dc.contributor.author | Farkas, Zsuzsa | |
dc.contributor.author | Országh, Erika | |
dc.contributor.author | Engelhardt, Tekla | |
dc.contributor.author | Zentai, Andrea | |
dc.contributor.author | Süth, Miklós | |
dc.contributor.author | Csorba, Szilveszter | |
dc.contributor.author | Jóźwiak, Ákos | |
dc.date.accessioned | 2024-09-04T11:14:48Z | |
dc.date.available | 2024-09-04T11:14:48Z | |
dc.date.issued | 2023-06 | |
dc.identifier.citation | Farkas, Zsuzsa, Erika Országh, Tekla Engelhardt, Andrea Zentai, Miklós Süth, Szilveszter Csorba, and Ákos Jóźwiak. "Emerging risk identification in the food chain–A systematic procedure and data analytical options." Innovative Food Science & Emerging Technologies 86 (2023): 103366. DOI: 10.1016/j.ifset.2023.103366 | en_US |
dc.identifier.uri | http://hdl.handle.net/10832/4026 | |
dc.description | CC BY-NC-ND 4.0 | en_US |
dc.description.abstract | Systematic screening for risks emerging in the food chain is essential for the protection of consumer health, however, timely identification of risks is not a trivial task because of the data and information gaps. By creating automated or semi-automated algorithms, a large amount of information can be pre-processed which helps experts to filter for the actual emerging risks that need further assessment. The present study gives an overview on the possible data analytical approaches that can be used for emerging risk screening and presents a practically applicable process management system. By using these methods, 58 emerging risks classified into 10 topics have been identified in 2020 and 2021 with the focus on Hungary and the European Union. The main goal is to aid authorities and industry in preparedness and timely acting to avoid or mitigate future risks. Experiences and limitations of the system and future directions are also presented. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.title | Emerging risk identification in the food chain – A systematic procedure and data analytical options | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/j.ifset.2023.103366 | |