The agri-food sector is an endless source of expansion for nourishing a vast population, but there is a considerable need to develop high-standard procedures through intelligent and innovative technologies, such as artificial intelligence (AI) and big data. This paper addresses the research concerning AI and big data analytics in the food industry, including machine learning, artificial neural networks (ANNs), and various algorithms. Logistics, supply chain, marketing, and production patterns are covered along with food sub-sector applications for artificial intelligence techniques. It is found that utilization of AI techniques and the intelligent optimization algorithm also leads to significant process and production management. Thus, digital technologies are a boon for the food industry, where AI and big data have enabled us to achieve optimum results in realtime.

SummaryFoodomics is an emerging probing method of phenotype investigation of the different milk proteins and their subtypes. The polymorphic nature of the β‐casein (β‐Cn) protein has shown fourteen different protein variants to date in bovines. The analysis of the β‐Cn genetic polymorphism from the milk of the crossbred dairy animals is crucial for the quality assurance of the consumers from the various health concerns, especially those linked with the A1 phenotype which yields β‐casomorphin‐7 on in‐vivo digestion. Jersey‐crossed Indian cattle have been widely utilized in dairy because of their better milk production and survival performance trait. In this investigation, an SDS‐PAGE coupled with a high‐resolution accurate mass spectrometry‐based proteomics approach has been applied to identify the presence of specific phenotype of the β‐Cn protein in the milk of the 24 Indian crossbred (Jersey crossed) animals. Amino acid sequential analysis has been done using different search modules, as MS Amanda and Sequest HT showed 17 cows are producing A2 β‐Cn (Pro~67) while only seven animals yielded the A1 variant (His~67). The maximum number of Indian Jersey‐crossed animals are lactating milk having A2 β‐Cn. The A2 milk from the crossbred animals is free from the negative impact on health caused by β‐casomorphin‐7 (BCM‐7) released during digestion of the A1 phenotype. Among the molecular biology techniques, top‐down proteomics has been an intriguing technique for the identification of protein genetic polymorphic products.

Background: The global dairy market is experiencing a massive transition as dairy farming has recently undergone modernization. As a result, the dairy industry needs to improve its operational efficiencies by implementing effective optimization techniques. Conventional and emerging optimization techniques have already gained momentum in the dairy industry. This study’s objective was to explore the optimization techniques developed for or implemented in the dairy supply chain (DSC) and to investigate how these techniques can improve the DSC. Methods: A systematic review approach based on PRISMA guidelines were adopted to conduct this review. The authors used descriptive statistics for statistical analysis. Results: Modernization has led the dairy industry to improve its operational efficiencies by implementing the most effective optimization techniques. Researchers have used mathematical modeling-based methods and are shifting to artificial intelligence (AI) and machine learning (ML) -based approaches in the DSC. The mathematical modeling-based techniques remain dominant (56% of articles), but AI and ML-based techniques are gaining traction (used in around 44% of articles). Conclusions: The review findings show insight into the benefits and implications of optimization techniques in the DSC. This research shows how optimization techniques are associated with every phase of the DSC and how new technologies have affected the supply chain.

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