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A Novel Visual Analysis Method of Food Safety Risk Traceability Based on Blockchain

Author

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  • Zhihao Hao

    (National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
    PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau 999078, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China)

  • Dianhui Mao

    (National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China)

  • Bob Zhang

    (PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau 999078, China)

  • Min Zuo

    (National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China)

  • Zhihua Zhao

    (The School of Law, Chinese University of Political Science and Law, Beijing 102249, China)

Abstract

Current food traceability systems have a number of problems, such as data being easily tampered with and a lack of effective methods to intuitively analyze the causes of risks. Therefore, a novel method has been proposed that combines blockchain technology with visualization technology, which uses Hyperledger to build an information storage platform. Features such as distribution and tamper-resistance can guarantee the authenticity and validity of data. A data structure model is designed to implement the data storage of the blockchain. The food safety risks of unqualified detection data can be quantitatively analyzed, and a food safety risk assessment model is established according to failure rate and qualification deviation. Risk analysis used visual techniques, such as heat maps, to show the areas where unqualified products appeared, with a migration map and a force-directed graph used to trace these products. Moreover, the food sampling data were used as the experimental data set to test the validity of the method. Instead of difficult-to-understand and highly specialized food data sets, such as elements in food, food sampling data for the entire year of 2016 was used to analyze the risks of food incidents. A case study using aquatic products as an example was explored, where the results showed the risks intuitively. Furthermore, by analyzing the reasons and traceability processes effectively, it can be proven that the proposed method provides a basis to formulate a regulatory strategy for regions with risks.

Suggested Citation

  • Zhihao Hao & Dianhui Mao & Bob Zhang & Min Zuo & Zhihua Zhao, 2020. "A Novel Visual Analysis Method of Food Safety Risk Traceability Based on Blockchain," IJERPH, MDPI, vol. 17(7), pages 1-18, March.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:7:p:2300-:d:338522
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    References listed on IDEAS

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    1. Sara Saberi & Mahtab Kouhizadeh & Joseph Sarkis & Lejia Shen, 2019. "Blockchain technology and its relationships to sustainable supply chain management," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2117-2135, April.
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    3. Dianhui Mao & Zhihao Hao & Fan Wang & Haisheng Li, 2018. "Innovative Blockchain-Based Approach for Sustainable and Credible Environment in Food Trade: A Case Study in Shandong Province, China," Sustainability, MDPI, vol. 10(9), pages 1-17, September.
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    2. Anandika Sharma & Anupam Sharma & Tarunpreet Bhatia & Rohit Kumar Singh, 2023. "Blockchain enabled food supply chain management: A systematic literature review and bibliometric analysis," Operations Management Research, Springer, vol. 16(3), pages 1594-1618, September.
    3. Mladen Krstić & Giulio Paolo Agnusdei & Snežana Tadić & Pier Paolo Miglietta, 2023. "Prioritization of e-traceability drivers in the agri-food supply chains," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 11(1), pages 1-26, December.
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