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

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Abstract

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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Acknowledgements

This work has been financially supported by the National Natural Science Foundation of China (31471646), the Natural Science Foundation of Zhejiang Province (Y16C160009), the Innovation Project of China Academy of Agricultural Sciences (CAAS-ASTIP-TRICAAS), and Science and Technology Project of Zhenjiang City (NY2016013).

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Authors and Affiliations

  1. School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, China

    Chunwang Dong, Jiewen Zhao & Quansheng Chen

  2. Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008, China

    Chunwang Dong, Hongkai Zhu, Jinjin Wang & Haibo Yuan

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  1. Chunwang Dong
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  2. Hongkai Zhu
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  3. Jinjin Wang
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  4. Haibo Yuan
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  5. Jiewen Zhao
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Correspondence to Quansheng Chen.

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Dong, C., Zhu, H., Wang, J. et al. Prediction of black tea fermentation quality indices using NIRS and nonlinear tools. Food Sci Biotechnol 26, 853–860 (2017). https://doi.org/10.1007/s10068-017-0119-x

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  • DOI: https://doi.org/10.1007/s10068-017-0119-x

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