Bee-derived products such as bee pollen, bee bread, propolis, and royal jelly represent complex natural matrices containing a diverse range of primary and secondary metabolites with potential nutritional and bioactive properties. Their composition is highly influenced by botanical origin, geographical location, environmental conditions, and processing practices, creating challenges for quality assessment, authentication, and the development of standardized functional food ingredients. Comprehensive metabolomic approaches provide an effective strategy for characterizing this chemical complexity and identifying molecular fingerprint associated with functional properties. Within the Horizon Europe BEE-TECH project, an integrated metabolomics approach is applied to investigate bee products originating from Turkey, Romania, and Germany. Methods-based metabolomics are employed to characterize the metabolic profiles of different bee-derived matrices targeted in BEE-TECH project, including bee pollen, bee bread, propolis, and royal jelly. The generated metabolomic datasets are processed using advanced computational workflows, including peak detection, metabolite annotation, spectral database matching, and multivariate statistical analysis, to identify characteristic metabolic patterns and discriminant features among samples from different geographical origins. The metabolomic profiling strategy enables the detection of a broad spectrum of bioactive metabolites, including phenolic compounds, flavonoids, lipids, amino acids, and other specialized metabolites contributing to the nutritional and functional potential of bee products. Chemometric approaches are applied to evaluate sample variability, identify geographical markers, and establish metabolite signatures related to product quality and authenticity. This research demonstrates the application of advanced metabolomics as a powerful analytical platform for the molecular characterization and valorization of bee-derived resources.
Acknowledgment: Funded by the European Union. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them. Grant agreement No 101236756 (BEE-TECH).