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Discrimination of black tea fermentation degree based on multi-data fusion of near-infrared spectroscopy and machine vision

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Abstract

Fermentation is a key step in the production of black tea and has an important impact on the quality of black tea. In this research, the fermentation degree of black tea was evaluated by a portable near-infrared spectrometer and a charge-coupled device camera. A total of 180 samples of black tea were taken at a variety of periods during the fermentation process, and their near-infrared spectra and images were measured. After the analyses of the changes in tea polyphenol and catechin contents measured by ultraviolet spectrophotometry and high-performance liquid chromatography, the fermentation degree for black tea was divided into three stages. Discrimination models based on spectra, images and their data fusion were established through linear discriminant analysis (LDA), random forest (RF) and support vector machine (SVM). Among them, the discrimination model established by successive projections algorithm (SPA) extraction of spectral variables and Pearson correlation analysis extraction of image variables obtained satisfactory results with 100.00% and 95.00% accuracies of the calibration set and prediction set, respectively. The study demonstrated that the middle-level data fusion of near-infrared spectroscopy and machine vision could be employed as a rapid and nondestructive technique to discriminate the fermentation degree of black tea.

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Acknowledgements

The authors wish to thank the Open Project of Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation and Utilization (2020KF02), Guangzhou Science and Technology Program Project (202002020079), and Guangdong Province Modern Agricultural Industry Technology System Innovation Team Construction Project with Agricultural Products as the Unit (Tea) (2023KJ120). Qinghua City Science and Technology Plan Project (2022KJJH065).

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Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation and Utilization, Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China

    Bai Zhang, Qiaoyi Zhou & Caijin Ling

  2. Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology, School of Mechanical Engineering, Jiangnan University, Wuxi, 214122, China

    Bai Zhang, Zhenfeng Li, Feihu Song, Guangyuan Jin & Chunfang Song

  3. Jiangsu Province International Joint Laboratory on Fresh Food Smart Processing and Quality Monitoring, Jiangnan University, Wuxi, 214122, China

    Bai Zhang

  4. Department of Bioresource Engineering, McGill University, 21111 Lakeshore Road, Ste-Anne-de-Bellevue, QC, H9X 3V9, Canada

    Vijaya Raghavan

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  1. Bai Zhang
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Zhang, B., Li, Z., Song, F. et al. Discrimination of black tea fermentation degree based on multi-data fusion of near-infrared spectroscopy and machine vision. Food Measure 17, 4149–4160 (2023). https://doi.org/10.1007/s11694-023-01935-3

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