Cross-Laboratory Comparative Study of the Impact of Experimental and Regression Methodologies on Salmonella Thermal...

Isothermal inactivation studies are commonly used to quantify thermal inactivation kinetics of bacteria. Meta-analyses and comparisons utilizing results from multiple sources have revealed large variations in reported thermal resistance parameters for Salmonella, even when in similar food materials. Different laboratory or regression methodologies likely are the source of methodology-specific artifacts influencing the estimated parameters; however, such effects have not been quantified. The objective of this study was to evaluate the effects of laboratory and regression methodologies on thermal inactivation data generation, interpretation, modeling, and inherent error, based on data generated in two independent laboratories. The overall experimental design consisted of a cross-laboratory comparison using two independent laboratories (Michigan State University and U.S. Department of Agriculture, Agricultural Research Service, Eastern Regional Research Center [ERRC] laboratories), both conducting isothermal Salmonella inactivation studies (55, 60, 62°C) in ground beef, and each using two methodologies reported in prior studies. Two primary models (log-linear and Weibull) with one secondary model (Bigelow) were fitted to the resultant data using three regression methodologies (two two-step regressions and a one-step regression). Results indicated that laboratory methodology impacted the estimated D60°C- and z-values (α = 0.05), with the ERRC methodology yielding parameter estimates ∼25% larger than the Michigan State University methodology, regardless of the laboratory. Regression methodology also impacted the model and parameter error estimates. Two-step regressions yielded root mean square error values on average 40% larger than the one-step regressions. The Akaike Information Criterion indicated the Weibull as the more correct model in most cases; however, caution should be used to confirm model robustness in application to real-world data. Overall, the results suggested that laboratory and regression methodologies have a large influence on resultant data and the subsequent estimation of thermal resistance parameters.