Search Results (4)

The distribution of moisture content in stacked tea leaves during processing significantly influences tea quality. Visualizing this moisture distribution is crucial for optimizing processing parameters. In this study, we utilized hyperspectral imaging (HSI) technology combined with machine learning algorithms to evaluate the moisture content and its distribution in the stacked tea leaves in West Lake Longjing and Tencha green tea products during the processing flow line. A spectral quantitative determination model was developed, achieving high accuracy (${\mathrm{R}}_{\mathrm{p}}^2$ > 0.940) The model demonstrated strong generalization ability, allowing it to predict moisture content in both types of tea. Through hyperspectral imaging, we visualized moisture distribution in seven key processing steps and observed that moisture content was non-uniform, with the leaf tips and petioles having higher moisture levels than the leaf surface. This study offers a novel solution for real-time moisture monitoring of stacked tea leaves in tea production, ensuring consistent product quality. Future research could focus on refining model transfer techniques and exploring additional tea varieties to further enhance the generalization of the approach. Full article

The quality and flavor of tea leaves are significantly influenced by chemical composition, with the content of free amino acids serving as a key indicator for assessing the quality of Tencha. Accurately and quickly measuring free amino acids during tea processing is crucial for monitoring and optimizing production processes. However, traditional chemical analysis methods are often time-consuming and costly, limiting their application in real-time quality control. Hyperspectral imaging (HSI) has shown significant effectiveness as a component detection tool in various agricultural applications. This study employs VNIR-HSI combined with machine learning algorithms to develop a model for visualizing the total free amino acid content in Tencha samples that have undergone different processing steps on the production line. Four pretreating methods were employed to preprocess the spectra, and partial least squares regression (PLSR) and least squares support vector machine regression (LS–SVR) models were established from the perspectives of individual processes and the entire process. Combining competitive adaptive reweighted sampling (CARS) and variable iterative space shrinkage approach (VISSA) methods for characteristic band selection, specific bands were chosen to predict the amino acid content. By comparing modeling evaluation indicators for each model, the optimal model was identified: the overall model CT+CARS+PLSR, with predictive indicators Rc² = 0.9885, Rp² = 0.9566, RMSEC = 0.0956, RMSEP = 0.1749, RPD = 4.8021, enabling the visualization of total free amino acid content in processed Tencha leaves. Here, we establish a benchmark for machine learning-based HSI, integrating this technology into the tea processing workflow to provide a real-time decision support tool for quality control, offering a novel method for the rapid and accurate prediction of free amino acids during tea processing. This achievement not only provides a scientific basis for the tea processing sector but also opens new avenues for the application of hyperspectral imaging technology in food science. Full article

Shandong matcha has the quality characteristics of bright green color, seaweed-like aroma and strong, fresh and brisk taste. In order to identify the characteristic aroma components and clarify the contribution of the grinding process to the aroma of Shandong matcha. Three grades of Shandong matcha and corresponding tencha material were firstly tested with sensory evaluation, and the volatile components were extracted with headspace solid-phase microextraction (HS-SPME) and solvent-assisted flavor evaporation (SAFE) and analyzed using GC–MS. The sensory evaluation results showed that high-grade matcha (M-GS) had prominent seaweed-like, fresh and roasted notes, whereas medium and low-grade matcha (M-G1, M-G2) were gradually coupled with grassy, fatty and high-fired aromas. GC–MS results showed that in the HS-SPME method, heterocyclic compounds (45.84–65.35%) were the highest in Shandong matcha, followed by terpenoids (7.44–16.92%) and esters (6.91–15.27%), while in the safe method, esters were the highest (12.96–24.99%), followed by terpenoids (10.76–25.09%) and heterocyclic compounds (12.12–17.07%). As a whole, the composition of volatile components between M-G1 and M-G2 is relatively close, and there are more differences in volatile components between them and M-GS. The volatile components unique to M-GS were screened using the odor activity value (OAV) evaluation method, with components such as 3-methyl-2-butene-1-thiol, 3-ethyl-Phenol, 2-thiophenemethanethiol, 2,4-undecadienal, (E,E)-2,6-nonadienal, (E,Z)- being evaluated. There were other differentially volatile components, that is, volatile components that coexist in the three grades of matcha, but with different concentrations and proportions. M-G1 and M-G2 contained more volatile substances with high-fired aroma, such as 2-ethyl-3-methyl-pyrazine, coumarin and 5,6,7,8-tetrahydroquinoxaline. The grinding process not only changes the appearance of tencha, but also increases the content of volatile components of matcha as a whole, enhancing the aroma and flavor characteristics of matcha. In this study, the contents of 24 volatile components in matcha were mainly increased, such as benzene, (2,2-dimethoxyethyl)-, cis-7-decen-1-al, safranal and fenchyl acetate. The dual factors of material tencha and matcha grinding technology are indispensable in forming the differences in aroma and flavor of Shandong matcha at different levels. Full article

Matcha, or powdered green tea (Camellia sinensis) of the Tencha type, is popular all around the world, and its consumption continues to rise. Because of its unique cultivation method, it is rich in phytochemicals and has many health-promoting properties; it contains high concentrations of polyphenols, theanine and chlorophyll. Tea, and by extension matcha, contains numerous minerals, one of which is fluorine. Under physiological conditions, this mineral plays a significant role in hard tissue mineralisation processes. However, even in low concentrations, with prolonged exposure, fluoride can accumulate in the body, leading to a number of harmful effects. The aim of this study was to evaluate, for the first time, the fluoride content of the matcha infusions from different harvests, brewed using water at different temperatures (25 °C, 70 °C, 80 °C and 90 °C). The content of fluoride ions was measured by the potentiometric method. The fluoride content ranged from 3.36 to 4.03 mg/L and was dependent on both the leaf harvest time and brewing temperature. The concentration of this mineral in the dry powder ranged from 118.39 to 121.65 mg/kg. Irrespective of the water temperature or harvest time, matcha was found to have a high fluoride content, with particularly high concentrations being noted in the powder itself. Full article