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
ISSN: 2278-9359 (Volume-6, Issue-1)
© 2017, IJERMT All Rights Reserved Page | 28
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
Sharma et al., International Journal of Emerging Research in Management &Technology
ISSN: 2278-9359 (Volume-6, Issue-1)
© 2017, IJERMT All Rights Reserved Page | 29
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
Sharma et al., International Journal of Emerging Research in Management &Technology
ISSN: 2278-9359 (Volume-6, Issue-1)
© 2017, IJERMT All Rights Reserved Page | 30
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
Sharma et al., International Journal of Emerging Research in Management &Technology
ISSN: 2278-9359 (Volume-6, Issue-1)
© 2017, IJERMT All Rights Reserved Page | 31
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
Sharma et al., International Journal of Emerging Research in Management &Technology
ISSN: 2278-9359 (Volume-6, Issue-1)
© 2017, IJERMT All Rights Reserved Page | 32
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.
REFERENCES
An easy way to comply with the conference paper formatting requirements is to use this document as a template and
simply type your text into it.
[1] R. Bennama, M. Fernandez, V. Ladero, M. A. Alvarez, S. N. Rechidi and A. Bensoltane, “Isolation of an
exopolysaccharide- producing Streptococcus thermophilusfrom Algerian raw cow milk”. Euro. F. Res Technol.
234: 119-125, 2012
Sharma et al., International Journal of Emerging Research in Management &Technology
ISSN: 2278-9359 (Volume-6, Issue-1)
© 2017, IJERMT All Rights Reserved Page | 33
[2] K. V. Madhuri and K. V. Prabhakar, “Microbial Exopolysaccharides: Biosynthesis and Potential Applications,”
Orient j. chem.. 30: 1401-1410, 2014
[3] D. L. Vuyst, F. Vanderveken, V.S Van and S.B Degeest,. “Production b y and isolation of exopolysaccharides
from Streptococcus thermophilus grown in a milk medium and evidence for their growthassociated
biosynthesis”. J. Appl Meteorol, 84, 1059-1068, 1998
[4] M.P Ruas and D. L. G Reyes, “Methods for the screening, isolation and characterization of exopolysaccharides
produced by lactic acid bacteria”. J. Dairy Sci. 88, 843-856. 2005
[5] O. Bunkoed and S.Thaniyavarn,” Isolation of exopolysaccharides producing-lactic acid bacteria for fermented
milks products”. In the 26th Annual Meeting of the Thai Society for Biotechnology and International
Conference- TSB2014".26–29 November 2014, Mae Fah Luang University, Chiang Rai, Thailand 2014
[6] H. M Stack, N. Kearney, C. Stanton, G. F Fitzgerald and R. P Ross, “ Association of beta-glucan endogenous
production with increased stress tolerance of intestinal Lactobacilli”. App Env. Microbiol. 76: 500-507. 2010
[7] P. J. Looijesteijn,J. Hugenholtz, “Uncoupling of growth and exopolysaccharide production by Lactococcus
lactis subsp. Cremoris NIZO B40 and optimization of its synthesis” . J. Biosci. Bioeng, 88: 178-182.1999
[8] A. N. Hassan,” ADSA Foundation Scholar Award: possibilities and challenges of exopolysaccharide producing
lactic cultures in dairy foods”. J Dairy Sci 91:1282–1298 ,2008
[9] F. Francis, A. Sabu and K.M. Nampoothiri , “Use of response surface methodology for optimizing process
parameters for the production of α–amylase by Aspergillus oryzae” . Biochem Eng J, 15: 107–115, 2003
[10] C. P. Xu, S. W. Kim , H. J. Hwang, J. W. Choi and J. W . Yun , “ Optimization of submerged culture
conditions for mycelial growth and exo-biopolymer production by Paecilomyces tenuipes C240”. P.Bio. 38,
1025-1030. 2003
[11] S. Mohana, A .Shah, J. Divecha and D. Madamwar, “ Xylanase production by Burkholderia sp. DMAX strain
under solid state fermentation using distillery spent wash” Bioresour. Technol. 99: 7553-7564. 2008.
[12] R.H Myers and D.C Montgomery, “Response Surface Methodology, John Wiley and Sons,
New York, NY “ 1995
[13] Y. Yao, Z. Lv , H. Min, Z. Lv , H. Jiao, ” Isolation, identification and characterization of a novel Rhodococcus
sp. strain in biodegradation of tetrahydrofuran and its medium optimization using sequential statistic-based
experimental designs”. Bioresour. Technol. 100: 2762-2769. 2009
[14] G. Ruchi, G. Anshu ,S. K Khare , “Lipase from solvent tolerant Pseudomonas aeruginosa strain: production
optimization by response surface methodology and application” Bioresour. Technol. 99: 4796-4802. 2008
[15] E. J. Faber, J. P Kamerling , J.F.G Vliegenthart “Structure of the extracellular polysaccharide produced by
Lactobacillus delbrueckii subsp. bulgaricus 291” Carbohydr Res .331:183–194, 2001
[16] W. Chen , W. P Wang, H. S. Zhang, Q. Huang , “ Optimization of ultrasonic-assisted extraction of water-
soluble polysaccharides from Boletus edulis mycelia using response surface methodology”. Carbohydr Polym.
87:614–619. 2012
[17] D. Gan , L. Ma ,C. Jiang ,R. Xu ,X. Zeng , “Production, preliminary characterization and antitumor activity in
vitro of polysaccharides from the mycelium of Pholiota dinghuensis.” Carbohydr Polym., 84:997–1003. 2011
[18] V. Zambare, “ Optimization of amylase production from Bacillus sp. using statistics based experimental
design.” Emir J Food Agric, 23:37–47. 2011
[19] J. Cerning, , C. Bouillanne, M.J Desmazeaud and M. Landon,” Isolation and charterization of exocellular
polysaccharide produced by Lactobacillus bulgaricus. “ Biotechnol. Lett. 8, 625-628.1986
Top Related