Evaluation of aroma quality using multidimensional olfactory information during black tea fermentation

文献类型： 外文期刊

第一作者： An, Ting

作者： An, Ting;Tian, Xi;Fan, Shuxiang;Huang, Wenqian;Duan, Dandan;Zhao, Chunjiang;An, Ting;Zhao, Chunjiang;An, Ting;Li, Yang;Zhao, Chunjiang;Dong, Chunwang

作者机构：

关键词： Hyperspectral imaging; Data fusion; Aroma quality; Porphyrin and metalloporphyrin (TPP); Black tea fermentation

期刊名称：SENSORS AND ACTUATORS B-CHEMICAL （ 影响因子：9.221； 五年影响因子：7.676 ）

ISSN：

年卷期： 2022 年 371 卷

页码：

收录情况： SCI

摘要： Thus far, the intelligent evaluation of aroma quality during black tea fermentation remains an unsolved problem due to the hysteresis quality of traditional sensory evaluation methods. In our study, a combination of hyper -spectral imaging technology and colorimetric sensing array (CSA) was used to collect the aroma information during black tea fermentation. Subsequently, different data fusion strategies coupled with the support vector regression (SVR) model were used to predict the aroma scores of finished tea at different fermentation times. The performance of the prediction model using data fusion strategies was better than that using each sensitive dye. The results demonstrated that the middle-level-competitive adaptive reweighted sampling (CARS) strategy showed the best performance, with the correlation coefficient of the prediction set (Rp) at 0.969, the relative percent deviation (RPD) at 4.091, and the variable compression rate at 96.83%. Based on the middle-level-CARS strategy, the discrimination rate of aroma quality for calibration and prediction set were 100% and 94.29%, respectively. The overall results sufficiently revealed that our proposed strategy provides a theoretical basis for the intelligent processing of black tea.

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