Integrating AI with detection methods, IoT, and blockchain to achieve food authenticity and traceability from farm-to-table

Background: Ensuring the authenticity and traceability of food is fundamental to reducing food fraud, safeguarding public health, and fostering consumer trust-cornerstones of global food safety. As food supply chains grow increasingly complex, artificial intelligence (AI), in conjunction with the Internet of Things (IoT) and blockchain, plays a pivotal role in enhancing detection accuracy, improving transparency, and addressing critical challenges in food traceability. Scope and approach: This paper provided a comprehensive review of AI applications in food safety, focusing on spectroscopy, mass spectrometry, imaging, and sensor-based detection. It also examined the integration of AI with IoT and blockchain, highlighting their potential in building safe, transparent, and scalable traceability frameworks. Furthermore, the study explored how this integrated framework advanced Food Industry 4.0, driving automation, real-time monitoring, and interconnected supply chains. Finally, the paper discussed current challenges and offered perspectives on advancing AI-driven systems for food authenticity detection and traceability. Key findings and conclusions: Meanwhile, the convergence of AI, IoT, and blockchain has facilitated cross-platform compatibility and scalability, optimized supply chain data collection, and strengthened the security of traceability information. The rapid advancement of the AI-IoT-blockchain framework is driving the evolution of Food Industry 4.0, fostering advancements in high-precision analysis, automation, cost-effectiveness, and quality control, thereby enhancing food safety and transparency.