Dilute acid pretreatment of rice straw, structural characterization and optimization of enzymatic hydrolysis conditions by response surface methodology

Abstract

Efficient conversion of fermentable sugars from cheap lignocellulosic biomass is a current need in viable ethanol production technology. In the present study, agricultural waste biomass such as rice straw was pretreated by using 0.5% sulfuric acid for 60 min at 121 °C in an autoclave. A statistical experimental design like central composite design (CCD) was used for optimization of the enzymatic hydrolysis conditions to achieve a significant reducing sugar yield using commercial cellulase. The optimal conditions for acid pretreated rice straw were found to be 40 FPU g⁻¹ enzymes loading, 17.50% biomass loading at 50 °C for 72 h. The reducing sugar yield was 0.359 g g⁻¹, achieved at the optimized conditions. Experimental results under optimum conditions fit well with CCD model predictions. The structural and morphological changes in native and dilute acid treated rice straw substrate were evaluated by FTIR, XRD and SEM analysis. The XRD pattern of biomass revealed an increase in the crystallite size and crystallinity index of pretreated biomass. Scanning electron micrography reported surface porosity and a distorted structure due to pretreatment. HPTLC analysis of sugars like glucose and xylose in hydrolysate produced after enzymatic hydrolysis was determined.

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1. Introduction

The global energy consumption has tremendously increased which has caused a shortage of crude oil reservoirs. Thus, research interest has been increased towards the development of alternative clean energy sources. Plants fix atmospheric carbon dioxide and produce complex carbohydrates by photosynthesis. Thus plants are a renewable energy source, and abundant lignocellulosic biomass has significant potential for biofuel production. Biofuel production by utilizing lignocellulosic biomass is a clean option as compared to nonrenewable energy sources like fossil fuels. Ultimately, it will help to reduce the dependency on fossil fuels for energy sources as well as greenhouse gas emission. At present, the production of second generation biofuels is desirable for control of the crisis related to the environment and society.¹

Lignocellulose is mainly composed of three major polymers like cellulose, hemicellulose, and lignin. Among which, cellulose and hemicellulose are packed densely in the lignocellulosic biomass while layer of lignin offer the resistance against enzymatic hydrolysis. Hence the pretreatment process is required for disruption of lignin layer and for easier accessibility of hydrolytic enzymes to cellulose and hemicellulose.² However lignocellulosic biomass can be converted into simpler and more biodegradable form by the pretreatment protocol like enzymatic hydrolysis of cellulose. The composition of lignocellulosic biomass depends upon various factors i.e. genetic variability, climate, soil type, environmental influences, etc.^3,4

Rice is one of the most important staple food crops grown in major farmland of India. Agricultural waste biomass such as rice straw is produced after post-cultivation of rice crop. Conversion of rice straw biomass to biofuels or other chemicals give rise to proper and efficient utilization of biomass.^5,6 To achieve best yield of fermentable sugar, pretreatment is strictly required in case of lignocellulosic waste biomass. There are various processes reported for the conversion of lignocellulosic biomass into biofuels production i.e. pretreatments, saccharification and fermentation.⁷ Pretreatments are required for increasing accessibility of surface area of cellulose for reducing sugar production by removing components such as hemicelluloses and lignin from the complex structure. Due to chemical pretreatment, cellulose can be easily digested in enzymatic saccharification procedure and thus increasing yield of fermentable sugars. Enzymatic hydrolysis is an ecofriendly strategy as well as energy efficient process. Comparatively less amount of energy is required for the production of reducing sugars from lignocellulosic biomass. Various efficient pretreatments are required to lignocellulosic biomass before enzymatic hydrolysis for the considerable yield of fermentable sugars.⁸

The fermentable sugars produced using enzymatic hydrolysis from dilute acid pretreated biomass considered as the most promising for bioethanol production.⁹ Dilute acid pretreatment helps in dissolution of cellulose and hydrolysis of hemicellulose by which sugar recovery increases in liquid fraction resulting in more availability of cellulose for enzymatic hydrolysis.^10–12 In enzymatic saccharification procedure biomass loading, enzyme loading, surfactant concentration and incubation time are the key factors that influences the efficiency of process. Optimization of enzymatic saccharification by conventional technique is time consuming as effect of single component is observed at a time. Response surface methodology (RSM) is a statistical method mainly used for modeling and optimization of the process. RSM gives correct scientific information about the optimum conditions. There are many reports of RSM used for enzymatic saccharification of wheat straw,^13,14 switch grass,¹⁵ prairie cord grass,¹⁶ rice straw^17,18 and sugarcane tops.¹⁹

In the present work, statistical method such as response surface methodology is used for the optimization of conditions for the production of reducing sugars using enzymatic hydrolysis of dilute acid pretreated rice straw. Central composite design is used to optimize various parameters of enzymatic hydrolysis such as biomass loading, incubation time, enzyme loading and surfactant loading for maximum yield of reducing sugars. The change in functional groups, cellulose crystallinity and structure of rice straw after dilute acid pretreatment was also investigated by using FTIR, XRD and SEM. This study reveals the change in the hydrolysis efficiency of lignocellulosic biomass after dilute acid pretreatment for the generation of liquid biofuel like bioethanol.

2. Experimental

2.1. Raw materials and chemicals

Rice straw a lignocellulosic waste was collected from local farmer, Kolhapur district, Maharashtra, India. The lignocellulosic waste was dried, milled using pulverizer, grinded to particle size <1 mm and stored at 37 °C. The commercial enzymes cellulase (Celluclast® 1.5 L), hemicellulase and β-glucosidase were obtained from Sigma Aldrich, USA. Tween-20 and other all chemicals used were analytical grade purchased from HI-Media (Mumbai).

2.2. Biomass pretreatment

Pretreatment with dilute sulfuric acid of rice straw was carried out using 250 mL screw cap bottle containing 10% (w/w) biomass loaded with 0.5% (w/v) H₂SO₄ and autoclaved (at 120 °C and 15 lb pressure for 60 min). Obtained solid biomass was washed with hot water until it attends pH 7.0. The biomass was dried and used for enzymatic hydrolysis process.

2.3. Compositional analysis of rice straw feedstock

The cellulose, hemicellulose and lignin composition in native and pretreated rice straw were analyzed.²⁰ For determination of hemicelluloses, 1 g biomass was treated with 0.5 M NaOH solution (150 mL) and kept at 80 °C for 3.5 h. Further, it was washed to remove Na⁺ ions (pH 7) and dried to a constant weight. Weight difference between before and after treatment of the sample was considered as the hemicellulose content. For acid soluble lignin, 0.5 g of hemicelluloses-free biomass was treated with 15 mL of 98% H₂SO₄ and incubated for 2 h at 30 °C. The sample mixture was diluted to 4% H₂SO₄ by de-ionized water, further autoclaved at 121 °C for 1 h. Filtrate aliquots of diluted sample were measured at the absorbance of 205 nm. Sulfate ions were removed by titration with 10% barium chloride from the remaining biomass and then dried to constant weight. This is considered as lignin content. The difference between total content and hemicelluloses plus lignin content corresponds to the cellulose content.²¹

2.4. Enzymatic hydrolysis

Enzymatic saccharification of pretreated rice straw was performed in 100 mL reagent bottle with screw cap by incubating 5 g of biomass in 50 mM citrate buffer (pH 4.8). Hydrolysis was performed using a commercial cellulase (Celluclast 1.5 L), hemicellulase from Aspergillus niger and β-glucosidase from almonds (Sigma Aldrich). The different combination ratio of enzymes were checked for enzymatic hydrolysis (1 : 0 : 0, 1 : 1 : 1, 2 : 1 : 1, 1 : 2.5 : 2 and 0 : 1 : 1), and the enzyme combination ratio of (1 : 2.5 : 2) were used for final enzymatic hydrolysis. The sample was incubated at 50 °C for 48 h in shaking incubator at 150 rpm. The reducing sugar produced in the hydrolysate was determined by 3,5-dinitrosalicylic acid (DNS) method.²²

2.5. Optimization of enzymatic hydrolysis using response surface methodology and central composite design

Central composite design (CCD) was employed to study the effect of independent variables on the response and mutual interactions among them. The variables selected were biomass loading, incubation time, enzyme loading, and surfactant loading. In this study four variables effect at three different levels and a total of 30 experimental runs were obtained. The software Design expert 9 (Stat-Ease, Inc., USA) was used for experimental design, data analysis and quadratic model building. The experimental setup of RSM is shown in Table 1.

Table 1 Coded values of the variables for the central composite design

Variable	Symbol coded	Actual and coded values
		−2	−1	0	1	2
Biomass loading (% w/w) variable	A	10	12.5	15	17.5	20
Incubation time (h)	B	0	24	48	72	96
Enzyme loading (FPU g^−1)	C	10	20	30	40	50
Surfactant concentration (% w/w)	D	0	0.050	0.125	0.200	0.275

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2.6. HPTLC analysis for determination of sugars

The reducing sugars produced after enzymatic hydrolysis was determined by HPTLC technique. In this analysis, silica gel 60 F₂₅₄ (Merck) HPTLC plate was used. Silica gel plate activation was done by using 0.02 M sodium acetate. After the plate activation, 10 μL of hydrolyzed samples and sugar standards of glucose, xylose and cellobiose were applied on plate by micro syringe using automatic nitrogen gas sample applicator (LinomatV, CAMAG, Switzerland). 8.0 mm bands size with track distance of 14.5 mm, 8.0 mm distance from the lower edge, and 20 mm distance from both edges were used. For chromatographic separation of sugars, mobile phase used was butanol : ethanol : water (5 : 5 : 3 v/v). The development of plate was carried out in the twin-trough chamber 10 × 10 cm (CAMAG) up to 85 mm migration distance. For detection of sugars, 0.3% α-naphthol in 5% H₂SO₄ in methanol solution was used for HPTLC plate.²³ Data processing and all instrumentation were performed with the software platform winCATS 1.4.4.6337 (CAMAG).

2.7. Structural characterization of biomass

2.7.1. FTIR analysis. FTIR spectra were analyzed to examine the functional group changes occurred in native and dilute acid pretreated rice straw. FTIR analysis was carried out using FTIR spectrometer (Agilent, Cary 630; USA). The FTIR spectra were recorded in the absorption band mode in the range of 650–4000 cm⁻¹ with a resolution of 4 cm⁻¹ and 32 scans. The IR crystallinity of cellulose in lignocellulosic biomass was evaluated by IR absorption ratios between the absorbance intensities 1375–1512 cm⁻¹, 1429–897 cm⁻¹ and 1372–2900 cm⁻¹.

2.7.2. XRD analysis. The waste biomass of native and dilute acid pretreated rice straw were analyzed by XRD instrument (BRUKER, Germany). D2-Phaser was set at 30 kV, 10 mA; 10 and 40° grade range and a step size of 0.02° with radiation Cu (1.54 A°) was used. Crystallinity index (CrI) of cellulose was calculated according to the peak intensity method.²⁴

CrI (%) = [(I002−Iam)/I002] × 100

where CrI is the crystallinity index, I002 is the maximum intensity at 2θ = 22.17°, and Iam is the minimum intensity corresponding to the amorphous content at 2θ = 18.0°.

The crystallite size (D) was calculated based on the following equation

D = K × λ/β cosθ

D (hkl) is the size of the crystallite (nm), K is the Scherrer constant (0.94), l is X-ray wavelength (for copper, 0.15418 nm). β is the full-width at half-maximum of the reflection hkl, measured at 2θ which is the corresponding Bragg angle.²⁵

The degree of crystallinity was calculated on the basis of area under crystalline peaks and non-crystalline region using following equation:

X_c = F_c/(F_a + F_c) × 100

where X_c is degree of crystallinity, F_c and F_a, respectively, are the area of crystalline and non-crystalline regions.²⁶

2.7.3. SEM analysis. The surface morphology changes in native and dilute acid pretreated rice straw were investigated by Scanning Electron Microscope (SEM). Surface morphology images of the native rice straw and that with chemical pretreatment were taken at magnification 500× and 2000× using a JEOL JSM-6360 SEM. The specimens to be coated were mounted on a conductive carbon tape and coated with gold then observed using a voltage of 20 kV.

3. Results and discussion

3.1. Dilute acid pretreatment of rice straw

The lignocellulosic biomass composition of rice straw was investigated before and after pretreatment with dilute acid. Composition of cellulose, hemicellulose and lignin content of native, and dilute acid pretreated biomass were shown in Table 2. The hemicellulose content was 19% in pretreated rice straw and 28% in native rice straw. It may be due to its amorphous nature and can easily hydrolyze during the dilute acid pretreatment. Cellulose content in acid pretreated rice straw was increased (47%) due to dissolution of amorphous materials from the biomass. It also results into increase in cellulose accessibility and maximum fermentable sugar production during enzymatic hydrolysis. The similar results were reported by many research groups.^27,28

Table 2 Composition of cellulose, hemicellulose and lignin in rice straw biomass native and after pretreatment

Rice straw	Cellulose (%)	Hemicellulose (%)	Lignin (%)
Native	32	28	17
Dilute acid pretreated	47	19	20.5

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3.2. Optimization of enzymatic hydrolysis

Response surface methodology and central composite design was employed to optimize the parameters of biomass loading, incubation time, enzyme loading, and surfactant concentration for improved reducing sugar yield (Table 4). The reducing sugar (g g⁻¹) and variables interactional relationship were shown in terms of coded values. The response predictions were analyzed by using coded values.

Reducing sugar (g g⁻¹) = 0.26 + 0.025A + 0.034B + 0.027C − 1.218E − 003D − 3.764E − 003AB − 0.014AC + 8.080E − 003AD − 7.742E − 003BC + 1.037E − 003BD + 9.575E − 003CD + 9.897E − 003A² − 9.666E − 003B² + 0.011C² + 7.179E − 003D² (1)

Table 3 IR crystallinity of native and dilute acid pretreated rice straw biomass

Rice straw	IR crystallinity ratio
	A1375/A1512	A1427/A895	A1372/A2900
Native	1.28	2.29	0.46
Dilute acid pretreated	1.19	2.18	0.47

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Table 4 Central composite design with four process variables and the experimental and predicted results

Run	Biomass loading (% w/w)	Incubation time (h)	Enzyme loading (FPU g^−1)	Surfactant concentration (% w/w)	Reducing sugar (g g^−1)
					Actual value	Predicted value
1	12.5	24	20	0.2	0.15	0.15
2	15	0	30	0.125	0.14	0.15
3	15	48	30	0.125	0.24	0.26
4	17.5	24	40	0.05	0.29	0.28
5	17.5	24	20	0.05	0.26	0.26
6	10	48	30	0.125	0.24	0.25
7	12.5	72	40	0.05	0.32	0.33
8	17.5	24	20	0.2	0.26	0.25
9	15	48	30	0.125	0.26	0.26
10	17.5	72	20	0.2	0.33	0.33
11	20	48	30	0.125	0.35	0.35
12	15	48	30	0.125	0.27	0.26
13	12.5	24	20	0.05	0.19	0.19
14	12.5	72	20	0.2	0.23	0.24
15	15	48	10	0.125	0.26	0.25
16	12.5	24	40	0.2	0.27	0.27
17	17.5	24	40	0.2	0.31	0.31
18	15	48	30	0.125	0.27	0.26
19	15	48	30	0.275	0.29	0.29
20	12.5	72	20	0.05	0.29	0.28
21	17.5	72	20	0.05	0.32	0.33
22	12.5	24	40	0.05	0.28	0.27
23	15	96	30	0.125	0.30	0.29
24	15	48	50	0.125	0.35	0.36
25	15	48	30	0.125	0.29	0.26
26	15	48	30	0.125	0.24	0.26
27	17.5	72	40	0.05	0.33	0.32
28	17.5	72	40	0.2	0.36	0.36
29	15	48	30	−0.025	0.28	0.29
30	12.5	72	40	0.2	0.34	0.33

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Eqn (1) is the predicted values of the reducing sugar (g g⁻¹), where A, B, C, and D are the coded values of biomass loading, incubation time, enzyme loading, and surfactant concentration, respectively. Validation of the statistical results and the model equation were analyzed using analysis of variance (ANOVA) presented in Table 5. The CCD model fits well and significant showing the F value of 24.89 and Prob > F of less than 0.0001.

Table 5 ANOVA for response surface quadratic model^a

Source	SS	df	MS	F Value	p-value Prob > F
a SS-sum of squares, MS-mean square, df-degree of freedom.
Model	0.079	14	2.273 × 10^4	24.89	<0.0001
Residual	3.410 × 10^3	15	1.789 × 10^4
Lack of fit	1.789 × 10^3	10	3.240 × 10^4	0.55
Pure error	1.620 × 10^3	5	2.273 × 10^4
Cor total	0.083	29

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The cellulase enzyme and the cellulosic substrates interaction is a very complicated process as hydrolysis efficiency is affected by the various variables in the process. Enzymatic hydrolysis of biomass loading, incubation time, enzyme concentration and surfactant concentration variables were analyzed at a time. The total 30 experimental runs were completed at different combinations of variables and the observed responses were presented in Table 4. The interactive effects of variables on reducing sugar yield were evaluated by three dimensional surface plots for obtaining maximum response and optimum level.

Fig. 1A represents the mutual interaction between biomass loading and incubation time which affect the reducing sugar concentration (g g⁻¹). The low concentration of biomass loading (12.5%) and minimum incubation time gives lower yield, whereas the higher incubation time (72 h) significantly affects the reducing sugar concentration. The yield increased proportionally with an incubation time and biomass loading. The results demonstrated that, an increase in the hydrolysis time from 24 to 72 h produces statistically higher saccharification yields in case of acid pretreated rice straw. The higher the biomass concentration and higher incubation time gives maximum yield. The highest reducing sugar production was reached at 72 h of hydrolysis. The biomass (17.5%) and enzyme loading concentration (40 FPU g⁻¹) showed maximum reducing sugar production (Fig. 1B).

Fig. 1 Response surface plot of reducing sugar concentration (g g⁻¹) from model equation: (A) interaction effects of biomass loading and incubation time (B) effects of biomass loading and enzyme loading (C) effects of biomass loading and surfactant concentration (D) effect of incubation time and enzyme loading (E) effects of incubation time and surfactant concentration (F) effects of enzyme loading and surfactant concentration.

Table 6 Comparison of enzymatic hydrolysis of different pretreated substrates by various enzyme sources

Source	Substrate	Reducing sugar (mg g^−1-substrate)	Ref.
Celluclast 1.5 L & Novozyme 188	Populus tormentosa Carr.	290	36 and 37
Celluclast 1.5 L & Novozyme 188	Olea europaea	302	38
Spezyme CP & Novozyme 188	Populus tremuloides	301	39
Celluclast 1.5 L & Novozyme 188	Olive tree	331	40
Celluclast 1.5 L & Cellulase from Trichoderma reesei and xylanase from Trametes versicolor (Sigma Aldrich®)	Yellow poplar	385	41
	Water hyacinth biomass	411	42
Celluclast 1.5 L & Novozyme 188	Populus nigra	378	43
Commercial cellulase (Zytex India Private limited, India)	Sugarcane tops	531	19
Celluclast 1.5 L, β-glucosidase from almonds and hemicellulase from Aspergillus niger (Sigma Aldrich)	Rice straw	359	This study

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The interactive effect of biomass loading and surfactant concentration on reducing sugar yield is shown in Fig. 1C. Surfactants have a positive effect on enzymatic hydrolysis. Addition of surfactants to enzymatic hydrolysis of lignocelluloses increased the conversion of cellulose into soluble sugars.²⁹ Surfactants reduce the unproductive enzyme adsorption to the lignin part of the biomass.²⁸ This is due to hydrophobic interaction of surfactant with lignin which releases nonspecifically bound enzyme. Reducing sugar was increased at the surfactant concentration increases.

The interactional effects of incubation time and enzyme loading on reducing sugar yield were presented in Fig. 1D. Low concentration of enzyme loading and low incubation time gave lower reducing sugar yield. Similarly the moderate level of incubation time and high level of enzyme loading gives higher reducing sugar yield. Both parameters showed mutual interactional effects on each other.

Fig. 1E explains the interaction of incubation time and surfactant concentration on the yield of reducing sugar. Maximum reducing sugar yield were achieved by low surfactant concentration and moderate incubation time. Surfactant concentration has positive interactional effect with incubation time for reducing sugar production.

Effect of enzyme loading and surfactant concentration on the yield of reducing sugar of acid pretreated rice straw is presented in Fig. 1F enzyme loading and surfactant concentration had mutual interactional effects on each other. Higher reducing sugar yield was obtained by increasing enzyme loading concentration at moderate surfactant concentration. Table 6 reveals the enzymatic hydrolysis of various substrates and reducing sugar production using commercial cellulase enzymes.

RSM study reports the optimum conditions for enzymatic hydrolysis of acid pretreated rice straw as biomass loading (17.50% w/w), enzyme loading (40 FPU g⁻¹), surfactant concentration (0.2% w/w) and incubation time (72 h) with reducing sugar yield of 0.359 g g⁻¹. Untreated rice straw after enzymatic hydrolysis produced 0.107 g g⁻¹ of reducing sugar. The results indicated that acid pretreatment and combination of cellulase cocktail ratio improved the hydrolysis yield by 3.36 fold. And using only single enzyme and low biomass loading reducing sugar yield was very low. The biomass loading (4.86% w/w), enzyme loading (29.45 FPU), surfactant concentration (0.13% w/w) and incubation time (47.35 h) with reducing sugar yield of 0.184 g g⁻¹. Untreated rice straw after enzymatic hydrolysis produced 0.089 g g⁻¹ of reducing sugar. The results indicated that acid pretreatment improved the hydrolysis yield by 2.06 fold.

3.3. HPTLC analysis

High performance thin layer chromatography (HPTLC) technique was used for qualitative analysis of reducing sugars produced after enzymatic hydrolysis of dilute acid pretreated rice straw. HPTLC results confirmed the production of glucose and xylose. The profile of reducing sugar produced after enzymatic hydrolysis in hydrolysate was shown in Fig. 5. The sugar standards used were glucose, xylose and cellobiose for the comparison of pretreated rice straw hydrolysate. The results indicated the production of xylose and glucose, where as no cellobiose and galactose were observed.

Fig. 2 FTIR spectra of (a) native (b) dilute acid pretreated rice straw biomass.

Fig. 3 X-ray diffraction pattern of native and dilute acid pretreated rice straw biomass.

Fig. 4 Scanning electron Microscopy (SEM) micrographs of rice straw biomass under 500× and 2000× magnification (A) native (control), (B) dilute acid pretreated.

Fig. 5 HPTLC analysis of enzymatic hydrolysate of dilute acid pretreated rice straw biomass. Standard: Glu-glucose, Xyl-xylose, Cel-cellobiose; samples run number: A-27, B-28, C-29, D-30.

3.4. Surface characterization

3.4.1. FTIR analysis. FTIR spectra revealed the structural changes in native and dilute acid treated rice straw biomass Fig. 2. The FTIR pattern of native and dilute acid pretreated rice straw was mostly similar because of crystalline structure of biomass was not hydrolyzed due to dilute acid pretreatment at 121 °C. The prominent peaks were observed as follows; the peaks 809, 901, 1160, 1249, 1320, 1373, 1439, 1512, 1640, 1726, 1879, 2130, 2346 and 2917 cm⁻¹ in pretreated biomass whereas, 808, 1170, 1365, 1430, 1516, 1643, 1879, 2140, 2924 cm⁻¹ in native biomass. Some peaks were absent and also the peak position was changed due to dilute acid pretreatment. The 1512 cm⁻¹ peak assigned for C=C stretching of the aromatic ring of lignin.³⁰ 1320 and 1430 cm⁻¹ peaks were defined as for the symmetric CH₂ bending and wagging,^31,32 the peak at 1373 cm⁻¹ assigned for the C–H bending.³² The glycosidic bond of C–O–C stretching at 1160 and 901 cm⁻¹ were earlier reported.^31,33 The broad peak at 3300 and 2900 cm⁻¹ defined for the stretching C–H and H–bonded OH groups.³² Fig. 2 demonstrates the structural changes and removal of hemicellulosic components due to acid pretreatment. The 1725 cm⁻¹ peak was absent due to acid hydrolysis of hemicelluloses which is present in native rice straw. Difference in the native and dilute acid pretreated rice straw FTIR spectral pattern suggests the change in the surface structure of the biomass.

IR crystallinity was investigated for native and acid pretreated samples using three IR ratios as A₁₃₇₅/A₁₅₁₂, A₁₄₂₇/A₈₉₅ and A₁₃₇₂/A₂₉₀₀ of their peak heights, 1375 cm⁻¹ peak for cellulose and the peak at 1512 cm⁻¹ for lignin.³⁰ IR crystallinity ratio of A₁₃₇₅/A₁₅₁₂ in dilute acid pretreated rice straw was lower (1.19) than native rice straw biomass (1.28). Similarly, IR crystallinity ratio of A₁₄₂₇/A₈₉₅ was lower as compared to native biomass. IR crystallinity ratio of A₁₃₇₂/A₂₉₀₀ was slightly higher in acid pretreated biomass (0.47) than native biomass (0.46) (Table 3), that might be due to amorphous cellulose in acid solution with hemicellulosic components as reported earlier.³²

3.4.2. XRD analysis. The XRD analysis investigates the change in cellulose crystallinity of rice straw after dilute acid pretreatment. XRD profile of native and dilute acid pretreated rice straw is shown in Fig. 3. The cellulose crystallinity is considered as one of the important factor which affects the enzymatic hydrolysis of lignocellulosic biomass.¹⁷ The crystallinity index of native and dilute acid pretreated rice straw was 40.84 and 51.49%, respectively. Increased crystallinity index after pretreatment might be due to hydrolysis of glycosidic linkages in the cellulose accessible regions. Previous studies also showed an increase in crystallinity index after acid pretreatment of switch grass,³⁴ sugarcane tops (SCT).⁷ The crystallinity index, crystallinity degree and crystalline size of native and acid pretreated rice straw are shown in Fig. 3. The crystalline size was found to be higher in pretreated biomass (3.08 nm) compared to native samples (2.60 nm).

Ionic liquid, steam explosion, lime and dilute acid pretreated sweet sorghum bagasse substrate showed highest crystalline size as compared to untreated sample.³⁵ The dilute acid pretreated rice straw showed higher crystallinity degree (67.2%) when compared to native (59.37%). Increase in degree of crystallinity due to the effect of acid pretreatment was more on amorphous region than the crystalline region. The identical observations are also reported earlier.³¹

3.4.3. Surface morphology analysis by scanning electron microscope. SEM analysis investigates the morphological changes in native and dilute acid pretreated rice straw samples. Fig. 4(A) showed the fibrous structure of native plant cell wall structures of rice straw. The Fig. 4(B) represented the distorted structure of lignocellulosic biomass of plant cell wall. The predominant destruction of plant cell wall takes place in dilute acid pretreated rice straw biomass. Dilute acid decreases the recalcitrant nature of lignocellulosic biomass by dissolving cellulose fibers and increases the cellulose accessibility.

4. Conclusion

The efficiency of enzymatic hydrolysis was found to increase after the dilute acid pretreatment of rice straw. Response surface methodology, the central composite design is found to be useful in identifying the important factors influencing enzymatic conversion of pretreated rice straw carbohydrate. The experimental results revealed interactions of biomass loading and enzyme loading with surfactant concentration. The combinational interactions significantly affect the saccharification efficiency in the pretreated rice straw. Reducing sugar yield had a good correlation with experimental and predicted results. Present study concludes that dilute acid pretreatment of rice straw provides a better option for increased enzymatic hydrolysis efficiency and also act as a good substrate for bioethanol production.

Acknowledgements

The author Siddheshwar Dnyandev Kshirsagar would like to acknowledge UGC-BSR (University Grants Commission), New Delhi for providing financial assistance through UGC-BSR-JRF fellowship during this research work. One of the authors Pankajkumar Ramdas Waghmare would like to acknowledge UGC (University Grants Commission), New Delhi for providing financial assistance through UGC-NET-JRF fellowship during this research work.

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Footnote

† Electronic supplementary information (ESI) available. See DOI: 10.1039/c5ra04430h

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