Enhanced production of lipase by the thermophilic Geobacillus stearothermophilus strain-5 using statistical experimental designs

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RESEARCH PAPER New Biotechnology � Volume 27, Number 4 � September 2010

Enhanced production of lipase bythe thermophilic Geobacillusstearothermophilus strain-5 usingstatistical experimental designsMohamed Sifour1,2, Taha I. Zaghloul2, Hesham M. Saeed2, Mahmoud M. Berekaa3 andYasser R. Abdel-fattah4

1Department of Molecular and Cell Biology, Faculty of Science, University of Jijel, Ouled Aissa, Jijel, Algeria2Department of Biotechnology, Institute of Graduate Studies and Research, Alexandria University, Egypt3Department of Environmental Science, Faculty of Science (Moharam Bay), Alexandria University, Egypt4Mubarak City for Scientific Research and Technology Applications, Alexandria, Egypt

Statistically based experimental designs were applied to optimize the cultural conditions for the

production of a glycerol-inducible lipase from the thermophilic Geobacillus stearothermophilus strain-5.

The effect of nineteen culture conditions on enzyme production was evaluated using Plackett–Burman

factorial design. Tween 80, K2HPO4, glycerol and glucose were the most significant factors in improving

enzyme production. The selected parameters were then further investigated using central composite

design to define the optimal process conditions. Maximal enzyme activity (578 U/ml) was reached under

the following conditions: glycerol, 2.24% (v/v); Tween 80, 0.76% (v/v); glucose, 0.76% (w/v) and

K2HPO4, 0.38% (w/v) which is about five folds the activity in basal medium. A verification experiment

was carried out to examine model validation and revealed more than 98% validity.

IntroductionRecently, the investigation on enzyme from thermophilic bac-

teria has gained a considerable attention because many industrial

processes operate best at high temperature [1–4]. Lipolytic

enzymes (lipases and esterases) are an important group of bioca-

talysts that found great applications in biotechnological

processes, they have been widely used in several industries

(food, dairy, detergent, and pharmaceuticals) [5,6]. As most of

the industrial processes operate at temperature exceeding 45 8C,

lipase should be active and stable at a temperature around 50 8C[1]. Several lipases have been isolated and purified from thermo-

philic bacteria, mainly from thermophilic Bacillus (Geobacillus)

[7–10].

Medium composition significantly affects product concentra-

tion, yield and productivity. There is a general practice of deter-

mining optimal concentration of media components by varying

one factor at a time. However, this method does not depict the net

effect of total interactions among the various media components

Corresponding author: Sifour, M. ([email protected])

330 www.elsevier.com/locate/nbt 1871-6784/$

[5]. Experimental design techniques present a more balanced

alternative to the one-factor-at-a time approach to fermentation

improvement [11]. The factorial design of a limited set of variables

is advantageous in relation to the conventional method of manip-

ulation of a single parameter per trial, as the latter approach

frequently fails to locate the optimal conditions for the process,

because of its failure to consider the effect of possible interactions

between factors [5].

A glycerol-inducible lipase was previously isolated from the

thermophilic Geobacillus stearothermophilus strain-5 [12]. The pre-

sent work aims at evaluating culture conditions affecting lipase

secretion from the same isolate by applying factorial experimental

design and at determining optimum conditions for its production

using central composite design (CCD).

Materials and methodsMicroorganismThe isolate used in this study was isolated from desert soil sample

and was purified and identified by 16S rRNA as Geobacillus stear-

othermophilus (accession number: DQ923400) [12].

- see front matter � 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.nbt.2010.04.004

New Biotechnology �Volume 27, Number 4 � September 2010 RESEARCH PAPER

TABLE 1

Media components and test levels for Plackett–Burman experi-ment.

Variable Variablecode

Lowlevel (�1)

Highlevel (+1)

Tween 80 (%) X1 0.2 1

Olive oil (%) X2 1 5

Glycerol (%) X3 0.2 1

Glucose (%) X4 0.1 1

Galactose (%) X5 0 1

Arabinose (%) X6 0 1

Xylose (%) X7 0 1

Sucrose (%) X8 0 1

Peptone (%) X9 0.2 1

Yeast extract (%) X10 0.2 1

Urea (%) X11 0.2 1

(NH4)2SO4 (%) X12 0.2 1

MgSO4 (%) X13 0.2 1

K2HPO4 (%) X14 0.1 0.5

KH2PO4 (%) X15 0.1 0.5

CaCl2 (%) X16 0.02 0.1%

pH X17 6 8

Aeration (200 rpm) X18 No baffles 2 baffles

Temperature X19 55 65

TABLE 2

Plackett–Burman experimental design for the evaluation of factorsactivity was measured after 48 h of incubation.

Trials X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11

1 1 �1 1 �1 �1 �1 �1 1 1 �1 �12 1 1 1 1 �1 1 �1 1 �1 �1 �13 1 �1 �1 1 �1 �1 1 1 1 1 �14 �1 �1 �1 �1 1 1 �1 �1 1 �1 �15 �1 1 �1 �1 1 1 1 1 �1 1 �16 1 �1 �1 �1 �1 1 1 �1 �1 1 �17 �1 1 1 1 1 �1 1 �1 1 �1 �18 �1 �1 �1 1 1 �1 �1 1 �1 �1 1

9 �1 �1 1 1 1 1 �1 1 �1 1 �110 �1 1 1 �1 �1 1 �1 �1 1 1 1

11 1 1 1 �1 1 �1 1 �1 �1 �1 �112 �1 �1 1 �1 �1 1 1 1 1 �1 1

13 �1 1 �1 1 �1 �1 �1 �1 1 1 �114 �1 �1 1 1 �1 �1 1 �1 �1 1 1

15 1 1 1 1 1 1 1 1 1 1 1

16 1 1 �1 1 �1 1 �1 �1 �1 �1 1

17 �1 1 �1 �1 �1 �1 1 1 �1 �1 1

18 1 �1 1 �1 1 �1 �1 �1 �1 1 1

19 1 �1 �1 1 1 1 1 �1 1 �1 1

20 1 1 �1 �1 1 �1 �1 1 1 1 1

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Lipase assayLipase activity was determined colorimetrically [13] using two solu-

tions.Solution1contained90 mgp-nitrophenylpalmitatedissolved

in30 ml2-propanol.Solution2contained2 gTritonX-100and0.5 g

gum arabic dissolved in 450 ml buffer (Tris–HCl, 50 mM, pH 8). The

assay reagent was prepared by adding 1 ml of solution 1 to 9 ml of

solution 2 dropwise to get an emulsion that remained stable for 2 h.

Theassaymixturecontained900 ml of theemulsion and 100 ml of an

appropriately diluted enzyme solution. The liberated p-nitrophenol

was measured at 410 nm. One unit of enzyme was defined as the

amount of enzyme that releases 1 mmol p-nitrophenol from the

substrate per minute. The assay was carried out at 60 8C.

Inoculum and enzyme preparationThe production medium was prepared in different formulae

according to the experimental design in Tables 1 and 4. At the

end of the incubation period, cells were removed by centrifugation

at 7000 rpm for 4 min and the supernatant was considered as crude

enzyme and used for the measurement of lipase activity. The

experiments were carried out in duplicate.

The production medium was inoculated with 4% (v/v) of the 16–

18 h bacterial culture (A600 = 2.1 corresponding to 4.2 � 107 CFU/

ml) grown on PY medium [14] (g/l—peptone: 10; yeast extract: 5;

NaCl: 5). Cells were removed by centrifugation at 7000 rpm for

4 min and suspended in normal saline then used as inoculum.

Plackett–Burman designFor screening purpose, various medium components as well as

environmental factors were evaluated. The different factors were

affecting lipase production by G. stearothermophilus strain-5;

X12 X13 X14 X15 X16 X17 X18 X19 Activity (U/ml)

1 �1 �1 1 1 1 1 �1 14.50

�1 1 1 �1 �1 1 �1 �1 467.00

1 �1 1 �1 �1 �1 �1 1 187.00

1 1 1 1 �1 1 �1 1 41.50

1 �1 �1 �1 �1 1 1 �1 34.60

�1 1 1 1 1 �1 1 �1 45.90

�1 �1 1 1 �1 �1 1 �1 34.20

1 1 1 �1 1 �1 1 �1 35.70

�1 �1 �1 1 1 �1 �1 1 46.00

1 �1 1 �1 1 �1 �1 �1 77.80

1 1 �1 �1 1 �1 �1 1 129.00

�1 1 �1 �1 �1 �1 1 1 38.90

�1 1 �1 �1 1 1 1 1 53.20

1 1 �1 1 �1 1 �1 �1 76.90

1 1 1 1 1 1 1 1 226.00

1 �1 �1 1 �1 �1 1 1 127.00

�1 �1 1 1 1 1 �1 1 38.50

�1 �1 1 �1 �1 1 1 1 263.00

�1 �1 �1 �1 1 1 �1 �1 103.00

�1 1 �1 1 �1 �1 �1 �1 100.00

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RESEARCH PAPER New Biotechnology � Volume 27, Number 4 � September 2010

TABLE 4

CCD matrix representing the effect of different significant vari-ables on lipase production by G. stearothermophilus strain-5.

Trials Tween 80 K2HPO4 Glycerol Glucose Activity (U/ml)

1 �1 �1 �1 �1 450.4

2 1 �1 �1 �1 293.05

3 1 �1 1 1 279.86

4 1 1 �1 �1 281.25

5 �1 �1 1 �1 511.11

6 0 0 0 �1.48258 370.6

7 1 1 �1 1 328.57

8 1 1 1 �1 252.77

9 0 0 0 0 402.77

10 0 �1.48258 0 0 445.4

11 �1 1 1 1 313.49

12 0 0 0 0 358.73

13 1 1 1 1 207.64

14 �1 1 �1 1 339.58

15 �1 �1 �1 1 240.27

16 �1 1 �1 �1 299.305

17 1.482579 0 0 0 313.94

18 1 �1 1 �1 395.3

19 0 0 0 1.482579 293.46

20 0 1.482579 0 0 345.5

21 0 0 1.482579 0 352.08

22 �1.48258 0 0 0 440

23 �1 1 1 �1 390.74

24 0 0 �1.48258 0 425.92

25 �1 �1 1 1 235.94

26 1 �1 �1 1 353.84

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prepared in two levels,�1 for low level and +1 for high level, based

on a Plackett–Burman statistical design [15]. This design is prac-

tical especially when the investigator is faced with large number of

factors and is unsure which settings are likely to produce optimal

or near optimum responses [16]. Table 1 illustrates the factors

under investigation as well as levels of each factor used in the

experimental design, whereas Table 2 represents the design

matrix. Plackett–Burman experimental design is based on the first

order model:

Y ¼ b0 þX

bixi

where Y is the response (enzyme activity), b0 is the model intercept

and bi is the variables estimates. This model describes no interac-

tion among factors and it is used to screen and evaluate the

important factors that influence the response (enzyme activity).

The maximum number of variables that can be evaluated in one

design is equal to one less than the number of individual experi-

ments. In the present work, 19 assigned variables were screened in

20 experiments that were carried out in Erlenmeyer flasks. Enzyme

activity was measured after 48 h of incubation. All experiments

were carried out in duplicate and the averages of lipase activity

were taken as responses. The variables whose confidence levels

were higher than 95% were considered to significantly influence

lipase activity.

Central composite design (CCD)To describe the nature of the response surface in the experi-

mental region, a central composite design [17] was applied. As

presented in Table 3, factors of highest confidence levels eluci-

dated through Plackett–Burman experimental design were pre-

scribed into five levels, coded �1.48, �1, 0, 1.48, and +1. Table 4

represents the design matrix of a 26 trials experiment. For

predicting the optimal point, a second-order polynomial func-

tion was fitted to correlate relationship between independent

variables and response (lipase activity). For the four factors the

equation is:

Y ¼ b0 þ b1X1 þ b2X2 þ b3X3 þ b4X4 þ b12X1X2 þ b13X1X3

þ b14X1X4 þ b23X2X3 þ b24X2X4 þ b34X3X4 þ b11X21 þ b22X2

2

þ b33X23 þ b44X2

4

where Y is the predicted response, b0 the model constant; X1, X2,

X3 and X4 independent variables; b1, b2, b3 and b4 are linear

coefficients; b12, b13, b14, b23, b24 and b34 are cross product

coefficients and b12, b22, b33 and b44 are the quadratic coeffi-

cients. Microsoft Excel 97 was used for the regression analysis of

the experimental data obtained. The quality of fit of the poly-

TABLE 3

Variables and their settings employed in central compositedesign for optimization of lipase production by G. stearother-mophilus strain-5.

Variables Variable code �1.48 �1 0 +1 +1.48

Tween (%) X1 0.76 1 1.5 2 2.24

K2HPO4 (%) X2 0.38 0.5 0.75 1 1.12

Glycerol (%) X3 0.76 1 1.5 2 2.24

Glucose (%) X4 0.76 1 1.5 2 2.24

332 www.elsevier.com/locate/nbt

nomial model equation was expressed by the coefficient of

determination R2.

Statistical analysis of the dataThe data on enzyme activity were subjected to multiple linear

regression using MICROSOFT EXCEL 97 to estimate t-value, P-

value and confidence level. The significance level (P-value) was

determined using the Student’s t-test. The t-test for any individual

effect allows an evaluation of the probability of finding the

observed effect purely by chance. If this probability is sufficiently

small, the idea that the effect was caused by varying the level of the

variable under test is accepted. Confidence level is an expression of

the P-value in percent. Optimal value of activity was estimated

using the solver function of MICROSOFT EXCEL tools.

Results and discussionThe strain used in this study is a thermophilic Geobacillus stear-

othermophilus that produce a glycerol-inducible lipase [12]. For

improvement of the enzyme production, a sequential optimiza-

tion approaches were applied. The first approach deals with screen-

ing for culture as well as nutritional factors affecting growth and

lipase production by G. stearothermophilus strain-5. The second

New Biotechnology �Volume 27, Number 4 � September 2010 RESEARCH PAPER

FIGURE 2

Pareto chart rationalizing the effect of each variable on the enzyme activity(U/ml) produced by G. stearothermophilus strain-5.

TABLE 5

Statistical analysis of Plackett–Burman design showing coeffi-cient values, t- and P-values for each variable.

Variables Coefficients t-Statistics P-value

Tween 80 (%) 59.291 7.47 0.0847

Olive oil (%) 21.721 2.74 0.2230

Glycerol (%) 30.239 3.81 0.1634

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approach is to optimize the factors that control the enzyme

production process.

Evaluation of the factors affecting lipase productivityNineteen different factors including fermentation conditions and

medium constitution were screened for their effect on lipase

production using the Plackett–Burman design. The independent

variables examined and their settings are shown in Table 1. The

design plan and the averages of lipase activity for the different

trials are given in U/ml and shown in Table 2. The main effect of

each variable was estimated as the difference between both

averages of measurements made at the high level (+1) and at

the low level (�1) of that factor. The data in Table 2 show a wide

variation from 14.5 to 467 U/ml of lipase activity. This variation

reflects the importance of medium optimization to attain higher

productivity. The analysis of the data from the Plackett–Burman

experiments involved a first order (main effects) model. The main

effects of the examined factors on the enzyme activity were

calculated and presented graphically in Fig. 1.

On the basis of the analysis of the regression coefficients of the 19

variables after 48 h of incubation, Tween 80, olive oil, glycerol,

glucose, arabinose, sucrose, K2HPO4, MgSO4, culture pH and tem-

perature showed positive effect on lipase activity. Xylose, galactose,

(NH4)2SO4, peptone, baffles (as an expression of medium aeration

factor), KH2PO4, and CaCl2 repressed enzyme production. Urea and

yeast extract have a slight effect on enzyme productivity. Fig. 2

shows the ranking of factor estimates in a Pareto chart. The Pareto

chart displays the magnitude of each factor estimate and it is a

convenient way to view the results of a Plackett–Burman design.

The polynomial model describing the correlation between the

19 factors and the lipase activity could be presented as follows:

Yactivity ¼ 107:01þ 59:29X1 þ 21:72X2 þ 30:23X3 þ 28:994X4

� 5:73X5 þ 13:82X6 � 15:64X7 þ 11:85X8 � 19:35X9

þ 4:02X10 þ 1:71X11 � 12:04X12 þ 14:38X13 þ 34:62X14

� 31:92X15 � 30:10X16 þ 24:81X17 � 19:74X18 þ 7:93X19

On the basis of calculated t-values and confidence level (%)

(Table 5), Tween 80, K2HPO4, glycerol and glucose, were found to

be the most significant variables affecting lipase activity, they were

FIGURE 1

Effect of different factors on lipase production (U/ml) byG. stearothermophilus

strain-5 as screened with Plackett–Burman design.

chosen for further optimization. Some variables of negative sig-

nificant effect were not included in the next optimization experi-

ment, but instead were used in all trials at their (�1) level. Most of

the reports state that lipases are generally induced by oils [18,19].

Tween 80 was one of the most important factors that affect the

production of lipase from Geobacillus thermoleovorans [19]; Tween

80 was also found to be the best carbon source inducing produc-

tion of lipase from a thermophilic Bacillus sp. [20]. In our study,

lipase production was induced by glycerol and glucose and it was

poorly induced by olive oil. This observation is in accordance with

the report of Gupta et al. [21], where glycerol and mannitol were

applied for lipase induction in a thermophilic Bacillus sp. instead

Glucose (%) 28.664 3.61 0.1791

Galactose (%) �5.735 �0.72 0.6016

Arabinose (%) 13.82 1.74 0.3318

Xylose (%) �15.645 �1.97 0.2989

Sucrose (%) 11.851 1.49 0.3756

Peptone (%) �19.355 �2.44 0.2477

Yeast extract (%) 4.024 0.51 0.7012

Urea (%) 1.714 0.22 0.8646

(NH4)2SO4 (%) �12.047 �1.52 0.3708

MgSO4 (%) 14.381 1.81 0.3210

K2HPO4 (%) 34.625 4.36 0.1434

KH2PO4 (%) �31.928 �4.02 0.1551

CaCl2 (%) �30.102 �3.79 0.1641

pH 24.812 3.13 0.1971

Aeration �19.742 �2.49 0.2433

Temperature 7.936 4.63 0.1354

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of lipid sources. On the other hand, supplementation of the

medium with 0.1% glucose enhanced the production of lipase

from the thermophilic Bacillus sp. THL027 [22].

Application of CCD and data analysisFurther experiments were carried out to obtain a quadratic model

consisting of 26 trials. As presented in Table 3, factors of highest

confidence levels were prescribed into five levels, coded�1.48,�1,

0, +1 and +1.48. The design of this experiment is given in Table 4

together with the experimental results. Regression analysis was

FIGURE 3

Three dimensional response surface graphs showing the behavior of lipase respo

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performed to fit the response function (lipase activity) with the

experimental data. The analysis of variance for the four variables

(Tween 80, glycerol, glucose and K2HPO4) indicated that enzyme

activity can be well described by a polynomial model with a

relatively high coefficient of determination (R2 = 0.85). The sta-

tistical analysis of the full model in Table 6 shows that Tween 80,

K2HPO4, glucose and glycerol each had a significant effect on

lipase synthesis. When presenting experimental results in the

form of surface plot (Fig. 3) it can be seen that near to moderate

levels of Tween 80, K2HPO4 and glucose and high level of glycerol

nse as affected by different culture conditions in CCD (A, B, C, D, E, and F).

New Biotechnology �Volume 27, Number 4 � September 2010 RESEARCH PAPER

TABLE 6

Regression coefficient of the full polynomial model representingrelationships between lipase activity and independent variables(Tween 80, K2HPO4, glycerol and glucose).

Coefficient symbol Estimatea P-value

b0 402.94 <1 � 10�4

b1 �28.21 1.3 � 10�2

b2 �24.24 2.8 � 10�2

b3 �5.33 0.59

b4 �33.78 5 � 10�3

b12 �9.82 0.38

b13 �15.17 0.19

b14 29.36 2 � 10�2

b23 �10.54 0.35

b24 31.57 1.4 � 10�2

b34 �28.20 2.5 � 10�2

b11 �17.41 0.24

b22 �9.01 0.53

b33 �11.94 0.41

b44 �37.86 2 � 10�2

a Estimates are the polynomial model coefficients.

FIGURE 4

Monitoring bacterial growth and extracellular lipolytic activity of

G. stearothermophilus strain-5 grown on (&) PY medium and (~) optimizedmedium. Open symbols represent bacterial growth and the closed ones

represent corresponding lipolytic activity.

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supported high lipase activity. For predicting the optimal point,

within experimental constrains, a second-order polynomial func-

tion was fitted to the experimental results of lipase activity:

Y ¼ 402:94� 28:21X1 � 24:24X2 � 5:33X3 � 33:78X4 � 9:82X1X2

� 15:17X1X3 þ 29:36X1X4 � 10:54X2X3 þ 31:57X2X4

� 28:2X3X4 � 17:41X21 � 9:01X2

2 � 11:94X23 � 37:86X2

4

where X1, X2, X3 and X4 represent codified values for Tween 80,

K2HPO4, glycerol and glucose, respectively. At the model level, the

correlation measures for the estimation of the regression equation

are the multiple correlation coefficient R and the determination

coefficient R2. The closer the value of R is to 1, the better is the

correlation between the observed and the predicted values. In this

experiment, the value of R was 0.92 for activity. This value indi-

cates a high degree of correlation between the experimental and

the predicted values. The value of determination coefficient

R2 = 0.85 being a measure of fit of the model, indicates that about

15% of the total variations are not explained by the activity model.

From statistical analysis, it can be concluded that among the test

variables, glycerol had the most significant effect on lipase activity.

The optimal levels of the three components as obtained from the

maximum point of the polynomial model were estimated using

the Solver function of MICROSOFT EXCEL tools, and found to be

(%): glycerol: 2.24, Tween 80: 0.76, glucose: 0.76 and K2HPO4:

0.38. with a predicted activity of 589.1 U/ml. The optimal value of

enzyme activity was about five times that in the basal conditions,

which reflects the necessity and the value of optimization process.

Results obtained in this study are in accordance with other find-

ings, where it was reported that glycerol is one of the most

important factors that affect lipase production from a thermo-

philic Bacillus sp. [18,21]. The importance of Tween 80 as carbon

source for the production of lipase was also reported [19,20]. In

addition, the presence of inorganic phosphate showed a remark-

able role in the lipase production. In addition to its role as an

important constituent of cellular biomolecules such as nucleic

acids and phospholipids, phosphate is known to play a regulatory

role in the synthesis of primary and secondary metabolites in

microorganisms [23].

The final production conditions were as follows (%): glycerol:

2.24; glucose: 0.76; Tween 80: 0.76; K2HPO4: 0.38; yeast extract: 1;

peptone: 0.2; (NH4)2SO4: 0.2; MgSO4:1; KH2PO4: 0.1; CaCl2: 0.02,

pH adjusted to 7.0–7.5 and incubated at 60 8C.

Verification of modelThe adequacy of the model was examined by an additional experi-

ment using the derived optimal conditions. The predicted value

was 589.1 U/ml and in the experimental value was 578 � 5 U/ml.

This is approximately 98% of the predicted value, which indicates

that the generated model gave an adequate prediction of the

enzyme activity.

Optimization through statistical experimental design has been

applied in the production of many lipolytic enzymes [24,25].

Plackett–Burman design was used to evaluate cultural conditions

affecting lipase production by a thermophilic G. thermoleovorans

YN [11], followed by determining the optimum conditions by

implementing Box–Behnken experimental design. The optimized

medium resulted in about 4-fold increase in enzyme production,

compared with that obtained in the basal medium [19]. Moreover,

the production of thermostable lipase from a thermophilic Bacillus

sp. was improved tremendously (around 193-fold) following med-

ium optimization involving both one-at-a-time and statistical

designing approaches [21].

Monitoring production of lipase in basal and optimized mediaThe bacterial growth and the extracellular lipolytic activity of the

G. stearothermophilus strain-5 grown on the basal (PY) medium [14]

and optimized medium were monitored. Bacterial growth was

determined by measuring the absorbance of the culture suspen-

sion at 420 nm. Lipolytic activity was measured using p-nitophe-

nyl palmitate as mentioned before. Data of Fig. 4 illustrated the

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growth andthe level of lipolytic activity through48 h of incubation.

Data showed that the level of extracellular enzyme production

started at late log phase of the bacterial growth and increased

gradually with bacterial growth till it reached its maximal level

after 24 and 48 h of incubation on the basal and on the optimized

medium, respectively. The level of the enzyme produced in opti-

336 www.elsevier.com/locate/nbt

mized medium was about five folds the activity in basal medium,

which confirms the necessity of the optimization process.

AcknowledgmentM. Sifour is very grateful to the ‘Ministry of Higher Education and

Scientific Research of Algeria’ for their financial support.

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