Metabolomics, the comprehensive analysis of low-molecular-weight metabolites (typically below 1500 DA) in biological systems, relies heavily on mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Each technique has inherent strengths and weaknesses. MS offers high sensitivity and is commonly coupled with chromatography to analyze complex matrices, yet it is destructive, has limited reproducibility, and provides limited structural information. NMR, while less sensitive, is non-destructive and enables structural elucidation and precise quantification. Recent studies increasingly employ data fusion (DF) strategies to combine the complementary information from NMR and MS, aiming to enhance metabolomic analyses. This review summarizes DF methodologies using NMR and MS data in metabolomics studies over the past decade. A comprehensive search of SciFinder, Scopus, and Clarivate Web of Science databases was conducted to analyze fusion techniques, methods, and statistical models. The review emphasizes the growing importance of DF in metabolomics, showing its capacity to provide a more comprehensive view of biochemical processes across diverse biological systems, including clinical, plant, and food matrices.

Wine, as a high-value product, is vulnerable to counterfeiting. To tackle increasingly sophisticated fraud, innovative analytical approaches are required. However, they must undergo rigorous validation. Proton nuclear magnetic resonance spectroscopy (¹H-NMR) is intrinsically quantitative, reproducible, and fast, making it a promising tool for official control. This study presents the development and validation of a standardised and fully automated workflow for the quantification of 20 oenologically relevant compounds, including organic acids, sugars, alcohols, esters, phenolics, and an alkaloid. The method combines optimised sample preparation, external quantification standards, spectrometer calibration, and a dedicated R package (RnmrQuant1D) for fully automated spectral processing, enabling high-throughput and operator-independent analysis. Validation was performed under intermediate precision according to OIV metrological standards, evaluating accuracy, precision, robustness, limits of quantification, and measurement uncertainty. The results demonstrated excellent linearity, trueness, and reproducibility, matching the targeted analytical performance. Measurement uncertainties were estimated both by conventional linear modelling and by a dynamic approach better suited to detection limits. The workflow is easy to implement, requires minimal sample consumption, and substantially reduces operator bias. Beyond validating a robust method, this study provides a framework for harmonised, transferable ¹H-NMR workflows that could support large-scale databases, integration with chemometric models, and ultimately, ¹H-NMR’s recognition as a relevant method for wine authentication and quality control. This work fills a crucial gap in wine analysis by uniting practical application and rigorous methods, enabling broader adoption in control laboratories worldwide.

Le secteur vitivinicole européen génère une production de richesse importante, mais il est confronté à un problème croissant de fraudes. La falsification des vins constitue l’une des fraudes alimentaires les plus anciennes et les plus répandues à l’échelle mondiale. Dès lors, la question de l’authenticité des vins, étroitement liée à celle de la traçabilité, est une problématique majeure. Pour y faire face, il est nécessaire de disposer de méthodes analytiques robustes et fiables. Dans ce contexte, les approches métabolomiques, en particulier par spectroscopie RMN et MS, offrent des perspectives prometteuses pour renforcer le suivi de l’authenticité des vins.L'objectif principal de ces travaux a été de développer et de valider, en vue d’une intégration aux contrôles officiels, des outils d’analyse du vin basés sur des approches de métabolomique par RMN et MS. Une méthode standardisée par RMN-1H pour l’analyse des vins, allant de la préparation des échantillons jusqu’au traitement des résultats, a été développée et validée dans le cadre d’une collaboration interlaboratoires. Cette méthode a été appliquée au suivi œnologique de l’évolution des vins au cours de leur vieillissement. Enfin, une approche par fusion de données a été explorée en intégrant des données de spectrométrie de masse à haute résolution afin de renforcer la traçabilité des vins.