Personalized Nutrition Platforms Driven by Multi-Omics Data and AI for Functional Food Development

The "one-size-fits-all" approach to functional foods is proving insufficient, leading to variable efficacy among consumers. This paper proposes a paradigm shift toward Personalized Nutrition (PN), utilizing Artificial Intelligence (AI) to integrate and analyze complex Multi-Omics data (Genomics, Metabolomics, and Microbiomics) to develop highly targeted and efficacious functional food products. The research gap lies in translating vast 'omics data into actionable, industrial-scale food formulations. This extended abstract explores the architecture of a PN platform that predicts an individual's metabolic response to specific bioactive ingredients (e.g., prebiotics, polyphenols) and guides the development of personalized functional food prototypes designed to optimize specific health markers (e.g., blood sugar control, gut health modulation). This approach moves beyond simple dietary advice to the creation of bespoke food matrices.

کلیدواژه ها:

Personalized Nutrition (PN) ، Functional Foods ، Multi-Omics ، Microbiome ، Artificial Intelligence (AI) ، Bioinformatics ، Targeted Bioactives ، Food Formulation