A predictive microbiology approach for thermal inactivation of Hepatitis A virus in acidified berries

Abstract

Hepatitis A virus (HAV) is a food-borne enteric virus responsible for outbreaks of hepatitis associated with consumption of raw vegetables. Soft fruits, such as red berries, exposed to faecal contamination are increasingly responsible for collective food-borne illnesses associated with HAV, when eaten raw or used in unprocessed foods. Heat is the most effective measure for the inactivation of HAV. Thermal treatments are used on fruits as a decontamination method, but they have to be adapted to product characteristics; indeed, factors such as sugar or pH may have an impact on the viral sensitivity to thermal treatments. A model was developed for the inactivation of HAV in red berries without supplemented sugar and with different pH values. Nonlinear inactivation curves in acidified raspberries were modelled using an integrated model, with a single equation nesting secondary models of temperature and pH in the primary model. Model predictions were then confronted to experimental results obtained in another laboratory on other berries with different pH values. Excellent predictions were obtained in most cases, while failed predictions provided safe results, with the model predicting higher residual virus titres than what was observed.