Enzyme activities of the crude extract
The solid-state fermentation of pineapple peels using A.niger KWM yielded crude extract with protein content of 145mg mL-1. This is indicated that the fungus released extra cellular enzymes onto the growth medium during fermentation. The choice of growth substrate is critical as it influences specific enzyme production during microbial fermentation of lignocellulosic biomass . The pineapple peels used as fermentation medium influenced the enzymes mixtures capable of hydrolyzing pineapple peel substrate. The crude extract displayed enzyme activities of 175UmL-1, 138UmL-1 and 221UmL-1 for CMCase, Fpase and xylanase respectively. Most filamentous fungi have been reported to produce cellulase and xylanase enzymes with either low or high activities [25-27]; however, this study showed that solid-state fermentation of pineapple peels using A.niger KWM can potentially produce enzyme cocktail with relatively close cellulase and xylanase activities which is important in many industrial bioprocesses.
Optimizing mushroom saccharification parameters for higher glucose yield
In the present study, the pH range of 4.5-6.5, reaction time ranging from 12-60h, temperature range of 30-50oC and enzyme loading of 1-5% (v/v) were chosen as key independent variables that affect enzymatic saccharification of mushroom biomass. Table 1 illustrates the coded and real values for the experimental variables.
Table 1 Coded and real values of reaction variables for central composite design
A second-order polynomial model that describes the effect of variables on glucose yield from the enzymatic saccharification of mushroom is presented in Eq 1.
Y(Glucose yield) = β0 + β1x1 + β2x2 +β3x3 + β4x4 + β11+ β22+ β33+ β44+ β12x1x2+ β13x1x3 + β14x1x4+ β23x2x3 + β24x2x4 + β34x3x4 (1)
where Y is the measured response; β0 is the intercept term; β1, β2, β3, and β4 are linear coefficients; β11, β22, β33, and β44 are quadratic coefficients; β12, β13, β14, β23, β24, and β34 are interaction coefficients; and x1, x2, x3, and x4 represent the independent variables (temperature, pH, time and enzyme loading) respectively. In developing the regression equation, the test variables for glucose yield were coded according to Eq 2.
where xi is the coded value, Xi is the real value of the independent variable, X0 is the value of the independent variable on the center point, and ΔXi is the step change value.
The CCD and their coded, experimental and predicted values are shown in Table 2. The model predicted maximum glucose yield of 0.934mg/mL under the reaction conditions of pH 6.0, temperature 45oC and enzyme loading of 4% (v/v) for a period of 48h (run 16). Experiments done under these conditions yielded slightly higher glucose (0.945mg/mL). The reaction at pH 5.5, temperature 40oC and enzyme loading of 5% (v/v) for a period of 36h yielded 1.046mg/mL (run 24); this was 2.8 folds higher than the glucose yield predicted by the model.
Table 2 Matrix of the CCD for the evaluation of the effect of independent variables
on the glucose yield during mushroom saccharification
The model expressed by equation (3) represents glucose yield (Y) as a function of pH (X1), time (X2), temperature (X3), and enzyme loading (X4).
Y (Glucose yield) = 0.367714 + 0.044625x1 + 0.062208x2+0.117292x3 + 0.147958x4 – 0.041085- 0.019710+ 0.022290+0.081415-.028438x1x2+0.025438x1x3+0.012062x1x4 – 0.005563x2x3 + 0.052563x2x4 – 0.094938x3x4 (3)
Table 3 illustrates the significance of all terms in the polynomial equation (2) as described statistically by F-test and the analysis of variance for response surface quadratic model.
Table 3 Analysis of variance of second order polynomial model for optimization of enzymatic mushroom saccharification.
Values of probability (p) ˃F<0.05 indicated that the model terms were significant. The F-value of 7.39 and a low p-value (p=0.000) suggest that the model is significant. The coefficient of determination (R2 =86.6%) indicated that the experimental and predicted values were in good agreement, and that the model can well be used to predict process performance and optimization. The lack-of-fit (F-value of 1.10) for regression of Eq. 3 was not significant (p-value=0.475). Non-significant lack-of-fit is good proof that the model equation is adequate to predict the response under any combination of values of the variables. The linear and quadratic terms in second order polynomial model (Eq. 3) were highly significant (p<0.01) and adequate to represent the relationship between glucose yield and the reaction variables of pH, enzyme loading, temperature and time (Table 3).
The t and p-values for linear, quadratic and combined effects of the variables are given in Table 4. The time, temperature and enzyme loading were linear variables which had significant (p<0.05) positive effects on mushroom hydrolysis. The medium pH had non-significant (p > 0.05) positive effect on mushroom hydrolysis. With regard to the quadratic terms, the medium pH and reaction time had negative effect on the glucose levels obtained. The temperature and enzyme loading positively (positive coefficients) affected mushroom hydrolysis and the glucose yield. The effect of enzyme loading on glucose yield was significant (p < 0.05). The absence of interactions between the variables (p > 0.05) implies that these variables had additive effects on mushroom hydrolysis. Similar results had been reported where non-interactive effect of reaction variables resulted in additive effects on the response .
Table 4 Estimated regression coefficients of second order polynomial model for optimization of glucose yield
Interaction effect of variables on enzymatic saccharification of mushroom biomass
To analyze the interaction and to determine the optimum value of each variable for maximum glucose yield, three dimensional response surface curves were drawn against two experimental variables while keeping the other variables constant at their central points. The interaction effect of reaction temperature and enzyme loading on glucose is illustrated in Figure 1. According to this contour plot, the glucose yield˃1.50mg/mL is achievable under the enzyme loading of 4.8-5.0% (v/v) and temperature range 48-50oC while keeping time and pH constant at 36h and 5.5 respectively.
Fig. 1 Response surface plot of the combined effect of reaction time and enzyme concentration on glucose yield.
Figure 2 shows the interaction effect of the reaction time and enzyme loading on glucose yield. Glucose yield of 1.00-1.25mg/mL can be obtained under the enzyme loading of 4.8-5.0% (v/v) and reaction time ˃40h while keeping the temperature and pH constant at 40oC and 5.5 respectively.
Fig. 2 Response surface plot of the combined effect of time and enzyme loading on glucose yield
According to the contour plot in Figure 3, mushroom hydrolysis at the temperature range of 30oC-60oC for 12h to 60h while keeping pH and enzyme loading constant at 5.5 and 3% (v/v) respectively is yields 0.5-0.75mg/mL glucose. The increase in temperature has been reported to have effects on the enzyme activity as a result of increasing thermal activation [29- 30].
Fig. 3 Response surface plot of the combined effect of temperature and time on glucose yield
According to Figure 4, glucose yield of 0.75-1.00mg/mL can be achieved when mushroom hydrolysis is done under pH 5.8-6.5 and enzyme loading 4.5-5.0% (v/v), while keeping time and temperature constant at 36h and 40oC respectively. It suggests that the enzymes in the crude extract are able to adapt well to the pH range of 5.8 to 6.5. Enzyme concentration is an important factor in the enzymatic saccharification of lignocellulosic biomass .
Fig. 4 Response surface plot of the combined effect of enzyme load and temperature on glucose yield
Figure 5 shows the interaction effect of medium pH and reaction temperature on glucose yield. Hydrolysis of mushroom at temperature range of 48-50oC and pH range 4.8-6.5 while keeping enzyme loading at 3% (v/v) for 36h respectively yields glucose of 0.5-0.75mg/mL.
Fig. 5 Response surface plot of the combined effect of pH and time on glucose yield
Figure 6 shows the interaction effect of medium pH and temperature on glucose yield. Glucose yield of 0.25- 0.5mg/mL can be achieved when mushroom hydrolysis is performed at pH 1.0-6.5 and temperature 40-50oC while keeping time and enzyme loading constant at 36h and 3% (v/v) respectively.
Fig. 6 Response surface plot of the combined effect of temperature and time on glucose yield
Figure 7 illustrates that enzymatic saccharification of mushroom biomass at temperature 50oC, medium pH 6.5, and enzyme loading of 5% (v/v) for 12h yields 1.490 mg/mL of glucose. The experiments conducted using optimal model conditions yielded glucose 1.582mg/mL which is 1.1 folds higher than the predicted value. This confirms the validity of the model as a viable tool that may be used for modeling and optimizing reaction parameters for mushroom hydrolysis using crude enzymatic extract. The glucose yield is likely to even increase with mushroom pretreatment and optimization of other reaction parameters such as agitation speed during enzymatic processing [32, 33].
Fig. 7 Optimization plot for enzymatic saccharification of mushroom
The glucose yield of 1.582mg/mL reported in the present study is within the range of glucose content of Pleurotus species previously reported [34, 35]. The hydrolysis of mushroom biomass was attributed to the ability of the crude extract to display adequate balance between the activities of β-glucosidase, FPase, and cellobiohydrolase which are key in mushroom cell-wall degradation [36-37]. An enzyme ratio of 2:1 for FPase and β-glucosidase enzymes is regarded as defining a suitable enzyme system .
Commercial enzymes have been used in mushroom processing to recover high value products. Ang and Ismail-Fitry  and Banjongsinsiri et al.  used commercial bromelain and papain enzymes respectively to enhance recovery of protein from mushroom. Similarly, Poojary et al  digested mushroom biomass with commercial enzymes to recover amino acids responsible for umami taste. The processing of mushrooms using commercial enzymes of some mushroom species such as Shiitake have been patented . However, for a long time, the cost of commercial enzymes has remained a major bottleneck in industrial bioprocessing. The application of crude enzymatic extracts in bioconversion processes has been therefore driven majorly by the need to make enzyme-based processing more cost-effective and competitive. Mahamud & Gomes  applied crude enzymatic extract for saccharification of sugarcane bagasse in bioethanol production; crude extracts showed higher saccharification efficiency compared to their commercial counterparts. The use of crude enzymatic extract has also been extended in many industrial bioprocesses including fruit processing industries for fruit juice clarification [44, 45]. Despite increasing research outputs in the applications of crude enzymatic extracts in industrial bioprocessing, the information on mushroom processing using crude enzymes is quite limited or probably not available.