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Using surface-enhanced Raman spectroscopy combined with chemometrics for black tea quality assessment during its fermentation process

文献类型： 外文期刊

作者： Luo, Xuelun 1 ; Gouda, Mostafa 1 ; Perumal, Anand Babu 1 ; Huang, Zhenxiong 1 ; Lin, Lei 1 ; Tang, Yu 4 ; Sanaeifar, Alireza 1 ; He, Yong 1 ; Li, Xiaoli 1 ; Dong, Chunwang 2 ;

作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China

2.Chinese Acad Agr Sci, Tea Res Inst, Hangzhou 310008, Peoples R China

3.Natl Res Ctr, Dept Nutr & Food Sci, Giza 12622, Egypt

4.Guangdong Polytech Normal Univ, Acad Interdisciplinary Studies, Guangzhou, Peoples R China

5.Shandong Acad Agr Sci, Tea Res Inst, Shandong 250100, Peoples R China

关键词： Black tea; Quality monitoring; Fermented tea detection; SERS

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 373 卷

页码：

收录情况： SCI

摘要： Developing a reliable and convenient method for monitoring the quality of black tea during fermentation could lead to a significant improvement in fermentation process. This work presented a rapid method based on surface-enhanced Raman spectroscopy (SERS) technology and chemometrics to determine the optimal fermentation stage and monitor the changes in 10 types of quality indicators of black tea throughout fermentation. First, the 10 different fermentation time points were clustered into 5 fermentation stages. Based on the SERS data, the fermentation stages were distinguished with an accuracy of 83.33 % by one-dimensional ResNet18 (1D-ResNet18). Furthermore, important Raman peaks at 317.71, 619.59, 731.48, 956.08 and 1326.70 cm(-1) were found for monitoring quality changes of black tea by density functional analysis and correlation analysis. The prediction r(2) for catechin (C) and epigallocatechin gallate (EGCG) reached 0.81 and 0.82, respectively, by integrated SERS with a one-dimensional convolutional neural network (1D-CNN). In conclusion, this study revealed the Raman fingerprint characteristics of key compounds associated with the fermentation quality of black tea, presenting an opportunity to quantify the quality changes of tea during fermentation using SERS data. With the monitoring method developed in this research, the optimal fermentation stage can be determined accurately, thus decreasing fermentation costs and improving tea quality.

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