Federica Nigro

Banana puree, due to its nutritional composition, is a good substrate for fermentation in the development of probiotic products. The production of banana puree mainly consists of three phases, i.e., raw material pretreatment, heat treatment, and the addition of anti-browning agents. In this study, we conducted three experimental protocols to evaluate the effect of ripeness grade, heat treatment, and ascorbic acid addition on fermentation performance. At the end of each protocol, the substrate was subjected to the fermentation process (37 °C, 48 h), and then measurements of pH reduction, microbial growth, and lactic acid production were used as markers in the analysis of fermentation performance. Ripe bananas produced better results than unripe bananas whose fermentation appeared to be inhibited. Therefore, ripe bananas were used to test the effect of two different heat treatments (sterilization (121 °C, 20 min) versus tyndallization (70 °C, 30 min; 37 °C, 30 min; 70 °C, 30 min)) on banana puree fermentation, and no significant differences were observed. Finally, 500 or 1000 ppm of ascorbic acid, normally used as an anti-browning agent, was added to ripe tyndallized bananas. No differences in fermentation results were observed between the two tested conditions, though values obtained for growth and lactic acid production were significantly lower than those from fermentation of banana puree without ascorbic acid.

The mathematical modeling of fermentation processes allows for the formulation of predictions about the kinetics of biomass growth and metabolite production as well as setting or verifying the best operative conditions in view of the economical convenience of the process. For this purpose, we performed a kinetic study of a rice flour fermentation process using Lactobacillus paracasei CBA L74 with and without pH control; the pH value was set to 5.8 under pH control. Monod, Logistic, and Contois models were proposed to describe the bacterial growth rate in both conditions. The best mathematical model, which was able to describe the experimental data obtained without pH control, was the Contois model, as the specific growth rate was influenced by both the glucose reduction (from 14.31 g/L to 10.22 g/L) and the biomass production (2 log growth) that occurred during fermentation. Conversely, when pH control was implemented, both Monod and Contois models satisfactorily described the specific growth rate trend. The estimated kinetic parameters confirmed that biomass production (2 log growth) and glucose consumption (from 14.31 g/L to 6.06 g/L) did not affect the microorganism’s growth capacity when the fermenting medium was maintained at an optimal pH. The lactic acid production rate described by the Luedeking–Piret model did not appear to be linked to growth in the absence of pH control while, on the other hand, this model was unsuitable for describing the experimental lactic acid concentration when pH control was applied. The kinetic modeling of lactic acid production and the percentage of added glucose in the protocol with controlled pH will be optimized in the future.