Bootstrapping for Estimating the Conservative Kill Ratio of the Surrogate to the Pathogen for Use in Thermal Process Validation at the Industrial Scale.

A surrogate is commonly used for process validations. The industry often uses the target log cycle reduction for the test (LCR) microorganism (surrogate) to be equal to the desired log cycle reduction for the target (LCR) microorganism (pathogen). When the surrogate is too conservative with far greater resistance than the pathogen, the food may be overprocessed with quality and cost consequences. In aseptic processing, the Institute for Thermal Processing Specialists recommends using relative resistance (D)/(D) to calculate LCR (product of LCR and relative resistance). This method uses the mean values of D and D and does not consider the estimating variability. We defined kill ratio (KR) as the inverse of relative resistance.The industry uses an extremely conservative KR of 1 in the validation of food processes for low-moisture foods, which ensures an adequate reduction of LCR, but can result in quality degradation. This study suggests an approach based on bootstrap sampling to determine conservative KR, leading to practical recommendations considering experimental and biological variability in food matrices. Previously collected thermal inactivation kinetics data of Salmonella spp. (target organism) and Enterococcus faecium (test organism) in Non-Fat Dried Milk (NFDM) and Whole Milk Powder (WMP) at 85, 90, and 95°C were used to calculate the mean KR. Bootstrapping was performed on mean inactivation rates to get a distribution of 1000 bootstrap KR values for each of the treatments. Based on minimum temperatures used in the industrial process and acceptable level of risk (e.g., 1, 5, or 10% of samples that would not achieve LCR), a conservative KR value can be estimated. Consistently, KR increased with temperature and KR for WMP was higher than NFDM. Food industries may use this framework based on the minimum processing temperature and acceptable level of risk for process validations to minimize quality degradation.