It is very difficult for humans to distinguish between two kinds of black tea obtained with similar processing technology. In this paper, an electronic tongue was used to discriminate samples of seven different grades of two types of Chinese Congou b...
Journal of the science of food and agriculture
Mar 12, 2019
BACKGROUND: Orthodox tea is known for its distinct aroma and superior quality. However, the oxidation step that is most crucial for developing these attributes needs precise control and is also time consuming. In the present study, a super-atmospheri...
Withering is the first step in the processing of congou black tea. With respect to the deficiency of traditional water content detection methods, a machine vision based NDT (Non Destructive Testing) method was established to detect the moisture conte...
Food research international (Ottawa, Ont.)
Nov 23, 2017
The purpose of this study was to investigate the applicability of green tea seed (GTS) extract as a natural preservative in food. Food preservative ability and mutagenicity studies of GTS extract and identification of antimicrobial compounds from GTS...
Pesticide biochemistry and physiology
Aug 30, 2017
Tea white scab (TWS) is a major disease affecting tea trees in mid-elevation regions and often occurs during rainy seasons with low temperatures. This disease is caused by the fungal pathogen Phoma sp. TWS can infect young stems, tender leaves, and t...
Understanding aroma compounds' changes during Shuixian roasting is vital for scientific guidance. This study used gas chromatography-ion mobility spectrometry (GC-IMS) and two-dimensional gas chromatography-olfactory-mass spectrometry (GC × GC-O-MS) ...
The withering process is a critical stage in developing the aroma profile of black tea. In this study, we presented an eco-friendly cellulose film-based colorimetric sensor array (CSA) for detecting volatile organic compounds (VOCs) and assessing wit...
Journal of the science of food and agriculture
Aug 30, 2025
BACKGROUND: The flavor profile and product quality of white tea, heavily dependent on its place of origin, significantly influence consumers' purchasing decisions. Quantitative adulteration testing for tea origin has encountered challenges due to the...
In this study, we introduce a groundbreaking deep learning (DL) model designed for the precise task of classifying common diseases in tea leaves, leveraging advanced image analysis techniques. Our model is distinguished by its complex multi-layer arc...
Tea diseases can significantly impact crop yield and quality, necessitating accurate and efficient recognition methods. This study presents WaveLiteNet, a lightweight model designed for tea disease recognition, addressing the challenge of inadequate ...
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