ReviewJournal of food science2026
Enhancing Food Safety in the Cold Chain Through Internet of Things and Artificial Intelligence.
Review in Journal of food science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Postharvest Management of Fruits: Navigating the Affordability, Efficacy, and Non-Destructive Quality Evaluation of Thermal Versus Non-Thermal Interventions.Foods (Basel, Switzerland) · 2026Review
- Enhancing Food Safety in the Cold Chain Through Internet of Things and Artificial Intelligence.Journal of food science · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Effective cold chain management is crucial for ensuring food safety, but traditional monitoring approaches remain reactive and fragmented. Industry 4.0 technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), offer transformative potential for real-time tracking, automation, and predictive analytics. Many studies have explored the application of IoT and AI in the cold chain, but the existing literature focuses on isolated technologies. This review presents an integrated perspective, examining the synergistic potential of IoT-enabled data acquisition and AI-driven predictive analytics in the cold chain. We reviewed 97 peer-reviewed papers published between 2010 and 2025, following PRISMA guidelines, covering IoT sensors, AI applications, and their integration across the food cold supply chain. Our analysis reveals a rapid growth in IoT and AI adoption, driven by regulatory and consumer demands for transparency, quality, and predictive risk assessment. IoT sensors enable real-time monitoring, providing early detection of potential safety risks. AI-powered models process sensor data to predict temperature deviations, assess food safety, and optimize logistics, reducing spoilage and contamination risks. We also highlight current limitations and future research directions, such as the limited number of studies on closed-loop systems, where IoT sensors provide real-time data and AI models respond dynamically. This review provides a comprehensive resource for selecting IoT and AI systems to enhance food safety and ensure a more resilient, transparent, and sustainable cold chain.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.