Multielemental Analysis Associated with Chemometric Techniques for Geographical Origin Discrimination of Tea Leaves (<i>Camelia sinensis</i>) in Guizhou Province, SW China

• Main Authors: Jian Zhang, Ruidong Yang, Rong Chen, Yuncong C. Li, Yishu Peng, Chunlin Liu
• Format: Article
• Language: English
• Published: MDPI AG 2018-11-01
• Series: Molecules
• Subjects: tea leaves; multielement; ICP-MS; chemometrics; geographical origin discrimination
• Online Access: https://www.mdpi.com/1420-3049/23/11/3013

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 (<i>p</i> &lt; 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&#8315;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.