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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 #338385

Research Project: Development of Predictive Microbial Models for Food Safety using Alternate Approaches

Location: Residue Chemistry and Predictive Microbiology Research

Title: Dynamic analysis of growth of Salmonella Enteritidis in liquid egg whites

Author
item Huang, Lihan
item Hwang, Cheng-An - Andy

Submitted to: Food Control
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/30/2017
Publication Date: 4/30/2017
Citation: Huang, L., Hwang, C. 2017. Dynamic analysis of growth of Salmonella Enteritidis in liquid egg whites. Food Control. 80:125-130. doi: 10.1016/j.foodcont.2017.04.044.

Interpretive Summary: Salmonella Enteritidis (SE) is a major foodborne pathogen frequently associated with eggs and egg-related products, and is a major public health hazard. This study applied a new one-step dynamic methodology to develop and validate a model to predict the growth and survival of SE in liquid egg whites (LEW) under sub-optimal and sub-lethal temperature conditions. The predictive model developed in this study can be used in risk assessment of SE and predict the microbial shelf-life of LEW.

Technical Abstract: Salmonella Enteritidis (SE) is a common foodborne pathogen associated with eggs and egg products. This research was conducted to study the kinetics of growth and survival of SE in liquid egg whites (LEW). A dynamic temperature profile that exposed SE to suboptimal temperatures and below the minimum growth temperature (Tmin) was used with two isothermal conditions to develop kinetic models. One-step dynamic analysis was used to directly construct a tertiary model for describing the growth and survival of SE and determine the kinetic parameters. The results of kinetic analysis showed that the Tmin was 7.7 degrees C and SE may die off at a rate of 2.78×10-3 log CFU/ml per h per degrees C below the Tmin. The root mean square error (RMSE) of the model was 0.5 log CFU/ml, with 76.6% of the residual errors within ± 0.5 log CFU/ml of the experimental observations. The model was validated under both dynamic temperature and isothermal conditions. Both growth and survival of SE was accurately predicted, with the RMSE of validation at < 0.5 log CFU/ml. For all the validation tests, nearly 75% of the residual errors were within ± 0.5 log CFU/ml of the experimental observations. This study clearly demonstrated that the one-step dynamic analysis method is an accurate and efficient method for direct construction of predictive models and estimation of the associated kinetic parameters that govern the growth and survival of microorganisms in food. Since the mathematical model has been validated, it can be used to predict the growth and survival of SE in LEW during storage and distribution and for conducting risk assessment of this microorganism in LEW.