期刊
MOLECULES
卷 23, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/molecules23113013
关键词
tea leaves; multielement; ICP-MS; chemometrics; geographical origin discrimination
资金
- National Natural Science Foundation of China [41463009]
- Innovation Group Major Research Project of Guizhou Province Education Department [KY[2016]024]
- Construction Project of the First-Class Subjects (Ecology) in Guizhou Province [GNYL[2017]007]
- Important and Special Project (Tea) of Guizhou Province Science and Technology Department
- Graduate Innovation Foundation Project of Guizhou Province Education [QJH-YJSCXJH-2018-049]
This study aimed to construct objective and accurate geographical discriminant models for tea leaves based on multielement concentrations in combination with chemometrics tools. Forty mineral elements in 87 tea samples from three growing regions in Guizhou Province (China), namely Meitan and Fenggang (MTFG), Anshun (AS) and Leishan (LS) were analyzed. Chemometrics evaluations were conducted using a one-way analysis of variance (ANOVA), principal component analysis (PCA), linear discriminant analysis (LDA), and orthogonal partial least squares discriminant analysis (OPLS-DA). The results showed that the concentrations of the 28 elements were significantly different among the three regions (p < 0.05). The correct classification rates for the 87 tea samples were 98.9% for LDA and 100% for OPLS-DA. The variable importance in the projection (VIP) values ranged between 1.01-1.73 for 11 elements (Sb, Pb, K, As, S, Bi, U, P, Ca, Na, and Cr), which can be used as important indicators for geographical origin identification of tea samples. In conclusion, multielement analysis coupled with chemometrics can be useful for geographical origin identification of tea leaves.
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