Optimization, kinetics and antioxidant activity of exopolysaccharide produced from rhizosphere isolate, Pseudomonas fluorescens CrN6

Carbohydrate Polymers 135 (2016) 35–43

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Carbohydrate Polymers journal homepage: www.elsevier.com/locate/carbpol

Optimization, kinetics and antioxidant activity of exopolysaccharide produced from rhizosphere isolate, Pseudomonas fluorescens CrN6 Abdul Razack Sirajunnisa ∗ , Velayutham Vijayagopal, Bhaskar Sivaprakash, Thangavelu Viruthagiri, Duraiarasan Surendhiran Bioprocess Laboratory, Department of Chemical Engineering, Annamalai University, Annamalai Nagar 608002, Tamilnadu, India

a r t i c l e

i n f o

Article history: Received 5 April 2015 Received in revised form 12 August 2015 Accepted 25 August 2015 Available online 29 August 2015 Keywords: Pseudomonas fluorescens Exopolysaccharide Rice bran Response surface methodology Kinetic models FTIR spectrometry Antioxidant activity

a b s t r a c t Pseudomonas fluorescens, isolated from rhizosphere soil, was exploited for the production of exopolysaccharide (EPS). A medium was constituted to enhance the yield of EPS. This study involved an agro waste as carbon substrate, rice bran, a replacement of glucose. Plackett–Burman statistical design was applied to evaluate the selected sixteen components from which, rice bran, peptone, NaCl and MnCl2 were found to be 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 concentrations significantly enhanced the production – rice bran 5.02%, peptone 0.35%, NaCl 0.51%, MnCl2 0.074%. Kinetic modeling was also performed to simulate the process parameters. Logistic model for microbial growth and Luedeking–Piret equation for product formation and substrate utilization were found to fit the experiment. The present investigation resulted in a maximum yield of 4.62 g of EPS/L at 48 h. High DPPH scavenging ability was a positive indication to use EPS as an antioxidant. The extracted polysaccharide could thus be ecofriendly due to its biodegradability and nontoxicity, and subjected to various industrial and pharmaceutical applications. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction Exopolysaccharide (EPS) is now a burgeoning research interest due to its ecofriendly characters like biodegradability, renewable, nontoxicity and nonpolluting secondary metabolites, hence a better replacement to synthetic polymers (Freitas, Alves, & Reis, 2011). Exopolysaccharides are high molecular weight polymeric materials with specific functions and rheological properties, secreted extracellularly into the environment. They are found either closely attached to the cell wall by covalent linkages as capsules or loosely bound onto cell surface as slime (de Vuyst & Degeest, 1999). EPS are highly important to any bacterium as a defense mechanism, prevent from dessication (Bhaskar & Bhosle, 2006) and for adhesions by forming biofilms (Hinsa & O’Toole, 2006), in industries as gelling agents, biosurfactants, emulsifiers, viscosifiers (Bryan, Linhardt, & Daniels, 1986; Poli, Anzelmo, & Nicolaus, 2010; Satpute, Banat, Dhakephalkar, Banpurkar, & Chopade, 2010), biosorbents (de Oliveira Martins, De Almeida, & Leite, 2008; Moppert et al., 2009) and biologically active as antimicrobials, anticancer agents,

∗ Corresponding author. E-mail address: [email protected] (A.R. Sirajunnisa). http://dx.doi.org/10.1016/j.carbpol.2015.08.080 0144-8617/© 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 exopolysaccharides. Extracellular slime is a salient feature of certain Pseudomonas strains and the formation of complex exocellular slime has been reported in strains of Pseudomonas aeruginosa under various cultural conditions (Williams & Wimpenny, 1977). Pseudomonas fluorescens is a common Gram negative, rod shaped bacterium (Osman, Fett, Irwin, Brouillette, & Connor, 1997) and yellow pigmented, highly mucoid, producing EPS (Hung, Santschi, & Gillow, 2005). Generally, Pseudomonas sp. produce bacterial alginates and also gellan type acidic heteropolysaccharides in a laboratory scale (Palleroni, 1984). The nature and composition of EPS produced by microorganisms are species and strain specific. Optimization is an indispensable procedure performed to devise optimal production medium, parameters and operation conditions involved in the fermentation process to maximize the EPS yield. Production variables are generally optimized by considering one factor at a time but the disadvantage is that the method is time consuming as a large number of experiments have to be carried out. To overcome this, response surface methodology (RSM), a statistical, non-linear multivariate model is employed to optimize the process (Montgomery, 1997). This method is performed as different

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stages like screening of nutrients using Plackett–Burman design, confirm optimum concentrations and conditions by central composite design for the production of required bioproduct. In a production medium, main nutrients like carbon and nitrogen 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 exopolysaccharide 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. fluorescens 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 possible 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 characterization 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 subjected 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 Pseudomonas 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 Biosystems 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 pairwise 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 onefactor at a time experiments, carbon, nitrogen, vitamin, amino acids, trace metal ions and minerals were screened by one factor 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 concentration levels were also selected by one factorial experiment. Plackett–Burman design is showed on the first order polynomial model, Y = ˇ0 +



ˇi Xi

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 significantly 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 independent variables were evaluated at five levels (−1, −2, 0, +1, +2) with 30 experimental runs and six repetitive central points. The experiments 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 + ˇ1 X1 + ˇ2 X2 + ˇ3 X3 + ˇ4 X4 + ˇ12 X1 X2 + ˇ13 X1 X3 + ˇ14 X1 X4 + ˇ23 X2 X3 + ˇ24 X2 X4 + ˇ34 X3 X4 + ˇ11 X12 + ˇ22 X22 + ˇ33 X32 + ˇ44 X42 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

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between each significant factor, thus evaluating the optimized medium components.

standard. Total protein content was estimated using Lowry et al. method (Lowry, Rosebrough, Farr, & Randall, 1951).

2.3. Kinetics and modeling

2.5. Fourier transform infra-red (FTIR) spectrometry

Kinetics is a key study done to know about the fermentation 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).

A quantity of 50 mg of lyophilized EPS was taken, mixed with 150 mg of KBr powder and ground well to fine mixture. The mixture 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).

2.3.1. 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 x = kx 1 − xs dt



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 dx dP =˛ + ˇx dt dt where ˛ and ˇ are product formation constants contributing to growth associated and non-growth associated fermentation conditions, 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 1 dx 1 dP =− − + kex Yx/s dt Yp/s dt dt where Yx/s is the yield coefficient for biomass with respect to substrate 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 volumes 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 content of the biopolymer was studied by phenol sulfuric acid method (Dubois, Giles, Hamilton, Rebers, & Smith, 1956) using glucose as

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 reference 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. fluorescens. 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 variables 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 significance of the experiment. The determination coefficient R2 value

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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 nitrogen sources play a vital role in cell’s growth and exopolysaccharide production (Gandhi, Rayand, & Patel, 1997). Carbohydrate components 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 presence 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 nitrogen concentration. Our results were in agreement with reports of Waseem Raza et al. (2012), resulting in 6.85 g L−1 at 1% concentration. 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 nitrogen 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. Sinorhizobium 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 concentrations may have caused instability in osmotic pressure in the bacterial cells leading to cell structure and metabolic activity deterioration (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 certain 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 regression analysis was developed for the EPS production as represented below: Y (EPS) = 4.62 + 0.217X1 + 0.073X2 + 0.05X3 + 0.064X4 + 0.033X1 X2 + 0.037X1 X3 + 0.019X1 X4 + 0.073X2 X3 − 0.014X2 X4 + 0.019X3 X4 − 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 evaluated 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 significant factors of this model are X1 , X12 , X22 , X32 and X42 . Values of p greater than F and less than 0.0500 indicate model terms are significant. p-Values greater than 0.1000 indicate the model terms are not

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Table 1 Central composite design matrix with responses. Run

X1 (rice bran)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

0 (5) 1 (6) 1 (6) 1 (6) 1 (6) 2 (7) 0 (5) 1 (6) −1 (4) −1 (4) 0 (5) 1 (6) 0 (5) 0 (5) −1 (4) −1 (4) 0 (5) −1 (4) −1 (4) 1 (6) 1 (6) 0 (5) 0 (5) 0 (5) 0 (5) 0 (5) −1 (4) −1 (4) −2 (3) 0 (5)

X2 (peptone)

0 (0.3) 1 (0.4) −1 (0.2) 1 (0.4) −1 (0.2) 0 (0.3) 2 (0.5) −1 (0.2) −1 (0.2) −1 (0.2) 0 (0.3) −1 (0.2) 0. (0.3) 0 (0.3) 1 (0.4) 1 (0.4) 0 (0.3) 1 (0.4) −1 (0.2) 1 (0.4) 1 (0.4) 0 (0.3) 0 (0.3) 0 (0.3) 0 (0.3) −2 (0.1) 1 (0.4) −1 (0.2) 0 (0.3) 0 (0.3)

X3 (NaCl)

0 (0.5) 1 (0.7) 1 (0.7) −1 (0.3) −1 (0.3) 0 (0.5) 0 (0.5) 1 (0.7) −1 (0.3) 1 (0.7) 0 (0.5) −1 (0.3) 0 (0.5) 2 (0.9) −1 (0.3) 1 (0.7) −2 (0.1) −1 (0.3) 1 (0.7) −1 (0.3) 1 (0.7) 0 (0.5) 0 (0.5) 0 (0.5) 0 (0.5) 0 (0.5) 1 (0.7) −1 (0.3) 0 (0.5) 0 (0.5)

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

Df

MS

F-value

F > prob

Model X1 X2 X3 X4 X1 X2 X1 X3 X1 X4 X2 X3 X2 X4 X3 X4 X12 X22 X32 X42

9.54 1.14 0.13 0.061 0.098 0.018 0.022 6.006E−003 0.086 3.306E−003 6.006E−003 6.79 1.40 0.63 1.28

14 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0.68 1.14 0.13 0.061 0.098 0.018 0.022 6.006E−003 0.086 3.306E−003 6.006E−003 6.79 1.40 0.63 1.28

17.55 29.36 3.29 1.57 2.51 0.45 0.56 0.15 2.20 0.085 0.15 174.83 36.14 16.14 33.01

<0.0001 <0.0001 0.0899 0.2291 0.1338 0.5115 0.4656 0.6996 0.1583 0.7744 0.6996 <0.0001 <0.0001 0.0011 <0.0001

Lack of fit Lack of fit Pure error

0.58 0.58 0.000

10 10 5

0.058 0.058 0.000

Cor total

SS

10.12

29

X4 (MnCl2 )

2 (0.13) −1 (0.07) −1 (0.07) −1 (0.07) −1 (0.07) 0 (0.09) 0 (0.09) 1 (0.11) 1 (0.11) −1 (0.07) 0 (0.09) 1 (0.11) −2 (0.05) 0 (0.09) 1 (0.11) −1 (0.07) 0 (0.09) −1 (0.07) 1 (0.11) 1 (0.11) 1 (0.11) 0 (0.09) 0 (0.09) 0 (0.09) 0 (0.09) 0 (0.09) 1 (0.11) −1 (0.07) 0 (0.09) 0 (0.09)

EPS (g L−1 ) Observed values

Predicted values

3.75 3.90 3.21 3.59 3.16 3.16 3.87 3.98 3.33 3.20 4.62 3.56 3.92 4.06 3.51 3.44 4.13 2.98 3.25 3.61 4.21 4.62 4.62 4.62 4.62 3.72 3.40 3.09 2.26 4.62

3.88 3.93 3.54 3.64 3.07 3.07 3.86 3.77 3.37 3.13 4.62 3.71 3.63 4.12 3.27 3.39 3.91 3.25 3.29 3.74 4.10 4.62 4.62 4.62 4.62 3.57 3.49 3.29 2.19 4.62

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 substrate 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 equation 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 evaluated (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 analysis 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

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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

P. fluorescens

Substrate consumption

Product formation

k (h−1 )

R2

Yx/s

Error %

˛

ˇ

Error %

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 linear 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 associated 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)

0 6 12 18 24 30 36 42 48 54 60 66

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%

1.036 1.428 2.014 2.414 3.986 5.002 6.789 7.136 8.771 8.771 8.771 8.771

1.036 1.593345 2.358844 3.322078 4.408413 5.492006 6.448431 7.206297 7.756798 8.131481 8.375118 8.528178

0 11.57878 17.12234 37.61715 10.59742 9.796202 5.016483 0.985104 11.56313 7.291289 4.513533 2.768464

1.897 1.768 1.642 1.521 1.502 1.207 0.924 0.816 0.608 0.606 0.605 0.604

1.897 1.795665 1.656483 1.481349 1.283834 1.086817 0.912922 0.775128 0.675037 0.606912 0.562615 0.534786

0 1.564762 0.882034 2.606903 14.52503 9.957167 1.198918 5.008824 11.02582 0.150495 7.005785 11.45927

0 0.352 0.702 1.275 1.917 2.474 3.381 3.704 4.861 4.861 4.861 4.861

0 0.334407 0.793706 1.371647 2.023448 2.673603 3.247458 3.702178 4.032479 4.257289 4.403471 4.495307

0 4.998011 13.06353 7.580157 5.552843 8.068027 3.949778 0.04919 17.04425 12.41948 9.41224 7.522999

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

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the FTIR spectrum of the isolated EPS. An absorption peak at 3430.23 cm−1 indicated the presence of hydroxyl group. Vibrational 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 presence 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

Fig. 3. Comparison of observed and simulated values of logistic and Luedeking–Piret experimental and predicted; models (cell mass of P. fluorescens, experimental and predicted; product formation by P. fluorescens experimental and predicted). substrate consumed by P. fluorescens

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 protein estimation showed that EPS constituted 9.76% proteins, thus indicating that EPS is majorly a polysaccharide. Fig. 4 represents

This activity results in the reduction of stable DPPH radical (purple) to non-radical DPPH-H (yellow) form. The isolated EPS along with the reference antioxidant was checked for their DPPH reducing 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. fluorescens 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 maximum 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 ability of antioxidants is attributed to their hydrogen donating abilities (Liu et al., 2010).

Fig. 4. FTIR spectrum of extracted EPS from P. fluorescens.

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Fig. 5. DPPH scavenging efficiency of EPS.

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