Dry roasting is used to control Salmonella contamination on peanuts. The goal of this research is to develop an enhanced understanding of modeling pathogen-reduction achieved during roasting of low-moisture foods by incorporating product and process factors in inactivation models. The objectives to accomplish this goal are: (1) Select and parameterize a model to quantify inactivation kinetics for Enterococcus faecium on the surface of peanuts during convective dry roasting, (2) Develop a heat and mass transfer model to quantify transient temperature and moisture at the surface of peanuts during convective dry roasting, (3) Evaluate the impact of heat transfer properties on predicted log reduction of E. faecium on peanuts.Shelled peanuts inoculated with E. faecium were treated at various air temperatures (121, 149, and 177℗ʻC) and air velocities (1.0 and 1.3 m/s) at several times. Sample temperature was measured during treatment. Moisture content was measured and E. faecium were enumerated after treatment. Inactivation was modeled as a function of time, product temperature, and product moisture for the whole data set, and then separately for each air velocity. Multiple primary model (log-linear and Weibull) and secondary model (Bigelow-type, and Bigelow-type modified to include water activity or moisture content) combinations were used and evaluated for fit. In general, the models with more terms tended to fit better than the simplest model form, with decreasing RMSE, residuals, and AICc as the model became more complex. However, when comparing observed data vs. predicted data for the four model forms evaluated, it was evident that the practical differences between the model forms were small. The recommended model form was the log-linear primary model combined with the Bigelow-type secondary model because it will be easier to implement in an industrial setting.A 1-D model was developed in COMSOL to determine the effects of dry roasting on temperature and moisture profiles at the surface of the peanut. Heat transfer was described using Fourier's law, where the heat was transferred to the peanut through convection and through the peanut by conduction. Mass transfer was described using Fick's law, where the water was driven out of the peanut through diffusion and evaporated from the surface through conduction. Values for the properties of the peanut and air, as well as thermal and mass transfer properties were either directly measured or obtained from the literature. Scaled sensitivity coefficients were calculated for the heat and mass transfer parameters - thermal conductivity, specific heat, convective heat transfer coefficient, mass diffusivity of water in peanut, and mass transfer coefficient. This analysis showed that the mass transfer properties (mass diffusivity of water in peanut and mass transfer coefficient) were unimportant to the model, and the most important property was the convective heat transfer coefficient, followed, by the specific heat, and thermal conductivity. With this information, these parameters were measured experimentally or optimized in the model to obtain more accurate values. The model's accuracy was improved from 9.61 to 3.97℗ʻC with these improved values.Sensitivity analysis was performed on a combined heat transfer and microbial inactivation model. This analysis entailed iterating heat transfer properties between an order of magnitude below and above their original baseline values. Results indicated that substantial changes in thermal conductivity did not make much of a difference in the time to reach a 5-log reduction, but overestimating specific heat and underestimating the convective heat transfer coefficient resulted in much longer times to a 5-log reduction.
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