|Title||A holistic approach to food safety risks : Food fraud as an example|
|Author(s)||Marvin, Hans J.P.; Bouzembrak, Yamine; Janssen, Esmée M.; Fels, Ine van der; Asselt, Esther D. van; Kleter, Gijs A.|
|Source||Food Research International 89 (2016). - ISSN 0963-9969 - p. 463 - 470.|
RIKILT - BU Toxicology Bioassays & Novel Foods
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Bayesian network - Food fraud - Food safety risks - Holistic approach - Prediction|
Production of sufficient, safe and nutritious food is a global challenge faced by the actors operating in the food production chain. The performance of food-producing systems from farm to fork is directly and indirectly influenced by major changes in, for example, climate, demographics, and the economy. Many of these major trends will also drive the development of food safety risks and thus will have an effect on human health, local societies and economies. It is advocated that a holistic or system approach taking into account the influence of multiple drivers on food safety is followed to predict the increased likelihood of occurrence of safety incidents so as to be better prepared to prevent, mitigate and manage associated risks. The value of using a Bayesian Network (BN) modelling approach for this purpose is demonstrated in this paper using food fraud as an example. Possible links between food fraud cases retrieved from the RASFF (EU) and EMA (USA) databases and features of these cases provided by both the records themselves and additional data obtained from other sources are demonstrated. The BN model was developed from 1393 food fraud cases and 15 different data sources. With this model applied to these collected data on food fraud cases, the product categories that thus showed the highest probabilities of being fraudulent were fish and seafood (20.6%), meat (13.4%) and fruits and vegetables (10.4%). Features of the country of origin appeared to be important factors in identifying the possible hazards associated with a product.The model had a predictive accuracy of 91.5% for the fraud type and demonstrates how expert knowledge and data can be combined within a model to assist risk managers to better understand the factors and their interrelationships.