~1H-NMR-based Metabolomics For Pu-erh Raw Tea From Four Different Mountain Origins

Plant metabolomics as an important branch of metabonomics research, which has been widely applied in studies on metabolites differences in plant from different species, different orgins area. Pu-erh tea is a kind of important Chinese tea tea. Because of its unique aroma palate combined with a variety of healthy pharmacological effect, Pu-erh tea becomes popular in floks. In this study, Pu-erh raw tea was selected as the research object, using modern analytical technology to analyze its water-soluble constituents for metabolomic research, trying to explore the difference in metabolites levels of Pu-erh raw tea from different mountain origins.At the beginning, analysis of the chemical composition of Pu-erh raw tea extract was achieved using UPLC/Q-TOF-MS under both positive and negative ion mode, respectively. According to retention time, mass spectrum data and reliable literature, water-soluble constituents of Pu-erh raw tea can be identified. By using this method 43 constituents was identified, including primary metabolites of amino acids, organic acids, secondary metabolites of polyphenols, alkaloids, flavonoids and its glycoside. This comprehensive analysis of Pu-erh raw tea’s water-soluble constituents could provide the basis for quality control or further pharmacological studies of Pu-erh raw tea.After understanding the composition of Pu-erh raw tea’s water-soluble constituents, the 1H-NMR metabolic fingerprint analysis method of Pu-erh raw tea’s water-soluble constituents was developed. The method is convenient and quick. Method validation by computing similarity and angle cosine showed that this method has good precision, stability and robustness, which provid a reliable method for further plant metabolomic analysis.For the study of metabolites differences of Pu-erh raw tea from different mountain origins, samples from four different mountains Bada, Jingmai, Nannuo and Yiwu was collected, 10 batches per mountain. Spectra were detected using the developed 1H-NMR method. The data was then processed with hierarchical cluster analysis, principal component analysis, partial least squares discriminant analysis and sparse partial least squares discriminant analysis for pattern recognition analysis.In unsupervised mode, hierarchical cluster analysis and principal component analysis showed that most of the original variables in the raw data have no contribution for classification, the sample can’t be distinguished. In supervised mode, PLS-DA magnify the difference between groups, samples can mainly be distinguished according to their origins, but the model is not ideal enough. In order to remove the variable has no contribution for distinguished and get a better classification, s PLS-DA was used for feature selection of original variables. Results show that s PLS-DA greatly improved the classification performance and accuracy of the traditional PLS-DA, successfully selected variables that have contribution to classification. These variables are mainly from valine, threonine, 3-Caffeoylquinic acid, quinic acid and(-)-Epiafzelechin-3-O-gallate.This study suggests that 1H-NMR combined with s PLS-DA methods has good performance for study on metabolites differences of Pu-erh raw tea from different tea mountain, which laid a foundation for further understanding of Pu-erh tea and provided a new train of thought for quality evaluation of Pu-erh tea.