Shimao Fang1,2, Jingming Ning1,2, Wen-Jing Huang2, Gang Zhang2, Wei-Wei Deng1,2, Zhengzhu Zhang1,2. 1. State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China. 2. School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China.
Abstract
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.
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.