Rapid identification of the aging time of Liupao tea using AI-multimodal fusion sensing technology combined with analysis of tea polysaccharide conjugates.

Identifying the aging time of Liupao Tea (LPT) presents a persistent challenge. We utilized an AI-Multimodal fusion method combining FTIR, E-nose, and E-tongue to discern LPT's aging years. Compared to single-source and two-source fusion methods, the three-source fusion significantly enhanced identifying accuracy across all four machine learning algorithms (Decision tree, Random forest, K-nearest neighbor, and Partial least squares Discriminant Analysis), achieving optimal accuracy of 98-100 %. Physicochemical analysis revealed monotonic variations in tea polysaccharide (TPS) conjugates with aging, observed through SEM imaging as a transition from lamellar to granular TPS conjugate structures. These quality changes were reflected in FTIR spectral characteristics. Two-dimensional correlation spectroscopy (2D-COS) identified sensitive wavelength regions of FTIR from LPT and TPS conjugates, indicating a high similarity in spectral changes between TPS conjugates and LPT with aging years, highlighting the significant role of TPS conjugates variation in LPT quality. Additionally, we established an index for evaluating quality of aging, which is sum of three fingerprint peaks (1029 cm-1, 1635 cm-1, 2920 cm-1) intensities. The index could effectively signify the changes in aging years on macro-scale (R2 = 0.94) and micro-scale (R2 = 0.88). These findings demonstrate FTIR's effectiveness in identifying aging time, providing robust evidence for quality assessment.

Fungal fermentation of Fuzhuan brick tea: A comprehensive evaluation of sensory properties using chemometrics, visible near-infrared spectroscopy, and electronic nose.

Fuzhuan brick tea (FBT) fungal fermentation is a key factor in achieving its unique dark color, aroma, and taste. Therefore, it is essential to develop a rapid and reliable method that could assess its quality during FBT fermentation process. This study focused on using electronic nose (e-nose) and spectroscopy combination with sensory evaluations and physicochemical measurements for building machine learning (ML) models of FBT. The results showed that the fused data achieved 100 % accuracy in classifying the FBT fermentation process. The SPA-MLR method was the best prediction model for FBT quality (R2 = 0.95, RMSEP = 0.07, RPD = 4.23), and the fermentation process was visualized. Where, it was effectively detecting the degree of fermentation relationship with the quality characteristics. In conclusion, the current study's novelty comes from the established real-time method that could sensitively detect the unique post-fermentation quality components based on the integration of spectral, and e-nose and ML approaches.

Matcha green tea targets the gut-liver axis to alleviate obesity and metabolic disorders induced by a high-fat diet.

Obesity induced by a high-fat diet (HFD) is an increasing global health problem, leading to many metabolic syndromes. As the emerging food additive rich in tea polyphenols, theanine, caffeine, and so on, matcha green tea has gained more and more popularity for its outstanding potential in ameliorating metabolic disorders. This study investigated the composition and antioxidant activity of matcha green tea and further explored its effects on gut-liver axis homeostasis in an HFD-induced obese mouse model. Male (7-8 weeks old) C57BL/6J mice were divided into four groups with the following dietary supplementation for 8 weeks: a normal chow diet (NCD), a normal chow diet+1.0% matcha (NCM), a high-fat diet (HFD), and a high-fat diet+1.0% matcha (HFM). The results demonstrated that matcha green tea ameliorated the development of obesity, lipid accumulation, and hepatic steatosis induced by HFD. Subsequently, dietary matcha supplementation restored the alterations in fecal bile acid profile and gut microbial composition. Meanwhile, the levels of mRNA expression in hepatocytes demonstrated that matcha intervention made significant regulatory on the multiple metabolic pathways of hosts involved in glucose, lipid, and bile acid metabolism. These findings present new evidence for matcha green tea as an effective nutritional strategy to mitigate obesity and relevant metabolic disorders through the gut-liver axis.

Matcha Green Tea Alleviates Non-Alcoholic Fatty Liver Disease in High-Fat Diet-Induced Obese Mice by Regulating Lipid Metabolism and Inflammatory Responses.

Lately, matcha green tea has gained popularity as a beverage and food additive. It has proved to be effective in preventing obesity and related metabolic syndromes. However, the underlying mechanisms of its control effects against non-alcoholic fatty liver disease (NAFLD) are complicated and remain elusive. In the present study, we performed an in vivo experiment using male C57BL/6 mice fed with a high-fat diet and simultaneously treated with matcha for six weeks. Serum biochemical parameters, histological changes, lipid accumulation, inflammatory cytokines, and relevant indicators were examined. Dietary supplementation of matcha effectively prevented excessive accumulation of visceral and hepatic lipid, elevated blood glucose, dyslipidemia, abnormal liver function, and steatosis hepatitis. RNA sequencing analyses of differentially expressed genes in liver samples indicated that matcha treatment decreased the activity of lipid droplet-associated proteins and increased the activity of cytochrome P450 enzymes, suggesting improved metabolic capacity and liver function. The current study provided evidence for new dietary strategies based on matcha supplementation to ameliorate lipotoxicity-induced obesity and NALFD.