Food Science and Biotechnology

Korean Society of Food Science and Technology (한국식품과학회)
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Development of a Predictive Mathematical Model for the Growth Kinetics of Listeria monocytogenes in Sesame Leaves

  • Park, Shin-Young (Institute of Biomedical Science, Hanyang University) ;
  • Choi, Jin-Won (Department of Food Science and Technology, Chung-Ang University) ;
  • Chung, Duck-Hwa (Division of Applied Life Science, Gyeongsang National University) ;
  • Kim, Min-Gon (Laboratory of Integrative Biotechnology, Korea Research Institute of Bioscience and Biotechnology) ;
  • Lee, Kyu-Ho (Department of Environmental Engineering and Biotechnology, Hankuk University of Foreign Studies) ;
  • Kim, Keun-Sung (Department of Food Science and Technology, Chung-Ang University) ;
  • Bahk, Gyung-Jin (Korea Health Industry Development Institute) ;
  • Bae, Dong-Ho (Division of Bioscience and Biotechnology, Konkuk University) ;
  • Park, Sang-Kyu (Bio/Molecular Information Center, Konkuk University) ;
  • Kim, Kwang-Yup (Department of Food Science and Technology, Chungbuk University) ;
  • Kim, Cheorl-Ho (Department of Biological Sciences, College of Natural Science, Sungkyunkwan University) ;
  • Ha, Sang-Do (Department of Food Science and Technology, Chung-Ang University)
  • Published : 2007.04.30
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Abstract

Square root models were developed for predicting the kinetics of growth of Listeria monocytogenes in sesame leaves as a function of temperature (4, 10, or $25^{\circ}C$). At these storage temperatures, the primary growth curves fit well ($R^2=0.898$ to 0.980) to a Gompertz equation to obtain lag time (LT) and specific growth rate (SGR). The square root models for natural logarithm transformations of the LT and SGR as a function of temperature were obtained by SAS's regression analysis. As storage temperature ($4-25^{\circ}C$) decreased, LT increased and SGR decreased, respectively. Square root models were identified as appropriate secondary models for LT and SGR on the basis of most statistical indices such as coefficient determination ($R^2=0.961$ for LT, 0.988 for SGR), mean square error (MSE=0.l97 for LT, 0.005 for SGR), and accuracy factor ($A_f=1.356$ for LT, 1.251 for SGR) although the model for LT was partially not appropriate as a secondary model due to the high value of bias factor ($B_f=1.572$). In general, our secondary model supported predictions of the effects of temperature on both LT and SGR for L. monocytogenes in sesame leaves.

Keywords

References

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