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Optimal temperature input design for estimation of the square root model parameters: parameter accuracy and model
Kristel Bernaerts1, Roos D Servaes, Steven Kooyman
1Bioprocess Technology and Control (BioTeC), Department of Food and Microbial Technology, Katholieke Universiteit Leuven, Belgium.
International Journal of Food Microbiology
|April 6, 2002
Summary
Optimal experiment design using dynamic temperature shifts improves parameter estimation accuracy for microbial growth models. Novel temperature profiles ensure sustained exponential growth, yielding precise Square Root model parameter estimates.
Area of Science:
- Microbial Physiology
- Bioprocess Engineering
- Mathematical Modeling
Background:
- Accurate parameter estimation is crucial for predictive microbial growth models.
- Optimal experiment design enhances parameter estimation accuracy, influencing prediction confidence limits.
- Previous studies suggest dynamic temperature conditions improve Square Root model parameter estimation.
Purpose of the Study:
- To design and experimentally implement an optimal temperature input profile for accurate Square Root model parameter estimation.
- To ensure model validity and maintain exponential growth of Escherichia coli K12 during dynamic temperature changes.
- To address limitations of common growth models in handling intermediate lag phases caused by extreme temperature shifts.
Main Methods:
- Utilized optimal experiment design principles for parameter estimation.
- Developed a novel, computer-controlled bioreactor experiment with precise sampling.
- Implemented a temperature input strategy comprising a sequence of smaller temperature increments, contrasting with single-step profiles.
- Generated starting values for experiment design using a traditional two-step static experiment procedure.
Main Results:
- Extreme temperature shifts were found to disturb exponential growth, inducing intermediate lag phases in Escherichia coli K12.
- The designed optimal temperature input successfully guaranteed model validity while yielding accurate Square Root model parameters.
- High-quality data obtained under optimally varying temperature conditions resulted in precise parameter estimates, validated by confidence intervals and regions.
Conclusions:
- Dynamic temperature profiles, when carefully designed, are effective for accurate microbial growth model parameter estimation.
- A novel temperature input strategy involving smaller, sequential increments is superior to abrupt shifts for maintaining exponential growth and model validity.
- The experimental implementation validated the effectiveness of the optimal temperature input in a computer-controlled bioreactor setting.