Predictive modelling of the microbial lag phase: a review

Area of Science:

• Microbiology
• Food Science
• Mathematical Modeling

Background:

• The microbial lag phase is a critical but complex aspect of microbial growth.
• Accurate estimation of lag time is essential for predictive microbiology applications.
• Existing models for microbial lag phenomena vary in their approach and complexity.

Purpose of the Study:

• To summarize recent advancements in predictive modeling of microbial lag phenomena.
• To critically assess major modeling approaches for lag phase estimation.
• To discuss factors influencing the lag phase, focusing on temperature and culture history.

Main Methods:

• Qualitative and quantitative analysis of microbial lag phase.
• Review of prevailing techniques for lag time determination.
• Critical assessment of primary (deterministic and stochastic) and secondary modeling approaches.

Main Results:

• Identified key factors influencing microbial lag, including temperature and prior culture history.
• Evaluated deterministic and stochastic primary models for microbial growth prediction.
• Discussed the role of secondary models in linking environmental factors to primary model parameters.

Conclusions:

• Predictive modeling of microbial lag phenomena requires careful consideration of both model type and influencing factors.
• Temperature and culture history are significant determinants of the microbial lag phase.
• Further research into sophisticated modeling techniques can improve the accuracy of microbial growth predictions.