“Coordinating intelligence”: From nanozyme sensing mechanisms to intelligent decoding for foodborne pathogens detection

Foodborne pathogens represent a major threat to global public health security, necessitating the development of rapid, accurate, and sensitive detection technologies to ensure food safety. In recent years, nanozymes, nanomaterials with enzyme-mimetic catalytic activities, have emerged as promising tools in biosensing due to their high stability, tunable catalytic properties, and ease of functionalization. Yet, the lack of a comprehensive review that systematically describes the entire sensing chain, from nanozyme–bacteria recognition and signal transduction to intelligent encoding, has hindered the rational design of nanozyme-based detection platforms and limited the convergence of nanotechnology with artificial intelligence in this field. Herein, we systematically elaborate the paradigm shift in nanozyme-based biosensing for foodborne pathogens, spanning from fundamental sensing mechanisms to intelligent decoding strategies. The catalytic characteristics and application principles of various types of nanozymes, such as peroxidase-, oxidase-, laccase-like, and others, are briefly reviewed. Various recognition strategies employing nanozymes modified with antibodies, nucleic acids, antibiotics, peptides, phages, and others are comprehensively discussed, highlighting their specific interaction mechanisms with target bacteria. Meanwhile, the role of nanozymes in signal transduction and amplification is equally importantly explored across multiple detection modalities. Afterwards, the nanozyme-integrated sensing platforms are comprehensively discussed. A special emphasis is placed on the integration of machine learning techniques for enhancing multiplex detection, sensitivity, and accuracy. Finally, we outline current challenges and future perspectives toward intelligent nanozyme-based sensing platforms for food safety monitoring. • Comprehensive review on nanozyme-based biosensing for foodborne pathogens are conducted. • Versatile recognition mechanisms and multimodal signal transduction are comprehensively discussed. • The integration of machine learning for intelligent signal decoding is highlighted. • The emerging platforms like LFIA, microfluidics, and sensor arrays are reviewed. • Prospects on intelligent nanozyme sensors for on-site food safety monitoring are carried out.