A recent study has established a framework for identifying and prioritizing microbiological risks in infant food products, which has ranked Salmonella, Cronobacter, and Shiga toxin-Producing Escherichia coli (STEC) as the most critical hazards.
Overall, eight risk ranking steps and seven criteria were defined and used to rank the microbial risks in infant foods. The risk-ranking framework developed in the study has been implemented into a web-based tool called the Microbiological Hazards Risk Ranking Decision Support System (MIRA DSS), available here.
The risk-ranking methodology employed in the study considered the prevalence of microbial pathogens in different infant food products, the conditions under which they thrive, and the likelihood of contamination at different stages of food production and storage. The researchers also considered severity of illness caused by each pathogen and the susceptibility of infants to infection.
Each criterion was given a semi-quantitative or quantitative score or risk value, contributing to the final microbial risk calculation via three aggregation methods: semi-quantitative risk scoring, semi-quantitative risk value, and outranking multi-criteria decision analysis. To validate the criteria and ranking approaches, the researchers used a case study to rank microbial risks in infant formula, compared the results of the three risk ranking methods, and evaluated the ranking results against expert opinions to ensure their accuracy.
The development of a risk ranking system that prioritizes microbiological hazards based on their potential impact on infant health can help identify which pathogens pose the greatest risk to public health and should therefore be the focus of stringent monitoring and control measures in the production of infant foods. For example, the study's findings could inform regulations on the acceptable levels of specific pathogens in infant foods or the implementation of more rigorous testing protocols.
The project was an effort of Safe Food for Infants in the EU and China (SAFFI), and was funded by an EU Horizon 2020 Research and Innovation program grant.
