Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Dong, Chunwang; Yang, Chongshan; Liu, Zhongyuan; Zhang, Rentian; Yan, Peng; An, Ting; Zhao, Yan; Li, Yang

doi:10.3390/s21238051

Cited by 12 publications

(2 citation statements)

References 28 publications

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“…Yang et al established a model to quantitatively predict the main endoplasmic components of Congou black tea under a different fermentation time series [ 25 ]. Dong et al applied hyperspectral technology with the chemometrics method to predict the catechin content of tea leaves at different fermentation times [ 26 ]. In 2013 and 2014, Xie et al realized the real-time detection of the color and water content of tea leaves during drying by using hyperspectral image technology [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Mao

Wang

et al. 2022

Foods

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The withering and fermentation degrees are the key parameters to measure the processing technology of black tea. The traditional methods to judge the degree of withering and fermentation are time-consuming and inefficient. Here, a monitoring model of the biochemical components of tea leaves based on hyperspectral imaging technology was established to quantitatively judge the withering and fermentation degrees of fresh tea leaves. Hyperspectral imaging technology was used to obtain the spectral data during the withering and fermentation of the raw materials. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) are used to select the characteristic bands. Combined with the support vector machine (SVM), random forest (RF), and partial least square (PLS) methods, the monitoring models of the tea polyphenols (TPs), free amino acids (FAA) and caffeine (CAF) contents were established. The results show that: (1) CARS performs the best among the three feature band selection methods, and PLS performs the best among the three machine learning models; (2) the optimal models for predicting the content of the TPs, FAA, and CAF are CARS-PLS, SPA-PLS, and CARS-PLS, respectively, and the coefficient of determination of the prediction set is 0.91, 0.88, and 0.81, respectively; and (3) the best models for quantitatively judging the withering and fermentation degrees are FAA-SPA-PLS and TPs-CARS-PLS, respectively. The model proposed in this study can improve the monitoring efficiency of the biochemical components of tea leaves and provide a basis for the intelligent judgment of the withering and fermentation degrees in the process of black tea processing.

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Section: Introductionmentioning

confidence: 99%

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Mao

Wang

et al. 2022

Foods

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“…Black tea fermentation is the key process in black tea production. Fermentation is mainly the process of catalyzing and oxidizing polyphenols, such as catechins and other polyphenols in tea leaves, by polyphenol oxidase and peroxidase of tea leaves, forming the intermediate product o-quinone and then further polymerizing o-quinone into thearubigins [2]. During the fermentation process, the color of the leaves changes from greenish green to greenish yellow and then to purple-red.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Discrimination and Quality Evaluation of Black Tea Fermentation Quality Using a Homemade Simple Machine Vision System

Yang,

An,

et al. 2023

Fermentation

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Fermentation is a key link in determining the quality and flavor formation of black tea. However, during the actual production, the judgment of black tea fermentation quality mainly relies on the sensory evaluation of the tea maker, which is more subjective and prone to cause inconsistency in tea quality. Traditional testing methods, such as physical and chemical analyses, are time-consuming, laborious, and costly and are unable to meet the needs of the actual production. In this study, a self-developed machine vision system was used to quickly and accurately identify the degree of black tea fermentation by acquiring color and texture information on the surface of fermented leaves. To accurately control the quality of black tea fermentation and to understand the dynamic changes in key endoplasmic components in the fermented leaves, a quantitative prediction model of the key endoplasmic components in the fermentation process of black tea was constructed. The experiments proved that the system achieved 100% accuracy in discriminating the degree of fermentation of black tea, and the prediction accuracy of catechin components and thearubigin content reached more than 0.895. This system overcomes the defects of accurate measurement of multiple sensors coupled together, reduces the detection cost, and optimizes the experimental process. It can meet the needs of online monitoring in actual production.

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Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains

Shi,

Gao,

Song

et al. 2024

Food Chemistry

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Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Cited by 12 publications

References 28 publications

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Real-Time Discrimination and Quality Evaluation of Black Tea Fermentation Quality Using a Homemade Simple Machine Vision System

Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains

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