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

Abstract

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°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 L. paracasei whereas new equations were developed for the effect of pH. The estimated T_min 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 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 log₁₀ 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.

Keywords: Lacticaseibacillus paracasei; fermented cabbage; growth models; predictive modeling; probiotic foods.

Figure 1

Effect of temperature (A) and of pH (B) on the maximum specific growth rate (μ_max) of different strains of Lacticaseibacillus paracasei. Comparison between the fitted model and observed maximum specific growth rates (■).

Figure 2

Comparison between in food growth of Lacticaseibacillus paracasei from all experimental data relevant to Table 1 (squares), additional data generated at 25°C not included in Sarvan et al. (Sarvan et al., 2013; circles) and the prediction in silico (line) with confidence interval (dashed lines). The stochastic simulations were performed using the normal distributions by the calculated mean values of the growth parameters and relevant standard deviations obtained for the four Lacticaseibacillus paracasei strains (Table 2).

Figure 3

The simulated distributions of Lacticaseibacillus paracasei population randomly generated that reached the targeted probiotic level fixed at 7 log₁₀ CFU/g during fermentation time at 20°C (grey), 25°C (light grey) and 35°C (white). Simulations were obtained in cabbage considering a virtual initial probiotic inoculum of 4 log₁₀ CFU/g ± 0.5 at pH 6.0 ± 0.2 during 4 days.

Figure 4

In silico growth simulations and probability of Lacticaseibacillus paracasei to reach the targeted probiotic level fixed at 7 log₁₀ CFU/g in cabbage. Simulations were obtained in cabbage during 4 days at different conditions (A: 18°C, pH 6.0, N₀ 5.0; B: 30°C, pH 6.0, N₀ 4.0; C: 28°C, pH 6, N₀ 4.5; D: 25°C, pH 6.0, N₀ 2.30). The distribution of Lacticaseibacillus paracasei population is indicated together with a targeted probiotic level threshold fixed at 7 log₁₀ CFU/g.