Dark tea: A popular beverage with possible medicinal application

Effects of solid-state fermentation with Aspergillus cristatus (MK346334) on the dynamics changes in the chemical and flavor profile of dark tea by HS-SPME-GC-MS, HS-GC-IMS and electronic nose

Aspergillus cristatus, the predominant microbe of Fuzhuan brick tea (FBT), is responsible for the creation of distinctive golden flower and unique floral aroma of FBT. The present study examined the alterations in chemical and aromatic components of raw dark tea by solid-state fermentation using A. cristatus (MK346334), the strain isolated from FBT. As results, catechins, total ployphenols, total flavonoids, theaflavins, thearubigins and antioxidant activity were significantly reduced after fermentation. Moreover, 112 and 76 volatile substances were identified by HS-SPME-GC-MS and HS-GC-IMS, respectively, primarily composed of alcohols, ketones, esters and aldehydes. Furthermore, the calculation of odor activity values revealed that 19 volatile chemicals, including hexanal, heptanal, linalool and methyl salicylate, were the main contributors to the floral, fungal, woody and minty aroma of dark tea. The present research highlights the pivotal role played by the fermentation with A. cristatus in the chemical composition, antioxidant property and distinctive flavor of dark tea.

The utilization of a data fusion approach to investigate fingerprint profiles of dark tea from China's different altitudes

Dark tea refers to a kind of post-fermented product, and its quality and price vary owing to the distinct altitudes at which it grows. In this study, a novel method based on high performance liquid chromatography with a diode-array detector (HPLC-DAD) and an evaporative light scattering detector (HPLC-ELSD) was proposed for the classification of dark teas from distinct altitudes in China. Through implementing a strategy fusing feature-level data to construct a combined dataset, the classification performance of dark teas from distinct altitudes in China was evaluated after preprocessing. The results suggested that, through the feature fusion strategy, the identification accuracy rate increased from <70% of a single detector to 76.923%. After the implementation of preprocessing, the identification accuracy rate was further improved. Typically, the model identification accuracy rate after short-time Fourier Transform (STFT) treatment reached 92.85%, and the AUROC value was higher than 0.84, exhibiting a favorable generalization ability. This study provides a new thinking for the identification technology of dark teas from different altitudes in China.

Pesticide residue detection technology for herbal medicine: current status, challenges, and prospects

The domains of cancer therapy, disease prevention, and health care greatly benefit from the use of herbal medicine. Herbal medicine has become the mainstay of developing characteristic agriculture in the planting area increasing year by year. One of the most significant factors in affecting the quality of herbal medicines is the pesticide residue problem caused by pesticide abuse during the cultivation of herbal medicines. It is urgent to solve the problem of detecting pesticide residues in herbal medicines efficiently and rapidly. In this review, we provide a comprehensive description of the various methods used for pesticide residue testing, including optical detection, the enzyme inhibition rate method, molecular detection methods, enzyme immunoassays, lateral immunochromatographic, nanoparticle-based detection methods, colorimetric immunosensor, chemiluminescence immunosensor, smartphone-based immunosensor, etc. On this basis, we systematically analyze the mechanisms and some of the findings of the above detection strategies and discuss the challenges and prospects associated with the development of pesticide residue detection tools.

Possible Mechanisms of Dark Tea in Cancer Prevention and Management: A Comprehensive Review

Tea is one of the most popular drinks in the world. Dark tea is a kind of post-fermented tea with unique sensory characteristics that is produced by the special fermentation of microorganisms. It contains many bioactive substances, such as tea polyphenols, theabrownin, tea polysaccharides, etc., which have been reported to be beneficial to human health. This paper reviewed the latest research on dark tea's potential in preventing and managing cancer, and the mechanisms mainly involved anti-oxidation, anti-inflammation, inhibiting cancer cell proliferation, inducing cancer cell apoptosis, inhibiting tumor metastasis, and regulating intestinal flora. The purpose of this review is to accumulate evidence on the anti-cancer effects of dark tea, the corresponding mechanisms and limitations of dark tea for cancer prevention and management, the future prospects, and demanding questions about dark tea's possible contributions as an anti-cancer adjuvant.

Oolong tea cultivars categorization and germination period classification based on multispectral information

Recognizing and identifying tea plant (Camellia sinensis) cultivar plays a significant role in tea planting and germplasm resource management, particularly for oolong tea. There is a wide range of high-quality oolong tea with diverse varieties of tea plants that are suitable for oolong tea production. The conventional method for identifying and confirming tea cultivars involves visual assessment. Machine learning and computer vision-based automatic classification methods offer efficient and non-invasive alternatives for rapid categorization. Despite advancements in technology, the identification and classification of tea cultivars still pose a complex challenge. This paper utilized machine learning approaches for classifying 18 oolong tea cultivars based on 27 multispectral characteristics. Then the SVM classification model was executed using three optimization algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimizer (GWO). The results revealed that the SVM model optimized by GWO achieved the best performance, with an average discrimination rate of 99.91%, 93.30% and 92.63% for the training set, test set and validation set, respectively. In addition, based on the multispectral information (h, s, r, b, L, Asm, Var, Hom, Dis, σ, S, G, RVI, DVI, VOG), the germination period of oolong tea cultivars can be completely evaluated by Fisher discriminant analysis. The study indicated that the practical protection of tea plants through automated and precise classification of oolong tea cultivars and germination periods is feasible by utilizing multispectral imaging system.