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基于挥发性成分结合多元统计分析鉴定祁门红茶的产地。

Identification of geographical origin of Keemun black tea based on its volatile composition coupled with multivariate statistical analyses.

机构信息

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.

出版信息

J Sci Food Agric. 2019 Jul;99(9):4344-4352. doi: 10.1002/jsfa.9668. Epub 2019 Apr 9.

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. © 2019 Society of Chemical Industry.

摘要

背景

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。

结果

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。

结论

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。

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