Untargeted metabolomics analysis based on LC-IM-QTOF-MS for discriminating geographical origin and vintage of Chinese red wine

Zhaoxiang Wang; Xiaoyi Chen; Qianqian Liu; Lin Zhang; Shuai Liu; Yingyue Su; Yamei Ren; Chunlong Yuan

doi:10.1016/j.foodres.2023.112547

Untargeted metabolomics analysis based on LC-IM-QTOF-MS for discriminating geographical origin and vintage of Chinese red wine

Food Res Int. 2023 Mar:165:112547. doi: 10.1016/j.foodres.2023.112547. Epub 2023 Feb 3.

Authors

Zhaoxiang Wang¹, Xiaoyi Chen¹, Qianqian Liu¹, Lin Zhang¹, Shuai Liu¹, Yingyue Su¹, Yamei Ren², Chunlong Yuan³

Affiliations

¹ College of Enology, Northwest A&F University, Yangling 712100, China.
² College of Food Science and Engineering, Northwest A&F University, Yangling 712100, China. Electronic address: 715189648@qq.com.
³ College of Enology, Northwest A&F University, Yangling 712100, China; Ningxia Helan Mountain's East Foothill Wine Experiment and Demonstration Station of Northwest A&F University, Yongning, Ningxia 750104, China. Electronic address: yuanchl69@nwsuaf.edu.cn.

PMID: 36869536
DOI: 10.1016/j.foodres.2023.112547

Abstract

Identifying wine geographical origin and vintage is vital due to the abundance of fraudulent activity associated with wine mislabeling of region and vintage. In this study, an untargeted metabolomic approach based on liquid chromatography/ion mobility quadrupole time-of-flight mass spectrometry (LC-IM-QTOF-MS) was used to discriminate wine geographical origin and vintage. Wines were well discriminated according to region and vintage with orthogonal partial least squares-discriminant analysis (OPLS-DA). The differential metabolites subsequently were screened by OPLS-DA with pairwise modeling. 42 and 48 compounds in positive and negative ionization modes were screened as differential metabolitesfor the discrimination of different wine regions, and 37 and 35 compounds were screened for wine vintage. Furthermore, new OPLS-DA models were performed using these compounds, and the external verification trial showed excellent practicality with an accuracy over 84.2%. This study indicated that LC-IM-QTOF-MS-based untargeted metabolomics was a feasible tool for wine geographical origin and vintage discrimination.

Keywords: Chemometrics; Geographical origin; LC-IM-QTOF-MS; Untargeted metabolomics; Wine discrimination; Wine vintage.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Alcoholic Beverages
Chromatography, Liquid
Ions
Mass Spectrometry
Wine*

Substances

Ions