Prediction of black tea fermentation quality indices using NIRS and nonlinear tools

文献类型： 外文期刊

第一作者： Dong, Chunwang

作者： Dong, Chunwang;Zhao, Jiewen;Chen, Quansheng;Dong, Chunwang;Zhu, Hongkai;Wang, Jinjin;Yuan, Haibo

作者机构：

关键词： Black tea;Fermentation;Near infrared spectroscopy;Nonlinear tools

期刊名称：FOOD SCIENCE AND BIOTECHNOLOGY （ 影响因子：2.391； 五年影响因子：2.108 ）

ISSN： 1226-7708

年卷期： 2017 年 26 卷 4 期

页码：

收录情况： SCI

摘要： Catechin content, the ratio of tea polyphenols and free amino acids (TP/FAA), as well as the ratio of theaflavins and thearubigins (TFs/TRs) are important biochemical indicators to evaluate fermentation quality. To achieve rapid determination of such biochemical indicators, synergy interval partial least square and extreme learning machine combined with an adaptive boosting algorithm, Si-ELM-AdaBoost algorithm, were used to establish quantitative analysis models between near infrared spectroscopy (NIRS) and catechin content and between TFs/TRs and TP/FAA, respectively. The results showed that prediction performance of the Si-ELM-AdaBoost mixed algorithm is superior than that of other models. The prediction results with root-mean-square error of prediction ranged from 0.006 to 0.563, the ratio performance deviation values exceeded 2.5, and predictive correlation coefficient values exceeded 0.9 in the prediction model of each biochemical indicator. NIRS combined with Si-ELM-AdaBoost mixed algorithm could be utilized for online monitoring of black tea fermentation. Meanwhile, the AdaBoost algorithm effectively improved the accuracy of the ELM model and could better approach the nonlinear continuous function.

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