Developing a detailed kinetic model for the production of yogurt starter bacteria in single strain cultures

Modeling the growth dependence of Streptococcus thermophilus and Lactobacillus bulgaricus as a function of temperature and pH

The growth of the lactic acid bacteria (LAB), Streptococcus thermophilus and Lactobacillus bulgaricus, widely used for yogurt production, results in acid production and the reduction of the milk [Formula: see text]. Industrial processes can show temperature ([Formula: see text]) changes due to the large scale of the equipment. As [Formula: see text] and [Formula: see text] affect the LAB growth, this study aimed to model the dependence of S. thermophilus and L. bulgaricus as a function of temperature and pH and to estimate and internally validate their growth parameters and confidence intervals with different modeling approaches. Twenty-four datasets regarding the growth kinetics of S. thermophilus and L. bulgaricus were used for estimating the kinetic parameters for each pure culture. The classical Baranyi and Roberts (sigmoidal) primary and Rosso and coworkers (cardinal parameter) secondary models successfully described the experimental data. The one-step modeling approach showed better statistical results than the two-step approach. The values of eight growth parameters ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]) for each culture estimated from the fitting with the one-step approach and the Monte-Carlo-based approach were similar. Low averaged root-mean-squared errors ([Formula: see text]) (0.125 and 0.090 log CFU/mL) and percent discrepancy factor [Formula: see text] ([Formula: see text] and [Formula: see text]) values for S. thermophilus and L. bulgaricus were obtained in the internal model validation, reinforcing the predictive ability of the model.

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°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.

The Effect of Salt and Temperature on the Growth of Fresco Culture

The effect of environmental factors, including temperature and water activity, has a considerable impact on the growth dynamics of each microbial species, and it is complicated in the case of mixed cultures. Therefore, the aim of this study was to describe and analyze the growth dynamics of Fresco culture (consisting of 3 different bacterial species) using predictive microbiology tools. The growth parameters from primary fitting were modelled against temperature using two different secondary models. The intensity of Fresco culture growth in milk was significantly affected by incubation temperature described by Gibson’s model, from which the optimal temperature for growth of 38.6 °C in milk was calculated. This cardinal temperature was verified with the Topt = 38.3 °C calculated by the CTMI model (cardinal temperature model with inflection), providing other cardinal temperatures, i.e., minimal Tmin = 4.0 °C and maximal Tmax = 49.6 °C for Fresco culture growth. The specific growth rate of the culture under optimal temperature was 1.56 h−1. The addition of 1% w/v salt stimulated the culture growth dynamics under temperatures down to 33 °C but not the rate of milk acidification. The prediction data were validated and can be used in dairy practice during manufacture of fermented dairy products.

Practicability of TTI application to yogurt quality prediction in plausible scenarios of a distribution system with temperature variations

Yogurt has high temperature sensitivity, resulting in the temperature variations from production to consumption. Cooling capacity of cold chain facilities and product storage height are regarded as factors contributing to temperature variations in this study. To find an effective method to monitor temperature history of every yogurt product, three measurements were used: the set point of a cold chamber, a data logger, and a time-temperature integrator (TTI). The mean measured yogurt quality factor (acidity, °T) of 30 samples was 92.1 °T, and predicted values were 91.8 °T from the set point, 93.3 °T from the data logger, and 92.4 °T from the TTI. In terms of individual prediction, the SSE of the TTI showed the smallest difference (5.76) followed by 81.5 of the set point and 118.9 of the data logger. Thus, the TTI showed the best performance and can be used to monitor the time-temperature history of yogurt in the cold chain system.