ABSTRACT
In this study, the mechanism of aged oolong tea (AOT) to alleviate colitis was investigated in terms of microbiome, metabolome, and fecal microbiota transplantation (FMT). AOT storage period could alleviate colitis in mice and there were some differences in AOT between storage periods, especially AOT-10. AOT improves UC by modulating oxidative stress and inflammatory factors and upregulating intestinal tight junction protein expression (Occludin, Claudin-1, ZO-1 and MUC2), which is associated with the recovery of gut microbiota. FMT and targeted metabolomics further demonstrate that the anti-inflammatory effects of AOT can reshape the gut microbiota through faecal bacterial transfer. Anti-inflammatory effects are exerted through the stimulation of metabolic pathways associated with amino acid, fatty acid and bile acid metabolites. Importantly, the study identified key bacteria (e.g., Sutterella, Clostridiaceae_Clostridium, Mucispirillum, Oscillospira and Ruminococcus) for the development and remission of inflammation. Conclusively, AOT may have great potential in the future adjuvant treatment of colitis.
ABSTRACT
Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea (n = 579) to produce models to discriminate these origins under different conditions. Continuous wavelet transform (CWT), min-max normalization (Minmax), multiplicative scattering correction (MSC) and standard normal variables (SNV) were used to preprocess the original spectra (OS). The approaches of principal component analysis (PCA), linear discriminant analysis (LDA) and successive projection algorithm (SPA) were used for features extraction. Subsequently, identification models of white tea from different provinces of China (DPC), different districts of Fujian Province (DDFP) and authenticity of Fuding white tea (AFWT) were established by K-nearest neighbors (KNN), random forest (RF) and support vector machine (SVM) algorithms. Among the established models, DPC-CWT-LDA-KNN, DDFP-OS-LDA-KNN and AFWT-OS-LDA-KNN have the best performances, with recognition accuracies of 88.97%, 93.88% and 97.96%, respectively; the area under curve (AUC) values were 0.85, 0.93 and 0.98, respectively. The research revealed that NIRS with machine learning algorithms can be an effective tool for the geographical origin traceability of white tea.
ABSTRACT
Compressed white tea (CWT) is a reprocessed tea of white tea. Long-term storage has greatly changed its aroma characteristics, but the material basis and transformation mechanism of its unique aroma are still unclear. In this study, flavor wheel, headspace gas chromatography ion mobility spectroscopy, chemometrics, and microbiomics were applied to study the flavor evolution and important aroma components during long-term storage of CWT, and core functional bacteria were screened. During long-term storage, the aroma of CWT gradually changed from sweet, fruity and floral to stale flavor, woody and herbal. A total of 56 volatile organic compounds (VOCs) were identified, 54 of which were significantly differences during storage. The alcohols content was the highest during 1-5 years of storage, the esters content was the highest during 7-13 years of storage, and the aldehydes content was the highest during 16 years of storage. Twenty-nine VOCs were identified as important aroma components, which were significantly correlated with 6 aroma sub-attributes (P < 0.05). The functional prediction of bacterial community reminded that bacterial community could participate in the transformation of VOCs during storage of CWT. Twenty-four core functional bacteria were screened, which were significantly associated with 29 VOCs. Finally, 23 characteristic differential VOCs were excavated, which could be used to identify CWT in different storage years. Taken together, these findings provided new insights into the changes in aroma characteristics during storage of CWT and increased the understanding of the mechanism of characteristic aroma formation during storage.
