OPTIMIZATION OF PROCESS PARAMETERS FOR DOCOSAHEXAENOIC ACID PRODUCTION BY SCHIZOCHYTRIUM SPECIES...

www.wjpps.com Vol 3, Issue 5, 2014.

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Umesh et al. World Journal of Pharmacy and Pharmaceutical Sciences

OPTIMIZATION OF PROCESS PARAMETERS FOR

DOCOSAHEXAENOIC ACID PRODUCTION BY SCHIZOCHYTRIUM

SPECIES USING STATISTICAL TECHNIQUE

Umesh Luthra*, Archana Tripathi, Sneha Khadpekar, Nishtha K.Singh, Aditi Trivedi

and Harish Kumar

Ipca Laboratories Ltd., Biotech R&D, Kandivali (W), Mumbai- 400067, Maharashtra, India

ABSTRACT

A marine microalgae Schizochytrium sp. was used for DHA production.

The aim of this study was to optimize the shake flask process

parameters for maximizing DHA production via statistical technique.

The process parameters play a significant role in DHA production. The

effect of four factors – Temperature, RPM, Inoculum% and pH was

determined. The significant factors i.e. temperature, pH and rpm were

screened using the first-order model based on Plackett-Burman design

which was further optimized by central composite design. Central

composite design was based on second degree polynomial used to

identify optimal culture conditions for the production of DHA. A maximum DHA production

was achieved 5.29 mg/g with the combination of optimized factors: temperature 26.93oC,

rpm 152.31 and pH 5.71. Validation experiments were also performed to verify the adequacy

and accuracy of the model. The predicted optimum levels were in agreement with the

verification experimental results.

Key words: Schizochytrium sp., Docosahexaenoic acid, Plackett-Burman design, Central

composite design.

INTRODUCTION

Docosahexaenoic acid (DHA) is long chain polyunsaturated fatty acid. It is commonly

referred as omega – 3 fatty acid. It plays an important role in human health. Omega – 3 fatty

acids are good for heart and brain. In infants, it is required for the development of brains,

eyes and nervous systems during the first six months. DHA is a primary structural component

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Article Received on 20 March 2014, Revised on 12 April 2014, Accepted on 05 May 2014

*Correspondence for Author

Dr. Umesh Luthra

Ipca Laboratories Ltd., Biotech

R&D, Kandivali (W), Mumbai-

400067, Maharashtra, India.


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of human brain, cerebral cortex, skin, sperm, testicles and retina. It can be obtained directly

from maternal milk and fish oil (Guesnet, 2011). Mammals can not synthesize omega-3 fatty

acids and therefore they need to obtain through diet (Cetrik and Shimizu, 1999). Cold water

marine fish are the rich source of DHA.

DHA is commercially produced from microalgae (Martek, 2007). Some marine microalgae

such as dinoflagellates and species in the heterokonta phylum contain high DHA (Barclay et

al., 1994; Apt and Behrens, 1999; De Swaaf et al., 2003; Wu & Lin, 2003; Wu et al., 2005).

One of the heterotrophic microalgae, Schizochytrium sp. is able to produce DHA and can be

used due to its fast growth rate.

DHA is necessary to prevent human diseases such as cancer (Roynette et al., 2004),

cardiovascular disease (Lee and Lip, 2003), schizophrenia and Alzheimer’s disease (Ursin,

2003) and generalized peroxisomal disorders (Kim, 2008). Omega–3 fatty acids play an

important role to control the expression of specific genes in human body (Sessler and Ntambi,

1998). Low level of DHA can affect the metabolism of dopamine and serotonin in brain

(Innis, 2007).

The higher productivity has been achieved by optimizing the process parameters and culture

conditions. It is necessary to study the factors influencing the production of secondary

metabolites. The “one factor at a time” is a classical technique of optimization which

involves sequential experiments where one factor is varied and others are held at constant

level. This technique is not only requires work and time but also unable to explain factor

interactions. Statistical experimental designs have been involved to eliminate the drawback of

single factor optimization process and utilized for the optimization of process parameters for

secondary metabolite production. Response surface methodology is widely employed for

optimization of shake flask process. The first order model based on Plackett-Burman design.

Plackett-Burman design experiments have been widely used for screening and selection of

efficient factors. The screened significant variables were further optimized by CCD and

response surface methodology.

The goal of this study was to use Plackett-Burman design followed by central composite

design and response surface methodology for the optimization of process parameters to

enhance the productivity of DHA by Schizochytrium sp.


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MATERIAL AND METHODS

MICROORGANISM

Schizochytrium sp. was used in this study for the production of DHA by submerged

fermentation. The culture was acquired from the American Type Culture Collection

(ATCC), P.O. Box 1549, Manassas, VA 20108, USA. The medium components were

procured from Merck, Mumbai, India. All chemicals were analytical grade. A stock culture

was maintained on agar media containing 40 g/l dextrose, 10 g/l yeast extract, 10 g/l tryptone,

35 g/l NaCl, 0.5 g/l MgSO4, 0.5 g/l CaCl2, 0.25 g/l KH2PO4, 0.1 g/l KCl and 20 g/l Agar.

CULTURE CONDITION

Inoculum was prepared by cultivating 48 h grown culture in 250 ml Erlenmeyer flasks

containing 35 ml seed medium and incubated at 28oC and 120 rpm in orbital shaker for 60 h.

Seed culture was used to inoculate production media.

ANALYTICAL METHOD

DHA productivity in the culture broth was analyzed by HPLC method. The extraction of

culture broth was done in ethanol. The extracted solution was injected into the HPLC.

Hypersil BDS C-18, 100X4.6mm, 5µ column was used for the estimation of DHA. 0.1%

ortho-phosphoric acid and acetonitrile was used as a mobile phase. The flow rate was set at

1.3 ml/min. Concentration of DHA was calculated by comparing the obtained peak area with

standard area.

EXPERIMENTAL DESIGN AND DATA ANALYSIS

PLACKETT-BURMAN DESIGN

The Plackett-Burman design was used to identify the most important variables with respect to

their main effects in the system, which is then optimized in the further studies. It is an

efficient method to determine the significant factors using a few experimental runs. Four

variables were selected in this study which can significantly affect the product yield. The

name of the factors was temperature, rpm, inoculum % and pH. Eight experiments were

generated with four variables and three dummy variables. The experimental design for the

selection of significant factors is shown in Table 1. Any factor that was not assigned as

variables is designated as dummy variables. Dummy variables are used to estimate

experimental errors in data analysis (Soliman et al., 2005). The factors were examined at two

levels, –1 for low level and +1 for high level (Rajendran et al., 2007). Plackett-Burman is a

fraction of two level of factorial design and allows the investigation of ‘n-1’ variables


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experiment with at least n experiments (Plackett-Burman, 1946). The number of high and low

variables should be equal in this design (Stowe et al., 1996).

Plackett-Burman experimental design is based on the first order model as given in equation 1.

Y = β0 + ∑ βi Xi ………………….(1)

Where, Y is the response, β0 is the intercept coefficient, βi is the variable estimates, and Xi is

independent variable.

All the experiments were carried out in triplicates and its average were taken as response.

Table 1: Experimental code and levels of factors in the Plackett-Burman design

Code Variables Low level (-) High level (+)

A Temperature (oC) 100 200

B RPM 26 30

C Inoculum % 5 10

D pH 5 7

Table 2: Plackett-Burman experimental design and its responses

Run

No A B C D D1 D2 D3

DHA activity

(mg/g)

1 + - - + - + + 2.48

2 + + - - + - + 2.46

3 + + + - - + - 2.56

4 - + + + - - + 4.48

5 + - + + + - - 2.77

6 - + - + + + - 4.16

7 - - + - + + + 3.32

8 - - - - - - - 2.44

CENTRAL COMPOSITE DESIGN (CCD)

The central composite design was used to determine the optimal process parameters for DHA

production. It is an experimental design useful in response surface methodology. Response

surface methodology was utilized for the optimization of screened factors to enhance the

productivity. It was used to determine the level of significant factors and the interaction

effects between them. In this study, three variables were selected on the basis of Plackett-


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Burman experiment design i.e. temperature, rpm and pH. Seventeen experiments were

generated with three factors at five different levels (-1.68, -1, 0, +1, +1.68) that included 8

trials of factorial design, 6 trials of axial points (2 for each variable) and 3 trials of centre

points (Box and Wilson, 1951). The variables and their levels were shown in table 3. CCD

was based on second degree polynomials which include all significant interaction terms. The

relationship of three factors was elucidated by quadratic model.

Y = β0 + ∑ βi Xi + ∑ βii Xi2 + ∑ βi j Xi Xj ………………….(2)

Where, Y is response variables, β0 is the interception coefficient, βi is the linear coefficient, βii

is the quadratic coefficient, βi j is the interaction coefficient and X is coded independent

variables.

Data obtained from central composite design was subjected to statistical analysis. Design-

Expert 8.0 Software was used to determine the regression analysis and analysis of variance

(ANOVA).

The significance of the model was determined by F-test (Khuri et al., 1987; Kiruthika et al.,

2011). The optimum levels of the screened variables were acquired by solving the regression

equation and also by analyzing the 3D surface plots (Sunitha et al., 1998).

Table 3: Experimental code and levels of factors in CCD

Code Variables -α -1 0 +1 +α A Temperature (oC) 25.32 26 27 28 28.68 B RPM 65.91 100 150 200 234.09 C pH 4.32 5 6 7 7.68

Table 4: CCD experimental design and its responses

Run A B C DHA Activity (mg/g) 1 - - - 3.56 2 + - - 3.58 3 - + - 4.26 4 + + - 3.77 5 - - + 2.67 6 + - + 3.85 7 - + + 2.84 8 + + + 3.10 9 -1.68 0 0 4.16 10 1.68 0 0 3.92 11 0 -1.68 0 3.02


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RESULT AND DISCUSSION

Statistical evaluation of factors by Plackett-Burman Design

Plackett-Burman experimental design is a major tool to find out the significant factors. The

experimental design for the evaluation of selected factors and its response is shown in table 2.

A variation in DHA activity was observed in the eight runs of Plackett-Burman ranging from

2.46 to 4.48 mg/g. The obtained data was studied to statistical analysis to estimate effect, F-

value and p-value. Among four factors, temperature, pH and RPM showed positive effect on

the DHA activity (Pareto chart, fig 1). On the basis of ANOVA calculation, temperature was

found most significant factor for the DHA production. The screened significant factors were

further optimized by CCD and response surface method to know the optimal value of

individual factors.

Table 5: ANOVA for Plackett-Burman to select the significant variables

(SS = sum square, df = degree freedom, MS = mean square, * = significant)

Variables SS df MS F-value p-value

A 2.132 1 2.132 37.750 0.009*

B 0.878 1 0.878 15.542 0.029*

C 0.316 1 0.316 5.595 0.099

D 1.209 1 1.209 21.406 0.019*

D1 0.070 0.070 1.245 0.381

D2 0.017

D3 0.082

Error 0.169 3 0.056

12 0 1.68 0 2.78 13 0 0 -1.68 3.51 14 0 0 1.68 2.56 15 0 0 0 5.10 16 0 0 0 4.58 17 0 0 0 4.89


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Pareto chart

0

0.3

0.6

0.9

1.2

1.5

A D B C

Variables

Mai

n ef

fect

Fig 3: Pareto chart for Main effects

Optimization of Screened Factors for DHA Production Using Central Composite Design

(CCD)

Seventeen experiments were performed using different combinations of three variables –

temperature, rpm and pH to determine the optimal levels (table 4). As shown in table 4, a

variation in DHA activity was observed depending upon the level of variables. The maximum

activity 5.10 mg/g was observed in run 15 and minimum 2.56 mg/g in run 14.

Multiple regression analysis was used to analyze the data and polynomial equation derived

from regression analysis for DHA production was shown in equation 3.

Y= 4.84 + 0.041A – 6.856E-003B - 0.32C - 0.18AB + 0.24AC - 0.18BC – 0.24A2 – 0.64B2 –

0.59C2…………………………….. (3)

Where, Y is response of DHA production, A is temperature, B is rpm and C is pH.

Analysis of variance (ANOVA) was used to check the adequacy of the model. F-value and p-

value was shown in table 7. The F-value of the model was 14.45 and p-value was 0.0010

represents the model was significant. The smaller p-value indicates the significance of the

level. The determination coefficient (R2) was used to check the goodness of the model (Park

et al., 2001). The R2 value always lies between 0 to 1. The R2 value closer to 1 denotes better

correlation between observed and predicted values. The multiple correlation coefficient (R2 =

0.9489) and adjusted coefficient (adjusted R2 = 0.8833) were high which indicates the

significance of the model. The coefficient of variation (CV) show the degree of precision to

which the experiments are compared. The lower value of CV (7.22) shows high reliability of

the experiments. The optimum level of variables and interaction effects were found out by 3D

surface plots.


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Table 6: Analysis of variance (ANOVA) for quadratic polynomial model

Variables SS df MS F-value p-value

Model 9.07 9 1.01 14.45 0.0010*

A 0.023 1 0.023 0.34 0.5798

B 6.419E-004 1 6.419E-004 9.206E-003 0.9263

C 1.36 1 1.36 19.49 0.0031*

AB 0.26 1 0.26 3.67 0.0971

AC 0.46 1 0.46 6.54 0.0377*

BC 0.27 1 0.27 3.87 0.0897

A2 0.65 1 0.65 9.28 0.0187*

B2 4.65 1 4.65 66.76 <0.0001*

C2 3.99 1 3.99 57.21 0.0001* R2 = 0.9489; adj R2 = 0.8833; pred R2 = 0.6860; F = Fisher’s function; * = significant The 3D surface plots and their corresponding contour curves were shown in fig. 2-4. It

explained the interaction between variables and the optimum concentration of each factor

involved in DHA production.

Fig. 2: 3D surface plot and 2D contour graph of DHA production showing interaction

between temperature and RPM.


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between temperature and pH.


between RPM and pH.

Each figure represents the effect of two factors on DHA production while the third factor was

held at zero level. The interaction between temperature and pH was significant for DHA

production. Synergetic effect of temperature, rpm and pH showed enhancement in DHA


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production. The optimal level of each variable obtained from polynomial model was 26.93oC

temperature, 152.31 rpm and 5.71 pH.

Validation of Model

To verify the adequacy of the model obtained from CCD for DHA production, three sets of

experiments were performed with the obtained optimal conditions. The mean value for DHA

production by Schizochytrium sp. was 5.29 mg/g, which was in agreement with the predicted

value.

CONCLUSION

Statistical technique is a major tool for the enhancement of secondary metabolite production.

In this study, Plackett-Burman and central composite design with response surface

methodology were employed for optimization of process parameters for production of DHA

by Schizochytrium sp.. In Plackett-Burman design, temperature, rpm and pH were selected as

a critical factor for DHA productivity. The favorable conditions for the individual variables

were further optimized by central composite design. The obtained optimum levels for

maximum DHA production were 26.93oC temperature, 152.31 rpm and 5.71 pH. Under such

conditions, the highest DHA production was 5.29 mg/g. Thus it is proposed that this

optimization approach would be effective for improving the microbial technology.

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OPTIMIZATION OF PROCESS PARAMETERS FOR DOCOSAHEXAENOIC ACID PRODUCTION BY SCHIZOCHYTRIUM SPECIES...

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