rsm chlorella

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RESPONSE SURFACE METHODOLOGICAL APPROACH TO OPTIMIZE PROCESS

PARAMETERS FOR THE BIOMASS PRODUCTION OF CHLORELLA PYRENOIDOSA

RAJASRI YADAVALLI1, RAMGOPAL RAO S2& C. S. RAO21Associate Professor, Department of Biotechnology, Sreenidhi Institute of Science and Technology (Autonomous)

Affiliated to Jawaharlal Nehru Technological University, Hyderabad, Andhra Pradesh, India

2Department of Biotechnology, Sreenidhi Institute of Science and Technology, Hyderabad, Andhra Pradesh, India

ABSTRACT

Biomass concentration and overall lipid productivity hold the key for economic feasibility of algal oil for

biodiesel production. For achieving higher yields of Chlorella pyrenoidosa, we employed Plackett-Burman design

followed by Response surface methodology using a Central-Composite design to derive the functional relationship

between the algal growth and process parameters in this study. Preliminary experiments revealed that light intensity, mode

of operation (Batch/Fed batch), sodium nitrate concentration had influence on biomass production of C. pyrenoidosa.

These factors were identified by using Plackett-Burman design and further optimized by response surface methodology.

The optimal process parameters obtained for achieving the maximum yield from C. pyrenoidosawere light intensity =

130.77 mol m-2s-1, and NaNO3= 1.78g/L, respectively. The maximum predicted value of biomass obtained 2.956 g/L was

about 1.3-fold higher than using the original medium. We conclude that RSM approach eventually helps in bulk production

of C. pyrenoidosafor future industrial applications.

KEYWORDS: Light Intensity, Nitrogen, Chlorella Pyrenoidosa, Response Surface Methodology

INTRODUCTION

Global warming caused by increasing concentrations of greenhouse gases resulting from human activity such as

fossil fuel burning and deforestation has become one of the most serious environment problems. In recent years, many

attempts are being made to reduce the quantity of CO2 in the atmosphere. Studies on photosynthesis, CO2 fixation and

utilization of micro algae biomass have been carried out. Chlorellastrains known widely for their high valued potential

substances such as chlorophyll, beta-carotene, protein, lipid content, can also be used as potential biomass albeit the

function of CO2fixation1.

Microalgal mass culture has been utilized for nearly six decades as a potentially important source of many

products, such as biofuels (biodiesel, biohydrogen), aquaculture feeds, human food supplements and pharmaceuticals 2-3.

Even though biodiesel production from algal biomass is pertinent, the relatively high cost is a major obstacle for

commercial production. Biomass concentration, increasing lipid content, and overall lipid productivity hold the key for

economic feasibility of algal oil for biodiesel production. While the overall lipid productivity determines the costs of the

cultivation process, biomass concentration and lipid content significantly enhance the downstream processing costs. In this

context, process optimization that can maneuver the algal biochemical production and fast growth helps to achieve

environmental and economic sustainability.

Currently, Chlorella sp is cultivated extensively in photoautotrophic conditions for enhancing biomass

productivity. As microalgae growth by autotrophic means produce lower biomass, process optimization efficiently

International Journal of Bio-Technology

& Research (IJBTR)

ISSN 2249-6858Vol. 3, Issue 1, Mar 2013, 37-48

TJPRC Pvt Ltd.


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38 Rajasri Yadavalli, Ramgopal Rao S& C. S. Rao

enhances the yield of a desired metabolic product in a microbial system4.In a fermentation process, optimization of the

variables provides information on their significant effects on the microbial growth and also their interaction at varying

levels. Conventionally, one independent variable is usually studied whilst all the other factors are maintained at a fixed

level. This approach could lead to unreliable results and erroneous conclusions. Furthermore, it does not guarantee the

optimization of process conditions, and fails to detect the frequent interactions that invariably occur between two or

more factors.

On the other hand, Response surface methodology (RSM) is a novel statistical method employed to analyze

problems wherein the response is dependent on several independent variables with an objective to maximize the process

variables for achieving optimum response5. RSM uses quantitative data from appropriate experiments to determine and

simultaneously solve multivariate equations 6-7. RSM reduces the number of experiments eventually saving time, chemicals

and labor. Furthermore, it offers a rapid and reliable prediction of response, making it a lucrative option for experimental

design. Research indicates that so far such experimental approach has not been reported for enhancing the Chlorella

biomass concentration.Thus, the main objective of this study is to optimize the process parameters for obtaining higher

biomass productivities of Chlorella pyrenoidosa employing Plackett-Burman design followed by Response surfacemethodology using a Central-Composite design (CCD).

MATERIALS AND METHODS

Algal Strain and Inoculum Preparation

Chlorella pyrenoidosasp. (NCIM NO: 2738) was obtained from National Centre for Industrial Microorganisms

(NCIM), Pune, India. Stock culture of Chlorella pyrenoidosawas grown photoautotrophically in BG11 media at 28o C

under continuous light illumination in four 100 ml borosil flasks. Basal medium was slightly modified for use in this

study. Each liter of the BG11 medium contained NaNO3-1.5g, K2HPO4-0.04g, MgSO47H2O-0.075g, CaCl22H2O-0.036g,

Citricacid-0.006g, NaCO3-0.02g, H3BO3-0.00286g, MnCl24H2O-0.00181g, ZnSO47H2O-0.00022g, Na2MoO42H2O-

0.00039g,CuSO45H2O-0.00008g,Co(NO3)26H2O-0.00005g, (NH4)6Mo7O24.4H2O-0.003 g, Na2EDTA-0.00001 g.

The inoculum was prepared by transferring the cells from stock culture, and incubated aseptically in a 250 ml

flask containing 100 ml of fresh BG11 media under continuous illumination of 34 mol m-2

s-1

at 28o

C for four days on an

orbital shaker set at 120 rpm. A 4 day old culture was used as inoculum at 10% volume for the preparation of stock

cultures.

Culture was prepared in 250 ml flasks by inoculating 39 ml of the 4 day old seed culture into 8 flasks, each

containing 100 ml of sterilized, fresh BG11 media of varying concentrations of sodium nitrate, respectively. Both low and

high concentrations of sodium nitrate were tested and denoted from N-1 to N-8 (Table 1). The flasks were incubated for 4

days at 28oC in a continuous light illumination of 110 mol m

-2s

-1and 135 mol m

-2s

-1on an orbital shaker set at 120 rpm.

Biomass Dry Cell Weight (DCW) Measurement

Biomass content was determined by measuring the optical density of samples at 600 nm (OD600). The conversion

factor was established by plotting OD600versus DCW of a series of samples of different biomass concentrations. Samples

were diluted by appropriate ratios to ensure that the measured OD600values were in the range of 0.20.9. DCW of a sample

was determined gravimetrically after drying, the algal cells were collected from samples with centrifugation (3,000g, 10

min) and washed with water. Linear regression equation obtained was found to be Y = 1.038557658*10-1

X -

7.295013686*10-4

and r = 9.83945870610-1

where y is DCW of algal cells and x is optical density at 600 nm.


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Response Surface Methodological Approach to Optimize Process 39Parameters for the Biomass Production of Chlorella Pyrenoidosa

Experimental Design and Optimization

Plackett-Burman Design

Pilot experiments revealed that four factors, including light intensity, mode of operation (Batch/Fed batch),

Sodium Nitrate concentration (nitrogen concentration) and pH had influence on biomass production of Chlorella

pyrenoidosa. It is well known that the Plackett-Burman design could evaluate the main effects of factors. The

factors having significant effects on biomass production of Chlorella pyrenoidosa were identified using SigmaXL

Version 6.1 Workbook (trial version). Each factor was investigated at a high (+1) and a low (-1) level . The factors,

with more effect (more contribution) were considered to have greater effects on the biomass production of Chlorella

pyrenoidosa and it is further optimized by response surface methodology using Central-Composite design. The first-

order model used to fit the results of Plackett-Burman design was represented as

(1)

Central Composite Design

CCD is employed to fit a second-order model. The design was generated by commercial statistical package,

Design-Expertversion 7.0 (Statease Inc.,Minneapolis, USA, Trial version). The levels were calculated and experiments

were performed using CCD. The two independent formulation variables selected for this particular study were light

intensity and nitrogen concentration (sodium nitrate).

The actual and corresponding coded values of different variables along with experimental data values were

summarized in Table 2. CCD is an efficient and proven design, especially for two factors. CCD is also rotatable, which

means that all the points in the design area are at equal distance from center.

This leads to distribution of errors among all points equally. The numbers of design points in CCD are based upon

a complete 2k factorial. The total numbers of experiments are

N = 2k+2k + m, (2)

where N is the total number of experiments, k is the number of factors, and m is the number of replicates. The test

variables were coded according to the following equation:

xi= (3)

Where, xi is the coded variable and Xiis the natural value of independent variable, Xiis the value of the variable

at the center point and X is the step change value.

Multiple linear regression analysis was used, and the data was fitted as a second-order equation. The general

equation that was fitted is

Y = 0+ iXi + iiXi2+ ijXiXj+ (4)

Where Y is the response variable, 0is the intercept term, iiis the squared effect and ijis the interaction between

Xiand Xj. The number of coefficients in the above equation is 6. The redundancy factor of the experimental design was

derived from Rf= number of experiments/number of coefficients.

For our current study k=2, the numbers of coefficients are 6 and hence the numbers of experiment are 10.

Therefore, the redundancy factor is 1.667.


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RESULTS AND DISCUSSIONS

Screening of the Important Factors by Plackett- Burman Design

Plackett-Burman design offered an effective screening procedure and we evaluated the significance of a large

number of factors in one experiment, which was time-saving and maintained convincing information on each factor. Table

2 shows the high and low levels of factors chosen for trials in Plackett-Burman design. Table 3 represents the four

independent factors and their concentrations at different coded levels and the experimental responses for 8 runs.

The biomass productivity showed considerable variation depending on the four independent factors in the

medium, by applying the regression analysis on the experimental data. The corresponding first-order model equation fitted

to the data obtained from the Plackett-Burman design experiment has the formula

Yield = 1.942 + 0.15225A + 0.8575B-0.0765C- 0.01175D (5)

The regression coefficients and determination coefficient (R2) for the linear regression model of biomass

productivity are presented in Table 4. The model was significant (P < 0.05) andR2= 98.7%, indicating that 98.7% of the

variability in the response could be explained by the model. Statistical analysis of the data showed that light intensity,

NaNO3concentration and mode of operation had a significant effect on biomass yield from Chlorella pyrenoidosa, with

the confidence level above 98% (P < 0.05). The other factors having confidence levels below 95% were considered

insignificant. The Pareto chart of Coefficients for Yield (Fig 1) clearly indicates the ranking of the factors in biomass

production.

In the present study, we observed that Chlorella pyrenoidosa, failed to grow in the absence of nitrogen source and

the concentration of nitrate in the medium had a profound effect on algal growth. Fig 1 substantiates that NaNO3has a

significant influence on the growth of Chlorella pyrenoidosa followed by light intensity. This was in accordance with

earlier studies which demonstrated a direct and linear relationship between low nitrate concentration and reduction in

biomass production8-11

. A decrease in Nannochloropsis spbiomass concentration in low nitrate concentration was also

reported12

. Further, increase in nitrogen concentration resulted in the enhancement of Chlorella pyrenoidosa biomass

suggesting that nitrogen is crucial for the survival and growth of this alga.

Figure 1: Effect on Variables on Biomass Production from Chlorella Pyrenoidosa


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Algae can be cultivated under certain conditions of temperature, light and sufficient nutrients to produce biodiesel.

Light intensity directly affects the growth and photosynthesis of microalgae since light itself acts as carbon source for their

photoautotrophic growth13.

It is well established that light intensities also play a major role in growth of microalgae14

.

Similarly, mode of operation has a significant role in biomass productivity. Regulation of nutrient feed rates to increase

productivity can be performed by fed-batch cultivation. Researchers have reported that maximum lipid productivity was

obtained with urea as a nitrogen limitation in a semi-continuous culture when compared with those in the batch and fed-

batch cultivations of Chlorella sp.15- 16

. In our previous study we had established that fed batch strategy enhances lipid

production in Chlorella pyrenoidosa.17

Hence, the concentration of NaNO3 and light intensity were selected for further

optimization in this study to achieve a maximum biomass yield under batch mode of cultivation.

Central Composite Design

Following screening, RSM using Central-Composite design was employed to determine the optimal levels of the

two selected factors that significantly affected biomass yield. The respective low, zero and high levels with the coded

levels for the factors were defined in Table 5. The experimental design and results are shown in Table 6. Based on a

regression analysis of the data from Table 7, the effects of two factors on biomass yield were predicted by a second-order

polynomial function, as

Biomass =2.49+0.18A+0.99B-0.011AB+0.081A2 -0.76B2 (6)

Where, Y was the predicted response and A, B were the Light intensity and NaNO3concentration, respectively.

The statistical significance of Equation (6) as checked by f-test, and the analysis of variance (ANOVA) for the

second-order polynomial model is shown in Table 7. It was evident that the model was highly significant, as suggested by

the model F value and a low probability value (P = 0.0051). The analysis of factor (f-test) showed that, the second-order

polynomial model was well adjusted to the experimental data and the coefficient of variation (CV) indicated the degree of

precision with which the treatments were compared. The precision of a model can be checked by the determination

coefficient (R2) and correlation coefficient (R). The determination coefficient (R

2) was calculated to be 0.9625, indicating

that 96.25% of the variability in the response could be explained by this model.

Normally, a regression model with an R2value higher than 0.9 was considered to have a very high correlation

18.

The closer the value of R to 1, better correlation is expected between the experimental and predicted values. Here, the

value of R (0.9824) for Equation (6) indicated a close agreement between the experimental results and the theoretical

values predicted by the model equation. Therefore, the quadratic model was selected in this optimization study.

The significance of the regression coefficients was tested by t-test. The regression coefficients and corresponding

P-values for the model are given in Table 8. The P-values were used as a tool to check the significance of each coefficient,

which is necessary to understand the pattern of the mutual interactions between the best factors. The smaller the p -value,

the significance of the corresponding coefficient will be greater19-21

. Our results showed that, among the two independent

factors NaNO3had more significant effect on biomass productivity. The positive coefficient of them showed a linear effect

to increase biomass productivity.

Comparison of Observed and Predicted Biomass Yield

A regression model could be used to predict future observations on the response Y(Biomass Yield) corresponding

to particular values of the regressor variables. In predicting new observations and in estimating the mean response at a

given point, one must be careful about extrapolating beyond the region containing the original observations. It was very


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possible that a model that fitted well in the region of the original data would no longer fit well outside the region. The

observed biomass yield (the response) versus those from the empirical model equation (6) was illustrated (Fig 2). The

figure proved that, the predicted data of the response from the empirical model is in good agreement with the observed

ones in the range of the operating variables.

Figure 2: Comparison of the Observed Biomass Yield (g/L) and the Predicted Biomass (g/L)

Localization of the Optimum Condition

The 3D response surface plots described by the regression model were drawn to illustrate the effects of the

independent factors and the interactive effects of each independent factor on the response factors. It also shows the

optimum concentration of each component required for the biomass production (Fig 3). These 3D plots provided a visual

interpretation of the interaction between two factors and facilitated the location of optimum experimental conditions. The

model predicted that the optimal values of the light intensity and NaNO3concentration were A = 130.77 mol m-2

s-1

, B =

1.78g/L, respectively. The maximum predicted value of biomass obtained was 2.956 g/L.

Figure 3: Response Surface Curve for Biomass Productivityby C. Pyrenoidosa vs Light Intensity and NaNO3

Concentration


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Model Adequacy Checking

Usually, it is necessary to check the fitted model to ensure that it provide an adequate approximation to the real

system. Unless the model showed an adequate fit, proceedings with the investigation and optimization of the fitted

response surface will likely give poor or misleading results. The residuals from the least squares fit played an important

role in judging model adequacy. By constructing a normal probability plot of the residuals, a check was made for the

normality assumption, was given (Fig 4).

Figure 4: Normal Probability of Internally Studentized Residuals

The normality assumption was satisfied as the residual plot was approximated along a straight line. Fig 5 presents

a plot of residuals versus the predicted response. The general impression was that the residuals scattered randomly on thedisplay, suggesting that the variance of the original observation was constant for all values of Y. Both plots (Figs 4 and 5)

were satisfactory implying that the empirical model was adequate to describe the biomass yield by response surface.

Figure 5: Plot of Internally Studentized Residuals vs the Predicted Response


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Verification of the Predicted Activity in the Optimal Medium

Three additional experiments in shake flasks were performed in batch mode in order to verify the predicted

biomass under the optimal medium compositions. The mean value of biomass productivity was 2.81g/L, which was in

excellent agreement with the predicted value. The final process parameters optimized were: light intensity= 130.77 mol

m-2s-1, NaNO3= 1.78g/L respectively .The maximum predicted value of biomass obtained was 2.956g/L.

CONCLUSIONS

In this study, we demonstrated that Plackett-Burman design and Response surface methodology using Central

Composite design effectively helps in optimizing biomass production by Chlorella pyrenoidosa. The maximum predicted

value of biomass (2.956g/L) obtained was increased by 1.3 times when compared with the original medium (2.276 g/L).

Validation experiments were also carried out to verify the adequacy and the accuracy of the model, and the results showed

that the predicted value agreed with the experimental values accurately. The optimized process parameters obtained in this

experiment has given a basis for further study with large scale fermentation in a photobioreactor for the production of

biomass from this strain.

ACKNOWLEDGEMENTS

The authors thank the Management of Sreenidhi Institute of Science and Technology (SNIST) for their financial

support in carrying out this in-house funded project. Special thanks to Mr.L.Saida Naik, Head, Centre for Biotechnology,

Institute of Science and Technology JNTU Hyderabad for his inputs.

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APPENDICES

Table 1: Concentration of Nitrate as Nitrogen Source Used in the Present Study

S.NoNitrogen Source

Concentration(g/L)Medium Label

1 0.025 N-1

2 0.05 N-2

3 0.1 N-3


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Table 1 Contd.,

4 0.15 N-4

5 0.5 N-5

6 1.0 N-6

7 1.5 N-7

8 2.0 N-8

Table 2: Coded and Real Values of the Factors Tested in the Plackett-Burman Design

Factor CodeLevels of Factor

-1 +1

Light Intensity(mol m-2

s-1)

A 110 135

NaNO3(g/L) B 0.025 2.0

Mode of Operation C Fed-Batch Batch

pH D 6.0 7.0

Table 3: Experimental Design and Results of the N = 8 Plackett-Burman Design

Run

Factor1A:

LightIntensity

(mol m-2

s-1

)

Factor2B:

NaNO3(g/L)

Factor3C:Mode of

Operation

Factor4

D:pH

ResponseBiomass(g/

L)

1 -1 1 -1 1 2.715

2 1 1 1 1 2.846

3 1 -1 -1 1 1.295

4 1 1 -1 -1 3.061

5 -1 -1 -1 -1 1.003

6 1 -1 1 -1 1.175

7 -1 -1 1 1 0.865

8 -1 1 1 -1 2.576

Table 4: Regression Results of the Plackett-Burman Design

Term CoefficientSE Coefficient T P-Value

Constant 1.942

A: Light 0.15225 0.01425 10.684 0.0049

B: nitrogen 0.8575 0.01425 60.175 0.0001

C: mode -0.0765 0.01425 -5.368 0.0234

D: pH -0.01175 0.01425 -0.824561 0.4854

Table 5: Coded and Real Values of Factors in Central-Composite Experimental Design

Factor CodeLevels of Factor

-1.414 -1 0 +1 +1.414

Light Intensity A 104.82 110 122.5 135 140.18

NaNO3 B -0.38 0.025 1.01 2.0 2.41


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Table 6: Experimental Conditions of Central Composite Design

Run NoLight

IntensityNaNO3

Biomass

Experimental

(g/L)

1 0 0 2.49

0.861.175

2.57

2.91

2.93

2.31

2.576

0.32

2.846

2 -1 -13 1 -1

4 0 0

5 1.414 0

6 0 1.414

7 -1.414 0

8 -1 1

9 0 -1.414

10 1 1

Table 7:Analysis of Variance (ANOVA) for the Second Order Polynomial Model

Source DF MS F-Value P>F

Model 5 1.56 22.17 0.0051

Residual 4 0.071 - -

Lack of Fit 3 0.093 29.08 0.1353

Pure error 1 3.2x10-3

- -

Total 9 - - -

R2 - - 0.9652

Coefficient of variation (CV) = 12.66%; correlation coefficient (R) = 0.9824; DF, degrees of freedom and MS,

mean square. Statistically significant at 95% confidence level (P < 0.05).

Table 8:Regression Result of Central Composite Design

Model termDegree of

FreedomEstimate P-Value

Intercept 1 2.49

A-Light Intensity 1 0.18 0.1291

B-NaNO3concentration 1 0.99 0.0008a

AB 1 -0.011 0.9366

A2 1 0.081 0.5227

B2 1 -0.76 0.0124

a

aStatistically significant at 95% confidence level (P < 0.05).


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rsm chlorella

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