Enhancing Food Safety in the Cold Chain Through Internet of Things and Artificial Intelligence
Abstract
Effective cold chain management is crucial for ensuring food safety, but traditional monitoring approaches remain reactive and fragmented. Industry 4.0 technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), offer transformative potential for real-time tracking, automation, and predictive analytics. Many studies have explored the application of IoT and AI in the cold chain, but the existing literature focuses on isolated technologies. This review presents an integrated perspective, examining the synergistic potential of IoT-enabled data acquisition and AI-driven predictive analytics in the cold chain. We reviewed 97 peer-reviewed papers published between 2010 and 2025, following PRISMA guidelines, covering IoT sensors, AI applications, and their integration across the food cold supply chain. Our analysis reveals a rapid growth in IoT and AI adoption, driven by regulatory and consumer demands for transparency, quality, and predictive risk assessment. IoT sensors enable real-time monitoring, providing early detection of potential safety risks. AI-powered models process sensor data to predict temperature deviations, assess food safety, and optimize logistics, reducing spoilage and contamination risks. We also highlight current limitations and future research directions, such as the limited number of studies on closed-loop systems, where IoT sensors provide real-time data and AI models respond dynamically. This review provides a comprehensive resource for selecting IoT and AI systems to enhance food safety and ensure a more resilient, transparent, and sustainable cold chain.
