Literature DB >> 12450800

Modeling the lag time of Listeria monocytogenes from viable count enumeration and optical density data.

F Baty1, J P Flandrois, M L Delignette-Muller.   

Abstract

The following two factors significantly influence estimates of the maximum specific growth rate ( micro (max)) and the lag-phase duration (lambda): (i) the technique used to monitor bacterial growth and (ii) the model fitted to estimate parameters. In this study, nine strains of Listeria monocytogenes were monitored simultaneously by optical density (OD) analysis and by viable count enumeration (VCE) analysis. Four usual growth models were fitted to our data, and estimates of growth parameters were compared from one model to another and from one monitoring technique to another. Our results show that growth parameter estimates depended on the model used to fit data, whereas there were no systematic variations in the estimates of micro (max) and lambda when the estimates were based on OD data instead of VCE data. By studying the evolution of OD and VCE simultaneously, we found that while log OD/VCE remained constant for some of our experiments, a visible linear increase occurred during the lag phase for other experiments. We developed a global model that fits both OD and VCE data. This model enabled us to detect for some of our strains an increase in OD during the lag phase. If not taken into account, this phenomenon may lead to an underestimate of lambda.

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Year:  2002        PMID: 12450800      PMCID: PMC134405          DOI: 10.1128/AEM.68.12.5816-5825.2002

Source DB:  PubMed          Journal:  Appl Environ Microbiol        ISSN: 0099-2240            Impact factor:   4.792


  9 in total

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2.  A combined discrete-continuous model describing the lag phase of Listeria monocytogenes.

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3.  Comparison of maximum specific growth rates and lag times estimated from absorbance and viable count data by different mathematical models.

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Journal:  J Microbiol Methods       Date:  2001-01       Impact factor: 2.363

4.  Stochastic modelling of bacterial lag phase.

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Journal:  Int J Food Microbiol       Date:  2002-03       Impact factor: 5.277

5.  Comparison of Stochastic and Deterministic Concepts of Bacterial Lag.

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Review 7.  A dynamic approach to predicting bacterial growth in food.

Authors:  J Baranyi; T A Roberts
Journal:  Int J Food Microbiol       Date:  1994-11       Impact factor: 5.277

8.  Estimation of bacterial growth rates from turbidimetric and viable count data.

Authors:  P Dalgaard; T Ross; L Kamperman; K Neumeyer; T A McMeekin
Journal:  Int J Food Microbiol       Date:  1994-11       Impact factor: 5.277

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Journal:  J Theor Biol       Date:  1994-05-07       Impact factor: 2.691

  9 in total
  8 in total

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Journal:  Appl Environ Microbiol       Date:  2006-07       Impact factor: 4.792

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Authors:  A Vimont; C Vernozy-Rozand; M P Montet; C Lazizzera; C Bavai; M-L Delignette-Muller
Journal:  Appl Environ Microbiol       Date:  2006-01       Impact factor: 4.792

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Journal:  Appl Environ Microbiol       Date:  2004-05       Impact factor: 4.792

6.  Antimicrobial properties, compressive strength and fluoride release capacity of essential oil-modified glass ionomer cements-an in vitro study.

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7.  Long-term live-cell imaging reveals new roles for Salmonella effector proteins SseG and SteA.

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8.  Comparison of Primary Models to Predict Microbial Growth by the Plate Count and Absorbance Methods.

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Journal:  Biomed Res Int       Date:  2015-10-11       Impact factor: 3.411

  8 in total

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