Modulation strategies of nanozyme activity applied to food quality and safety analysis: From rational design to intelligent assistance

Nanozymes combine the catalytic properties of natural enzymes with the distinctive structural features of nanomaterials, offering significant potential for applications in food quality and safety analysis. Machine learning (ML) algorithms enable precise control over the active sites and electronic structures of nanozymes by integrating data mining, predictive modeling, and theoretical calculations. This review systematically summarizes key strategies for enhancing nanozyme activity, including morphology modulation, optimization of interfacial electron transfer, micro-environment engineering and ML-assisted design, and highlights their emerging applications in food quality and safety analysis. Particular emphasis is placed on ML-enabled high-throughput screening, which elucidates complex structure–activity relationships, accelerates the identification of high-performance nanozymes, and supports their practical application in food contaminants detection, product quality monitoring, and adulteration identification. The current challenges and future prospects for ML-assisted activity modulation, with the aim of advancing both nanozyme engineering and their translation for food quality and safety field.