Optimization of Process Parameters for Cellulase Production from Bacillus sp. JS14 in Solid Substrate Fermentation Using Response Surface Methodology

Abstract

The aim of this work was to isolate the potent bacterial strains for the production of cellulose enzyme. A total 30 bacterial isolates showed positive results for the cellulase production but highest enzyme activity was shown by isolate JS 14. From the morphological and biochemical reactions, the isolate was identified as Bacillus sp. Cellulase production was studied by this strain using response surface methodology (RSM). A central composite design (CCD) quadratic response surface was applied to explicate the parameters that significantly affected cellulase production in solid substrate fermentation (SSF). The wheat bran concentration and incubation period were significant factors. The process parameters optimized with response surface methodology was wheat bran concentration 400 g/L; pH, 6.5; temperature, 400C and incubation period 5 days when inoculum 10 % (1x107 cells/ ml) was used for cellulase production in SSF. Supplementation of lactose and CMC to the wheat bran medium favored the enzyme formation.

Full text

Braz. Arch. Biol. Technol. v.55 n.4: pp. 505-512, July/Aug 2012
505
Vol.55, n. 4: pp. 505-512, July-August 2012
ISSN 1516-8913 Printed in Brazil
BRAZILIAN ARCHIVES OF
BIOLOGY AND TECHNOLOGY
A N I N T E R N A T I O N A L J O U R N A L
Optimization of Process Parameters for Cellulase
Production from Bacillus sp. JS14 in Solid Substrate
Fermentation Using Response Surface Methodology
Jagdish Singh
*
and Pawandeep Kaur
Department of Biotechnology; Mata Gujri College Fatehgarh Sahib Punjab - 147 002, India
ABSTRACT
The aim of this work was to isolate the potent bacterial strains for the production of cellulose enzyme. A total 30
bacterial isolates showed positive results for the cellulase production but highest enzyme activity was shown by
isolate JS 14. From the morphological and biochemical reactions, the isolate was identified as Bacillus sp.
Cellulase production was studied by this strain using response surface methodology (RSM). A central composite
design (CCD) quadratic response surface was applied to explicate the parameters that significantly affected
cellulase production in solid substrate fermentation (SSF). The wheat bran concentration and incubation period
were significant factors. The process parameters optimized with response surface methodology was wheat bran
concentration 400 g/L; pH, 6.5; temperature, 400C and incubation period 5 days when inoculum 10 % (1x107 cells/
ml) was used for cellulase production in SSF. Supplementation of lactose and CMC to the wheat bran medium
favored the enzyme formation.
Key words: Cellulase, response surface methodology, optimization
*
Author for correspondence: jagdish122@rediffmail.com
INTRODUCTION
Cellulosic material is the most abundant renewable
carbon source in the world. Cellulose may be
hydrolyzed using enzymes to produce glucose,
which can be used for the production of ethanol,
organic acids and other chemicals. Cellulase (E.C
3.2.1.4) refers to a class of enzyme that catalyze
the hydrolysis of 1, 4 β-D glycosidic linkages in
cellulose are mainly produced by fungi, bacteria
and protozoans (Beguin and Aubert 1994) and
have broad range of industrial and commercial
applications (Bhat 2000; Adsul et al. 2007; Kaur et
al. 2007). An important obstruction in the
exploitation of cellulase is expensive production
affecting the overall cost of hydrolysis (Chahal et
al. 1992; Duff and Murray 1996; Reczey et al.
1996; Nieves et al. 1998). Considerable progress
has been made for high cellulase production by
optimization of best possible fermentation
conditions for the development of economically
feasible bioprocess. Optimization of growth and
product conditions by classical methods, which
involves the change of one variable at a time, is
extremely time consuming and expensive.
Combinational interactions of medium
components for the production of desired
compound are numerous and the optimum
processes may be developed using an effective
experimental design procedure. Response surface
methodology (RSM), which is a collection of
statistical techniques for designing experiments,

Singh, J. and Kaur, P.
Braz. Arch. Biol. Technol. v.55 n.4: pp. 505-512, July/Aug 2012
506
building models, evaluating the effects of factors
and searching for the optimum conditions, has
successfully been used in the optimization of
bioprocesses (Dey et al. 2001; Wejse et al. 2003;
Kristo et al. 2003; Chen et al. 2005). The objective
of this work was to isolate an efficient cellulase
producing microorganism and produce the enzyme
in SSF.
MATERIALS AND METHODS
Isolation screening and identification of
microorganisms
Cellulase producing bacteria were isolated from
the soils, decomposing logs and composts
collected from the Fatehgarh Sahib Punjab (India)
by the spread plate techniques using CMC agar
media. The plates were incubated at 37
0
C for 24 h.
To visualize the hydrolysis zone, the plates were
flooded with an aqueous solution of 0.1% Congo
red for 15 min and washed with 1 M NaCl (Ariffin
et al. 2008). To indicate the cellulase activity of
the organisms, diameters of clear zone around
colonies on CMC agar were measured. Besides the
cellulase activity of the selected bacterial isolate in
liquid medium. The cellulase activity of each
culture was measured by determining the amount
of reducing sugar liberated by using a
dinitrosalicylic acid (DNS) method (Miller 1959).
The bacterial isolates with high enzyme
production were identified by means of the
morphological and biochemical characterizations.
The parameters investigated included colonial
morphology, Gram staining, catalase production,
starch hydrolysis and nitrate reduction test. The
results were compared with Bergey’s Manual of
Determinative Bacteria (Buchanan and Gibbons
1974).
Experimental design and optimization of
process parameters for cellulase production
A central composite design (CCD) was used to
pick the factors that influence cellulase production
significantly with software Design expert 8.01. In
CCD, the range and the levels of the variables
investigated in this study are given in Table 1. The
central values (zero level) chosen for the
experimental design were; wheat bran
concentration (X1) 35%; pH (X2) 6.25;
temperatures (X3) 35
0
C and incubation time (X4)
5.5 days. Different combination of variables of
wheat bran and pH was adjusted according to the
design (Table 2) in the alkaline soya casein
medium. Inoculation was carried with 10% (1×10
7
cells/mL) inoculum and incubated at various
temperature and time combinations, according to
RSM design (Table 2). Low and high factor
settings were coded as –1 and 1, the midpoint was
coded as 0. The factor settings of trails that ran
along the axes drawn from the middle of the cube
through the centers of each face of the tube were
coded as 1.414 or –1.414. The Design expert 8.0.1
software, was used for regression and graphical
analyses of the data obtained. The optimal
concentrations of the critical medium components
were obtained by the ridge analysis and also by
analyzing the contour plots. The statistical analysis
of the model was performed in the form of
analysis of variance (ANOVA). The enzyme was
extracted by phosphate buffer (0.1M and pH 6.5)
from the solid medium on rotary shaker at 200 rpm
by filtration and enzyme activity in filtrate was
determined.
Table 1 - Factors involved in RSM for optimization of cellulose production.
Factors and Codes value (X) Value (-1) Value (0) Value (+1)
Wheat Bran % (X1) 30 35 40
pH (X2) 5 6.25 7.5
Temperature (°C) (X3) 30 35 40
Incubation Period in days (X4) 2 5.5 9
Effect of inoculum size on cellulase production
Inoculum concentration of Bacillus sp. JS14 was
varied from 5-20% (1×10
7
cells /mL) in different
batches of 200 mL of the sterilized alkaline soya
casein medium containing what bran 40% pH 6.5
and Incubation was carried out at 40
0
C for 5 days.
Enzyme was harvested by filtration method and
enzyme activities was determined
Determination of cellulase activity
CMCase activity assay was carried out according
to the methods developed by Mandels, (1985).

Optimization of Process Parameters for Cellulase Production from Bacillus sp. JS14
Braz. Arch. Biol. Technol. v.55 n.4: pp. 505-512, July/Aug 2012
507
Reducing sugar was measured by the DNS method
using glucose as the standard (Miller 1959). In this
study, one international unit (IU) of enzyme
activity was defined as the amount of enzyme that
liberated one µmol of glucose per minute under the
specified conditions from the appropriate
substrate.
RESULTS AND DISCUSSION
Isolation, screening and identification of
cellulase producing microorganisms
Total 30 bacterial isolates when applied the Congo
red test, showed positive results with clear zone
ranging from 1 to 7 mm (data not shown). Upon
further quantitative determination of cellulose
degrading enzyme, all the isolates displayed
activity of cellulase with the highest enzyme
activity from the isolate JS14. Although the Congo
red test was sensitive enough for primary isolation
and screening of cellulytic bacteria, but the clear
zone width did not indicate the amount of cellulase
activity, Krootdilaganandh, (2000) reported that
bacterial isolates CMU4-4 grown on CMC agar,
exhibited the highest enzyme activity in the liquid
medium whereas its clear zone was smaller than
other isolates. The isolate JS14 showed white
colonies on CMC agar. A microscopic
examination of the isolate revealed that it was a
Gram positive bacterium and produced enzyme
catalase. Furthermore, the JS14 displayed starch
and nitrate reduction test. From these
morphological and biochemical characterization,
the isolate was identified as Bacillus sp.
Optimization of process parameters in SSF by
Response surface methodology (RSM) for
cellulase production
The concentration of wheat bran (X1 400 g/L), pH
(X2 6.5), temperature (X3 40
0
C) and incubation
period (X4 5days) in solid substrate were chosen
as optimum for cellulase production by SSF. Table
2 shows the design and the results of this
experiment.
Table 2 - A central composite designs for cellulase enzyme production using RSM.
S.No Values of factors (X) Cellulase activity
(IU/L)
X1 X2 X3 X4
1.
35
5.50
30
5.50
540
2.
35
6.50
30
9
420
3.
35
7.
50
35
2
210
4.
40
6.50
40
5
2040
5.
35
5.50
40
5.50
1070
6.
35
6.50
40
9
520
7.
30
6.50
35
2
130
8.
30
6.50
30
5.50
590
9.
35
7.50
40
5.50
2010
10.
40
6.50
35
9
460
11.
35
5.50
35
9
290
12.
40
5.50
35
5.50
1030
13.
30
7.50
35
5.50
650
14.
35
6.50
35
5.50
1330
15.
35
6.50
30
2
230
16.
35
6.50
35
5.50
1330
17.
35
6.50
35
5.50
1330
18.
30
6.50
40
5.50
1080
19.
40
6.50
35
2
290
20.
35
6.50
40
2
250
21.
35
7.50
35
9
330
22.
35
6.50
35
5.50
1330
23.
30
6.50
35
9
280
24.
40
6.50
30
5.50
1550
25.
35
7.50
30
5.50
200
26.
35
5.50
35
2
170
27.
35
6.50
35
5.50
1330
28.
40
7.50
35
5.50
1490
29.
30
5.50
35
5.50
780
30.
40
7.50
33
5.45
1250

Singh, J. and Kaur, P.
Braz. Arch. Biol. Technol. v.55 n.4: pp. 505-512, July/Aug 2012
508
Regression analysis was performed to fit the
response function with the experimental data. The
statistical significance of the second order model
equation was checked by an F-test (ANOVA) and
the data were shown in Table 3.The regression
model for cellulase production was highly
significant (p<0.003) with a satisfactory value of
determination coefficient (R2=0.95), indicating
that 95 % of the variability in the response could
be explained by the second-order model equation
given as below:
y=284.09494+37.31286X
1
+225.23766X
2
43.4724
3X
3
+790.58161X
4
+28.95995X
1
X
2
+0.043204X
1
X
3
+0.28792X
1
X
4
+64.21602X
2
X
3
+0.011021X
2
X
4
+
1.14198X
3
X
4
-2.47136X
1
2
-261.78389X
2
2
where y is the measured response, and X1, X2,
X3 and X4 are coded independent variables. The
Model F-value of 8.16 implied the model was
significant (Table 3). There is only a 0.01%
chance that a "Model F-Value" this large could
occur due to noise. Values of "Prob > F" less than
0.0500 indicated that the model terms were
significant. The "Pred R-Squared" of 0.7664 was
as close to the "Adj R-Squared" of 0.7757 as one
might normally expect (Table 4). "Adeq
Precision" measures the signal to noise ratio. A
ratio greater than 4 is desirable. Ratio of 9.309
indicated an adequate signal. This model could be
used to navigate the design space.
Table 3 - ANOVA for Response Surface Quadratic Model Analysis of variance table [Partial sum of squares - Type
III].
Source*
Sum of Squares
Df
Mean Square
F Va
lue
P value Prob > F
Model
X1
X2
X3
X4
X1 X2
X1 X3
X1 X4
X2 X3
X2X4
X3X4
X1
2
X2
2
X3
2
X4
2
Residual
Lack of Fit
Pure Error
Cor Total
8.405E+006
6.060E+005
3.812E+005
4.427E+005
67779.73
98643.63
321.72
75.80
4.225E+005
6.007E-003
450.17
23311.90
4.621E+005
11264.03
5.386E+006
1.103E+006
1.103E+006
0.000
9.509E+006
14
1
1
1
1
1
1
1
1
1
1
1
1
1
1
15
11
4
29
6.004E+005
6.060E+005
3.812E+005
4.s427E+005
67779.73
98643.63
321.72
75.80
4.225E+005
6.007E-003
450.17
23311.90
4.621E+005
11264.03
5.386E+006
73559.26
1.003E+005
0.000
8.16
8.24
5.18
6.02
0.92
1.34
4.374E-003
1.030E-003
5.74
8.166E-008
6.120E-003
0.32
6.28
0.15
73.22
0.0001
0.0117
0.0379
0.0269
0.3523
0.2650
0.9481
0.9748
0.0300
0.9998
0.9387
0.5818
0.0242
0.7011
< 0.0001
*X1 Concentration of wheat bran (%), X2 pH, X3 Temperature (
0
C) and X4 incubation period (days).
Table 4 - ANOVA results for cellulase production obtained from CCD.
S.NO
Parametr
Value
1.
Std. Dev
271.22
2.
Mean 817.00
3.
Adj R-Squared 0.7757
4.
C.V. % 33.20
5.
R-Squared
0.8840
6.
Pred R-Squared 0.7664
7.
Adeq Precision 9.309

Optimization of Process Parameters for Cellulase Production from Bacillus sp. JS14
Braz. Arch. Biol. Technol. v.55 n.4: pp. 505-512, July/Aug 2012
509
The 3D response surface based on the dependent
variables such as wheat bran concentration, pH,
temperature and incubation period are shown in
Figure 1. The canonical analysis revealed that
maximum CMCase activity of 2040 IU/L was
achieved at wheat bran concentration 400 g/L; pH
6.5; temperature 40
0
C and incubation period of
five days.
Effect of inoculum size on cellulase production
Lower inoculum size requires longer time for the
cells to multiply to sufficient number to utilize the
substrate and produce enzyme. An increase in the
number of cells in the inoculums would ensure a
rapid proliferation and biomass synthesis. When
inoculum size was increased from 5 to 10% there
was increase in enzyme production but after that
the activity was decreased (Fig. 2) due to depletion
of nutrients by the enhanced biomass, which
resulted dwindle in metabolic activity (Kashyap et
al. 2002). A balance between the increasing
biomass and accessible nutrient would yield an
optimal enzyme production (Ramachandran et al.
2004).
Figure 1 - Surface plot for the effect of (a) wheat bran and pH (b) temp and pH (c) incubation
period and substrate conc. and (d) temp and substrate conc. on CMCase activity.
A
B
C
D
E F

Singh, J. and Kaur, P.
Braz. Arch. Biol. Technol. v.55 n.4: pp. 505-512, July/Aug 2012
510
Figure 2 - Effect of inoculum (%) on enzyme production.
Effect of supplementation of what barn with
different carbon sources and ions
Although wheat bran supported the growth of
Bacillus sp. JS14 and cellulase production, but it
might not supply sufficient nutrients needed by the
organism for maximum enzyme production.
Hence, the addition of different carbon sources to
the medium was conceded to improve the cell
growth and enzyme production. The
supplementation of wheat straw, rice husk and
baggase had little effect on cellulase production,
while lactose and carboxy methyl cellulose
enhanced enzyme production. Among them,
lactose improved the cellulase production the
most, which increased 40% compared to the
control (Fig. 3). Lactose was also considered as a
good inducer for cellulase production by the
Seiboth et al (2005).
The production of cellulase was enhanced by the
addition of NaCl and MgSO
4
while EDTA reduced
the production (Fig.4).
Figure 3 - Effect of different supplement carbon sources on the cellulase production.

Optimization of Process Parameters for Cellulase Production from Bacillus sp. JS14
Braz. Arch. Biol. Technol. v.55 n.4: pp. 505-512, Jul/Aug 2012
511
Figure 4 - Effect of different ions on the cellulase production.
REFERENCES
Adsul MG, Bastawde KB, Varma AJ, Gokhale DV.
Strain improvement of Penicillium janthinellum
NCIM 1171 for increased cellulaseproduction.
Bioresour Technol. 2007; 98: 1467-1473.
Ariffin HN, Abdullah MS, Umikalsom Y, Shirai,
Hassan MA. Production of bacterial endoglucanase
from pretreated oil palm empty fruit bunch by
Bacillus pumilus EB3. J Biosci Bioeng. 2008; 106:
231-236.
Beguin P, Aubert JP. The biological degration of
cellulose. FEMS Microbiol Rev. 1994; 13: 25-28.
Bhat MK. Cellulases and related enzymes in
biotechnology. Biotechnol Adv. 2000; 18:355-383.
Buchanan RE, Gibbons NE. Bergey’s of determinative
bacteriology. America: United States of America.
1974; 529-563.
Chahal PS, Chahal DS, Andre G. Cellulase production
profile of Trichoderma reesei on different cellulosic
substrates at various pH levels. J Ferment Bioeng.
1992; 74: 126-128.
Chen F, Cai TY, Zhao GH, Liao XJ, Guo LY, Hu XS.
Optimizing conditions for the purification of crude
octacosanol extract from rice bran wax by molecular
distillation analyzed using response surface
methodology. J Food Eng. 2005; 70: 47–53.
Dey G, Mitra A. Banerjee R, Maiti BR. Enhanced
production of amylase by optimization of nutritional
constituents using response surface methodology.
Biochem. Eng. J. 2001; 7: 227–231.
Duff SJ, Murray WD. Bioconversion of forest products
industry waste cellulosics to fuel ethanol: a review.
Bioresour Technol. 1996; 55: 1-33.
Kashyap P, Sabu A, Pandey A, Szakacs G. Extra-
cellular Lglutaminase production by
Zygosaccharomyces rouxii under solidstate
fermentation. Process Biochem. 2002; 38: 307–312
Kaur J, Chadha BS, Kumar BA, Saini HS. Purification
and characterization of two endoglucanases from
Melanocarpus sp. MTCC 3922. Biores Technol.
2007;98: 74-81
Kristo E, Biliaderis CG, Tzanetakis N. Modelling of the
acidification process and rheological properties of
milk fermented with a yogurt starter culture using
response surface methodology. Food Chem. 2003;
83: 437–446.
Krootdilaganandh J. Isolation and selection of
thermotolerant bacteria capable of producing
cellulase. Chiang Mai: Chiang Mai University Press.
2000;88: 20-21.
Lee SL, Chen WC. Optimization of medium
composition for the production of glucosyltransferase
by Aspergillus niger with response surface
methodology. Enzyme Microb Technol. 1997; 21:
436–440.
Mandels M. Applications of cellulases. Biochem Soc
Trans. 1985; 13: 414–416.
Miller GL. Use of dinitrosalicylic acid regent for
determination of reducing sugar. Anal Chem. 1959;
31: 426- 428.

Singh, J. and Kaur, P.
Braz. Arch. Biol. Technol. v.55 n.4: pp. 505-512, July/Aug 2012
512
Nieves RA, Ehrman CL, Adney WS, Elander RT,
Himmel ME. Technical communication: survey and
analysis of commercial cellulase preparations suitable
for biomass conversion to ethanol. W J Microbiol
Biotechnol. 1998; 14: 301-304.
Ramachandran S, Patel AK, Nampoothiri KM, Francis
F , Nagy V, Szakacs G, Pandey A. Coconut oil cake–
–a potential raw material for the production of
amylase. Biores Technol. 2004; 93: 169–174.
Reczey K, Szengyel Z, Eklund R, Zacchi G. Cellulase
production by T. reesei. Biores Technol. 1996; 57:
25-30.
Seiboth B, Lukas H, Salovuori N, Karin L, Robson DG,
Vehmaanpera J, Penttila ME, Kubicek CP. Role of
the bga1-encoded extracellular galactosidase of
Hypocrea jecorina in cellulase induction by lactose.
Appl Environ Microbiol. 2005 ;71:851–857.
Silva CJ, Roberto C. Optimization of xylitol production
by Candida guilliermondii FTI 20037 using response
surface methodology. Process Biochem. 2001; 36:
1119–1124.
Teruel MLA, Gontier E, Bienaime C, Saucedo JE,
Barbotin JN. Response surface analysis of
chlortetracycline and tetracycline production with K-
carrageenan mmobilized Streptomyces aureofaciens.
Enzyme Microb Technol. 1997; 21: 314–320.
Wejse PL, Ingvorsen K, Mortensen KK. Xylanase
production by a novel halophilic bacterium increased
20-fold by response surface methodology. Enz
Microb Technol. 2003; 32: 721–727.
Received: February 26, 2011;
Revised: August 28, 2011;
Accepted: March 05, 2012.