A Predictive Growth Model for Pro-technological and Probiotic Lacticaseibacillus paracasei Strains Fermenting White...

Area of Science:

• Microbiology
• Food Science
• Predictive Modeling

Background:

• Lacticaseibacillus paracasei strains are crucial for food fermentations and as probiotics.
• Understanding their growth parameters is essential for optimizing their use.

Purpose of the Study:

• To determine the cardinal growth parameters (temperature and pH) for four L. paracasei strains.
• To develop predictive models for L. paracasei growth in food matrices.
• To simulate and predict probiotic levels in fermented foods.

Main Methods:

• Incubation of L. paracasei strains across a range of temperatures (5.5°C-40°C) and pH values (3.2-9.1).
• Application of cardinal temperature models and development of new pH-effect equations.
• Calculation of correction factors and development of bi-linear models for growth in white cabbage.
• In silico simulations to predict growth and probiotic levels.

Main Results:

• Estimated cardinal temperatures (Tmin) ranged from -0.97°C to 1.95°C, with some strains tolerating sub-zero conditions.
• Strain P40 exhibited a narrow temperature growth range, while IMPC4.1 showed tolerance to extreme pH.
• Predictive models were established for growth rate and maximum population density in white cabbage.
• In silico simulations successfully predicted time to reach target probiotic levels.

Conclusions:

• L. paracasei strains exhibit diverse growth characteristics, including tolerance to extreme conditions.
• Predictive microbiology offers a powerful tool for optimizing fermentation processes and ensuring probiotic efficacy.
• This research facilitates the strategic application of L. paracasei in food production.