Optimization Of Lipase Catalyzed Synthesis Of Nonyl Caprylate Biology Essay

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Flavour ester, a short-chain ester is the class of compound that widely distributed in nature. This kind of ester also known as a carboxylic acid ester is extensively used in foods, beverages, cosmetics and pharmaceutical industries. Flavour esters are fine organic compound that forming part of natural aromas in flowers and fruit. Traditionally, this kind of compounds has been isolated from natural sources such as flowers, fruits and vegetables. However, these natural flavour esters extracted from plant materials are often limited and expensive for commercial use. Thus, it is economically important to synthesize flavour ester using cheaper and more broadly available material to meet the consumer demand.

Conventional chemical synthesis of flavour ester between acid and alcohol using polluting liquid acids as a catalyst, require post-treatment. Thus, the use of biotechnology appears to be an attractive in various ester preparations under milder conditions and the product may be given the natural label. Therefore, the use of enzymes, such as lipases as biocatalysts may offer many significant advantages over chemical synthesis such as lower energy requirement since enzymes function under mild reaction conditions and enhanced selectivity and quality of product.

However, the use of native lipase form brings about significant practical problems such as the high cost of the enzyme. As a mean of reducing the cost, the use of immobilized lipases is significantly advantageous. Immobilized lipase are lipases attached to solid materials, which make them easily be recovered from the reaction mixtures, thus offers reutilizations of the biocatalyst and thereby making the process economically feasible. Another aims of using immobilized lipase is that the functional activities as well as the stability of enzymes are improved compared to the native lipase.

Optimization of reaction process is very important in enzymatic synthesis in order to improve the reaction performance. Since ester synthesis is a specific problem as it is affected by various factors depending upon the reaction condition used. The conventional method of optimization process involves varying one parameter at-a-time and keeping the other constant. Being single-dimensional, this method is inefficient as it fails to understand relationships between the variables (reaction time, temperature, amount of enzyme and substrate concentration) and response (percentage conversion). These procedures are time consuming, burdensome, require a lot of experimental data sets and do not provide information about the mutual interactions of the parameters and sometimes it also can lead to misinterpretation of the results. Therefore understanding and modeling of both conventional and interactive effects of important parameters are essential in order to obtain a high performance synthesis.

A statistical based technique commonly used for this purpose is Response Surface Methodology (RSM). This method which is an efficient statistical tool for optimization of multiple variables with several designs is an effective technique for prediction and the investigation of complex processes. By carrying out just a few selected experiments such designs explain the reaction completely bringing out the finer details. Statistical analysis of Response surface methodology quantifies the relationships between variables and response (yield) thus determines the optimum operating conditions in a process. This methodology leads to fantastic breakthroughs in process understanding, thus improving quality, reducing costs and increasing profits.

Therefore, the purpose of this research is to study the optimization process of green synthesis of flavor esters via lipase-catalyzed reactions in solvent free system via statistical approach of Response surface methodology (RSM). Nonyl caprylate which is known as the main component in citrus and rose flavor was the targeted flavor ester in this investigation. It is necessary to identify the important factors that affect the performance of the process in small scale system, so that a suitable approach of scaling-up and a design model could be proposed prior to commercialization.

Materials and Methods

Materials

Novozym 435 as 10,000 PLU (from Candida antarctica lipase immobilized onto macroporous acrylin resin) was received from Novo Nordisk (Denmark). Nonanol (purity, 98 %) and caprylic acid (purity, 97 %) were obtained from Merck (Germany). All other reagents were of analytical grade and used as received.

Enzymatic Synthesis

The reaction system consisted of nonanol and caprylic acid (1:1) and 5 % of enzyme (w/w) were mix in screw-capped vial. The mixture was incubated at 37°C using a horizontal waterbath shaker. The shaking speed was set at 150 rpm and the reaction mixture was continuously reacted for 12 hours.

Analysis of Reaction Product

Determination of the percentage conversion of ethyl valerate (%):

The percentage conversion (%) of nonyl caprylate was measured by determining the remaining unreacted fatty acids in the reaction mixture by titration with 1.0 M NaOH in an automatic titrator (Methrom, Switzerland). All the samples were assayed in triplicate and the experiment was repeated twice.

Conversion of flavour ester (%) =

Volume of NaOH used (without enzyme) - Volume of NaOH used (with enzyme) X 100 Ex. (1)

Volume of NaOH used (without enzyme)

Experimental Design

A five-level, four-factor central composite rotatable design (CCRD) was employed, requiring 30 experiments. The fractional factorial design consisted of sixtheen factorial points, eigth axial points and six centre points. The variables and their respective levels are presented in Table 1. Table 2 represents the actual experiments carried out for developing the model. The data obtained were fitted to a second-order polynomial equation:

Ex. (2)

Where Y= % conversion of flavour ester, b0, bi, bii and bij are constant coefficients and xi are the uncoded independent variables. Subsequent regression analysis, analysis of variance (ANOVA) and response surfaces were performed using Design Expert Software (version 7.1.6) from stat ease (Minneapolis, MN). Optimal reaction parameters for maximum conversion were generated using the software's numerical optimization function.

Table 1. Coded and actual levels of variables for design of experimenta

Coded values of variables

Factor

Name

Unit

-1

0

1

α

A

Time

hour

0.5

3

5

8

10.5

B

Enzyme Amount

% (w/w)

5

10

15

20

25

C

Temperature

°C

20

30

40

50

60

D

Shaking Speed

rpm

50

100

150

200

250

aStudy type, response surface; No. of experiments, 30; design, CCRD; response, Y1, name, flavour ester, unit, % conversion.

Table 2. Design matric of the actual experiments carried out for developing the model

Standard

A (hour)

B % (w/w)

C (°C)

D (rpm)

Actual (%)

Predicted (%)

1

3

10

30

100

81.337

82.116

2

8

10

30

100

85.722

85.976

3

3

20

30

100

89.603

89.114

4

8

20

30

100

90.361

91.112

5

3

10

50

100

84.115

83.777

6

8

10

50

100

85.785

86.013

7

3

20

50

100

89.783

89.731

8

8

20

50

100

89.898

90.154

9

3

10

30

200

86.937

86.510

10

8

10

30

200

88.716

89.119

11

3

20

30

200

89.687

89.810

12

8

20

30

200

90.441

90.607

13

3

10

50

200

87.210

86.811

14

8

10

50

200

87.528

87.846

15

3

20

50

200

89.543

89.117

16

8

20

50

200

88.817

88.339

17

0.5

15

40

150

83.149

83.829

18

10.5

15

40

150

87.720

86.861

19

5.5

5

40

150

84.756

84.412

20

5.5

25

40

150

91.688

89.069

21

5.5

15

20

150

90.463

89.748

22

5.5

15

60

150

88.556

89.092

23

5.5

15

40

50

88.019

87.389

24

5.5

15

40

250

89.467

89.918

25

5.5

15

40

150

88.698

89.069

26

5.5

15

40

150

89.657

89.069

27

5.5

15

40

150

88.388

89.069

28

5.5

15

40

150

89.207

89.069

29

5.5

15

40

150

88.966

89.069

30

5.5

15

40

150

89.499

89.069

Result and Discussion

3.1 Model fitting and ANOVA

The coefficients of the empirical model and their statistical analysis, evaluated using Design Expert Software, are presented in Tables 3-5. The model F-value of 19.17 with a 'Prob > F' value of 0.0001 implied that the model was significant at the 1 % confidence level. The high coefficient of determination (R2= 0.9471) of the model indicated the suitability of the model for adequately representing the real relationship among the parameters studied. A high value of R2 (>0.950) has been also reported by Hari Krishna et al [6], for the lipase-catalysed synthesis of isoamyl isobutyrate and by Jei et al [7] for the enzymatic optimization of propylene glycol monolaurate by direct esterification. In this study, quadratic model was shown to be the most significant model due to the low value of probability (P=0.0001) and high value of coefficient determination (R2=0.9471). Similar quadratic response models have been reported by Shieh et al [8] and Chen et al [9] in the optimization of lipase-catalyzed synthesis of biodiesel (soybean oil methyl ester) and kojic acid monolaurate, respectively. The model indicates the significant terms was observed for linear (A and C), quadratic (C) and interactive effect (AC) according to the value of 'Prob > F' < 0.050. The final equation was derived in terms of coded factors for the synthesis of ethyl valerate as shown in Equation (3):

Y = +77.01 + 3.23A + 12.10B + 0.40C + 1.36D - 3.51AB - 1.32AC + 0.63AD - 0.11BC - 1.44BD + 1.25CD - 0.89A2- 4.83B2- 0.57C2- 0.18D2

Ex. (3)

where A is the time; B is the temperature; C is the amount of enzyme; D is the shaking speed.

Table 3. Statistical analysis: ANOVA

Source

Sum of squares

Degrees of freedom

Mean Square

F-value

Prob >F

Model

155.92

14

11.14

24.74

<0.0001

Residual

6.75

15

0.45

Lack of fit

5.59

10

0.56

2.41

0.1720

Pure error

1.16

5

0.23

Total

162.67

29

aSignificance at 'Prob>F' is <0.0500

Table 4. Statistical analysis: regression analysis

Std. Dev.

0.67

Mean

88.12

R-squared

0.9585

Adj-R-Squared

0.9198

Pred-R-Squared

0.7917

Adeq Precision

20.419

Table 5. Statistical analysis: coefficient of models

Factor

Coefficient Estimate

Prob >F

Intercept

89.07

<0.0001

A-Time

0.76

<0.0001

B-Temperature

1.86

<0.0001

C-Enzyme Amount

-0.16

0.2496

D-Shaking Speed

0.63

0.0003

AB

-0.45

0.0164

AC

-0.39

0.0331

AD

-0.30

0.0936

BC

-0.25

0.1590

BD

-0.91

<0.0001

CD

-0.33

0.0698

A2

-0.93

<0.0001

B2

-0.23

0.0875

C2

0.088

0.5042

D2

-0.10

0.4298

aA = Time; B = Temperature; C = Enzyme amount; D = Shaking Speed.

bSignificance at 'Prob > F' is <0.0500

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