Exploring the potential of digital twins in the food industry and their role in developing food formulations

abstract

Digital twins have been used across various industries to reduce workload and costs while enhancing process efficiency, aligning with the goals of process intensification. Their application in the food industry is limited, particularly in the industrial-scale food product formulation. This review provides a comprehensive analysis of the current status, benefits, limitations, and prospects of DT applications in the food industry. Authors perform an extensive literature review for the last 20 years using different databases like Scopus, Pubmed, ResearchGate, and academic editorials. Food formulation using DTs allows for the development of personalized recipes tailored to specific nutritional profiles, sensory properties, and target groups. They also facilitate the optimization of food formulas to improve food security, shelf-life, and ingredient composition. Sensory evaluation can be enhanced using virtual environments, biometric sensors, and AI-generated food images. Challenges in DT implementation in the food industry include high digitalization requirements, data accessibility and ownership issues. The integration of DTs with AI, IoT, and big data analytics further enhances their capabilities in predictive maintenance, quality control, and supply chain optimization. Despite these challenges, the potential benefits of DTs in food formulation are considerable, demanding a multidisciplinary approach to effectively and swiftly address consumer demand and industry issues. © 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.