Food Science and Biotechnology
→ Food Science and Biotechnology 2017 ; 26(4): 853-860
Prediction of black tea fermentation quality indices using NIRS and nonlinear tools
Chunwang Dong1,2, Hongkai Zhu2, Jinjin Wang2, Haibo Yuan2, Jiewen Zhao1, Quansheng Chen1
1School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China 2Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
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-ELMA-daBoost 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.
Black tea, Fermentation, Near infrared spectroscopy, Nonlinear tools
Food Science and Biotechnology 2017 ; 26(4): 853-860