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Carbohydrate Polymers 135 (2016) 35–43

Contents lists available at ScienceDirect

Carbohydrate Polymers

j ourna l ho me page: www.elsev ier .com/ locate /carbpol

ptimization, kinetics and antioxidant activity of exopolysaccharideroduced from rhizosphere isolate, Pseudomonas fluorescens CrN6

bdul Razack Sirajunnisa ∗, Velayutham Vijayagopal, Bhaskar Sivaprakash,hangavelu Viruthagiri, Duraiarasan Surendhiran

ioprocess Laboratory, Department of Chemical Engineering, Annamalai University, Annamalai Nagar 608002, Tamilnadu, India

r t i c l e i n f o

rticle history:eceived 5 April 2015eceived in revised form 12 August 2015ccepted 25 August 2015vailable online 29 August 2015

eywords:seudomonas fluorescensxopolysaccharide

a b s t r a c t

Pseudomonas fluorescens, isolated from rhizosphere soil, was exploited for the production of exopolysac-charide (EPS). A medium was constituted to enhance the yield of EPS. This study involved an agro waste ascarbon substrate, rice bran, a replacement of glucose. Plackett–Burman statistical design was applied toevaluate the selected sixteen components from which, rice bran, peptone, NaCl and MnCl2 were found tobe effective and significant on the fermentation process. To study the concentration of each component,central composite design was carried out and response surface plots indicated that the following con-centrations significantly enhanced the production – rice bran 5.02%, peptone 0.35%, NaCl 0.51%, MnCl20.074%. Kinetic modeling was also performed to simulate the process parameters. Logistic model for

ice branesponse surface methodologyinetic modelsTIR spectrometryntioxidant activity

microbial growth and Luedeking–Piret equation for product formation and substrate utilization werefound to fit the experiment. The present investigation resulted in a maximum yield of 4.62 g of EPS/L at48 h. High DPPH scavenging ability was a positive indication to use EPS as an antioxidant. The extractedpolysaccharide could thus be ecofriendly due to its biodegradability and nontoxicity, and subjected tovarious industrial and pharmaceutical applications.

© 2015 Elsevier Ltd. All rights reserved.

. Introduction

Exopolysaccharide (EPS) is now a burgeoning research interestue to its ecofriendly characters like biodegradability, renewable,ontoxicity and nonpolluting secondary metabolites, hence a bet-er replacement to synthetic polymers (Freitas, Alves, & Reis, 2011).xopolysaccharides are high molecular weight polymeric mate-ials with specific functions and rheological properties, secretedxtracellularly into the environment. They are found either closelyttached to the cell wall by covalent linkages as capsules or looselyound onto cell surface as slime (de Vuyst & Degeest, 1999). EPSre highly important to any bacterium as a defense mechanism,revent from dessication (Bhaskar & Bhosle, 2006) and for adhe-ions by forming biofilms (Hinsa & O’Toole, 2006), in industriess gelling agents, biosurfactants, emulsifiers, viscosifiers (Bryan,inhardt, & Daniels, 1986; Poli, Anzelmo, & Nicolaus, 2010; Satpute,

anat, Dhakephalkar, Banpurkar, & Chopade, 2010), biosorbentsde Oliveira Martins, De Almeida, & Leite, 2008; Moppert et al.,009) and biologically active as antimicrobials, anticancer agents,

∗ Corresponding author.E-mail address: siraj.razack@gmail.com (A.R. Sirajunnisa).

ttp://dx.doi.org/10.1016/j.carbpol.2015.08.080144-8617/© 2015 Elsevier Ltd. All rights reserved.

antioxidants (Kocharin, Rachathewe, Sanglier, & Prathumpai, 2010;Liu et al., 2010; Liu, Chu, Chou, & Yu, 2011; Onbasli & Aslim, 2008).

Pseudomonads are one of the richest sources of exopolysaccha-rides. Extracellular slime is a salient feature of certain Pseudomonasstrains and the formation of complex exocellular slime has beenreported in strains of Pseudomonas aeruginosa under various cul-tural conditions (Williams & Wimpenny, 1977). Pseudomonasfluorescens is a common Gram negative, rod shaped bacterium(Osman, Fett, Irwin, Brouillette, & Connor, 1997) and yellow pig-mented, highly mucoid, producing EPS (Hung, Santschi, & Gillow,2005). Generally, Pseudomonas sp. produce bacterial alginates andalso gellan type acidic heteropolysaccharides in a laboratory scale(Palleroni, 1984). The nature and composition of EPS produced bymicroorganisms are species and strain specific.

Optimization is an indispensable procedure performed to deviseoptimal production medium, parameters and operation conditionsinvolved in the fermentation process to maximize the EPS yield.Production variables are generally optimized by considering onefactor at a time but the disadvantage is that the method is time con-

suming as a large number of experiments have to be carried out. Toovercome this, response surface methodology (RSM), a statistical,non-linear multivariate model is employed to optimize the pro-cess (Montgomery, 1997). This method is performed as different

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tages like screening of nutrients using Plackett–Burman design,onfirm optimum concentrations and conditions by central com-osite design for the production of required bioproduct.

In a production medium, main nutrients like carbon and nitro-en sources are inevitable, but cost of chemicals is one of the failingactors in a fermentation process. Biowastes from agriculturalndustries are one of the richest resources of such nutrients, hencetilizing these could make the production economically feasible.arious agricultural waste materials are used for exopolysaccha-ide production in several studies. Rice bran is being utilized as theubstitute for carbon source in this study. Rice bran is one of theost common agro industrial wastes of Indian rice mills, obtained

uring dehulling process. It is composed of 24.6 g total fats, 1.1 gotal sugars (glucose – 0.2 g), 24.8 g dietary fiber, 7.2 g water and1.8 g ash. Our research uses this for the first time on P. fluores-ens for EPS production and only very few reports are available onse of rice bran for exopolymer production from other organisms.PS is often produced at a lower temperature required for growthhan optimum (Fett, 1993). It also requires higher carbon contentn the medium and decreased nitrogen quantity. Factors that couldnfluence the production of EPS are composition of the medium,specially carbon and nitrogen sources and the parameters like pH,emperature and incubation time.

For better understanding of the fermentation process and itsptimization, a mathematical model is of great help. A kineticodel describes the behavior of the cellular processes through pos-

ible mathematical equations and it serves to be a very effectiveool to test and eliminate the extremities (Bailley & Ollis, 1986). Inhe present study, unstructured models had been used to elaboratehe stoichiometric relationship between variables namely growth,ubstrate utilization and product formation, studied.

P. fluorescens was used for our study, isolated from an herballant, Cantharanthus roseus. Only scanty reports are found on char-cterization of EPS from this plant. A medium was optimized tonhance the production of exopolymer using a statistical tool,esponse Surface Methodology (RSM). For the production of EPS

rom P. fluorescens, a Plackett–Burman design was performed firsto screen the significant nutrients that enhance the yield of EPS,nd then a central composite design was carried out to optimize theoncentration of essential medium components that were screenedy Plackett–Burman design. The production dynamics were alsotudied using mathematical models for the process variables.

. Materials and methods

.1. Bacterial culture isolation

The culture was isolated from the rhizosphere soil of C. roseusrown in the campus of Annamalai University (Tamilnadu, India).he soil sample was suspended in sterile distilled water and sub-ected to serial dilution (10−1–10−7). An aliquot of 0.1 ml of eachilution mixture was spread on nutrient agar medium containingeptone (5 g L−1), yeast extract (2 g L−1), NaCl (5 g L−1) and Agar20 g L−1). From the plates incubated at 37 ◦C for 24 h, mucoid andellow pigmented colonies were selected and purified on Pseu-omonas Agar F medium (HiMedia Laboratories, Mumbai, India).he isolated organism was identified and confirmed by 16S rRNAequencing. PCR analysis was performed with 16SrRNA primers:7F (5′-AGA GTT TGA TCC TGG CTC AG-3′) and 1492R (5′-TAC GGTAC CTT GTT ACG ACT T-3′). A volume of 25 �l reaction mixtureor PCR was carried out using 10 ng of genomic DNA, 1× reaction

uffer (10 mM Tris HCl, pH 8.8, 1.5 mM MgCl2, 50 mM KCl and 0.1%riton X 100), 0.4 mM dNTPs each, 0.5 U DNA polymerase and 1 mMeverse and forward primers each. The reaction was performed in5 amplification cycles at 94 ◦C for 45 s, 55 ◦C for 60 s, 72 ◦C for 60 s

e Polymers 135 (2016) 35–43

and an extension step at 72 ◦C for 10 min. The sequencing of 16Samplico2n was performed according to manufacturer instructionsof Big Dye terminator cycle sequencing kit (Applied BioSystems,USA). Sequencing products were resolved on an Applied Biosys-tems model 3730XL automated DNA sequencing system (AppliedBioSystems, USA). The 16S rRNA gene sequence obtained from theorganism 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 sequenceswere performed using Clustal Omega version of EBI (www.ebi.ac.uk/Tools/msa/clustalo). Phylogenetic tree was constructed byClustal Omega of EBI (www.ebi.ac.uk/Tools/phylogeny/clustalw2phylogeny) using neighbor joining method.

2.2. Media optimization

2.2.1. Plackett–Burman (PB) designThe 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, aminoacids, 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 2concentrations (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 polynomialmodel,

Y = ˇ0 +∑

ˇiXi

where, Y is the response (EPS yield), ˇ0 is the model intercept andˇi is the linear coefficient, and Xi is the level of the independentvariable. This model does not derive the interactive effects but usedto screen the essential nutrients implementing the yield of EPS (Y).The experimental design and statistical analysis of the data weredone by Minitab statistical software package (v 16.0). In the presentstudy the trials were carried out in duplicates and the analyzedEPS was taken as the response. Regression analysis determined thecomponents, 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 (Version8.0.7.1 Trial, Stat-Ease Inc., Minneapolis, USA) was used to framethe experimental designs and statistical analyses. The four indepen-dent variables were evaluated at five levels (−1, −2, 0, +1, +2) with30 experimental runs and six repetitive central points. The exper-iments were conducted in 250 ml Erlenmeyer flasks with 100 mlof media, under non-agitating condition 37 ◦C for 48 h, preparedaccording to the design.

The response obtained could be represented by a second degreepolynomial equation as:

Y = ˇ0 + ˇ1X1 + ˇ2X2 + ˇ3X3 + ˇ4X4 + ˇ12X1X2 + ˇ13X1X3

+ ˇ14X1X4 + ˇ23X2X3 + ˇ24X2X4 + ˇ34X3X4 + ˇ11X12 + ˇ22X22

+ ˇ33X32 + ˇ44X42

where Y is the predicted response, ˇ0 was the constant, X1, X2, X3and X4 were the input variables, ˇ1, ˇ2, ˇ3 and ˇ4 were the linearcoefficients, ˇ12, ˇ13, ˇ14, ˇ23, ˇ24 and ˇ34 were the second order

interactive coefficients and ˇ11, ˇ22, ˇ33 and ˇ44 were the quadraticcoefficients. The experiments were carried out in triplicates. Theresponse (yield of EPS g L−1) was the dependent variable. The 3Dgraphical plots obtained would illustrate the mutual interactions

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etween each significant factor, thus evaluating the optimizededium components.

.3. Kinetics and modeling

Kinetics is a key study done to know about the fermenta-ion reactions involved in scaling up a product. Fundamentalnstructured kinetic models were employed in this study. The ratequation is expressed by the process variables – cell concentrationx), product formed (p) and substrate concentration (s).

.3.1. Growth dynamicsMicrobial growth kinetics of P. fluorescens was investigated

sing an unstructured kinetic model, the logistic model. Verlhurstn 1844, and Pearl and Reed in 1920 contributed to a theory, whichncluded an inhibiting factor to population growth. Assuming thatnhibition is proportional to x2, they used

dx

dt= kx

(1 − x

xs

)

here t is the time (h), x is the cell mass, xs is the saturated cell mass, is the carrying capacity (cell mass the environment can hold). Theogistic curve is sigmoidal and leads to a stationary population ofize, xs = 1/ˇ.

.3.2. Product formation kineticsA typical and widely used product kinetic model is

uedeking–Piret model (1959) (Luedeking & Piret, 1959), whichs an unstructured approach contributed to both growth andon-growth associated phenomena for product formation (Bailley

Ollis, 1986). According to this model, the product formation rateepends linearly upon the growth rate and the cell concentration

dP

dt= ˛

dx

dt+ ˇx

here ̨ and ̌ are product formation constants contributing torowth associated and non-growth associated fermentation con-itions, and vary with the fermentation dynamics. The productormation rate, dP/dt, allowed a correlation between cell mass androduct concentration.

.3.3. Substrate utilization kineticsSubstrate utilization kinetics is given as the modification of the

uedeking–Piret model, which considers substrate conversion toell mass, to product and substrate consumption maintenance,

dS

dt= − 1

Yx/s

dx

dt− 1

Yp/s

dP

dt+ kex

here Yx/s is the yield coefficient for biomass with respect to sub-trate consumed and Yp/s is the yield coefficient for product formedith respect to the substrate consumption.

.4. Isolation of exopolysaccharides

EPS was extracted by precipitation using ethanol. The cultureas centrifuged at 11,000 rpm for 10 min at 4 ◦C. The supernatant

btained was mixed with two volumes of ice cold ethanol and keptt 4 ◦C for 24 h. The mixture was then centrifuged at 2500 rpm for0 min at 4 ◦C. The obtained pellet was suspended in distilled water,hich was centrifuged at 2500 rpm for 30 min at 4 ◦C with two vol-mes of ice cold ethanol (Savadogo, Savadogo, Barro, Ouattara, &

raore, 2004). The process was repeated twice and the EPS obtainedas dried, weighed and lyophilized. The total carbohydrate con-

ent of the biopolymer was studied by phenol sulfuric acid methodDubois, Giles, Hamilton, Rebers, & Smith, 1956) using glucose as

Polymers 135 (2016) 35–43 37

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 with150 mg of KBr powder and ground well to fine mixture. The mix-ture was pressed to a disc using a hydraulic press. The disc wassubjected to FTIR spectral measurement in the frequency range of4000–400 cm−1. The exopolysaccharide was characterized usinga Fourier Transfer Infrared Spectrophotometer (Bruker Optics,GmBH, Germany).

2.6. Antioxidant activity

The antioxidant activity of the isolated EPS was evaluated onthe basis of the free radical scavenging effect of 1,1-diphenyl-2picrylhydrazyl (DPPH), by the method of Liu et al. (2010) with slightmodification (Liu et al., 2010). In brief, sample solutions at variousconcentrations of 0.2, 0.4, 0.6, 0.8 mg/ml were made up to 1 ml withdistilled water. 1 ml of DPPH solution (0.004% in methanol) wasadded to sample and standard solutions. After the solutions wereincubated 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 wascalculated 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 ahigh score, when compared with BLAST. The target rRNA wasaligned with all homologous sequences using Clustal W2 and aphylogenetic tree was eventually constructed (Fig. 1A and 1B). Thephylogenetic analysis confirmed that the isolated strain was P. fluo-rescens. The nucleotide sequence of the organism, referred to asP. fluorescens CrN6, had been deposited in the GenBank databaseunder the accession number KF359766.

3.2. Plackett–Burman design

Plackett–Burman design was employed for preliminaryscreening of nutrients, through one factor at a time approach. Theaverages of EPS yield (g L−1) were obtained using 16 selected vari-ables for 20 experimental runs. The variables which had significanteffect on EPS production (p < 0.05) were selected and were used forfurther optimization. The results showed that the response variedfrom 3.55 to 4.51 g L−1. Except M, all the other selected variablesshowed positive effect on EPS production. Four variables werefound 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 insignificantvariables, describing the correlation between the variables usedfor 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 byANOVA. F-test and p-test values (p < 0.05) indicated the signifi-cance of the experiment. The determination coefficient R2 value

38 A.R. Sirajunnisa et al. / Carbohydrate Polymers 135 (2016) 35–43

F enetic

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f the model was 0.9752, indicating 97.52% of the variability in theesponse could be explained by the model.

Various carbon and nitrogen sources were checked for theirnvolvement in EPS generation by P. fluorescens. Carbon and nitro-en sources play a vital role in cell’s growth and exopolysaccharideroduction (Gandhi, Rayand, & Patel, 1997). Carbohydrate compo-ents of the medium affect the yield of EPS but do not influenceheir chemical structure. They also affect viscosity of EPS, possiblywing to the heterogeneity in the molecular weight. Our result wasonsistent with similar reports. William and Wimpenny reportedhat glucose and sucrose influenced polymer synthesis the mostWilliams & Wimpenny, 1977). Ganoderma lucidum also utilizedlucose as the carbon source at 60 g L−1, for exopolymer productionYuan, Chi, & Zhang, 2012). Beijernicka indica produced 5.52 g L−1

f EPS when lactose MSM was supplemented with 4 g L−1 glucoseWu, Son, Kim, Lee, & Kim, 2006). Ganoderma was able to produce.7 g L−1 EPS with glucose concentration as 70 g L−1 (Kim et al.,006). Lentinus edodes produced 6.88 g L−1 biopolymer in the pres-nce of 15.88 g L−1 glucose (Feng, Li, Wu, Cheng, & Ma, 2010).

Peptone influenced many other cultures in EPS production.he study revealed that the production of polysaccharide wasreatly influenced by higher amounts of carbon and limiting nitro-en concentration. Our results were in agreement with reports ofaseem Raza et al. (2012), resulting in 6.85 g L−1 at 1% concen-

ration. Peptone might have stimulated the production due to itsontents of proteins, amino acids and vitamins (Raza et al., 2012).he present study also showed that organic nitrogen sources gave

higher yield than that of inorganic ones which was in completegreement with study by Kim et al. (2005). It was suggested thatssential aminoacids cannot be synthesized from inorganic nitro-en components (Wu, Liang, Lu, & Wu, 2008), hence the decreasen cell growth and EPS metabolism.

For an effective fermentative large scale production of EPS,gro industrial wastes and residues are used as cheap carbonubstrates. Rice bran, a rich source of glucose was used in thistudy. Certain studies had used rice bran as substrate. Sinorhizo-ium meliloti produced 12 g L−1 EPS using 20% rice bran hydrolysateDevi, Vijayendra, & Shamala, 2012). Choi et al. used rice bran asne of the substrates for producing 198 mg/ml EPS from Cordycepsp. (Choi et al., 2010). Glucose being the simplest sugar, abundantn rice bran, was utilized easily by the organism, thus producing aigher yield, by glycolysis to nucleotides in turn getting convertedo exopolysaccharides.

Salinity was an essential parameter in EPS production. Higherr lower the optimal concentration, 0.5%, of NaCl, the decreasen extracellular metabolite were observed. Al-Nahas reported thatseudoalteromonas sp. required 3% NaCl for EPS production (Al

tree of isolated P. fluorescens aligned with other homologous P. fluorescens strains.

Nahas, Darwish, Ali, & Amin, 2011). The changes in salt concen-trations may have caused instability in osmotic pressure in thebacterial 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 andproduction in this work. At very low concentrations, mineral saltsdid not show much effect on EPS production. It is reported that cer-tain minerals Mn2+, Ca2+, Co2+, Fe2+ and K+ favored mycelial growthand exopolysaccharide production by Paecilomyces sinclairii and asconcentration was increased, EPS was found to be increasing (Kimet 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 responsesobtained 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 representedbelow:

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 bemeasured by adjusted determination coefficient. The R2 value of0.9425 and adjusted R2 of 0.8888 shows that the model couldbe significant predicting the response and explaining 95% of thevariability in the EPS synthesis. Adequate precision measures thesignal, i.e. response to noise (deviation) ratio. A ratio greater than4 is desirable. The ratio of 17.41 indicates an adequate signal forthis model. The statistical significance of the equation was eval-uated by F-test and ANOVA (analysis of variance) which showedthat the model was statistically significant at 95% confidence level(p < 0.05). ANOVA reported the model F-value of 17.55 implyingthat 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 , X22 , X2

3 and X24 . 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

A.R. Sirajunnisa et al. / Carbohydrate Polymers 135 (2016) 35–43 39

Table 1Central 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 3.882 1 (6) 1 (0.4) 1 (0.7) −1 (0.07) 3.90 3.933 1 (6) −1 (0.2) 1 (0.7) −1 (0.07) 3.21 3.544 1 (6) 1 (0.4) −1 (0.3) −1 (0.07) 3.59 3.645 1 (6) −1 (0.2) −1 (0.3) −1 (0.07) 3.16 3.076 2 (7) 0 (0.3) 0 (0.5) 0 (0.09) 3.16 3.077 0 (5) 2 (0.5) 0 (0.5) 0 (0.09) 3.87 3.868 1 (6) −1 (0.2) 1 (0.7) 1 (0.11) 3.98 3.779 −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 3.1311 0 (5) 0 (0.3) 0 (0.5) 0 (0.09) 4.62 4.6212 1 (6) −1 (0.2) −1 (0.3) 1 (0.11) 3.56 3.7113 0 (5) 0. (0.3) 0 (0.5) −2 (0.05) 3.92 3.6314 0 (5) 0 (0.3) 2 (0.9) 0 (0.09) 4.06 4.1215 −1 (4) 1 (0.4) −1 (0.3) 1 (0.11) 3.51 3.2716 −1 (4) 1 (0.4) 1 (0.7) −1 (0.07) 3.44 3.3917 0 (5) 0 (0.3) −2 (0.1) 0 (0.09) 4.13 3.9118 −1 (4) 1 (0.4) −1 (0.3) −1 (0.07) 2.98 3.2519 −1 (4) −1 (0.2) 1 (0.7) 1 (0.11) 3.25 3.2920 1 (6) 1 (0.4) −1 (0.3) 1 (0.11) 3.61 3.7421 1 (6) 1 (0.4) 1 (0.7) 1 (0.11) 4.21 4.1022 0 (5) 0 (0.3) 0 (0.5) 0 (0.09) 4.62 4.6223 0 (5) 0 (0.3) 0 (0.5) 0 (0.09) 4.62 4.6224 0 (5) 0 (0.3) 0 (0.5) 0 (0.09) 4.62 4.6225 0 (5) 0 (0.3) 0 (0.5) 0 (0.09) 4.62 4.6226 0 (5) −2 (0.1) 0 (0.5) 0 (0.09) 3.72 3.5727 −1 (4) 1 (0.4) 1 (0.7) 1 (0.11) 3.40 3.49

sle

eeoiibFb0

TA

28 −1 (4) −1 (0.2) −1 (0.3)

29 −2 (3) 0 (0.3) 0 (0.5)

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ignificant. The model also depicted the statistically non-significantack of fit (p > 0.05), indicating that the responses are adequate formploying in this model.

Three dimensional response surface plots represent regressionquations and illustrate the interactions between the response andxperimental levels of each variable. These plots let us locate theptimum levels of each variable for the highest EPS yield. Fig. 2llustrates the response surface plots and represents the pair wisenteraction of the four variables. Higher interaction between rice

ran, peptone resulted in larger significance of EPS production.rom this optimization study, the optimal concentration of riceran, peptone, sodium chloride and MnCl2 were found as 5.02%,.35%, 0.51% and 0.074% respectively. The maximum production

able 2nalysis of variance of the model.

Source SS Df MS F-value F > prob

Model 9.54 14 0.68 17.55 <0.0001X1 1.14 1 1.14 29.36 <0.0001X2 0.13 1 0.13 3.29 0.0899X3 0.061 1 0.061 1.57 0.2291X4 0.098 1 0.098 2.51 0.1338X1X2 0.018 1 0.018 0.45 0.5115X1X3 0.022 1 0.022 0.56 0.4656X1X4 6.006E−003 1 6.006E−003 0.15 0.6996X2X3 0.086 1 0.086 2.20 0.1583X2X4 3.306E−003 1 3.306E−003 0.085 0.7744X3X4 6.006E−003 1 6.006E−003 0.15 0.6996X2

1 6.79 1 6.79 174.83 <0.0001X2

2 1.40 1 1.40 36.14 <0.0001X2

3 0.63 1 0.63 16.14 0.0011X2

4 1.28 1 1.28 33.01 <0.0001

Lack of fit 0.58 10 0.058Lack of fit 0.58 10 0.058Pure error 0.000 5 0.000

Cor total 10.12 29

−1 (0.07) 3.09 3.290 (0.09) 2.26 2.190 (0.09) 4.62 4.62

was estimated to be 4.62 g L−1 and the actual production obtainedwith the optimal medium was also 4.62 g L−1, which is in completeagreement with the prediction of the model. The validation of themodel was done by carrying out three experiments in non-agitated,optimized medium formulation for EPS production. The mean valueobtained was 4.57 g L−1, which was in good agreement with thepredicted response.

3.4. Kinetic studies

Cell growth, substrate utilization and product formation wereexamined and simulated with the experimental data, which wereobtained for EPS yield. The logistic equation was used for thecellular growth kinetic study and Luedeking–Piret model for sub-strate consumption and product formation studies. The simulationof the experiment was carried out using MATLAB (v.7.10.0.0499,The Mathworks, USA) software. Kinetic parameter ‘k’ of logisticmodel was obtained using curve fitting (cftool) tool kit of thesame 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, thekinetic 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 obtainedby solving the differential equations by Runge Kutta’s numericalintegration using ODE23 solver, in same software (Sivaprakash,Karunanithi, & Jayalakshmi, 2011b).

A plot of logistic kinetic model with experimental data fittedwell and followed the model with R2 value of 0.9825 with rice branas 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 utilizationlinearly related the specific rate of biomass growth and the specificrate of the substrate consumption through the yield coefficient Yx/s,a measure for the conversion efficiency of a growth substrate into

40 A.R. Sirajunnisa et al. / Carbohydrate Polymers 135 (2016) 35–43

Fig. 2. Illustrates the interactive effects of the four independent variables.

Table 3Model parameters for EPS production.

Organism Logistic model Luedeking–Piret model

Growth Substrate consumption Product formation

c5seaTd

TE

E

k (h−1) R2 Yx/s

P. fluorescens 0.08422 0.9852 5.5

ell material. The growth yield coefficient Yx/s was evaluated to be.5 g of biomass/g sucrose. Applying the Luedeking–Piret’s model,pecific EPS production and growth rates were correlated by a lin-ar regression plot. The values for the stoichiometric coefficients ˛

nd ̌ were calculated to be 0.6 g/g and 0.114 g/g/h respectively.he correlation coefficient value (R2 = 92%) of this linear modelescribes well the relationship between product formation rate

able 4xperimental and predicted values of cell mass concentration, substrate utilization and p

Time, t (h) Cell concentration, x (g L−1) Substrate consu

E P E% E P

0 1.036 1.036 0 1.897 16 1.428 1.593345 11.57878 1.768 1

12 2.014 2.358844 17.12234 1.642 118 2.414 3.322078 37.61715 1.521 124 3.986 4.408413 10.59742 1.502 130 5.002 5.492006 9.796202 1.207 136 6.789 6.448431 5.016483 0.924 042 7.136 7.206297 0.985104 0.816 048 8.771 7.756798 11.56313 0.608 054 8.771 8.131481 7.291289 0.606 060 8.771 8.375118 4.513533 0.605 066 8.771 8.528178 2.768464 0.604 0

– experimental values; P – predicted values; E% – % error.

Error % ̨ ̌ Error %

5.45 0.6 0.114 7.47

and cell growth with a high level of confidence, with minimalerrors of 5.45% and 7.47% respectively. Fig. 3 illustrates the overallcomparison of experimental and simulated values obtained fromexperiments of proposed models.

Furthermore, it can be stated that product formation is asso-ciated with bacterial growth, since the value estimated for thestoichiometric coefficient ̨ was found to be higher than that of

roduct formation of Pseudomonas fluorescens.

mption, s (g L−1) Product formation, P (g L−1)

E% E P E%

.897 0 0 0 0

.795665 1.564762 0.352 0.334407 4.998011

.656483 0.882034 0.702 0.793706 13.06353

.481349 2.606903 1.275 1.371647 7.580157

.283834 14.52503 1.917 2.023448 5.552843

.086817 9.957167 2.474 2.673603 8.068027

.912922 1.198918 3.381 3.247458 3.949778

.775128 5.008824 3.704 3.702178 0.04919

.675037 11.02582 4.861 4.032479 17.04425

.606912 0.150495 4.861 4.257289 12.41948

.562615 7.005785 4.861 4.403471 9.41224

.534786 11.45927 4.861 4.495307 7.522999

A.R. Sirajunnisa et al. / Carbohydrate

Fig. 3. Comparison of observed and simulated values of logistic and Luedeking–Piretmps

ˇpaptct

3

eti

odels (cell mass of P. fluorescens, experimental and predicted;roduct formation by P. fluorescens experimental and predicted;ubstrate consumed by P. fluorescens experimental and predicted).

, the maintenance coefficient. The specific rates for growth androduct formation were in a sense of measures of the metabolicctivity of the individual cells. It would be expected that if the laghase could be ignored, the specific rates were found to be high inhe initial log phases of the fermentation and the EPS along withell multiplication were found to be ceased and stationary due tohe depletion of nutrients.

.5. Confirmation of presence of EPS

The total carbohydrate content analysis revealed that thextracted EPS consisted of 84.12% of total sugars and total pro-ein estimation showed that EPS constituted 9.76% proteins, thusndicating that EPS is majorly a polysaccharide. Fig. 4 represents

Fig. 4. FTIR spectrum of extracte

Polymers 135 (2016) 35–43 41

the FTIR spectrum of the isolated EPS. An absorption peak at3430.23 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 at1610–1400 cm−1. An intense peak at 1233.56 denoted the pres-ence of esters. An absorption peak at 1062.93 cm−1 revealed thepresence of methoxyl group. A sharp peak at 811.24 indicatedthe characteristic peak of heteropolysaccharide moieties (El-AnwarOsman, 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 alongwith the reference antioxidant was checked for their DPPH reduc-ing capability. The crude EPS was found to be a stronger antioxidantthan the standard vitamin C (Vc). As the concentration increased thereducing capacity also elevated. The maximum antioxidant activityof EPS, was at the concentration of 1 mg/ml (Fig. 5). EPS from P. fluo-rescens exhibited antioxidant activity with a maximum percentageinhibition of 39.98%, which was comparable with that of reference(27.81%). This report was consistent with a study on antioxidantactivity of EPS isolated from Paenibacillus polymyxa, showing a max-imum of 45.4% inhibition at 4 mg/ml concentration.[12] Our studyshowed that DPPH scavenging activity of EPS was higher than thatof reference, even at very low concentrations (0.2, 0.4, 0.6, 0.8 and1 mg/ml). The reducing activity is apparently due to the presence ofreducing sugars or the monosaccharides, proteins, peptides, aminoacids and other micro elements along with EPS (Kanmani et al.,

2011; Khalaf, Shakya, Al-Othman, El-Agbar, & Farah, 2008; Razaet 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).

d EPS from P. fluorescens.

42 A.R. Sirajunnisa et al. / Carbohydrat

4

lErdctdmtmmTiowEo

R

A

B

B

B

C

d

d

D

D

E

F

in mathematical biosciences for modeling and simulation of the behavior of

Fig. 5. DPPH scavenging efficiency of EPS.

. Conclusion

Synthetic polymers are malicious to environment being a pol-utant and non-degradable material. Production of cheap, microbialPS from different sources is the recent interest of polymeresearch. The present study was an extensive investigation on pro-uction of exopolysaccharides by P. fluorescens using a cheaperarbon substrate, rice bran. Optimization studies were carried outhat resulted in four significant nutritive components for EPS pro-uction viz. rice bran, peptone, NaCl and MnCl2. Unstructuredodels befitted the experiments which were performed to learn

he dynamics of growth, substrate utilization and product for-ation by the organism. FTIR analysis revealed the presence ofajor functional groups indicating the presence of sugar moieties.

he biopolymer also proved to be a potent antioxidant. Furthernvestigations could be carried out to study about other potentialrganisms producing biopolymer using various other agro-wastes,ith efficient applications and elucidating the structure of isolated

PS. The isolated biopolymer could be used effectively in the fieldsf pharmaceuticals, therapeutics and biotechnology.

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