A meta-analysis of microbial thermal inactivation in low moisture foods

Abstract

Microbial thermal inactivation in low moisture foods is challenging due to enhanced thermal resistance of microbes and low thermal conductivity of food matrices. In this study, we leveraged the body of previous work on this topic to model key experimental features that determine microbial thermal inactivation in low moisture foods. We identified 27 studies which contained 782 mean D-values and developed linear mixed-effect models to assess the effect of microorganism type, matrix structure and composition, water activity, temperature, and inoculation and recovery methods on cell death kinetics. Intraclass correlation statistics (I²) and conditional R² values of the linear mixed effects models were: E. coli (R²-0.91, I²-83%), fungi (R²-0.88, I²-85%), L. monocytogenes (R²-0.84, I²-75%), Salmonella (R²-0.69, I²-46%). Finally, global response surface models (RSM) were developed to further study the non-linear effect of a_w and temperature on inactivation. The fit of these models varied by organisms from R² 0.88 (E. coli) to 0.35 (fungi). Further dividing the Salmonella data into individual RSM models based on matrix structure improved model fit to R² 0.90 (paste-like products) and 0.48 (powder-like products). This indicates a negative relationship between data diversity and model performance.

Keywords: Composition; Food matrix; Linear mixed-effects model; Response surface model; Water activity.