AI-driven Approaches: Enhancing Food Safety and Security in the Supply Chain (International Journal of Food and Nutritional Sciences, 2022)
This paper explores the role of AI-driven approaches in enhancing food safety and security throughout the supply chain. With the increasing complexity and globalization of food supply networks, ensuring the safety and integrity of food products has become a paramount concern. AI technologies offer innovative solutions to address the diverse challenges facing the food industry, from quality control and inspection to predictive analytics, traceability, supply chain optimization, early detection of contamination, consumer engagement, and regulatory compliance. Through a comprehensive review of existing literature, this paper examines how AI-powered systems can improve the accuracy and efficiency of quality control processes by detecting defects and anomalies in food products. It also discusses how AI algorithms analyze data from multiple sources to predict and prevent food safety risks, such as contamination outbreaks and supply chain disruptions, enabling proactive risk management. Furthermore, the paper explores the role of blockchain technology and smart contracts in providing transparent and traceable supply chains, ensuring the integrity and authenticity of food products from production to consumption. Moreover, the paper discusses how AI-driven optimization algorithms enhance the efficiency and resilience of food supply chain logistics, minimizing waste and ensuring timely delivery of products while maintaining safety standards. It also examines the use of AI-driven sensor networks to monitor environmental conditions and detect deviations that may indicate contamination, enabling timely intervention to prevent foodborne illnesses. Additionally, the paper explores how AI-driven applications deliver personalized recommendations and real-time alerts to consumers, empowering them to make informed decisions about food safety. Despite the promise of AI-driven approaches, several challenges remain, including data quality, interoperability, privacy, algorithm bias, cost, regulatory compliance, and ethical considerations. Addressing these challenges is crucial to realizing the full potential of AI technologies in ensuring the safety and integrity of the global food supply chain.