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.

This study aimed to explore the origin discrimination technology for White Peony tea from small regions based on aroma characterization, so as to provide the technical support for tea origin traceability and brand building.

The aroma composition of White Peony tea from the main production areas (Zhenghe, Songxi, and Fu'an) was analyzed using the gas chromatography-mass spectrometry (GC-MS) with a headspace solid-phase microextraction method. A model for identifying the origin of White Peony tea was constructed by integrating mathematical and statistical methods to screen for characteristic compounds.

The aroma compounds in White Peony tea were comprised with aldehydes (24), ketones (23), alcohols (22), esters (14), acids (8), and other compounds (alkanes, alkenes, furans, ethers, anhydrides, phenols, amines, naphthalenes, etc.). The average aroma content in the tea samples was (3 487.93±1 014.04) μg·kg^-1. The average aroma content in samples from Zhenghe County was significantly higher than that in samples from Songxi County and Fu'an City (P<0.01). For samples from Songxi and Fu'an, there was no significant difference. The total content of alcohols and acids in samples were not significantly different across the three regions (P>0.05), while the total content of ketones, aldehydes, and esters in samples from Zhenghe were significantly higher than those in samples from Fu'an and Songxi (P<0.01). Furthermore, 58 aromatic compounds, including hexanol, 1-octen-3-ol, and benzyl alcohol, showed no significant differences among the three regions (P>0.05). The PLS-DA and OPLS-DA models were well-validated with prediction indices of 0.732 and 0.728, respectively, and the discrimination accuracy of them was 98.89% and 96.67%, respectively. Based on two models, 38 and 46 characteristic aroma substances were identified with VIP values ≥1. The stepwise discriminant analysis also proved stable and reliable, screening 20 characteristic aroma compounds with 100% accuracy in both initial and cross-validation. Using combined OPLS-DA, PLS-DA, and stepwise discriminant analysis, 1H-Pyrrole-2-carboxaldehyde, 1-ethyl-, Geranial, Undec-8-enal <cis->, Benzyl alcohol, Cyclohexanol, 2,6-dimethyl-, Hept-5-en-2-one <6-methyl->, Trans-3-Nonen-2-one, Cyclodecanone, Nonanoic acid, Ethyl ester, and Benzyl acetate were identified as the main characteristic aroma compounds in samples from Zhenghe County. Ionone <(E)-, beta-> was the main characteristic aroma compound for samples from Songxi County. The characteristic aroma compounds in samples from Fu'an City were Enanthaldehyde, Hexanol <n->, Nerol, 3-Nonen-5-one, Nonanoic acid, ethyl ester, and Geranyl acetate.

A method for discriminating the origin of White Peony tea from small regions based on aroma characteristics was developed.