Smart logistics of perishable goods: emerging nanomaterial-based sensing technologies for real-time monitoring and...

Perishable supply chains lose substantial value because product deterioration is rarely measured directly during distribution, while static date labels and temperature records provide only indirect assurance of quality. This review surveys nano-enabled sensor elements that translate headspace chemistry and microbial activity into actionable freshness information, prioritizing studies evaluated on real foods and under realistic cold chain excursions. We synthesize progress across three technology clusters. Optical indicators combine pH responsive dyes, polydiacetylene assemblies, quantum dots, and carbon dots to deliver visible or camera-readable outputs that track amine accumulation and ripening-related metabolites. Gas sensors and electronic noses employ semiconducting metal oxides, graphene, and carbon nanotube networks to quantify volatile organic compounds (VOCs) such as ammonia, trimethylamine, ethylene, and carbon dioxide, with increasing attention to room temperature operation and pattern-based classification. Biosensors extend specificity through antibodies, aptamers, and molecularly imprinted polymers on conductive nanostructures, frequently using electrochemical impedance spectroscopy (EIS) to detect bacteria, toxins, or enzymatic byproducts at low loads. Integration pathways are reviewed from passive color labels to wireless tags using radio frequency identification (RFID), near field communication (NFC), and Bluetooth Low Energy (BLE), enabling package-level data capture and cloud analytics for remaining shelf-life estimation. We also consolidate cross-cutting constraints including calibration drift, humidity cross sensitivity, limited independent validation, added system cost, and safety and regulatory requirements around nanomaterial migration. Taken together, the literature indicates that multi-parameter designs coupled to data models are central to translating laboratory sensitivity into deployable decision support in practice.