Predictive Modeling for Estimation of Bacterial Behavior from Farm to Table

Abstract

Microbial contamination is inevitable for raw and/or minimally processed ready-to-eat foods. As a consequence of the pathogenic bacterial contamination, the risk of food-borne illness increases during distribution and storage until consumption. Prediction of microbial growth and/or inactivation in/on those foods provides important information for ensuring the microbial food safety. Although numerous predictive models for bacterial growth have been proposed for various microorganisms, this review focuses on the modeling of pathogenic bacterial growth in raw and minimally processed ready-to-eat foods such as fresh-cut produce and raw minced-tuna, a common ingredient for sushi. The growth models described here take into account both the environment temperature and microbial competition in the food matrix. Microbial competition plays a key role in real food environments. Food-based predictive models enable not only to directly estimate the microbial growth on those foods, but also to apply to validation of culture-medium-based predictive models. Furthermore, toward a development of accurate and/or realistic bacterial dose-response models, a model for inactivation of pathogenic bacteria during simulated gastric fluid is also introduced.