Advances in food metabolomics: Validating NMR-based non-targeted methods and fostering collaborative NMR applications

Food metabolomics has emerged as a powerful tool for characterizing complex food systems, offering a nontargeted highly discriminative approach for detecting authenticity, assessing quality, and ensuring safety across an array of food matrices. By capturing the complete spectral signature of a sample and reducing it to manageable variables, this technique provides an extensive metabolite snapshot that encompasses everything from minor compounds to major constituents. A key advantage lies in the reproducibility and robustness of NMR spectroscopy, allowing the comparison of spectra even across different instruments and laboratories. Such comparability fosters collaborative efforts and facilitates the establishment of large, community-built datasets, which are critical for advancing reliable classification models and enabling wide-scale deployment of non-targeted protocols. Rigor in each step, ranging from selecting representative authentic samples to optimizing acquisition parameters, data processing, and classification algorithms, proves essential for achieving consistent, high-quality metabolomics data. As validation and standardization practices become more widely accepted, NMR-based non-targeted approaches will accelerate innovations in food product monitoring and labeling, reduce analytical uncertainties, and address emerging challenges in food fraud detection. Ultimately, by combining best-in-class protocols, collaborative networks, and open-access data repositories, non-targeted NMR metabolomics has the potential to revolutionize traceability and foster global consumer confidence in the authenticity and quality of the food supply chain.