Submitted to: Journal of Food Protection
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/31/2003
Publication Date: 6/1/2004
Citation: Oscar, T.P. 2004. Predictive simulation model for enumeration of salmonella on chicken as a function of polymerase chain reaction detection time score and sample size. Journal of Food Protection. 67(6):1201-1208.
Interpretive Summary: The use of risk assessments to guide policy decisions directed at improving the microbiological safety of the U.S. food supply is increasingly used among government regulatory agencies. A data gap that is routinely identified by risk assessors is the lack of information on the number and distribution of pathogens on and in food. Enumeration of pathogens in food is not often done because they occur infrequently and in low numbers. As a result, many food samples must be analyzed to obtain enough positive samples to generate a probability distribution that is useful for risk assessment. Although the most probable number (MPN) method can be used to enumerate low numbers of pathogens on and in food, the MPN method is expensive and time consuming due to multiple steps that take up to one week to complete. With the advent of new DNA based methods that are highly specific for detecting pathogens on and in food, it is now possible to develop rapid methods that eliminate most of the steps in the MPN method. In the present study, a commercial DNA system for rapid detection of Salmonella on food samples was converted to a rapid assay for enumerating Salmonella on food. The new method was developed specifically for risk assessment and thus, will help government regulatory agencies obtain information needed to more effectively protect the nation's food supply.
Technical Abstract: Concepts of microbial growth kinetics, polymerase chain reaction (PCR) detection of pathogens and simulation modeling were combined to develop a predictive simulation model for enumeration of Salmonella on chicken as a function of PCR detection time score (PCRDTS) and sample size. Data from challenge studies with Salmonella and chicken homogenates (25 g of chicken plus 225 ml of sterile buffered peptone water) were used to build the model. Chicken homogenates were inoculated with 10(0)exponents to 10(6)exponents CFU of Salmonella followed by incubation at 37 C for 24 h. Subsamples were collected at 0, 2, 4, 6, 8, 10, 12 and 24 h of incubation and tested for Salmonella using a commercial PCR system. A PCRDTS, which was based on the widths of the PCR bands of the subsamples in the electrophoresis gel, was obtained for each inoculated chicken homogenate. Standard curves relating PCRDTS to initial density of inoculated Salmonella were developed for sterile and nonsterile chicken homogenates and two serotypes of Salmonella. Of the factors examined, microbial competition had the largest effect on the standard curve. Microbial competition suppressed growth and thus, PCRDTS at low (< 100 CFU) but not at high initial density of inoculated Salmonella and resulted in nonlinear standard curves as compared to linear standard curves for sterile chicken homogenates. Data from two experiments with nonsterile chicken homogenates were combined to yield the standard curve that was used to develop a simulation model that predicted the incidence and distribution of Salmonella contamination on naturally contaminated chicken as a function of PCRDTS and sample size. Simulation results from the model indicated that the incidence and extent of Salmonella contamination of naturally contaminated chicken increased in a non-linear manner as a function of sample size. Thus, linear extrapolation of enumeration results, a common practice in microbial risk assessment, is not appropriate.