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

Bacterial strains belonging to Lacticaseibacillus paracasei species are generally used as starters in food fermentations and/or as probiotics. In the current study, the growth cardinal parameters of four L. paracasei strains (IMPC2.1, IMPC4.1, P40 and P101), isolated from table olives or human source, were determined. Strains were grown in liquid medium and incubated at several temperatures (10 values from 5.5 degrees C-40 degrees C) and pH (15 values from 3.2 to 9.1) along the growth range. The cardinal temperature model was used to describe temperature effects on the maximum specific growth rate of L. paracasei whereas new equations were developed for the effect of pH. The estimated T-min values ranged between -0.97 degrees C and 1.95 degrees C and were lower than 0 degrees C for strains IMPC4.1 and P101. Strain P40 was able to grow in the most restricted range of temperature (from 1.95 degrees C to 37.46 degrees C), while strain IMPC4.1 was estimated to survive at extreme conditions showing the lowest pH(min). Maximum specific growth rates of L. paracasei IMPC2.1 in white cabbage (Brassica oleracea var. capitata) were used to calculate the correction factor (C-f) defined as the bias between the bacterial maximum specific growth rate in broth and in the food matrix. A simple bi-linear model was also developed for the effect of temperature on the maximum population density reached in white cabbage. This information was further used to simulate the growth of L. paracasei strains in cabbage and predict the time to reach the targeted probiotic level (7 log(10) CFU/g) using in silico simulations. This study demonstrates the potential of the predictive microbiology to predict the growth of beneficial and pro-technological strains in foods in order to optimize the fermentative process.

Automated summary

This study determined the growth parameters of four Lacticaseibacillus paracasei strains and developed models to predict their growth in cabbage. The study demonstrates the potential of predictive microbiology in optimizing food fermentation processes.