State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China.
School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China.
J Sci Food Agric. 2019 Jul;99(9):4344-4352. doi: 10.1002/jsfa.9668. Epub 2019 Apr 9.
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.
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.
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.
由于其独特的风味和潜在的健康益处,祁门红茶(KBT)是中国最受欢迎的茶饮料之一。KBT 的地理来源影响其质量和价格。本研究旨在应用顶空固相微萃取方法和气相色谱-质谱联用结合化学计量学分析,对来自五个产地的 KBT 中的挥发性化合物进行分析。
在 61 个 KBT 样品中检测到 31 个峰。采用层次聚类分析、主成分分析(PCA)、k-最近邻(k-NN)和逐步线性判别分析(SLDA)对挥发性馏分进行可视化处理。使用相似性和区别检验比较了无监督统计工具的结果,表明不同的来源可能相关。PCA-k-NN 获得了令人满意的平均识别率(91.7%)和交叉验证预测能力(84.6%)的组合。在所有统计工具中,SLDA 提供了有希望的结果,具有 100%的识别能力和 96.4%的预测能力。
本研究结果表明,挥发性化合物可用作识别 KBT 地理来源的指标。 © 2019 化学工业协会。