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Bioinactivation: Software for modelling dynamic microbial inactivation.

Alberto Garre1, Pablo S Fernández1, Roland Lindqvist2

  • 1Departamento de Ingeniería de Alimentos y del Equipamiento Agrícola, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena (ETSIA), Paseo Alfonso XIII, 48, 30203 Cartagena, Spain.

Food Research International (Ottawa, Ont.)
|March 15, 2017
PubMed
Summary
This summary is machine-generated.

The bioinactivation software models microbial inactivation under dynamic conditions, offering user-friendly tools for the food and pharmaceutical industries. It uses Markov Chain Monte Carlo (MCMC) for better uncertainty characterization compared to traditional regression.

Keywords:
Freeware toolModel fittingNon-isothermal microbial inactivationPredictive microbiology

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Area of Science:

  • Microbiology
  • Food Science
  • Computational Biology

Background:

  • Microbial inactivation modeling is crucial for food safety and shelf-life prediction.
  • Existing models often lack flexibility for dynamic conditions and robust uncertainty quantification.

Purpose of the Study:

  • To introduce the bioinactivation software for modeling isothermal and non-isothermal microbial inactivation.
  • To provide user-friendly tools for dynamic conditions, various fitting algorithms, and prediction intervals.
  • To demonstrate the software's utility through a case study on Bacillus sporothermodurans inactivation.

Main Methods:

  • Development of the bioinactivation software in two formats: Bioinactivation core (R package) and Bioinactivation SE (user-friendly interface).
  • Implementation of established inactivation models (Bigelow, Peleg, Mafart, Geeraerd).
  • Application of non-linear regression and Markov Chain Monte Carlo (MCMC) algorithms for model fitting.
  • Case study involving non-isothermal inactivation of Bacillus sporothermodurans.

Main Results:

  • The bioinactivation software enables user-friendly modeling of microbial inactivation under dynamic temperature conditions.
  • The Markov Chain Monte Carlo (MCMC) algorithm provided a superior characterization of biological uncertainty and variability compared to non-linear regression.
  • A full characterization of Bacillus sporothermodurans response to dynamic temperatures, including confidence and prediction intervals, was achieved.

Conclusions:

  • The bioinactivation software is a valuable tool for microbial inactivation modeling, particularly under dynamic conditions.
  • MCMC offers improved biological uncertainty assessment in microbial inactivation studies.
  • The software supports quantitative microbial risk assessment in the food and pharmaceutical industries and for regulatory agencies.