Meat Freshness Monitoring in the Modern Supply Chain: Analytical Advances, Smart Packaging, and Digital Integration

Monitoring meat freshness across the modern supply chain remains challenging because meat quality changes dynamically with fluctuating cold-chain, packaging, and logistics conditions. Conventional assessment methods, sensory inspection, physicochemical indices, and microbiological testing remain essential reference tools but are destructive, retrospective, and poorly suited to real-time decision-making. Growing research attention has therefore focused on sensor-based systems, intelligent packaging, spectroscopic and imaging techniques, and digitally enabled monitoring platforms. However, many technologies that perform well in the laboratory do not scale into robust solutions under realistic supply-chain variability. This review takes a translation-oriented approach, reframing meat spoilage pathways in terms of their analytically detectable signals rather than their mechanistic biochemistry. Conventional freshness indices are critically assessed to explain their structural inability to operate under real logistics dynamics. Emerging technologies are evaluated not on analytical novelty alone, but on their capacity to integrate multiple spoilage signals, function under variable conditions, and support actionable freshness management, with particular attention to sensor arrays, data-fusion schemes, IoT connectivity, machine-learning-based prediction, and smartphone-assisted interpretation. This review provides a critical, criteria-based synthesis to guide the development of robust, scalable, and decision-oriented freshness monitoring systems capable of reducing waste, improving food safety, and strengthening supply-chain resilience.