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Mould germination: data treatment and modelling.

Philippe Dantigny1, Sonia Marín, Marco Beyer

  • 1Laboratoire de microbiologie, UMR Université de Bourgogne/INRA 1232, ENS.BANA, 1 Esplanade Erasme, F-21000 Dijon, France. phdant@u-bourgogne.fr

International Journal of Food Microbiology
|December 26, 2006
PubMed
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This study compared the Gompertz and logistic models for fitting fungal spore germination data. The logistic model offered more precise parameter estimation, especially for symmetrical germination curves.

Area of Science:

  • Food microbiology
  • Mycology
  • Mathematical modeling

Background:

  • Fungal spore germination is crucial for food spoilage.
  • Accurate modeling of germination kinetics is essential for predicting food safety.
  • Environmental factors significantly influence fungal growth.

Purpose of the Study:

  • To analyze germination data for eight food spoilage molds under varying conditions.
  • To compare the fitting capabilities of the Gompertz and logistic models.
  • To simulate germination by assessing latent period distributions.

Main Methods:

  • Analysis of percentage germination data over time for eight mold species.
  • Fitting experimental data using Gompertz and logistic equations.
  • Simulation of germination curves using different latent period distributions.

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Main Results:

  • No significant correlation between mean percentage and variance of germination data.
  • Both Gompertz and logistic models fitted the data adequately, with logistic showing more precise parameter estimation.
  • Normal distributions aligned with logistic model fits, while skewed distributions matched Gompertz model fits.

Conclusions:

  • The logistic function is preferred for symmetrical fungal spore germination curves.
  • The Gompertz model is suitable for asymmetric germination curves.
  • Further research is needed to validate assumed latent period distributions against experimental data.