Search Results (78)

Tianshanhong (TSH), black tea products originating from the Ningde Tianshan Mountain, has gained significant recognition in the market. However, the chemical characteristics contributing to the flavor of TSH have not yet been reported. To systematically investigate the non-volatile and volatile compounds in TSH, four grades of TSH were evaluated using national standard sensory methods, revealing that overall quality improved with higher grades. Based on the detection of ultra-performance liquid chromatography–mass spectrometry (UPLC-MS), the content of ester-type catechins was relatively high and decreased with lower grades. A total of 19 amino acids (AAs) were clustered, among them, three amino acids, L-Theanine (L-Thea), Arg, and GABA, showed highly significant correlations with the refreshing taste of TSH. Notably, the content of Arg had the highest correlation with TSH grade, with a coefficient of 0.976 (p < 0.01). According to gas chromatography mass spectrometry (GC-MS) analysis, a total of 861 kinds of volatile compounds were detected, with 282 identified and aroma-active compounds across grades selected using the PLS model. Methyl salicylate and geraniol were particularly notable, showing strong correlations with TSH grades at 0.975 and 0.987 (p < 0.01), respectively. Our findings show that non-volatile and volatile compounds can rationally grade TSH and help understand its flavor quality. Full article

Tea has become one of the most popular drinks worldwide thanks to its pleasant sensory attributes and diverse health benefits. However, tannin-rich compositions have several negative effects and significantly impact the quality of tea beverages. Among various detannification methods, tannase treatment appears to be the most secure and environmentally friendly strategy. Although numerous microbial tannases have been identified and used in food processing, they are predominantly mesophilic with compromised heat tolerance, which limit their application in high-temperature tea extraction processing. Computer-assisted rational design and site-directed mutagenesis has emerged as a promising strategy in enzyme engineering to improve the thermostability of industrial enzymes. Nevertheless, relevant studies for tannase thermostability improvement remain lacking. In the present study, a novel thermophilic tannase called TanPL1 from marine fungus Penicillium longicatenatum strain SM102 was expressed in the food-grade host Yarrowia lipolytica. After purification and characterization, the thermostability of this enzyme was improved through site-directed mutagenesis guided by computer-aided rational design and molecular dynamics simulations. Then the thermostable mutant MuTanPL1 was applied in green tea processing for both polyphenol extraction and ester catechin hydrolysis. The tannase yield and specific activity values of 166.4 U/mL and 1059.3 U/mg, respectively, were achieved. The optimum pH and temperature of recombinant TanPL1 were determined to be 5.5 and 55 °C, respectively, and the enzyme exhibited high activity toward various gallic acid ester substrates. The site-directed mutagenesis method successfully generated a single-point mutant, MuTanPL1, with significantly enhanced thermostability and a higher optimum temperature of 60 °C. After 2 h of detannification by MuTanPL1, nearly all gallated catechins in green tea infusion were biotransformed. This resulted in a 202.4% and 12.1-fold increase in non-ester catechins and gallic acid levels, respectively. Meanwhile, the quality of the tea infusion was also markedly improved. Sensory evaluation and antioxidant activity assays revealed notable enhancements in these properties, while turbidity was reduced considerably. Additionally, the α-amylase inhibition activity of the tannase-treated tea infusion declined from 50.49% to 8.56%, revealing a significantly lower anti-nutritional effect. These findings suggest that the thermostable tannase MuTanPL1 holds strong application prospects in tea beverage processing. Full article

To explore the taste characteristics and flavor characteristics of Baiyun 0492 white tea, Fuyun 6 was used as a comparison to describe and analyze the taste of the tea by the standard tea sensory evaluation method and quantitative descriptive analysis. A total of 1083 nonvolatile metabolites were identified by ultrahigh-performance liquid chromatography and mass spectrometry (UPLC–MS–MS). N-Feruloyl tyramine, with strong biological activity, may be the specific metabolite of Baiyun 0492 white tea, and 3,4,5-tricaffeoylquinic acid, with a bitter taste, may be the specific metabolite of Fuyun 6 white tea. Most of the different metabolite contents were negatively correlated with the Baiyun 0492 and Fuyun 6 white tea grades. KEGG metabolic pathway analysis showed that Baiyun 0492 and Fuyun 6 white teas had higher enrichment levels of flavonoids, caffeine, amino acids and carbohydrate-related metabolic pathways. Correlation analysis using widely targeted metabolomics and quantitative taste description showed that most flavonoid differences and some amino acid and phenolic acid differences were significantly positively correlated with bitterness and astringency, including quercetin-3-o-rhamnoside (quercetin), L-arginine, 3,4,5-tricaffioylquinic acid, and neochlorogenic acid. Some soluble sugars were positively correlated with sweetness, including D-maltose and D-sucrose, which were the key taste components of Baiyun 0492 and Fuyun 6. These metabolites may be responsible for the taste characteristics of Baiyun 0492, which is characterized as sweet and mellow, while the taste of Fuyun 6 is mainly mellow. In this study, a wide range of targeted metabolomics techniques were used to screen out different metabolites related to the quality of Baiyun 0492 and Fuyun 6 white teas, providing a reference for clarifying the quality characteristics of Baiyun 0492. Full article

Wuyi Shuixian tea, a premium oolong tea known for its complex floral-fruity aroma, exhibits significant quality variations across different grades. This study systematically analyzed the aroma characteristics and key fragrant compounds of four grades (Grand Prize SA, First Prize SB, Outstanding Award SC, and Non-award SD) using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS), odor activity value (OAV) analysis, and multivariate statistical methods. A total of 159 volatile compounds were identified, with similar compound categories but distinct concentration gradients between grades. OAV-splitting analysis (based on OAV ≥ 1 as the threshold for aroma activity) identified β-ionone (fruity), octanal (fatty), and linalool (floral) as core aroma-active contributors, as their OAV values significantly exceeded 10 in awarded grades (SA, SB, SC), indicating dominant roles in sensory perception. Notably, linalool, a floral marker, showed a concentration gradient (SA > SB > SC) and was absent in SD, serving as a critical determinant of grade differentiation. Orthogonal partial least squares-discriminant analysis (OPLS-DA) further distinguished awarded grades (SA, SB, SC) by balanced fruity, floral, and woody notes, while SD lacked floral traits and exhibited burnt aromas. This classification was supported by hierarchical clustering analysis (HCA) of volatile profiles and principal component analysis (PCA). Electronic nose data validated these findings, showing strong correlations between sensor responses (W5S/W2W) and key compounds like hexanal and β-ionone. This study elucidates the molecular basis of aroma-driven quality grading in Wuyi Shuixian tea, providing a scientific framework for optimizing processing techniques and enhancing quality evaluation standards. The integration of chemical profiling with sensory attributes advances precision in tea industry practices, bridging traditional grading with objective analytical metrics. Full article

The evaluation of region-specific aroma characteristics in green tea remains critical for quality control. This study systematically analyzed eight Tongcheng Xiaohua tea samples (standard and premium batches) originating from four distinct regions using sensory analysis, electronic nose (E-nose), headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS), and chemometrics. The E-nose results demonstrated that the volatile characteristics of Tongcheng Xiaohua tea exhibit distinct geographical signatures, confirming the regional specificity of its aroma. HS-SPME-GC-MS identified 66 volatile metabolites across samples, with 18 key odorants (OAV > 1) including linalool, geraniol, (Z)-jasmone, and β-ionone driving aroma profiles. The partial least squares–discriminant analysis (PLS-DA) model, combined with variable importance in projection (VIP) scores and OAV, identified seven compounds that effectively differentiate the origins, among which α-pinene and β-cyclocitral emerged as novel markers imparting unique regional characteristics. Further comparative analysis between standard and premium grades revealed 2-methyl butanal, 3-methyl butanal, and dimethyl sulfide as main differential metabolites. Notably, the influence of geographical origin on metabolite profiles was found to be more significant than batch effects. These findings establish a robust analytical framework for origin traceability, quality standardization, and flavor optimization in tea production, providing valuable insights for the tea industry. Full article

Matcha, a finely powdered green tea, has been cherished in Japan for centuries, used in the traditional tea ceremony and nowadays also valued for its health-promoting properties. Cultivated under shaded conditions to enhance chlorophyll production, which gives the typical vibrant green color, matcha is rich in important bioactive compounds, including caffeine, catechins, and theanine. This study analyzes three matcha grades—ceremonial grade 1 (G1), grade 4 (G4), and food grade (FG)—to assess variations in their metabolite profiles. The Bligh–Dyer method was employed to extract polar and non-polar metabolites from organic and hydroalcoholic phases. High-performance thin-layer chromatography (HPTLC) was used for qualitative metabolite analysis, while nuclear magnetic resonance (NMR) spectroscopy was employed for both qualitative and quantitative analyses. Results reveal a decreasing gradient of amino acids and caffeine from grade 1 to food grade, while other metabolites, such as polyphenols, display an increasing trend. These findings suggest that factors such as harvesting time and leaf maturity significantly influence matcha’s chemical composition, providing a scientific basis for its quality differentiation and potential nutraceutical uses. Full article

Background: An increasing number of total elbow arthroplasty (TEA) procedures are performed in trauma patients every year through a variety of approaches. We have devised the Anconeus-reflected Triceps tongue (ARTT) approach for TEA, which optimizes soft tissue management and implant placement, particularly in post-trauma patients, where extensive scar tissue and/or marked bone deformity hamper joint exposure and carry a risk of component malposition. We describe the ARTT surgical technique, discuss its advantages, and report its preliminary results. Methods: Six consecutive patients with malunion of the articular elbow surfaces with severe soft tissue retraction and multiple previous surgeries underwent TEA using the ARTT approach, which spares the triceps tendon insertion on the olecranon and reflects the anconeus and triceps muscles as one. Results: At a mean follow-up of 29 months, the Mayo Elbow Performance Score had increased from 39 to 95 points, whereas the visual analog score for pain had fallen from 7.5 to 1. None of the patients had insufficiency or secondary detachment of the triceps tendon and all achieved grade 4 or 5 on the Medical Research Council scale. Discussion: The ARTT approach provides enhanced joint exposure, resulting in the preservation of the triceps tendon insertion on the olecranon and enabling earlier active rehabilitation. Our preliminary results indicate that it is a viable alternative to traditional techniques, particularly in post-trauma patients with severe elbow dysfunction, who often suffer from extensive scarring, soft tissue damage, and bone deformity. Full article

Low-grade tea, often underutilized due to its coarse texture and limited bioavailability, represents a significant resource waste. This study systematically investigated the synergistic effects of steam explosion (SE) and superfine grinding on enhancing the structural deconstruction, powder property, instant solubility, and diffusivity of low-grade. SE treatment induced critical physicochemical modifications, including hemicellulose degradation, lignin recondensation, and cellulose crystalline reorganization, which significantly weakened the lignocellulosic matrix. Subsequent superfine grinding via ball milling achieved ultrafine particles, with median diameter D₅₀ = 10.4 ± 0.17 μm, and almost completely destroyed the cell wall by 99.9%. Extraction kinetics revealed that SE-ball milling synergistically accelerated the diffusion behavior of bioactive compounds, reducing equilibrium time by 2~4 times and increasing maximum yields of polysaccharides, polyphenols, caffeine, and water-soluble solids by 9~25% compared to untreated samples. Homogenization combined with 0.08 mg/mL CMC-Na further improved the suspension stability of tea powder and reduced its centrifugal sedimentation to 9.85%. These findings demonstrate a scalable strategy to transform low-grade tea into high-value ingredients with enhanced accessibility and solubility of bioactive compounds, offering promising applications in instant beverages, fortified foods, and nutraceuticals. Full article

This study aimed to thoroughly investigate the quality differences and influencing factors of Dahongpao tea of different grades. Through sensory evaluation, electronic nose analysis, electronic tongue analysis, biochemical component analysis, and HS-SPME-GC-MS, the taste and aroma characteristics of Dahongpao samples of different grades (superfine, first, and second grades) were comprehensively studied. The results showed that there were significant differences in sensory quality, aroma components, and taste components among Dahongpao of different grades. Superfine Dahongpao has a rich aroma and mellow taste, containing a higher content of esters and aromatic hydrocarbons such as benzaldehyde (2-hydroxy-5-methoxy), hexyl benzoate, and cyclohexanecarboxylic acid 2,3-dichlorophenyl ester, which endow it with fruity, floral, and woody characteristics. In contrast, first- and second-grade Dahongpao contain more alkanes, pyrazines, and furans such as benzene (1-ethyl-1-propenyl), dodecane (2,6,10-trimethyl), and pyrazine (2,6-dimethyl), which impart floral, roasted, and nutty flavors. Moreover, superfine Dahongpao has a more bitter and astringent taste, but the bitterness and astringency dissipate more quickly, while the taste of first- and second-grade Dahongpao is relatively bland. These differences provide a scientific basis for the grade classification of Dahongpao tea and offer references for improving tea quality and standardized production. Full article

The conventional hand-picking of tea buds is inefficient and leads to inconsistent quality. Innovations in tea bud identification and automated grading are essential for enhancing industry competitiveness. Key breakthroughs include detection accuracy and lightweight model deployment. Traditional image recognition struggles with variable weather conditions, while high-precision models are often too bulky for mobile applications. This study proposed a lightweight YOLOV5 model, which was tested on three tea types across different weather scenarios. It incorporated a lightweight convolutional network and a compact feature extraction layer, which significantly reduced parameter computation. The model achieved 92.43% precision and 87.25% mean average precision (mAP), weighing only 4.98 MB and improving accuracy by 6.73% and 2.11% while reducing parameters by 2 MB and 141.02 MB compared to YOLOV5n6 and YOLOV5l6. Unlike networks that detected single or dual tea grades, this model offered refined grading with advantages in both precision and size, making it suitable for embedded devices with limited resources. Thus, the YOLOV5n6_MobileNetV3 model enhanced tea bud recognition accuracy and supported intelligent harvesting research and technology. Full article

Objectives: The effects of brain tumors located in the supplementary motor area (SMA) have so far been described mainly in the context of motor and speech disorders. There are few studies that have considered other cognitive domains, so this study aimed to fill this gap by focusing on examining attention and working memory in a population of patients with gliomas in the SMA region. Methods: This study included 50 patients diagnosed with gliomas located in the SMA who have not yet had any treatment and 57 demographically matched healthy individuals. A set of neuropsychological tests was conducted to assess attention and working memory: Digit Span from WAIS-R, Visual Elevator from TEA, Verbal Fluency Test (switching condition), and Color Trails Test (CTT). Results: The analyses showed that patients scored lower in most of the evaluated tests and indicators, namely in Digit Span-forward (t = −2.05; p = 0.022), Digit Span-backward (t = −2.63; p = 0.005), CTT-2 (t = 4.24; p = 0.001), CTT-interference (t = 2.31; p = 0.012), Visual Elevator-time (t = 1.83; p = 0.035), Visual Elevator-accuracy (t = −2.42, p = 0.010), and Verbal Fluency-switching (t = −3.41; p = 0.001). A significant relationship was also demonstrated between the grade of tumor malignancy and the results achieved in some of the neuropsychological tests. The lateralization of the tumor, the size of the lesion, and the presence of epilepsy did not prove to be particularly significant. Conclusions: Due to the significant decline in cognitive performance in terms of attention and working memory, we believe that every patient with a tumor in the SMA should undergo a detailed neuropsychological examination, which will profile their functioning and help tailor the best possible psychological care. Full article

As people attempt to elude the environmental issues associated with synthetic dyes, interest in natural dyes has recently increased significantly. Oolong tea stems act as a common agricultural waste yet offer advantages like high production, low cost, and a stable supply. The objectives of this research are to investigate the potential utilization of oolong tea waste, specifically tea stems, as a natural dye source for the development of organic cotton fabrics with added health benefits. In this study, using the Kubelka–Munk (K/S) value as an indicator, the dyeing process was refined through response surface methodology (RSM) by investigating the pH of the dye solution, temperature, and duration. Accordingly, it was demonstrated that the optimal effect was achieved at a pH value of 7.9, a duration of 80 min, and a temperature of 90 °C. Furthermore, under these conditions, the color fastness and functional performance of dyed organic cotton were compared with and without chitosan as a mordant. The results showed that the organic cotton dyed with oolong tea stem extract not only had a good color fastness grade but also presented excellent antibacterial properties, ultraviolet protection properties, and oxidation resistance. Especially with the assistance of chitosan, the dyed fabric achieved excellent performance above grade 4 in all color fastness test items; moreover, its antibacterial activity against Staphylococcus aureus reached more than 90%, the ultraviolet protection coefficient reached 25.3, and the antioxidant activity exceeded 90%. Consequently, considering environmental concerns, natural dyes extracted from discarded oolong tea stems are promising substitutes for synthetic dyes in the textile sector. Full article

Anxi Tieguanyin belongs to the oolong tea category and is one of the top ten most famous teas in China. In this study, hyperspectral imaging (HSI) technology was combined with chemometric methods to achieve the rapid determination of free amino acid and tea polyphenol contents in Tieguanyin tea. Here, the spectral data of Tieguanyin tea samples of four quality grades were obtained via visible near-infrared hyperspectroscopy in the range of 400–1000 nm, and the free amino acid and tea polyphenol contents of the samples were detected. First derivative (1D), normalization (Nor), and Savitzky–Golay (SG) smoothing were utilized to preprocess the original spectrum. The characteristic wavelengths were extracted via principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and the successive projection algorithm (SPA). The contents of free amino acid and tea polyphenol in Tieguanyin tea were predicted by the back propagation (BP) neural network, partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM). The results revealed that the free amino acid content of the clear-flavoured Tieguanyin was greater than that of the strong-flavoured type, that the tea polyphenol content of the strong-flavoured Tieguanyin was greater than that of the clear-flavoured type, and that the content of the first-grade product was greater than that of the second-grade product. The 1D preprocessing improved the resolution and sensitivity of the spectra. When using CARS, the number of wavelengths for free amino acids and tea polyphenols was reduced to 50 and 70, respectively. The combination of 1D and CARS is conducive to improving the accuracy of late modelling. The 1D-CARS-RF model had the highest accuracy in predicting the free amino acid (R_P² = 0.940, RMSEP = 0.032, and RPD = 4.446) and tea polyphenol contents (R_P² = 0.938, RMSEP = 0.334, and RPD = 4.474). The use of hyperspectral imaging combined with multiple algorithms can be used to achieve the fast and non-destructive prediction of free amino acid and tea polyphenol contents in Tieguanyin tea. Full article

Yunnan Province, China, known for its superior ecological environment and diverse climate conditions, is home to a rich resource of tea-plant varieties. However, the subtle differences in shape, color and size among the fresh leaves of different tea-plant varieties pose significant challenges for their identification and detection. This study proposes an improved YOLOv8 model based on a dataset of fresh leaves from five tea-plant varieties among Yunnan large-leaf tea trees. Dynamic Upsampling replaces the UpSample module in the original YOLOv8, reducing the data volume in the training process. The Efficient Pyramid Squeeze Attention Network is integrated into the backbone of the YOLOv8 network to boost the network’s capability to handle multi-scale spatial information. To improve model performance and reduce the number of redundant features within the network, a Spatial and Channel Reconstruction Convolution module is introduced. Lastly, Inner-SIoU is adopted to reduce network loss and accelerate the convergence of regression. Experimental results indicate that the improved YOLOv8 model achieves precision, recall and an mAP of 88.4%, 89.9% and 94.8%, representing improvements of 7.1%, 3.9% and 3.4% over the original model. This study’s proposed improved YOLOv8 model not only identifies fresh leaves from different tea-plant varieties but also achieves graded recognition, effectively addressing the issues of strong subjectivity in manual identification detection, the long training time of the traditional deep learning model and high hardware cost. It establishes a robust technical foundation for the intelligent and refined harvesting of tea in Yunnan’s tea gardens. Full article

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. Full article