Metabolomics-based approaches on wine authentication: a review with case studies

Wine is a natural product with a unique production method, being considered an art due to its unique features. Due to the singularity of its components and the high production cost, wine adulteration events happen frequently, aiming to achieve higher profits, compromising its authenticity.

By using analytical techniques, such as nuclear magnetic resonance spectroscopy or mass spectrometry, it is possible to acquire large amounts of metabolomics data related to specific metabolites over distinct samples. A number of multivariate statistical and machine learning methods may be applied, with high discriminative power allowing to achieve information with added-value about important features such as cultivar, age and geographic origin, and also to detect possible adulteration events. Nonetheless, metabolomics data analysis still constitutes a challenge, specially over complex matrices, such as wine.

This work entails a comprehensive survey of research work related to metabolomics-based approaches for wine authentication, with particular emphasis on supervised and unsupervised multivariate data analysis.

To illustrate the main tasks and steps of metabolomics data analysis, but also to highlight existing challenges in wine authentication issues, two case studies were performed, using the metabolomics data analysis R package specmine. These cases encompass one published dataset, which is re-analyzed here, and a new dataset of Portuguese and Brazilian wines. In both cases, exploratory data analysis in conjunction with multivariate statistical analysis, including principal component analysis and clustering, were performed. It was possible to discriminate the wines according to their cultivar and geographical origin (in the first case) and age (in the second) based on NMR profiles and metabolite identification.