Designing cost-effective monitoring programs for chemical hazards in feed using machine learning
Chemical hazards in feed can affect animal and human health. Feed containing such food safety hazards may be unsafe for animals and humans and must be withdrawn from the supply chain. Monitoring programs to control the presence of chemical hazards in feed have been designed and implemented by both the industry and governmental agencies. Since the checking the presence of all food safety hazards in the endless number of feed ingredients is resource-demanding such monitoring programs are ideally carried out with a risk-based approach to monitor the hazards that pose the highest risk to animal and human health. In addition, monitoring plans could be conducted in a cost-effective way, meaning the plan provides the highest effectiveness of food safety monitoring given available resources. Machine Learning (ML) algorithms can be used to design such food safety monitoring schemes. Results showed ML - embedded cost-effective monitoring plans increased the effectiveness and reduced the monitoring costs as compared to the current monitoring plan.