Optimization of fermentation parameters for the biomass and DHA production of Schizochytrium limacinum OUC88 using response surface methodology

Process Biochemistry 42 (2007) 1391–1397 www.elsevier.com/locate/procbio

Optimization of fermentation parameters for the biomass and DHA production of Schizochytrium limacinum OUC88 using response surface methodology Xiaojin Song a, Xuecheng Zhang a,*, Chenghong Kuang a, Luying Zhu a,b, Nan Guo a a

College of Marine Life Sciences, Ocean University of China, Qingdao, Shandong 266003, PR China b College of Life Sciences, Ludong University, Yantai, Shandong 264025, PR China Received 4 December 2006; received in revised form 22 June 2007; accepted 16 July 2007

Abstract Fermentation parameters for biomass and DHA production of Schizochytrium limacinum OUC88 in a fermenter (working volume 7 L) were optimized using Plackett–Burman and central composite rotatable design. Out of 10 factors studied by Plackett–Burman design, 4 influenced the biomass production significantly. Central composite rotatable design was used to optimize the significant factors and response surface plots were generated. Using these response surface plots and point prediction, optimized values of the factors were determined as follows temperature (8C) 23 8C, aeration rate 1.48 L min1 L1, agitation 250 rpm and inoculum cells in mid-exponential phase, the maximum yield of DCW and DHA were 24.1 and 4.7 g L1, respectively. These predicted values were also verified by validation experiments. # 2007 Elsevier Ltd. All rights reserved. Keywords: Biomass; Docosahexaenoic acid (DHA); Optimization; Plackett–Burman design; Response surface methodology; Schizochytrium limacinum

1. Introduction Docosahexaenoic acid (C22:6 n-3, DHA), is an important substance for the development of both invertebrates and vertebrates [1–5]. It has been verified that DHA plays a key role in improving neural and retinal development in infants and lowering the incidence of certain cardiovascular diseases. In addition, it is an essential component of cell membranes in some human tissues, mainly in brain and retina [6,7]. Schizochytrium limacinum OUC88, a UV-induced mutant from S. limacinum SR21 [8,9], a heterotrophic marine fungus, contains large amount of DHA. As with any heterotrophic microbe, optimization of medium and fermentation conditions is important for establishing a protocol for production of DHA. In this paper optimization of fermentation parameters (temperature, aeration rate, pH, agitation, inoculum volume, fermentation volume, fermentation pressure, inoculum age,

* Corresponding author. Fax: +86 532 85902708. E-mail address: [email protected] (X. Zhang). 1359-5113/$ – see front matter # 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.procbio.2007.07.014

harvesting time and Tween 80 concentration) for the biomass (dry cell weight, DCW) and DHA production by S. limacinum OUC88 has been performed, while optimization of medium composition by S. limacinum OUC88 has been reported in another paper [9]. The conventional approach to optimization investigates one factor at a time while keeping the others constant. This approach is tedious, lacks the completeness to predict response under untested sets of variables and does not study the interaction amongst these factors. Statistical methods, on the other hand with factorial experimental designs, offer the simultaneous study of many factors. These methods also allow the study of interactive effects of many factors together and facilitate the prediction of the response to values not yet tested in the experiment. One of the statistical designs for the screening of the independent variables is Plackett–Burman (PB) design, which has been frequently used for screening the key factors. It is a two factorial design and offers the screening of a large number of independent factors (N) in a small number of experiments (N + 1) [10–13]. Factors chosen for study can be either nutritional components or environmental conditions. This is followed by a process

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optimization tool, such as response surface methodology (RSM), whereby the optimum values are studied [14–16]. These are a group of techniques that are used to study the relations between one or more measured dependent factors (responses) and a number of input (independent) factors. The optimization of process parameters would improve process productivity. The present study deals with the identification of factors influencing biomass and DHA production using Plackett–Burman as a tool followed by determination of their optimum values by RSM using central composite rotatable design. 2. Materials and methods

Table 1 Range of different factors studied in the Plackett–Burman design Variable

Variable code

Low level (1)

High level (+1)

Temperature/T (&) Aeration rate/Q (L min1 L1) pH Agitation/R (rpm) Inoculum volume/I (%) Fermentation volume/V (L) Fermentation pressure/P (Mpa) Inoculum age/IA

X1 X2 X3 X4 X5 X6 X7 X8

26 1.48 7 250 10 8 0.08 Stationary phase

Harvesting time/HT (h) Tween 80 concentration/Tw (ml)

X9 X10

23 1.02 6 150 7 6 0.06 Midexponential phase 108 2

132 10

2.1. Microorganism and medium S. limacinum OUC88 used in this study is derived from S. limacinum SR21 which was obtained from the Institute for Fermentation, Osaka (Japan, IFO number is 32693) by UV-mutation (put S. limacinum SR21 under ultraviolet radiation on 35–40 s). The culture was maintained on GSA slant agar (20 g L1 glucose, 10 g L1 soybean cake hydrolysate and 2.0% (w/v) agar in half the salt concentration of seawater) and inoculated monthly.

2.2. Inoculum preparation Glucose soybean cake hydrolysate medium (GSM) were used as the basal medium. Seed culture was grown in 0.5 L flasks containing 0.25 L of GSM (30 g L1 glucose and 20 g L1 soybean cake hydrolysate [17] in half the salt concentration of natural seawater) at 25 8C on a rotary shaker incubator at 200 rpm, then transferred into fermentation reactor (DS-Y-10L fermentation reactor, whose volume is 10 L) with GSM (60 g L1 glucose and 40 g L1 soybean cake hydrolysate in half the salt concentration of natural seawater, the C/N-ratio is about 15:1).

analyzed using an Agilent 6890 GC equipped with an FID and a DB-23 capillary column (30 m  0.25 mm) [9]. Nitrogen was used as carrier gas. Initial column temperature was set at 50 8C for 1 min, then raised to 175 8C at 25 8C min1, which was subsequently raised to 230 8C at 4 8C min1 and maintained 5 min. The FID detector temperature was set at 280 8C. FAMEs were identified by chromatographic comparison with authentic standards (Sigma Chemical Co., USA). The quantity of DHA was estimated from the peak areas on the chromatogram using nonadecanoic acid (19:0) as an internal standard.

2.5. Plackett–Burman experimental design

The biomass of S. limacinum OUC88 was expressed as dry cell weight (DCW). The biomass was centrifuged (7000  g, 10 min) at 4 8C, and then freeze-dried to constant weight at 408C for about 30 h.

A Plackett–Burman design of the experiments was formulated for 10 factors using the SAS version 8.1 software (SAS# Institute Inc., Cary, NC). The 10 factors tested were: temperature (T), aeration rate (Q, volumes of air per volume of medium per minutes VVM), pH, agitation (R), inoculum volume (I), fermentation volume (V), fermentation pressure (P), inoculum age (IA), harvesting time (HT) and Tween 80 concentration (Tw). Each factor was tested at two levels, high (+1) and low (1) (Table 1). A design of a total of 12 experiments was generated and response was measured in terms of DCW and DHA production in Table 2. The effect of each variable was determined by the following equation:

2.4. Lipid analysis

EðxiÞ ¼

The dried cells were suspended in 5 ml 0.4 M methanolic KOH for 1 h at 60 8C, and esterified for 1 h in 5 ml BF3-methanol (14%, w/w) reagent at 60 8C. After extraction with 10 ml n-hexane, followed by evaporation, the fatty acid methyl esters (FAMEs) were dissolved in 1 ml n-hexane and

where E(xi) is the concentration effect of the tested variable, Mi+ and Mi are the DCW and DHA yields from the trials where the variable (Xi) measured was present at high and low concentrations, respectively, and N is the number of experiments. The standard error (S.E.) of the concentration effect was the

2.3. Determinations of S. limacinum OUC88 biomass

P

M iþ  M i N

(1)

Table 2 Plackett–Burman design for screening of fermentation parameters for DCW and DHA production by S. limacinum OUC88 Run #

X1

X2

X3

X4

X5

X6

X7

X8

X9

X10

DCW (g L1)

DHA (g L1)

1 2 3 4 5 6 7 8 9 10 11 12

+1 +1 1 +1 +1 +1 1 1 1 +1 1 1

1 +1 +1 1 +1 +1 +1 1 1 1 +1 1

+1 1 +1 +1 1 +1 +1 +1 1 1 1 1

1 +1 1 +1 +1 1 +1 +1 +1 1 1 1

1 1 +1 1 +1 +1 1 +1 +1 +1 1 1

1 1 1 +1 1 +1 +1 1 +1 +1 +1 1

+1 1 1 1 +1 1 +1 +1 1 +1 +1 1

+1 +1 1 1 1 +1 1 +1 +1 1 +1 1

+1 +1 +1 1 1 1 +1 1 +1 +1 1 1

1 +1 +1 +1 1 1 1 +1 1 +1 +1 1

10.6 14.6 18.2 12.7 18.2 13.7 21.1 16.3 16.9 10.4 16.5 15.9

1.7 3.1 3.9 2.4 3.8 2.5 4.7 3.9 3.5 2.0 3.5 3.0

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Table 3 Design and responses of the central composite rotatable design Run #

Tx1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Qx2

Rx3

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0

DCW (g L1)

IAx4

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0

Experimental

Predicted

Experimental

Predicted

14.5 12.4 19.3 18.1 20.1 17.5 24.2 22.9 10.4 9.5 14.8 13.7 15.4 14.4 18.9 18.0 20.2 11.4 12.1 19.7 12.1 18.2 18.5 16.3 18.6 18.4 19.1 18.5 19.1 19.3 19.6

14.0 12.9 19.2 17.7 19.8 18.2 24.1 22.2 10.7 9.9 14.7 13.5 15.1 13.9 18.4 17.0 20.1 11.7 11.7 20.8 11.7 19.4 19.1 16.2 18.9 18.9 18.9 18.9 18.9 18.9 18.9

2.6 2.2 3.9 3.8 4.5 3.9 4.9 4.7 1.6 1.4 2.4 1.7 2.9 2.8 3.6 3.7 4.2 1.8 2.1 3.7 2.2 3.8 3.9 3.0 3.6 3.5 4.1 4.0 3.8 3.8 4.0

2.6 2.6 3.8 3.8 3.9 3.9 4.8 4.8 1.5 1.5 2.2 2.2 2.9 2.9 3.4 3.4 4.2 1.7 1.8 4.2 2.1 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7

square root of the variance and the P-value (significance level) of each concentration effect was determined using the Student’s t-test: tðxiÞ ¼

EðxiÞ S:E:

DHA (g L1)

(2)

where E(xi) is the effect of variable Xi. The variables with confidence levels greater than 95% were considered to influence DCW and DHA production significantly.

2.6. Central composite rotatable design and response surface methodology (RSM) Response surface methodology was used to optimize the screened components for enhanced DCW and DHA production using the central composite design (CCD). According to this design, the total number of treatment combinations was 2k + 2K + n0, where k is the number of independent variables and n0 is the number of repetition of experiments at the centre point [18–20]. For statistical calculations, the variables Xi were coded as xi according to the

Table 4 Effect estimates for DCW and DHA production from the result of Plackett–Burman design Term

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10

For DCW production

For DHA production

Estimate

S.E.

t

Pr > jtj

Estimate

S.E.

t

Pr > jtj

123.127 98.730 6.683 72.874 9.302 7.399 15.770 49.265 5.216 43.469

3.744 3.744 3.744 3.744 3.744 3.744 3.744 3.744 3.744 3.744

32.889 26.372 1.785 19.466 2.485 1.977 4.212 13.160 1.393 11.611

0.019 0.024 0.325 0.033 0.244 0.298 0.148 0.048 0.396 0.055

1.167 0.807 0.057 0.810 0.183 0.160 0.203 0.260 0.067 0.053

0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057

20.588 14.235 1.000 14.294 3.235 2.824 3.588 4.588 1.176 0.941

0.031 0.045 0.500 0.044 0.191 0.217 0.173 0.137 0.448 0.519

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following equation: xi ¼

Xi  X0 dX

(3)

where xi is the dimensionless coded value of the variable Xi, X0 the value of the Xi at the centre point and dX is the step change. The behaviour of the system was explained by the following quadratic equation: Y ¼ b0 þ

X

bi xi þ

X

bii x2i þ

X

bi j xi x j

1.167, 0.807, 0.810, respectively, and were considered to influence DHA production significantly. The remaining variables, i.e. X3 (pH), X5 (I), X6 (V), X7 (P), X9 (HT) and X10 (Tw), had confidence levels below 95% and hence, were considered insignificant. 3.2. Optimization of DCW and DHA production by Schizochytrium limacinum OUC88

(4)

where Y is predicted response; b0 the offset term, bI the linear effect; bii the squared effect and bij is the interaction effect. Four factors (T, Q, R and IA), screened from Plackett–Burman design, were studied for determination of optimum values for DCW and DHA production. These parameters were tested at three levels, 1 (high), 0 (medium), 1 (low). An experimental design of 31 experiments including 7 centre points and eight axial point (a = 2) was formulated using SAS software (Table 3). Other parameters (pH, I, V, P, HT, Tw), which did not significantly influence the DCW and DHA production, were kept at constant level. Response was measured in the terms of DCW and DHA production. Actual responses and predicted responses (from software) are shown in Table 3.

The variables showing confidence level 95% and above in the Plackett–Burman design were selected and optimized using a central composite design. In order to study the combined effect of these variables, experiments were performed using different combinations. Table 3 summarizes the central composite experimental plan along with the experimental and predicted response from each individual experiment. By applying multiple regression analysis on the experimental data, the following second-order polynomial equation (Eq. (5)) was found to describe DCW production:

2.7. The measurement of oxygen transfer coefficients, kLa, in bioreactor

Y DCW ¼ 2:94101  0:134681  Tx1 þ 0:142611  Qx2 þ 0:127872  Rx3  0:040884  IAx4  0:052494

The sulfite method (Na2SO3 method) is proposed for the measurement of oxygen transfer coefficients, kLa, in bioreactors [21].

 Tx21  0:048167  Qx22  0:029813  Qx2  Rx3  0:057095  Rx23  0:018264  IAx24

(5)

3. Results and discussion 3.1. The effect of the studied factors on production of DCW and DHA The experimental corresponding DCW and DHA yields are shown in Table 2. When the sign of the concentration effect of the tested variable is positive, the influence of the variable upon DCW and DHA yield is greater at a high concentration, and when negative, the influence of the variable is greater at a low concentration. For DCW production, the effect of variables X1 (T), X2 (Q), X4 (R), and X8 (IA) are 123.127, 98.730, 72.874 and 49.265, respectively (Table 4). These variables had confidence levels above 95% and were considered to influence DCW production by S. limacinum OUC88 significantly; as the same time, the effect of variables X1 (T), X2 (Q) and X4 (R) are

where YDCW is the predicted response variable; x1–x4 the coded values of the independent variables, viz., T, Q, R and IA, respectively. The statistical significance of Eq. (5) was checked by F-test, and the analysis of variance (ANOVA) for response surface quadratic model is summarized in Table 5. It is evident from Table 5 that the model is highly significant, as is evident from the model F-value and a very low probability value (P model, F < 0.0001). The goodness of the model can be checked by the determination coefficient R2 and the multiple correlation coefficient R. The value of adjusted R2 (0.9734) for Eq. (5) suggests that the total variation of 97% for DCW is attributed to the independent variables and only about 3% of the total variation cannot be explained by the model. The closer the values of R (multiple correlation coefficient) to 1, the better the correlation between the experimental and predicted values

Table 5 Analysis of variance (ANOVA) for the selected quadratic model of DCW Source

DF

SS

MS

F

Pr > F

Tx1 Qx2 Rx3 IAx4 Tx1  Tx1 Qx2  Qx2 Qx2  Rx3 Rx3  Rx3 IAx4  IAx4

1 1 1 1 1 1 1 1 1

0.435336 0.488107 0.392432 0.040115 0.0788 0.066344 0.014221 0.093217 0.009539

0.435336 0.488107 0.392432 0.040115 0.0788 0.066344 0.014221 0.093217 0.009539

307.1266 344.356 276.8578 28.30118 55.59283 46.80531 10.03275 65.76413 6.729712

0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.004641 0.0001 0.016931

Model Error Total R2 = 0.9815

9 21 30

1.567668 0.029766 1.597434 Adj-R2 = 0.9734

0.174185 0.001417

122.8864

0.0001

R = 0.9907

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Fig. 1. Response surface plot and corresponding contour plot of DCW vs. rotate speed and aerate rate.

[22,23]. Here, the value of R (0.9907) indicates good agreement between the experimental and predicted values of DCW. Fitted response surface for the DCW by the above model is given in Fig. 1. It is clear from the results shown here, the DCW production by S. limacinum OUC88 is sensitive to minor alterations of the test variables. The shapes of the contour plots, circular or elliptical, indicate if the mutual interactions between the variables are significant or not. A circular contour plot indicates that the interactions between the corresponding variables are negligible. An elliptical nature of the contour plots indicates that the interactions between the corresponding variables are significant [24]. Through these three-dimensional plots and their respective contour plots, it is very easy and convenient to understand the interactions between two variables and also to locate their optimum levels. By analyzing the plots, the optimal values of the fermentation conditions in order to obtain approximately 24.1 g L1 of DCW production fell with in the following ranges: temperature = 22–23.5 8C; aeration rate = 1.48 L min1 L1; agitation = 200–250 rpm and inoculum cells in mid-exponential phase. By applying multiple regression analysis on the experimental data given in Table 3, the second-order polynomial equation for the yield of DHA, YDHA, as a function of the coded values of temperature (T), aeration rate (Q) and agitation (R) is as follows:

order polynomial prediction Eq. (6) is 0.9258, indicating of variability experiences in DHA production of approximately 92% could be explained by the fitted model. The high values of correlation coefficient R (0.9801) and determinant coefficient R2 (0.9604) show a close agreement between the experimental results and the values predicted by the polynomial model [25,26]. The coefficient estimates of Eq. (6), along with the corresponding P-values are presented in Table 6.The P-values are used as a tool to check the significance of each coefficient, which in turn may indicate the pattern of the interaction between the coefficients [27]. The smaller the value of P, the more significant is the corresponding coefficient [28,29]. It can be seen from Table 6 that two cross-product coefficients (Tx1  Qx2 and Qx2  Rx3) are significant (P = 0.036, 0.042). The fitted response surface for the yield of DHA by the above empirical model was generated using the SAS software and is given in Figs. 2 and 3. Fig. 2 shows the effect of interaction of temperature (T) and aeration rate (Q) on DHA. It is evident that the DHA production of the fungus is sensitive even to minor alterations of fermentation temperature. Specifically, the production of DHA reached maximum yields between 22.7 and 23.6 8C, but decreased sharply when

Y DHA ¼ 1:308349  0:217188  Tx1 þ 0:21765  Qx2

Source

þ 0:138966  Rx3  0:079629  Tx21 þ 0:054958  Tx1  Qx2  0:076178  Qx22  0:043702  Qx2  Rx3  0:067254  Rx23

(6)

The predicted values of DHA obtained using the above equation, were shown in Table 3. The statistical significance of the second-order model Eq. (6) was checked by F-test (ANOVA) and data shown in Table 6. As can be seen from Table 6, the value of ‘‘Pr > F’’ is much less than 0.0001, indicating that the model is highly significant. The fit-value, adj-R2, of the second-

Table 6 Analysis of variance (ANOVA) for the selected quadratic model of DHA

Tx1 Qx2 Rx3 Tx1  Tx1 Tx1  Qx2 Qx2  Qx2 Qx2  Rx3 Rx3  Rx3 Model Error Total R2 = 0.9604

SS

MS

F

Pr > F

1.132095 1.136921 0.463474 0.183397 0.048325 0.167843 0.030557 0.130821

1.132095 1.136921 0.463474 0.183397 0.048325 0.167843 0.030557 0.130821

95.14974 95.55532 38.95387 15.41406 4.061619 14.10682 2.568251 10.99516

0.0001 0.0001 0.0001 0.000722 0.036247 0.001092 0.042329 0.003141

8 3.216313 22 0.261757 30 3.47807 Adj-R2 = 0.9258

0.402039 0.011898

33.79038

0.0001

DF 1 1 1 1 1 1 1 1

R = 0.9801

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Fig. 2. Response surface plot and corresponding contour plot of DHA vs. temperature and aerate rate.

Fig. 3. Response surface plot and corresponding contour plot of DHA vs. rotate speed and aerate rate.

temperature increased beyond 25 8C. With an increase in aeration rate (Q) (1.02–1.48 L min1 L1), DHA yields increased gradually from 2.4 to 4.7 g L1, and negligibly after 1.5 L min1 L1of aeration rate. As can be seen from Figs. 2 and 3, the optimum values of the test variables for the maximum yield of DHA were temperature (T) = 23 8C; aeration rate (Q) = 1.48 L min1 L1 and agitation (R) = 220 rpm. Using these variables, a maximum DHA production rate 4.8 g L1 was obtained. 3.3. Effects of different kLa on the DCW and DHA production of S. limacinum OUC88 S. limacinum OUC88 is an aerobe. Effects of different kLa on the DCW and DHA production of S. limacinum OUC88 are shown in Fig. 4. Along with the increasing of kLa, both the DCW and DHA production are increased significantly (P < 0.05). But after kLa > 192 h1, the production stop increasing or increase insignificantly. So the best kLa value for the S. limacinum OUC88, is about 192 h1.

Fig. 4. Effects of different kLa on the growth and DHA content of S. limacinum OUC88.

4. Conclusion Conventional process studies are usually time-consuming and expensive. To overcome these problems, statistical techniques were followed where variables were changed simultaneously to study their collective effect on DCW and

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DHA production. Wu and Lin had applied response surface methodology to optimize docosahexaenoic acid production by Schizochytrium sp. S31 [30]. Based on contour plots and canonical analysis, they have obtained maximizing DHA production of 516 mg L1. Through these optimization experiments in this study, the optimal temperature (T), aeration rate (Q), agitation (R) and inoculum cells in mid-exponential phase resulted in 24.1 g L1 DCW production rate at 23 8C, 1.48 L min1 L1 and 250 rpm, respectively. To obtain 4.8 g L1 DHA production, the optimal values of the fermentation conditions are temperature (T) = 23 8C; aeration rate (Q) = 1.48 L min1 L1 and agitation (R) = 220 rpm, respectively. In order to obtain the maximum yields of DCW and DHA jointly, optimal fermentation conditions are found to be: temperature (T) = 23 8C; aeration rate (Q) = 1.48 L min1 L1, agitation (R) = 250 rpm and inoculum cells in midexponential phase. In this situation, the maximum yield for DCW and DHA are 24.1 and 4.7 g L1, respectively. After optimization, the yield of DCW and DHA are rose 27.2 and 28.6%, respectively, and both increased significantly (P < 0.05). Acknowledgement This work was supported by the Science and Technology Program of Qingdao, China (grant no. 04-2-HH-76). References [1] Harel H, Koven W, Lein I, Bar Y, Behrens P, Stubblefield J. Advanced DHA, EPA and ArA enrichment materials for marine aquaculture using single cell heterotrophs. Aquaculture 2002;213:347–62. [2] Hayashi M, Matsumoto R, Yoshimatsu T, Tanaka S, Shimizu S. Isolation of highly DHA-accumulated Labyrinthulales and their utilization for nutritional enrichment of rotifers and Artemia. Nippon Suisan Gakkaishi 2002;68(5):674–8. [3] Kanazawa A. Importance of DHA in organisms. Pros Puslitbangkan 1993;18:62–70. [4] Kanazawa A. Effects of docosahexaenoic acid and phospholipids on stress tolerance of fish. Aquaculture 1997;255:129–34. [5] Watanabe T. Importance of Docosahxaenoic acid in marine larval fish. J World Aquacult Soc 1993;24:152–61. [6] Kang JX, Leaf A. The cardiac antiarrhythmic effects of polyunsaturated fatty acid. Lipids 1996;31:41–4. [7] Kromann N, Green A. Epidemiological studies in the Upernavik district, Greenland. Incidence of some chronic diseases 1950–1974. Acta Med Scand 1980;208:401–6. [8] Honda D, Yokochi T, Nakahara T, Erata M, Higashihara T. Schizochytrium limacinum sp. nov, a new thraustochytrid from a mangrove area in the west Pacific Ocean. Mycol Res 1998;102(4):439–48. [9] Zhu LY, Zhang XC, Ji L, Song XJ, Kuang CH. Changes of lipid content and fatty acid composition of Schizochytrium limacinum in response to different temperatures and salinities. Proc Biochem 2007;42:210–4. [10] Plackett RL, Burman JP. The design of optimum multifactorial experiments. Biometrika 1946;33:305–25. [11] Vaidya R, Vyas P, Chhatpar HS. Statistical optimization of medium components for the production of chitinase by Alcaligenes xylosoxydans. Enzyme Microb Technol 2003;33:92–6.

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