Multi-omics analysis enhances dairy nutritional quality, safety, and health function understanding

Dairy products are complex matrices with significant variability in nutritional content, safety profiles, and health-promoting bioactives, influenced by species, rearing, and processing. Traditional analytical methods often fall short in comprehensively capturing these intricate details, leading to challenges in ensuring consistent nutritional quality, detecting adulteration, and fully understanding the mechanisms of health functions. A critical gap exists in integrating diverse data types to provide a holistic view, hindering the development of targeted improvements and personalized dairy nutrition strategies.

This comprehensive review systematically evaluated the current applications of multi-omics technologies, including genomics, proteomics, metabolomics, and metagenomics, in dairy research. The authors synthesized findings across three key domains: enhancing nutritional quality, improving safety evaluation, and elucidating health functions. They analyzed how multi-omics approaches contribute to identifying nutrient variations, detecting contaminants, characterizing microbial communities, and understanding the mechanisms of bioactive compounds, also assessing the role of machine learning in these applications.

Multi-omics integration significantly advanced the understanding of dairy products. The review highlighted how these methods reveal nutrient differences influenced by species, rearing practices, and processing techniques, while also identifying specific protein patterns and allergen profiles. For safety, multi-omics constructed unique adulteration detection fingerprints and species-specific peptide markers, improving assessment timeliness and accuracy. The coupling of metagenomics and metabolomics effectively predicted spoilage-related microbial risks, enhancing risk control. Furthermore, multi-omics approaches systematically elucidated the functional mechanisms of bioactive peptides, such as ACE-inhibitory peptides, and clarified the prebiotic effects of functional oligosaccharides. This also helped build intricate interaction networks between dairy components and the gut microbiota. The review noted that machine learning applications enabled origin and shelf-life prediction, alongside the discovery of novel biomarkers.

Multi-omics integration significantly improved the timeliness and accuracy of dairy product safety assessment by identifying specific adulteration fingerprints and species-specific peptide markers.