Food Processing Commons^™

Knowledge-Informed Data-Driven Modeling Of Microbial Inactivation In Food, Steve Zhang, Firnaaz Ahamed, Hyun-Seob Song

UNL Student Research Days Posters, Graduate

Prevention of the growth of harmful microorganisms in food products is an important requirement for ensuring food safety and quality. Mathematical models to predict the quantitative changes in microbial populations in food to the variations of environmental conditions are useful tools in this regard. Current approaches that use empirical formulation generate arbitrary forms of model equations, impeding systematic analysis towards identifying key factors governing microbial growth and inactivation in food. To address this challenge, we present a data-driven modeling pipeline that enables automatic discovery of model equations (through parsimonious selection of relevant terms in a pre-built library) without having to …