Prediction of Moisture Content for Congou Black Tea Withering Leaves Using Image Features and Nonlinear Method

Abstract

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 content of withered leaves. First, according to the time sequences using computer visual system collected visible light images of tea leaf surfaces, and color and texture characteristics are extracted through the spatial changes of colors. Then quantitative prediction models for moisture content detection of withered tea leaves was established through linear PLS (Partial Least Squares) and non-linear SVM (Support Vector Machine). The results showed correlation coefficients higher than 0.8 between the water contents and green component mean value (G), lightness component mean value (L*) and uniformity (U), which means that the extracted characteristics have great potential to predict the water contents. The performance parameters as correlation coefficient of prediction set (Rp), root-mean-square error of prediction (RMSEP), and relative standard deviation (RPD) of the SVM prediction model are 0.9314, 0.0411 and 1.8004, respectively. The non-linear modeling method can better describe the quantitative analytical relations between the image and water content. With superior generalization and robustness, the method would provide a new train of thought and theoretical basis for the online water content monitoring technology of automated production of black tea.

Delivery of acetamiprid to tea leaves enabled by porous silica nanoparticles: efficiency, distribution and metabolism of acetamiprid in tea plants

“…In tea, the metabolites can help the tea plants resist abiotic and biotic stresses and ensure the quality of tea [23][24][25][26]. Metabolomics can identify and quantify the metabolites, metabolic pathways and regulatory mechanisms [27,28], and has been widely used in tea research, including to screen fine tea, for tea quality evaluation [29][30][31], to understand how cultivation conditions induce physiological changes [32,33], to study the metabolism of non-biological and biological stress reactions, and to outline the metabolic pathways [34][35][36][37][38][39][40][41][42].…”

Effects of Variety, Season, and Region on Theaflavins Content of Fermented Chinese Congou Black Tea

“…e annual consumption of tea may reach about 2.5 million metric tons (processed leaves), 75% of which is black tea [1]. Congou black tea is a unique variety of Chinese black tea, which is made with careful skill to produce thin, tight strips without breaking the leaves [2]. e tea processing includes withering, rolling, fermenting, and drying.…”

“…Fermentation as a key step that can enhance the activation level of enzymes, promote the oxidative condensation of polyphenols, and produce a strong fragrance, thus forming the special desirable color and avor of black tea ready for drying and packaging. e nal drying will assure that the leaves will produce the expected dark color and strong fragrance when "brewed" with boiling water [2,3]. e content of thea avins is the most important evaluation index of the quality of Congou black tea.…”

Improving black tea quality through optimization of withering conditions using artificial neural network and genetic algorithm

“…The physical and chemical changes during withering are influenced by duration of withering and environmental conditions like air temperature, relative humidity, air flow rate etc. In recent days studies have been reported to correlate degree of withering with textural properties, color of leaves, or machine vision-based image analysis (Liang et al, 2018;Ye et al, 2019;Yu et al, 2019). and aroma of black tea.…”