processing (Zscore) and mixed variable screening (VCPA-IRIV) can effectively improve the performance of the model. The number of variables introduced in the VCPA-IRIV-PLS model was reduced from 162 to 31, and the compression rate reached 80.86%. RMSECV and RMSEP were reduced to 0.630 and 1.116, respectively. Rp and RPD were increased to 0.941 and 2.956. The research results show that the electrical characteristics detection technology is feasible for the rapid non-destructive detection of the content of tea polyphenols in black tea fermentation.
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