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Precise, High-throughput Analysis of Bacterial Growth
Published on: September 19, 2017
Comparing uncertainty resulting from two-step and global regression procedures applied to microbial growth models
1Department of Biosystems and Agricultural Engineering, A. W. Farrall Hall, Michigan State University, East Lansing, Michigan 48824, USA.
Journal of Food Protection
|December 22, 2007
Summary
Global regression modeling for Listeria monocytogenes growth is more accurate and robust than the traditional two-step method. This approach offers improved predictions for microbial safety in food products.
Area of Science:
- Food microbiology
- Predictive modeling
- Quantitative risk assessment
Background:
- Listeria monocytogenes is a significant foodborne pathogen.
- Accurate microbial growth modeling is crucial for food safety.
- Traditional two-step regression models have limitations in predicting microbial behavior.
Purpose of the Study:
- To compare the performance of global regression and two-step regression for modeling Listeria monocytogenes growth.
- To validate these modeling procedures using independent data from meat and poultry products.
- To assess the robustness and predictive accuracy of each method.
Main Methods:
- Utilized the Gompertz equation as the primary model and a response surface model as the secondary model.
- Employed a global regression approach combining primary and secondary models.
- Compared global regression against a traditional two-step regression procedure.
- Validated models with independent data from meat and poultry products.
Main Results:
- Global regression demonstrated lower standard errors of calibration (0.95 log CFU/ml aerobic, 1.21 log CFU/ml anaerobic) compared to the two-step procedure (1.35 log CFU/ml aerobic, 1.62 log CFU/ml anaerobic).
- Global regression was more robust in 65% of food product cases studied.
- Robustness index for global regression ranged from 0.27 to 2.60, while for the two-step method, it ranged from 0.42 to 3.88.
- Predictions were overestimated (fail-safe) in over 50% of cases with global regression and over 70% with the two-step regression.
Conclusions:
- Global regression offers superior accuracy and robustness for modeling Listeria monocytogenes growth compared to the two-step procedure.
- This improved modeling approach enhances the reliability of microbial safety predictions in food.
- The findings support the adoption of global regression for more effective food safety management.
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