# A Model for Probiotic Fermented Food Production
## 3 Results and Discussion
### Evaluation of the Capacity to Grow during Cheese-Making Process  
The HN001 and LR06 strains were evaluated for their ability to survive and grow during the cheese-making process for up to one month of ripening using absolute quantification via real-time PCR before inoculation, immediately after inoculation, in discarded whey, and at three days and one month after inoculation. The investigated samples were treated with the same extraction method, and the Ct value for each sample was calculated using the standard curve to obtain the bacterial content (shown as log10 copies of bacterial DNA per gram; Figure 2). All the analyses were performed in triplicate. As we can observe in Figure 2, strain HN001 showed a slightly higher adaptation to cheese technological processes, showing a slightly faster growth rate and a higher concentration after one month of ripening compared to strain LR06 (2.8 \(\times\) 10\({}^{8}\) CFU/mL versus 1.5 \(\times\) 10\({}^{8}\) CFU/mL, respectively). However, the difference was not significant (\(p\) = 0.46). \begin{table}
\begin{tabular}{c c} \hline
**Slim Milk 37 \({}^{\circ}\)C \(\times\) 72 h** & **pH** \\ \hline HN001 & 3.7 \(\pm\) 0.1 \\ \hline LR06 & 3.5 \(\pm\) 0.2 \\ \hline LR04 & 7.3 \(\pm\) 0.5 \\ \hline Negative control & 7.1 \(\pm\) 0.2 \\ \hline
**Slim Milk 52 \({}^{\circ}\)C \(\times\) 30 min 37 \({}^{\circ}\)C \(\times\) 72 h** & \\ \hline HN001 & 3.7 \(\pm\) 0.2 \\ \hline LR06 & 3.5 \(\pm\) 0.2 \\ \hline LR04 & 6.7 \(\pm\) 0.2 \\ \hline Negative control & 7.2 \(\pm\) 0.1 \\ \hline \end{tabular}
\end{table} 
Table 1: pH values of the milk cultured with the three probiotic _L. rhamn_ strains. Figure 1: Impedometric evaluation of the three _L. rhamn_ strains (HN001, LR04, LR06) in milk at 37 \({}^{\circ}\)C and 52 \({}^{\circ}\)C.  
Both _L. rhamnus_ strains (HN001 and LR06) showed the ability to survive and grow in the thermal and nutritional conditions typical of dairy cultures, providing the possibility to use them as adjunct probiotics in a multitude of fermented products characterized by a similar environment. Also, the use of more probiotic strains simultaneously could offer the possibility, as in this case, to combine the immune support and allergy prevention benefits associated with the HN001 probiotic strain with the ability of the LR06 strain to restore the gastric barrier [64,75,76, 77]. Moreover, the ability to carry out a large probiotic screening based on thermal treatment tolerance and the ability of following probiotic growth using real-time PCR can help promote the production of probiotic fermented foods characterized by beneficial effects, such as aged dairy products with long fermentation-ripening times in which the glycolysis is enhanced, and lactose is exhausted by LAB and other lactic acid microorganisms. This process also creates the necessary sensory characteristics of fermented foods thanks to longer lipolysis and proteolysis activities that influence not only the generation of myriad of flavor compounds, their precursors, and the SCFAs [78] but also the rheology and external appearance by softening its texture. With our model, we strive to promote the production and diffusion of a large variety of probiotic fermented products from vegetables, beverages, dairy and non-dairy fermentation. Our model uses impedometric evaluation as a first step to obtain a list of probiotic strains able to grow in the specific conditions dictated by the product ingredients. The next step is using quantitative real-time PCR to identify the initial inoculum amount of probiotics for the subsequent fermentation process. The inoculum amount is affected by the different sources of nutrients, the length of the fermentation and aging, and the relative serving size of the product. Considering that the adequate amount of probiotics per serving is generally reported to be higher than 10\({}^{9}\) CFU [79]; it is important to identify a specific initial inoculum amount that fulfills the probiotic food labeling requirements. An example is reported in Figure 3, where the growth of _L.