Amino acids contribute to the nutritional value and quality of black tea. Fermentation is the most important stage of the black tea manufacturing process. In this study, we investigated protein degradation and proteinaceous amino acid metabolism associated with enzymatic reactions during fermentation in the black tea manufacturing process. The results showed that the concentrations of both protein and free amino acids decreased during fermentation. We also confirmed that proteins were broken down into free amino acids by artificially synthesized dipeptide benzyloxycarbonyl glutamyl-tyrosine (Z-Glu-Tyr). Metabolites of the amino acid metabolic pathway increased significantly during fermentation. Furthermore, we confirmed that free amino acids were degraded to volatile compounds in a tracer experiment with the isotope precursor. These results provide information that will help black tea manufacturers improve the quality of black tea.

Photosynthesis is the central energy conversion process for plant metabolism and occurs within mature chloroplasts. Chloroplasts are also the site of various metabolic reactions involving amino acids, lipids, starch, and sulfur, as well as where the production of some hormones takes place. Light is one of the most important environmental factors, acting as an essential energy source for plants, but also as an external signal influencing their growth and development. Plants experience large fluctuations in the intensity and spectral quality of light, and many attempts have been made to improve or modify plant metabolites by treating them with different light qualities (artificial lighting) or intensities. In this review, we discuss how changes in light intensity and wavelength affect the formation of chloroplast-located metabolites in plants.

Accurate bud detection is a prerequisite for automatic tea picking and yield statistics; however, current research suffers from missed detection due to the variety of singleness and false detection under complex backgrounds. Traditional target detection models are mainly based on CNN, but CNN can only achieve the extraction of local feature information, which is a lack of advantages for the accurate identification of targets in complex environments, and Transformer can be a good solution to the problem. Therefore, based on a multi-variety tea bud dataset, this study proposes RT-DETR-Tea, an improved object detection model under the real-time detection Transformer (RT-DETR) framework. This model uses cascaded group attention to replace the multi-head self-attention (MHSA) mechanism in the attention-based intra-scale feature interaction (AIFI) module, effectively optimizing deep features and enriching the semantic information of features. The original cross-scale feature-fusion module (CCFM) mechanism is improved to establish the gather-and-distribute-Tea (GD-Tea) mechanism for multi-level feature fusion, which can effectively fuse low-level and high-level semantic information and large and small tea bud features in natural environments. The submodule of DilatedReparamBlock in UniRepLKNet was employed to improve RepC3 to achieve an efficient fusion of tea bud feature information and ensure the accuracy of the detection head. Ablation experiments show that the precision and mean average precision of the proposed RT-DETR-Tea model are 96.1% and 79.7%, respectively, which are increased by 5.2% and 2.4% compared to those of the original model, indicating the model’s effectiveness. The model also shows good detection performance on the newly constructed tea bud dataset. Compared with other detection algorithms, the improved RT-DETR-Tea model demonstrates superior tea bud detection performance, providing effective technical support for smart tea garden management and production.

‘Dancong’ tea is a famous traditional Oolong tea. In order to keep the original taste of “ancient tea trees”, most of the ‘Dancong’ tea plants are planted in a single plant pattern without pruning. The objective of this study was to explore the effects of long-term non-pruning on main quality constituents in ‘Dancong’ tea. The results showed that the contents of free amino acids, chlorophylls, and floral-honey aromatic substances in tea leaves of unpruned tea plants were higher than those in every year pruned tea plants, while the catechin content in leaves of pruned tea plants was higher than that in leaves of unpruned tea plants. Quantitative proteomics analysis showed that most enzymes involved in biosynthesis of catechins were downregulated in leaves of unpruned tea plants. Five proteins involved in chlorophyll metabolism and 12 proteins related to photosynthesis were upregulated, and the results suggested that higher chlorophyll content and more efficient photosynthetic energy conversion may be important for the higher accumulation of special quality components in leaves of unpruned tea plants. The findings of this study will advance our understanding of the mechanism of formation of different metabolites in leaves of unpruned and pruned tea plants.

Salicylic acid (SA) is a well-known immune-related hormone that has been well studied in model plants. However, less attention has been paid to the presence of SA and its derivatives in economic plants, such as tea plants (Camellia sinensis). This study showed that tea plants were rich in SA and responded differently to different pathogens. Feeding experiments in tea tissues further confirmed the transformation of SA into salicylic acid 2-O-β-glucoside (SAG) and methyl salicylate. Nonaqueous fractionation techniques confirmed that SA and SAG were mostly distributed in the cytosol of tea leaves, consistent with distributions in other plant species. Furthermore, the stem epidermis contained more SA than the stem core both in C. sinensis cv. “Jinxuan” (small-leaf species) and “Yinghong No. 9” (large-leaf species). Compared with cv. “Yinghong No. 9”, cv. “Jinxuan” contained more SAG in the stem epidermis, which might explain its lower incidence rate of wilt disease. This information will improve understanding of SA occurrence in tea plants and provide a basis for investigating the relationship between SA and disease resistance in tea plants.

This study addresses two key challenges in tea nutrient analysis: the limited range of detectable nutrient elements in tea gardens and the interference caused by moisture in fresh tea leaves during spectral data acquisition. To overcome these issues, hyperspectral technology combined with effective spectral intelligent processing algorithms was used to develop quantitative, non-destructive prediction models for four essential nutrients—nitrogen, phosphorus, potassium and carbon—in fresh tea leaves. This study utilises EPO to address the issue of moisture interference in the spectra of fresh tea leaves, and combines it with SG for spectral data processing, namely SG-EPO. Through comparative analysis with traditional pre-processing algorithms, this method was found to effectively reduce moisture interference in fresh tea leaves and enhance prediction accuracy (R²). Finally, based on a multi-stage feature selection strategy involving SG-EPO-VCPA-IRIV-SVM_RFE and SG-EPO-BOSS-SVM_RFE, and in combination with three machine learning models—XGBoost, BP and SVR—quantitative prediction models were developed. The results indicated that the R² for nitrogen is 0.896; phosphorus, 0.954; potassium, 0.913; and carbon, 0.928. The RMSEP values for N, P, K, and C were 0.062, 0.075, 0.438, and 0.157, respectively. Using this model, nitrogen, phosphorus, potassium and carbon contents were rapidly predicted in tea leaves after the exogenous application of GABA at different concentrations, enabling an assessment of the effects of exogenous GABA application on these nutrient levels. Furthermore, the Shapley Additive Explanation method was employed to identify the feature wavelengths that had the greatest contribution to the XGBoost model, effectively explaining the information underlying the improved model predictions regarding the correlation between spectral and chemical values. Finally, the accuracy of the predictions was confirmed using 20 samples from independent data, demonstrating that the proposed model can achieve rapid, non-destructive detection of multiple nutrient elements in fresh tea leaves under in situ conditions in tea plantations.