ArticleScientific reports2024
Tracking perishable foods in the supply chain using chain of things technology.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- IoT-Enabled Quality-Triggered Markdown Pricing for Perishable Food: Equity and Waste Implications.Foods (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern food supply chains are intrinsically sophisticated due to their multi-participant and multi-echelon structure, which are challenging to handle high turbulent business environment. The development of Perishable Food Supply Chains (PFSC) has to be strong enough to manage any type of disruptions in the food industry. At the same time, the food processing industry must also take responsibility for the social and environmental consequences of their deeds. This has further led to performance deterioration and intensified design complexity. Recently, digitalization and Blockchain technology (BCT) have brought unfathomed rebellions in PFSC. Despite the potential and market hype, the application of BCT to track the perishable products and status of in-transit shipments is still a challengingtask for the food industry due to privacy and security issues, restricted transactional and scalability performance, deficiency of industry standards and managerial abilities, etc. However, integrating the BCT with the eventual benefits of the Internet of Things (IoT) (i.e., Chain of Things (CoT)) increases the performance of good traceability in any supply chain. The proposed CoT-based Track and Trace system (CoT-TTS) employs a set of IoT devices, BCT, and Adaptive Neuro-Fuzzy Inference System (ANFIS). The performance of CoT-TTS is evaluated through a case study using an EOSIO platform. The effectiveness of the proposed system is evaluated in terms of depth, breadth, access, and precision of the transactions.
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