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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Residue Chemistry and Predictive Microbiology Research » Research » Publications at this Location » Publication #324725

Research Project: DEVELOPMENT OF PREDICTIVE MICROBIAL MODELS FOR FOOD SAFETY AND THEIR ASSOCIATED USE IN INTERNATIONAL MICROBIAL DATABASES

Location: Residue Chemistry and Predictive Microbiology Research

Title: Mathematical modeling and validation of growth of Salmonella Enteritidis and background microorganisms in potato salad – one-step kinetic analysis and model development

Author
item Huang, Lihan

Submitted to: Food Control
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/2/2016
Publication Date: 3/24/2016
Citation: Huang, L. 2016. Mathematical modeling and validation of growth of Salmonella Enteritidis and background microorganisms in potato salad – one-step kinetic analysis and model development. Food Control. 68(10):69-76. doi: 10.1016/j.foodcont.2016.03.039.

Interpretive Summary: Salmonella Enteritidis (SE) is a major foodborne pathogen frequently associated with eggs and egg-related products. Potato salad can be a vehicle for transmission of SE to consumers. This study investigates the growth kinetics of this pathogen and background microorganisms in potato salad and develops mathematical models to accurately predict the microbial growth under different temperature conditions. The models developed in this study can be used in risk assessment of SE and predict the microbial shelf-life of potato salad.

Technical Abstract: This study was conducted to examine the growth of Salmonella Enteritidis (SE) in potato salad caused by cross-contamination and temperature abuse, and develop mathematical models to predict its growth. The growth of SE was investigated under constant temperature conditions (8, 10, 15, 20, 25, 30, and 37 degrees C) to evaluate the effect of temperature on growth rates and lag times. Duplicated experiments were conducted. The data set from one replicate was used to develop kinetic models and determine kinetic parameters. The data from the other replicate served as an independent data set for model validation. The growth of background microorganism (BK) was also examined. One step-kinetic analysis method was used to directly construct both primary (Huang) and secondary (Ratkowsky square-root) models. Nonlinear regression was used to minimize the global residual sum of squares (RSS) for SE and BK. The results showed that both primary and secondary models can be used to analyze the growth curves, with the kinetic parameters closely matching the characteristics of SE and BK. The validation results showed that the root-mean-square error (RMSE) was only 0.39 Log CFU/g for SE and 0.55 Log CFU/g for BK, with the residual errors of predictions following logistic distributions. This study showed that one-step kinetic analysis is a useful and efficient method for analyzing the entire data set to directly construct primary and secondary growth models and determine kinetic parameters. Since the models were validated, they can be used to predict the growth of SE and conduct risk assessment, and to predict the microbiological shelf-life of potato salad.