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BACKGROUND: Non‐volatile compounds play a key role in the quality and price of Keemun black tea (KBT). The non‐volatile compounds in KBT samples from different producing areas normally vary greatly. The development of rapid methods for tracing the geographical origin of KBT is useful. In this study, we develop models for the discrimination of KBT's geographical origin based on non‐volatile compounds. RESULTS: Seventy‐two KBT samples were collected from five towns in Anhui province to determine 13 KBT compounds by high‐performance liquid chromatography (HPLC). Analysis of variance showed that the content of 13 compounds in KBT indicated significant differences (P < 0.05) among five towns. Three multivariate statistical models including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were built to discriminate origin. Principal component analysis effectively extracted three principal components, namely theaflavins, galloylated catechins, and simple catechins. The high sensitivity (64.5%–99.2%) was achieved of SIMCA model. To establish the discriminant functions, six variables (gallic acid, (+)‐catechin, (−)‐epigallocatechin gallate, theaflavin‐3‐gallate, theaflavin‐3,3′‐di‐gallate, and total theaflavins) were chosen from 13 variables, and LDA was applied. This gave a satisfactory overall correct classification rate (94.4%) and cross‐validation rate (88.9%) for KBT samples. CONCLUSION: The results showed that HPLC analysis together with chemometrics is a reliable approach for tracing KBT and guaranteeing its authenticity. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

BACKGROUND Keemun black tea (KBT) is one of the most popular tea beverages in China as a result of its unique flavor and potential health benefits. The geographical origin of KBT influences its quality and price. The present study aimed to apply a head‐space solid phase microextraction approach and gas chromatography–mass spectrometry combined with chemometric analysis to profile the volatile compounds of KBT collected from five production areas. RESULTS: Thirty‐one peaks were detected in 61 KBT samples. Hierarchical cluster analysis, principal component analysis (PCA), k‐nearest neighbor (k‐NN) and stepwise linear discriminant analysis (SLDA) were employed to visualize the volatile fractions. The results of unsupervised statistical tools were compared using a test for similarities and distinctions, which showed that different sources may be associated. A satisfying combination of average recognition (91.7%) and cross‐validation prediction abilities (84.6%) was obtained for the PCA–k‐NN. Among all of the statistical tools, SLDA provided promising results, with 100% recognition and 96.4% prediction ability. CONCLUSION: The results obtained in the present study indicate that the volatile compounds can be used as indicators to identify the geographical origin of KBT. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]