 Open Access
 Total Downloads : 600
 Authors : Varinder Kaur, M. B. Bera, P. S. Panesar
 Paper ID : IJERTV2IS110050
 Volume & Issue : Volume 02, Issue 11 (November 2013)
 Published (First Online): 01112013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Modeling for Exopolysaccharide Production by Alcaligenes faecalis B14
Varinder Kaur a, M. B. Bera a*, P. S. Panesar a
Bioatechnology Research Laboratory,
Department of Food Engineering and Technology, Sant Longowal Institute of Engineering and Technology, Longowal148106, Punjab, India,
Abstract
Exopolysaccharides (EPS) are environment friendly natural polymers secreted by microorganisms in the surrounding medium. EPS shows diverse applications such as food formulations, pharmaceutical and chemical industry etc. In the present study, optimization of fermentation process parameters and fermentation kinetics with respect to exopolysacharide production from the Alcaligenes faecalis B14 was done. A simple kinetic model was proposed using the logistic, the LuedekingPiret and LuedekingPiretlike equation for growth, EPS production and glucose consumption. The maximum Logistic specific growth rate was determined as max = 0. 0.225 h1 and the values of LuedekingPiret constants and indicated that the EPS production using Alcaligenes faecalis was growth associated. A CCD (central composite Design) was used to study the influence of different parameters on the production of EPS. Optimized values of fermentation parameters include: glucose (3.15% w/v); yeast extract (0.48
% w/v); MgSO4 (0.05% w/v); temperature (30.13 Â°C); and pH (7.20). The maximum EPS production obtained was 12.76Â±0.2 g/L under optimal parameter conditions stated above.
Keywords: Exopolysaccharide, Alcaligenes sp., optimization, Kinetics, fermentation

Introduction
Bacterial exopolysaccharides (biopolymer) are environment friendly natural polymers. They are either homopolysaccharide or heteropolysaccharide biosynthesized by a wide range of bacteria [1,2]. Biopolymers have potential applications in the food, pharmaceutical, and other industries due to their unique structure and physical properties. Few exopolysaccharides (EPS) like xanthan from Xanthomonas campestris, sphingans from Sphingomonas sp., cellulose from Acetobacter xylinium, etc. have gained commercial importance [3,4,5]. In food industry natural polymers are known for their application as thickeners, stabilizers, emulsifiers, binders, gelling agents, and film former etc. Due to its wide industrial applications, it has generated much interest in microbial EPS research [6,7]. Further, production of EPS by bacteria have created great interest among researchers, because bacteria can be easily cultivated under controlled environment as well as secrete wide range of biopolymers with characteristic composition and properties [2,8,9,10,11].
The production of microbial EPS depends on many fermentation factors, which includes strain type, composition of fermentation medium and fermentation conditions [12]. Thus, the optimization of nutritional (concentration of carbon, nitrogen, and mineral salts source) and physical parameters (pH, temperature, etc) is required to get optimum production of EPS from microbial stains.
Response surface methodology (RSM) is a statistical method based on the multivariate nonlinear model that has been widely used method for optimization process. It is also useful in the study of interactions of the various parameters affecting the process. A central composite design (CCD) helps to develop the matrix for optimization of the variables in cultivation medium [13]. It has been used in the optimization of EPS production by strains Zunongwangia profunda SMA87 and Frateuria aurentia [14,15]. Also the optimization of gellan gum production by Sphingomonas paucimobilis ATCC 31461 using central composite design has been reported [16].
In our laboratory, exopolysaccharide containing monomer composition similar to welan gum and having good viscosity and emulsifying properties was produced by fermentation process from newly isolated strain Alcaligenes faecalis B14.
The development of kinetic models is crucial for understanding, controlling, and optimizing fermentation processes. The information gained using kinetic models along with designed experiments can aid bioengineers to design and control microbial processes. Gilani et al., [17] and Wang et al., [18] reported growth associated EPS (xanthan and gellan) production and glucose consumption in the fermentation kinetics studies on Xanthomonas campestris and Sphingomonas paucimobilis ATCC 31461.
Keeping in view of the industrial importance of EPS, the present investigation was carried out to study fermentation kinetics using unstructured models of cell growth, product formation and substrate consumption and optimization of fermentation parameters using RSM to obtain maximum EPS production by A. faecalis B14.

Material and methods

Materials
All the chemicals used in the experiments were purchased from HiMedia Laboratories Pvt. Ltd, Mumbai, India.

Microorganism and growth conditions
The strain used for the present study was Alcaligenes faecalis B14 (accession numberKF035058), isolated from subsoil of Punjab state of India [19]. The inoculum was prepared in medium containing glucose (2%), yeast extract (0.1%), peptone (0.3%), dihydrogen potassium phosphate (0.2%) and magnesium sulphate (0.01%) and incubated at 30 oC in a rotary shaker (100 rpm). 5% culture was inoculated in fermentation medium which was optimized using RSM. The EPS was recovered by following the method of Banik & Santhiagu (2006) [20], with slight modification. The modification included heat treatment (80oC) for 15 min, cooling at room temperature and neutralization of pH of fermentation broth. The broth was centrifuged at 10,000 Ã— g for 10 min to separate the cells. Isopropanol was added to the supernatant and kept at 4 oC for 24 hrs to precipitate the EPS. Again centrifuged to obtain the EPS, freeze dried and weighed.

Experimental design and statistical analysis
A Central Composite Rotatable Design (CCRD) with five variables at five levels each were used to investigate the effects of concentration of glucose, yeast extract, MgSO4 and process parameters (pH, temperature) on EPS production. Based on preliminary experiment, low and high levels were selected for each five independent variable to get maximum EPS production. The design was generated by Design Expert, Trial version 6.0.10 statistical software (StatEase INC., Minneapolis, MN, USA). In this design, the experiments were conducted randomly. The matrix designs for the levels of variables in coded and uncoded form for EPS production are given in Table 1 and Table 2, respectively. A second order polynomial equation (Eq. 1) was fitted to study the effect of variable as given below
n n1 n n
Y x x x x2
(1)
i 0 i i i1
i1
ji1
ij i j
i1
ii i
Where Yi = EPS production (g/L)
xi & xj= Independent variables, (x1 = glucose concentration, x2 = yeast extract concentration, x3 = MgSO4 concentration, x4 = Temperature (Â°C), x5 = pH), o is the value of coefficient of fitted response at the central point of design, i, ij, ii are the linear, quadratic and cross product regression coefficients, respectively.
Total number of experiments = 2 No. of Variables + 2* No. of variables + Central Points
For five variables, total no. of experiments = 25 +2*5 + 8 = 50 Five different levels for each experiment in coded form are
– , 1, 0, +1, +
Where, = 2 No. of variables / 4 = 2 5/4 =2.378
_
2 X i X i
R
R
i
i
Coded value x
i
(2)
_
p>i
i
Where Xi is the actual setting in the uncoded units of the ith factor X is the average of the low and high settings
for the ith factor and Ri is the range between the low and high settings. Analysis of variance (ANOVA) and regression analysis were carried out.

Kinetic models
The logistic equation: In many biopolymer fermentation systems, the microbial processes do not follow the classical kinetic model of substratelimited cell growth and product formation proposed by Monod in 1949. Alternative to this, the logistic equation, a substrateindependent model, is used [21]. Therefore, in fermentation process, the cell growth has been characterized by the logistic equation as shown in equation (3)
(3)
Where, Âµm is the maximum specific growth rate (h1) and Xm is the maximum attainable biomass concentration (g/L). The integrated form of equation (3) using X= X0 (t= 0) gives a sigmoid variation of X as a function of t, which may represent both an exponential and a stationary phase (equation 4).
(4)
The LuedekingPiret equation: The production of microbial metabolites was based on the LuedekingPiret equation [21]. Where, the product formation rate (rp) depends on both the instantaneous biomass concentration and the growth rate in a linear manner.
.. (5)
where and are the product formation constants and may differ under different fermentation conditions. The integration of equation (5) using P = P0 at t = 0 with equation (6) provides:
.. (6) The modified LuedekingPiret equation: Glucose is used in the present fermentation process, which is required by the microbe to form cell components and metabolic products as well as for the maintenance of cells. The modified LuedekingPiret equation (equation 7) was used to explain the glucose consumption.
. (7)
where mS is the maintenance coefficient (g substrate Â· g cell/h), YX/S and YP/S are the is the cell yield coefficient and product yield coefficient, respectively for glucose
Combining equations 5 and 7 gives:
Equation 8 can be simplified to express glucose consumption by the following model:
(8)
(9)
where, is the sum of 1/YX/S and /YP/S and is the sum of /YP/S and mS. The integration of equation 9 using S=S0 at t=0 gives:


Results and Discussion
(10)
A Central Composite Rotatable Design was used to select the concentration of various nutritional and physical parameters to get conditions responsible for maximum exopolysaccharide production. The preliminary experiments were followed to choose the low and high levels for the five independent variables i.e. pH (6.58), incubation temperature (2535oC), glucose concentration (%, w/v; 15), yeast extract concentration (%, w/v; 0.30.7) and MgSO4 (%, w/v; 0.040.06).
Table 1. Coded levels of different variables in the central composite design variables (CCD)
Factor Variable Coded Levels
2.378
1
0
+1
+2.378
X1
Glucose (%, w/v)
0.62
1.0
3.0
5.0
5.38
X2
Yeast extract (%, w/v)
0.02
0.3
0.5
0.7
0.98
X3
MgSO4
0.03
0.04
0.05
0.06
0.07
X4
Temperature (Â°C)
18.11
25
30
35
41.89
X5
pH
5.47
6.5
7.25
8
9.03

Statistical analysis for EPS production
A total of 50 trials of CCD were carried out randomly to optimize the production of EPS, the results of these trails were presented in Table 2. The change of concentration of variables affects the EPS production ranging from 1.23 to
12.76 g/L. A secondorder polynomial regression model (equation 11) was established from the experimental data:
Y = +12.17 + 0.43×1 – 0.31 x2 – 0.34 x3+0.21 x40.22 x50.28 x1 x20.25 x1 x30.13 x1 x4 + 0.11
x1 x5+9.375E003 x2 x30.14 x2 x4 +0.049 x2 x50.11 x3 x4+0.061 x3 x50.15 x4 x51.63 x 21.59 x221.61 x 21.81
1 3
x 21.65 x 2 (11)
4 5
where Y was the EPS production, x1 to x5 are independent variables.
ANOVA analysis was performed (Table 3) which indicated that the quadratic regression to produce the second order model was significant (p<0.001; F value 65.83). The value of the coefficient of determination near to 1 indicates better correlation of quadratic model to the experimental data [15]. The coefficient of determination R2 was 0.9784 for EPS production, indicating that only 2.16% of the total variation was not explained by the model.
The pvalue was used to ensure the significance of coefficients and to determine the interaction strength between each independent variable [22]. The smaller pvalue points to the bigger significance of the corresponding coefficient [23]. The coefficients of quadratic polynomial equation along with pvalues are given in Table 3. It indicated that linear and quadratic terms of glucose, yeast extract, MgSO4, temperature, and pH have significant effect on EPS production at 5% level of significance (p<0.05).
The relative magnitude of coefficients revealed that the linear term of glucose had maximum positive effect (= 0.43) followed by temperature (= 0.21), whereas, yeast extract (= 0.31), MgSO4 (= 0.34), pH (= – 0.22) had negative effect on EPS production. The interactive terms glucose and pH ( = 0.11), had the maximum positive effect followed by MgSO4 and pH (= 0.061), yeast extract and pH (= 0.049), yeast extract and MgSO4 (= 9.375Ã—103) on EPS production. The interaction of glucose and yeast extract ( = 0.28), glucose and MgSO4 ( = 0.25), glucose and temperature ( = 0.13), yeast extract and temperature (= 0.14) MgSO4 temperature (= 0.11), temperature and pH (= 0.015), had negative effect on EPS production at 5% level of signifince (p< 0.05). The interactive effects of these parameters are also shown in the Fig. 1A1C.
Table 2. The matrix of CCD and values of experimental data
Glucose Conc.
Yeast extract Conc.
MgSO4
Incubation temp.
pH
Yield (g/L)
(%, w/v)
(%, w/v)
(oC)
2.00
0.30
0.04
25.00
6.50
2.97
4.00
0.30
0.04
25.00
6.50
4.25
2.00
0.70
0.04
25.00
6.50
3.29
4.00
0.70
0.04
25.00
6.50
4.02
2.00
0.30
0.06
25.00
6.50
3.24
4.00
0.30
0.06
25.00
6.50
4.21
2.00
0.70
0.06
25.00
6.50
3.54
4.00
0.70
0.06
25.00
6.50
2.25
2.00
0.30
0.04
35.00
6.50
4.65
p>4.00 0.30
0.04
35.00
6.50
5.79
2.00
0.70
0.04
35.00
6.50
3.92
4.00
0.70
0.04
35.00
6.50
4.37
2.00
0.30
0.06
35.00
6.50
3.98
4.00
0.30
0.06
35.00
6.50
4.68
2.00
0.70
0.06
35.00
6.50
4.21
4.00
0.70
0.06
35.00
6.50
2.94
2.00
0.30
0.04
25.00
8.00
2.33
4.00
0.30
0.04
25.00
8.00
4.89
2.00
0.70
0.04
25.00
8.00
3.16
4.00
0.70
0.04
25.00
8.00
3.78
2.00
0.30
0.06
25.00
8.00
2.98
4.00
0.30
0.06
25.00
8.00
4.11
2.00
0.70
0.06
25.00
8.00
3.15
4.00
0.70
0.06
25.00
8.00
3.65
2.00
0.30
0.04
35.00
8.00
4.23
4.00
0.30
0.04
35.00
8.00
4.87
2.00
0.70
0.04
35.00
8.00
2.97
4.00
0.70
0.04
35.00
8.00
3.88
2.0
0.3
0.06
35.00
8.00
3.43
4.0
0.3
0.06
35.00
8.00
3.96
2.0
0.7
0.06
35.00
8.00
3.67
4.0
0.7
0.06
35.00
8.00
2.89
0.62
0.5
0.05
30.00
7.25
1.23
5.38
0.50
0.05
30.00
7.25
5.34
3.00
0.02
0.05
30.00
7.25
4.45
3.0
0.98
0.05
30.00
7.25
2.62
3.0
0.5
0.03
30.00
7.25
5.12
3.0
0.5
0.07
30.00
7.25
1.67
3.00
0.50
0.05
25.24
7.25
2.16
3.00
0.50
0.05
34.76
7.25
2.43
3.00
0.50
0.05
30.00
5.47
4.33
3.00
0.50
0.05
30.00
9.03
2.09
3.00
0.50
0.05
30.00
7.25
11.69
3.00
0.50
0.05
30.00
7.25
12.11
3.00
0.50
0.05
30.00
7.25
12.11
3.00
0.50
0.05
30.00
7.25
12.1
3.00
0.50
0.05
30.00
7.25
12.63
3.00
0.50
0.05
30.00
7.25
11.87
3.00
0.50
0.05
30.00
7.25
12.54
3.00
0.50
0.05
30.00
7.25
12.76
From the present study, the 3D response surfaces (Figure 1) were generated to investigate the interactions between any two variables for EPS production. Figure 1A shows the effect of glucose and yeast extract on EPS production and their interaction when the other variables were kept constant. Appropriate carbon and nitrogen concentration is required for microbial growth and to obtain high production. The maximum EPS production was obtained with glucose 3%, yeast extract 0.05%. Further increase in concentration of glucose and yeast extract in the media, the production was decreased. The decrease is probably due to osmotic stress in the fermentation medium for cell
growth thus affecting the EPS production [24]. In comparison to carbon concentration, low nitrogen concentration is essential parameter for EPS production [7]. The maximum EPS production from Alternaria alternate using yeast extract as organic nitrogen source has been reported [25].
It was observed that there was an increases in the EPS production, with the increase in glucose (up to 3%) and MgSO4 (up to 0.05%) concentration and beyond these concentrations, EPS production was decreased (Figure 1B). This may be due to influence of mineral ions on the catalytic activities of enzymes involved in growth and EPS production [26].
The effect of pH and temperature on EPS production and their interaction is shown in Figure 1C. There was an increase in EPS production with increase in pH (6 to 7) and temperature (25 to 30 Â°C) and then decreased. The incubation temperature of 30 Â°C for EPS production was found optimum and beyond 30 Â°C temperature an inhibitory effect on EPS production was seen. Also the initial pH increase found to be favorable for growth and EPS production. The variation in pH causes changes in hydrogen ion concentration in the fermentation medium that effects the cell growth and EPS production [27].
Source
value
Sum of Squares
DF
Mean Square
F value
Prob.
Model
12.17
530.24
1
26.51
65.83
< 0.0001
Glucose
0.43
7.98
1
7.98
19.82
0.0001
Yeast extract
0.31
4.04
1
4.04
10.04
0.0036
MgSO4
0.34
4.98
1
4.98
12.36
0.0015
Temp
0.21
1.98
1
1.98
4.92
0.0346
pH
0.22
2.17
1
2.17
5.38
0.0276
Glucose2
1.63
148.08
1
148.08
367.68
< 0.0001
Yeast extract 2
1.59
140.17
1
140.17
348.05
< 0.0001
MgSO 2
1.61
144.57
1
144.57
358.98
< 0.0001
temp2
1.81
181.53
1
181.53
450.75
< 0.0001
pH2
1.65
150.49
1
150.49
373.68
< 0.0001
Glucose * Yeast extract
0.28
2.58
1
2.58
6.40
0.0171
Glucose * MgSO4
0.25
1.92
1
1.92
4.77
0.0372
Glucose * temp
0.13
0.55
1
0.55
1.36
0.2538
Glucose * pH
0.11
0.36
1
0.36
0.90
0.3514
Yeast extract * MgSO4
9.375Ã—103
2.813Ã—103
1
2.813Ã—103
6.984Ã—103
0.9340
Yeast extract * temp
0.14
0.66
1
0.66
1.64
0.2102
Yeast extract * pH
0.049
0.078
1
0.078
0.19
0.6631
MgSO4 * temp
0.11
0.35
1
0.35
0.88
0.3570
MgSO4 * pH
0.061
0.12
1
0.12
0.29
0.5930
temp * pH
0.015
0.76
1
0.76
1.88
0.1810
Residual
11.68
29
0.40
Lack of Fit
10.68
22
0.49
Pure Error
1.00
7
0.14
3.38
0.0516
Cor Total
541.92
49
RSquared
0.9784
Adj RSquared
0.9636
Source
value
Sum of Squares
DF
Mean Square
F value
Prob.
Model
12.17
530.24
1
26.51
65.83
< 0.0001
Glucose
0.43
7.98
1
7.98
19.82
0.0001
Yeast extract
0.31
4.04
1
4.04
10.04
0.0036
MgSO4
0.34
4.98
1
4.98
12.36
0.0015
Temp
0.21
1.98
1
1.98
4.92
0.0346
pH
0.22
2.17
1
2.17
5.38
0.0276
Glucose2
1.63
148.08
1
148.08
367.68
< 0.0001
Yeast extract 2
1.59
140.17
1
140.17
348.05
< 0.0001
MgSO 2
1.61
144.57
1
144.57
358.98
< 0.0001
temp2
1.81
181.53
1
181.53
450.75
< 0.0001
pH2
1.65
150.49
1
150.49
373.68
< 0.0001
Glucose * Yeast extract
0.28
2.58
1
2.58
6.40
0.0171
Glucose * MgSO4
0.25
1.92
1
1.92
4.77
0.0372
Glucose * temp
0.13
0.55
1
0.55
1.36
0.2538
Glucose * pH
0.11
0.36
1
0.36
0.90
0.3514
Yeast extract * MgSO4
9.375Ã—103
2.813Ã—103
1
2.813Ã—103
6.984Ã—103
0.9340
Yeast extract * temp
0.14
0.66
1
0.66
1.64
0.2102
Yeast extract * pH
0.049
0.078
1
0.078
0.19
0.6631
MgSO4 * temp
0.11
0.35
1
0.35
0.88
0.3570
MgSO4 * pH
0.061
0.12
1
0.12
0.29
0.5930
temp * pH
0.015
0.76
1
0.76
1.88
0.1810
Residual
11.68
29
0.40
Lack of Fit
10.68
22
0.49
Pure Error
1.00
7
0.14
3.38
0.0516
Cor Total
541.92
49
RSquared
0.9784
Adj RSquared
0.9636
Table 3. Regression summery and ANOVA table for enzyme activity for uncoded value of process variable
4
3.1.1Optimization of EPS production
A numerical optimization technique was used to optimize the process variables for EPS production. The optimum values of the variables to get maximum EPS production were: glucose concentration 3.15% (w/v); yeast extract concentration 0.48 % (w/v); MgSO4 concentration 0.05% (w/v); temperature 30.13 Â°C; pH 7.20. Under these optimal conditions of variables the predicted value for EPS production was found to be 12.76 g/L. The experiments were carried out in triplicate under the optimal conditions as given above to confirm the result. The mea value of EPS production obtained was 12.76Â± 0.2 g/L which were in accordance with the predicted value. From the results, it was observed that optimum concentration of glucose, nitrogen and mineral ion along with optimum physical parameters (temperature and pH) is critical factor for higher EPS production.
E P S y i e l d ( g / L )
E P S y i e l d ( g / L )
12.8
11.7
10.6
9.5
8.4
0.70
0.60
0.50
3.00
3.50
4.00
12.8
E P S y i e l d ( g / L )
E P S y i e l d ( g / L )
11.675
10.55
9.425
8.3
0.06
3.50
4.00
Yeast extract (%)
A
0.40
0.30 2.00
2.50
Glucose (%)
0.05
Magnesium Sulphate (%)
B
0.04 2.00
2.50
3.00
Glucose (%)
12.8
E P S y i e l d ( g / L )
E P S y i e l d ( g / L )
11.7
10.6
9.5
8.4
8.00
7.25
30.00
31.00
32.00
pH
C 6.50
28.00
29.00
Temperature
Figure 1: 3D plots showing effect of variables: glucose and YE (A); glucose and MgSO4 (B); pH and temperature (C) on EPS yield (g/L)
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126
3.2 Fermentation kinetics
The batch fermentation was carried out under the optimized conditions in one liter production medium and observed the production of EPS.
The EPS production in batch cultivation was rapid during the exponential phase and continued slowly during the stationary phase. Similar trends in the EPS production such as gellan gum and xanthan gum using Sphingomonas paucimobilis ATCC 31461 and Xanthomonas campestris [17,18] and also Pediococcus damnosus strain 2.6 [28] were reported. The maximum exopolysaccharide obtained were (12.76 g /L) with the residual glucose concentration of 11.1 g/L (Figure 2).
Figure 2. Batch fermentation (solid symbolsexperimental; solid lines model prediction) for formation of biomass (), EPS () and glucose consumption () using Alcaligenes faecalis B14
Microbial growth: Logistic equation: In batch fermentation using A. faecalis B14, maximum cell growth obtained were 3.5 g/L at 144 h. A logistic equation was employed to estimate the cell growth and specific growth rate as shown in Figure 2 and 3a. The calculated parameters for A. faecalis B14 were: Xm= 3.5 g/L; X0 was 0.9 g/L and
Âµm was 0.225 (h1). The correlation coefficient for cell growth and specific growth rate were 0.939 and 0.886
respectively. The fitting of experimental values in logistic equation was found to be satisfactory.
Product formation: LuedekingPiret equation: In batch fermentation, the values for the kinetic parameters were 2.058 g Â· g cell1 and 0.067 g Â· g cell h1, respectively. LuedekingPiret equation (Equation 5) was used to estimate the product formation. A comparison between kinetic model predictions and the experimental data were found to be consistent as given in Figure 2 and 3b. The correlation coefficient EPS production and specific product formation rate were 0.987 and 0.941, respectively. In comparing (growthassociated) and (non growthassociated) constants, the value of is higher than , which showed that the rate of exopolysaccharide formation is high during the exponential growth phase. The small value of indicated that product formed at low rate during the stationary growth phase.
(a)
(b)
(c)
Figure 3. Comparison between experimental results (solid symbols) and the model prediction (solid lines) for specific growth rate (a), specific product formation rate (b), and specific substrate consumption rate (c); in batch fermentation.
Substrate consumption: Modified LuedekingPiret equation: Glucose is required by the cells to form cell material and metabolic products as well as the maintenance of cells. As the cell concentration increased, there was decrease in residual glucose concentration. Modified LuedekingPiret equation was used for glucose consumption. At the beginning the glucose concentration was 30 g/L and at the end of fermentation the residual glucose concentration was 11.5 g/L. The comparisons between model prediction data and experimental results are shown in Figure 2 and 3c. The correlation coefficients for glucose consumption and specific glucose consumption rate were 0.898 and 0.869, respectively. The parameters and , provided by the model, were 2.013 g Â· g cell1 and 0.057 g Â· g cell h1, respectively.
On examination of various fermentation kinetics models studied above (Logistic equation, LuedekingPiret equation, Modified LuedekingPiret equation), it was observed that the exopolysaccharide production was growth associated. In comparing growthassociated (, ) and nongrowthassociated (, ) constants, the values of former are higher than later, which showed that the rate of exopolysaccharide formation and glucose consumption is high during the exponential growth phase and at low rate during the stationary growth phase. Similar pattern of growth associated exopolysaccharide (xanthan and gellan) production and glucose consumption was reported in the fermentation kinetics studies using Xanthomonas campestris and Sphingomonas paucimobilis ATCC 31461 [17,18].


Conclusions
Response surface methodology proved to be effective and reliable tool in selecting optimum process conditions for EPS production. The optimized values obtained for the production of EPS production were glucose 3.15% (w/v); yeast extract 0.48 % (w/v); MgSO4 0.05% (w/v); temperature 30.13 Â°C; and pH 7.20. The maximum production obtained was 12.76Â±0.2 g/L under optimal parameter conditions. Using the optimized conditions, the kinetic model for the exopolysaccharide production with Alcaligenes faecalis B14 has been analyzed. The calculated data revealed that the exopolysaccharide production was growth associated. Further research on other functional properties and food toxicology of welan gum like exopolysaccharide is needed to predict its possible use in food industry.

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