Modeling the temperature-dependent microbial reduction of Enterococcus faecium NRRL B-2354 in radio-frequency pasteurized wheat flour

Abstract Radio-frequency (RF) pasteurization has been identified as a potential technology to pasteurize low-moisture foods. Recent studies demonstrated that soft wheat organic flour with water activity of 0.45 at 25°C subjected to RF heating at 80-85°C followed by 10-25 min nature cooling could result in 2.5-3.7 log 10 reduction of Enterococcus faecium NRRL B-2354 ( E. faecium ), a valid surrogate for Salmonella Enteritidis PT30. Bigelow model was used to predict the temperature-time dependent microbial reduction of bacteria during a RF process. However, reported studies only validate the accuracy of this model by testing microbial reductions of target microorganism at limited locations and at a fixed heating rate. RF processing with a natural cooling may lead to a relocation of the least lethality zone, which could fail in predicting the worst scenario. In this study, microbial reduction of E. faecium was evaluated at 15 locations (evenly distributed in top, middle and bottom layers) in a 1.8 kg-wheat flour container (water activity 0.45±0.02 at 22°C) after RF heating to 80°C with three different RF heating rates (36.0, 11.3, 5.5°C·min -1 ), followed by a 20 min nature cooling. Fiber optic sensors were used to monitor the temperatures at the geometric center of three layers throughout the process. Bigelow model was applied to predict the temperature-time dependent reductions of E. faecium in each layer. RF heating to 80°C combined with a 20 min nature cooling could achieve an average microbial reduction of 1.21-4.64 log 10 CFU/g in wheat flour at water activity 0.45±0.02 at 22°C. Fast-heating rate resulted in non-uniformity in terms of temperature and inactivation. Using least sum of squares technique, Bigelow model yielded D -value of 8.3 min at 80°C with a given z -value of 11.7 °C. The fitting results using the Bigelow model were in good agreement with that from the experiment ( R 2 =0.81). This study provides comprehensive evidence on using Bigelow model to predict the real-time microbial reduction of bacteria in a RF process.