Advancing dairy science through integrated analytical approaches based on multi-omics and machine learning

Using advanced platforms such as nuclear magnetic resonance spectroscopy, gas chromatography–mass spectrometry, and liquid chromatography–mass spectrometry, metabolomics enables the comprehensive profiling of small molecules in milk, providing insights into its nutritional value, contamination levels, and processing effects. The integration of metabolomics with other omics approaches, such as metagenomics and proteomics, has demonstrated great potential. This multi-omics strategy enhances the understanding of the biochemical complexity underlying milk production and quality, paving the way for innovative research into the interactions between different molecular components in dairy products. Furthermore, combining multi-omics with machine learning (ML) has revolutionized data interpretation by uncovering patterns and correlations within complex data sets. Researchers can effectively predict and classify milk quality attributes, detect adulteration, and authenticate product origin by employing multivariate statistics and ML algorithms. This short review underscores the role of integrated omics approaches in dairy science, illustrating their capacity to enhance practices, ensure quality, and strengthen traceability. • Single-omics fails to capture milk’s complex biochemical networks and pathways. • LC-MS leads metabolomics, offering high sensitivity and broad metabolite coverage. • Multi-omics reveals genetic, microbial, and metabolic drivers of milk quality. • Integrative tools enhance insights into milk’s composition and functional traits. • Machine learning in milk metabolomics is limited but offers future opportunities.