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Introduction: Effective cold chain management is essential for maintaining the safety, nutritional quality, and sensory attributes of perishable food products throughout storage, transportation, and distribution. Conventional cold chain systems often rely on manual inspections and intermittent data logging, which limit real-time deviation detection, leading to increased spoilage and significant food losses. The rapid advancement of the Internet of Things (IoT) has enabled intelligent cold chain systems capable of continuous monitoring, real-time data transmission, and predictive quality management. Materials and methods: A systematic literature search was conducted in Scopus (February 2026) using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-based screening. Search terms combined “Internet of Things,” “cold chain,” “food supply chain,” “predictive quality management,” and “smart packaging sensors” with Boolean operators. The initial search returned 451 records, of which 318 were excluded during title/abstract screening (due to topic irrelevance n = 141, non-English publications n = 23, conference abstracts n = 35, duplicate records n = 21, and non-food IoT studies n = 98), and a further 46 were excluded during full-text eligibility assessment (not meeting IoT monitoring criteria n = 28, and insufficient methodological detail n = 18), resulting in 87 articles included in the final synthesis. Results: Key technological components identified include sensor networks, wireless communication protocols (Wi-Fi, Long-Range (LoRa), and Narrowband IoT (NB-IoT)), cloud–edge computing, machine learning (ML) models, and smart packaging systems. Emerging innovations such as biosensors, nano-enabled materials, and electronic nose technologies for freshness detection are highlighted. The integration of predictive analytics with IoT sensor data enables dynamic shelf-life estimation, anomaly detection, and automated quality management across food supply chains. Temporal bibliometric analysis reveals a clear shift from foundational monitoring keywords (2010–2016) toward predictive analytics, blockchain, and digital twins (2018–2026). Advanced technologies including blockchain, digital twins, and Tiny Machine Learning (TinyML) enhance traceability, transparency, and operational efficiency. Current challenges related to system interoperability, cost, regulatory compliance, and data security are analyzed. The integration of edge and cloud computing represents the optimal architecture for responsive, scalable cold chain systems. Conclusions: IoT-driven predictive quality management represents a transformative approach for ensuring food safety, minimizing postharvest losses, and improving transparency in modern global food supply systems. Future research should prioritize interoperability standards, low-cost biosensor development, and comprehensive regulatory frameworks to enable large-scale deployment.
Mudoi et al. (Wed,) studied this question.