Response Surface Methodology for Salmonella Inactivation during Extrusion Processing of Oat Flour

An increase in the number of foodborne outbreaks and recalls due to Salmonella in low-moisture foods has resulted in the need for the development and validation of process controls to ensure their microbiological safety. Furthermore, the Food Safety Modernization Act Preventive Controls for Human Food final rule requires fixed processors to validate their process controls to ensure food safety. The objective of this study was to develop a response surface model to predict Salmonella inactivation in oat flour, as affected by moisture, fat content, screw speed, and temperature. Oat flour was adjusted to different moisture (14 to 26% wet basis) and fat (5 to 15% [w/w]) contents and was then inoculated with a five-strain cocktail of Salmonella. Inoculated material was extruded through a single-screw extruder running at different screw speeds (75 to 225 rpm) and temperatures (65 to 85 degrees C), without a die. Once steady-state conditions were attained, extruded samples were collected, cooled, and stored under refrigeration, and Salmonella survivors were enumerated. A split-plot central composite second-order response surface design was used, with the central point replicated six times. Temperature showed a significant (P < 0.0005) positive effect on microbial reduction. Moisture content showed significant linear (P = 0.0014) and quadratic (P = 0.0005) effects, whereas higher fat content showed a significant (P < 0.0001) protective effect on Salmonella destruction. The screw speed did not play a major role in inactivating Salmonella, but it had a significant (P=0.0004) interactive effect with temperature. Results indicated that a >5.5-log reduction was achieved in oat flour extruded at a temperature above 85 degrees C at all moisture and fat contents evaluated at a screw speed of 150 rpm. The developed response surface model can be used to identify the extrusion process conditions to achieve a desired reduction of Salmonella based on the moisture and fat contents of the product.