P3-29 Cross-laboratory Comparative Study of the Impact of Experimental and Regression Methodologies on Salmonella Thermal Inactivation Parameters

Wednesday, July 25, 2012
Exhibit Hall (Rhode Island Convention Center)
Ian Hildebrandt, Michigan State University, East Lansing, MI
Bradley Marks, Michigan State University, East Lansing, MI
Vijay Juneja, U.S. Department of Agriculture-ARS-ERRC, Wyndmoor, PA
Angie Osoria, U.S. Department of Agriculture-ARS-ERRC, Wyndmoor, PA
Nicole Hall, Michigan State University, East Lansing, MI
Introduction: Thermal inactivation studies often are aimed at testing the effects of various product factors on the inactivation rate of pathogens. However, the lack of standardized methods causes significant difficulties when trying to compare results across studies, and also limits the utility of merged data sets for meta-analyses or improved inactivation models.

Purpose: Therefore, the objective was to conduct a comparative study across two laboratories to determine whether experimental methodologies for isothermal inactivation tests, or data processing methods, significantly affect resulting inactivation parameters.

Methods: Using the same batch of irradiated ground beef (2.3% fat) and an identical 8-serovar Salmonella cocktail, two separate laboratory groups (MSU and USDA) each performed two different isothermal inactivation trials (reps ≥ 2) at 60 °C, each using two methods previously published by each group. The mean initial inoculation level across all trials was 7.2 log CFU/g. The raw data were then pooled and analyzed independently by each group, with MSU computing D-values using linear regression of the log CFU/g data, and USDA computing D-values from the rate parameter from regression of the same data using DMFit.  The net result was a 2x2x2 study, with two laboratories, two experimental methodologies, and two analysis/regression methods. 

Results: In pairwise comparisons of the survivor data using analysis of covariance, the P values for intra-laboratory results (across methods) were 0.07 and 0.01, and for intra-method results (across laboratories) were 0.07 and 0.45.  When comparing the D-values determined using the two different regression methods by the respective groups, the differences were 3-6% or 21-40% when analyzing data generated by the two respective experimental methodologies, with D-values ranging from 0.88 to 2.2 min. 

Significance: Overall, the results indicate that experimental methodologies and data processing methods can significantly impact reported inactivation parameters across multiple laboratories, even when using identical cultures and food materials, supporting the importance of fully disclosing methodology details and considering standardization of methodologies in the field.