AI-integrated smart biosensors for real-time multi-contaminant detection in food systems

Abstract

Food safety systems are under growing pressure and threat due to the presence of multiple chemical and biological contaminants across complex food networks. Conventional detection techniques remain time-consuming, lab-intensive, and single-target-oriented, with limited reliable, real-time data for rapid decision-making. Recent biosensor advancements, driven by artificial intelligence (AI), machine learning (ML), and the integration of the Internet of Things (IoT), have enabled smarter on-field deployment and greater accuracy. The current mini-review summarises recent advancements in AI-integrated biosensors, such as multi-analyte sensing designs, paper-based platforms, smartphone-assisted detection, and nanozyme-based sensors. It critically examines their potential to enable rapid, cost-effective, and scalable monitoring with an emphasis on AI-enabled signal resolution and representative detection performance across food matrices. The challenges associated with multi-contaminant detection, data integration, model reliability, and practical implementation in resource-constrained environments are given special attention. The review proposes a conceptual framework for intelligent biosensing systems that integrate sensing, data analytics, and decision support. This work demonstrates the transition from prototype-level sensors to reliable, real-time food safety monitoring systems by integrating technological innovation with sustainability and policy considerations.