Screening of the factors affecting biomass and LEPLs production
One-factor-at-a-time
Although one-factor-at-a-time methods are tedious, and overlook the interaction between different factors, this method was helpful for the selection of levels in SSF, making the results more reasonable and credible (
17). Five variables were chosen in the solid-state fermentation process to efficiently screen out the key factors on the Biomass and LEPLs production (in
Table 1). The data reported in
Figures 1-
5 showed a substantial variation in Biomass and LEPLs production yield among the experimental setting runs, going from 0.033 mg/g to 0.053 mg/g and 21.05 mg/g to 43.67 mg/g, respectively, under different levels of factors, suggesting that the screened parameters were important for the solid-state fermentation of the biomass and LEPLs.
Effect of inoculum size on solid-state production
Inoculum size has been reported to play a significant role in the production of biomass and bioactive compounds (
27,
28) .
In the present study, in order to determine the actual inoculums size that gave maximal biomass and LEPLs production, fungal spawn was used as the inoculum and different inoculum′s sizes, that is, 5-20 percent (W/W) were studied to enhance the fermentation of substrate and thereby improving biomass and LEPLs production by
L.edodes in solid-state fermentation (
Figure 1).
L.edodes D.P.B 319 was observed at all the different level of inoculum size studied. When low inoculum level i.e. 5% was used, both of the biomass and LEPLs productions were minimum but as the inoculum level increased, both of two responses were also increased. Maximum yield of biomass and LEPLs was observed at 20% inoculum size (
Figure 1, a & b respectively). Statistical analyses were performed by the (ANOVA) with Tukey′s multiple comparisons test (
p < 0.05). Increased level of inoculum mostly reduced production in the solid-state fermentation process. This may be due to the depletion of nutrients from the fermentation medium which resulted decline in Biomass and LEPLs production. Min Shi
etin. (2013) reported inoculum size of 12.5% (with a maximum yield of 20.44 mg/g) was best for total polysaccharides production by
Ganoderma lucidum (G. Lucidum) using Soybean Curd Residue in solid-state fermentation (
29). The results indicated 20% of inoculum size was fit for the mycelial growth and enhanced total polysaccharide production. Indeed, there was not much different in biomass and LEPLs production at 20% inoculum size. Thus, it needed to be suitable, when the inoculum size was too small, the fermentation starting time was long. In contrast, a greater inoculum size caused the nutrition to be consumed more quickly, and the fermentation could be interrupted (
30-
32).
Effect of Inoculum size (a) on the mycelial biomass (b) and LEPLs production in L. edodes cultures. The p-values (p < 0/0001) for significant differences (obtained through one-way ANOVA) are shown
Effect of C/N ratio on solid-state production
The C/N ratio is a major factor and an important essential requirement for the growth rate of the mycelium and development of bioactive compounds for the fermentation goals (
8). The effect of different ratios of C/N was evaluated for the polysaccharide production by the mushroom.
Based on the total carbon and total nitrogen in WSB, the C/N ratio was calculated (
33). C/N ratios of media from 5 to 25 were packed in 250 mL flasks and tested in order to estimate the production of biomass and LEPLs. Sucrose and yeast extracts were used to adjust the C/N ratios.
All of the selected C/N ratios resulted in good mycelial growth and product yield. Among the five different C/N ratios we examined, C/N ratio 10 was the most effective for enhancing the LEPLs production (43.67 mg/g) and biomass (0.053 mg/g) by L. edodes. Statistical analyses were performed by the (ANOVA) with Tukey′s multiple comparisons test (p < 0.05). However, WSB was low priced and biologically was the most effective energy source, so we chose C/N ratios 10 to be the best C/N ratio for polysaccharides production. Both biomass and LEPLs production increased with increases in C/N ratio from 5 to 10. However, further increases had a negative effect
Effect of C/N ratio (a) on the mycelial biomass (b) and LEPLs production in L. edodes cultures. The p-values (p < 0/0001) for significant differences (obtained through one-way ANOVA) are shown
Effect of Incubation time on solid-state production
A time course of LEPLs production in the solid-state fermentation is presented in
Figure 3, b. The result obviously revealed that the yield of the biomass or LEPLs was significantly affected by the fermentation time. The biomass production gradually increased with the increasing period of incubation up to 35 days. It was found that highest LEPLs production (42.43 mg/g) was shown at 25 days of fermentation period (
Figure 3, b). Statistical analyses were performed by the (ANOVA) with Tukey′s multiple comparisons test (
p < 0.05). Different experiments were conducted to study the optimum period for maximum LEPLs production in the solid-state fermentation process. Similar observations on maximum total polysaccharides production at 20 days incubation period by
G. lucidum was reported 43.96 mg/g. Further increase of fermentation period beyond this resulted in a decline in LEPLs production which might be due to the production of toxic metabolites or reduction of nutritional elements during microbial growth which inhibits the mycelial growth or LEPLs production. When TC (2016) studied on total polysaccharides production by
Ganoderma atrum (G. atrum) and reported that maximum total polysaccharides production was observed in 20 days using wheat as a substrate (
34).
Effect of Incubation time (a) on the mycelial biomass (b) and LEPLs production in L. edodes cultures. The p-values (p < 0/0001) for significant differences (obtained through one-way ANOVA) are shown
Effect of moisture content on solid-state production
Although the fermentation with a rang from low initial moisture to very high moisture content has been reported (
28,
35), it has been observed that insufficient moisture content led to the decrease in the fungal growth, whereas very high moisture content has an inhibitory effect on biomass and LEPLs production (
36-
40). To investigate the effect of moisture content on production of biomass and LEPLs by
L. edodes, WSB substrate was moistened with distilled water from 60-75% (
29,
39). The optimal initial moisture for both biomass and LEPLs productions was found to be 65%, (0.033 mg/g and 22.98 mg/g respectively) by
L. edodes.). Statistical analyses were performed by the (ANOVA) with Tukey′s multiple comparisons test (
p < 0.05). (
Figure 4a, b).
Effect of moisture content (a) on the mycelial biomass (b) and LEPLs production in L. edodes cultures. The p-values (p < 0/0001) for significant differences (obtained through one-way ANOVA) are shown
Effect of pH on solid-state production
Different pH ranges from 5 to 6.5 was tested to estimate the optimum production of biomass and LEPLs by
L. edodes in solid-state fermentation. The result was shown in
Figure 5. Similar findings were also reported by Min Shi
et al. (2013) showing optimum pH of 5.5 for polysaccharides production by
G. Lucidum in basidiomycete culture (
29,
41). The study revealed that 5.5 of an initial pH was the optimum pH with 29.4 mg/g of the maximum polysaccharide production. Statistical analyses were performed by the (ANOVA) with Tukey′s multiple comparisons test
(p < 0.05).
Effect of pH (a) on the mycelial biomass (b) and LEPLs production in L. edodes cultures. The p-values (p < 0/0001) for significant differences (obtained through one-way ANOVA) are shown
Optimization by RSM design
Based on the results of the One-factor-at-a-time procedure, three factors (inoculum size, Incubation time, C/N ratio) were selected and RSM was used to optimize the fermentation process. The statistical combinations of critical variables in coded and actual values along with the maximum predicted and experimental responses are listed in
Table 2.
Experimental values
| Type | Units | Factors | Symbol code |
|---|
| High | Mean | Low |
|---|
| 20 | 15 | 10 | Numeric | % | Inoculum Size | A |
| 45 | 30 | 5 | Numeric | | C/N | B |
| 15 | 10 | 5 | Numeric | Days | Incubation time | C |
Optimization of biomass production by RSM
Biomass production by L. edodes was optimized using central composite design in the Quadratic model, using Design Expert, version 8.0 software (Stat-Ease. Minneapolis, Minn.). Biomass production of this mushroom was optimized by varying the conditions of the fermentation especially the inoculum size, C/N ratio, and the incubation time, at controlled temperature without change in pH during the fermentation. There was a considerable variation in the biomass depending on the fermentation conditions, as shown in Table S1. The replication at the center point conditions resulted in higher biomass than at other levels. Response surface methodology helps in evaluation of the relationship between the dependent variable (biomass yield) and independent variables (fermentation conditions). Observed and predicted values of the biomass yield are shown in Table S1. The accuracy of the model can be seen by the difference between observed and predicted values.
The statistical significance of Equ. (
2) was confirmed by an F-test, and the co-efficient and the analysis of variance (ANOVA) for the response surface quadratic model are presented in Table S2, S3. The fitness of the model expressed using the value of the determination co-efficient (R2).
The ANOVA of the quadratic regression model demonstrated that the model was significant, with an F-test of a very low probability value (P >F) <0.0001. In the present study, R2 comes out to be 0.9475 for L. edodes. The value of adjusted co-efficient of determination adjusted R2 = 0.9287 for L. edodes indicates the high significance of the model. The goodness of the model was indicated by the determination coefficient (R2) and the multiple correlation coefficients (R). The application of RSM yielded the following order polynomial equation.
Y biomass = +0.033 +0.0064A + 0.0021B +0.00235C−0.00453B2-0.0051C2 Equ. 2
Where Y biomass is the response, the logarithmic value of biomass production (mg/g) and also A, B, and C are the uncoded values of the test variables Inoculum size, C/N ratio, and Incubation time. The model coefficients of variation are shown in Figure. S1, S2, and S3- a. the response surface Figures, obtained by the analysis of the experimental data of CCD, indicated a relationship between two variables at a time, while maintaining the third variable at the fixed level. These data are very useful in understanding both linear and interaction effect of the two variables. Figure S1 (a) shows the interaction of Inoculum size and incubation time. As the amount of inoculum size increased between 15 and 20%, at higher incubation period (30 to 45 days), the biomass of L. edodes also increased. Figure S2 (a) shows that 10-15 C/N ratio and 15-20% inoculum size gave high biomass yield. Figure S3 (a) shows that biomass yield was maximum, at 30 and 45 days of incubation and C/N ratio 10- 15 was equally effective in increasing the biomass of L. edodes.
The (ANOVA) analysis of biomass production by L. edodes showed that P > F value was 0.0001 and the model was significant for biomass production (response 1) for the L. edodes. Also,
the results indicated that the lack of fit test was insignificant. This result suggested that the sample variation of 94.75% for biomass was attributed to the independent variables, and only about 5.25% of the total variation could not be explained by the Quadratic model. Regression equation (Y Biomass) for the levels of biomass production as functions of Inoculum size (A), C/N ratio (B), and Incubation time (C) suggested that all the three factors influenced biomass production by the L. edodes.
The results predicted the maximum biomass production 0.043 mg/g by L. edodes in 1 g of solid walnut shell substrate under the experimental conditions of 23.41% inoculum size, 10 C/N ratio, and 30 days of Incubation time (Table.S1, Run 10). The P-value was used to assess the significance of the correlation coefficient; the smaller the value of P, the more significant was the corresponding coefficient. As can be seen from results, the coefficients were significant; indicating biomass production depended on all the three factors selected and optimum conditions for maximum biomass production was determined by three-dimensional response surface plots.
Optimization of LEPLs production by RSM
LEPLs production by L. edodes was optimized using central composite design in the Quadratic model, using Design Expert, version 8.0 software (Stat-Ease. Minneapolis, Minn.). LEPLs production by L. edodes mushroom was optimized by varying the conditions of the fermentation especially the inoculum size, C/N ratio, and the incubation time, at controlled temperature without change in pH during fermentation.
There was a considerable variation in the LEPLs depending upon the fermentation conditions, as shown in Table S1. The replication at the center point conditions resulted in higher biomass than at other levels. Response surface methodology helps in evaluation of the relationship between the dependent variable (LEPLs yield) and independent variables (fermentation conditions). The observed and predicted values of the biomass yield are shown in Table S1. The accuracy of the model can be seen by the difference between the observed and predicted values.
The statistical significance of Eq. (
3) was confirmed by an F-test, and the co-efficient and the analysis of variance (ANOVA) for the response surface quadratic model are presented in Table S4, S5. The fitness of the model was expressed using the value of the determination co-efficient (R2). The (ANOVA) of the quadratic regression model demonstrated that the model was significant, with an F-test of a very low probability value (
P >
F) <0.0001. In the present study, R2 comes out to be 0.9956 for
L. edodes. The value of adjusted co-efficient of determination adjusted R2 = 0.9931 for
L. edodes indicating the high significance of the model. The goodness of the model was indicated by the determination coefficient (R2) and the multiple correlation coefficients (R). The application of RSM yielded the following order polynomial equation.
Y LEPLs = +45.65 +7.47A + 2.79B +5.63C + 1.87AB −4.04A2 −5.35B2 −7.42C2… Equ. 3
Where Y LEPLs is the response, the logarithmic value of LEPLs production (mg/g) and also A, B, and C are the uncoded values of the test variables Inoculum size, C/N ratio, and Incubation time. The model coefficients of variation are shown in Figure. S1, S2, S3- b. Response surface figures, obtained by the analysis of the experimental data of CCD, showed a relationship between two variables at a time, while maintaining the third variable at the fixed level. These Figures are helpful in understanding both linear and interaction effect of the two variables. Figure S1 (b) shows the interaction of Inoculum size and incubation time. As the amount of inoculum size increased between 15 and 20%, at higher incubation period (30 to 45 days), the LEPLs of L. edodes also increased. Figure S2 (b) shows that 10-15 C/N ratio and 15-20% inoculum size gave high LEPLs yield. Figure S3 (b) shows that biomass yield was maximum, at 30 and 45 days of incubation and C/N ratio 10- 15 was equally effective in increasing the LEPLs of L. edodes.
The (ANOVA) analysis of LEPLs production by L. edodes showed that P > F value was 0.0001 and the model was significant for LEPLs production (response 2) for the L. edodes. Also, the results indicated that the lack of fit test was insignificant. This result suggested that the sample variation of 99.56% for biomass was attributed to the independent variables, and only about 0.44% of the total variation could not be explained by the Quadratic model. Regression equation (Y LEPLs) for the levels of LEPLs production as functions of Inoculum size (A), C/N ratio (B), and Incubation time (C) suggested that all the three factors influenced LEPLs production by the L. edodes.
The results predicted the maximum LEPLs production 46.80 mg/g by
L. edodes in 1 g of solid walnut shell substrate under the experimental conditions of 23.41% inoculum size, 10 C/N ratio, and 30 days of Incubation time (Table.S1, Run 10). Similar observations by
G.lucidum were reported (
29). The
P-value was used to assess the significance of the correlation coefficient; the smaller the value of
P, the more significant was the corresponding coefficient. As can be seen from the results, the coefficients were significant; indicating LEPLs production depended on all the three factors was selected and the optimum conditions for maximum biomass production were determined by three-dimensional response surface plots.
Validation of the models
In order to confirm the predicted results of the model, experiments under optimal conditions were carried out, yielding a biomass of 0.045 mg/g and a LEPLs yield of 47.62 mg/g.
Various substrates using solid-state fermentation technology have been used for the production of polysaccharides by the edible mushrooms (
8,
12,
27,
29,
34). In Iran, Walnut shell by-products that are generated from food processing are discarded as waste materials. Based on the literatures, the Walnut shell by-products have many properties that render them suitable substrates for the production of total polysaccharides by solid-state fermentation (
14). However, the production of polysaccharides using WSB as a raw material has not been reported. Thus, it was selected as a suitable substrate for total polysaccharides production, which could make full use of the WSB. In the present study, the main factors for the solid-state fermentation conditions of
L. edodes were scientifically selected for further optimization studies, using an RSM design. In brief, we describe a design process for obtaining the best values and value model using RSM assessments. Through these optimization experiments, highest yield of LEPLs has been 46.91 mg/g (Actual value).
The results from this work indicated that solid-state fermentation of WSB substrate by selected mushroom could be potentially used as an industrial method for total polysaccharides production.