2. 36 A.R. Sirajunnisa et al. / Carbohydrate Polymers 135 (2016) 35–43
stages like screening of nutrients using Plackett–Burman design,
confirm optimum concentrations and conditions by central com-
posite design for the production of required bioproduct.
In a production medium, main nutrients like carbon and nitro-
gen sources are inevitable, but cost of chemicals is one of the failing
factors in a fermentation process. Biowastes from agricultural
industries are one of the richest resources of such nutrients, hence
utilizing these could make the production economically feasible.
Various agricultural waste materials are used for exopolysaccha-
ride production in several studies. Rice bran is being utilized as the
substitute for carbon source in this study. Rice bran is one of the
most common agro industrial wastes of Indian rice mills, obtained
during dehulling process. It is composed of 24.6 g total fats, 1.1 g
total sugars (glucose – 0.2 g), 24.8 g dietary fiber, 7.2 g water and
11.8 g ash. Our research uses this for the first time on P. fluores-
cens for EPS production and only very few reports are available on
use of rice bran for exopolymer production from other organisms.
EPS is often produced at a lower temperature required for growth
than optimum (Fett, 1993). It also requires higher carbon content
in the medium and decreased nitrogen quantity. Factors that could
influence the production of EPS are composition of the medium,
especially carbon and nitrogen sources and the parameters like pH,
temperature and incubation time.
For better understanding of the fermentation process and its
optimization, a mathematical model is of great help. A kinetic
model describes the behavior of the cellular processes through pos-
sible mathematical equations and it serves to be a very effective
tool to test and eliminate the extremities (Bailley & Ollis, 1986). In
the present study, unstructured models had been used to elaborate
the stoichiometric relationship between variables namely growth,
substrate utilization and product formation, studied.
P. fluorescens was used for our study, isolated from an herbal
plant, Cantharanthus roseus. Only scanty reports are found on char-
acterization of EPS from this plant. A medium was optimized to
enhance the production of exopolymer using a statistical tool,
Response Surface Methodology (RSM). For the production of EPS
from P. fluorescens, a Plackett–Burman design was performed first
to screen the significant nutrients that enhance the yield of EPS,
and then a central composite design was carried out to optimize the
concentration of essential medium components that were screened
by Plackett–Burman design. The production dynamics were also
studied using mathematical models for the process variables.
2. Materials and methods
2.1. Bacterial culture isolation
The culture was isolated from the rhizosphere soil of C. roseus
grown in the campus of Annamalai University (Tamilnadu, India).
The soil sample was suspended in sterile distilled water and sub-
jected to serial dilution (10−1–10−7). An aliquot of 0.1 ml of each
dilution mixture was spread on nutrient agar medium containing
peptone (5 g L−1), yeast extract (2 g L−1), NaCl (5 g L−1) and Agar
(20 g L−1). From the plates incubated at 37 ◦C for 24 h, mucoid and
yellow pigmented colonies were selected and purified on Pseu-
domonas Agar F medium (HiMedia Laboratories, Mumbai, India).
The isolated organism was identified and confirmed by 16S rRNA
sequencing. PCR analysis was performed with 16SrRNA primers:
27F (5 -AGA GTT TGA TCC TGG CTC AG-3 ) and 1492R (5 -TAC GGT
TAC CTT GTT ACG ACT T-3 ). A volume of 25 l reaction mixture
for PCR was carried out using 10 ng of genomic DNA, 1× reaction
buffer (10 mM Tris HCl, pH 8.8, 1.5 mM MgCl2, 50 mM KCl and 0.1%
Triton X 100), 0.4 mM dNTPs each, 0.5 U DNA polymerase and 1 mM
reverse and forward primers each. The reaction was performed in
35 amplification cycles at 94 ◦C for 45 s, 55 ◦C for 60 s, 72 ◦C for 60 s
and an extension step at 72 ◦C for 10 min. The sequencing of 16S
amplico2n was performed according to manufacturer instructions
of Big Dye terminator cycle sequencing kit (Applied BioSystems,
USA). Sequencing products were resolved on an Applied Biosys-
tems model 3730XL automated DNA sequencing system (Applied
BioSystems, USA). The 16S rRNA gene sequence obtained from the
organism was compared with other Pseudomonas strains for pair-
wise identification using NCBI-BLAST (http://blast.ncbi.nlm.nih.
gov/Blast.cgi) and multiple sequence alignments of the sequences
were performed using Clustal Omega version of EBI (www.ebi.
ac.uk/Tools/msa/clustalo). Phylogenetic tree was constructed by
Clustal Omega of EBI (www.ebi.ac.uk/Tools/phylogeny/clustalw2
phylogeny) using neighbor joining method.
2.2. Media optimization
2.2.1. Plackett–Burman (PB) design
The screening of significant nutrients was carried out using
Plackett–Burman design (Plackett & Burman, 1946). Based on one-
factor at a time experiments, carbon, nitrogen, vitamin, amino
acids, trace metal ions and minerals were screened by one fac-
tor at a time and the significant nutrients were used for study.
Based on this, 16 independent variables were selected for the study,
evaluated in 20 experiments trials. Each nutrient was used at 2
concentrations (high and low), designated as ± levels. The con-
centration levels were also selected by one factorial experiment.
Plackett–Burman design is showed on the first order polynomial
model,
Y = ˇ0 + ˇiXi
where, Y is the response (EPS yield), ˇ0 is the model intercept and
ˇi is the linear coefficient, and Xi is the level of the independent
variable. This model does not derive the interactive effects but used
to screen the essential nutrients implementing the yield of EPS (Y).
The experimental design and statistical analysis of the data were
done by Minitab statistical software package (v 16.0). In the present
study the trials were carried out in duplicates and the analyzed
EPS was taken as the response. Regression analysis determined the
components, based on the significant level of 95% (p < 0.05).
2.2.2. Central composite design (CCD)
A central composite design was experimented to optimize the
four variables screened by Plackett–Burman design that signifi-
cantly influenced EPS production. Design Expert software (Version
8.0.7.1 Trial, Stat-Ease Inc., Minneapolis, USA) was used to frame
the experimental designs and statistical analyses. The four indepen-
dent variables were evaluated at five levels (−1, −2, 0, +1, +2) with
30 experimental runs and six repetitive central points. The exper-
iments were conducted in 250 ml Erlenmeyer flasks with 100 ml
of media, under non-agitating condition 37 ◦C for 48 h, prepared
according to the design.
The response obtained could be represented by a second degree
polynomial equation as:
Y = ˇ0 + ˇ1X1 + ˇ2X2 + ˇ3X3 + ˇ4X4 + ˇ12X1X2 + ˇ13X1X3
+ ˇ14X1X4 + ˇ23X2X3 + ˇ24X2X4 + ˇ34X3X4 + ˇ11X12 + ˇ22X2
2
+ ˇ33X32 + ˇ44X42
where Y is the predicted response, ˇ0 was the constant, X1, X2, X3
and X4 were the input variables, ˇ1, ˇ2, ˇ3 and ˇ4 were the linear
coefficients, ˇ12, ˇ13, ˇ14, ˇ23, ˇ24 and ˇ34 were the second order
interactive coefficients and ˇ11, ˇ22, ˇ33 and ˇ44 were the quadratic
coefficients. The experiments were carried out in triplicates. The
response (yield of EPS g L−1) was the dependent variable. The 3D
graphical plots obtained would illustrate the mutual interactions
3. A.R. Sirajunnisa et al. / Carbohydrate Polymers 135 (2016) 35–43 37
between each significant factor, thus evaluating the optimized
medium components.
2.3. Kinetics and modeling
Kinetics is a key study done to know about the fermenta-
tion reactions involved in scaling up a product. Fundamental
unstructured kinetic models were employed in this study. The rate
equation is expressed by the process variables – cell concentration
(x), product formed (p) and substrate concentration (s).
2.3.1. Growth dynamics
Microbial growth kinetics of P. fluorescens was investigated
using an unstructured kinetic model, the logistic model. Verlhurst
in 1844, and Pearl and Reed in 1920 contributed to a theory, which
included an inhibiting factor to population growth. Assuming that
inhibition is proportional to x2, they used
dx
dt
= kx 1 −
x
xs
where t is the time (h), x is the cell mass, xs is the saturated cell mass,
k is the carrying capacity (cell mass the environment can hold). The
logistic curve is sigmoidal and leads to a stationary population of
size, xs = 1/ˇ.
2.3.2. Product formation kinetics
A typical and widely used product kinetic model is
Luedeking–Piret model (1959) (Luedeking & Piret, 1959), which
is an unstructured approach contributed to both growth and
non-growth associated phenomena for product formation (Bailley
& Ollis, 1986). According to this model, the product formation rate
depends linearly upon the growth rate and the cell concentration
dP
dt
= ˛
dx
dt
+ ˇx
where ˛ and ˇ are product formation constants contributing to
growth associated and non-growth associated fermentation con-
ditions, and vary with the fermentation dynamics. The product
formation rate, dP/dt, allowed a correlation between cell mass and
product concentration.
2.3.3. Substrate utilization kinetics
Substrate utilization kinetics is given as the modification of the
Luedeking–Piret model, which considers substrate conversion to
cell mass, to product and substrate consumption maintenance,
dS
dt
= −
1
Yx/s
dx
dt
−
1
Yp/s
dP
dt
+ kex
where Yx/s is the yield coefficient for biomass with respect to sub-
strate consumed and Yp/s is the yield coefficient for product formed
with respect to the substrate consumption.
2.4. Isolation of exopolysaccharides
EPS was extracted by precipitation using ethanol. The culture
was centrifuged at 11,000 rpm for 10 min at 4 ◦C. The supernatant
obtained was mixed with two volumes of ice cold ethanol and kept
at 4 ◦C for 24 h. The mixture was then centrifuged at 2500 rpm for
20 min at 4 ◦C. The obtained pellet was suspended in distilled water,
which was centrifuged at 2500 rpm for 30 min at 4 ◦C with two vol-
umes of ice cold ethanol (Savadogo, Savadogo, Barro, Ouattara, &
Traore, 2004). The process was repeated twice and the EPS obtained
was dried, weighed and lyophilized. The total carbohydrate con-
tent of the biopolymer was studied by phenol sulfuric acid method
(Dubois, Giles, Hamilton, Rebers, & Smith, 1956) using glucose as
standard. Total protein content was estimated using Lowry et al.
method (Lowry, Rosebrough, Farr, & Randall, 1951).
2.5. Fourier transform infra-red (FTIR) spectrometry
A quantity of 50 mg of lyophilized EPS was taken, mixed with
150 mg of KBr powder and ground well to fine mixture. The mix-
ture was pressed to a disc using a hydraulic press. The disc was
subjected to FTIR spectral measurement in the frequency range of
4000–400 cm−1. The exopolysaccharide was characterized using
a Fourier Transfer Infrared Spectrophotometer (Bruker Optics,
GmBH, Germany).
2.6. Antioxidant activity
The antioxidant activity of the isolated EPS was evaluated on
the basis of the free radical scavenging effect of 1,1-diphenyl-2
picrylhydrazyl (DPPH), by the method of Liu et al. (2010) with slight
modification (Liu et al., 2010). In brief, sample solutions at various
concentrations of 0.2, 0.4, 0.6, 0.8 mg/ml were made up to 1 ml with
distilled water. 1 ml of DPPH solution (0.004% in methanol) was
added to sample and standard solutions. After the solutions were
incubated for 30 min in dark, the absorbance was read at 517 nm.
Vitamin C and distilled water with DPPH were used as the refer-
ence and blank, respectively. The percent scavenging ability was
calculated using the formula:
Percent (%) scavenging activity = 1 − (A/B) × 100
3. Results and discussion
3.1. Molecular Identification of the strain
P. fluorescens exhibited maximum percentage of similarity,
100%, with the sequences of other P. fluorescens strains with a
high score, when compared with BLAST. The target rRNA was
aligned with all homologous sequences using Clustal W2 and a
phylogenetic tree was eventually constructed (Fig. 1A and 1B). The
phylogenetic analysis confirmed that the isolated strain was P. fluo-
rescens. The nucleotide sequence of the organism, referred to as
P. fluorescens CrN6, had been deposited in the GenBank database
under the accession number KF359766.
3.2. Plackett–Burman design
Plackett–Burman design was employed for preliminary
screening of nutrients, through one factor at a time approach. The
averages of EPS yield (g L−1) were obtained using 16 selected vari-
ables for 20 experimental runs. The variables which had significant
effect on EPS production (p < 0.05) were selected and were used for
further optimization. The results showed that the response varied
from 3.55 to 4.51 g L−1. Except M, all the other selected variables
showed positive effect on EPS production. Four variables were
found to be the most significant, namely rice bran (A), peptone (B),
NaCl (C) and MnCl2 (D). Based on the results of design, a polynomial,
first order equation was developed, excluding the insignificant
variables, describing the correlation between the variables used
for study. The EPS yield, Y (g L−1) could be represented as:
Y = 4.51 + 0.106X1 + 0.076X2 + 0.053X3 + 0.062X4
where Y is the response, X1, X2, X3 and X4 are the coded values of
rice bran, peptone, NaCl and MnCl2 respectively.
The statistical significance of the model was evaluated by
ANOVA. F-test and p-test values (p < 0.05) indicated the signifi-
cance of the experiment. The determination coefficient R2 value
4. 38 A.R. Sirajunnisa et al. / Carbohydrate Polymers 135 (2016) 35–43
Fig. 1. (A) shows the gene sequence of isolated strain and (B) represents the phylogenetic tree of isolated P. fluorescens aligned with other homologous P. fluorescens strains.
of the model was 0.9752, indicating 97.52% of the variability in the
response could be explained by the model.
Various carbon and nitrogen sources were checked for their
involvement in EPS generation by P. fluorescens. Carbon and nitro-
gen sources play a vital role in cell’s growth and exopolysaccharide
production (Gandhi, Rayand, & Patel, 1997). Carbohydrate compo-
nents of the medium affect the yield of EPS but do not influence
their chemical structure. They also affect viscosity of EPS, possibly
owing to the heterogeneity in the molecular weight. Our result was
consistent with similar reports. William and Wimpenny reported
that glucose and sucrose influenced polymer synthesis the most
(Williams & Wimpenny, 1977). Ganoderma lucidum also utilized
glucose as the carbon source at 60 g L−1, for exopolymer production
(Yuan, Chi, & Zhang, 2012). Beijernicka indica produced 5.52 g L−1
of EPS when lactose MSM was supplemented with 4 g L−1 glucose
(Wu, Son, Kim, Lee, & Kim, 2006). Ganoderma was able to produce
1.7 g L−1 EPS with glucose concentration as 70 g L−1 (Kim et al.,
2006). Lentinus edodes produced 6.88 g L−1 biopolymer in the pres-
ence of 15.88 g L−1 glucose (Feng, Li, Wu, Cheng, & Ma, 2010).
Peptone influenced many other cultures in EPS production.
The study revealed that the production of polysaccharide was
greatly influenced by higher amounts of carbon and limiting nitro-
gen concentration. Our results were in agreement with reports of
Waseem Raza et al. (2012), resulting in 6.85 g L−1 at 1% concen-
tration. Peptone might have stimulated the production due to its
contents of proteins, amino acids and vitamins (Raza et al., 2012).
The present study also showed that organic nitrogen sources gave
a higher yield than that of inorganic ones which was in complete
agreement with study by Kim et al. (2005). It was suggested that
essential aminoacids cannot be synthesized from inorganic nitro-
gen components (Wu, Liang, Lu, & Wu, 2008), hence the decrease
in cell growth and EPS metabolism.
For an effective fermentative large scale production of EPS,
agro industrial wastes and residues are used as cheap carbon
substrates. Rice bran, a rich source of glucose was used in this
study. Certain studies had used rice bran as substrate. Sinorhizo-
bium meliloti produced 12 g L−1 EPS using 20% rice bran hydrolysate
(Devi, Vijayendra, & Shamala, 2012). Choi et al. used rice bran as
one of the substrates for producing 198 mg/ml EPS from Cordyceps
sp. (Choi et al., 2010). Glucose being the simplest sugar, abundant
in rice bran, was utilized easily by the organism, thus producing a
higher yield, by glycolysis to nucleotides in turn getting converted
to exopolysaccharides.
Salinity was an essential parameter in EPS production. Higher
or lower the optimal concentration, 0.5%, of NaCl, the decrease
in extracellular metabolite were observed. Al-Nahas reported that
Pseudoalteromonas sp. required 3% NaCl for EPS production (Al
Nahas, Darwish, Ali, & Amin, 2011). The changes in salt concen-
trations may have caused instability in osmotic pressure in the
bacterial cells leading to cell structure and metabolic activity dete-
rioration (Al Nahas et al., 2011). Mineral salts are essential for cells’
metabolism. MnCl2 had greatly influenced the cell’s growth and
production in this work. At very low concentrations, mineral salts
did not show much effect on EPS production. It is reported that cer-
tain minerals Mn2+, Ca2+, Co2+, Fe2+ and K+ favored mycelial growth
and exopolysaccharide production by Paecilomyces sinclairii and as
concentration was increased, EPS was found to be increasing (Kim
et al., 2002). Cationic salts involve in metabolic activities of the cells,
thus aiding in the production of exopolymer (Yuan et al., 2012).
3.3. Central composite design
Based on the results using Plackett–Burman design, rice bran,
peptone, NaCl and MnCl2 were selected for CCD. The responses
obtained at different experimental runs are represented in Table 1.
An overall second order polynomial equation by multiple regres-
sion analysis was developed for the EPS production as represented
below:
Y (EPS) = 4.62 + 0.217X1 + 0.073X2 + 0.05X3 + 0.064X4
+ 0.033X1X2 + 0.037X1X3 + 0.019X1X4 + 0.073X2X3
− 0.014X2X4 + 0.019X3X4 − 0.5X12 − 0.226X22
− 0.151X32 − 0.216X42
where, Y is the EPS yield, X1 is rice bran, X2 is peptone, X3 is NaCl,
X4 is MnCl2 respectively.
The goodness of fit of regression equation developed could be
measured by adjusted determination coefficient. The R2 value of
0.9425 and adjusted R2 of 0.8888 shows that the model could
be significant predicting the response and explaining 95% of the
variability in the EPS synthesis. Adequate precision measures the
signal, i.e. response to noise (deviation) ratio. A ratio greater than
4 is desirable. The ratio of 17.41 indicates an adequate signal for
this model. The statistical significance of the equation was eval-
uated by F-test and ANOVA (analysis of variance) which showed
that the model was statistically significant at 95% confidence level
(p < 0.05). ANOVA reported the model F-value of 17.55 implying
that the model is significant (Table 2).
p-Value denotes the importance of each coefficient, helping in
understanding the interactions among the variables. The most sig-
nificant factors of this model are X1, X2
1
, X2
2
, X2
3
and X2
4
. Values of p
greater than F and less than 0.0500 indicate model terms are signifi-
cant. p-Values greater than 0.1000 indicate the model terms are not
5. A.R. Sirajunnisa et al. / Carbohydrate Polymers 135 (2016) 35–43 39
Table 1
Central composite design matrix with responses.
Run X1 (rice bran) X2 (peptone) X3 (NaCl) X4 (MnCl2) EPS (g L−1
)
Observed values Predicted values
1 0 (5) 0(0.3) 0 (0.5) 2(0.13) 3.75 3.88
2 1(6) 1(0.4) 1(0.7) −1 (0.07) 3.90 3.93
3 1(6) −1 (0.2) 1(0.7) −1 (0.07) 3.21 3.54
4 1(6) 1(0.4) −1 (0.3) −1 (0.07) 3.59 3.64
5 1(6) −1 (0.2) −1 (0.3) −1 (0.07) 3.16 3.07
6 2(7) 0(0.3) 0 (0.5) 0(0.09) 3.16 3.07
7 0 (5) 2(0.5) 0 (0.5) 0(0.09) 3.87 3.86
8 1(6) −1 (0.2) 1(0.7) 1(0.11) 3.98 3.77
9 −1(4) −1 (0.2) −1 (0.3) 1(0.11) 3.33 3.37
10 −1(4) −1 (0.2) 1(0.7) −1 (0.07) 3.20 3.13
11 0 (5) 0(0.3) 0 (0.5) 0(0.09) 4.62 4.62
12 1(6) −1 (0.2) −1 (0.3) 1(0.11) 3.56 3.71
13 0 (5) 0. (0.3) 0 (0.5) −2 (0.05) 3.92 3.63
14 0 (5) 0(0.3) 2(0.9) 0(0.09) 4.06 4.12
15 −1(4) 1(0.4) −1 (0.3) 1(0.11) 3.51 3.27
16 −1(4) 1(0.4) 1(0.7) −1 (0.07) 3.44 3.39
17 0 (5) 0(0.3) −2 (0.1) 0(0.09) 4.13 3.91
18 −1(4) 1(0.4) −1 (0.3) −1 (0.07) 2.98 3.25
19 −1(4) −1 (0.2) 1(0.7) 1(0.11) 3.25 3.29
20 1 (6) 1(0.4) −1 (0.3) 1(0.11) 3.61 3.74
21 1(6) 1(0.4) 1(0.7) 1(0.11) 4.21 4.10
22 0 (5) 0(0.3) 0 (0.5) 0(0.09) 4.62 4.62
23 0 (5) 0(0.3) 0 (0.5) 0(0.09) 4.62 4.62
24 0 (5) 0(0.3) 0 (0.5) 0(0.09) 4.62 4.62
25 0 (5) 0(0.3) 0 (0.5) 0(0.09) 4.62 4.62
26 0 (5) −2 (0.1) 0 (0.5) 0(0.09) 3.72 3.57
27 −1(4) 1(0.4) 1(0.7) 1(0.11) 3.40 3.49
28 −1(4) −1 (0.2) −1 (0.3) −1 (0.07) 3.09 3.29
29 −2(3) 0(0.3) 0 (0.5) 0(0.09) 2.26 2.19
30 0 (5) 0(0.3) 0 (0.5) 0(0.09) 4.62 4.62
significant. The model also depicted the statistically non-significant
lack of fit (p > 0.05), indicating that the responses are adequate for
employing in this model.
Three dimensional response surface plots represent regression
equations and illustrate the interactions between the response and
experimental levels of each variable. These plots let us locate the
optimum levels of each variable for the highest EPS yield. Fig. 2
illustrates the response surface plots and represents the pair wise
interaction of the four variables. Higher interaction between rice
bran, peptone resulted in larger significance of EPS production.
From this optimization study, the optimal concentration of rice
bran, peptone, sodium chloride and MnCl2 were found as 5.02%,
0.35%, 0.51% and 0.074% respectively. The maximum production
Table 2
Analysis of variance of the model.
Source SS Df MS F-value F > prob
Model 9.54 14 0.68 17.55 <0.0001
X1 1.14 1 1.14 29.36 <0.0001
X2 0.13 1 0.13 3.29 0.0899
X3 0.061 1 0.061 1.57 0.2291
X4 0.098 1 0.098 2.51 0.1338
X1X2 0.018 1 0.018 0.45 0.5115
X1X3 0.022 1 0.022 0.56 0.4656
X1X4 6.006E−003 1 6.006E−003 0.15 0.6996
X2X3 0.086 1 0.086 2.20 0.1583
X2X4 3.306E−003 1 3.306E−003 0.085 0.7744
X3X4 6.006E−003 1 6.006E−003 0.15 0.6996
X2
1
6.79 1 6.79 174.83 <0.0001
X2
2
1.40 1 1.40 36.14 <0.0001
X2
3
0.63 1 0.63 16.14 0.0011
X2
4
1.28 1 1.28 33.01 <0.0001
Lack of fit 0.58 10 0.058
Lack of fit 0.58 10 0.058
Pure error 0.000 5 0.000
Cor total 10.12 29
was estimated to be 4.62 g L−1 and the actual production obtained
with the optimal medium was also 4.62 g L−1, which is in complete
agreement with the prediction of the model. The validation of the
model was done by carrying out three experiments in non-agitated,
optimized medium formulation for EPS production. The mean value
obtained was 4.57 g L−1, which was in good agreement with the
predicted response.
3.4. Kinetic studies
Cell growth, substrate utilization and product formation were
examined and simulated with the experimental data, which were
obtained for EPS yield. The logistic equation was used for the
cellular growth kinetic study and Luedeking–Piret model for sub-
strate consumption and product formation studies. The simulation
of the experiment was carried out using MATLAB (v.7.10.0.0499,
The Mathworks, USA) software. Kinetic parameter ‘k’ of logistic
model was obtained using curve fitting (cftool) tool kit of the
same software and the high R2 values represented that the equa-
tion fit the experiment (Sivaprakash, Karunanithi, & Jayalakshmi,
2011a). Using the obtained ‘k’ values for each biological system, the
kinetic constants of Luedeking–Piret model, ˛ and ˇ, were eval-
uated (Table 3). Predicted values of this model, given in Table 4,
which were consistent with the observed values, were obtained
by solving the differential equations by Runge Kutta’s numerical
integration using ODE23 solver, in same software (Sivaprakash,
Karunanithi, & Jayalakshmi, 2011b).
A plot of logistic kinetic model with experimental data fitted
well and followed the model with R2 value of 0.9825 with rice bran
as the carbon source. The results showed that the regression anal-
ysis and kinetic parameters obtained were reasonably acceptable
(k = 0.08422). The link between growth and substrate utilization
linearly related the specific rate of biomass growth and the specific
rate of the substrate consumption through the yield coefficient Yx/s,
a measure for the conversion efficiency of a growth substrate into
6. 40 A.R. Sirajunnisa et al. / Carbohydrate Polymers 135 (2016) 35–43
Fig. 2. Illustrates the interactive effects of the four independent variables.
Table 3
Model parameters for EPS production.
Organism Logistic model Luedeking–Piret model
Growth Substrate consumption Product formation
k (h−1
) R2
Yx/s Error % ˛ ˇ Error %
P. fluorescens 0.08422 0.9852 5.5 5.45 0.6 0.114 7.47
cell material. The growth yield coefficient Yx/s was evaluated to be
5.5 g of biomass/g sucrose. Applying the Luedeking–Piret’s model,
specific EPS production and growth rates were correlated by a lin-
ear regression plot. The values for the stoichiometric coefficients ˛
and ˇ were calculated to be 0.6 g/g and 0.114 g/g/h respectively.
The correlation coefficient value (R2 = 92%) of this linear model
describes well the relationship between product formation rate
and cell growth with a high level of confidence, with minimal
errors of 5.45% and 7.47% respectively. Fig. 3 illustrates the overall
comparison of experimental and simulated values obtained from
experiments of proposed models.
Furthermore, it can be stated that product formation is asso-
ciated with bacterial growth, since the value estimated for the
stoichiometric coefficient ˛ was found to be higher than that of
Table 4
Experimental and predicted values of cell mass concentration, substrate utilization and product formation of Pseudomonas fluorescens.
Time, t (h) Cell concentration, x (g L−1
) Substrate consumption, s (g L−1
) Product formation, P (g L−1
)
E P E% E P E% E P E%
0 1.036 1.036 0 1.897 1.897 0 0 0 0
6 1.428 1.593345 11.57878 1.768 1.795665 1.564762 0.352 0.334407 4.998011
12 2.014 2.358844 17.12234 1.642 1.656483 0.882034 0.702 0.793706 13.06353
18 2.414 3.322078 37.61715 1.521 1.481349 2.606903 1.275 1.371647 7.580157
24 3.986 4.408413 10.59742 1.502 1.283834 14.52503 1.917 2.023448 5.552843
30 5.002 5.492006 9.796202 1.207 1.086817 9.957167 2.474 2.673603 8.068027
36 6.789 6.448431 5.016483 0.924 0.912922 1.198918 3.381 3.247458 3.949778
42 7.136 7.206297 0.985104 0.816 0.775128 5.008824 3.704 3.702178 0.04919
48 8.771 7.756798 11.56313 0.608 0.675037 11.02582 4.861 4.032479 17.04425
54 8.771 8.131481 7.291289 0.606 0.606912 0.150495 4.861 4.257289 12.41948
60 8.771 8.375118 4.513533 0.605 0.562615 7.005785 4.861 4.403471 9.41224
66 8.771 8.528178 2.768464 0.604 0.534786 11.45927 4.861 4.495307 7.522999
E – experimental values; P – predicted values; E% – % error.
7. A.R. Sirajunnisa et al. / Carbohydrate Polymers 135 (2016) 35–43 41
Fig. 3. Comparison of observed and simulated values of logistic and Luedeking–Piret
models (cell mass of P. fluorescens, experimental and predicted;
product formation by P. fluorescens experimental and predicted;
substrate consumed by P. fluorescens experimental and predicted).
ˇ, the maintenance coefficient. The specific rates for growth and
product formation were in a sense of measures of the metabolic
activity of the individual cells. It would be expected that if the lag
phase could be ignored, the specific rates were found to be high in
the initial log phases of the fermentation and the EPS along with
cell multiplication were found to be ceased and stationary due to
the depletion of nutrients.
3.5. Confirmation of presence of EPS
The total carbohydrate content analysis revealed that the
extracted EPS consisted of 84.12% of total sugars and total pro-
tein estimation showed that EPS constituted 9.76% proteins, thus
indicating that EPS is majorly a polysaccharide. Fig. 4 represents
the FTIR spectrum of the isolated EPS. An absorption peak at
3430.23 cm−1 indicated the presence of hydroxyl group. Vibra-
tional stretching band of CH group was observed at 2918 cm−1.
Carboxylate group was denoted by vibrational stretching band at
1610–1400 cm−1. An intense peak at 1233.56 denoted the pres-
ence of esters. An absorption peak at 1062.93 cm−1 revealed the
presence of methoxyl group. A sharp peak at 811.24 indicated
the characteristic peak of heteropolysaccharide moieties (El-Anwar
Osman, El-Shouny, Talat, & El-Zahaby, 2012; Sathyanarayanan,
Kiran, & Joseph, 2013).
3.6. Antioxidant activity
This activity results in the reduction of stable DPPH radical (pur-
ple) to non-radical DPPH-H (yellow) form. The isolated EPS along
with the reference antioxidant was checked for their DPPH reduc-
ing capability. The crude EPS was found to be a stronger antioxidant
than the standard vitamin C (Vc). As the concentration increased the
reducing capacity also elevated. The maximum antioxidant activity
of EPS, was at the concentration of 1 mg/ml (Fig. 5). EPS from P. fluo-
rescens exhibited antioxidant activity with a maximum percentage
inhibition of 39.98%, which was comparable with that of reference
(27.81%). This report was consistent with a study on antioxidant
activity of EPS isolated from Paenibacillus polymyxa, showing a max-
imum of 45.4% inhibition at 4 mg/ml concentration.[12] Our study
showed that DPPH scavenging activity of EPS was higher than that
of reference, even at very low concentrations (0.2, 0.4, 0.6, 0.8 and
1 mg/ml). The reducing activity is apparently due to the presence of
reducing sugars or the monosaccharides, proteins, peptides, amino
acids and other micro elements along with EPS (Kanmani et al.,
2011; Khalaf, Shakya, Al-Othman, El-Agbar, & Farah, 2008; Raza
et al., 2012). Thus the study showed that the DPPH scavenging abil-
ity of antioxidants is attributed to their hydrogen donating abilities
(Liu et al., 2010).
Fig. 4. FTIR spectrum of extracted EPS from P. fluorescens.
8. 42 A.R. Sirajunnisa et al. / Carbohydrate Polymers 135 (2016) 35–43
Fig. 5. DPPH scavenging efficiency of EPS.
4. Conclusion
Synthetic polymers are malicious to environment being a pol-
lutant and non-degradable material. Production of cheap, microbial
EPS from different sources is the recent interest of polymer
research. The present study was an extensive investigation on pro-
duction of exopolysaccharides by P. fluorescens using a cheaper
carbon substrate, rice bran. Optimization studies were carried out
that resulted in four significant nutritive components for EPS pro-
duction viz. rice bran, peptone, NaCl and MnCl2. Unstructured
models befitted the experiments which were performed to learn
the dynamics of growth, substrate utilization and product for-
mation by the organism. FTIR analysis revealed the presence of
major functional groups indicating the presence of sugar moieties.
The biopolymer also proved to be a potent antioxidant. Further
investigations could be carried out to study about other potential
organisms producing biopolymer using various other agro-wastes,
with efficient applications and elucidating the structure of isolated
EPS. The isolated biopolymer could be used effectively in the fields
of pharmaceuticals, therapeutics and biotechnology.
References
Al Nahas, M. O., Darwish, M. M., Ali, A. E., & Amin, M. A. (2011). Characterization of
an exopolysaccharide-producing marine bacterium, isolate Pseudoalteromonas
sp. AM. African Journal of Microbiological Research, 5(22), 3823–3831.
Bailley, J. F., & Ollis, D. F. (1986). Biochemical engineering fundamentals (second ed.,
pp. 408–440). Tata McGraw Hill Publishers.
Bhaskar, P. V., & Bhosle, N. B. (2006). Bacterial extracellular polymeric substance
carrier of heavy metals in the marine food-chain. Environment International,
32(2), 191–198.
Bryan, B. A., Linhardt, R. J., & Daniels, L. (1986). Variation in composition and yield
of exopolysaccharides produced by Klebsiella sp. strain K32 and Acenitobacter
calcoaceticus BD4. Applied Environmental Microbiology, 51(6), 1304–1308.
Choi, J. W., Ra, K. S., Kim, S. Y., Yoon, T. J., Yu, K. W., Shin, K. S., et al. (2010).
Enhancement of anti-complementary and radical scavenging activities in the
submerged culture of Cordyceps sinensis by addition of citrus peel. Bioresource
Technology, 101(15), 6028–6034.
de Oliveira Martins, P. S., De Almeida, N. F., & Leite, S. G. F. (2008). Application of a
bacterial extracellular polymeric substance in heavy metal adsorption in a
co-contaminated aqueous system. Brazilian Journal of Microbiology, 39(4),
780–786.
de Vuyst, L., & Degeest, B. (1999). Heteropolysaccharides from lactic acid bacteria.
FEMS Microbiology Reviews, 23(2), 153–177.
Devi, E. S., Vijayendra, S. V. N., & Shamala, T. R. (2012). Exploration of rice bran, an
agro-industry residue, for the production of intra and extra cellular polymers
by Sinorhizobium meliloti MTCC 100. Biocatalysis and Agricultural Biotechnology,
1(1), 80–84.
Dubois, M., Giles, K. A., Hamilton, J. K., Rebers, P. A., & Smith, F. (1956). Colorimetric
method for determination of sugars and related substances. Analytical
Chemistry, 28(3), 350–356.
El-Anwar Osman, M., El-Shouny, W., Talat, R., & El-Zahaby, H. (2012).
Polysaccharides production from some Pseudomonas syringae pathovars as
affected by different types of culture media. Journal of Microbiology
Biotechnology and Food Sciences, 1(5), 1305–1318.
Feng, Y. L., Li, W. Q., Wu, X. Q., Cheng, J. W., & Ma, S. Y. (2010). Statistical
optimization of media for mycelial growth and exo-polysaccharide production
by Lentinus edodes and a kinetic model study of two growth morphologies.
Biochemical Engineering Journal, 49(1), 104–112.
Fett, W. F. (1993). Bacterial exopolysaccharides: Their nature, regulation and role
in host–pathogen interactions. Current Topics in Botanical Research, 1, 367–390.
Freitas, F., Alves, V. D., & Reis, M. A. M. (2011). Advances in bacterial
exopolysaccharides: From production to biotechnological applications. Trends
in Biotechnology, 29(8), 388–398.
Gandhi, H. P., Rayand, R. M., & Patel, R. M. (1997). Exopolymer production by
Bacillus species. Carbohydrate Polymers, 34(4), 323–327.
Hinsa, S. M., & O’Toole, G. A. (2006). Biofilm formation by Pseudomonas fluorescens
WCS365: A role for LapD. Microbiology, 152, 1375–1383.
Hung, C. C., Santschi, P. H., & Gillow, J. B. (2005). Isolation and characterization of
extracellular polysaccharides produced by Pseudomonas fluorescens Biovar II.
Carbohydrate Polymers, 61(2), 141–147.
Kanmani, P., Kumar, R. S., Yuvaraj, N., Paari, K. A., Pattukumar, V., & Arul, V. (2011).
Production and purification of a novel exopolysaccharide from lactic acid
bacterium Streptococcus phoacae PI80 and its functional characteristics activity
in vitro. Bioresource Technology, 102(7), 4827–4833.
Khalaf, N. A., Shakya, A. K., Al-Othman, A., El-Agbar, Z., & Farah, H. (2008).
Antioxidant activity of some common plants. Turkish Journal of Biology, 32(1),
51–55.
Kim, S. W., Hwang, H. J., Xu, C. P., Na, Y. S., Song, S. K., & Yun, J. W. (2002). Influence
of nutritional conditions on the mycelial growth and exopolysaccharide
production in Paecilomyces sinclairii. Letters in Applied Microbiology, 34(6),
389–393.
Kim, H. M., Paik, S. Y., Ra, K. S., Koo, K. B., Yun, J. W., & Choi, J. W. (2006). Enhanced
production of exopolysaccharides by fed-batch culture of Ganoderma
resinaceum DG-6556. Journal of Microbiology, 44(2), 233–242.
Kim, H. O., Lim, J. M., Joo, J. H., Kim, S. W., Hwang, H. J., Choi, J. W., et al. (2005).
Optimization of submerged culture condition for the production of mycelial
biomass and exopolysaccharides by Agrocybe cylindracea. Bioresource
Technology, 96(10), 1175–1182.
Kocharin, K., Rachathewe, P., Sanglier, J. J., & Prathumpai, W. (2010). Exobiopolymer
production by Ophiocordyceps diterigena BCC 2073: Optimization, production
in bioreactor and characterization. BMC Biotechnology, 10(51)
Liu, C. T., Chu, F. J., Chou, C. C., & Yu, R. C. (2011). Antiproliferative and anticytotoxic
effects of cell fractions and exopolysaccharides from Lactobacillus casei 01.
Mutation Research, 721(2), 157–162.
Liu, J., Luo, J., Ye, H., Sun, Y., Lu, Z., & Zeng, X. (2010). In vitro and in vivo antioxidant
activity of exopolysaccharides from endophytic bacterium Paenibacillus
polymyxa EJS-3. Carbohydrate Polymers, 82(4), 1278–1283.
Lowry, O. H., Rosebrough, N. J., Farr, A. L., & Randall, R. J. (1951). Protein
measurement with the Folin phenol reagent. Journal of Biological Chemistry,
193, 265.
Luedeking, R., & Piret, E. L. (1959). A kinetic study of the lactic acid fermentation:
Batch process at controlled pH. Journal of Biochemical and Microbiological
Technology Engineering, 1(4), 393–431.
Montgomery, D. C. (1997). Response surface methods and other approaches to
process optimization. In D. C. Montgomery (Ed.), Design and analysis of
experiments (pp. 427–510). New York, USA: John Wiley and Sons.
Moppert, X., Costaouec, T. L., Ragunenes, G., Courtois, A., Simon-Colin, C., Crassous,
P., et al. (2009). Investigations into the uptake of copper, iron and selenium by
a highly sulphated bacterial exopolysaccharide isolated from microbial mats.
Journal of Industrial Microbiology and Biotechnology, 36(4), 599–604.
Onbasli, D., & Aslim, B. (2008). Determination of antimicrobial activity and
production of some metabolites by Pseudomonas aeruginosa B1 and B2 in sugar
beet molasses. African Journal of Biotechnology, 7(24), 4614–4619.
Osman, S. F., Fett, W. F., Irwin, P., Brouillette, J. N., & Connor, J. V. O. (1997). The
structure of the exopolysaccharides of Pseudomonas fluorescens strain H13.
Carbohydrate Research, 300(4), 323–327.
Palleroni, N. J. (1984). Pseudomonadaceae – Bergey’s manual of systematic
bacteriology. Baltimore: The Williams and Wilkins Co.
Plackett, R. L., & Burman, J. P. (1946). The design of optimum multifactorial
experiments. Biometrika, 33(4), 305–325.
Poli, A., Anzelmo, G., & Nicolaus, B. (2010). Bacterial exopolysaccharides from
extreme marine habitats: Production, characterization and biological
activities. Marine Drugs, 8(6), 1779–1802.
Raza, W., Yang, W., Jun, Y., Shakoor, F., Huang, Q., & Shen, Q. (2012). Optimization
and characterization of a polysaccharide produced by Pseudomonas fluorescens
WR-1 and its antioxidant activity. Carbohydrate Polymers, 90(2), 921–929.
Sathyanarayanan, G., Kiran, G. S., & Joseph, S. (2013). Synthesis of silver
nanoparticles by polysaccharide bioflocculant produced from marine Bacillus
subtilis MSBN17. Colloids and Surface B: Biointerfaces, 102, 13–20.
Satpute, S. K., Banat, I. M., Dhakephalkar, P. K., Banpurkar, A. G., & Chopade, B. A.
(2010). Biosurfactants, bioemulsifiers and exopolysaccharides from marine
microorganisms. Biotechnology Advances, 28(4), 436–450.
Savadogo, A., Savadogo, C. W., Barro, N., Ouattara, A. S., & Traore, A. S. (2004).
Identification of exopolysaccharides producing lactic acid bacteria from
Burkino Faso fermented milk samples. African Journal of Biotechnology, 3(3),
189–194.
Sivaprakash, B., Karunanithi, T., & Jayalakshmi, S. (2011a). Application of software
in mathematical biosciences for modeling and simulation of the behavior of
multiple interactive microbial populations. Communications in Computer and
Informative Science, 145, 28–37.
Sivaprakash, B., Karunanithi, T., & Jayalakshmi, S. (2011b). Modeling of microbial
interactions using software and simulation of stable operating conditions in a
9. A.R. Sirajunnisa et al. / Carbohydrate Polymers 135 (2016) 35–43 43
chemostat. Proceedings Published by International Journal of Computer
Applications, 15–21.
Williams, A. G., & Wimpenny, J. W. T. (1977). Exopolysaccharide production by
Pseudomonas NCIB11264 grown in batch culture. Journal of General
Microbiology, 102, 13–21.
Wu, C. Y., Liang, Z. C., Lu, C. P., & Wu, S. H. (2008). Effect of carbon and nitrogen
sources on the production and carbohydrate composition of exopolysaccharide
by submerged culture of Pleurotus citrinopileatus. Journal of Food Drug and
Analysis, 16(1), 61–67.
Wu, J. R., Son, J. H., Kim, K. M., Lee, J. W., & Kim, S. K. (2006). Beijerinckia indica L3
fermentation for the effective production of heteropolysaccharide-7 using the
dairy byproduct whey as medium. Process Biochemistry, 41,
289–292.
Yuan, B., Chi, X., & Zhang, R. (2012). Optimization of exopolysaccharides
production from a novel strain of Ganoderma lucidum cau 5501 in submerged
culture. Brazilian Journal of Microbiology, 43(2), 490–497.
