Literature DB >> 2592289

A predictive model for combined temperature and water activity on microbial growth during the growth phase.

K R Davey1.   

Abstract

An empirical and generalized model is presented, based on a modified Arrhenius equation, for predicting the combined effect of temperature and water activity on the growth rate of bacteria. When it was applied to seven separate sets of wide ranging published results, spanning some 50 years and including a spore-former and a silage micro-organism, predictions explained between 92.9 and 99.0% of the variation in the results with an overall mean of 96.6%. Advantages over existing models are that it is relatively easy to fit to data using least squares regression and requires only five coefficients. These, together with its simplicity and demonstrated wide application, will facilitate its practical use.

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Year:  1989        PMID: 2592289     DOI: 10.1111/j.1365-2672.1989.tb02519.x

Source DB:  PubMed          Journal:  J Appl Bacteriol        ISSN: 0021-8847


  10 in total

1.  Quantifying the sensitivity of G. oxydans ATCC 621H and DSM 3504 to osmotic stress triggered by soluble buffers.

Authors:  B Luchterhand; T Fischöder; A R Grimm; S Wewetzer; M Wunderlich; T Schlepütz; J Büchs
Journal:  J Ind Microbiol Biotechnol       Date:  2015-02-03       Impact factor: 3.346

2.  Effects of Temperature, pH, and NaCl on Growth and Pectinolytic Activity of Pseudomonas marginalis.

Authors:  J M Membré; P M Burlot
Journal:  Appl Environ Microbiol       Date:  1994-06       Impact factor: 4.792

3.  Statistical approach for comparison of the growth rates of five strains of Staphylococcus aureus.

Authors:  E Dengremont; J M Membré
Journal:  Appl Environ Microbiol       Date:  1995-12       Impact factor: 4.792

4.  The effect of handwashing at recommended times with water alone and with soap on child diarrhea in rural Bangladesh: an observational study.

Authors:  Stephen P Luby; Amal K Halder; Tarique Huda; Leanne Unicomb; Richard B Johnston
Journal:  PLoS Med       Date:  2011-06-28       Impact factor: 11.069

5.  Modeling of the Coral Microbiome: the Influence of Temperature and Microbial Network.

Authors:  Laís F O Lima; Maya Weissman; Micheal Reed; Bhavya Papudeshi; Amanda T Alker; Megan M Morris; Robert A Edwards; Samantha J de Putron; Naveen K Vaidya; Elizabeth A Dinsdale
Journal:  mBio       Date:  2020-03-03       Impact factor: 7.867

6.  Predictive Modeling for the Growth of Salmonella spp. in Liquid Egg White and Application of Scenario-Based Risk Estimation.

Authors:  Mi Seon Kang; Jin Hwa Park; Hyun Jung Kim
Journal:  Microorganisms       Date:  2021-02-25

7.  Global Grassland Diazotrophic Communities Are Structured by Combined Abiotic, Biotic, and Spatial Distance Factors but Resilient to Fertilization.

Authors:  Maximilian Nepel; Roey Angel; Elizabeth T Borer; Beat Frey; Andrew S MacDougall; Rebecca L McCulley; Anita C Risch; Martin Schütz; Eric W Seabloom; Dagmar Woebken
Journal:  Front Microbiol       Date:  2022-03-28       Impact factor: 5.640

8.  Dimensional Analysis Model Predicting the Number of Food Microorganisms.

Authors:  Cuiqin Li; Laping He; Yuedan Hu; Hanyu Liu; Xiao Wang; Li Chen; Xuefeng Zeng
Journal:  Front Microbiol       Date:  2022-02-08       Impact factor: 5.640

9.  Effect of temperature on growth of Vibrio parahaemolyticus [corrected] and Vibrio vulnificus in flounder, salmon sashimi and oyster meat.

Authors:  Yoo Won Kim; Soon Ho Lee; In Gun Hwang; Ki Sun Yoon
Journal:  Int J Environ Res Public Health       Date:  2012-12       Impact factor: 3.390

10.  Development and Validation of Predictive Model for Salmonella Growth in Unpasteurized Liquid Eggs.

Authors:  Young-Jo Kim; Hye-Jin Moon; Soo-Kyoung Lee; Bo-Ra Song; Jong-Soo Lim; Eun-Jeong Heo; Hyun-Jung Park; Sung-Hwan Wee; Jin-San Moon
Journal:  Korean J Food Sci Anim Resour       Date:  2018-07-31       Impact factor: 2.622

  10 in total

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