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Updated: Jun 23, 2026

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Theil error splitting method for selecting the "best model" in microbial inactivation studies.

Federico Harte1, Glenn Black, P Michael Davidson

  • 1Department of Food Science and Technology, University of Tennessee, 2605 River Drive, Knoxville, Tennessee 37996, USA. fede@utk.edu

Journal of Food Protection
|May 14, 2009
PubMed
Summary

This study evaluated models for predicting bacterial inactivation by heat, finding the best model minimizes errors and maximizes random variability. This research aids in understanding thermal inactivation kinetics for Escherichia coli.

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Area of Science:

  • Microbiology
  • Food Science
  • Biotechnology

Background:

  • Thermal inactivation is crucial for microbial safety.
  • Predictive models aid in optimizing sterilization processes.
  • Escherichia coli (E. coli) is a key indicator organism.

Purpose of the Study:

  • To evaluate three established models for predicting thermal inactivation of E. coli K-12.
  • To assess model accuracy using Theil's error splitting method.
  • To identify the best-fitting model based on statistical error analysis.

Main Methods:

  • E. coli K-12 cultures were subjected to isothermal inactivation at 58°C under various conditions.
  • Survival data were analyzed using three predictive inactivation models.
  • Theil's error splitting method decomposed prediction errors into bias, regression, and random components.

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Main Results:

  • Theil's method quantified bias, regression, and random error sources for each model.
  • Statistical tests assessed the significance of error terms and normality of random error.
  • Model performance was evaluated based on minimizing total error and maximizing random error.

Conclusions:

  • The optimal model demonstrated minimal bias and regression errors, with significant random error.
  • Accurate prediction of microbial thermal inactivation requires careful model selection and validation.
  • Understanding error components is vital for reliable microbial inactivation predictions.