Region identification of Xinyang Maojian tea using UHPLC‐Q‐TOF/MS‐based metabolomics coupled with multivariate statistical analyses

Abstract

Xinyang Maojian tea is a kind of famous roasted green tea produced in the middle of China. Ultra‐high performance liquid chromatography‐quadrupole time of flight‐mass spectrometry (UHPLC‐Q‐TOF/MS)‐based metabolomics coupled with multivariate statistical analyses, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), were carried out in XMMJTs collected from Luoshan, Shangcheng, and Shihe Counties, respectively. Additionally, seven catechins, four flavonoids, two purine alkaloids, and gallic acid contents were determined by HPLC. Differential metabolites were selected by p‐value <0.05, and fold change >1.50 or < 0.66 among 745 detected metabolites in metabolomics analysis. The results showed significant (p < 0.05) differences of three catechins including (‐)‐epicatechin, (‐)‐epicatechin gallate, and (‐)‐gallocatechin gallate, four flavonoids (i.e. quercetin, kaempferol, myricetin, and rutin), and theobromine among three various regions, and significant (p < 0.05) differences of (‐)‐epicatechin gallate, (‐)‐epigallocatechin, (+)‐catechin, gallic acid, and kaempferol between Shuchazao and Group cultivar. The HCA showed that, except for two samples (i.e. LS 2 and SH 2) of Shuchazao cultivar clustered together, others could be clustered completely according to production place. The 63 relevant differential metabolites could achieve the purpose of region identification through PCA. Kyoto encyclopedia of genes and genomes (KEGG) metabolic pathway analysis elaborated the impact of geographical origin and tea cultivar on physiological metabolism in tea tree. Practical Application Ultra‐high performance liquid chromatography‐quadrupole time of flight‐mass spectrometry (UHPLC‐Q‐TOF/MS)‐based liquid chromatography‐tendem mass spectrometry (LC‐MS/MS) metabolomics coupled with multivariate statistical analyses, such as principal component analysis (PCA) and hierarchical cluster analysis (HCA), revealed 63 differential metabolites related to production place, which contributed to the region identification of Xinyang Maojian teas.

Discrimination and characterization of the volatile profiles of five Fu brick teas from different manufacturing regions by using HS–SPME/GC–MS and HS–GC–IMS

“…Meanwhile, α-ionone had the maximum OAV value (215.29 ± 10.72) in the GX sample and the minimum OAV value (26.52 ± 5.23) in the SX sample. Previous literature ( Wang et al, 2021 ) stated that the continuous postfermentation of dark tea caused the degradation of carotenoids, leading to the accumulation of α- and β-ionones as demonstrated by our findings. Ionones (including α- and β-forms) were reported to have a remarkable effect on the aroma of teas ( Zhou et al, 2019 ), and the different ionone values of FBT samples could be used to discriminate teas from diverse production regions.…”

Dynamic Changes of Volatile Compounds during the Xinyang Maojian Green Tea Manufacturing at an Industrial Scale

“…A few studies have focused on the volatile compounds of XYMJ green tea [ 14 , 15 ] and the key odorants responsible for the XYMJ green tea have been identified [ 16 , 17 ]. To get a deeper insight into the effect of the single manufacturing process on the XYMJ green tea aroma formation, the key odorants and their dynamic changes during manufacturing processes from tea shoots to drying must be elucidated.…”

Discriminant Analysis of Pu-Erh Tea of Different Raw Materials Based on Phytochemicals Using Chemometrics

“…The reason might be that the new variables generated in the dimensionality reduction process of PCA produced different results. The clusters-B, C, E, F, and G included seven, twenty-one, two, nine, and eleven TTs, respectively, among which clusters-F and G corresponded to the sample distribution in the gray and green circles in Figure 1 a,b, respectively; while other samples in Figure 1 b corresponded to clusters-B, C, and E. In short, PCA and HCA can be used to explain whether teas could be distinguished according to season, year, production place, processing technologies, and category variations [ 32 , 33 , 34 , 35 ]. In this study, the results revealed that phytochemicals were the dominating influence factors in the classification of AT and TT, which could be used as identification indicators of both.…”