Available online at www.sciencedirect.com

Bioresource Technology 99 (2008) 937–945

Production and recovery of an alkaline exo-polygalacturonasefrom Bacillus subtilis RCK under solid-state fermentation

using statistical approach

Shefali Gupta, Mukesh Kapoor, Krishna Kant Sharma, Lavanya M. Nair,Ramesh Chander Kuhad *

Lignocellulose Biotechnology Laboratory, Department of Microbiology, University of Delhi South Campus, Benito Juarez Marg, New Delhi 110021, India

Received 24 November 2006; received in revised form 7 March 2007; accepted 8 March 2007Available online 24 April 2007

Abstract

The empirical models developed through two independent RSM (RSM-I, 23; RSM-II, 25) in terms of effective operational factors ofinoculum age, inoculum volume, wheat bran-to-moisture ratio (RSM-I) and contact time, extraction temperature, agitation, fermentedbran-to-solvent ratio and SDS (RSM-II) were found adequate to describe the optimization of exo-polygalacturonase from Bacillus

subtilis RCK under solid-state fermentation (SSF) conditions. Through the analysis of RSM-I, wheat bran-to-moisture ratio and inoc-ulum volume were found to be the most significant factors and an increment in both had a positive effect in enhancing enzyme yield,while in RSM-II all the factors significantly affected enzyme recovery except fermented bran-to-solvent ratio, which had the leastimpact within the ranges investigated in enhancing enzyme recovery. Based on contour plots and variance analysis, optimum opera-tional conditions for maximum exo-polygalacturonase yield were achieved when 1.5% (v/w) of 24 h old (OD600 nm � 2.7 ± 0.2) B. sub-

tilis RCK cells were inoculated on moistened wheat bran (1:7 solid substrate-to-moisture ratio) and enzyme was harvested by additionof solvent (1:6 fermented bran-to-solvent ratio) under shaking conditions (200 rpm) in presence of SDS (0.25% w/v) for 15 min at35 �C. An over all 3.4 fold (1.7-fold RSM-I; 2.0 fold RSM-II) increase in enzyme production was attained because of optimizationby RSM.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Bacillus subtilis; Exo-polygalacturonase; Response surface methodology; Solid-state fermentation

1. Introduction

Pectins are a heterogeneous group of high molecularweight complex, acidic structural polysaccharides consist-ing largely of D-galactopyranosyluronic acid that area(1! 4) glycosidically linked to polygalacturonic acid withsmall amounts of L-rhamnose (2–4%) and various sidechains comprising of L-arabinose, D-galactose and b-D-xylose (Esquivel et al., 1999; Singh et al., 1999a). Theenzymes that hydrolyze pectic substances are broadly

0960-8524/$ - see front matter � 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.biortech.2007.03.009

* Corresponding author. Tel.: +91 11 24112972; fax: +91 11 24115270.E-mail address: kuhad@hotmail.com (R.C. Kuhad).

known as pectinases, which include polygalacturonase(exo-polygalacturonase and endo-polygalacturonase), pec-tin esterase, pectin lyase and pectate lyase on the basis oftheir mode of action. Bacteria, yeast, actinomycetes and fil-amentous fungi have been reported to produce pectinases(Kapoor et al., 2001; Hoondal et al., 2002; Kuhad et al.,2004; Jayani et al., 2005; Torres et al., 2006).

The acidophilic pectinases have been extensivelyreported for extraction and clarification of fruit juicesand wines (Alkorta et al., 1998; Ortega et al., 2004; Vaillantet al., 2005; Ingallinera et al., 2005), while alkaline pectin-ases are employed for the pretreatment of waste watersfrom food processing industries containing pectinaceouswaste (Tanabe et al., 1988) and processing and degumming

938 S. Gupta et al. / Bioresource Technology 99 (2008) 937–945

of bast fibers like ramie (Boehmeria nivea) (Kapoor et al.,2001), sunn hemp (Crotalaria juncea) (Kapoor et al.,2001), buel (Grewia optiva) (Kashyap et al., 2001) and jute(Chorchorus capsularis) (Sreenath et al., 1996). However,usage of alkaline pectinases at commercial scale remainsabysmally low due to paucity of cultures producing highenzyme yields (Hoondal et al., 2002; Kashyap et al.,2003; Kuhad et al., 2004; Li et al., 2005).

Pectinase production from microorganisms has beenreported under both submerged (SmF) and solid-state(SSF) fermentation conditions. SSF offers numerous eco-nomical and practical advantages over SmF but has onlymarginal adoption at commercial scale (Pandey, 2003).One of the prime reasons is the scanty information ondownstream processing parameters like recovery and con-centration, which comprise an integral part of downstreamprocessing under SSF (Castilho et al., 1999). Optimizationof recovery and concentration operations in SSF wouldincrease the process efficacy by generation of concentratedenzyme extracts, reduction in the number of downstreamprocessing stages and prevention of enzyme losses in thediscarded solid residues.

The optimization of process and downstream conditionsunder SSF is generally done by varying one factor at a timeapproach (Silva et al., 2005; Patil and Dayanand, 2006).However, these strategies are laborious and time-consum-ing especially for a large number of variables and oftendo not consider interactions among variables. Understand-ing and modeling of both individual and interactive effectsenables each reaction parameter to be optimized in coher-ence with others for achieving maximum product yield. Inthis respect, optimization carried out by response surfacemethodology (RSM), not only allows quick screening oflarge experimental domain but also reflects role of each fac-tor. This optimization process involves three major stepsviz. performing the statistically designed experiments, esti-mating the coefficients in a mathematical model and pre-dicting the response and checking the adequacy of themodel.

It is necessary to develop efficient extraction techniquesto make solid-state fermentation applicable for the produc-tion of high purity enzymes and to enable its effective com-mercial availability. Thus, the objective of the presentinvestigation was to statistically optimize the productionand recovery conditions, in order to obtain concentratedcrude extracts and to reduce the enzyme losses in the dis-carded residues for an alkaline exo-polygalacturonase fromBacillus subtilis RCK under SSF.

2. Methods

2.1. Chemicals

Citrus pectin, dinitrosalicylic acid, galacturonic acid,digalacturonic acid, trigalacturonic acid and polygalactu-ronic acid were purchased from Sigma (St. Louis, MO,

USA). Wheat bran was procured locally. All other mediacomponents and chemicals used were of highest puritygrade available commercially.

2.2. Microorganism

Bacillus sp. RCK was isolated from decomposingkitchen waste at University of Delhi South Campus, NewDelhi and maintained on modified pectin–agar medium(Kapoor et al., 2000) containing (g l�1): pectin 2.5, peptone5.0, yeast extract 5.0, KH2PO4 1.0, MgSO4 Æ 7H2O 0.1, pH9.0 at 37 �C. Pure cultures were stored at 4 �C and sub-cul-tured every fortnight.

2.3. Exo-polygalacturonase production from B. subtilis RCKunder solid-state fermentation (SSF)

In SSF, each 250 ml Erlenmeyer flask containing wheatbran (5.0 g) was moistened with mineral salt solution con-taining (g l�1): KH2PO4 1.0, MgSO4 Æ 7H2O 0.1, CaCl2 0.5,NaCl 1.0 (pH 9.0) in 1:6 ratio of solid substrate-to-mois-ture. The pH was adjusted to 9.0 using 0.1 N NaOH andthe contents were thoroughly mixed and autoclaved at121 �C (15 psi) for 15 min. The flasks were cooled and inoc-ulated with 1.0% (v/w) of 16 h old (OD600 nm � 2.0 ± 0.2)bacterial seed culture and incubated at 37 �C for 48 h inhumidity (99%) controlled incubator (Hycon humidifier,India). After fermentation, solvent (distilled water forextraction of exopolygalacturonase under culture condi-tions optimized by one at a time approach and RSM-I;buffer 100 mM glycine–NaOH, pH 10.5 under RSM-II)was added to a beaker containing fermented solids in 1:5ratio of fermented bran-to-solvent, SDS 0.2% (w/v) andstirred mechanically (150 rpm) at 30 �C for 10 min. Theresulting solid suspensions were centrifuged at 8000g for10 min at 4 �C and supernatant obtained was used to esti-mate enzyme activity. The pellet containing fermented sol-ids was oven dried overnight at 80 �C to estimate the dryweight of fermented substrate.

2.4. Analytical procedures

Exo-polygalacturonase activity was measured by quan-tifying reducing groups expressed as galacturonic acidequivalents liberated during the incubation of 0.4 ml of0.5% (w/v) citrus pectin prepared in 100 mM glycine–NaOH buffer (pH 10.5) and 0.1 ml of appropriately dilutedsupernatant at 60 �C for 10 min using Miller’s method(Miller, 1959). One unit of exo-polygalacturonase wasdefined as the amount of enzyme required to release 1 lmolof galacturonic acid from citrus pectin under the assay con-ditions (Kapoor et al., 2000). Protein content was deter-mined by Lowry method using bovine serum albumin(BSA) as standard (Lowry et al., 1951). The absorbance(OD600 nm) of bacterial culture was measured after dilution(1:10) in a double beam UV–visible spectrophotometer(Analytical Jena, Specord 2000, Konrad-Zuse-Strasse,

Table 2Experimental design used in RSM-I by using three independent variablesfor exo-polygalacturonase production by B. subtilis RCK under SSFconditions

Runorder

Inoculumvolume%

Inoculumage (h)

Wheat branto-moisture

Exo-polygalacturonaseyield (IU/g dry substrate)

S. Gupta et al. / Bioresource Technology 99 (2008) 937–945 939

Jena, Germany). The retention times of products (galact-uronic acid, digalacturonic acid, trigalacturonic acid)obtained after hydrolysis of polygalacturonic acid (PGA)by polygalacturonase were compared with known stan-dards using HPLC (model no. SCL-10AVP).

(v/w) ratio Experimental Predicted

1 1.5 8.0 1:7 1626.7 1580.52 1.0 16.0 1:8 1400.0 1537.03 2.2 16.0 1:6 1519.5 1652.54 2.0 32.0 1:6 1262.0 1504.55 1.5 24.0 1:7 2770.5 2697.56 0.5 24.0 1:7 1245.5 1227.177 2.0 32.0 1:8 1739.0 1674.58 1.0 32.0 1:6 1895.5 1747.09 1.5 24.0 1:9 1312.0 1173.0

10 1.0 32.0 1:8 1871.0 1817.511 1.0 16.0 1:6 1407.5 1371.012 1.5 24.0 1:7 2770.5 2697.513 1.5 24.0 1:7 2770.5 2697.514 1.5 24.0 1:5 888.5 837.515 1.5 24.0 1:7 2770.5 2697.516 1.5 24.0 1:7 2770.5 2697.517 1.5 40.0 1:7 1856.5 1812.518 2.5 24.0 1:7 1266.0 1231.019 1.5 24.0 1:7 2770.5 2697.520 2.0 16.0 1:8 1760.5 1818.0

2.5. Response surface methodology (RSM)

In order to maximize exo-polygalacturonase productionand understand the role of interacting variables, produc-tion and recovery conditions for exo-polygalacturonaseproduction under SSF were optimized separately byemploying two different RSM as follows:

RSM I Optimization of process parameters for exo-poly-galacturonase production from B. subtilis RCKunder SSF using wheat bran as the prime solidsubstrate.

RSM II Optimization of parameters for efficient recoveryof exo-polygalacturonase from wheat bran fer-mented under conditions optimized by RSM-I.

RSM (RSM-I and RSM-II) was carried out using statis-tical software package Design Expert� 6.0, (Stat-Ease, Inc.Minneapolis, USA). The level of independent variables inRSM-I (inoculum age (A), wheat bran-to-moisture ratio(B) and inoculum volume (C)) and RSM-II (contact time(P), fermented bran-to-solvent ratio (Q), extraction tem-perature (R), agitation (S) and SDS (T)) were optimizedby studying each factor in the designs at five different levels(�1, �a, 0, a, 1) (Table 1). All the variables were taken at acentral coded value considered as zero. The minimum andmaximum ranges of variables used and the full experimen-tal plan for RSM-I and RSM-II with respect to their valuesare listed in Tables 2 and 3, respectively. The quadraticmodels for RSM-I and RSM-II predicting the optimalpoints were expressed according to the quadratic Eqs. (1)and (2), respectively.

Table 1Experimental range and levels of independent variables studied usingCCD in terms of actual and coded factors

Variable Coded level of variable

�a �1 0 +1 +a

RSM-I

Inoculum volume% (v/w) 0.5 1.0 1.5 2.0 2.5Inoculum age (h) 8.0 16.0 24.0 32.0 40.0Wheat bran-to-moisture

ratio1:5 1:6 1:7 1:8 1:9

RSM-II

Contact time (min) 5.0 10.0 15.0 20.0 25.0Fermented bran-to-solvent

ratio1:4 1:5 1:6 1:7 1:8

Temperature (�C) 25.0 30.0 35.0 40.0 45.0Agitation (rpm) 100.0 150.0 200.0 250.0 300.0SDS% (w/v) 0.15 0.20 0.25 0.30 0.35

Y ¼ b0 þ b1Aþ b2Bþ b3C þ b11A2 þ b22B2 þ b33C2

þ b12ABþ b23BC þ b13AC; ð1Þ

where Y is predicted response, b0 is intercept; b1b2b3

are linear coefficient and b12b23b13b33 are interactioncoefficients.

Y ¼ b0 þ b1P þ b2Qþ b3Rþ b4S þ b5T þ b11P 2

þ b22Q2 þ b33R2 þ b44S2 þ b12PQþ b13PR

þ b14PS þ b15PT þ b23QRþ b24QS þ b25QT

þ b34RS þ b35RT þ b55T 2 þ b45ST ; ð2Þ

where Y is predicted response, b0 is intercept; b1b2-b3b4b5 are linear coefficient and b11b12b15b22b23b25b13b35-b34b24b14b22b33b44b45 are interaction coefficients.

Eqs. (1) and (2) were analysed to estimate the responsesof the dependent variables and regression analysis of exper-imental data (Xu et al., 2002). The quality of the fit of thequadratic model equation was expressed by the coefficientof determination (R2) and its statistical significance waschecked by Fischer’s test value (F-value).

2.6. Validation of the models

The validation of statistical models was performed byvarying the enzyme production (wheat bran-to-moistureratio, inoculum volume and age) and recovery (fermentedbran-to-solvent ratio, extraction temperature, contact time,agitation and SDS) variables at various levels within thedesign space.

Table 3Experimental design used in RSM-II by using five independent variables for exo-polygalacturonase recovery by B. subtilis RCK under SSF conditions

Runorder

Contact time(min)

Fermented bran-to-solvent ratio

Extraction temperature(�C)

Agitation(rpm)

SDS%(w/v)

Exo-polygalacturonase yield(IU/g dry substrate)

Experimental Predicted

1 15.0 1:6 35.0 300 0.25 3578.6 3620.62 10.0 1:5 30.0 250 0.30 2526.7 2441.73 15.0 1:4 35.0 200 0.25 3971.2 4232.84 10.0 1:7 30.0 150 0.20 3622.8 3608.95 20.0 1:5 30.0 150 0.20 3131.9 3073.96 15.0 1:6 35.0 200 0.25 5769.2 5748.17 25.0 1:6 35.0 200 0.25 4773.2 4711.48 20.0 1:5 30.0 250 0.30 3236.1 3244.89 15.0 1:6 35.0 200 0.25 5769.2 5748.1

10 20.0 1:5 40.0 250 0.20 4463.2 4443.211 15.0 1:6 35.0 200 0.25 5769.2 5748.112 15.0 1:6 35.0 200 0.35 2389.8 2387.713 10.0 1:7 30.0 250 0.30 2359.2 2406.814 15.0 1:6 35.0 200 0.25 5769.2 5748.115 10.0 1:7 30.0 150 0.30 3422.8 3445.416 20.0 1:7 30.0 150 0.30 3354.2 3438.017 20.0 1:7 30.0 150 0.20 3391.6 3340.118 15.0 1:6 35.0 200 0.25 5769.2 5748.119 15.0 1:6 25.0 200 0.25 3292.8 3202.820 10.0 1:5 40.0 250 0.30 2358.6 2344.221 10.0 1:7 30.0 250 0.20 2700.4 2730.422 15.0 1:6 35.0 200 0.15 2952.8 3006.523 10.0 1:5 40.0 150 0.30 1725.6 1704.724 10.0 1:5 40.0 250 0.20 3090.5 3060.925 15.0 1:6 45.0 200 0.25 3366.1 3307.726 10.0 1:7 40.0 150 0.20 2693.1 2770.827 20.0 1:7 30.0 250 0.30 3087.1 3157.828 15.0 1:6 35.0 200 0.25 5769.2 5748.129 20.0 1:7 30.0 250 0.20 2916.3 3005.830 15.0 1:8 35.0 200 0.25 2822.6 2729.231 10.0 1:5 30.0 150 0.30 2886.4 2802.232 10.0 1:5 30.0 250 0.20 2956.4 2875.933 20.0 1:5 30.0 150 0.30 3019.5 3061.634 20.0 1:5 40.0 250 0.30 4016.8 3987.835 10.0 1:5 30.0 150 0.20 3136.8 3075.936 15.0 1:6 35.0 200 0.25 5769.2 5748.137 15.0 1:6 35.0 200 0.25 5769.2 5748.138 10.0 1:7 40.0 250 0.30 2184.8 2286.339 10.0 1:7 40.0 150 0.30 2403.7 2324.840 20.0 1:5 40.0 150 0.30 2846.8 2804.841 20.0 1:7 40.0 250 0.20 3949.1 4008.342 20.0 1:7 40.0 150 0.30 3087.9 3157.843 20.0 1:5 30.0 250 0.20 3436.9 3417.744 10.0 1:5 40.0 150 0.20 2225.7 2260.845 20.0 1:7 40.0 250 0.30 3588.8 3663.046 5.0 1:6 35.0 200 0.25 3223.2 3336.647 20.0 1:7 40.0 150 0.20 3287.2 3342.548 20.0 1:5 40.0 150 0.20 3146.8 3099.349 10.0 1:7 40.0 250 0.20 3018.7 3061.550 15.0 1:6 35.0 100 0.25 3305.7 3315.7

940 S. Gupta et al. / Bioresource Technology 99 (2008) 937–945

3. Results and discussion

Bacillus sp. RCK, isolated from decomposing kitchenwaste at University of Delhi South Campus, New Delhi,was identified by 16 S rDNA sequencing as B. subtilis

RCK (Genbank accession no. AJ937677). The partial16 S rDNA sequence of the isolate showed 98% similarityto B. subtilis (Genbank accession numbers: AY030330

and AY833569). Under conditions optimized by one at atime approach, B. subtilis RCK produced 1610.0 IU/gdry substrate of exo-polygalacturonase after 48 h of incu-bation at 37 �C under SSF conditions.

The hydrolysis of polygalacturonic acid by pectinasefrom B. subtilis RCK produces digalacturonic acid asmajor end product without the intermediary accumulationof longer oligogalacturonic acids. This shows that the

Table 4Analysis of variance (ANOVA) for response surface quadratic models forexo-polygalacturonase production (RSM-I) and recovery (RSM-II) underSSF

Term RSM-I (Exo-polygalacturonase yield)

RSM-II (Exo-polygalacturonase yield)

F-valueb 93.08 353.53P > Fa <0.0001 <0.0001Mean 32796.16 3542.04R2 0.9882 0.9959Adj. R2 0.9776 0.9931Pred. R2 0.9296 0.9825Coefficient of

variance5.02 2.64

Adequateprecision

27.933 66.74

PRESS 21018.19 1.084E + 006

a Value of ‘‘P > F’’ less than 0.0500 indicate model terms are significant.b The model F-value of 93.08 and 353.53 for exopolygalacturonase

production and recovery, respectively implies that the models are signifi-cant. There is only 0.01% chance that a ‘‘model F-value’’ this large couldoccur due to noise.

S. Gupta et al. / Bioresource Technology 99 (2008) 937–945 941

chain-splitting activity of pectinase could be best describedas an exo-poly(1,4-a-D-galacturonide) digalacturonohydro-lase (EC 3.2.1.82) (called exo-polygalacturonase hereafter)(Jayani et al., 2005).

3.1. Statistical optimization of exo-polygalacturonase

production and recovery

The input variables that have the maximum influence onthe final response (exo-polygalacturonase yield) of the sys-tem were identified by one-factor-at-a-time approach (datanot shown) and the interactions of various chosen factorson exo-polygalacturonase production (inoculum age,inoculum volume and wheat bran-to-moisture ratio) andrecovery (fermented bran-to-solvent ratio, extractiontemperature, contact time, agitation and SDS) were exam-ined through RSM following CCD. The results obtainedafter CCD were then analyzed by standard analysis of var-iance (ANOVA), which gave regression equations (in termsof coded factors) 3 and 4 for exo-polygalacturonase pro-duction and recovery, respectively.

Y ¼ 2750:27� 3:59Aþ 144:70Bþ 191:63C � 15:5A2

� 106:4B2 � 171:71C2 � 123:85ABþ 375:50AC

þ 109:50BC: ð3Þ

Eq. (3)-exo-polygalacturonase yield (Y) as a function ofinoculum age (A), wheat bran-to-moisture ratio (B) andinoculum volume (C):

Y ¼ 5725:59þ 687:40P þ 5066:43Qþ 1421:72R

þ 73:93S þ 1:55E þ 005T � 17:24P 2 � 21:54Q2

� 22:70R2 � 0:22S2 � 3:05E þ 005T 2 � 13:34PQ

þ 8:4PRþ 0:54PS þ 26:13PT � 1:15QR� 3:39QS

þ 550:62QT þ 1:0RS � 28:252RT � 16:05: ð4Þ

Eq. (4)-exo-polygalacturonase yield (Y) as a function ofcontact time (P), fermented bran-to-solvent ratio (Q),extraction temperature (R), agitation (S) and SDS (T).

The regression equations obtained indicated R2 (coeffi-cient of determination) values of 0.9882 and 0.9959 (a valueof R2 > 0.75 indicates the aptness of the model) for exo-polygalacturonase production and recovery, respectivelyand thus the models could explain more than 98.82% ofthe variability in the responses (Table 4). Moreover, R2 val-ues were in reasonable agreement with adjusted R2 valuesof 0.9776 (exo-polygalacturonase production) and 0.9931(exo-polygalacturonase recovery). The adjusted R2 correctsthe R2 value for the sample size and number of terms in themodel. If there are many terms in the model and the samplesize is not very large, the adjusted R2 may be noticeablysmaller then predicted R2. The purpose of statistical analy-sis was to determine the experimental factors, which gener-ate signals that are large in comparison to noise. Theadequate precision measuring the signal to noise ratiowas found to be 27.93 and 66.74 for exo-polygalacturonase

production and recovery, respectively (Table 4). A signal tonoise ratio greater than 4.0 is desirable. The statisticalmodels were thus fit and could be used to navigate thedesign space. The model F-values of 93.08 (exo-polygalac-turonase production) and 353.53 (exo-polygalacturonaserecovery) indicated that model terms are significant(Table 4). The predicted sum of squares (PRESS), whichis a measure of how a particular model fits each point inthe design was 21018.19 (exo-polygalacturonase produc-tion) and 1.084E + 006 (exo-polygalacturonase recovery).Values of ‘‘Prob > F’’ less than 0.0500 indicated that modelterms were significant. A2, B2, C2 and AB were significantmodel terms for exo-polygalacturonase production, whilefor exo-polygalacturonase recovery, P, Q, S, T, P2, Q2,R2, S2, T2, PQ, PR, PS, PT, QS, RS, RT and ST werefound to be significant model terms.

Three dimensional response surface curves were plottedto study the interactions among the various selected factorsand to determine their optimum concentrations/values forattaining maximum yield of exo-polygalacturonase. Theplots were generated by plotting the response using the z-axis against two independent variables while keeping theother independent variables at their O-level. The coordi-nates of the central point within the highest contour levelsin each of the figures corresponded to the optimum concen-trations of the respective components.

Fig. 1 indicates a linear increase in exo-polygalacturo-nase production when wheat bran-to-moisture ratio wasincreased up to 1:7. The enzyme yield was reduced whenwheat bran-to-moisture ratios were adjusted to eitherhigher or lower values. The decrease in the enzyme activitywith an increase in moisture might be attributed to the phe-nomenon of flooding of inter particle space of the sub-strate, causing decreased porosity and reduced oxygentransfer. Similarly, a moisture level lower than optimumleads to higher water tension, a lower degree of swelling

-199.8

529.4

1258.2

1987.0

2715.7

Exo

-pol

ygal

actu

rona

se y

ield

(IU

/g)

8.0

16.0

24.0

32.0

40.0

1: 5

1: 6

1: 7

1: 8

1: 9

Inoculum age (h)Wheat bran-to-moisture ratio

Fig. 1. Response surface plot of exo-polygalacturonase yield from B.

subtilis RCK as a function of wheat bran-to-moisture ratio and inoculumage under optimal conditions.

-1058.2

-116.0

826.1

1768.4

2710.6

Exo

-pol

ygal

actu

rona

se y

ield

(IU

/g)

0.5

1.0

1.5

2.0

2.5

1: 5

1: 6

1: 7

1: 8

1: 9

Inoculum volume % (v/w)Wheat bran-to-moisture ratio

Fig. 2. Response surface plot of exo-polygalacturonase yield from B.

subtilis RCK as a function of wheat bran-to-moisture ratio and inoculumvolume under optimal conditions.

4141.3

4560.1

4979.0

5397.8

5816.7

Exo

-pol

ygal

actu

rona

se y

ield

(IU

/g)

10.0

12.5

15.0

17.5

20.0

30.0

32.5

35.0

37.5

40.0

Contact time (min)Extraction temperature (ºC)

Fig. 3. Response surface plot of exo-polygalacturonase yield fromfermented wheat bran as a function of extraction temperature and contacttime under optimal conditions.

942 S. Gupta et al. / Bioresource Technology 99 (2008) 937–945

and a reduced solubility and accessibility of nutrients pres-ent in the solid substrate (Matsumoto et al., 2004). In gen-eral, the moisture levels in SSF processes vary between 30%and 85%. For bacteria, the moisture of the solid matrixmust be higher than 70% and in the case of filamentousfungi it could be as wide as 20–70%. Blandino et al.(2002) reported maximum polygalacturonase productionon wheat bran by Aspergillus awamori when the initialmoisture level was maintained at 60%. Castilho et al.(1999) reported that initial moisture content of 40% pro-vided the best conditions for production of pectinase byAspergillus niger. Enzyme production in SSF using Bacillus

sp. has been reported for other enzymes such as xylanases(Gessesse and Mamo, 1999) and amylases (Babu and Sat-yanarayana, 1995), but few reports are available on polyga-lacturonase production by SSF using bacteria (Kapooret al., 2000, 2001; Kapoor and Kuhad, 2002). This mightbe due to the general belief that the solid-state fermentationtechnique is applicable only to filamentous fungi (Lonsaneand Ghildyal, 1992). Recently, (Li et al., 2005) has reportedalkaline pectinase production (3600.0 U/g) from Bacillus

gibsonii S-2 using sugar beet pulp as substrate under solidstate fermentation conditions.

The response between inoculum age and wheat bran-to-moisture ratio at the ‘O’ level of inoculum volume, indi-cated that late log phase cells (24 h old) gave maximumexo-polygalacturonase yield (Fig. 2).

In SSF processes, the enzyme loss in the discarded solidsis a major factor affecting process productivity. Therefore,it is necessary to develop efficient extraction techniques tomake SSF applicable for commercial exploitation (Rama-das et al., 1995). In the present study an attempt was madeto enhance exo-polygalacturonase recovery from fermentedwheat bran after completion of SSF. Figs. 3 and 4 are the

response surface curves for the variation in the yields ofexo-polygalacturonase recovery. Fig. 3 showing the inter-action between contact time and extraction temperature,reveals that exo-polygalacturonase yields increases gradu-ally with increase in extraction temperature and contacttime (between substrate and extraction solvent). This maybe due to the fact that high temperature (35 �C) increasessolubility and diffusivity of proteins and no protein loss isindicated through thermal denaturation (Castilho et al.,2000) as present exo-polygalacturonase is very much stableeven up to 60 �C (residual activity 100% for 1.5 h, pH 8.0–

4233.3

4614.4

4995.5

5376.5

5757.6

Exo

-pol

ygal

actu

rona

se y

ield

(IU

/g)

150.0

175.0

200.0

225.0

250.0

0.20

0.23

0.25

0.28

0.30

Agitation (rpm)SDS % (w/v)

Fig. 4. Response surface plot of exo-polygalacturonase yield fromfermented wheat bran as a function of agitation and SDS under optimalconditions.

S. Gupta et al. / Bioresource Technology 99 (2008) 937–945 943

10.5). A contact time of 15.0 min and temperature 35 �Cresulted in maximum (5769.2 IU/g dry substrate) enzymeextraction. Interestingly, exo-polygalacturonase yieldreached 77% (4450.6 IU/g) of the optimal value within12.5 min of contact time. The extended period requiredto recover the remainder of the exo-polygalacturonase indi-cated that during the fermentation, the exo-polygalacturo-nase diffuses throughout the substrate and is not restrictedto the liquid film at the substrate surface (Lonsane andKrishnaiah, 1992). The amount of contact time requiredfor recovery of any enzyme from solid-state fermentedmedia depends upon enzyme solubility, distribution ofthe enzyme with in the substrate and physical characteris-tics of the leaching system (Ikasari and Mitchell, 1996).

The interaction between SDS and agitation reveals thataddition of SDS 0.25% (w/v) and agitation (200 rpm) hadpositive effects on enzyme recovery (Fig. 4). Agitation notonly improved the contact frequency between solvent and

Table 5Validation of reduced quadratic model for exo-polygalacturonase production

RSM-I RSM-II

Inoculumvolume%(v/w)

Inoculumage (h)

Wheat bran-to-moistureratio

Exo-polygalacturonaseyielda (IU/g drysubstrate)

Contacttime(min)

Fbso

Experimental Predicted

1.0 20.0 1:8 1714.5 1726.5 15.0 11.5 16.0 1:7 1848.5 1854.5 20.0 12.0 24.0 1:6 1609.0 1704.0 10.0 11.5 32.0 1:7 2367.5 2439.5 12.0 11.0 16.0 1:7 1374.0 1422.0 15.0 11.5 24.0 1:7 2770.5 2697.5 15.0 1

a The extraction of exo-polygalacturonase from fermented wheat bran was ca30 �C, fermented bran-to-solvent ratio 1:5, SDS 0.2% (w/v), agitation 150 rpm

solid substrate due to breakage of aggregated clots of solidsubstrate but also resulted in better mixing and relativelysmall retention of exo-polygalacturonase in the leached sol-ids. Similar levels of agitation have been reported in litera-ture for extraction of enzymes produced under SSF(Castilho et al., 1999; Singh et al., 1999b). Addition ofSDS facilitated enzyme recovery in leachates probablydue to desorption of enzyme from the lignocellulosic sub-strate (Park et al., 1992; Helle et al., 1993) and preventionof enzyme denaturation during agitation by stabilization ofenzyme activity (Karr and Holtzapple, 1998). However,prolonging of extraction period (for more than 15 min) athigher agitation (250 rpm) in presence of SDS resulted inreduction in enzyme yield. This may be due to excessivefoam formation leading to entrapment and partial denatur-ation of amphiphilic exo-polygalacturonase (Linke et al.,2007).

The results predicted by the model equation from CCDfor exo-polygalacturonase production showed that a com-bination of increasing the wheat bran-to-moisture ratio to1:7, inoculum age of 24 h and an inoculum volume of 1.5%(v/w) favor maximum (2697.5 IU/g dry weight of solid sub-strate) enzyme yield after 48 h of incubation. However,optimum conditions for maximum enzyme recovery pre-dicted by the model equation from CCD showed that acombination of contact time of 15.0 min, fermented bran-to-solvent ratio 1:6, temperature 35 �C, agitation 200 rpmand SDS 0.25% (w/v) would enhance enzyme yield by 2.1fold (5748.1 IU/g dry weight of solid substrate). Whenthese values were experimentally verified, exo-polygalactu-ronase production of 2770.5 IU/g and recovery of5769.2 IU/g were observed, which are in close agreementwith the model predictions.

Validation of the statistical models and regressionequations were carried out by taking random set of combi-nations of variables (inoculum age (A), wheat bran-to-moisture ratio (B), inoculum volume (C) – RSM-I) and(contact time (P), fermented bran-to-solvent ratio (Q),extraction temperature (R), agitation (S) and SDS (T) –RSM-II) with in the design space under conditions pre-dicted by the model. The experimental values were found

under SSF within the design space

ermentedran-to-lvent ratio

Extractiontemperature(�C)

Agitation(rpm)

SDS%(w/v)

Exo-polygalacturonaseyield (IU/g drysubstrate)

Experimental Predicted

:6 40.0 170 0.20 4496.5 4535.0:5 35.0 180 0.25 5139.2 5082.8:6.5 30.0 225 0.35 3374.6 3300.4:7 37.0 175 0.28 4436.4 4457.9:6 35.0 150 0.25 5197.4 5122.5:6 35.0 200 0.25 5769.2 5748.1

rried out under the following conditions: contact time 10 min, temperature.

944 S. Gupta et al. / Bioresource Technology 99 (2008) 937–945

to be very close to the predicted values, and hence, themodels were successfully validated (Table 5).

4. Conclusions

It is important to discover new bacterial strains that pro-duce enzymes with novel properties of industrial interest.In the present study, increase (1.7-fold, 2770.0 IU/g) inproduction of an alkaline exo-polygalacturonase from B.

subtilis RCK under SSF was obtained by optimizing med-ium components by RSM. Efficient extraction of exo-poly-galacturonase from fermented substrate using RSM furtherincreased (2.0 fold, 5769.2 IU/g) enzyme yield and is oftechnological interest since lower enzyme concentrationsin the crude extracts significantly increases the final costof the purified enzyme. The alkaline exo-polygalacturonaseholds strong eco-friendly candidature as compared to con-ventional toxic chemicals for degumming of bast fiberssuch as ramie and sunn hemp and treatment of highly alka-line pectic wastewaters from vegetable and food processingindustries.

Acknowledgements

The authors acknowledge the research grant fromDepartment of Biotechnology (DBT), India. M.K andK.K are grateful to Council of Scientific and Industrial Re-search (CSIR) and Ministry of Environment and Forest(MoEF), respectively for grant of senior research fellow-ship (SRF). The assistance provided by Mr. Manwar singhis also gratefully acknowledged.

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