# Statistical Approach to Potentially Enhance the Postbiotication of Gluten-Free Sourdough
## 3 Results and Discussion
### Sourdough Fermentation Process Optimization
#### 3.1.1 Selection of the Most Important Fermentation Parameters by Plackett-Burman Design (PBD)
The meaning of the factors \(A\) to \(H\) is given in Table 1.  
Another key-variable for the TTA response was represented by the fermentation time (Table 3). Our results suggested that an extension of the incubation time up to 96 h could result in higher levels of TTA, depending on the LAB strains used as inoculum. In a work from [PERSON] et al. (2018), the TTA values above 10.0 mL NaOH 0.1N were obtained with some selected _Lactobacillus_ spp. strains, after 72 h of fermentation at 30 \({}^{\circ}\)C of a wheat-based sourdough. Furthermore, the amount and type of flours and other ingredients used in the sourdough recipes for bakery products, as well as the number and type of LAB strains employed during the fermentation and the consistency of the sourdoughs (i.e., the DY) may cause significant changes upon the acidification potential. Accordingly, different types of cereal flours have different buffer capacities, and different LAB strains in the same or different dough mixtures lead to different types of organic acids and respective yields of production ([PERSON] and [PERSON], 2018; [PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019). The high antimicrobial activities of the fermented products (Table 3) were determined against _Aspergillus niger_ and _Aspergillus flavus_ as indicator fungal strains, after 96 h of incubation at 45 \({}^{\circ}\)C and after 24 h of fermentation at 25 \({}^{\circ}\)C, respectively. The data showed in Table 3 indicate that some LAB strains used for the fermentation of sourdough were not able to inhibit the fungal or bacterial growth, namely of _Penicillium_ spp. or _Bacillus_ spp. strains, respectively. However, the antimicrobial compounds produced during the SSF process of the gluten-free flours formulated media by LAB strains used in this study possessed different inhibitory potentials against the indicator strains of molds and bacterium (Table 3). According to the ANOVA results displayed in Tables S1-S5, the type of LAB strains used as the inoculum, the addition of okara in the fermentation medium, as well as the fermentation temperature and time led to distinct impacts on the analyzed responses. Particularly, the factors \(A\) (dough yield), \(D\) (inoculum of \(L\). _parabuchneri_ MIUG BL 24 strain, % \(v/w\)), \(F\) (okara, % \(w/w\)), \(G\) (fermentation temperature, \({}^{\circ}\)C) and \(H\) (fermentation time, h) had significant impacts (\(p<0.05\)) on the TTA. The mathematical model generated on the TTA was also significant (\(p=0.014\)) with a regression coefficient (R\({}^{2}\)) of 0.9827 (Table S1). The growth of the fungal indicator strains used in this study revealed different behaviors, depending on the strain. _Aspergillus niger_'s growth was significantly inhibited (\(p<0.05\)) by the variation of the factors \(A\) (dough yield), \(D\) (\(L\). _parabuchneri_ MIUG BL 24 strain, 24 strain inoculum, % \(v/w\)), and \(H\) (fermentation time, h) involved in the sourdoughs' fermentation processes, as showed in Table S2. Moreover, the analysis of variance (ANOVA) from Tables S3 and S4 unfolded the influence of the factor \(B\) (\(L\). _paracasei_ ssp. _paracasei_ MIUG BL 21 strain inoculum, % \(v/w\)) on the inhibitory potential of the sourdoughs against _Aspergillus flavus_ and _Penicillium_ spp. strains. The temperature (factor \(G\)) through the SSF greatly influenced the sourdoughs' antifungal activity against _Aspergillus flavus_. In contrast, the same factor \(G\) had no significant effect against _Penicillium_ spp. Moreover, the \(L\). _parabuchneri_ MIUG BL 24 strain inoculum (factor \(D\)), along with the fermentation time (factor \(H\)), contributed significantly to attain a sourdough with an inhibitory effect against _Penicillium_ spp. The antibacterial activity of the sourdoughs against _Bacillus_ spp. was strongly influenced (\(p<0.05\)) by the factor \(G\) (fermentation temperature, \({}^{\circ}\)C). This statistical model was significant (\(p=0.026\)) but its regression coefficient was only 66.66% (R\({}^{2}=0.6666\)) (Table S5).