3.2. Optimization of culture media composition and fermentation conditions
In this study, it was employed Bacillus megaterium to synthetize biopolymers via microbial fermentation. According to López et al. (2012) and Porras et al.(2017), this strain is capable of producing PHB and PHA copolymers, respectively. To optimize culture media composition, it was carried out 15 experiments given by the Box–Behnken experimental design and obtained PHB productivity (P) values are included in Table 2. The vinasse sample employed in these assays as carbon source was V2017. The influence of the different tested independent variables (carbon, nitrogen, and phosphorus concentration) on the biopolymer productivity are given by the p-value: those that presented p < 0.05 significantly affected the response parameter, meanwhile variables that showed p > 0.05 were considered not statistically significant.
The p-values for the three independent variables and their interactions are given in Table 5. It is important to note that not all variables affected the productivity in the same way. It can be seen that C and N statistically influenced PHB productivity, considering a significance level α = 0.05 (p = 0.0441 and 0.0280, respectively). On the other hand, phosphorus concentration was not statistically significant (p = 0.8941). Interactions between studied variables were not remarkable on PHB productivity, except the quadratic term N2 (p = 0.0167).
Table 5
Coefficient and p-values for independent variable and their interactions, obtained from optimization of culture media composition.
Variable
|
Coefficient
|
p-value
|
C
|
-0.2956
|
0.044
|
N
|
-0.3384
|
0.028
|
Ph
|
-0.0155
|
0.894
|
C2
|
-0.0783
|
0.651
|
C-N
|
0.2130
|
0.231
|
C-Ph
|
-0.3106
|
0.104
|
N2
|
0.5745
|
0.017
|
N-Ph
|
-0.2287
|
0.203
|
Ph2
|
0.3570
|
0.080
|
C: carbon; N: nitrogen; Ph: phosphorous |
Table 6
Coefficient and p-values for independent variable and their interactions, obtained from optimization of fermentation conditions.
Variable
|
Coefficient
|
p-value
|
C/N
|
-0.5340
|
0.278
|
T
|
-0.1878
|
0.702
|
t
|
-2.2338
|
0.003
|
C/N-T
|
-0.1580
|
0.756
|
C/N-t
|
1.8160
|
0.032
|
T-t
|
0.6016
|
0.370
|
(C/N)2
|
-1.7675
|
0.495
|
T2
|
-0.0898
|
0.901
|
t2
|
0.4981
|
0.495
|
C/N: carbon/nitrogen ratio; T: fermentation temperature; t: fermentation time |
For predicting the optimal culture media composition, a polynomial function was fitted to the experimental data (Eq. 3).
P = 0.286–0.296 C – 0.338 N – 0.010 Ph – 0.080 C2 + 0.213 C N – 0.310 C Ph + 0.570 N2 – 0.228 N Ph + 0.357 Ph2 [3]
where P is PHB productivity (mg/Lˑh), meanwhile C, N, Ph represents the carbon, nitrogen, and phosphorus concentration (mg/L) in the culture media.
This polynomial function represents a good fitted of experimental data since the determination coefficient (R2) was 0.8924. C and N have similar negative standardized coefficients (-0.296 and -0.338, respectively). The fact that these coefficients were negative means that PHB productivity decreased when C or N concentration were increased. Even though the coefficient of Ph was also negative, it was very low (-0.010), indicating that the effect of this variable on PHB productivity was not significant. Quadratic terms are model fit coefficients which demonstrate that there is a curvature and a local optimum point could be found. Thus, the optimum level for each studied variable, estimated from the maximum point of the polynomial PHB model, was estimated using the solver function of Statgraphics Centurion XV.II X64 tools. Optimum level of C, N, and Ph were -1, -1 and 0.935 respectively. Comparison with data found in the bibliography is difficult because several factors should be taken into account to analyze the effect of these variables on PHB productivity. For example, Bora (2013) studied the PHA synthesis by Bacillus megaterium, using fructose as carbon source, and K2HPO4 and Na2HPO4 as phosphorus sources. This author reported that fructose and its interaction with Na2HPO4 significantly affected PHB productivity. On the other hand, Nygaard et al. (2019) carried out fermentations for PHA production using Cupriavidus necator, and evaluated the effect of carbon, nitrogen and phosphorus concentration, as well as medium pH on the biopolymer productivity. Obtained results showed that productivity was statistically affected by N, pH, and C2. These examples from the literature demonstrate that the effect of fermentation variables on PHA productivity depends on many factors such as strain, nutrient sources, and fermentation conditions, among others. Therefore, the comparison with results obtained in this work is not suitable.
As it was aforementioned, phosphorus concentration did not have a significant effect on PHB productivity, so the response surface was built taking into account only carbon and nitrogen concentrations as independent parameters and the productivity as response variable (Figure 2). These response surface plots reinforced the discussed results about the effect of C and N concentrations on PHB productivity. As can be seen, productivity increased when the carbon and nitrogen concentration decreased. This could be attributed to the fact that vinasse contains phenolic compounds, difficult to be biologically degraded by bacteria, that have antimicrobial and phytotoxic properties (Parsaee et al. 2019). Besides, the decrease in PHB productivity by increasing nitrogen concentration may be due to that Bacillus sp. bacteria require the limitation of this nutrient for the production of PHB as metabolite (Kanekar et al. 2015).
From the optimum levels, PHB maximum productivity was calculated obtaining a value of 2.43 mg/Lˑh. This is a low productivity compared to values found in the literature. For example, Bhattacharyya et al.(2012) reported a PHB productivity of 0.21 g/Lˑh employing vinasse as carbon source and Haloferax meditaerranei as strain. Therefore, in order to improve PHB productivity by B. megaterium employing vinasse as carbon source, it was carried out an additional experimental design to optimize the C/N ratio and growing conditions (fermentation time and temperature). Therefore, it was performed 15 experiments given by the Box–Behnken experimental design and obtained PHB productivity (P) values are included in Table 3. The vinasse sample employed in these assays as carbon source was V2018. The p-value for the three variables and their interactions are given in Table 6. Taking into account the different independent variables, fermentation time had a statistically significant effect on PHB productivity (p = 0.0034). Meanwhile, fermentation temperature and C/N ratio did not significantly affect PHB productivity by B. megaterium (p = 0.7016 and 0.2777, respectively). Regarding the interaction between the variables, the only one that had a notable influence on PHB productivity was the one between C/N and time (0.0318), probably due to the significant effect of fermentation time. None of the quadratic terms resulted statistically significant. Equation 4 described the polynomial model which good-fitted to the experimental data with a determination coefficient (R2) of 0.9093.
P = 6.57 – 0.524 C/N – 0.175T – 2.25 t – 1.7 C/N2 – 0.2 C/N. T + 1.8 C/N. t – 0.08 T2 + 0.6 T.t + 0.5 t2 [2]
where P is the PHB productivity (mg/Lˑh), t is the fermentation time (h), and T is the fermentation temperature (°C).
The time variable had the largest negative standardized coefficient (-2.25), indicating that an increase in the fermentation time led to a decrease in PHB productivity. The longer the fermentation time the lower the productivity. This tendency is in good agreement with the low PHB production rate, not accumulating more PHB until the end of the cultivation. Similar behavior was reported by Dalsasso et al. (2019) studying PHB production by Cupriavidus necator using a blend of vinasse and sugarcane molasses as substrate. On the other hand, coefficients of C/N and T variables presented very low values, demonstrating that they had not a significant effect on the response variable. The quadratic coefficients and those of the interactions between variables resulted negligible, except the one corresponding to C/N and t interaction, mainly attributed to the effect of the fermentation time. In Figure 3 are shown the three response surface plots representing the productivity as response variable and C/N ratio and fermentation time (Figure 3a), temperature and fermentation time (Figure 3b), and C/N and temperature (Figure 3c) as independent variables. The only significant interaction was between C/N and t; when t value was minimal and the ratio C/N increased, the production of PHB increased. Besides, when t reached its highest value, the slope of PHB production as a function of C/N became negative (Figure 3a). The factor associated with the quadratic contribution of C/N presented its maximum value in the vicinity of the central point. Thus, the maximum PHB production occurred when C/N had a value of 23.95 (Figure 3a,c). As it can be seen, the temperature range assayed in these experiences did not significantly affect the PHB productivity (Figure 3b,c), despite these values are within the optimal growing temperature range for this strain (Porras et al. 2017).
The optimal value of C/N ratio, fermentation time and temperature to produce PHB by B. megaterium and vinasse as carbon source were 24.01, 30.25 °C and 24 h. The optimal productivity value in this case was 10.6 mg/Lˑh. Pramanik et al. (2012) reported a PHB productivity in the same magnitude order (0.015 g/L h) using a culture medium with 10 % raw vinasse as carbon source and Haloarcula marismortui as strain. Otherwise, reported values of PHB production by Bacillus megaterium by other authors are very variable according to the carbon source used. Jimenez (2011) informed a PHB productivity of 0.082 g/Lˑ h from glucose as carbon source, Obruca et al.(2011) reported a value of 0.056 g/Lˑh employing cheese whey, and Porras (2012) obtained a PHB productivity of 0.0125 g/Lˑh using starch.
As it was aforementioned, vinasse sample employed in the optimization of the media composition was V2017, meanwhile for the optimization of growing conditions it was employed V2018. The variability in physicochemical properties of both vinasse samples led to significant differences in PHB productivity. As it can be seen, the best values were obtained using V2018 in the second experimental design. When V2018 was employed, it reached a productivity 4.4 times higher than value obtained with V2017, estimated for the optimal conditions. This difference could be associated with the higher BOD and COD values of V2017 than those of V2018. Particularly, BOD high values indicate that microorganisms need more oxygen to degrade it (Porras 2012) and this issue could affect the PHB production by B. megaterium from V2017.
3.3. PHA extraction and characterization
Fermentations were carried out using the optimal composition. PHB was extracted and a characterization was done. Considering the mass of dry cells and extracted PHA, a polymer yield of 37 % was obtaining. Valappil et al. (2007) reported a similar value for PHA yield (31 %), using glucose as substrate and Bacillus cereus as microorganism. On the other hand, using vinasse as carbon source, Pramanik et al.(2012) obtained a PHA yield of 23 % employing Haloarcula marismortui as strain and Zanfonato et al.(2018) reported a maximum PHA yield of 26 % with Cupriavidus necator. In addition, Bacillus sp. accumulated 75.5 % (Das et al. 2018), 54.6 % (Mohanrasu et al. 2020) and 59 % (Jimenez 2011) PHA using cheese whey in the first case and glucose in the others, respectively.
FTIR spectra of synthesized PHA is shown in Fig. 4 and it is in consonance with the PHB structure reported in the bibliography. A high intense band at 1726 cm− 1 was observed which corresponds to the C = O stretch of the ester group (Lathwal et al. 2018). Absorption band at 1455 cm− 1 is attributed to the asymmetric deformation of C-H bonds in -CH2 groups, while the band at 1380 cm− 1 is assigned to the symmetric vibration of -CH3 groups. The band at 1230 cm− 1 is assigned to CH2 vibrations (Jimenez 2011).
Figure 4 also includes a SEM micrograph of the extracted biopolymer, showing the morphology of PHA granules. The microstructure gives a fairly porous material with fine grains interconnected and a strong tendency to form multigrain agglomerates. The morphology shows grains that are pseudo-spherical in shape with fairly uniform distribution. Similar observations were reported by Nwinyi and Owolabi (2019). They used microbial species obtained from an abattoir employing different carbon sources (acetate and molasses) in the mineral medium.
The melting temperature of obtained PHB was taken at the maximum of the endothermic peak in the DSC second heating thermogram. A value of 177.7°C was obtained which is in good agreement with values reported by Pradhan et al. (2018). These authors reported values of 175°C and 176°C for PHA obtained by Bacillus megaterium and Cupriavidus necator, respectively. The melting enthalpy determined of the PHA obtained in this study was 79.6 J/g. Pradhan et al.(2018) using B. megaterium and fructose as carbon source reported a melting enthalpy of 33 J/g and Ansari and Fatma (2016) obtained 83.2 J/g by Nostoc muscorum NCCU- 442 and glucose as carbon source.
TGA was performed to detect the thermal stability of PHA. The maximum degradation temperature for the PHA synthesized was determined using the first derivative of thermogravimetric curve. PHA degradation occurred in two stages: in the first step, the degradation started at 225°C and extended until 300°C with a maximum degradation occurring at 255°C and the second step began after 310°C and the maximum degradation took place at 325°C. The degradation in more than one stage was reported by various authors (Pradhan et al. 2018, Hassan et al. 2016, Liu et al. 2014) Particularly Hassan et al. (2016) showed that PHB from Bacillus sp. was decomposed in 3 stages and resisted until 320°C.