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Contribution Details

Type Master's Thesis
Scope Discipline-based scholarship
Title Towards Improving the Classification and Ranking of Relevant Information in an Early Detection Process of Food Safety Risks: A Case Study in the Swiss Federal Food Safety and Veterinary Offce
Organization Unit
  • Kathrin Wardatzky
  • Cristina Sarasua
  • Luca Rossetto
  • English
Institution University of Zurich
Faculty Faculty of Business, Economics and Informatics
Date 2021
Abstract Text Early risk detection in food safety aims to identify emerging risks and trends before they can impact the health of consumers. In Switzerland, the FSVO established a network-based process to find microbiological, chemical, and nutritional issues in food, food stuffs, and commodities that might impact the Swiss population in the future. This case study investigates the feasibility of improving the current early detection process by implementing crowdsourcing methods. A series of interviews with people who are involved in the process determined the state-of-the-art and main challenges but left questions about the assessment process of potentially relevant information open. These questions were addressed by an online study that concluded that a crowdsourcing-based information filtering process might be feasible. A literature survey that presents different crowdsourcing implementations in the food domain completes the case study. Following up on the results from the case study, the proposal presents ideas on how the next steps towards an improved early detection process at the FSVO could look like.
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