Through multi-omics integration, multi-omics approaches reveal nutrient differences driven by species, rearing practices, and processing techniques, identify protein patterns and allergen profiles, and construct adulteration detection fingerprints and species-specific peptide markers, thereby improving the timeliness and accuracy of safety assessment.
Abstract
This review evaluates multi-omics applications in dairy research across nutrition, safety, and health. Through multi-omics integration, we reveal nutrient differences driven by species, rearing practices, and processing techniques, identify protein patterns and allergen profiles, and construct adulteration detection fingerprints and species-specific peptide markers, thereby improving the timeliness and accuracy of safety assessment. The coupling of metagenomics and metabolomics effectively predicts spoilage-related microbial risks, enabling better risk control. Furthermore, multi-omics approaches systematically elucidate the functional mechanisms of bioactive peptides (e.g., ACE-inhibitory peptides), clarify the prebiotic effects of functional oligosaccharides, and build interaction networks between dairy components and gut microbiota. The introduction of machine learning enables origin and shelf-life prediction, as well as the discovery of novel biomarkers, promoting personalized nutrition and precision fermentation strategies. However, the field is currently constrained by severe reproducibility issues arising from the absence of standardized operating procedures, excessive optimism regarding machine learning models that rarely generalize across laboratories or product matrices, and a persistent disconnect between laboratory-scale biomarker discovery and industrial implementation. Without rigorous cross-platform validation and openly shared multi-omics reference datasets, most published markers remain unfit for regulatory or industrial application. Future efforts should establish standardized workflows and expand the evidence base to drive the dairy industry toward safer, healthier, and more traceable directions.
Despite the challenges in standardization, cost, and large-scale validation, advances in artificial intelligence, portable analytics, and open access omics data bases are accelerating the translation of these technologies into practical feed innovation.
Mir Ishfaq Nazir, Pankaj Gargotra, O. Asimi et al.· Frontiers in Fish Science· 0 citations
Flavor is a key determinant of food quality, and elucidating its formation mechanisms is of great significance for product optimization and satisfying consumer preferences. Traditional sensory analysis is inherently subjective, while instrumental analysis can objectively assess flavor components but provides limited insight into the formation mechanisms. This article adopts a narrative literature review to summarize the applications of omics technologies in food flavor research in recent years. It focuses on genomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics technologies. It points out that current studies remain largely confined to identifying differential molecular features, lacking in-depth exploration from molecular interactions to causal mechanisms. At the same time, single-omics methods can only provide partial information and cannot capture the multi-level regulatory networks of flavor formation. A causal inference framework for analyzing flavor mechanisms at the omics level through multi-omics integration and intervention experiments has been proposed. It emphasizes that artificial intelligence technology provides a promising solution for the interactive analysis of complex multi-omics datasets. Future research should focus on developing low-cost, high-throughput real-time analysis methods, cross-omics data fusion strategies, and the rigorous exploration of scientific causal relationships.
Yi Xiao, Xiulian Wang, Jia Zhang et al.· Foods· 0 citations
In dairy production, the fermentation process is a complex biochemical system that plays a significant role in determining the quality criteria of the final product. Traditional methods for controlling fermentation rely on limited and non-standard process parameters. In recent years, omics technologies have come to the forefront, enabling the monitoring of fermentation dynamics at the molecular level with their current, efficient, and reliable approaches. Thanks to omics approaches such as metabolomics, metagenomics, proteomics, and lipidomics, starter culture behavior, metabolite formation, aroma–texture formation, and microbial interactions in the fermentation process can be characterized more comprehensively. On the other hand, evaluating or calculating high-dimensional omics data using traditional statistical methods presents a challenge. Artificial intelligence applications are overcoming this challenge, offering significant opportunities for the accurate and reliable evaluation of data. Artificial intelligence-powered models hold promise in areas such as predicting fermentation kinetics, process control, optimizing quality criteria, and revealing the therapeutic potential of products through metabolites. This compilation aims to comprehensively address current approaches to modeling the fermentation process and quality parameters of dairy products using multi-omics technologies and artificial intelligence applications. In this respect, it will provide current and important perspectives for industrial applications and future studies.
Murat Emre Terzioğlu, Zeynep Çağla Tekgül· Fermentation· 0 citations
Dairy products are an important source of high-quality protein, calcium, and various micronutrients in the human diet. The detection of milk components spans the entire industrial chain, including raw milk quality control, processing management, and market supervision. It has evolved from traditional pass/fail judgment toward an integrated approach encompassing risk early warning, quality enhancement, and traceability and authentication. This paper reviews the classification of dairy components targeted for detection and summarizes major technical systems, including conventional physicochemical methods, chromatography and spectroscopy techniques, biosensing and microfluidic rapid detection, as well as multi-omics-based deep profiling. The principles, advantages, and application limitations of each method are compared. Key challenges such as complex matrix interference, lack of reference standards, and standardization of rapid detection methods are analyzed. Future directions toward intelligent, on-site, precise, and panoramic detection are also discussed, aiming to provide technical references for quality and safety control, quality improvement, and high-quality development of the dairy industry.
Food characterization is fundamental to ensuring food quality, safety, authenticity, traceability, and nutritional value in increasingly complex global food systems. Conventional analytical approaches often provide limited information on the molecular complexity of food matrices, necessitating the adoption of advanced, high-throughput technologies. Omics sciences, including genomics, transcriptomics, proteomics, metabolomics, lipidomics, and microbiomics, have emerged as powerful tools for comprehensive food characterization by enabling the large-scale analysis of genes, transcripts, proteins, metabolites, lipids, and microbial communities. The integration of these disciplines within the framework of foodomics has transformed food research from conventional assessments to systems-level investigations of food composition, functionality, and biological effects. This review provides a comprehensive overview of the major omics platforms and their applications in food quality evaluation, authenticity verification, safety monitoring, traceability, and nutritional assessment. Particular emphasis is placed on the role of multi-omics approaches in identifying molecular biomarkers associated with food origin, processing, adulteration, and health-promoting attributes. Advances in analytical technologies, including high-resolution mass spectrometry, nuclear magnetic resonance spectroscopy, and next-generation sequencing, are discussed in the context of foodomics workflows. Furthermore, the integration of chemometrics, machine learning, and artificial intelligence with omics datasets is examined as a means of improving data interpretation, predictive modeling, and real-time decision-making throughout the food supply chain. The review also highlights current challenges related to data integration, standardization, analytical reproducibility, and bioinformatics infrastructure, while outlining emerging opportunities in precision nutrition, digital food systems, and sustainable food production. By synthesizing recent developments across multiple omics disciplines, this review underscores the pivotal role of foodomics and multi-omics strategies in advancing comprehensive food characterization and supporting the development of safer, more authentic, and nutritionally optimized food systems.
Muhammad Suhail Ibrahim, Wajahat Azeem, H. A. Sakandar et al.· Frontiers in Nutrition· 0 citations
Probiotics play an important role in human health, food safety, and industrial applications due to their ability to modulate the gut microbiota and support host physiological functions. Their therapeutic potential has expanded beyond gastrointestinal health to include benefits in metabolic, inflammatory, and infectious diseases. This review synthesizes current evidence on probiotics, their health effects, and the scientific principles underlying their viability and functionality under different processing and storage conditions. Despite their wide-ranging applications, conventional probiotic research and development face important limitations related to experimental speed, predictive accuracy, and the analysis of large and complex biological datasets. These challenges necessitate the integration of advanced artificial intelligence (AI) and machine learning (ML) approaches to accelerate discovery and optimize probiotic performance. Particular focus is placed on applying ML and bioinformatics tools to improve probiotic strain selection, predict survival under environmental stresses, and analyze genomic features associated with probiotic efficacy. Genera such as Lactobacillus and Bifidobacterium have demonstrated clinical benefits in conditions including diarrhea, gastroenteritis, inflammatory bowel diseases, allergic disorders, and metabolic dysfunction, through mechanisms including pathogen inhibition, immune regulation, and enhancement of gut barrier integrity. AI- and ML-based models enable rapid, accurate, and scalable analysis of multidimensional probiotic datasets, overcoming traditional trial-and-error limitations. ML enables accurate prediction of probiotic stability and performance across variable pH, temperature, and formulation environments, while bioinformatics provides deeper insight into strain-level genomic traits and functional pathways. Together, these computational advances contribute to the development of more effective, targeted, and scientifically optimized probiotic interventions.