Food Science and Biotechnology

Korean Society of Food Science and Technology (한국식품과학회)
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Predictive Modeling of the Growth and Survival of Listeria monocytogenes Using a Response Surface Model

  • Jin, Sung-Sik (Division of Food and Biotechnology, Kangwon National University) ;
  • Jin, Yong-Guo (Division of Food and Biotechnology, Kangwon National University) ;
  • Yoon, Ki-Sun (Center for Food Science and Technology, University of Maryland Eastern Shore) ;
  • Woo, Gun-Jo (Department of Food Microbiology, Korea Food and Drug Administration) ;
  • Hwang, In-Gyun (Department of Food Microbiology, Korea Food and Drug Administration) ;
  • Bahk, Gyung-Jin (Korea Health Industry Development institute) ;
  • Oh, Deog-Hwan (Division of Food and Biotechnology, Kangwon National University)
  • Published : 2006.10.30
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Abstract

This study was performed to develop a predictive model for the growth kinetics of Listeria monocytogenes in tryptic soy broth (TSB) using a response surface model with a combination of potassium lactate (PL), temperature, and pH. The growth parameters, specific growth rate (SGR), and lag time (LT) were obtained by fitting the data into the Gompertz equation and showed high fitness with a correlation coefficient of $R^2{\geq}0.9192$. The polynomial model was identified as an appropriate secondary model for SGR and LT based on the coefficient of determination for the developed model ($R^2\;=\;0.97$ for SGR and $R^2\;=\;0.86$ for LT). The induced values that were calculated using the developed secondary model indicated that the growth kinetics of L. monocytogenes were dependent on storage temperature, pH, and PL. Finally, the predicted model was validated using statistical indicators, such as coefficient of determination, mean square error, bias factor, and accuracy factor. Validation of the model demonstrates that the overall prediction agreed well with the observed data. However, the model developed for SGR showed better predictive ability than the model developed for LT, which can be seen from its statistical validation indices, with the exception of the bias factor ($B_f$ was 0.6 for SGR and 0.97 for LT).

Keywords

References

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