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A Novel Visual Analysis Method of Food Safety Risk Traceability Based on Blockchain
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 H...
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Published in: | International journal of environmental research and public health 2020-03, Vol.17 (7), p.2300 |
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description | 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. |
doi_str_mv | 10.3390/ijerph17072300 |
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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. 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Mao, Dianhui ; Zhang, Bob ; Zuo, Min ; Zhao, Zhihua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c418t-d933fc34c1899291e0fcc28d9d1fe6e989e4cd0e3b22f107050a22f854bee44b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Blockchain</topic><topic>Cooperation</topic><topic>Data storage</topic><topic>Data structures</topic><topic>Datasets</topic><topic>Efficiency</topic><topic>Failure rates</topic><topic>Food</topic><topic>Food production</topic><topic>Food quality</topic><topic>Food safety</topic><topic>Food supply</topic><topic>Graph theory</topic><topic>Information storage</topic><topic>Internet of Things</topic><topic>Product recalls</topic><topic>Radio frequency identification</topic><topic>Risk analysis</topic><topic>Risk assessment</topic><topic>Safety</topic><topic>Sampling</topic><topic>Supervision</topic><topic>Supply chains</topic><topic>Technology</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hao, Zhihao</creatorcontrib><creatorcontrib>Mao, Dianhui</creatorcontrib><creatorcontrib>Zhang, Bob</creatorcontrib><creatorcontrib>Zuo, Min</creatorcontrib><creatorcontrib>Zhao, Zhihua</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection (Proquest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database (Proquest)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal of environmental research and public health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hao, Zhihao</au><au>Mao, Dianhui</au><au>Zhang, Bob</au><au>Zuo, Min</au><au>Zhao, Zhihua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Novel Visual Analysis Method of Food Safety Risk Traceability Based on Blockchain</atitle><jtitle>International journal of environmental research and public health</jtitle><addtitle>Int J Environ Res Public Health</addtitle><date>2020-03-29</date><risdate>2020</risdate><volume>17</volume><issue>7</issue><spage>2300</spage><pages>2300-</pages><issn>1660-4601</issn><issn>1661-7827</issn><eissn>1660-4601</eissn><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. 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subjects | Blockchain Cooperation Data storage Data structures Datasets Efficiency Failure rates Food Food production Food quality Food safety Food supply Graph theory Information storage Internet of Things Product recalls Radio frequency identification Risk analysis Risk assessment Safety Sampling Supervision Supply chains Technology Visualization |
title | A Novel Visual Analysis Method of Food Safety Risk Traceability Based on Blockchain |
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