Lingtou Dancong oolong tea is a famous Chinese oolong tea due to its special honey-like aroma. However, little is known about its specific aroma profile and key contributors. Furthermore, whether the aroma characteristics of Lingtou Dancong oolong tea are affected by the environmental conditions at different altitudes is unknown. In this study, the aromas in Lingtou Dancong oolong tea were extracted and analyzed by stir-bar sorptive extraction (SBSE) combined with gas chromatography-olfactometry (GC-O) and GC-mass spectrometry (GC-MS), and the aroma profiles of tea plants grown at different altitudes were compared. We detected 59 odor compounds in Lingtou Dancong oolong tea. Eight compounds with honey and floral odors were identified as key components on the basis of GC-O, GC-MS, odor activity value, and flavor dilution analyses. Differences in the contents of precursor geranyl diphosphate and transcript levels of structural genes were found to be responsible for the differential accumulation of linalool and hotrienol among plants grown at different altitudes. This is the first report on the aroma characteristics and key contributors of Lingtou Dancong oolong tea and their differences, as affected by altitude. These results provide details of the chemical basis of the aroma quality of Lingtou Dancong oolong tea.

Acute alcoholic intoxication (AAI) is a pathological process of multiple system damage caused by a large amount of alcohol, especially in the liver. Although tea extracts alleviate AAI and alcohol-induced liver damage, the mechanisms underlying the protective actions of different types of Chinese tea are unclear. In this study, the AAI mice model was used to explore the functions and mechanisms of six types of tea extract (WEATs) in alleviating AAI. The losing righting reflexes of mice were evaluated to assess the effects of the WEATs on AAI. The levels of the ethanol metabolism enzymes (ADH, ALDH2, CYP2E1), the oxidative stress-related indicators (NRF-2, HO-1, SOD, GSH, CAT, and TG) and the inflammatory factors (TNF-α, iNOS, IL-6, and IL-10) were determined. Black tea and dark tea significantly shortened the sleep time (duration of the loss of righting reflex) and had a good sobering effect. Green tea and oolong tea had the dual effect of prolonging tolerance time (time of losing righting reflex) and shortening sleep time. While white tea had the most significant effect on prolonging tolerance time but with no obvious sobering effect. Black tea, dark tea, and oolong tea significantly up-regulated ADH and ALDH2, and down-regulated CYP2E1. Green tea and white tea significantly increased the levels of Nrf2, GSH, and CAT. Black tea, dark tea and oolong tea markedly increased the levels of HO-1, IL-10, and inhibited TG. Therefore, it is possible that black tea, dark tea and oolong tea reduced AAI by increasing ethanol metabolism, suppressing oxidative stress and inflammation. While green tea was mainly by regulating oxidative stress. White tea may prolong the tolerance time by increasing ethanol metabolism and reducing oxidative stress. Different types of tea have specific chemical compositions and can alleviate AAI. In conclusion, despite variations in the composition and mechanism of action, tea is a potent natural product to alleviate a hangover and protect the liver.

Pest infestations in tea gardens are one of the common issues encountered during tea cultivation. This study introduces an improved YOLOv8 network model for the detection of tea pests to facilitate the rapid and accurate identification of early-stage micro-pests, addressing challenges such as small datasets and the difficulty of extracting phenotypic features of target pests in tea pest detection. Based on the original YOLOv8 network framework, this study adopts the SIoU optimized loss function to enhance the model’s learning ability for pest samples. AKConv is introduced to replace certain network structures, enhancing feature extraction capabilities and reducing the number of model parameters. Vision Transformer with Bi-Level Routing Attention is embedded to provide the model with a more flexible computation allocation and improve its ability to capture target position information. Experimental results show that the improved YOLOv8 network achieves a detection accuracy of 98.16% for tea pest detection, which is a 2.62% improvement over the original YOLOv8 network. Compared with the YOLOv10, YOLOv9, YOLOv7, Faster RCNN, and SSD models, the improved YOLOv8 network has increased the mAP value by 3.12%, 4.34%, 5.44%, 16.54%, and 11.29%, respectively, enabling fast and accurate identification of early-stage micro pests in tea gardens. This study proposes an improved YOLOv8 network model based on deep learning for the detection of micro-pests in tea, providing a viable research method and significant reference for addressing the identification of micro-pests in tea. It offers an effective pathway for the high-quality development of Yunnan’s ecological tea industry and ensures the healthy growth of the tea industry.

To facilitate the realization of automated tea picking and enhance the speed and accuracy of tea leaf grading detection, this study proposes an improved YOLOv8 network for fresh tea leaf grading recognition. This approach integrates a Hierarchical Vision Transformer using Shifted Windows to replace segments of the original YOLOv8’s network architecture, thereby alleviating the computational load of dense image processing tasks and reducing computational expenses. The incorporation of an Efficient Multi-Scale Attention Module with Cross-Spatial Learning serves to attenuate the influence of irrelevant features in complex backgrounds, which in turn, elevates the model’s detection Precision. Additionally, the substitution of the loss function with SIoU facilitates a more rapid model convergence and a more precise pinpointing of defect locations. The empirical findings indicate that the enhanced YOLOv8 algorithm has achieved a marked improvement in metrics such as Precision, Recall, F1, and mAP, with increases of 3.39%, 0.86%, 2.20%, and 2.81% respectively, when juxtaposed with the original YOLOv8 model. Moreover, in external validations, the FPS enhancements over the original YOLOv8, YOLOv5, YOLOX, Faster RCNN, and SSD deep-learning models are 6.75 Hz, 10.84 Hz, 12.79 Hz, 28.24 Hz, and 21.57 Hz, respectively, and the mAP improvements in practical detection are 2.79%, 2.92%, 3.08%, 7.07%, and 3.84% respectively. The refined model not only ensures efficient and accurate tea-grading recognition but also boasts high recognition rates and swift detection capabilities, thereby establishing a foundation for the development of tea-picking robots and tea quality grading devices.

Mellow and thick taste (MTT) is considered to be a typical taste characteristic of high-quality Pu-erh ripe tea. However, the role of polysaccharide conjugates remains unclear. In this study, the infusion of different grades of Pu-erh ripe tea was isolated to fractions by sensory-guided ultrafiltration technology and the key taste substances of MTT in Pu-erh ripe tea were identified and confirmed in the sensory reconstruction experiment. Further separation, purification and structural identification of the polysaccharide conjugates were carried out. Involving in aggregation morphology, the ultrafiltration fraction exhibited obvious MTT than other fractions. The main MTT compound (PRTPS-5), mainly composed of the rhamnose, galactose, arabinose and mannose, had a molecular weight of 22.93 kDa. The main chain of PRTPS-5 comprised α-L-Araf-(1→, →2,4)-α-L-Rhap-(1→, →2)-α-L-Rhap-(1→, α-D-Galp-(1→, →4)-α-D-GalpA-6-OMe-(1→, →4)-α-D-Manp-(1→, →3,6)-β-D-Galp-(1 → and →5)-α-L-Araf-(1 → and contained multiple pectic characteristic peaks. This result had scientific guiding significance for the quality enhancement of Pu-erh ripe tea.