Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values

Area of Science:

• Food Microbiology
• Mathematical Modeling
• Food Safety

Background:

• Meat's high nutrient content, water activity, and pH (around 5.6) promote microbial growth, leading to spoilage.
• Aerobic psychrotrophic bacteria like Pseudomonas spp. cause slime and off-flavors in fresh meat exposed to oxygen.

Purpose of the Study:

• To predict the growth of Pseudomonas fluorescens in fresh meat.
• To evaluate microbial growth under refrigeration and temperature abuse conditions.
• To assess the impact of pH on bacterial growth.

Main Methods:

• Applied microbial mathematical modeling, including the Baranyi and Roberts and modified Gompertz primary models.
• Utilized the Ratkowsky extended model to analyze the effect of pH and temperature on the maximum growth rate (μmax).
• Employed DMFit 3.0 software for model fitting and validation.

Main Results:

• Both primary models (Baranyi and Roberts, modified Gompertz) demonstrated a good fit to experimental data.
• The Baranyi and Roberts models, including secondary models, showed superior validation for predicting bacterial growth.
• The Ratkowsky model effectively determined the influence of pH and temperature on μmax.

Conclusions:

• Mathematical models, particularly those based on Baranyi and Roberts, can accurately predict Pseudomonas fluorescens growth in fresh meat.
• These predictive models are valuable tools for managing meat safety under various storage conditions.
• Understanding the impact of pH and temperature is crucial for controlling microbial spoilage in meat products.