AUTHOR=Di Biase Mariaelena , Le Marc Yvan , Bavaro Anna Rita , De Bellis Palmira , Lonigro Stella Lisa , Lavermicocca Paola , Postollec Florence , Valerio Francesca 

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

JOURNAL=Frontiers in Microbiology

VOLUME=13

YEAR=2022

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2022.907393

DOI=10.3389/fmicb.2022.907393

ISSN=1664-302X

ABSTRACT=<p>Bacterial strains belonging to <italic>Lacticaseibacillus paracasei</italic> species are generally used as starters in food fermentations and/or as probiotics. In the current study, the growth cardinal parameters of four <italic>L. paracasei</italic> 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°C–40°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 <italic>L. paracasei</italic> whereas new equations were developed for the effect of pH. The estimated <italic>T<sub>min</sub></italic> values ranged between −0.97°C and 1.95°C and were lower than 0°C for strains IMPC4.1 and P101. Strain P40 was able to grow in the most restricted range of temperature (from 1.95°C to 37.46°C), while strain IMPC4.1 was estimated to survive at extreme conditions showing the lowest <italic>pH<sub>min</sub></italic>. Maximum specific growth rates of <italic>L. paracasei</italic> IMPC2.1 in white cabbage (<italic>Brassica oleracea</italic> var. <italic>capitata</italic>) were used to calculate the correction factor (<italic>C<sub>f</sub></italic>) 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 <italic>L. paracasei</italic> strains in cabbage and predict the time to reach the targeted probiotic level (7 log<sub>10</sub> CFU/g) using <italic>in silico</italic> 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.</p>