Application of Bayesian Networks in the development of herbs and spices sampling monitoring system. (January 2018) - Bouzembrak, Yamine; Camenzuli, Louise; Janssen, Esmée; van der Fels-Klerx, H.J.
Abstract: Knowing which products and hazards to monitor along the food supply chain is crucial for ensuring food safety. In this study, we developed a model to predict which types of herbs and spices products and food safety hazards should preferentially be monitored at each level of the supply chain (suppliers, border inspection points, market and consumers). A Bayesian Network method was used to develop a model based on notifications reported in the Rapid Alert System for Food and Feed and the database of the Dutch national monitoring program for chemical contaminants in food and feed over the period 2005–2014. The model was constructed by randomly selecting ca. 80% of the 3126 data records and validated using the remaining ca. 20% of the records. Model validation showed that the prediction accuracy was higher than 85%. Results showed that the sampling plan is closely related to the place where the products are checked along the supply chain, the products and the country of origin. Our approach of integrating different data sources and considering the entire supply chain can support industry and authorities at border inspection points and at all control points along the herbs and spices supply chain in setting priorities for their monitoring program. Highlights: A Bayesian Network model to predict herbs/spices products and food safety hazards. The Bayesian network model is based on integrating different data sources. Results showed that the prediction accuracy was higher than 85%. The monitoring plan depends on where in the supply chain the product is sampled.