High-speed identification system for fresh tea leaves based on phenotypic characteristics utilizing an improved genetic algorithm.

BACKGROUND: High-quality tea requires leaves of similar size and tenderness. The grade of the fresh leaves determines the quality of the tea. The automated classification of fresh tea leaves improves resource utilization and reduces manual picking costs. The present study proposes a method based on an improved genetic algorithm for identifying fresh tea leaves in high-speed parabolic motion using the phenotypic characteristics of the leaves. During parabolic flight, light is transmitted through the tea leaves, and six types of fresh tea leaves can be quickly identified by a camera. RESULTS: The influence of combinations of morphology, color, and custom corner-point morphological features on the classification results were investigated, and the necessary dimensionality of the model was tested. After feature selection and combination, the classification performance of the Naive Bayes, k-nearest neighbor, and support vector machine algorithms were compared. The recognition time of Naive Bayes was the shortest, whereas the accuracy of support vector machine had the best classification accuracy at approximately 97%. The support vector machine algorithm with only three feature dimensions (equivalent diameter, circularity, and skeleton endpoints) can meet production requirements with an accuracy rate reaching 92.5%. The proposed algorithm was tested by using the Swedish leaf and Flavia data sets, on which it achieved accuracies of 99.57% and 99.44%, respectively, demonstrating the flexibility and efficiency of the recognition scheme detailed in the present study. CONCLUSION: This research provides an efficient tea leaves recognition system that can be applied to production lines to reduce manual picking costs. © 2022 Society of Chemical Industry.

Recent Advances in Analytical Methods for Determination of Polyphenols in Tea: A Comprehensive Review.

Polyphenols, the most abundant components in tea, determine the quality and health function of tea. The analysis of polyphenols in tea is a topic of increasing interest. However, the complexity of the tea matrix, the wide variety of teas, and the difference in determination purposes puts forward higher requirements for the detection of tea polyphenols. Many efforts have been made to provide a highly sensitive and selective analytical method for the determination and characterization of tea polyphenols. In order to provide new insight for the further development of polyphenols in tea, in the present review we summarize the recent literature for the detection of tea polyphenols from the perspectives of determining total polyphenols and individual polyphenols in tea. There are a variety of methods for the analysis of total tea polyphenols, which range from the traditional titration method, to the widely used spectrophotometry based on the color reaction of Folin-Ciocalteu, and then to the current electrochemical sensor for rapid on-site detection. Additionally, the application of improved liquid chromatography (LC) and high-resolution mass spectrometry (HRMS) were emphasized for the simultaneous determination of multiple polyphenols and the identification of novel polyphenols. Finally, a brief outline of future development trends are discussed.

Metabolic Flux Enhancement and Transcriptomic Analysis Displayed the Changes of Catechins Following Long-Term Pruning in Tea Trees ( Camellia sinensis).

The tea tree is a perennial woody plant, and pruning is one of the most crucial cultivation measurements for tea plantation management. To date, the relationship between long-term pruning and metabolic flux enhancement in tea trees has not been studied. In this research, 11-year-old pruned tea trees from four different cultivars were randomly selected for transcriptome analysis and characteristic secondary metabolite analysis together with controls. The findings revealed that epigallocatechin gallate (EGCG) accumulation in pruned tea trees was significantly higher than that in unpruned tea trees. SCPL1A expression (encoding a class of serine carboxypeptidase), which has been reported to have a catalytic ability during EGCG biosynthesis, together with LAR, encoding leucoanthocyanidin reductase, was upregulated in the pruned tea trees. Moreover, metabolic flux enhancement and transcriptome analysis revealed low EGCG accumulation in the leaves of unpruned tea trees. Because of the bitter and astringent taste of EGCG, these results provide a certain understanding to the lower bitterness and astringency in teas from "ancient tea trees", growing in the wild with no trimming, than teas produced from pruned plantation trees.