The moisture content of Yinghong No. 9 tea leaves is an important indicator for their processing. The traditional method used to detect the moisture content of tea leaves is not suitable for large-scale production. To improve the efficiency of tea processing, a moisture content detection system for Yinghong No. 9 tea leaves based on machine vision was developed, and the relationship between the moisture content and the fresh tea leaves was researched. Firstly, nine color features and five texture features of the tea leaves images were extracted, and two different tea leaves databases were constructed based on linear discriminant analysis (LDA) and principal component analysis (PCA). Secondly, two models of moisture prediction for fresh tea leaves were built using a backpropagation (BP) neural network, which were then optimized by particle swarm optimization (PSO) and a genetic algorithm (GA), respectively. After, the two preprocessing methods and the two optimization algorithms were cross-combined to optimize the models for moisture content prediction. Finally, the models above were filtered using segmental analysis for the segmental moisture content prediction. It was verified by experiments that the coefficient of determination (R2) of the combined model of PCA-GA-BP and PCA-PSO-BP was 94.1073%, the RMSE was 1.1490%, and the MAE was 0.9982%. The results of this paper can help in the instantaneous detection of the moisture content of fresh tea leaves during processing, improving the production efficiency of Yinghong No. 9 tea.
Proper postharvest storage preserves horticultural products, including tea, until they can be processed. However, few studies have focused on the physiology of ripening and senescence during postharvest storage, which affects the flavor and quality of tea. In this study, physiological and biochemical indexes of the leaves of tea cultivar ‘Yinghong 9′ preserved at a low temperature and high relative humidity (15–18 °C and 85–95%, PTL) were compared to those of leaves stored at ambient conditions (24 ± 2 °C and relative humidity of 65% ± 5%, UTL). Water content, chromatism, chlorophyll fluorescence, and key metabolites (caffeine, theanine, and catechins) were analyzed over a period of 24 h, and volatilized compounds were determined after 24 h. In addition, the expression of key biosynthesis genes for catechin, caffeine, theanine, and terpene were quantified. The results showed that water content, chromatism, and chlorophyll fluorescence of preserved leaves were more similar to fresh tea leaves than unpreserved tea leaves. After 24 h, the content of aroma volatiles and caffeine significantly increased, while theanine decreased in both groups. Multiple catechin monomers showed distinct changes within 24 h, and EGCG was significantly higher in preserved tea. The expression levels of CsFAS and CsTSI were consistent with the content of farnesene and theanine, respectively, but TCS1 and TCS2 expression did not correlate with caffeine content. Principal component analysis considered results from multiple indexes and suggested that the freshness of PTL was superior to that of UTL. Taken together, preservation conditions in postharvest storage caused a series of physiological and metabolic variations of tea leaves, which were different from those of unpreserved tea leaves. Comprehensive evaluation showed that the preservation conditions used in this study were effective at maintaining the freshness of tea leaves for 2–6 h. This study illustrates the metabolic changes that occur in postharvest tea leaves, which will provide a foundation for improvements to postharvest practices for tea leaves.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.