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Monte Carlo procedures can be used to evaluate the uncertainty of food safety and quality estimations associated with the variability in the parameters for the calculation model used. Procedures for possible inclusion in an undergraduate process engineering course covering the assessment of uncertainty in thermal processing decisions were developed using spreadsheets and operations found in the Excel™ Analysis ToolPack. Published thermal decimal reduction time (DT, T = 110 °C) and initial spore load (No, spores/container) level for Clostridium botulinum Type B in mushroom were used to estimate a thermal processing time (FT). For a survival probability (N) of 1 spore in 109 containers and using mean values for the parameters DT and log No yielded F110°C = 5.96 min. Unique combinations of D T ∗ and N o ∗ datasets generated assuming normal and lognormal distributions, respectively, were used to obtain the distribution for the spore survival probability and the associated percentage of under processing. Next, the coefficient of variation (CV) for the percentage of under processing based on 2–500 generated datasets was calculated and used as a criterion to determine that 100 was an acceptable minimum number of datasets to estimate a recommended thermal process (F110°C = 9.6 min) considering the reported variability of the parameters DT and No. This thermal process would yield a 10−9 failure rate with a 95% confidence based on the frequency distribution for spore survival probability. The same procedures were used to assess the impact of lowering the standard deviation of both No and D110°C by 10%, 50%, and 90% yielding 8.6, 7.8, and 6.4 min, respectively, as a recommended thermal process at 110 °C with 95% confidence. |
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