Characterization of Non-Volatile Metabolites of White Peony Tea Make of Camellia sinensis Fu'an-dabaicha from Different Origins

This study aimed to explore the discrimination technology of the origin of small-region white peony tea based on non-volatile metabolite characteristics.

Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was used to determine the composition and content of non-volatile metabolites in white peony tea made of Camellia sinensis Fu'an-dabaicha varieties from three different production areas, including Zhenghe County (ZH), Songxi County (SX), and Fu'an City (FA). Combined with mathematical statistics methods, characteristic compounds were screened, and a discrimination model of the origin of white peony tea was constructed.

After screening and filtering, 219 kinds of non-volatile metabolites were identified in the white peony tea from the three regions. Among them, polyphenol metabolites accounted for the highest proportion in the tea samples from the three production areas, with 30.921%, 29.046%, and 29.110% in Fu'an, Songxi, and Zhenghe, respectively. Analysis of variance showed that polyphenols and amino acids and their derivatives had significant differences among the three production areas (the order of polyphenol content from high to low was FA, SX, and ZH, while the order of amino acid and their derivatives content from high to low was ZH, FA, and SX (P<0.01), while organic acids and nucleotides and their derivatives had no significant differences among the three production areas (P>0.05). Among the 219 metabolites, 158 non-volatile metabolites, such as 5-formylsalicylic acid and quercetin 3-D-galactoside, showed significant or extremely significant differences. The orthogonal partial least squares discriminant analysis (OPLS-DA) model did not show overfitting, and the model validation effect was good. The prediction index and discrimination accuracy of the model were 0.859 and 98.89%, respectively. There were 85 characteristic non-volatile metabolites with variable importance in projection (VIP)≥1. Stepwise linear discriminant analysis (SLDA) selected 16 metabolites into the model, and the initial and cross-validation discrimination accuracy of the constructed discrimination model were both 100.00%.

A discrimination technology of the origin of small-region white peony tea based on non-volatile metabolite characteristics was established. Both OPLS-DA and SLDA discrimination models can effectively discriminate against the origin of white peony tea based on non-volatile metabolites, which could provide the theoretical references for the traceability and brand building of tea origins.