Artificial Intelligence and Big Data in food safety
The majority of Big Data applications requires dealing with large volumes, diverse sources and forms of data, ranging from highly structured to unstructured data, which is due to the rise in the sharing and availability of data. To deal with this complexity, new tools are needed to automatically extract process, integrate, visualize and organize this data into both human and machine readable summaries. Wageningen Food Safety Research is exploring the application of Artificial intelligence (AI) and Big Data technologies in food safety to keep and make food safe. Progress in these areas will allow us to greatly improve our knowledge in AI and Big Data, and will help us to utilise better the available data for the generation of knowledge.
Key publications
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Big Data in food safety- A review
Current Opinion in Food Science 36 (2020). - ISSN 2214-7993 - p. 24 - 32. -
Determination and Metrics for Emerging Risks Identification DEMETER: Final Report
EFSA Supporting Publications 17 (2020)7. - ISSN 2397-8325 -
Expert-driven methodology to assess and predict the effects of drivers of change on vulnerabilities in a food supply chain: Aquaculture of Atlantic salmon in Norway as a showcase
Trends in Food Science and Technology 103 (2020). - ISSN 0924-2244 - p. 49 - 56. -
A system approach towards prediction of food safety hazards : Impact of climate and agrichemical use on the occurrence of food safety hazards
Agricultural Systems 178 (2020). - ISSN 0308-521X -
Internet of Things in food safety: Literature review and a bibliometric analysis
Trends in Food Science and Technology 94 (2019). - ISSN 0924-2244 - p. 54 - 64. -
Effective sampling strategy to detect food and feed contamination : Herbs and spices case
Food Control 83 (2018). - ISSN 0956-7135 - p. 28 - 37. -
Application of Bayesian Networks in the development of herbs and spices sampling monitoring system
Food Control 83 (2018). - ISSN 0956-7135 - p. 38 - 44.