enhanced production of cellulase from … 12.4.pdf · riswan ali, muthuvelayuhdam, viruthagiri:...

Innovative Romanian Food Biotechnology Vol. 12, Issue of March, 2013© 2013 by “Dunărea de Jos” University – Galaţi Received December 6, 2012/ Accepted February 1, 2013

RESEARCH ARTICLE

*Corresponding author: [email protected]

ENHANCED PRODUCTION OF CELLULASE FROM TAPIOCA STEMUSING RESPONSE SURFACE METHODOLOGY

S.B. RISWAN ALI*, R. MUTHUVELAYUDHAM, T.VIRUTHAGIRI

Department of Chemical Engineering, Annamalai University, Annamalainagar, Tamilnadu 608002, India

Abstract

Medium composition for production of cellulase by Cellulomonas fimi NCIM-5015 from tapioca stem as a substrate insubmerged fermentation was optimized using a stepwise strategy. Plackett-Burman design was employed for screening,the most significant nutrient components which influence the cellulose production from 12 nutrient components. Resultsrevealed that peptone, yeast extract, KH2PO4 and CaCl2.2H2O, have significant effects on cellulase production, withconfidence levels above 95%. Central composite design (CCD) was used to determine the optimal concentrations ofthese four components and the experimental results were fitted with a second-order polynomial model at 95% level (P <0.05). The optimum conditions are peptone (0.957 g/L), yeast extract (2.5 g/L), KH2PO4 (3.09 g/L), and CaCl2

.2H2O(0.409 g/L). Under these conditions, the production of cellulase was found to be 0.58 IU/ml.

Keywords: cellulase, Plackett-Burman Design, tapioca stem, response surface methodology

Introduction

Lignocellulosic biomass like agricultural, forestryresidues and municipal solid waste etc., isconsidered to be an attractive alternative energysource because it is renewable, abundant,production of such resources does not competewith food crops for land, which offer economic,environmental and strategic advantages (Perez etal., 2002; Wyman, 1996).

Lignocellulose mainly consists of cellulose,hemicellulose and lignin (Sanchez, 2009; Juha´szet al., 2005; Muthuvelayudham and Viruthagiri,2010). Where cellulose consists of linear chains ofhundreds or thousands of glucose molecules, whilethe most abundant hemicelluloses are comprised ofxylans, they frequently consist of heterogeneousmixtures of pentoses (xylose, arbinose), hexoses(mannose, glucose, galactose) and sugar acids(Saha, 2003).

Lignocellulosic materials could be naturallydegraded to monomeric sugars by enzymatichydrolysis done by cellulase and xylanase, whichare widely spread among bacterial and fungalspecies (Xia and Sheng, 2004).

The enzymatic hydrolysates of lignocellulosicmaterials containing reducing sugars can be furtherused for the production of biofuels (e.g. hydrogen,ethanol) and other high-value chemicals (Amouriand Gargouri, 2006).The term cellulase is a groupof enzymes consists of three types of enzymes: β-1,4-D-glucan cellobiohydrolase (EC 3.2.1.91),which cleaves cellobiosyl units from the ends ofcellulose chains: endo-β-1,4-D-glucanase (EC3.2.1.4),which cleaves internal glucosidic bonds;β-1,4-glucosidase (EC 3.2.1.21), which cleavesglucose units from cellooligosaccharides (Zhang,2006).

The cost of enzymes plays an important role indetermining the economics of an enzymatic

40

Riswan Ali, Muthuvelayuhdam, Viruthagiri: Enhanced Innovative Romanian Food Biotechnology (2013) 12, 40-51production of cellulase from tapioca stem usingresponse surface methodology

This paper is available on line at http://www.bioaliment.ugal.ro/ejournal.htm

hydrolysis and it can be reduced by findingoptimum conditions for their production (Lynd etal., 2002). Reducing the costs of enzymeproduction by optimizing the fermentation mediumis the basic research for industrial application.

The composition of fermentation media includingcarbon sources, nitrogen sources, mineral salts,trace elements, vitamins and other growth factors)is a very important factor that determines thenutrimental and chemical environment for thewhole cell biocatalysts in the reactor and thus mayinfluence the production of cellulase (Lynd et al.,2002).

The use of different statistical designs for mediumoptimization has been recently employed forcellulase and xylanase production by fungalcultures (Senthilkumar et al., 2005; Hao et al.,2006; Francis et al., 2003; Dey et al., 2001). Thesestatistical methods, as compared to the common”one-factor-at-a-time” method proved to bepowerful and useful tools, since optimization byone-factor-at-a-time method involves changing onevariable while fixing the others at certain levels,which is laborious and time consuming, especiallyfor large number of variables (Xu et al., 2003).

However, statistical method offers severaladvantages one-factor-at-a-time method beingquick and reliable, screens significant nutrients,helps understanding the interactions among thenutrients at various concentrations and reduces thetotal number of experiments resulting in reducingchemicals, glass wares, experimental time, andmanpower (Srinivas et al., 1994; Carvalho et al.,1997). Statistical methods such as Plackett–Burman design, the Box–Behnken design andcentral composite design are most widely usedtechniques (Rafaat et al., 2012; Soni et al., 2010).

Placket–Burman design is mainly used asstatistical technique for screening and selection ofsignificant variables followed by central compositedesign (CCD) of response surface methodology(RSM) which provides important informationregarding the optimum level of each variable, theirinteraction with other variables and effect on theproduct yield. It involves minimum number ofexperiments for large number of factors, by whichimprovement in enzyme production has been

successfully observed (Rodrigues et al., 2003;Majumder, and Goyal, 2008; Ghanem et al., 2000).

In the present study the production of cellulase byCellulomonas fimi NCIM-5015 in batch process,using tapioca stem as substrate was enhanced bymedium optimization. The medium optimization ofthe bioprocess was carried out by three steps (1)screening the most significant medium componentswhich enhance enzymes production by usingPlackett-Burman design. (2) Optimization of themost significant medium components byapplication of central composite design (CCD) ofresponse surface methodology (RSM), and (3)verification of the model.

Materials and methods

Microorganism and culture media

Cellulomonas fimi NCIM-5015 used in this studywas purchased from the National ChemicalLaboratory, Pune, India. Stock cultures weremaintained on nutrient agar slants which contain 1g of beef extract, 0.5 g of NaCl, 1 g of peptone, 2 gof agar, in 100 ml of distilled water. pH 7.0 to 7.5,at 4oC. The sub culturing was performed every 15days to assure its viability. All the chemicals usedhere are analytical grade procured from Himedia,India.

Substrate preparation

Pretreatment decreases the crystallinity of tapiocastem while removing lignin and other inhibitorsthereby enabling its enzymatic hydrolysis. 100 g ofthe washed ground tapioca stem was treatedseparately with 2000 mL of 2% NaOH solutionand autoclaved at 121°C for 30 minutes.

Then it was filtered, washed with distilled waterand excess alkali present was neutralized with 1Nphosphoric acid. Again it was filtered and theresidue material was dried at 65°C in a hot air ovento constant weight. To the cellulosic materialobtained, the same volume of distilled water wasadded and heated at 121°C for 30 minutes. Thesuspension was filtered and the solid material wasdried at 65°C to constant weight in hot air oven.The dried tapioca stem powder was used as acarbon source.

41



Cultivation of Cellulomonas fimi NCIM-5015

Alkali pretreated tapioca stem powder was used assubstrate for cellulase production. Fermentationwas carried out in Erlenmeyer flasks (250 mL)with 10 g of alkali pretreated tapioca stem powder,supplemented with nutrients concentrationsdefined by the experimental design, pH of themedium was adjusted to 7.0 with 1 N NaOH or 1 NHCl. Each flask was covered with hydrophobiccotton and autoclaved at 121oC for 20 min. Aftercooling, each flask was inoculated with 10% v/v ofinoculum incubated at 28oC on a rotary shaker(150 rpm) (Orbital shaking incubator-Remiinstruments Ltd, Mumbai, India) (Emtiazi andNahvi, 2000). During the preliminary screeningprocess, the experiments were carried out for 96hours, and it was found that at the 72 h, themaximum enzyme production occurs. Hence,experiments were carried out for 72 h. All theexperiments were carried out in triplicate and theaverage values are reported.

Cellulase enzyme assay

In culture filtrate, 20 – 90% ammonium sulfate[(NH4)2SO4] was added and precipitated.Precipitates were separated by centrifugation andredissolved in citrate buffer (0.05 M) andcentrifuged. Filter paper activity (FPA) wasdetermined according to the method of theInternational Union of Pure and Applied Chemistry(IUPAC) and expressed as international units (IU)

(Ghose, 1987). A rolled Whatman No. 1 filterpaper strip of dimension 1.0 x 6 cm (50 mg) wassaturated with 1 mL of sodium citrate buffer (0.05mol/L, pH 4.8) in a tube, and 1 mL of anappropriately diluted enzyme was added to thetube and incubated at 50°C for 30 min. Thereafter,tubes were removed from the water bath, and thereaction was stopped by addition of 3 mL of 3,5-dinitrosalicylic acid reagent per tube. The tubeswere incubated for 5 min in a boiling water bathfor color development and were cooled rapidly.The reaction mixture was diluted appropriately andwas measured against a reagent blank at 540 nm inan UV-VIS spectrophotometer (Elico-India).

One international unit of cellulose activity is theamount of enzyme that forms 1 μmol glucose(reducing sugars as glucose) per minute during thehydrolysis reaction. Reducing sugar wasdetermined by the dinitrosalicylic acid (DNS)method (Miller, 1959).

Plackett–Burman design

For screening purpose, various mediumcomponents were evaluated. Based on Plackett–Burman factorial design, each factor was examinedin two levels, -1 for low level and +1 for high level(Plackett and Burman, 1946).

Table 1 shows the factors under investigation aswell as levels of each factor used in theexperimental design, whereas Table 2 representsthe design matrix.

Table 1. Nutrient Screening Using A Plackett-Burman DesignNutrient Code Nutrient (g/L) Low (-1) High (+1)A Peptone 0.25 1.25B Yeast extract 0.5 3.5

C MnSO4.7H2O 0.0010 0.002D KH2 PO4 2.0 4.0E K2H PO4 1.0 5.0F NaNO3 0.5 1.0G CoCl2 .6H2O 0.02 2.0H MgSO4 .7H2O 0.3 0.5I CaCl2.2H2O 0.3 0.5J (NH4)2SO4 0.06 0.1K Corn steep liquor 0.4 0.8L FeSO4.7H2O 0.005 0.1

42



Table 2. Plackett-Burman experimental design matrix for screening of important variables for cellulase

RunOrder A B C D E F G H I J K LFPA

IU/mL

1 1 -1 -1 -1 -1 1 -1 1 -1 1 1 1 0.142 -1 -1 1 1 -1 1 1 -1 -1 -1 -1 1 0.143 -1 1 1 1 1 -1 -1 1 1 -1 1 1 0.334 -1 -1 1 -1 1 -1 1 1 1 1 -1 -1 0.155 1 1 1 -1 -1 1 1 -1 1 1 -1 -1 0.296 1 1 1 1 -1 -1 1 1 -1 1 1 -1 0.237 1 -1 1 1 -1 -1 -1 -1 1 -1 1 -1 0.38 -1 1 1 -1 1 1 -1 -1 -1 -1 1 -1 0.139 -1 1 -1 1 -1 1 1 1 1 -1 -1 1 0.210 1 1 -1 -1 1 1 -1 1 1 -1 -1 -1 0.2411 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 0.0612 1 1 -1 -1 -1 -1 1 -1 1 -1 1 1 0.2613 1 -1 -1 1 1 -1 1 1 -1 -1 -1 -1 0.1814 1 -1 1 -1 1 1 1 1 -1 -1 1 1 0.1215 -1 1 -1 1 1 1 1 -1 -1 1 1 -1 0.1816 -1 -1 -1 -1 1 -1 1 -1 1 1 1 1 0.1517 1 -1 1 1 1 1 -1 -1 1 1 -1 1 0.3218 1 1 -1 1 1 -1 -1 -1 -1 1 -1 1 0.3119 -1 1 1 -1 -1 -1 -1 1 -1 1 -1 1 0.1220 -1 -1 -1 1 -1 1 -1 1 1 1 1 -1 0.19

In the present work, 12 assigned variables werescreened in 20 experimental designs. Allexperiments were carried out in triplicate and theaverage of the cellulase activity was taken asresponses (Table 2).

From the regression analysis the variables, whichwere significant at 95% level (P < 0.05) wereconsidered to have greater impact on cellulaseproduction. Significant nutrient components forcellulase production such as nitrogen source, andinorganic salts were screened and identified by thePlackett-Burman design using statistical softwarepackage MINITAB (Release 15.1, PA, USA).

Central composite design

Response surface methodology (RSM) was used tooptimize the selected four significant nutrientcomponents, namely, peptone, yeast extract,

KH2PO4 and CaCl2.2H2O which enhances the

cellulase.

The four independent variables were studied at fivedifferent levels (Table 3), and sets of 30experiments were carried out (Table 4).

It consists of a complete 2k factorial design, wherek is the number of the test variables and is equal to4, six replications of the center points to estimatethe experimental error and have a satisfactoryorthogonality for the coefficients estimation (allfactors at level 0), six star points (2 axis points onthe axis of each variable at a distance of α (= 2k/4, =2 for k = 4), whereas the other two factors are atlevel 0.

Hence, the total number of design points is N = 2k

+ 2k + n0 = 30 experiments, where n0 the numberof replicate runs at center point of the variables.

43



Table 3. Ranges of the independent variables used in RSM

Table 4. Central composite design (CCD) of factors in coded levels with enzymes activities as responses

Run X1 X2 X3 X4FPA IU/mL

Observed Predicted1 1 -1 -1 -1 0.27 0.282 0 0 0 0 0.52 0.523 0 0 0 0 0.52 0.524 0 0 0 0 0.52 0.525 0 0 0 -2 0.28 0.276 -1 -1 1 1 0.24 0.237 -2 0 0 0 0.16 0.228 -1 -1 1 -1 0.17 0.159 -1 -1 -1 1 0.17 0.1410 1 -1 1 1 0.41 0.4111 1 1 1 1 0.51 0.5212 0 0 0 0 0.52 0.5213 0 -2 0 0 0.16 0.1614 1 1 -1 1 0.43 0.4415 0 0 -2 0 0.27 0.2516 0 2 0 0 0.47 0.4617 -1 1 1 1 0.39 0.3718 0 0 0 2 0.36 0.3619 -1 1 -1 -1 0.35 0.3420 -1 1 1 -1 0.32 0.2921 0 0 0 0 0.51 0.5222 1 -1 1 -1 0.29 0.323 -1 1 -1 1 0.32 0.3224 1 1 1 -1 0.4 0.4225 2 0 0 0 0.55 0.4926 1 -1 -1 1 0.25 0.2927 0 0 0 0 0.52 0.5228 -1 -1 -1 -1 0.16 0.1629 1 1 -1 -1 0.41 0.4330 0 0 2 0 0.31 0.32

In this study, the Central composite design (CCD),using statistical software package “Design Expert7.1.5” (Stat Ease Inc., Minneapolis, USA) was

employed .which was used to obtain a quadraticmodel, consisting of factorial trials and star points(α=2) to estimate quadratic effects and central

Variable CodeLevels (g/L)

-2 -1 0 1 21. Peptone X1 0.25 0.5 0.75 1.0 1.252. Yeast extract X2 0.5 1.25 2.0 2.75 3.53. KH2 PO4 X3 2.0 2.5 3.0 3.5 4.04. CaCl2.2H2O X4 0.3 0.35 0.4 0.45 0.5

44



points to estimate the pure process variability withcellulase production as response.

The statistical software package “Design Expert7.1.5” was used to analyze the experimental data.All variables were taken at a central coded value ofzero. The minimum and maximum ranges ofvariables investigated are listed in Table 3. Upon

the completion of experiments, the averagemaximum cellulase was taken as the response (Y1).

A multiple regression analysis of the data wascarried out for obtaining an empirical model thatrelates the response measured to the independentvariables. A second-order polynomial equation is

where Y is the measured response, β0 is theintercept term, βi are linear coefficients, βii arequadratic coefficient, βij are interaction coefficient,and Xi and Xj are coded independent variables. Thefollowing equation was used for coding the actualexperimental values of the factors in the range of(−1 to +1):

, i = 1, 2, 3, . . . , k, (3)

where xi is the dimensionless value of anindependent variable, Xi is the real value of anindependent variable, X0 is the value of Xi at thecenter point, and ΔXi is the step change. Statisticalanalysis of the data was performed by designpackage Design Expert 7.1.5 to evaluate theanalysis of variance (ANOVA) to determine thesignificance of each term in the equations fittedand to estimate the goodness of fit in each case.The fitted polynomial equation was then expressed

in the form of three-dimensional response surfaceplots to illustrate the main and interactive effects ofthe independent variables on the dependent ones.The combination of different optimized variables,which yielded the maximum response, wasdetermined to verify the validity of the model. Inorder to verify the accuracy of the predicted modelan experiment was conducted with initial andoptimized media. The optimal concentrations ofthe critical variables were obtained by analyzing3D plots. The statistical analysis of the model wasrepresented in the form of analysis of variance(ANOVA).

Results and Discussion

Plackett-Burman experiments (Table 2) showed awide variation in cellulase activity. This variationreflected the importance of optimization to attainhigher productivity.

Figure 1. Pareto chart showing the effect of media components on cellulase activity

45



5

Actual

Predict

ed

Predicted vs. Actual

0.14

0.24

0.34

0.45

0.55

0.16 0.26 0.35 0.45 0.55

Figure 2. Predicted response Vs Actual value for FPA

From the Pareto charts (Figure 1,2) the variables,namely, Peptone, Yeast extract, KH2PO4andCaCl2.2H2O were selected for further optimizationto attain a maximum production of cellulase. Thelevels of factors Peptone, Yeast extract,KH2PO4and CaCl2

.2H2O and the effect of theirinteractions on cellulase production wasdetermined by central composite design of RSM.

Thirty experiments were performed at differentcombinations of the factors shown in Table 3.

The observed responses along with design matrixare presented in Table 4, and the results wereanalyzed by ANOVA.

The second-order regression equations providedthe levels of cellulase as the function of peptone,yeast extract, KH2PO4and CaCl2

.2H2O which canbe presented in terms of coded factors as in thefollowing equations:

where Y1 is the cellulase activity (IU/mL) X1, X2,X3 and X4 are peptone, Yeast extract, KH2PO4 andCaCl2.2H2O, respectively. ANOVA for theresponse surface is shown in Table 5.

Table 5: Analysis of Variance (ANOVA) for FPA

Source Coefficientfactor

Sum ofSquares Df Mean

Square F Value p-value Prob > F

Model 0.52 0.46929 14 0.03352 37.7345 < 0.0001

X1 0.068 0.1107 1 0.1107 124.62 < 0.0001

X2 0.075 0.1335 1 0.1335 150.286 < 0.0001

X3 0.019 0.00844 1 0.00844 9.49812 0.0076

X4 0.021 0.01084 1 0.01084 12.1998 0.0033

X1 X2 -6.875E-03 0.00076 1 0.00076 0.85131 0.3708

X1 X3 8.125E-03 0.00106 1 0.00106 1.18902 0.2927

X1 X4 6.875E-03 0.00076 1 0.00076 0.851310.3708

46



X2 X3 -9.375E-03 0.00141 1 0.00141 1.58302 0.2276

X2 X4 -6.25E-04 6.25E-06 1 6.25E-06 0.00704 0.9343

X3 X4 0.024 0.00951 1 0.00951 10.7012 0.0052

X12 -0.041 0.0455 1 0.0455 51.2199 < 0.0001

X22 -0.051 0.07059 1 0.07059 79.4589 < 0.0001

X32 -0.057 0.08905 1 0.08905 100.244 < 0.0001

X42 -0.049 0.06715 1 0.06715 75.5913 < 0.0001

Residual 0.01333 15 0.00089Lack of Fit 0.01299 10 0.0013 19.4875 0.0022Pure Error 0.00033 5 6.67E-05Cor Total 0.48262 29

Std. Dev. 0.030, R-Squared 0.9724, Mean 0.36 , Adj R-Squared 0.9466, C.V. %8.32 , Pred R-Squared 0.8440, PRESS 0.075, AdeqPrecision 18.209

The Model F-value of 37.73 for cellulase impliesthe model is significant. There is only 0.01%chance that a "Model F-Value" this large couldoccur due to noise. Values of “Prob > F” less than0.05 indicate that model terms are significant.Values greater than 0.1 indicate that the modelterms are not significant.

In the present work, the linear effects of X1 , X2 ,X3,X4,the interactive effects of X3 X4 and square effectsof X1

2, X22, X3

2, X42 are significant model terms for

cellulase production. To test the fit of the modelequation, the regression - based determinationcoefficient R2 was evaluated. The nearer the valuesof R2 to 1, the model would explain better forvariability of experimental values to the predictedvalues. The coefficient of determination (R2) forcellulase activity was calculated as (R2=0.9724 forcellulase) which are nearly equal to 1) 97.24% ofthe responses. The predicted R2 value of cellulaseactivity was 84.40% was in reasonable agreementwith the adjusted R2 value of 94.66%. "AdeqPrecision" measures the signal to noise ratio. Aratio greater than 4 is desirable. Adeq Precision of18.209 indicates an adequate signal. This modelcan be used to navigate the design space. Theabove models can be used to predict the cellulaseproduction within the limits of the experimentalfactors. Figure 2 shows that the actual responsevalues agree well with the predicted responsevalues of cellulase. The interaction effects ofvariables on cellulase was studied by plotting 3Dsurface curves against any two independent

variables, while keeping another variable at itscentral (0) level. The plotting 3D surface curves ofthe calculated responses (cellulase activity) fromthe interactions between the variables are shown inFigures 3(a)-3(f). Figures 3(a), 3(b), 3(c) show thedependency of cellulase on Peptone. The cellulaseactivity increases with increase in peptone about0.957 g/L and thereafter cellulase decreases withfurther increase in peptone. The same trend wasobserved in Figures 3(a), 3(d), 3(e) which showsthe dependency of cellulase on yeast extract. Thecellulase activity increases with increase in yeastextract about 2.5g/L and thereafter cellulaseactivity decreases with further increase in yeastextract. The Figures 3(b), 3(d), 3(f) shows thedependency of cellulase on KH2PO4. The cellulaseactivity increases with increase in KH2PO4 about3.09g/L and thereafter cellulase activity decreaseswith further increase in KH2PO4. The dependencyof cellulase on CaCl2.2H2O shows from theFigures 3(c), 3(e), 3(f). The cellulase increaseswith increase in CaCl2.2H2O about 0.409g/L andthereafter cellulase activity decreases with furtherincrease in CaCl2.2H2O.

The optimum conditions for the maximumproduction of cellulase was determined byresponse surface analysis and also estimated byoptimizer tool using statistical software package“Design Expert 7.1.5”. The optimum conditionsare Peptone (0.957g/L), Yeast extract (2.5g/L),KH2PO4 (3.09g/L), and CaCl2.2H2O (0.409g/L).The predicted results are shown in Table 4.

47


*Corresponding author: [email protected]

-2.00 -1.00

0.00 1.00

2.00

-2.00-1.00

0.001.00

2.00

-0.17

0.0175

0.205

0.3925

0.58

FPA IU

/mL

A: Peptone B: Yeast Extract

Figure 3(a). Three-dimensional response surface plot for cellulase production showing the interactive effects ofpeptone and yeast extract

-2.00 -1.00

0.00 1.00

2.00

-2.00-1.00

0.001.00

2.00

-0.02

0.1225

0.265

0.4075

0.55

FPA IU

/mL

A: Peptone C: KH2PO4

Figure 3(b). Three-dimensional response surface plot for cellulase production showing the interactive effects ofpeptone and KH2PO4

-2.00 -1.00

0.00 1.00

2.00

-2.00-1.00

0.001.00

2.00

0

0.1375

0.275

0.4125

0.55

FPA IU

/mL

A: Peptone D: CaCl2.2H2O

Figure 3(c). Three-dimensional response surface plot for cellulase production showing the interactive effects ofpeptone and CaCl2

.2H2O

48



-2.00 -1.00

0.00 1.00

2.00

-2.00-1.00

0.001.00

2.00

-0.15

0.025

0.2

0.375

0.55 FP

A IU/mL

B: Yeast Extract C: KH2PO4

Figure 3(d). Three-dimensional response surface plot for cellulase production showing the interactive effects of yeastextract and KH2PO4

-2.00 -1.00

0.00 1.00

2.00

-2.00-1.00

0.001.00

2.00

-0.08

0.0775

0.235

0.3925

0.55

FPA IU

/mL

B: Yeast Extract D: CaCl2.2H2O

Figure 3(e). Three-dimensional response surface plot for cellulase production showing the interactive effects of yeastextract and CaCl2.2H2O

-2.00 -1.00

0.00 1.00

2.00

-2.00-1.00

0.001.00

2.00

-0.02

0.1175

0.255

0.3925

0.53

FPA I

U/mL

C: KH2PO4 D: CaCl2.2H2O

Figure 3(f). Three-dimensional response surface plot for cellulase production showing the interactive effects ofKH2PO4 and CaCl2

.2H2O

49



Validation of the experimental

Validation of the experimental model was testedby carrying out the batch experiment under optimaloperation conditions. Three repeated experimentswere performed, and the results are compared. Thecellulase activity obtained from experiments wasvery close to the actual response predicted by theregression model, which proved the validity of themodel. At these optimized conditions, themaximum cellulase activity was found to be 0.58IU/ml.

Conclusions

In this work medium components for highercellulase activities from Cellulomonas fimi NCIM-5015 were optimized by RSM. Using Placket-Burman design peptone, yeast extract, KH2PO4andCaCl2

.2H2O was found to be the most significantvariables, which significantly enhanced cellulaseactivity.

Central composite design was employed tooptimize these selected nutrients. The optimallevels of components were obtained as peptone(0.957g/L), yeast extract (2.5g/L), KH2PO4

(3.09g/L), and CaCl2.2H2O (0.409g/L).This study

showed that the tapioca stem constitutes a goodcarbon source for the production of cellulase.Using the optimized conditions, the producedenzyme activity of cellulase reaches 0.58 IU/ml.The results show a close agreement between theexpected and obtained activity level.

Acknowledgment

The authors gratefully acknowledge UGC, NewDelhi, for providing financial support to carry outthis research work under UGC – Major ResearchProject Scheme. The authors also wish to expresstheir gratitude for the support extended by theauthorities of Annamalai University,Annamalainagar, India, in carrying out the researchwork in Bioprocess Laboratory, Department ofChemical Engineering.

References

Amouri B., Gargouri A. (2006) Characterization of anovel β-glucosidase from a Stachybotrys strain,Biochemical Engineering Journal, 32, 191-197.

Carvalho C.M.L., Serralheiro Cabral J.M.S., AirebarrosM.R. (1997) Application of factorial design to the studyof transesterification reactions using cutinase in AOT-reversed micelles, Enzyme and Microbial Technology,27(2),117-123.

Dey G., Mitra A., Banerjee R., Maiti B.R., (2001)Enhanced production of amylase optimization ofnutritional constituents using response surfacemethodology, Biochemical Engineering Journal, 7(3),227–31.

Emtiazi G., Nahvi I., (2000) Multi-enzyme productionby Cellulomonas sp. grown on wheat straw, Biomassand Bioenergy, 19 (1), 31-37.

Francis F., Sabu A., Nampoothiri K.M., (2003) Use ofresponse surface methodology for optimizing processparameters for the production of α - amylaseby Aspergillus oryzae. Biochemical EngineeringJournal, 15(2), 107-115.

Ghanem N.B., Yusef H.H., Mahrouse H.K. (2000)Production of Aspergillus terreus xylanase in solid-statecultures: application of the Plackett-Burmanexperimental design to evaluate nutritionalrequirements, Bioresource Technology, 73(2), 113-121.

Ghose T.K. (1987). The measurment of celluloseactivities, Pure& Applied Chemistry, 59 (2), 257-268.

Hao X.C., Yu X., Yan Z.L. (2006). Optimization of theMedium for the Production of Cellulase by the MutantTrichoderma reesei WX-112 Using Response SurfaceMethodology. Food Technology and Biotechnology.44(1), 89-94.

Juhasz T., Szengyel Z., Reczey K., Siika-Aho M.,Viikari L. (2005). Characterization of cellulases andhemicellulases produced by Trichoderma reesei onvarious carbon sources, Process Biochemistry. 40(11),3519-3525.

Lynd L., Weimer P., Zyl W., Pretorius I. (2002)Microbial cellulose utilization: fundamentals andbiotechnology, Microbiology and Molecular BiologyReviews, 66(3), 506-577.

50



Majumder A., Goyal A. (2008) Enhanced production ofexocellular glucansucrase from Leuconostocdextranicum NRRL B-1146 using response surfacemethod, Bioresource Technology, 99 (9), 3685-3691.

Miller G.L. (1959) Use of dinitrosalicyclic acid reagentfor determination of reducing sugar, AnalyticalChemistry, 31(3), 426-428.

Muthuvelayudham R., Viruthagiri T. (2010) Applicationof Central Composite Design Based Response SurfaceMethodology in Parameter Optimization and onCellulase Production Using Agricultural Waste.International Journal of Chemical and BiologicalEngineering, 3(2), 97-104.

Perez J., Munoz-Dorado J., De-la-Rubia T., Martínez J.(2002) Biodegradation and biological treatments ofcellulose, hemicellulose and lignin: an overview,International Microbiology, 5(2), 53-63.

Plackett R.L., Burman J.P. (1946) The design ofoptimum multifactorial experiments, Biometrika, 33,305-325.

Rafaat M., Elsanhoty I.A., Al-Turki, Ramadan M.F.(2012) Screening of medium components by Plackett–Burman design for carotenoid production using date(Phoenix dactylifera) wastes, Industrial Crops andProducts, 36(1), 313-320.

Rodrigues R.C.L.B., Felipe M.A ., Silva J.B.A., VitoloM. (2003) Response surface methodology for xylitolproduction from sugarcane bagasse hemicellulosichydrolyzate using controlled vacuum evaporationprocess variables, Process Biochemistry, 38(8), 1231-1237.

Saha B.C. (2003) Hemicellulose bioconversion, Journalof Industrial Microbiology and Biotechnology, 30(5),279-291.

Sanchez L. (2009) Research review paperLignocellulosic residues: Biodegradation andbioconversion by fungi, Biotechnology Advances, 27(2),185-194.

Senthilkumar S.R., Ashokkumar B., Chandra Raj K.,Gunasekaran P. (2005) Optimization of mediumcomposition for alkali-stable xylanase production byAspergillus fischeri Fxn 1 in solid-state fermentationusing central composite rotary design, BioresourceTechnology, 96(12), 1380-1386.

Soni R., Nazir A., Chadha B.S. (2010) Optimization ofcellulase production by a versatile Aspergillus fumigatusFresenius strain (AMA) capable of efficient deinkingand enzymatic hydrolysis of Solka floc and bagasse.Industrial Crops and Products, 31(2), 277-283.

Srinivas M.R.S., Naginchand, Lonsane B.K. (1994) Useof Plackett– Burman design for rapid screening ofseveral nitrogen sources, growth product promoters,minerals and enzyme inducers for the production ofalpha-galactosidase by Aspergillus niger MRSS 234 insolid state fermentation, Bioprocess Eng. 10, 139-144.

Weuster-Botz D. (2000) Experimental design forfermentation media development: statistical design orglobal random search? Journal of Bioscience andBioengineering, 90(5), 473-83.

Wyman C. (1996) In C. E. Wyman (Ed.), Handbook onbioethanol: Production and utilization, Applied energytechnologyseries, Washington, DC: Taylor and Francis,105-118.

Xia L.M., Sheng X.L. (2004) High-yield cellulaseproduction by Trichoderma reesei ZU-02 on corncobresidues, Bioresource Technology, 91(3), 259-62.

Xu C.P., Kim S.W., Hwang H.J., Choi J.W., Yun J.W.(2003) Optimization of submerged culture conditionsfor mycelial growth and exo-biopolymer production byPaecilomyces tenuipes C240. Process Biochemistry.38(7), 1025-1030.

Zhang P., Himmel M., Mielenz J. (2006) Outlook forcellulose improvement: screening and selectionstrategies, Biotechnology Advances, 24(5), 452-481.

51

enhanced production of cellulase from … 12.4.pdf · riswan ali, muthuvelayuhdam, viruthagiri:...

Documents