Untargeted metabolomics analysis based on LC-IM-QTOF-MS for discriminating geographical origin and vintage of Chinese red wine
Graphical abstract
Introduction
Wine is a complex mixture that consists of thousands of chemical compositions (Sherman et al., 2020). This complexity means that wine is susceptible to adulteration. Especially taking into consideration its economic importance, the abundance of economically interest-driven frauds associated with mislabeling of vintage and geographical origins to counterfeit high-value wines, has become of great concern among the wine industry, researchers, and consumers. Therefore, many studies have been focused on the urgent issue of wine authentication (Pan et al., 2022, Phan and Tomasino, 2021, Ranaweera et al., 2021, Solovyev et al., 2021).
Metabolomics, also generally regarded as “systems biology”, seeks an analytical description of complex biological samples, aims to characterize all the small molecules in such a sample and focuses on the object as a whole for analysis (Nicholson & Lindon, 2008). Up to now, metabolomics has been successfully applied in food, agriculture, medicine and other fields (Oyedeji et al., 2021, Pinu, 2015, Reher et al., 2022, Wurtzel and Kutchan, 2016). The term “wine-omics”, a novel analysis of wine data based on metabolomics, was first proposed by Wohlgemuth (2008) in the journal Nature. With the continuous development of metabolomics, wine metabolomics combined with powerful statistical techniques has been widely applied in wine authentication.
In general, metabolomics can be probably divided into two categories: targeted and untargeted analysis approaches. The targeted analysis is used for the detection or quantification of specific compounds, such as polyphenols, organic acids, carbohydrates, amino acids, mineral elements, aromatic substances, etc (Sun et al., 2021). Combining these characteristic constituents with multivariate statistical techniques proved to be a potential tool for wine authentication. For example, LC–MS determination of 37 anthocyanin derivatives was performed by Zhang et al. (2021) for wine classification according to vintages and aging stages. Wu et al. (2021) verified the geographical origins of red wines imported into China using 16 mineral elements. Nevertheless, it has to be noted that targeted metabolomic approaches are not preferable in food authentication unless the suspected targets are specific markers as the targeted metabolomic method detects only limited compounds at a time (Ballin & Laursen, 2019).
On the contrary, untargeted analyses are therefore gaining territory in food authentication, especially in highly complex authentication issues such as vintage or geographic origin discrimination (Ballin and Laursen, 2019, McGrath et al., 2018). Untargeted analyses, also known as fingerprinting, can simultaneously detect numerous unspecified targets, providing high sensitivity and good resolution. In the last decade, untargeted metabolomic techniques have been widely applied in food authentication (Li et al., 2021, Rubert et al., 2014, Springer et al., 2014). In the early days of metabolomics development, nuclear magnetic resonance (NMR) was the most-used technique for the detection of metabolites in foods (Cassino et al., 2019, Consonni and Cagliani, 2019). For example, a previous study reported that the authenticity, the grape variety, the geographical origin, and the year of the vintage of wines produced in Germany were successfully distinguished by 1H NMR spectroscopy in combination with multivariate data analysis (Mannu et al., 2020, Mannu et al., 2020). In recent years, with the continual development and spread of mass spectrometry (MS), it gradually substituted the use of NMR (Zhou et al., 2019). On the one hand, MS is more suitable for combination with a separation technique such as Liquid chromatography/gas chromatography. On the other hand, for most researchers, developing or improving detection methods matching high-resolution MS (HRMS) equipment is feasible and affordable, which can greatly enhance the capabilities for the identification of unknown metabolites (Lacalle-Bergeron et al., 2021). Therefore, based on the aforementioned facts, the MS-based untargeted metabolomic analysis has been regarded as an attractive method for the discriminative, predictive, informative objectives involving food quality, safety, and authentication (Cavanna et al., 2018, Hu et al., 2022, Sherman et al., 2020, Zhang et al., 2015). However, the information regarding the application of LC-MS-based untargeted metabolomic analysis approaches in the identification and analysis of differential metabolites in Chinese red wines (i) from different geographical origins in the present literature are far from adequate (Pan et al., 2022), (ii) from different vintages have yet to be reported.
Therefore, this study aimed to develop a suitable approach for screening differential metabolites and predicting the vintages and regions of Chinese red wines by combining the untargeted metabolomics based on LC-IM-QTOF-MS with chemometrics. For this purpose, untargeted metabolomic fingerprints of 114 Chinese red wine samples from four famous geographical origins in China with vintages ranging from 2011 to 2019 were investigated. The differences in metabolites among different wine sample groups were revealed by multivariate chemometric techniques. Principal component analysis (PCA) models were initially performed to roughly discriminate wine samples from different origins and vintages. OPLS-DA models were subsequently constructed to screen the differential metabolites. Based on these differential metabolites, effective OPLS-DA models were re-constructed and used successfully for predicting the wine sample geographical origins and vintages. This work indicated that LC-IM-QTOF-MS-based untargeted metabolomics in combination with chemometrics showed strong potential for screening reliable markers and predicting the Chinese red wine geographical origins and vintages.
Section snippets
Chemicals
Acetonitrile, methanol, and formic acid were purchased from Roe Scientific Inc. (Newark, NJ, USA). All of these chemicals and standards were HPLC grade. Ultrapure water was obtained from a Milli-Q water purification system (Millipore, Bedford, MA, USA).
Wine samples
In total, 114 Chinese red wine samples from four famous geographical origins: Qinhuangdao, Hebei (HB); the eastern foot of Helan Mountain, Ningxia (Ningxia), the north foot of Mt. Tianshan, Xinjiang (XJ); and Wuhai, Inner Mongolia (IM), in China
Untargeted metabolomic analysis of wine samples using LC-IM-QTOF-MS
In this study, an advanced LC-IM-QTOF-MS analytical technique was used for the analysis of metabolites in wine samples. In particular, 1863 features were obtained in positive ionization modes (ESI+), and 521 features were obtained in negative ionization modes (ESI–). On account of the analytical characteristics of untargeted metabolomics, more metabolic features used in chemometric analysis produces more reliable results. Furthermore, flavonols and phenolic acids in red wine tend to ionize in
Conclusion
In this study, LC-IM-QTOF-MS-based untargeted metabolomics combined with chemometrics was successfully applied in the discrimination of wine geographical origin and vintage. PCA and OPLS-DA models were performed to explore the fingerprint data acquire from LC-IM-QTOF-MS-based untargeted metabolomics and screen differential metabolites. In total, 42 differential metabolites in the ESI+ model and 48 differential metabolites in the ESI – model were screened for discriminating wine samples from four
CRediT authorship contribution statement
Zhaoxiang Wang: Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft, Writing – review & editing. Xiaoyi Chen: Formal analysis, Methodology, Writing – review & editing. Qianqian Liu: Investigation, Formal analysis, Data curation, Methodology. Lin Zhang: Formal analysis, Data curation, Methodology, Writing – review & editing. Shuai Liu: Formal analysis. Yingyue Su: Formal analysis, Methodology. Yamei Ren: Supervision, Investigation, Writing – review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment
This study was funded by the Ningxia National Key Research and Development Program (No. 2021BEF02016), and the Shaanxi Province Agriculture Key Industry Innovation Chain (Group) (No. 2020ZDLNY05-05).
References (43)
- et al.
Using untargeted metabolomics to profile the changes in roselle (Hibiscus sabdariffa L.) anthocyanins during wine fermentation
Food Chemistry
(2021) - et al.
Untargeted metabolomic analysis using liquid chromatography quadrupole time-of-flight mass spectrometry for non-volatile profiling of wines
Analytica Chimica Acta
(2015) - et al.
To target or not to target? Definitions and nomenclature for targeted versus non-targeted analytical food authentication
Trends in Food Science & Technology
(2019) - et al.
Wine evolution during bottle aging, studied by 1H NMR spectroscopy and multivariate statistical analysis
Food Research International
(2019) - et al.
The scientific challenges in moving from targeted to non-targeted mass spectrometric methods for food fraud analysis: A proposed validation workflow to bring about a harmonized approach
Trends in Food Science & Technology
(2018) - et al.
(1)H NMR metabolomics applied to Bordeaux red wines
Food Chemistry
(2019) - et al.
Integration of lipidomics and metabolomics for the authentication of camellia oil by ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry coupled with chemometrics
Food Chemistry
(2022) - et al.
Chromatography hyphenated to high resolution mass spectrometry in untargeted metabolomics for investigation of food (bio) markers
TrAC Trends in Analytical Chemistry
(2021) - et al.
What are the scientific challenges in moving from targeted to non-targeted methods for food fraud testing and how can they be addressed?–Spectroscopy case study
Trends in Food Science & Technology
(2018) - et al.
Metabolomic approaches for the determination of metabolites from pathogenic microorganisms: A review
Food Research International
(2021)
Untargeted metabolomic analysis of Chinese red wines for geographical origin traceability by UPLC-QTOF-MS coupled with chemometrics
Food Chemistry
1H NMR metabolite fingerprints of grape berry: Comparison of vintage and soil effects in Bordeaux grapevine growing areas
Analytica Chimica Acta
Untargeted lipidomic approach in studying pinot noir wine lipids and predicting wine origin
Food Chemistry
Metabolomics-The new frontier in food safety and quality research
Food Research International
Authentication of the geographical origin of Australian Cabernet Sauvignon wines using spectrofluorometric and multi-element analyses with multivariate statistical modelling
Food Chemistry
Chemical typicality of South American red wines classified according to their volatile and phenolic compounds using multivariate analysis
Food Chemistry
Influence of attenuated reflected solar radiation from the vineyard floor on volatile compounds in Cabernet Sauvignon grapes and wines of the north foot of Mt. Tianshan
Food Research International
Community succession of the grape epidermis microbes of cabernet sauvignon (Vitis vinifera L.) from different regions in China during fruit development
International Journal of Food Microbiology
Origin verification of French red wines using isotope and elemental analyses coupled with chemometrics
Food Chemistry
Discrimination of conventional and organic rice using untargeted LC-MS-based metabolomics
Journal of Cereal Science
A metabolomics approach for authentication of Ophiocordyceps sinensis by liquid chromatography coupled with quadrupole time-of-flight mass spectrometry
Food Research International
Cited by (15)
Untargeted chromatographic methods coupled with chemometric strategies for the analysis of food and related samples
2024, TrAC - Trends in Analytical ChemistryUHPLC-QTOF-MS-based untargeted metabolomic authentication of Chinese red wines according to their grape varieties
2024, Food Research International
- 1
These authors contributed equally to this work.