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ptimization of saccharification of sweet sorghum bagasse using responseurface methodology
itendra K. Sainia,∗, Rahul K. Anuragb, Arti Aryaa, B.K. Kumbharc, Lakshmi Tewaria
Department of Microbiology, CBSH, G.B. Pant University of Agriculture & Technology, Pantnagar, Udham Singh Nagar, Uttarakhand 263145, IndiaDepartment of Food Technology, College of Agriculture, G.B. Pant University of Agriculture & Technology, Pantnagar, Udham Singh Nagar, Uttarakhand 263145, IndiaDepartment of Post Harvest Process and Food Engineering, College of Technology, G.B. Pant University of Agriculture & Technology, Pantnagar, Udham Singh Nagar, Uttarakhand63145, India
r t i c l e i n f o
rticle history:eceived 16 June 2012eceived in revised form 6 October 2012ccepted 1 November 2012
The lignocellulose rich sweet sorghum bagasse (SSB) is a good feedstock for bioethanol production afterconversion of its insoluble carbohydrates, mainly cellulose, to fermentable sugars. Main focus of thepresent investigation was therefore, to determine the optimum conditions for enzymatic saccharificationof SSB using indigenously produced cellulases from a novel fungal consortium of Aspergillus flavus F-80and Aspergillus niger MTCC-2425. Response surface methodology was adopted by using a three factor-three level Box–Behnken design by selecting substrate concentration (%, w/v), saccharification time (h)and enzyme loading (FPU g−1 substrate) as the main process parameters. Data obtained from RSM weresubjected to the analysis of variance (ANOVA) and analyzed using a second order polynomial equation.
The developed model was found to be robust and was used to optimize the % saccharification yieldduring enzymatic hydrolysis. Under optimized conditions (substrate concentration 6%, w/v, time 48 hand enzyme loading of 22 FPU g−1 substrate), maximum saccharification yield of 51.21% was achieved.Structural modification of SSB due to enzymatic saccharification was supported by changes in ther-mal decomposition behavior and pore formation observed during thermogravimetric and SEM analysis,respectively.
. Introduction
Increased energy consumption over the past few decades hasade it inevitable to look toward renewable and replenish-
ble sources of energy. Bioethanol, as an alternative fuel energyesource, has been a subject of great interest since the oil cri-is in the 1970s (Tao et al., 2005). Obviously, the production ofrst generation bioethanol from food-based biomass has induced
competition between fuel and food. Therefore, the secondeneration bioethanol, by utilizing inedible biomass, is gradu-lly attracting worldwide attention. Lignocellulosic biomass is anttractive and cheap feedstock for bioethanol production becausef its abundance and renewable nature (Wyman, 1994). Sweetorghum (Sorghum bicolor (L.) Moench) has the potential of becom-ng a useful energy crop with a promising future (FAO, 2002) withrimary advantages being its adaptability to diverse climates and
oil conditions, low requirement of fertilizers and high water usagefficiency (1/3 of sugarcane and 1/2 of corn) (Ratnavathi et al.,011). It is a high biomass (20–30 dry tons/ha) and sugar-yielding
(16–18% fermentable sugar in juice) crop and its stalk containsapproximately equal quantities of soluble carbohydrates (glucose,fructose and sucrose, nearly 9.5% mass fraction) and insolublecarbohydrates (cellulose and hemicellulose, nearly 10.0% mass frac-tion) (Sipos et al., 2009; Yu et al., 2010). These desirable agriculturalcharacteristics make sweet sorghum a promising alternative feed-stock for fuel ethanol production in India (Reddy et al., 2005). Theutilization of sweet sorghum stem to produce ethanol in itself isnot altogether new, nor difficult. However, a major challenge forlarge scale applications of bioethanol production is how to dealwith the bagasse (insoluble carbohydrates) remaining after extrac-tion of juice from stem. Increased and concerted efforts are calledfor in order to develop new processes to convert this bagasse tobioethanol.
Lignocellulosic plant biomass is composed of cellulose, hemi-cellulose, lignin, and small amounts of extractives, etc., which aredistributed in a lamellar structure (Fengel and Wegener, 1989).The hydrolysis is crucial for the conversion of bagasse polysac-charides, mainly the cellulose, into valuable products such as
bioethanol. Although the current market price of cellulases, theenzyme responsible for cellulose hydrolysis, makes the processless favorable compared to technologies using acid catalysts, work-ing with enzymes makes it possible to combine the cellulose
Table 1Codes and actual levels of the independent variables for design of experiment.
Independent variables Symbols Coded
−1 0 1
Substrate concentration (%, w/v) X1 2 6 10
12 J.K. Saini et al. / Industrial Cro
ydrolysis with the ethanol fermentation, due to the significantlyilder processing conditions applied. The cellulosic and hemicel-
ulosic sugars obtained through acid or enzymatic hydrolysis canfficiently be used for ethanol fermentation either by separateydrolysis and fermentation (SHF) or simultaneous saccharificationnd fermentation (SSF). However, the temperature optima for theeast and the cellulolytic enzymes differ, which means that the con-itions used in SSF cannot be optimal for both the enzymes and theeast and might result in lower efficiency and lower product yield.ence, for better efficiency of ethanol production, the approach ofHF is preferred (Karin et al., 2007).
Optimization of saccharification process is one of the mostmportant stages in the development of an efficient and costffective saccharification strategy. The traditional ‘one-factor-at--time approach’ is time consuming and moreover the interactionsetween independent variables are not considered. Response sur-ace methodology (RSM) is an effective optimization tool wherein
any factors and their interactions affecting the response can bedentified with fewer experimental trials. RSM mainly includesentral composite design, Box–Behnken design, one-factor design,-optimal design, user-defined design, and historical data design.tatistical designs such as RSM have proved efficient for opti-ization in food process operations, including extrusion, new
roduct development, biotechnology-media composition, to bio-rocessing such as enzymatic hydrolysis and fermentation ofarious lignocellulosic feedstocks (Li et al., 2010; Karunanithy anduthukumarappan, 2011). Box–Behnken design (BBD) is second-
rder design based on three-level incomplete factorial design andas the advantage that it does not contain combinations for whichll factors are simultaneously at their highest or lowest levels,hereby, it is useful in avoiding experiments performed underxtreme conditions, for which unsatisfactory results might occurFerreira et al., 2007).
In order to explore the potential of crude cellulase produced byungal consortium of Aspergillus spp. in obtaining maximum sac-harification yield of SSB, a three factor-three level Box–Behnkenesign was employed to achieve best combination of the variablessubstrate concentration, saccharification time and enzyme load-ng).
. Material and methods
.1. Microbial cultures
Cellulolytic fungi were isolated from various sources, screenedualitatively and quantitatively for cellulolytic potential. The besttrains Aspergillus flavus F-80 and Aspergillus niger MTCC-2425standard culture) were used in a consortium for production of cel-ulase enzyme. The cultures were maintained on potato dextrosegar (PDA) medium at 28 ± 2 ◦C, stored at 4 ◦C and were subculturednce in a month.
.2. Production of cellulolytic enzymes
Cellulase production by the fungal consortium was carried outn modified Mandel’s medium (Mandel et al., 1976) containing 8%,
/v wheat bran as carbon source. Inoculum (2 × 106 spores ml−1)as prepared by harvesting spores from one week old potato dex-
rose agar slants of A. flavus F-80 and A. niger MTCC-2425 in equalroportion in sterile distilled water containing 0.15% Tween-80.
noculum was added to the medium of fermentation and the pro-
uction of cellulolytic enzymes was carried out at 28 ± 2 ◦C for 8 d,fter which the contents of flasks were squeezed with a muslinloth, centrifuged and analyzed for the enzyme activities. The crudenzyme was concentrated using a 10 kDa molecular weight cut
off polyethersulfone membrane in a MinimateTM Tangential FlowUltrafiltration system (Pall Lifesciences, USA).
2.3. Determination of enzyme activities
Overall cellulase (FPase) and ˇ-glucosidase enzyme activitieswere determined according to Ghose (1987) and Kubicek andPenttila (1998), respectively. Filter paper unit was defined as the�mol of glucose equivalent liberated per ml per minute of cul-ture filtrate under assay conditions. One unit of ˇ-glucosidasewas defined as the amount of enzyme liberating 1 �mol of p-nitrophenol per ml per min. Soluble protein was measured usingbovine serum albumin as standard (Bradford, 1976).
2.4. Substrate (SSB) and its pretreatment
SSB sample was obtained from the Department of Agronomy,G.B. Pant University of Agriculture & Technology, Pantnagar, Indiaand made into 8 mesh (2 mm) powder using Willy mill. Samplewas preserved in a sealed plastic bag at 4 ◦C to prevent any possibledegradation or spoilage. The ash content, cellulose, hemi-cellulose,lignin and ether extractives in SSB were determined (Van Soest,1963). The alkali pretreatment of SSB was carried out using 1%(w/v) sodium hydroxide solution with a solid to liquid ratio of1:5.75 at 95 ◦C for a period of approximately 12 min. After reactionthe residual solid material was separated and washed with water(until neutral pH). The resulting solid was dried at 50 ◦C and usedas substrate for enzymatic hydrolysis.
2.5. Enzymatic saccharification of pretreated SSB
Enzymatic saccharification was performed by following theprocedure of Baboukani et al. (2012) with the modification thatreaction mixture (20 ml) was incubated for 72 h. Aliquots ofthe enzyme hydrolysates were taken at different time inter-vals (Table 2) and the concentration of the reducing sugar inthe hydrolysate was measured by the dinitrosalicylic acid (DNS)method (Miller, 1959). % saccharification yield was determinedusing the following formula:
To determine the best combination of parameters for optimizingthe enzymatic saccharification of SSB, a second order Box–Behnkendesign (BBD) (Ferreira et al., 2007) was employed. The substrateconcentration (%, w/v), saccharification time (h) and enzyme load-ing (FPU g−1 substrate) were selected as independent variables
(Tables 1 and 2). The Design-Expert software, version 7.0 (Stat-Ease Inc., Minneapolis, MN, USA) was used to build and analyze theexperimental design (e.g. analysis of variance (ANOVA), determi-nation of the estimated effects and interaction, regression equation
J.K. Saini et al. / Industrial Crops and Products 44 (2013) 211– 219 213
Table 2Three level Box–Behnken design and the experimental responses of dependent variable (% saccharification).
Run no. Substrate conc. (%, w/v) Time (h) Enzyme loading (FPU g−1 substrate) % saccharification
hich has been fitted to the data). A second order polynomial Eq.1) was used to describe the effects of variables on the response:
= ˇ0 +k∑
i=1
ˇixi +k∑
i=1
ˇiixi2 +
k∑
i=1
k∑
j=i+1
ˇijxixj (1)
here y is the response variable; x is the independent variable; ˇ0s the constant term; ˇi, ˇii, ˇij are regression coefficients of theinear, interaction and quadratic parameters, respectively; and k ishe number of variables studied during the experiments.
TG-DTG-DTA analysis of SSB (untreated and alkali pretreated-nzyme hydrolyzed) samples was done at Institute Instrumenta-ion Centre, Indian Institute of Technology, Roorkee, Uttarakhand,ndia, using a TG analyzer (Perkin Elmer, Pyris Diamond) at tem-erature ranging from 20 ◦C to 600 ◦C under nitrogen atmosphere400 ml min−1) with a heating ramp of 5 ◦C min−1 in a platinum pan.
.8. Scanning electron microscopy (SEM) analysis of SSB
The surface morphology and characteristics of the substrateere studied with the help of SEM. SEM analysis of the untreated
nd alkali pretreated enzyme hydrolyzed SSB samples was doney following the method of Li et al. (2010) by using Scanning Elec-ron Microscope (Leo, 435VF) at Institute Instrumentation Centre,ndian Institute of Technology, Roorkee, Uttarakhand, India.
. Results and discussion
.1. Compositional analysis of SSB
The compositional analysis of untreated SSB revealed that hemi-ellulose, cellulose, lignin, ash, ether extractives and crude proteinontents were, respectively, 25.2%, 42.6%, 13.4%, 4.1%, 1.5% and.4% (dry wt. basis). The composition of the raw SSB indicated thatoth carbohydrate and lignin contents were similar with the earlier
eports for SSB (Li et al., 2010) and other typical herbaceous residuesuch as corn stover, and lower than those of typical hardwoodspoplar) and softwood (Douglas-fir) (Yang et al., 2002). The lowersh content (4.1%) in comparison to other agricultural residues
) 43.59 43.91) 42.98 43.91) 41.72 43.91
might have proved potentially beneficial for enzymatic hydrolysisas suggested by Yu and Chen (2010).
3.2. Pretreatment and enzymatic saccharification of SSB
Pretreatments enhance saccharification rates and yields duringenzymatic hydrolysis of biomasses by decreasing the crystallinityof cellulose and breaking the lignin-hemicellulose sheath thatsurrounds the cellulose (Wright, 1988). Presently, several pretreat-ment methods including physical and chemical procedures havebeen in use but more attention is now focused on chemical treat-ments as these are considered to be relatively more effective andless energy-intensive than physical pretreatments (Varga et al.,2002). In a different study, we optimized dilute sulphuric acidand sodium hydroxide based pretreatments and the latter methodresulted in more hydrolysis of SSB (unpublished data) and wastherefore, used in the present work.
There are several reports on improvement in enzyme produc-tion by co-culturing of two or more cellulolytic microbial strains.Improved cellulose hydrolytic activities have been achieved byco-cultivation of Aspergillus ellipticus and Aspergillus fumigatus inthe semi-solid-state fermentation systems (Gupte and Madamwar,1997). The present investigation has also indicated that A. flavusF-80 and A. niger MTCC-2425 together in the consortium were effec-tive for cellulase and ˇ-glucosidase production (unpublished data).Co-cultivation of A. flavus F-80 and A. niger MTCC-2425 increasedthe production of cellulase and ˇ-glucosidase as compared to whenthey were used alone. There appears to be a good compatibilitybetween these fungi, which offers the possibility of eliminatingcostly fermentation and recovery steps traditionally used to pro-duce these enzymes.
Based on our previous studies (unpublished data) maximumthermal and pH stability of the cellulase produced by the fungalconsortium was at its optimum temperature and pH of 50 ◦C and5.0, respectively. Therefore, these conditions were prefixed for theexperimental setup. In previous reports, the temperature of 50 ◦Cand pH 5 were also found optimum for enzymatic saccharificationof different lignocellulosic feedstocks (Sharma et al., 2002). More-over, the preliminary studies using different combinations of thesubstrate concentrations, time and enzyme loading revealed that
maximum saccharification occurred between 6 and 10% substrateand around 15 FPU g−1 cellulase loading for a period of around40–60 h. Therefore, these conditions were chosen as central valuesin designing the experiment matrix (Tables 1 and 2).
214 J.K. Saini et al. / Industrial Crops and Products 44 (2013) 211– 219
Table 3ANOVA for response surface quadratic model for enzymatic saccharification of SSB.
between X1 and X2 also have significant effects on the sacchari-fication yield. A statistically significant model (Table 4) only with
Table 4Results of regression analysis of enzymatic saccharification of SSB.
Source Coefficients F-value P-value (%)
ˇ0 43.91 46.70 0.01***
ˇ1 4.13 33.90 0.06*
ˇ2 −0.13 0.032 86.23ˇ3 10.40 215.29 0.01***
ˇ12 3.31 10.88 1.31*
ˇ13 −0.26 0.067 80.29ˇ23 2.33 5.41 5.29ˇ2
1 −6.51 44.44 0.03**
ˇ22 −8.61 77.93 0.01***
ˇ23 −4.14 17.93 0.39*
R2 0.9836F 46.70LOF N.S.
* Significant at 10% level of significance.** Significant at 5% level of significance.
*** Significant 1% level of significance.
Table 5Total effect of parameters on enzymatic saccharification of alkali treated SSB.
Source SS DF MS F-value
A. Total individual parameter effectSubstrate conc. (X1) 358.79 4 89.70 22.31***
Time (X2) 377.78 4 94.44 23.49***
Enzyme Loading (X3) 959.24 4 239.81 59.65***
B. Total effect of all parameters atLinear level 1001.51 3 333.84 83.04***
Lack of fit 16.03 3 5.34 0.2923Pure error 12.10 4 3.03Total 1717.35 16
.3. Development of a model for enzymatic saccharification of SSB
Three independent variables: substrate concentration (%, w/v),accharification time (h) and enzyme loading (FPU g−1 substrate)nd their values at different coded and actual levels were employedn the design matrix (Table 1). Table 2 shows the levels of selectedariables for the BBD and summarizes the response values alongith the predicted values. The percent saccharification yield of SSB
anged from 17.28% (experimental run #5) to 48.71% (experimentalun #8). The data obtained from the three level BBD matrix yieldedhe following regression equation (Eq. (2)), which was an empiricalelationship between % saccharification yield and the test variablesn coded form:
here the coded variables were: Y = % saccharification;1 = substrate concentration (%, w/v); X2 = time (h); and3 = enzyme loading (FPU g−1 substrate).
The ANOVA was carried out to determine the significance ofhe model equation and the model terms (Table 3). The statisti-al significance of the above equation was checked by the F test.he model F-value of 46.7 and a low probability value (P < 0.01)howed that the model terms were significant. The standard devi-tion and coefficient of variation (CV) (2.00 and 5.75, respectively)ere reasonably low and acceptable. The coefficient of variationercent (CV%) is a measure of residual variation of the data relativeo the size of the mean and is inversely related to the reliabilityf experiment. The coefficient of determination (R2) value (0.98),
measure of the amount of variation around the mean, explainedy the model indicated that only 2% of all variation for responseould not be explained by the model and expresses well enough fit.ormally, a regression model with R2 > 0.90 is considered to have
very high correlation (Haaland, 1989). Fig. 1 shows that observedaccharification yields (the response) agreed well with the pre-icted data. For the developed model in our study the ‘predictedoefficient of determination’, R2
pred of 0.84 was in reasonable agree-
ent with the ‘adjusted coefficient of determination’, R2adj of 0.96.
his indicated a good adjustment between the observed and pre-icted values. ‘Adequate precision’ represents the signal-to-noiseS/N) ratio and values >4.0 indicates that the model precision isdequate (Ferreira et al., 2007). ‘Adequate precision’ ratio of 19.01
ndicated an adequate signal in our study.
Table 4 shows the F-test and the corresponding P-values alongith the parameter estimate. The smaller the P-values, the bigger
he significance of the corresponding coefficient. The parameter
Fig. 1. Observed percent saccharification yield vs the predicted response.
estimates and the corresponding P-values suggest that, amongthe independent variables, X1 (substrate concentration) and X3(enzyme loading) have significant effects on % saccharificationyield. The quadratic terms of X1, X2, and X3 and interactions
Quadratic level 562.82 3 187.61 46.67***
Interactive level 65.74 3 21.91 5.45**
** Significant at 5% level of significance.*** Significant 1% level of significance.
J.K. Saini et al. / Industrial Crops and Products 44 (2013) 211– 219 215
Table 6Optimum levels of variables during enzymatic saccharification of SSB.
Independent variables Goal Coded levels Actual levels
Substrate concentration (X1) In range 0 6%Time (X2) In range 0 48 hEnzyme loading (X3) In range 1 22 FPU g−1
s
Y
3
owtlf
trt(tasccoiittFsc
3
opcdroIcwc
wydcwVygm
Response Goal Predicted value Observed value
% saccharification Maximum 50.41 51.21
ignificant terms can be written as follows (Eq. (3)):
.4. Effect of parameters on percent saccharification yield
Table 5 indicated that the total effect of all individual variablesn the % saccharification yield was significant and maximum effectas shown by enzyme loading, followed by the substrate concen-
ration and time. Furthermore, the total effects of all parameters atinear, quadratic and interactive level were also significant in theollowing decreasing order: linear > quadratic > interactive.
To examine the interaction of the variables and to determinehe optimum level of each variable for maximum response, 3-Desponse surface curves were plotted against two experimental fac-ors while maintaining the other factor constant at its central valueFig. 2). The effects of SSB concentration and enzyme loading onhe % saccharification yield, when the time was at its center level,re shown in Fig. 2a. When enzyme loading was at a low level,accharification yield was low. Significant improvement in the sac-harification yield could be obtained by increasing the amount ofellulase to some extent. The effects of SSB concentration and timen the % saccharification yield, when the enzyme loading was atts center points, are shown in Fig. 2b. It was observed that max-mum response of percent saccharification yield was observed athe central level of both substrate concentration and time whenhe enzyme loading was held constant at 15 FPU g−1 (central level).ig. 2c shows the effects of enzyme loading and time on the %accharification yield, when the substrate concentration was at itsenter level.
.5. Optimization of percent saccharification yield of SSB
The second-order polynomial regression equation (Eq. (3))btained from the experimental data can be used to predict theercent saccharification of SSB at any enzyme loading, substrateoncentration and time within the range of the experimentalesign. Numerical optimization of saccharification process was car-ied out using Design Expert software, version 7.0, to evaluate theptimum values of different parameters from the developed model.n optimization, the desired goal was to maximize the percent sac-harification yield with the highest importance and the factorsere selected to be within the mentioned range. All weights were
onsidered equal to 1.Based on the developed model, the optimal working conditions
ere obtained to maximize saccharification yield. Response anal-sis predicted the maximum % saccharification yield of 50.41%uring enzyme hydrolysis of SSB under the optimum processonditions i.e. when substrate concentration was 6% (w/v), timeas 48 h and enzyme loading was 22 FPU g−1 substrate (Table 6).
alidation experiment under these predicted optimum conditionsielded 51.21% saccharification of SSB experimentally. Thus, overallood agreement was observed between experimentally deter-ined (observed) response and predicted optimum response.
Fig. 2. 3-D response surface plots showing effects of various parameters on enzy-matic saccharification of SSB.
3.6. Comparison of cellulose conversion from SSB with otherlignocellulosic materials
The effect of substrate concentration on enzymatic hydrolysis ofcellulose has also been evaluated for other lignocellulosic materials,including sugarcane bagasse (Manonmani and Sreekantiah, 1987),softwood (Tengborg et al., 2001), and corncob (Chen et al., 2007).
2 ps and
Ftcntoda
sgsl(nhibitb(necitlm
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16 J.K. Saini et al. / Industrial Cro
or all these cases, as well as in the present work, it was observedhat maximum percent saccharification was observed at a substrateoncentration below 10% (w/v). Earlier studies concluded that lig-ocellulosic substrate concentrations of 6% and 10% were adequateo release optimum reducing sugars. Stirring difficulties, reductionf the aqueous movable phase and end product inhibition can hin-er the enzymatic hydrolysis of pretreated lignocellulosic residuest further higher substrate concentrations (Sharma et al., 2002).
Cellulase dosages around 10 FPU g−1 are often used in laboratorytudies because it provides a hydrolysis profile with high levels oflucose yield within acceptable reaction times (48–72 h) at a rea-onable enzyme cost (Gregg and Saddler, 1996). With an enzymeoading of 15 FPU g−1 the maximum percent saccharification yield46.12%) was achieved (run #13) in this study. Enzyme loading sig-ificantly influenced the enzymatic hydrolysis of SSB. In general,igher enzyme loading resulted in better hydrolysis probably by
ncreasing the rate and yield of saccharification. This is partiallyecause the ratio of total substrate to total enzyme increases with
ncrease in enzyme loading (Huang and Penner, 1991). Although,he conversion of SSB was promoted (with 48.71% saccharification)y further increasing the cellulase loading to the maximum level22 FPU g−1), increasing the loading of cellulase did not show pro-ounced effect on the enzymatic digestibility as has been reportedarlier (Zhang et al., 2010). There is certain limiting enzyme con-entration after which higher enzyme loadings have no significantnfluence on the product formation. It has also been reportedhat substrates having a good pretreatment usually require lowerimiting enzyme concentrations than those with poorer pretreat-
ents (Chambel, 2008).Our results indicated that increasing the saccharification time
id not have much effect on percent saccharification yield of SSB inccordance with previous studies where saccharification rate wasigher at the start of incubation period (up to 24 h in our case)nd then it slowed down, resulting in comparatively lower rate ofydrolysis of various lignocellulosic feedstuffs between 48 and 72 hf incubation (Eklund et al., 1990; Sreenath et al., 1999). This behav-or might be due to the decrease in the extent of adsorbed enzyme,ransformation of the structure of cellulose into less digestible formnd inhibition of the enzyme action by the accumulated hydrolysisroducts (Lee and Fan, 1982).
In general, optimization with RSM enhances experimentalesults more than the conventional optimization methods. Inresent study, the saccharification yield was enhanced to 51.21%hrough optimization, which was higher than the conversion yield
f 32% and 41% for rice hull (Saha and Cotta, 2008) and bagasseKaar et al., 1998), respectively. Enzymatic saccharification condi-ions and yields from various studies on SSB have been comparedn Table 7. In a previous study, different preparations of sweet
able 7nzymatic saccharification conditions and yields from various studies on SSB.
S. no. Pretreatment conditions Saccharification
Enzyme used
1 H2SO4 (0.2%) at 190 ◦C for10 min
Commercial cellulase3–14 FPU g−1
2 Ammonia fiber expansion(AFEX)
Commercial cellulase
3 NaOH (2%) at 60 ◦C for 90 min Commercial cellulase at 2.5and 5.0 FPU g−1 with xylanaseand �-glucosidase
4 Phosphoric acid at 50 ◦C for30 min and NaOH (12%) at 0 ◦Cfor 3 h
Commercial cellulase with�-glucosidase enzymes
5 Liquid hot water (LHW) Commercial cellulase, 30%substrate concentration
6 NaOH (1%, w/v) at 95 ◦C for12 min
Crude fungal cellulase at22 FPU g−1
Products 44 (2013) 211– 219
sorghum bagasse were subjected to 72 h hydrolysis by addition ofcellulase enzyme. The yield of hydrolysis of native bagasse waseffectively improved after pretreatments with sodium hydrox-ide and concentrated phosphoric acid. The sugar content in thehydrolyzates increased sharply in the first 12 h and continued grad-ually until 72 h, resulting in 42% and 65% conversion after 12 h and72 h, respectively (Goshadrou et al., 2011). It is also noteworthythat most of the research groups have utilized commercial cellulaseenzymes, which are highly purified with very high activities andare costly. Therefore, results of present study are significant fromeconomic point of view as only partially purified enzyme has beenused for decreasing the cost of enzyme production and enzymaticsaccharification.
3.7. Characterization of native and hydrolyzed biomass: TG andSEM analyses
Thermal analysis and SEM images support the structural andmechanical changes in alkali pretreated SSB during enzymatic sac-charification. The weight loss of SSB due to the hydrolysis processwas explained through TG analysis (Fig. 3). A preliminary indi-cation about the enzymatic hydrolysis of SSB may be assignedthrough comparison of the second step decomposition with respectto untreated (control) SSB sample during the simultaneous TG-DTG-DT. The enzymatic saccharification process hydrolyzed thecellulose fibers to liberate the monomer glucose in the hydrolysate,which leads indirectly to increase the percentage of lignin con-centration in the fiber. The untreated sample showed second stepdecomposition at 383 ◦C with 23.8% residue (Fig. 3a), whereas theenzymatically hydrolyzed SSB showed degradation under similarconditions at 363 ◦C with 28.8% residue (Fig. 3b). TG analysis hasalso been used earlier to study the changes in various lignocellu-loses during pretreatment and saccharification (Sasmal et al., 2011;Chen et al., 2012).
Electron micrographs depicting morphological features ofuntreated, alkali pretreated and alkali pretreated-enzymehydrolyzed SSB samples are shown in Fig. 4. Untreated sam-ples had deposits on the surface which might include waxes,hemicellulose, lignin, and other binding materials (Fig. 4a). Thesurface layer was removed and broken or made loose during chem-ical (alkali) treatment, resulting in exposure of internal structureand fibers (Fig. 4b). SEM image of the saccharified substrate (alkalipretreated SSB) indicated the removal of compacted outer layerand pore formation in the cellulose fibrils (Fig. 4c). As visualized
by SEM analysis, several visible breaks, terraces, steps and kinks,and pores were formed in SSB after its enzymatic saccharificationconfirming the disintegration of its lignocellulosic structure dueto the enzymatic conversion of cellulose to constituent sugars. We
J.K. Saini et al. / Industrial Crops and Products 44 (2013) 211– 219 217
(b)
(a)
Temp Cel60055050045040035030025020015010050
DT
A u
V
30.00
25.00
20.00
15.00
10.00
5.00
0.00
-5.00
-10 .00
-15 .00
-20 .00
-25 .00
-30 .00
TG
%
220.0
200.0
180.0
160.0
140.0
120.0
100.0
80.0
60.0
40.0
20.0
DT
G
mg
/min
1.00 0
0.00 0
-1.00 0
-2.00 0
-3.00 0
-4.00 0
-5.00 0
-6.00 0
-7.00 0
20Cel100.0%
603Cel16.3%
383Cel23.8%
100Cel92.9%
200Cel92.3%
300Cel79.5%
500Cel18.2%
367Cel13.91 uV
364Cel1.049 mg/min
318Cel0.470 mg/min
63Cel0.164 mg/min
70Cel0.64 uV
44.4 uJ/mg
34.9 uJ/mg
350Cel52.6%
250Cel90.2%
Temp Cel60055050045040035030025020015010050
DT
A uV
40.00
30.00
20.00
10.00
0.00
-10 .00
-20 .00
-30 .00
-40 .00
-50 .00
TG
%
240.0
220.0
200.0
180.0
160.0
140.0
120.0
100.0
80.0
60.0
40.0
20.0
0.0
DT
G
mg
/min
2.00
1.00
0.00
-1.00
-2.00
-3.00
-4.00
-5.00
-6.00
-7.00
-8.00
-9.00
-10 .00
-11 .00
346Cel1.675 mg/min
68Cel0.103 mg/min
30Cel100.0%
100Cel95.7%
605Cel-0.2%
570Cel0.2%
359Cel12.04 uV
535Cel14.48 uV
250Cel93.0%
363Cel28.8%
450Cel19.5%
200Cel94.7%
300Cel85.5%
82Cel-0.97 uV
26.1 mJ/mg
-1.94 J/mg
Fig. 3. Thermal analysis of SSB samples: (a) untreated SSB and (b) alkali pretreated SSB enzymatically hydrolyzed under optimized conditions.
218 J.K. Saini et al. / Industrial Crops and
Fig. 4. Scanning electron micrographs of (a) untreated, (b) alkali pretreated, and (c)alkali pretreated SSB enzymatically hydrolyzed under optimized conditions.
prih
Gupte, A., Madamwar, D., 1997. Solid state fermentation of lignocellulosic waste forcellulase and �-glucosidase production by co-cultivation by Aspergillus ellipticusand Aspergillus fumigatus. Biotechnol. Prog. 13, 166–169.
Haaland, P.D., 1989. Separating Signals from the Noise, Experimental Design in
ropose that these structural modifications were the results ofemoval of very reactive amorphous cellulose from SSB and weren good agreement with SEM analysis of various enzymatically
ydrolyzed lignocelluloses (Samir et al., 2005; Zhao et al., 2007).
Products 44 (2013) 211– 219
4. Conclusion
Despite the availability of large number of cellulase producingorganisms, the high cost and poor stability of current commer-cially available cellulase enzymes hamper their application andefficacy for saccharification process during bioethanol production.Therefore, attempts were made during the present study to deter-mine the optimum conditions for enzymatic saccharification oflignocellulose rich SSB using cellulase produced by novel, efficientfungal consortium of A. flavus F-80 and A. niger MTCC-2425. Out ofthe three process parameters-substrate concentration (%, w/v) andenzyme loading (FPU g−1 substrate) had significant effect on sac-charification yield and enzyme loading produced greatest effects.Efficient and cost-effective conversion of SSB to reducing sugars(51.21%, w/v) has been presented in this study by using compar-atively lower enzyme loading (22 FPU g−1 substrate) and shortersaccharification period (48 h) with a substrate concentration of 6%(w/v). The developed model can be further used as a platform fordeveloping kinetic models for enzyme hydrolysis of other poten-tial lignocellulosic substrates. It is evident from the study that SSB,as a renewable bioresource, can be hydrolyzed to reducing sugarsand can be used as initial feedstock for bioethanol production byuse of crude cellulase of fungal origin. In order to make full use ofwaste sweet sorghum resources for bioethanol production ethanolconversion efficiency of the enzyme hydrolysates of SSB should beinvestigated further.
Acknowledgements
We thank Director, Experiment Station, GBPUA&T, Pantnagar,India, for providing necessary facilities to carry out research; andMr. Anil and (Late) Mrs. Rekha, Institute Instrumentation Centre,IIT-Roorkee, Roorkee, India for technical assistance during TGA andSEM analysis.
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