Sensor technologies in the food industry under industry 4.0: A systematic review

The food industry is undergoing a rapid transformation driven by the integration of advanced sensor technologies and Industry 4.0 digital enablers. Ensuring food quality, safety, sustainability, and traceability across increasingly complex supply chains requires real-time, scalable, data-driven monitoring solutions. This paper presents a comprehensive analysis of the scientific literature on sensor technologies applied to the food industry within the Industry 4.0 framework, using both bibliometric and content-based analyses. The former aims to examine publication trends, geographical distribution, and keyword co-occurrence patterns, identifying dominant research themes and technological directions. The latter concentrates on the principal sensor classifications employed in food systems, including chemical, biological, optical, physical, and nano-enabled sensors. The study further investigates how these sensing technologies are integrated with key digital enablers such as the Internet of Things, artificial intelligence, machine learning, cloud computing, blockchain, and additive manufacturing. The focus is on the role of sensors at each step of the food supply chain, from growing and processing to storage, transport, sales, and consumption. The results indicate that sensor-based solutions are becoming more mature and have the potential to improve food safety, operational efficiency, sustainability, and traceability. At the same time, the paper identifies the main challenges that still limit their large-scale adoption and outlines future research directions toward fully integrated smart food systems. • A bibliometric analysis of food sensor research is presented • A systematic literature review is conducted based on bibliometric results • Industry 4.0 and food sensors are reviewed with a conceptual integration framework • A smart food system enabling real-time monitoring and adaptive control is outlined. • The study supports innovation via sensor evaluation in real and risky environments.