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Published on 1 August 2023
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Tang,J. (2023). Food Supply Chain Traceability System Based on Blockchain Technology. Applied and Computational Engineering,8,311-317.
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Food Supply Chain Traceability System Based on Blockchain Technology

Jiawei Tang *,1,
  • 1 Southwest University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/8/20230176

Abstract

In recent years, with the Internet industry’s development, more and more industries are developing in a more informative and transparent direction, especially traceability system. The databases of traditional supply chain traceability systems are all relational (RDBMS). The data is easy to be lost and tampered with. This shortcoming is particularly obvious in the food processing product supply chain traceability system. The data of each supply chain sector is stored in different databases. When regulatory authorities conduct information investigation and traceability, they must search multiple databases. The steps could be more convenient and easier to hold accountable by using the blockchain technology. A supply chain traceability system based on blockchain technology is designed to respond to these problems. The supply chain traceability system for processed food products is taken as an example to implement. This system is developed on the hyperledger fabric (HLF) platform, which realizes decentralization and tamper-proof, one-click traceability of the complete information and other functions. Consumers can not only query the whole process information of the food supply chain but also lodge complaints and defend their rights in this system.

Keywords

blockchain, food supply chain, traceability, smart contract

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Cite this article

Tang,J. (2023). Food Supply Chain Traceability System Based on Blockchain Technology. Applied and Computational Engineering,8,311-317.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

Conference website: http://www.confseml.org
ISBN:978-1-915371-63-8(Print) / 978-1-915371-64-5(Online)
Conference date: 19 April 2023
Editor:Anil Fernando, Marwan Omar
Series: Applied and Computational Engineering
Volume number: Vol.8
ISSN:2755-2721(Print) / 2755-273X(Online)

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