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Identification of geographical origin of Keemun black tea based on its volatile composition coupled with multivariate statistical analyses
- Source :
- Journal of the science of food and agriculture. 99(9)
- Publication Year :
- 2018
-
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.
- Subjects :
- China
030309 nutrition & dietetics
Solid-phase microextraction
Camellia sinensis
Gas Chromatography-Mass Spectrometry
03 medical and health sciences
0404 agricultural biotechnology
Statistics
Black tea
Solid Phase Microextraction
Mathematics
0303 health sciences
Principal Component Analysis
Volatile Organic Compounds
Nutrition and Dietetics
Geography
Tea
Discriminant Analysis
04 agricultural and veterinary sciences
Composition (combinatorics)
Linear discriminant analysis
040401 food science
Hierarchical clustering
Identification (information)
Principal component analysis
Multivariate Analysis
Multivariate statistical
Agronomy and Crop Science
Food Science
Biotechnology
Subjects
Details
- ISSN :
- 10970010
- Volume :
- 99
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- Journal of the science of food and agriculture
- Accession number :
- edsair.doi.dedup.....b469cddfd868e4a0ecfe47ac9e485c1d