Search Results (248)

Kombucha is a fermented tea beverage of Asian origin, widely consumed due to its functional properties; yet, it typically lacks sufficient levels of probiotic micro-organisms to be classified as a probiotic product. This review analyzes the occurrence of lactic acid bacteria (LAB) in kombucha, reporting that concentrations rarely exceed 4–5 log CFU/mL and often decline during fermentation or storage. Strategies to enhance probiotic viability, including the use of robust LAB strains and encapsulation technologies, are critically evaluated. Notably, encapsulation using pea and whey protein has been shown to sustain LAB levels above 6 log CFU/mL during fermentation and up to 21 days under refrigerated storage for whey protein. Fortified kombucha beverages with probiotic strains have also been shown to possess enhanced functional and health-promoting benefits compared to traditional control samples. Despite promising approaches, inconsistencies in microbial survival and regulatory constraints remain key challenges. Future research should focus on the optimization of delivery systems for probiotic cultures, identification of kombucha-compatible LAB strains and standardized protocols to validate probiotic efficacy in real-world beverage conditions. Full article

Volatile organic compounds (VOCs) are important for teas’ quality and act as a critical evaluative criterion in teas. The distinctive aromatic profile of tea not only facilitates tea classification but also has potential applications in aroma-driven product innovation. In this review, we summarized the tea aroma from tea classification, VOCs extraction methodologies, and VOCs detection techniques. Moreover, the potential utilization of tea aroma in the future, such as applications in essential oil refinement, food flavor enhancement, and functional fragrance for personal health care, was proposed. Our review will provide a solid foundation for further investigations in tea aroma and offer significant insights into the development and application of tea fragrance. Full article

The molecular identification of valuable genotypes is an important problem of germplasm management. In this study, we evaluated the potential of 11 universal primer pairs for the DNA barcoding of locally derived cultivars of subtropical crops (actinidia, feijoa, citrus, and tea). A total of 47 accessions (elite cultivars, forms, and breeding lines) of these four genera were included in the study. The efficiency of the following universal primers was assessed using Sanger sequencing: ITS-p5/ITS-u4, ITS-p5/ITS-u2, ITS-p3/ITS-u4, 23S,4.5S&5S, 16S, petB/petD, rpl23/rpl2.l, rpl2 intron, rpoC1 intron, trnK intron, and trnE-UUC/trnT-GUU. Among these primers, trnE-UUC/trnT-GUU showed greater intraspecific polymorphisms, while rpl2 intron and 16S displayed the lowest polymorphism levels in all crops. In addition, the 23S,4.5S & 5S, and rpoC1 intron were efficient for intraspecific analysis of tea and actinidia species. Using five efficient chloroplast primers, a total of 22/6 SNPs/InDels were observed in tea accessions, 45/17 SNPs/InDels in actinidia, 23/3 SNPs/InDels in mandarins, and 5/4 SNPs/InDels in feijoa. These results will be useful for the further development of DNA barcodes of related accessions. Full article

Accurate identification of non-food crops underpins food security by clarifying land-use dynamics, promoting sustainable farming, and guiding efficient resource allocation. Proper identification and management maintain the balance between food and non-food cropping, a prerequisite for ecological sustainability and a healthy agricultural economy. Distinguishing large-scale non-food crops—such as oilseed rape, tea, and cotton—remains challenging because their canopy reflectance spectra are similar. This study proposes a novel phenology-aware Vision Transformer Model (PVM) for accurate, large-scale non-food crop classification. PVM incorporates a Phenology-Aware Module (PAM) that fuses multi-source remote-sensing time series with crop-growth calendars. The study area is Hunan Province, China. We collected Sentinel-1 SAR and Sentinel-2 optical imagery (2021–2022) and corresponding ground-truth samples of non-food crops. The model uses a Vision Transformer (ViT) backbone integrated with PAM. PAM dynamically adjusts temporal attention using encoded phenological cues, enabling the network to focus on key growth stages. A parallel Multi-Task Attention Fusion (MTAF) mechanism adaptively combines Sentinel-1 and Sentinel-2 time-series data. The fusion exploits sensor complementarity and mitigates cloud-induced data gaps. The fused spatiotemporal features feed a Transformer-based decoder that performs multi-class semantic segmentation. On the Hunan dataset, PVM achieved an F1-score of 74.84% and an IoU of 61.38%, outperforming MTAF-TST and 2D-U-Net + CLSTM baselines. Cross-regional validation on the Canadian Cropland Dataset confirmed the model’s generalizability, with an F1-score of 71.93% and an IoU of 55.94%. Ablation experiments verified the contribution of each module. Adding PAM raised IoU by 8.3%, whereas including MTAF improved recall by 8.91%. Overall, PVM effectively integrates phenological knowledge with multi-source imagery, delivering accurate and scalable non-food crop classification. Full article

The undertaking of distant hybridization holds paramount significance for the innovation of tea germplasm resources and the cultivation of superior, specialized tea varieties. However, challenges manifest during the process of tea plant distant hybridization breeding, with reproductive barriers impeding the successful acquisition of hybrid progeny; the precise stages at which these barriers occur remain unclear. In this study, utilizing Camellia sinensis cv. Jinxuan as the maternal parent, as well as C. gymnogyna Chang and C. sinensis cv. Yinghong No.9 as the paternal parents, interspecific distant hybridization (DH) and intraspecific hybridization (IH) were conducted. The investigation involved the observation of pollen germination and pollen tube behavior on the stigma, the scrutiny of the developmental dynamics of the ovary post-hybridization, and the examination of the stages and reasons for reproductive disorders during tea tree distant hybridization. The findings indicate that both IH and DH exhibit pre-fertilization barriers. The pre-embryonic development of hybrids obtained from DH is normal, but there is a significant fruit drop during the stage of fruit development. The germination rate of mature seeds obtained from DH is low, and there are pronounced post-fertilization disorders, which are the primary reasons for the difficulty in achieving successful tea plant distant hybridization. An analysis of the genetic variation in phenotypes and chemical components in the progeny after distant hybridization revealed widespread variation and rich genetic diversity. The identification of progeny with a high amino acid and caffeine content holds promise for future production and breeding, providing valuable theoretical references for the selection of parents in the creation of low-caffeine-content tea germplasm resources. Full article

Tea disease detection is of great significance to the tea industry. In order to solve the problems such as mutual occlusion of leaves, light disturbance, and small lesion area under complex background, YOLO-SSM, a tea disease detection model, was proposed in this paper. The model introduces the SSPDConv convolution module in the backbone of YOLOv8 to enhance the global information perception of the model under complex backgrounds; a new ESPPFCSPC module is proposed to replace the original spatial pyramid pool SPPF module, which optimizes the multi-scale feature expression; and the MPDIoU loss function is introduced to optimize the problem that the original CIoU is insensitive to the change of target size, and the positioning ability of small targets is improved. Finally, the map values of 89.7% and 68.5% were obtained on a self-made tea data set and a public tea disease data set, which were improved by 3.9% and 4.3%, respectively, compared with the original benchmark model, and the reasoning speed of the model was 164.3 fps. Experimental results show that the proposed YOLO-SSM algorithm has obvious advantages in accuracy and model complexity and can provide reliable theoretical support for efficient and accurate detection and identification of tea leaf diseases in natural scenes. Full article

To achieve an efficient, non-destructive, and intelligent identification of tea plant seedlings under high-temperature stress, this study proposes an improved YOLOv11 model based on chlorophyll fluorescence imaging technology for intelligent identification. Using tea plant seedlings under varying degrees of high temperature as the research objects, raw fluorescence images were acquired through a chlorophyll fluorescence image acquisition device. The fluorescence parameters obtained by Spearman correlation analysis were found to be the maximum photochemical efficiency (Fv/Fm), and the fluorescence image of this parameter is used to construct the dataset. The YOLOv11 model was improved in the following ways. First, to reduce the number of network parameters and maintain a low computational cost, the lightweight MobileNetV4 network was introduced into the YOLOv11 model as a new backbone network. Second, to achieve efficient feature upsampling, enhance the efficiency and accuracy of feature extraction, and reduce computational redundancy and memory access volume, the EUCB (Efficient Up Convolution Block), iRMB (Inverted Residual Mobile Block), and PConv (Partial Convolution) modules were introduced into the YOLOv11 model. The research results show that the improved YOLOv11-MEIP model has the best performance, with precision, recall, and mAP50 reaching 99.25%, 99.19%, and 99.46%, respectively. Compared with the YOLOv11 model, the improved YOLOv11-MEIP model achieved increases of 4.05%, 7.86%, and 3.42% in precision, recall, and mAP50, respectively. Additionally, the number of model parameters was reduced by 29.45%. This study provides a new intelligent method for the classification of high-temperature stress levels of tea seedlings, as well as state detection and identification, and provides new theoretical support and technical reference for the monitoring and prevention of tea plants and other crops in tea gardens under high temperatures. Full article

Hybrid tea roses (Rosa hybrida) are economically important horticultural crops and highly susceptible to heat stress, which significantly impacts flower quality and yield. As a key member of the heat shock protein (HSP) family, HSP70 protein acts as a molecular chaperone and exhibits diverse abiotic stress response functions in plants. A total of 113 HSP70 transcription factors (TFs) with varying physical and chemical properties were identified in the genome of hybrid tea rose. Phylogenetic analysis showed that the identified TFs could classify into three (I, II, and III) subfamilies, with most members (51 TFs) falling in subfamily II. Wide gene structural variations were observed among the three subfamilies, with group I and II members lacking introns, while group III members only harbored 1~4 exons and introns. Numerous cis-acting elements associated with abiotic stress, hormone response, growth and development responses, as well as light response were detected in the HSP70 gene promoters. In addition, protein interaction networks predicted a wide range of interactions between different hybrid tea rose HSP70 subfamilies. Gene expression analysis revealed that 57 HSP70 genes had strong organ specificity and response to heat stress in the hybrid tea rose plants. Notably, the expression levels of two RhHSP70 genes, RhHSP70-69 and RhHSP70-88, were significantly increased after heat stress, indicating that these two genes might be crucial for plant heat stress response. Subcellular localization of RhHSP70-69 and RhHSP70-88 revealed that their proteins were located in the nucleus. Our results are not only useful for future evaluation of the regulatory roles of RhHSP70 genes in the hybrid tea rose growth and development, but also provides key genes for future molecular breeding of heat tolerant plants. Full article

Tea is one of the most widely consumed beverages globally and a significant dietary source of fluoride. This systematic review aimed to identify and evaluate the factors influencing fluoride concentration in tea infusions. A comprehensive literature search was conducted in March 2025 across PubMed, Scopus, and Web of Science databases, following PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the PICO framework. Eligible studies investigated fluoride release in tea infusions, published in English from the year 2000 onward. Thirty articles met the inclusion criteria, and the risk of bias in the articles was assessed using the Joanna Briggs Institute (JBI) quality checklist. Fluoride concentration in tea infusions varied widely across studies, ranging from 0.008 to over 8 mg/L. Key factors influencing fluoride release included tea type (with black and green teas showing the highest values), leaf form (powdered and bagged teas released more fluoride than loose leaves), brewing time and temperature, water composition, and the presence of additives such as spices. A longer brewing time and higher temperature consistently increased fluoride extraction. Lower pH or water hardness also significantly affected fluoride availability. Regional origin of tea and production methods were additional sources of variation. Fluoride release in tea is influenced by a complex interplay of botanical, environmental, and preparation-related factors. These findings are clinically relevant, particularly for populations at risk of fluoride overexposure. Further standardized research is needed to inform safe consumption guidelines and public health recommendations. Full article

The analysis and identification of the genetic diversity of plant germplasm resources and varieties are crucial for plant breeding. DNA fingerprinting using genomic molecular markers is crucial for precisely identifying germplasm resources. In this study, the SLAF-seq was performed using 47 germplasm resources of the Wanzhou tea plant population and 5 common cultivated varieties from the Yunnan, Sichuan, and Fujian provinces. A total of 7,447,940 SNPs were identified from 1,641,569 SLAF tags with an averaged sequencing depth of 11.73 (Q30 94.93% and GC 41.37%), which were used to analyze the population composition and genetic diversity. Results showed a large degree of genetic diversity and genetic variation among the samples. The cluster analysis showed that the tea plant population was categorized into three groups, indicating that these germplasms could not be fully classified by their geographical origin, and the linkage disequilibrium analysis indicates that the population resources of the XJ production area are more modern in evolution. A total of 371 uniformly distributed SNP loci were selected and successfully employed to construct the first SNP fingerprint and quick response code (QR code) for tea resources in Wanzhou. These findings offer new insights for genotyping, classifying, and identifying germplasm and genetic resources in the breeding of the Wanzhou tea population. Full article

During the harvesting and preparation of tea, it is common for tea to become mixed with some impurities. Eliminating these impurities is essential to improve the quality of famous green tea. At present, this sorting procedure heavily depends on manual efforts, which include high labor intensity, low sorting efficiency, and high sorting costs. In addition, the hardware performance is poor in actual production, and the model is not suitable for deployment. To solve this technical problem in the industry, this article proposes a lightweight algorithm for detecting and sorting impurities in premium green tea in order to improve sorting efficiency and reduce labor intensity. A custom dataset containing four categories of impurities was created. This dataset was employed to evaluate various YOLOv8 models, ultimately leading to the selection of YOLOv8n as the base model. Initially, four loss functions were compared in the experiment, and Focaler_mpdiou was chosen as the final loss function. Subsequently, this loss function was applied to other YOLOv8 models, leading to the selection of YOLOv8m-Focaler_mpdiou as the teacher model. The model was then pruned to achieve a lightweight model at the expense of detection accuracy. Finally, knowledge distillation was applied to enhance its detection performance. Compared to the base model, it showed advancements in P, R, mAP, and FPS by margins of 0.0051, 0.0120, and 0.0094 and an increase of 72.2 FPS, respectively. Simultaneously, it achieved a reduction in computational complexity with GFLOPs decreasing by 2.3 and parameters shrinking by 860350 B. Afterwards, we further demonstrated the model’s generalization ability in black tea samples. This research contributes to the technological foundation for sophisticated impurity classification in tea. Full article

Background: The colour of tea beverages during processing and storage significantly influences their visual quality. However, natural pink tea products are rare. This study investigated the mechanism behind the pink colouration in the mixed infusion of Biluochun (a green tea) and rosemary. Methods: Infusions of Biluochun (B), rosemary (R), and their mixture (BR), brewed with boiling water for 10 min, were analysed using liquid chromatography-mass spectrometry (LC-MS). Additionally, the pH value and tea pigment content were measured. Results: A total of 134 differential metabolites (DEMs) were detected. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that phenylalanine metabolism and tyrosine metabolism pathways were enriched with abundant DEMs. Some amino acids in BR showed degradation. The content of pelargonin, a compound in the anthocyanin biosynthetic pathway, was significantly elevated in BR compared to that in B and R. DEMs related to fatty acid metabolism were at low levels in BR. Other compounds, such as quercetin, caffeate, rosmarinic acid, and isoferulic acid, were also more abundant in BR. No significant differences in pH value and tea pigment content were found among the three infusions. Conclusions: A model of pink colouration formation in BR was proposed based on the results of this study. Some substances in Biluochun and rosemary were released during the brewing process. Tyrosine was converted into p-coumaric acid, which further reacted to form pelargonin. Pelargonin, an orange-red (pH ≈ 5.0) anthocyanin, was the primary contributor to the pink colouration in BR. Additionally, p-coumaric acid formed co-pigments such as quercetin, caffeic acid, rosmarinic acid, and isovaleric acid. These co-pigments stabilised or enhanced the colour of pelargonin through co-pigmentation. The findings provide a theoretical basis for optimising tea processing techniques and improving quality control in beverage production. Full article

This study aims to develop a system combining fluorescence spectroscopy and machine learning through a convolutional neural network (CNN) to identify the origins of various Japanese green teas (Sayama tea, Kakegawa tea, Yame tea, and Chiran tea). Although food origin labeling is important for ensuring consumer quality and safety, ac-curate identification remains a priority for the food industry due to the emergence of problems with false origin labeling. In this study, image data of the fluorescent fingerprints of green teas were collected using fluorescence spectroscopy and analyzed using a CNN model implemented in Python (ver. 3.13.2), TensorFlow (ver. 2.18.0), and Keras (ver. 3.9). The fluorescence of each sample was measured in the range of 250 to 550 nm, highlighting the differences in chemical composition that reflect each region. Using these data, a CNN suitable for image recognition successfully identified the origins of the teas with an average accuracy of 92.83% in 10 trials. For Chiran tea and Yame tea, precision and recall rates of over 95% were achieved, showing clear differences from other regions. In contrast, the classification of Kakegawa and Sayama teas proved challenging due to their similar fluorescence patterns in the 300–350 nm spectral range, corresponding to catechins and polyphenolic compounds. These similarities are presumed to reflect the comparable growing conditions and processing methods characteristic of the two regions. This study shows the potential of this system in food origin identification, suggesting applications in preventing origin fraud and quality control. Future research will aim to extend the system to other regions and foods, enhance data preprocessing to improve accuracy, and develop a versatile identification system. Full article

Nanchuan Dachashu (Camellia nanchuanica), an arboreal tea species from Chongqing, China, exhibits valuable germplasm characteristics and tea production quality. However, the morphological diversity and genetic basis of key traits, such as tree architecture, leaf anatomy, and ovary trichomes, within this natural population remain to be elucidated. In this study, we conducted a survey on 90 wild individuals from this population, with a special focus on ovary trichomes—an important taxonomic trait. Considerable variations were observed in tree architecture, leaf size and shape, and anatomical structures. Through association analysis, we identified the SNP locus Chr9_89939207 to be associated with the glabrous/hairy ovary trait. A KASP marker was subsequently developed based on this locus, which could accurately distinguish between glabrous and hairy ovary individuals of Nanchuan Dachashu, as well as differentiate this species from C. sinensis or other hairy ovary species. The SNP locus Chr9_89939207 resides in the exon of a predicted protein phosphatase 2C (PP2C) gene, CSS0003297, which potentially regulates ovary trichome development in tea plants. These results reveal extensive morphological variation within the Nanchuan Dachashu population, establish a molecular tool for the identification of valuable interspecific hybrids, and provide insights into the breeding and industrial applications of this germplasm. Full article

Pre- and post-milking teat antisepsis is one of the most effective methods for reducing the incidence of mastitis. Plant extracts have proven effective in reducing microorganisms, providing a natural alternative for antisepsis, along with additional benefits for teat health. This study aimed to evaluate the antimicrobial and antiseptic effects of two newly formulated products, used as pre-dipping and post-dipping agents, respectively, based on plant extracts from papain (Carica papaya), aloe vera (Aloe barbadensis), andiroba (Carapa guianensis), copaiba (Copaifera officinalis), tea tree (Melaleuca alternifolia), and barbatimão (Stryphnodendron barbatiman). Minimum inhibitory concentration tests were performed in vitro, along with microbiological analyses of different bacterial groups and in vivo compliance assessments to evaluate the antimicrobial activity of the formulations, as well as the molecular identification of Staphylococcus aureus. The results demonstrated that the products were effective in vitro against the main pathogens that cause mastitis, including Staphylococcus aureus, Streptococcus agalactiae, and Escherichia coli. In the in vivo assays, similar performance was observed between the tested products and the control products, indicating that the plant-extract-based formulations, applied as pre-dipping and post-dipping agents, effectively reduced the microbial load on the teats. The effects were equivalent to those of hydrogen-peroxide- and iodine-based products, being able to reduce the total microbial load by more than 80%. Full article