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Cited by 445

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Modeling of bacterial growth as a function of temperature

Open access
Zwietering, M H, de Koos, J T, Hasenack, B E, de Witt, J C, van't Riet, K·1991·Applied and Environmental Microbiology

Abstract

The temperature of chilled foods is a very important variable for microbial safety in a production and distribution chain. To predict the number of organisms as a function of temperature and time, it is essential to model the lag time, specific growth rate, and asymptote (growth yield) as a function of temperature. The objective of this research was to determine the suitability and usefulness of different models, either available from the literature or newly developed. The models were compared by using an F test, by which the lack of fit of the models was compared with the measuring error. From the results, a hyperbolic model was selected for the description of the lag time as a function of temperature. Modified forms of the Ratkowsky model were selected as the most suitable model for both the growth rate and the asymptote as a function of temperature. The selected models could be used to predict experimentally determined numbers of organisms as a function of temperature and time.

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Crossref · 10.1128/aem.57.4.1094-1101.1991

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Metadata

Authors
Zwietering, M. H., de Koos, J. T., Hasenack, B. E., de Witt, J. C., & van't Riet, K.
Venue
Applied and Environmental Microbiology
Published
1991-04
Volume
57
Issue
4
Pages
1094-1101
ISSN
  • 0099-2240 (ISSN-L)
  • 1098-5336
OpenAlex
W1508208342
Cited by
445
References
1
FWCI
8.56
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Modeling of bacterial growth as a function of temperature. https://app.cochat.ai/papers/10.1128/aem.57.4.1094-1101.1991
The paper
Zwietering, M. H., de Koos, J. T., Hasenack, B. E., de Witt, J. C., & van't Riet, K. (1991). Modeling of bacterial growth as a function of temperature. Applied and Environmental Microbiology. https://doi.org/10.1128/aem.57.4.1094-1101.1991

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.