Issue 34, 2022

Nontargeted metabolomics coupled with multivariate modelling techniques for discrimination of Cyclocarya paliurus (Batal.) Ijinskaja leaves from different geographic altitudes

Abstract

Altitude-associated nutrition-compositional evaluation is critical for quality control and value determination of plants. Herein, an exploratory study was applied to investigate the differences in the metabolites of Cyclocarya paliurus (CP) leaves from different altitudes (200–1000 m) using a UPLC-QTOF-MS-based metabolomics method, employed to create models for discrimination of CP leaves. On the one hand, 70 metabolites exhibiting significant distinctions within various components in different altitude environments were detected and identified, of which majority showed a close connection. High altitude environments with a decrease in temperature accompanied by enhanced UV-B radiation significantly influenced the profile of flavonoids and organic acids. On the other hand, the PLS-DA model (R2 = 0.994 and Q2 = 0.990) with the VIP variable selection method and P-value were selected to characterize fifteen potential differential metabolites. Moreover, the DD-SIMCA model involving the above-mentioned differential compounds showed both good specificity and accuracy of 100%. These results provide guidance for the discrimination of CP leaves from different geographic altitudes, which may be extended to improve the growing conditions of CP leaves.

Graphical abstract: Nontargeted metabolomics coupled with multivariate modelling techniques for discrimination of Cyclocarya paliurus (Batal.) Ijinskaja leaves from different geographic altitudes

Supplementary files

Article information

Article type
Paper
Submitted
20 Ақп. 2022
Accepted
12 Мам. 2022
First published
01 Мау. 2022

Anal. Methods, 2022,14, 3270-3279

Nontargeted metabolomics coupled with multivariate modelling techniques for discrimination of Cyclocarya paliurus (Batal.) Ijinskaja leaves from different geographic altitudes

Y. Li, H. Xi, L. Feng, L. Liang, T. Yang, X. Mao and Y. Wang, Anal. Methods, 2022, 14, 3270 DOI: 10.1039/D2AY00292B

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