Smart Recognition of Chemical Contaminants in Food: Experimental and Computational Perspectives on MIP-Based Sensors

Abstract

The growing concern about chemical contaminants in food has enhanced the need for rapid, selective, and cost-efficient sensing technology. Molecularly imprinted polymers (MIPs) have emerged as potential artificial sensing elements because of their high sensitivity, stability, selectivity, and reproducibility. Recent advances further highlight the growing role of computational tools including molecular docking, molecular dynamics (MD), quantum chemical calculations (QC), and molecular mechanics (MM) in rational MIP design. These methods guide the rational selection of monomers, solvents, and cross-linkers by predicting their effects on template interactions, solvent polarity, and cavity stability, thereby minimizing trial-and-error in MIP design. This review presents a comprehensive overview of recent progress in MIP-based sensors for detection of chemical contaminants in food, emphasizing both experimental and computational perspectives. In addition, this review also covers chromatography-integrated MIP systems, where imprinted polymers are used as selective recognition elements within separation-based methods for food contaminant analysis. The reviewed platforms enable not only sensitive detection but also reliable quantification of food contaminants across diverse matrices. Special focus is given to case studies that demonstrate the applications of MIPs in food analysis and the role of in silico strategies in optimizing sensor performance. By bridging experimental innovation with computational design, this work aims to provide researchers with an integrated framework for developing next-generation sensing platforms that are selective, sensitive, and practical for real-world food safety monitoring.