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Predictive growth model of the effects of temperature on the growth kinetics of generic Escherichia coli in the Korean traditional rice cake product “Garaetteok

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Abstract

In this study, a predictive growth model of generic Escherichia coli in Garaetteok at a range of storage temperatures (T, 10–40 °C) was developed. The primary models of specific growth rate (SGR) and lag time (LT) fit well (R2 ≥ 0.985) using a Gompertz equation. Secondary polynomial models were obtained by non-linear regression and calculated as SGR = − 0.01,570 + 0.0183T + 0.000008T2; LT = 43.2064 − 2.4824T + 0.0355T2. The appropriateness of the secondary models was verified by mean square error (MSE; 0.0006 for SGR, 0.282 for LT), bias factor (B f ; 0.948 for SGR, 0.942 for LT), accuracy factor (A f ; 1.163 for SGR, 1.355 for LT), and coefficient of determination (r2; 0.986 for SGR, 0.996 for LT), and these models were found to be in good agreement with the experimental values used for validation. The secondary models developed in this study may thus be used as practical prediction models for generic E. coli growth in Garaetteok. These newly developed secondary models of SGR and LT for generic E. coli in Garaetteok may thus be incorporated into tertiary modeling programs such as the Korea Pathogen Modeling Program, in which they can easily be used to predict the growth kinetics of E. coli as a function of storage temperature. Ultimately, model developed in this study may be a vital tool for the reduction of E. coli levels in food production, processing, and distribution processes, which in turn will lead to enhanced safety of rice products.

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Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A4A01).

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Correspondence to Sang-Do Ha.

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Park, S.Y., Ha, SD. Predictive growth model of the effects of temperature on the growth kinetics of generic Escherichia coli in the Korean traditional rice cake product “Garaetteok”. J Food Sci Technol 55, 506–512 (2018). https://doi.org/10.1007/s13197-017-2959-z

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  • DOI: https://doi.org/10.1007/s13197-017-2959-z

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