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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 #314093

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: Direct construction of predictive models for describing growth Salmonella enteritidis in liquid eggs – a one-step approach

Author
item Huang, Lihan

Submitted to: Food Control
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/24/2015
Publication Date: 4/24/2015
Publication URL: http://handle.nal.usda.gov/10113/61056
Citation: Huang, L. 2015. Direct construction of predictive models for describing growth Salmonella enteritidis in liquid eggs – a one-step approach. Food Control. DOI: 10.1016j.foodcont.2015.03.051.

Interpretive Summary: Salmonella Enteritidis is a major foodborne pathogen frequently associated with egg products. This study was conducted to develop mathematical models to predict the growth of S. Enteritidis in liquid egg white and egg yolk using a novel one-step approach. Using this approach, more accurate mathematical models can be developed with minimized global errors. The models developed in this study can be used by the food industry and regulatory agencies to more accurately estimate the growth of S. Enteritidis in and conduct risk assessments of the safety of liquid egg products.

Technical Abstract: The objective of this study was to develop a new approach using a one-step approach to directly construct predictive models for describing the growth of Salmonella Enteritidis (SE) in liquid egg white (LEW) and egg yolk (LEY). A five-strain cocktail of SE, induced to resist rifampicin at 100 mg/L, was used to inoculate LEW and LEY. Kinetic studies were conducted isothermally at different temperatures between 8 and 43 deg. C to generate growth curves at each temperature. Once the growth curves were generated, they were assembled and analyzed using nonlinear regression to construct both primary and secondary models in one step, with an objective to minimize the global residual sum of squares (RSS) for the entire data set. For growth in LEW, a three-parameter logistic model was used. For growth in LEY, the Huang model was used as the primary model. The Ratkowsky square-root model was used to evaluate the growth rates. The results showed that the one-step approach successfully constructed the predictive models for describing the growth of SE in LEY and LEW. The estimated nominal minimum growth temperatures of SE were 7.4 deg. C and 9.9 deg. C, while the estimated maximum temperatures were 45.2 deg. C and 46.8 deg. C, respectively, in LEW and LEY. As a validation, the predictive models were tested with growth curves of SE in LEY and LEW at 37 deg. C. The root mean square error (RMSE) was only 0.36 and 0.28, respectively, for the growth models of SE in LEY and LEW, suggesting that the one-step approach can generate accurate models for predicting the growth of SE in LEY and LEW. The results from study can be used to predict the growth of SE and evaluate the safety of LEY and LEW.