Rapid determination by near infrared spectroscopy of theaflavins-to-thearubigins ratio during Congou black tea fermentation process

文献类型： 外文期刊

第一作者： Dong, Chunwang

作者： Dong, Chunwang;Li, Jia;Wang, Jinjin;Liang, Gaozhen;Jiang, Yongwen;Yuan, Haibo;Yang, Yanqin;Liang, Gaozhen;Meng, Hewei

作者机构：

关键词： Congou black tea fermentation; Theaflavin-to-thearubigin ratio; NIR spectroscopy; Extreme learning machine; Variable selection

期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.098； 五年影响因子：3.464 ）

ISSN： 1386-1425

年卷期： 2018 年 205 卷

页码：

收录情况： SCI

摘要： The theaflavin-to-thearubigin ratio (TF/TR) is an important parameter for evaluating the degree of fermentation and quality characteristics of Congou black tea. Near infrared (NIR) spectroscopy, one of the most promising techniques for evaluating large-scale tea processing quality, in association with chemometrics, can be used as a selection tool when a fast determination of the requested parameters is required. The aim of this work is to develop a unique model for the determination of TF/TR. First, 11 key wavelength variables were screened by synergy interval partial least-squares regression (SI-PLS) and competitive adaptive reweighted sampling (CARS). Based on these characteristic variables, a new extreme learning machine (ELM) combined with an adaptive boosting (ADABOOST) algorithm (ELM-ADABOOST) was applied to construct the nonlinear prediction model for TF/TR, and an independent external set was used for the validation. A determinate coefficient (R-P(2)) of 0.893, root mean square error of prediction (RMSEP) of 0.0044, RSD below 10%, and RPD above 3 were acquired in the prediction model. These results demonstrate that NIR can be used to rapidly determine the TF/TR value during fermentation, and it effectively simplify the model and improve the prediction accuracy when combined with the SI-CARS variable. (C) 2018 Elsevier B.V. All rights reserved.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序