Statistical optimization of molasses based exopolysaccharide and biomass production by Aureobasidium pullulans MTCC 2195

Statistical optimization of molasses based exopolysaccharide and biomass production by Aureobasidium pullulans MTCC 2195

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Original Research Paper

Statistical optimization of molasses based exopolysaccharide and biomass production by Aureobasidium pullulans MTCC 2195 S. Srikanth a, M. Swathi a, M. Tejaswini a, G. Sharmila a, C. Muthukumaran a,n, M.K. Jaganathan a, K. Tamilarasan b a b

Bioprocess Laboratory, Department of Biotechnology, School of Bioengineering, SRM University, Kattankulathur-603203, Chennai, Tamilnadu, India Department of Chemical Engineering, School of Bioengineering, SRM University, Kattankulathur 603203, Chennai, Tamilnadu, India

art ic l e i nf o

a b s t r a c t

Article history: Received 19 June 2013 Received in revised form 23 October 2013 Accepted 27 November 2013

In the present study, optimization of exopolysaccharide (pullulan) and biomass production by Aureobasidium pullulans was carried out by four factor—five level central composite design (CCD) of response surface methodology (RSM). Four factors namely molasses, KH2PO4, yeast extract and pH were chosen for the optimization studies and their significance on exopolysaccharide and biomass production was statistically analyzed by ANOVA. A second order polynomial model for exopolysaccharide and biomass production was constructed by using the estimated regression coefficients. Optimized values of molasses, KH2PO4, yeast extract and pH for targeted response of pullulan (45 g/L) and biomass (12.5 g/L) were predicted as 5.0%, 0.22%, 0.25% and 6.4%, respectively. Result of this study shows that utilization of molasses has significantly improved the exopolysaccharide production and also make the process cost effective. & 2013 Elsevier Ltd. All rights reserved.

Keywords: Pullulan Molasses Central composite design Response surface methodology

1. Introduction Currently, the environmental pollution is the major challenging problem in worldwide. Our environment is mainly polluted by the industrial effluent discharges and accumulation of synthetic nondegradable polymeric materials. These polluting agents affect the ecosystem in land, waterways and also atmosphere by burning the synthetic wastes (Luckachan and Pillai, 2011). Presently, the use of biodegradable polymers for several purposes is significantly increased to reduce the environmental risk. Biopolymers are derived from various sources like plants, microorganisms, animals and can be easily degraded in the environment. This advantage makes the biopolymers more popular than synthetic one. Microbial based biopolymers have gained more industrial importance because of easy scale up and several industrial applications (Nagane et al., 2009; Gounga et al., 2008; Yuen, 1974). Biopolymers such as xanthan gum (Kalogiannis et al., 2003), gellan gum (Banik et al., 2007), pullulan (Sharmila et al., 2013; Gao et al., 2010) and polyglutamic acid (Zhang et al., 2012) by fermentation route were reported in the literature. Pullulan is a white, non hygroscopic and water soluble biopolymer produced extracellularly by yeast like fungi Aureobasidium

n Correspondence to: Department of Industrial Biotechnology, Government College of Technology, Coimbatore, Tamilnadu, India. Tel.: þ 91 9894595995. E-mail address: [email protected] (C. Muthukumaran).

pullulans (Leathers, 2003). It is composed of maltotriose subunits connected by α-1,6 glycosidic linkages. It is less viscous in nature and shows high stability towards pH and temperature. It is used as filler in beverages, stabilizer and thickener in food industries, binder and tablet coating agents in pharmaceutical industries (Goksungur et al., 2011). Nutrient components of the production medium mainly affect the yield, product quality and also process cost (Kennedy and Krouse, 1999). Researchers are involved to search and identify a cheap carbon sources for cost effective fermentation process. Utilization of several agricultural based products and byproducts such as jack fruit seed, beet molasses, carob pod, sweet potato and brewery wastes are reported in the literature for pullulan production by fermentative process (Sharmila et al., 2013; Goksungur et al., 2004; Roukas and Biliaderis, 1995; Wu et al., 2009; Roukas, 1999). In this study, molasses is utilized as a low cost substrate for production of pullulan. Molasses is brown viscous liquor waste generated from the sugar industry which consists of fermentable sugars (48–60%) that includes glucose and fructose, organic content (9–12%), inorganic ash (10–15), solids (70–85%) and water (15–30%) (Soni, 2007). It is utilized as a low cost substrate for the production of different bioproducts like ethanol (Bouallagui et al., 2013), biopolymer (Gouda et al., 2001), enzymes (Vohra and Satyanarayana, 2004), pigments (Goksungur et al., 2002) and organic acids etc. (Chan et al., 2012; Jiang et al., 2009). Process optimization is an important step to minimize the cost of any industrial operation. RSM is well accepted tool for optimization

1878-8181/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.bcab.2013.11.011

Please cite this article as: Srikanth, S., et al., Statistical optimization of molasses based exopolysaccharide and biomass production by Aureobasidium pullulans MTCC 2195. Biocatal. Agric. Biotechnol. (2013), http://dx.doi.org/10.1016/j.bcab.2013.11.011i

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Table 1 Experimental range and variable levels for RSM optimization. Factors

Molasses, % (v/v) KH2PO4, % (w/v) Yeast extract,%(w/v) pH

Table 2 RSM experimental design (in actual units) for production of exopolysaccharide (pullulan) and biomass with observed and predicted responses.

Symbol Lower (  2)

Low (  1)

Middle (0)

High ( þ 1)

Higher (þ 2)

X1 X2 X3 X4

2 0.3 0.1 6

3 0.4 0.15 6.5

4 0.5 0.2 7

5 0.5 0.25 7.5

1 0.2 0.05 5.5

studies because of possessing advantage to study the interaction between the variables with less experiments, time and cost (Goksungur et al., 2011; Yu et al., 2008). The main objective of this study is to optimize the molasses based medium components for the maximized production of exopolysaccharide (pullulan) and biomass by employing RSM. The interaction effect between the responses and the medium variables is also studied.

2. Materials and methods 2.1. Microorganism and inoculum preparation Fungi culture A. pullulans (MTCC 2195) was used in this study. It was procured from MTCC, Institute of Microbial Technology, Chandigarh, India. It was maintained in PDA (Potato Dextrose Agar) slants at 4 1C and sub cultured at every 15 days interval. A loopful of well grown culture was transferred to 100 mL of the inoculum media which consists of glucose 30 g/L, peptone 5 g/L, yeast extract 2.5 g/L, dihydrogen potassium phosphate 1 g/L, magnesium sulphate 0.5 g/L and pH was adjusted to 6.5.

Run order

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 31

X1

2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 1 5 3 3 3 3 3 3 3 3 3 3 3 3 3

X2

0.3 0.3 0.5 0.5 0.3 0.3 0.5 0.5 0.3 0.3 0.5 0.5 0.3 0.3 0.5 0.5 0.4 0.4 0.2 0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

X3

0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.15 0.15 0.15 0.15 0.05 0.25 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15

X4

6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 6.5 6.5 6.5 6.5 6.5 6.5 5.5 7.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5

Pullulan (g/L)

Biomass(g/L)

Observed

Predicted

Observed

Predicted

25.4 16.8 39.8 33.4 34.3 26.6 31.5 24.1 13.2 11.8 21.0 28.5 24.6 28.5 14.9 18.7 32.5 25.5 29.9 25.1 15.1 21.5 29.4 18.6 17.9 19.2 23.2 21.9 20.5 21.2 19.7

25.7 16.1 38.7 31.9 36.3 27.1 29.2 22.8 13.7 15.1 21.5 25.7 27.1 28.9 14.8 19.4 31.4 26.4 25.7 29.2 16.0 20.4 31.6 16.2 20.5 20.5 20.5 20.5 20.5 20.5 20.5

8.7 9.7 8.5 8.8 9.8 10.4 9.5 10.2 7.6 9.7 8.8 9.5 8.1 9.2 8.5 10.2 9.2 10.7 8.6 9.0 8.9 9.9 9.2 7.9 8.5 8.9 8.7 9.1 8.4 8.2 8.6

8.8 9.6 8.5 8.9 9.7 10.5 9.7 10.1 7.9 9.4 8.6 9.7 7.8 9.3 8.8 9.9 9.0 10.9 8.7 9.0 8.9 10.0 9.1 8.0 8.6 8.6 8.6 8.6 8.6 8.6 8.6

2.2. Preparation of molasses medium

2.5. Extraction of pullulan

Molasses was obtained from the local sugar industry near Chennai, Tamilnadu. The raw molasses was pretreated with sulphuric acid to eliminate the heavy metals and other undesired compounds (Goksungur et al., 2004). In brief, molasses was acidified to pH 4 with 2 N H2SO4 and boiled for 15 min in the water bath. After boiling, the molasses solution was centrifuged at 6000  g to remove the precipitate and the supernatant was adjusted to pH 6.5 using 2 N NaOH. The pretreated molasses was suitably diluted with distilled water to obtain the final desired sugar concentration for 100 mL and it was used in further studies. The molasses medium consists of molasses 40 g/L, peptone 5 g/L, yeast extract 2.5 g/L, dihydrogen potassium phosphate 1 g/L, magnesium sulphate 0.5 g/L and pH 6.5.

Pullulan levels were determined by precipitating the polysaccharide in the clarified supernatant obtained from previous step with twice the volume of isopropyl alcohol kept at 4 1C for 12 h. Crude biopolymer precipitate was separated by centrifuging the content at 10,000  g for 10 min followed by drying at 80 1C overnight and then weighed (Singh et al., 2009).

2.3. Fermentation conditions One hundred milliliters of molasses medium was prepared in 500 mL Erlenmeyer flask and autoclaved at 121 1C. Five percent inoculum was used to inoculate the flask and kept in temperature controlled rotary shaker at 150 rpm, 35 1C for five days. For statistical optimization studies, 100 mL of production medium was prepared in 250 mL Erlenmeyer flask according to the experimental design given in Table 2. 2.4. Biomass estimation Five milliliters of the culture broth was centrifuged at 11,000  g for 10 min and the supernatant was preserved for the biopolymer extraction. The collected biomass pellet was dried in a hot air oven at 80 1C to a constant weight (Singh et al., 2009).

2.6. Response surface methodology Statistical optimization of the pullulan production by RSM was performed by using Minitab software (Myers and Montgomery, 2002). Central composite design (CCD) was used to determine the optimal level of the selected factors. Four factors (Molasses (%), KH2PO4 (%), yeast extract (%) and pH) were selected for the optimization at five levels. The experimental range and design were given in Tables 1 and 2, respectively. Experiments were carried out as per the combination of the selected factors and the responses (pullulan concentration and biomass concentration) were tabulated in Table 2. A second order polynomial model shown in Eq. (1) was used to represent the relation exists between the dependent and independent variables. Y ¼ b0 þ b1 X 1 þ b2 X 2 þ b3 X 3 þ b4 X 4 þ b11 X 21 þb22 X 22 þb33 X 23 þ b44 X 24 þ b12 X 12 þ b13 X 13 þb14 X 14 þ b23 X 23 þ b24 X 24 þ b34 X 34

ð1Þ

where Y is a response, b0 is constant, b1, b2, b3, b4 are linear effect coefficients, b11, b22, b33, b44 are quadratic effect coefficients, b12, b13, b14, b23, b24, b34 are interaction effect coefficients and X1, X2, X3, X4 are the independent variables. The obtained data were

Please cite this article as: Srikanth, S., et al., Statistical optimization of molasses based exopolysaccharide and biomass production by Aureobasidium pullulans MTCC 2195. Biocatal. Agric. Biotechnol. (2013), http://dx.doi.org/10.1016/j.bcab.2013.11.011i

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Table 3 Estimated regression coefficients (in actual units), t and p values for the pullulan and biomass production. Terms

Symbol

Constant X1 X2 X3 X4 X11 X22 X33 X44 X1X2 X1X3 X1X4 X2X3 X2X4 X3X4 n

b0 b1 b2 b3 b4 b11 b22 b33 b44 b12 b13 b14 b23 b24 b34

Pullulan (g/L)

Biomass (g/L)

Coefficient

t-Value

p-Value

Coefficient

t-Value

p-Value

268.28  52.63 168.39 306.30  62.14 2.10 172.46  230.18 3.40 7.06 1.87 5.49  1003.75  25.88 27.75

2.707  5.491 1.722 1.598  2.338 4.292 3.525  1.176 1.737 1.080 0.143 4.195  7.674  1.978 1.061

0.016 o0.001n 0.104 0.130 0.033n 0.001n 0.003n 0.257 0.102 0.296 0.888 0.001n o0.001n 0.065 0.305

23.38  3.63  34.84 38.61  1.44 0.33 4.70 78.81  0.06  0.87  0.00 0.37 12.50 5.00  9.50

2.170  3.481  3.278 1.852  0.500 6.290 0.884 3.704  0.291  1.231  0.000 2.637 0.879 3.516  3.340

0.045 0.003n 0.005n 0.083 0.624 o 0.001n 0.390 0.002n 0.775 0.236 1.000 0.018n 0.392 0.003n 0.004n

Significant model terms.

Table 4 Analysis of Variance (ANOVA) statistics for pullulan and biomass using molasses media. Source

Regression Linear Square Interaction Residual Error Lack-of-Fit Pure error Total

Degrees of freedom

14 4 4 6 16 10 6 30

Pullulan (g/L)

Biomass (g/L)

Sum of squares

Mean square

F value

p Value

Sum of squares

Mean square

F value

p Value

1232.80 440.16 226.58 566.06 109.50 90.67 18.83 1342.29

88.06 72.79 56.64 94.34 6.84 9.06 3.13

12.87 10.64 8.28 13.79

o 0.001n o 0.001n 0.001n o 0.001n

o0.001n 0.002n o0.001n 0.003n

0.103

1.121 0.537 1.021 0.442 0.080 0.074 0.092

13.86 6.64 12.62 5.46

2.89

15.69 8.96 4.08 2.65 1.29 0.74 0.55 16.99

0.80

0.640

R2 ¼0.918, R2Adj ¼0.847 n

R2 ¼ 0.924, R2Adj ¼ 0.857

Significant model terms.

subjected to multiple regression analysis to estimate the coefficients and the statistical significance of the regression model was evaluated by F test of ANOVA statistics results.

3. Results and discussion 3.1. Response surface analysis of pullulan and biomass production Thirty one experiments were performed according to CCD of RSM and the results were given in Table 2. The two responses, pullulan and biomass concentration were subjected to regression analysis and the corresponding coefficients and the estimated p values were represented in Table 3. The estimated coefficients (in actual units) were used to construct the model Eqs. (2) and (3) for pullulan and biomass production, respectively. Y Pullulanðg=LÞ ¼ 268:28–52:63X 1 þ 168:39X 2 þ 306:30X 3 –62:14X 4 þ 2:10X 21 þ 172:46X 22 –230:18X 23 þ 3:40X 24 þ 7:06X 12 þ 1:87X 13 þ 5:49X 14 –1003:74X 23 –25:88X 24 þ 27:75X 34

ð2Þ

Y Biomassðg=LÞ ¼ 23:38–3:63X 1 –34:84X 2 þ 38:61X 3 –1:44X 4 þ 0:33X 21 þ 4:70X 22 þ 78:81X 23 –0:06X 24 –0:87 X 12 –0:00X 13 þ 0:37X 14 þ12:50X 23 þ 5:00X 24 –9:50X 34

ð3Þ

ANOVA statistical results for pullulan and biomass production were shown in Table 4. High values of F-test for regression indicating that the model was fitted well with the experimental

results and can explain the variation observed in pullulan and biomass concentration within the designed levels of selected factors. ANOVA results demonstrated that the model chosen can adequately provide the details of linear, quadratic and interaction effects of four factors on pullulan and biomass production. Coefficient of determination (R2) is used as a measure for the goodness of fit and the R2 value closer to unity usually considered best fit. In our study, R2 value was observed as 0.918 and 0.924 for pullulan and biomass concentration, respectively. These values showed that there was a good relation existed between the experimental and predicted model.

3.2. Contour plots The regression model was graphically represented by 2D contour plots which explain the interaction effect between the selected variables and to determine the optimal conditions of each variable for the maximized output. Contour plots shown in Figs. 1 and 2 represent the interaction effects of molasses, yeast extract, KH2PO4 and pH on pullulan and biomass concentration, respectively. The shape of the contour plots is used understand the interactions between the variables. According to Yu et al. (2008), when the contour lines are circular in nature there is no interaction exists between the variables whereas the significant interaction is observed with elliptical shape contour lines.

Please cite this article as: Srikanth, S., et al., Statistical optimization of molasses based exopolysaccharide and biomass production by Aureobasidium pullulans MTCC 2195. Biocatal. Agric. Biotechnol. (2013), http://dx.doi.org/10.1016/j.bcab.2013.11.011i

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Fig. 1. Contour plots showing the combined effect of the medium variables (molasses, KH2PO4, yeast extract and pH) on pullulan production by Aureobasidium pullulans.

3.3. Response optimization The optimal value of the selected variables was predicted by the response optimizer tool of Minitab software for a targeted response of pullulan (45 g/L) and biomass (12.5 g/L) with the desirability factor equals to 1. The optimized parameter values for the targeted response were predicted as molasses 5.0%, KH2PO4 0.22%, yeast extract 0.25% and pH 6.4.

for the microbial metabolism. The maximum pullulan and biomass production were observed with 5% of molasses. Linear and square effect of molasses (po0.005) was found significant for both pullulan elaboration and biomass synthesis (Table 3). Figs. 1 and 2a–c showed that the pullulan and biomass concentration were significantly increased with the increase in molasses level and reached maximum at 5%. High level of molasses provides the energy for biomass synthesis and also enhances the pullulan production.

3.4. Effect of molasses on pullulan and biomass production

3.5. Effect of KH2PO4 on pullulan and biomass production

Carbon source is an important medium variable, which determines the yield of the product and also serves as an energy source

Presence of mineral salts in the production medium is essential for the metabolic activities of microorganisms and also plays

Please cite this article as: Srikanth, S., et al., Statistical optimization of molasses based exopolysaccharide and biomass production by Aureobasidium pullulans MTCC 2195. Biocatal. Agric. Biotechnol. (2013), http://dx.doi.org/10.1016/j.bcab.2013.11.011i

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Fig. 2. Contour plots showing the combined effect of the medium variables (molasses, KH2PO4, yeast extract and pH) on biomass production by A. pullulans.

a significant role in the utilization rate of carbon and nitrogen sources (Gao et al., 2010). Inorganic salts such as ammonium phosphate, dihydrogen potassium phosphate, dipotassium hydrogen phosphate etc., are supplemented in the medium to satisfy the phosphorus requirement of the microorganisms which are mainly used to synthesize the nucleotides, ATP and also act as cofactor for enzymes. In our study, low level (0.22%) of KH2PO4 plays an important role in pullulan and biomass production. From Table 3, it was understood that the presence of KH2PO4 in the medium greatly influenced the biomass production than pullulan production. Linear effect of KH2PO4 was found significant (p o0.005) for biomass and insignificant for pullulan synthesis. Fig. 1b shows that the interaction between the molasses and KH2PO4 on pullulan production is negligible since the shape of the contour lines are

circular in nature. For biomass synthesis, significant interaction effect was observed when KH2PO4 is combined with pH (Table 3 and Fig. 2d) and yeast extract (Fig. 2e). 3.6. Effect of yeast extract on pullulan and biomass production Yeast extract acts as a nitrogen source in the production medium which contains mixture of amino acids, peptides, vitamins and minerals (Auer and Seviour, 1990). Linear term of yeast extract was observed to be insignificant for both pullulan and biomass production but quadratic term and combined effect with KH2PO4 and pH (p o0.05) was highly significant (Table 3). Figs. 1 and 2(a), (e) and (f) showed that both pullulan and biomass concentration was observed maximum at high level of yeast

Please cite this article as: Srikanth, S., et al., Statistical optimization of molasses based exopolysaccharide and biomass production by Aureobasidium pullulans MTCC 2195. Biocatal. Agric. Biotechnol. (2013), http://dx.doi.org/10.1016/j.bcab.2013.11.011i

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extract (0.25%). Previous literature on biopolymer production suggested that the synthesis of biopolymers were enhanced in nitrogen deficient medium or supplement with less amount of nitrogen sources (Chung et al., 2001). In this study, the optimal level of yeast extract was observed as 0.25%, which is considered sufficient for the maximum biopolymer production. 3.7. Effect of pH on pullulan and biomass production Pullulan production is highly influenced by medium pH during the fermentation process. Previous literature showed that acidic pH of the medium was found favorable environment for pullulan and biomass production by A. pullulans (Cheng et al., 2009; Li et al., 2009; Youssef et al., 1998; Roukas and Biliaderis, 1995). Linear effect of pH showed significance (p o0.05) for pullulan production but no significance was observed for biomass production (Table 3). Table 3 shows that the interaction effect of pH with molasses has good impact on both pullulan (p o0.005) and biomass production (p o0.05). Ono et al. (1977) studied the effect of initial pH on pullulan elaboration and reported that the pullulan concentration was found maximum at acidic pH 6 and decrease in polysaccharide levels with fall in pH. Our predicted result of pH 6.4 had good agreement with the results reported by Ono et al. (1977) and Roukas and Biliaderis (1995) using carob pod extracts as substrate for pullulan production. From the available reports, it was understood that the optimal initial pH for pullulan synthesis depends on various fermentation parameters like type of microorganism used, the production medium composition and physical conditions (Pollock et al., 1992). 4. Conclusion Response surface method was successfully employed to study the effect of process variables and to determine the optimal conditions for maximum pullulan and biomass production by A. pullulans. Assays for pullulan and biomass production were conducted and individual, quadratic and interaction effects of the variables were studied. Linear, quadratic and interaction effects of molasses were found significant in this study. Response was targeted to pullulan (45 g/L) and biomass (1.25 g/L) to obtain the optimal values and the optimized parameter values were found to be molasses 5.0%, KH2PO4 0.22%, yeast extract 0.25% and pH 6.4. Molasses was found more significant factor in this study and it may be employed as an alternate carbon source for the economical production of pullulan on industrial scale since it is easily available and much cheaper than the synthetic carbon sources. Acknowledgement Authors are acknowledging with thanks to the Management of SRM University, Director (E&T) for supporting the research and also thank the Department of Biotechnology for providing the facilities to carry out this study References Auer, D.P.F., Seviour, R.J., 1990. Influence of varying nitrogen sources on polysaccharide production by Aureobasidium pullulans in batch culture. Appl. Microbiol. Biotechnol. 32, 637–644. Banik, R.M., Santhiagu, A., Upadhyay, S.N., 2007. Optimization of nutrients for gellan gum production by Sphingomonas paucimobilis ATCC-31461 in molasses based medium using response surface methodology. Bioresour. Technol 98, 792–797. Bouallagui, H., Touhami, Y., Hanafi, N., Ghariani, A., Hamdi, M., 2013. Performances comparison between three technologies for continuous ethanol production from molasses. Biomass Bioenergy 48, 25–32.

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Please cite this article as: Srikanth, S., et al., Statistical optimization of molasses based exopolysaccharide and biomass production by Aureobasidium pullulans MTCC 2195. Biocatal. Agric. Biotechnol. (2013), http://dx.doi.org/10.1016/j.bcab.2013.11.011i