Statistical Modeling and Optimization of Reducing Sugar Production for Enzymatic Digestibility of Bamboo Grass by Box-Behnken Design Methodology

Bamboo plants are plants that can grow quickly and easily, and is an abundant and renewable resource plants analogous to deciduous tree and conifer. In this study, bamboo species of Yushania alpina was pretreated with sodium hydroxide (NaOH) and hydrolyzed by dilute sulfuric acid to produce reducing sugar. Phenol-sulphur acid method was used to determine the response. Seventeen experimental runs were carried out at temperature range (110-128oC), concentration of H2SO4 range of (1-7%) and residence time range of (30-60 min) respectively. The effect of the parameters was measured by concentration of reducing sugar produced for each, and the optimization of glucose production was done by using Box-Behnken design methodology. The results reveal that, the temperature, concentration and time significantly affected the glucose production. At range of low temperature, concentration and time, the yield was decreased while at high value of factors the yield of reducing sugar increased. At 113.17oC, 3.43wt%, 33.65min and desirability of 0.71 the optimum reducing sugar of 19.9 found.


Introduction
Production processes based on renewable resources has become one of the most important concerns of today, especially due to the depletion of non-renewable resources such as petroleum. In the past eras, the significant reduction in fossil-based resources containing petroleum, natural gas as well as coal, and conservational anxieties linked to the consumption of these resources, has led to attention more and more on renewable raw materials. It is known that plantbased raw materials and chiefly lingo cellulosic biomass have the prospective to exchange a major part of fossil-based resources for the manufacturing production of energy, chemicals, and materials. Lignocelluloses represent the most copious and lowest-cost biomass in the biosphere and, thus, can be used as substitute raw materials for the invention of fuel ethanol [1].
Bamboo, which goes to the Gramineae family (grasses), with a growth period of 3-8years, develops more quickly than any extra woods on the earth [2]. According to, Bamboo is a hopeful class for use as a feedstock in a biorefinery for the fabrication of pulp, bioethanol, and other chemicals because of numerous reasons. For instance, it not single matures profligate but also has a dissimilar chemical composition, short renovation, and easy propagation. Open Access Journal utilizing alkaline are conceded out at milder situations, specific of them even at ambient temperature, as demonstrated by wringing in NaOH, or NH₄OH. Such methods can eradicate the requirement for expensive materials and unusual designs to handle with deterioration and Spartan feedback conditions. Alkaline pretreatments are more effective in grass species than wood species in delignification [21].

Sci Forschen
The enzymatic activities of the yeast can be enhanced by applying the best hydrolysis process to convert cellulose to simple sugar for a high yield of bioethanol. A lot of literature is written about chemical and enzymatic hydrolysis approaches to enhance the ferment-ability of lignocellulosic material. Almost all researchers' focus on the enzymatic hydrolysis of bamboo [22][23][24][25][26][27][28]. A few studies have been done on chemical hydrolysis [29][30][31][32][33]2].
More than 7% of the biosphere`s bamboo enclosed area is found in Ethiopia above 1ha [34], and which shelters 67% of African bamboo capitals. Currently, the bamboo (highland bamboo or lowland bamboo) found in Ethiopia is not utilized and there is no conversion of this resource into glucose and other chemicals. That is maybe due to a lack of awareness about the multiple uses of bamboo and scientific knowledge about their derived products and main properties [35]. Moreover, limited researches, especially in Ethiopia, was done on acid hydrolysis of bamboo and optimization of glucose production.
In the present study, pretreatment and hydrolysis of bamboo were done using sodium hydroxide and dilute sulfuric acid respectively. The hydrolysis process was improved by recycling the water and sulfuric acid, which could reduce the environmental impact of the whole process. The production of glucose was optimized using a design expert version 11.1.2.0 and results were analyzed by ANOVA.

Raw material collection and preparation
Highland bamboo (Yushania alpina) stems, were harvested from Jimma (Ethiopia). The collected bamboo culm was permitted to dehydrate in the open air for a week. The dehydrated bamboo Culm was then expurgated into minor fragments and, desiccated in an oven (Labquip Ltd., UK) for 1day at 50°C. The desiccated bamboo culm was crushed into powder in a hammer mill (Glen Creston Ltd., UK) and filtered to 2mm particle dimensions [31] then store at room temperature for further work.

Pretreatment of Bamboo
The pretreatment process was done according to [31,32]. Accordingly, bamboo powder (dry weight) was pretreated with prepared reagents of NaOH, at a temperature of 860C for 2h at a 1:10wt ratio of solid and alkaline solution loading. After pretreatment, the solids (alkaline insoluble) were centrifuged and the solid remainder was desiccated in the open environment to removing moister content and preparing for the hydrolysis process.

Dilute acid hydrolysis of Bamboo
Pretreated bamboo residues were hydrolyzed with different concentrations sulfuric acid of (1, 4, and 7 wt %)to convert the cellulose to glucose. Hydrolysis lasted for a time of 30, 45, and 60 minutes, and at different temperatures of 110, 118, and 126°C.The liquid and remainder later hydrolysis were parted using a vacuum screen [31].

Total reducing sugar determination using the phenolsulfuric acid method
The total reducing sugar after hydrolysis was resolved by phenol sulphur acid methods and the absorbance of the reduced was dignified with a UV-visible spectrophotometer (6505, Jenway Ltd. UK) at 540 nm wavelength of maximum glucose absorbance. Quantification was made from a standardization curve of maximum absorption against concentration of glucose as standard ( Figure 1) and calculation was achieved by equation of the linear regression obtained from the standardization curve. The standard system was prepared as follows. 2 mL aliquot of a sample solution was mixed with 0.4 mL of 5% aqueous solution of phenol in a test tube. Subsequently, 2 mL of concentrated sulfuric acid was added rapidly to the mixture. The test tubes were allowed to keep for 10 min at room temperature, and placed in a water bath for 20 min for color development. Absolute solutions were arranged in the same way as above, excluding that the 2 mL aliquot of a sample solution was substituted by distilled water [36].

Standard and reagent solution preparation
3.6 g of glucose in 100 mL of distilled water was dissolved to prepare a glucose stock solution. By pipetting a known volume of the stock solution (1, 2, 3, 4 and 5 mL) into a 100 mL volumetric flask, several thinning the stock glucose solutions were made and filling the capacity with distilled water as far as the mark. The concentrations completed for this study were: 0.036, 0.072, 0.108, 0.144 and 0.181 g/mL. To resolve the standardization curve for typical glucose, 2 mL of each of the typical solutions were pipetted out and taken into a distinct test tube. Then 0.4 mL of 5% aqueous solution of phenol chemical and 2 mL of 96% sulfuric acid were added. The combination was reserved for 10 min at room temperature and sited in a water wash for 20 min. Then the absorbance was read at 540 nm using a UV-visible spectrophotometer (6505, Jenway Ltd. UK). Blank solutions were ready in the same way as above, except that the 2 mL of the standard solution was replaced by distilled water. Then, the quantity of total reduced sugar content existing in the sample solution was calculated using the standard graph and expressed as gram glucose equivalents (GE) per 10 g of sample [36].

Experimental design
For experimental design, Response surface methodology (RSM) was implemented in this study. Mainly RSM is employed to reduce the number of investigational rounds needed to deliver adequate information for statistically satisfactory results. Box-Behnken Design (BBD) of response surface methodology is based on the construction of balanced incomplete block designs and requires at least three levels for each factor. The level of one of the factors is fixed at the center level while combinations of all levels of the other factors are applied in the Box-Behnken experimental design [37].This strategy requires an The effects of unexplained variability in the observed responses due to extraneous factors were maximized by randomized experimental work. The levels of the independent variables as shown in table 1 were selected based on preliminary experiments.
The three main factors selected for hydrolysis Bamboo were acid concentration, exposure time, and temperature with three levels for each factor. The factors among their levels were described in Table 1. The experiments were performed as a completely randomized design. The responses that were considered during the optimization of the Processing variables were reducing sugar.

Analysis of variance
The competence of the model was patterned by analysis of variance (ANOVA) and diagnostic plots. Analysis of variance (ANOVA) was engaged to test the implication of the developed models. The significance of the result was performed using analysis of variance (ANOVA) which was developed using the software (Stat-ease, Inc. Minneapolis, USA). A 2nd-degree polynomial was snug to the experimental data to approximate the response of the dependent variable and predict the optimal point.

Measurement of Reducing Sugar
In this work, the total reduced sugar content was produced by the phenol sulfuric acid method was investigated (Figure 1). The effect of acid concentration, hydrolysis time, and temperature on the hydrolysis of bamboo was determined.

Statistical analysis
Usually, it is critical to approve first whether the fitted model delivers a satisfactory estimation of the real values or not. Table 2 shows the summary of the analysis of variance (ANOVA) of the reducing sugar. Values of p-values less than 0.05 indicate model terms are significant. In this study, A, B, C, A2, B2, C2, AC, BC are significant model terms, while AB was not significant. The Lack of Fit is not significant relative to the pure error since its value is 1.95. There is a 26.29% casual that a "Lack of Fit F-value" this large might happen due to noise. A nonsignificant lack of fit is good --we need the model to fit.
For a respectable arithmetical model, the R 2 value (correlation coefficient) should be near to one. The result is given in (Table 3) and shows a reasonable modification of the quadratic model to the investigational data. It was indicated that approximately 99.79 % of the variability in the dependent variable reducing sugar could be explained by this model. The value of R 2 was very high and near to the one which shows a good agreement among experimental and forecast values. The "Pred R-Squared" of 0.9789 is in realistic agreement with the "Adj R-Squared" of 0.9953.

Effect of Process Parameters on the reducing sugar
Here, direct effect, interaction effect or a comparison between any two input parameters have been discussed and the third parameter would be on its central level. 3D surface plots were drawn by using BBD methods to examine the effect of all the independent variables on the out puts. The individual effect of factors was shown in figures 2-4. Figure 2 demonstrates the effect of temperature at a fixed time on the glucose concentration. As it is shown (Figure 2), the total reduced sugar from bamboo was increase as temperature increases from 110°C to 119°C. The maximum reduced sugar was achieved at 119 o C with 26.44g/ml. Nevertheless, when temperature increases beyond 119°C decrease in concentration was observed which is due to sugar being degraded into not fermentable products such as hydroxymethyl furfural (HMF) and furfural that are toxic to the cell of microorganisms [10]. Figure 3 shows the consequence of hydrolysis time on the concentration of reducing sugar at constant temperature and acid concentration in the center point. As observed in figure 3 the concentration was first positively affected by hydrolysis time, as the hydrolysis time increased from 30min to 45min. However, beyond 45min the concentration of sugars lightly decreased and reached 23.43g/ml in agreement with previously reported work [5,38,33].     At constant temperature and hydrolysis time, an increase in acid concentration increased the concentration of reducing sugar, as shown in figure 4. The curve (Figure 4) shows a sharp increase of reducing sugar up to 5%, there after the increase steadily falls and at 7% it's almost no further increase of reducing sugar. The decrement of reduced sugar content with increasing acid concentration may be due to the decomposition of the sugars and the development of some inhibitor such as hydroxymethylfurfural (HMF) and methyl furfural.

Sci Forschen
Not only single factors but also interaction factors affected reducing sugar concentration, as was clear from table 2. The collaboration effect of factors on the reducing sugar concentration was illustrated in figures 5-7. Figure 5 shows the 3D plots developed as a function of temperature and time, while the acid concentration was kept at the center point on reducing sugar concentration. At lower hydrolysis time with increasing temperature, a slight decrease in reducing sugar was observed. However, at lower temperatures with increasing time the reducing sugar increased which higher lower than at lower exposure time with increased temperature. This is observed when the cellulose is exposed to high temperature, the sugar obtained from cellulose degraded into not fermentable products.
The interaction effect of acid concentration and temperature at constant time on reducing sugar was drawn (Figure 6). At a definite acid concentration, the sugar yield first increased slightly with   hydrolysis temperature up to a peak. However, upon further increasing the hydrolysis temperature there was a gradual decline in the yield. This means that, as the acid concentration increased the reducing sugar yield increased as the temperature decreased; for the reason, that sugar degrade to the toxic hydroxymethyl-furfural (HMF) which leads to decreased glucose yield might be occurred [26].

Optimizations
Optimization of reducing sugar concentration was carried out by a numerous output method called desirability (D) function to improved is similar groupings of process parameters. The objective of optimization was to exploit economic benefit or increasing sugar concentration by reducing process costs, so a lower temperature, a short hydrolysis time, and low acid use. The conditions to investigate the optimum values of sugar from bamboo through dilute acid hydrolysis were summarized in table 4.
The optimum possible solutions in the hydrolysis of bamboo for the production of total reducing sugar were presented in figure 8. The predicted optimum yield of TRS was 19.97g/ml observed at the process variables 113.17 o C, 33.65min, and 3.43wt% at 0.71 desirability.

Conclusion
The production process of monosaccharaides from lignocellulosic biomass was effective through optimization of independent variables. The current study indicates that RSM with engaging BBD has provided a consistent and precise procedure in optimizing the reducing sugar production from the bamboo species Yushania alpina. The independents variables: temperature, hydrolysis time and acid concentration were affecting the concentration of reducing sugar individual and interaction each other. In the order of single effect: acid concentration temperature and hydrolysis time were affect mainly the production process respectively. The optimum condition of reducing sugar was obtained at temperature, acid concentration and hydrolysis time of 13.179°C, 3.43wt% and 33.65 min respectively with the desirability of 0.71. Concentration of reducing sugar (g/ml) Maximize 9.8 27 Table 4: Optimization criteria for optimum concentration of reducing sugar.