Hybrid CQD sensors for food safety: MOF/MIP composites, AI integration, and real-matrix performance

Carbon quantum dots (CQDs) have emerged as promising photoluminescent nanomaterials for food safety monitoring owing to their tunable optical properties, high stability, low toxicity, and versatile surface chemistry. This review critically examines recent advances in CQD-based sensing platforms, with particular emphasis on hybrid architectures incorporating metal-organic frameworks (MOFs) and molecularly imprinted polymers (MIPs). These hybrid systems significantly improve analyte enrichment, molecular recognition, sensitivity, and selectivity, enabling the detection of heavy metals, pesticides, veterinary drugs, food additives, adulterants, pathogens, and spoilage indicators in complex food matrices. The underlying sensing mechanisms, including fluorescence resonance energy transfer, photoinduced electron transfer, inner filter effects, and ratiometric fluorescence strategies, are discussed. Particular attention is given to sensor performance in real food samples, highlighting validation studies, matrix effects, and practical limitations associated with stability, reproducibility, and large-scale implementation. The review further explores the emerging role of artificial intelligence and machine learning in spectral analysis, matrix-effect compensation, multi-analyte discrimination, and smartphone-enabled sensing platforms. Finally, current challenges, future opportunities, and pathways toward portable, intelligent, and field-deployable food safety monitoring systems are critically evaluated. The analysis demonstrates that CQD-MOF and CQD-MIP hybrid sensors represent a promising direction for next-generation food safety technologies that combine high analytical performance with digital intelligence.