Artificial Intelligence in Personalized Functional Food Nutrition

Artificial intelligence and big data management and analytics allow to fingerprint all layers of the agri-food sector from farm to fork/human in a unified omics approach, which encompasses a multispectral analysis within the supply chain from food production to industrial processing and intake by humans in terms of efficient and sustainable agriculture, food safety and public health. Across the food blockchain, a varying number of factors can alter and determine the final quality and composition of functional foods delivered to the population and their safety attributes. In depth, multi-omics analyses of the products during each stage of the agri-food supply chain can yield invaluable information about structure, function, and mapping of food genomes (genomics), the quantification and variability of their protein content in different states and conditions (proteomics), their complete transcript profiling and differential expression levels under different conditions (transcriptomics), the epigenetic effect through post-transcriptional RNA modifications (epitranscriptomics), post-transcriptomic pre-translatomically interference (interferomics), and the identification of specific food derived ingredients. All in all, foodomics encompasses a set of omics approaches for food microbiology analysis, safety assessment, quality control, and nutritional component analysis, will complement the analysis of agri-food blockchain. Artificial intelligence can create and facilitate the framework that seamlessly links agri-food to health via the prism of Big Data and AI, through an interoperable data acquisition, data management and data analysis system between the aforementioned levels of our uni-omics approach, according to the societal and timely conditions monitored, aims for the standardization of good practices and blockchain optimization in terms of food safety. Additionally, based on the populations’ genetic background, personalized guidelines for food compatibility and diet adherence can be designed and discovered. In this special issue, articles are invited that address the management and analyses of multi-omics functional food raw data (molecular level, genotyping, climate conditions, food, health and disease, environmental and societal factors) using efficient AI algorithms. AI in functional nutrition multi-omics is expected to deliver a unified approach by encompassing all previous datasets whilst being capable of mining and deducing highly specialized and personalized practices and advise.

29/05/2025