Revealing novel insights into the enhancement of quality in black tea processing through microbial intervention

Wang, Ailing; Lei, Qingqing; Zhang, Beibei; Wu, Junhai; Fu, Zheyang; He, Jiangfeng; Wang, Yanbo; Wu, Xinying

doi:10.1016/j.fochx.2024.101743

Food Chemistry: X

2024

DOI: 10.1016/j.fochx.2024.101743

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Revealing novel insights into the enhancement of quality in black tea processing through microbial intervention

Ailing Wang,

Qingqing Lei,

Beibei Zhang

et al.

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2024

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Cited by 3 publications

References 36 publications

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Optimization of Fermentation Time, Temperature, and Tea Bed Thickness in CFM to Enhance the Biological Composition of CTC Black Tea

Hoque,

Padhiary,

Prasad

et al. 2024

J. Inst. Eng. India Ser. A

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Optimization of Fermentation Time, Temperature, and Tea Bed Thickness in CFM to Enhance the Biological Composition of CTC Black Tea

Hoque,

Padhiary,

Prasad

et al. 2024

J. Inst. Eng. India Ser. A

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Study on volatile metabolites and microbial community in the fermentation process of black tea (Jiuqu hongmei tea)

Li,

Huang,

Chen

et al. 2024

International Journal of Gastronomy and Food Science

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Research on Rapid Detection Methods of Tea Pigments Content During Rolling of Black Tea Based on Machine Vision Technology

Zou,

Lan,

Jiang

et al. 2024

Foods

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As a crucial stage in the processing of black tea, rolling plays a significant role in both the color transformation and the quality development of the tea. In this process, the production of theaflavins, thearubigins, and theabrownins is a primary factor contributing to the alteration in color of rolled leaves. Herein, tea pigments are selected as the key quality indicators during rolling of black tea, aiming to establish rapid detection methods for them. A machine vision system is employed to extract nine color feature variables from the images of samples subjected to varying rolling times. Then, the tea pigment content in the corresponding samples is determined using a UV-visible spectrophotometer. In the meantime, the correlation between color variables and tea pigments is discussed. Additionally, Z-score and PCA are used to eliminate the magnitude difference and redundant information in original data. Finally, the quantitative prediction models of tea pigments based on the images’ color features are established by using PLSR, SVR, and ELM. The data show that the Z-score–PCA–ELM model has the best prediction effect for tea pigments. The Rp values for the model prediction sets are all over 0.96, and the RPD values are all greater than 3.50. In this study, rapid determination methods for tea pigments during rolling of black tea are established. These methods offer significant technical support for the digital production of black tea.

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Revealing novel insights into the enhancement of quality in black tea processing through microbial intervention

Cited by 3 publications

References 36 publications

Optimization of Fermentation Time, Temperature, and Tea Bed Thickness in CFM to Enhance the Biological Composition of CTC Black Tea

Optimization of Fermentation Time, Temperature, and Tea Bed Thickness in CFM to Enhance the Biological Composition of CTC Black Tea

Study on volatile metabolites and microbial community in the fermentation process of black tea (Jiuqu hongmei tea)

Research on Rapid Detection Methods of Tea Pigments Content During Rolling of Black Tea Based on Machine Vision Technology

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