Smart Cold Chains for Perishable Foods: Sensor Fusion, Artificial Intelligence and Dynamic Shelf-Life Management

Abstract

Food cold chains are increasingly instrumented with connected temperature sensors, intelligent packaging, product-quality measurements and predictive algorithms. Yet the transition from monitoring refrigeration conditions to managing actual product deterioration is neither automatic nor equally reliable across commodities. This critical narrative review evaluates how sensing, multimodal data fusion, artificial intelligence and remaining shelf-life models can support decisions throughout refrigerated food supply chains. Peer-reviewed work published from 2000 to 12 August 2026 was considered, alongside selected foundational studies, through accessible scholarly indexes, bibliographic verification and backward citation examination. Evidence was assessed for biological relevance, measurement validity, independent validation, operational deployment and decision-level consequences. Temperature histories provide an essential but incomplete representation of product condition: packaging atmosphere, initial microbiological or physiological state, humidity, mechanical injury and spatial thermal gradients can materially affect deterioration. Laboratory studies demonstrate promising performance for electronic noses, spectroscopy, flexible indicators and machine-learning models, whereas external route-level validation and calibration across product lots remain comparatively limited. Kinetic models offer interpretable relationships between exposure and quality loss but depend on commodity-specific parameters and assumptions; data-driven models can represent complex patterns but are vulnerable to distribution shift and leakage between training and test samples. Digital-twin and remaining-life allocation studies establish plausible pathways towards first-expired-first-out distribution, yet simulated reductions in waste should not be mistaken for demonstrated commercial benefits. A central distinction is made between marketable quality and microbiological safety: freshness estimates cannot independently establish pathogen absence, extend legally defined use-by dates or replace food-safety controls. The review proposes an uncertainty-aware sequence linking measurement, state estimation, validated remaining-life prediction and constrained operational action. Research priorities include prospective multi-site studies, robust ground truth, calibration transfer, human–algorithm decision trials and transparent accounting of energy, waste and equity. Smart cold chains are best understood as governed decision systems rather than collections of sensors or predictive models.