Dimensional Analysis Model Predicting the Number of Food Microorganisms

Area of Science:

• Food Science
• Microbiology
• Mathematical Modeling

Background:

• Accurate prediction of microbial populations is crucial for food safety and shelf-life management.
• Traditional methods for predicting microbial growth can be complex and time-consuming.

Purpose of the Study:

• To develop a novel, simple, and effective Dimensional Analysis Model (DAM) for predicting microorganism numbers.
• To apply and validate the DAM for forecasting *Pseudomonas* levels in Niuganba (NGB), a traditional Chinese fermented dry-cured beef.

Main Methods:

• The Dimensional Analysis Model (DAM) was formulated using dimensionless analysis and the Pi theorem.
• The DAM was applied to predict *Pseudomonas* counts in NGB samples stored at 278 K, 283 K, and 288 K.
• Model performance was rigorously validated using internal and external metrics (R², RMSE, %SEP, A, B).

Main Results:

• The DAM demonstrated high accuracy in predicting microbial numbers and NGB storage time, evidenced by high R² and low RMSE/%SEP values.
• Validation parameters (A and B) were close to 1, indicating strong model reliability.
• A high correlation was observed between the predicted and actual *Pseudomonas* counts.

Conclusions:

• The Dimensional Analysis Model (DAM) is a simple, unified, and effective tool for predicting microbial populations.
• This model offers significant potential for enhancing food safety and storage time predictions in the food industry.