Weighted Bayesian network for the classification of unbalanced food safety data: Case study of risk ‐based monitoring of heavy metals

This study proposes a weighted Bayesian network (WBN) classifier to improve the model prediction accuracy for the presence of food and feed safety hazards using unbalanced monitoring data, specifically for the presence of heavy metals in feed. Applying different weight values resulted in different classification accuracies for each involved class; the optimal weight value was defined as the value that yielded the most effective monitoring plan, that is, identifying the highest percentage of contaminated feed batches. Results showed that the Bayesian network classifier resulted in a large difference between the classification accuracy of positive samples (20%) and negative samples (99%). With the WBN approach, the classification accuracy of positive samples and negative samples were both around 80%, and the monitoring effectiveness increased from 31% to 80% for pre-set sample size of 3000. Results of this study can be used to improve the effectiveness of monitoring various food safety hazards in food and feed.

Source: Risk Analysis - March 3, 2023 Category: International Medicine & Public Health Authors: Xinxin Wang, Yamine Bouzembrak, A. G. J. M. Oude Lansink, H. J. Fels ‐Klerx Tags: ORIGINAL ARTICLE Source Type: research