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Research Article

January 2017

© 2017, IJERMT All Rights Reserved Page | 27

International Journal of

Emerging Research in Management &Technology

ISSN: 2278-9359 (Volume-6, Issue-1)

Optimization of Various Process Parameters Using Response Surface

Methodology for Exopolysaccharide Production from a Novel Strain

Pediococcus acidilactici KM0 (Accession Number KX671557)

Isolated from Milk Cream Kanika Sharma

*, Nivedita Sharma,

Jasveen Bajwa, Sunita Devi

Microbiology Research Laboratory, Department of Basic Sciences, Dr Y S Parmar University of Horticulture and

Forestry, Nauni, Solan, HP, India

DOI: 10.23956/ijermt/V6N1/117

Abstract—

ediococcus acidilactici KM0 (accession number KX671557) isolated from milk cream was optimized by

response surface methodology by using the Design Of Experiments (DOE) to evaluate the interactive effects

of the process parameters i.e. incubation time (h), temperature (◦C), pH, carbon concentration, nitrogen

concentration production for the production of EPS . Maximum EPS production of 32.64 mg/ml observed after 24 h

incubation at 35 ◦C using 1.50 % and 3% of carbon and nitrogen concentration respectively, at pH 6.5.

Keywords— Lactic acid bacteria, Exopolysaccharides, Milk cream, Pediococcus acidilactici KMO, RSM, DOI

I. INTRODUCTION

Lactic acid bacteria (LAB) are generally regarded as safe, gram-positive microorganisms [1] that play an

essential role in the industrial production of various products and are excellent producers of exopolysaccharides (EPS).

As bacteria belonging to this group are harmless to human and animal health [2] therefore investing in the production of

EPS for industrial use including food industry has become particularly interesting. It has been suggested that the health

promoting effect of EPS-producing strains are related to the biological activities of these biopolymers The main

beneficial effects for the human health assigned to these EPS are such as cholesterol lowering, antitumoural or

immunomodulating activities etc. [3,4] Demand for natural polymers for various industrial applications has lead to a

vibrant interest in exopolysaccharides production . Most of the EPS producing LAB can be isolated from different

fermented foods such as dahi, lassi, yoghurt, cultured buttermilk, cheeses, kefir, and other fermented dairy products[5]

Incorporation of EPS or EPS-producing potential starters in food products can provide viscosifying, stabilizing,

and water-binding functions [6]. The composition of EPS produced by LAB are strain dependent and affected by the

nutritional and environmental conditions. Keeping in view the increasing demand of microbial EPS, it has become

essential to increase the polymer production by manipulating the culture conditions [7,8].

Thus it has become important to design a statistical experiment to achieve an excellent yield of microbial EPS

with an optimal production medium and process parameters. Usually the production parameters are optimized

considering only single factor at a time without taking account of interactions between parameters [9]. This method is

time consuming and requires a large number of experiments to determine the optimum levels of production and process

parameters These limitations can be overcome by Response surface methodology (RSM) which is a classical method for

evaluating the interactions between a set of independent experimental factors and observed responses and a collection of

mathematical techniques for designing experiments[10] , while at the same time reducing the number of experiments

required to determine optimal conditions[11,12]. RSM has been recently used in optimization of bioprocesses such as

cultivation and process conditions [13,14]

The information regarding isolation of EPS producing cultures from different fermented food products of India

and to enhance production of RSM by optimizing different process parameters is scanty. The present study was

undertaken to optimize the interaction of various process parameters i.e incubation time (h), temperature (◦C), pH,

carbon concentration and nitrogen concentration using response surface methodology for maximum EPS production

from a first time reported a potential EPS producing strain of Pediococcus acidilactici KM0 (accession number

KX671557) from milk cream

II. MATERIALS AND METHODS

II. A Microorganism and culture medium

Hyper EPS producing strain of Pediococcus acidilactici KM0 (accession number- KX671557) used in this study

was isolated first time from milk cream 32.64 mg/ml of EPS production . The stock culture was maintained on agar slants

at 40C using MRS agar medium.

II. B Optimization of Process Parameters

Response Surface Methodology (RSM), is used to explain the individual as well as combined effects of all the

factors in a production process [15]. RSM is an empirical statistical technique employed for multiple regression analysis

P

Sharma et al., International Journal of Emerging Research in Management &Technology



by using quantitative data obtained from designed experiments to solve multivariate equations simultaneously. The

dependent variable was EPS production and the independent variables chosen were incubation time (A), temperature (B),

pH (C), carbon source (C) and nitrogen source (E). The central composite design (CCD) with five factors at five levels

was employed to investigate the first and higher order main effects of each factor and the interactions among them. The

experiment divided into 1 block contained 50 runs. The minimum and maximum ranges of the independent variables are

given in Table 1. The experimental design included 50 flasks with three replicates at their central coded values. The

mathematical relationship of response (EPS production) and variables i.e. A, B, C, D and E was approximated by a

quadratic model equation. The response value in each case is the average of triplicate experiments.

II. C Central composite design (CCD)

The central composite design (CCD) was employed. The coded terms and actual values are presented in Table 1.

Table I. Range of Values for Independent Variables Used in Central Composite Design (CCD) Of RSM

Independent variables Units Low High

Incubation time(A) H 12 36

Temperature (B) °C 12 40

pH (C) - 30 50

Carbon source (D) % 0.31 2

Nitrogen source (E) % 1.81 4.19

Regression analysis was performed on the data obtained. A second-order polynomial equation was used to fit

the data by multiple regression procedure. This resulted in an empirical model that related the response measured to the

independent variables of the experiment. For a 5-factor system the model equation is

Y=β0+β1A+β2B+β3C+β4D+β5E+β11A2+β22B2+β33C2+β44D2+β55E2+β12AB+β13AC

+β14AD+β15AE+β23BC+β24BD+β25BE+β34CD+β35CE+β45DE

where Y (EPS) is the predicted response; β0 is the intercept; β1, β2, β3, β4 and β5 are the linear coefficients;

β11, β22, β33, β44 and β55 are the squared coefficients; β12, β13, β14, β15, β23, β24, β25, β34, β35 and β45 are the

interaction coefficients and A, B, C,D, E, A2, B2, C2, D2 E2, AB, AC, AD, AE, BC, BD, BE, CD, CE and DE are

independent variables. The proportion of variance explained by the polynomial models obtained was given by the

multiple coefficient of determination, R2. The fitted polynomial equation was expressed as 3-dimensional response

surface plots to find the concentration of each factor for maximum EPS production. These shows the relationship

between the responses and the experimental levels of each factor used in the design. To optimize the level of each factor

for maximum response „numerical optimization‟ process was employed. The combination of different optimized

parameters, which gave maximum EPS yield, was tested experimentally to validate the model. Although there were

many articles about EPS extraction of microorganism [16,17] while in the present study, the extraction condition was

firstly optimized with CCD design.

II.D Model validation

The mathematical model generated during RSM implementation was validated by conducting check point

studies. The experimentally obtained data were compared with the predicted one, and the prediction error was calculated.

III. RESULT AND DISCUSSION

III. A Optimization of Process Parameters by RSM

Table II. Optimization of Process Parameters for EPS Production

Run

Incubation

time(h)

Temperature

(ᵒC)

pH Carbon

Concentration

(%)

Nitrogen

Concentration

(%)

Actual value

(mg/ml)

Predicted

value(mg/ml)

1 12.00 30.00 6.00 1.00 3.50 11.56 10.68

2 24.00 35.00 6.50 2.69 3.00 22.61 21.45

3 24.00 35.00 6.50 1.50 3.00 32.64 32.86

4 24.00 35.00 6.50 1.50 3.00 32.64 32.86

5 36.00 30.00 6.00 2.00 2.50 22.61 22.41

6 12.00 40.00 7.00 2.00 3.50 14.45 14.53

7 24.00 35.00 6.50 1.50 3.00 32.64 32.86

8 52.54 35.00 6.50 1.50 3.00 12.24 17.24

9 36.00 30.00 6.00 1.00 3.50 20.23 19.00

10 36.00 40.00 7.00 1.00 3.5 21.41 20.01

11 12.00 40.00 7.00 1.00 2.50 12.75 12.62

12 24.00 35.00 6.50 1.50 1.81 32.30 34.23




13 24.00 23.11 6.50 1.50 3.00 0 -1.71

14 36.00 40.00 7.00 2.00 2.50 22.61 21.34

15 36.00 30.00 7.00 2.00 2.50 23.205 22.67

16 24.00 35.00 6.50 1.50 3.00 32.64 32.86

17 36.00 30.00 6.00 1.00 2.50 20.82 19.07

18 24.00 35.00 7.00 1.50 3.00 32.64 32.86

19 24.00 35.00 6.50 1.50 4.19 32.47 33.83

20 12.00 30.00 7.00 2.00 2.50 14.28 14.72

21 24.00 35.00 7.69 1.50 3.00 12.03 11.30

22 12.00 40.00 7.00 1.00 3.50 12.49 12.19

23 12.00 30.00 6.00 2.00 2.50 12.15 12.72

24 12.00 30.00 7.00 1.00 2.50 12.07 12.14

25 12.00 30.00 7.00 2.00 3.50 12.66 14.01

26 12.00 30.00 6.00 2.00 3.50 12.58 12.18

27 36.00 40.00 7.00 1.00 2.50 21.06 19.81

28 36.00 40.00 6.00 2.00 2.50 22.64 21.85

29 24.00 35.00 6.50 1.00 3.00 32.81 32.86

30 24.00 35.00 6.50 2.00 3.00 12.92 13.02

31 12.00 40.00 6.00 1.50 2.50 13.43 13.11

32 12.00 30.00 7.00 0.31 3.50 11.98 11.76

33 36.00 30.00 7.00 2.00 3.50 20.40 19.40

34 24.00 35.00 6.50 1.00 3.00 32.64 32.86

35 36.00 40.00 6.00 1.00 2.50 21.42 19.56

36 24.00 46.89 6.50 1.50 3.00 0 1.05

37 12.00 40.00 6.00 1.00 3.50 12.58 11.94

38 12.00 40.00 7.00 1.50 2.50 14.45 14.79

39 24.00 35.00 5.31 1.00 3.00 4.79 8.82

40 12.00 30.00 6.00 2.00 2.50 11.76 10.89

41 24.00 35.00 6.50 1.50 3.00 32.04 32.86

42 36.00 30.00 7.00 1.00 2.50 20.58 19.64

43 36.00 40.00 6.00 2.00 3.50 22.61 21.42

44 36.00 40.00 7.00 2.00 3.50 22.49 22.29

45 12.00 40.00 6.00 1.00 2.50 12.83 11.70

46 36.00 40.00 6.00 1.00 3.50 22.01 19.93

47 -4.54 35.00 6.50 1.50 3.00 0 2.24

48 36.00 30.00 7.00 2.00 3.50 22.37 22.09

49 36.00 30.00 6.00 2.00 3.50 21.89 20.94

50 12.00 40.00 6.00 2.00 3.50 12.92 13.02

Optimum levels of the above mentioned factors, and the effect of their interactions on exopolysaccharide

production were determined by CCD. Table 2 and Fig 1, lists the details of the actual values employed in the RSM as

well as the predicted and observed responses for EPS production (Y). Second order regression equation provided the

levels of EPS production as function of initial values of incubation period, temperature, pH, carbon concentration and

nitrogen concentration which can be predicted by the following equation

Exopolysaccharides production= +32.86 + 3.98 *A+ 0.25 *B + 0.52 *C + 1.03 *D - 0.083 *E -4.44 *A2 - 5.52

*B2 - 4.03 *C2- 2.38 *D2 + 0.21 *E2- 0.081 *A *B - 0.17 *A *C + 0.11 *A *D + 0.033 *A *E-0.082 *B *C- 0.10 *B

*D + 0.11 *B *E + 0.19 *C *D - 0.043 *C *E - 0.083 *D *E

Where A=Incubation period, B=Temperature, C=pH, D=Carbon concentration, E=Nitrogen concentration

where Y is exopolysaccharide production (mg/ml), incubation time (A), temperature (B), pH (C), carbon

concentration (D) and nitrogen concentration (E). According to Table 1, central values for independent variables for P.

acidilactici KM0 were obtained at 24 h, 35◦C, 6.5 pH, 1.50% (w/v) and 3 % (w/v) for incubation hour, temperature, pH,

carbon source concentration and nitrogen source respectively where, maximum response (Y) 32.81 mg/ml was achieved.

According to CCD of RSM, A, D, A2, B2, C2, D2 were significant model terms. Interactions of other factors were also

found equally important for exopolysaccharide production. The response surface curves were plotted for the variation in

exopolysaccharide production. Quadratic terms of all the variables were significant. Among the interactions, were found

to contribute to the response at a significant level. A positive P-values for E2, AD, AE, BE indicated a linear effect of




these variables on exopolysaccharide production. Interactions of other factors were also found equally important for EPS

production of the organisms. These experimental findings are in close agreement with the model predictions.

a b

c d

e f

g h




i j

Fig 1. Three dimensional response surface curves for Pediococcus acidilactici KM0 exopolysaccharide production

plotted between a) incubation period & temperature; b) incubation period & pH; c) incubation period & carbon conc.; d)

incubation period & nitrogen conc.; e) temperature & pH; f) temperature & carbon conc.; g) temperature & nitrogen

conc.; h) pH & carbon conc.; i) pH & nitrogen conc.; j)carbon conc. & nitrogen conc.

ANOVA results for the RSM quadratic equation for response Y are shown in Table 3. ANOVA for EPS

production (Y, mg/ml) indicated the „F-value‟ to be 21.75, which implied the model to be significant. Model terms

having values of „Prob>F‟ less than 0.05 are considered significant, whereas those greater than < 0.0001 are insignificant.

The coefficient of determination (R²) was calculated as 0.9742 for exopolysaccharide production of P. acidilactici KM0,

indicating that the statistical model can explain 92.83% of variability in the response. The R² value was between 0 and 1

which indicated that the model was significant in predicting the response. The closer the R2 to 1.0, the stronger the model

and better it is predicted (Oleskowicz et al., 2012).

Table III. ANOVA for Response Surface of P. acidilactici KM0 Analysis of variance table [Partial sum of squares]

Source Sum of

squares

DF Mean

Squares Square

F value Prob > F

Model 2089.36 20 104.47 21.75 <0.0001significant

A 16.04 1 16.04 3.34 0.0779

B 32.40 1 32.40 6.75 0.0146

C 0.30 1 0.30 0.061 0.8059

D 27.12 1 27.12 5.65 0.0243

E 0.31 1 0.31 0.065 0.8001

A2

180.41 1 180.41 37.56 <0.0001

B2

1617.29 1 1617.29 336.74 <0.0001

C2 369.79 1 369.79 76.99 <0.0001

D2 62.99 1 62.99 13.12 0.0011

E2

14.33 1 14.33 2.98 0.0948

E2 14.33 1 14.33 2.98 0.0948

AB 5.15 1 5.15 1.07 0.3089

AC 1.031 1 1.03 0.21 0.646

AD 0.52 1 0.52 0.11 0.7456

AE 0.19 1 0.19 0.039 0.8453

BC 6.46 1 6.46 1.35 0.2555

BD 0.76 1 O.76 0.16 0.6932

BE 0.45 1 0.45 0.094 0.7614

CD 2.12 1 2.12 0.44 0.5115

CE 1.25 1 1.25 2.60 0.9987

DE 0.68 1 0.68 0.14 0.7097

Residual 139.28 29 4.80

Lack of Fit 133.16 22 6.05 6.92 0.0069 significant

Pure Error 6.13 7 0.88

Corrected Total 2228.65 49

R2 =0.9660, Adj R

2 = 0.9564




The „lack of fit p-value‟ of 0.0069 implies the „lack of fit‟ is significant relative to pure error and that the model

fits. ANOVA indicated the R2 value of 0.9660 This again ensured a satisfactory adjustment of the quadratic model to the

experimental data. The adequate precision which measures the signal to noise ratio was 29.30. The „Pred R2‟ of 0.9660is

in reasonable agreement with the „Adjusted R2‟ of 0.9564 for good correlation between observed and predicted results

reflected the accuracy and applicability of the central composite design for the process optimization.

III. B Model adequacy checking

it is important to check the fitted model to ensure that it provides an adequate approximation to the real system.

The residuals from the least squares fit play an important role in judging model adequacy. By constructing a normal

probability plot for the residuals, a check was made for the normality assumption, as given in Fig 2 for P. acidilactici

KM0, the normality assumption was satisfied as the residual plot approximated along a straight line. The general

impression is that the residuals scatter randomly on the display, suggesting that the variance of the original observation is

constant for all values of predicated response (Y). Fig. 2 are satisfactory, so we conclude that the empirical model is

adequate to describe the exopolysaccharide activity by response surface. DESIGN-EXPERT PlotResponse 1

Studentized Residuals

Norm

al %

Pro

babi

lity

Normal Plot of Residuals

-3.99 -2.62 -1.25 0.11 1.48

1

5

10

20

30

50

70

80

90

95

99

Fig 2. Normal probability of internally studentized residuals for P. acidilactici KM0

III. C Validation of the model

The statistically optimal values of factors were obtained when moving along the major and minor axis of the

contour, and the response at the centre point yielded maximum exopolysaccharide production. These observations were

also verified from canonical analysis of the response surface. The canonical analysis revealed a minimum region for the

model. The stationary point presenting a maximum exopolysaccharide production for P. acidilactici KM0 had the critical

values as 32.81 mg/ml at 35°C temperature and 6.5 pH, 1.5% carbon concentration and 3% nitrogen concentration. The

optimum levels of the said variables were then determined by employing RSM. Present findings are also in accordance

with the findings of Zambare, [18] in terms of the independent variables obtained. There are a number of reports in

literature which suggests that lower temperatures enhance the production of EPS [19,7]. Although a higher EPS

production has also been associated with optimal growth conditions [3]

IV. CONCLUSION

Potential probiotic lactic acid bacteria capable of excellent production of EPS Pediococcus acidilactici KM0

(accession number KX671557) isolated from milk cream. The five important parameters (incubation time, temperature ,

pH, carbon concentration and nitrogen concentration) had significant positive effects on the EPS production. The

optimum values of these five variables were optimized by RSM by using Design of Experiments(DOE) and interactive

effects of the process parameters were evaluated. Maximum EPS production of 32.64 mg/ml was observed with 653.81

percent increase after 24 h incubation at 35ºC using 1.50 % and 3% of carbon and nitrogen concentration respectively, at

pH 6.5. These factors resulted in an impressive increase in exopolysaccharide activity. Thus, the isolated strain

Pediococcus acidilactici KM0, accession number KX671557 proves to have a great potential for exopolysaccharide

production.

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