ORIGINAL PAPER

Product Selectivity and Optimization of Lipase-Catalyzed1,3-Propylene Glycol Esters by Mixture Design and RSM

Hsiao-Ching Chen • Chia-Hung Kuo • Wei-Chuan Tsai •

Yi-Lin Chung • Wen-Dee Chiang • Chieh-Ming J. Chang •

Yung-Chuan Liu • Chwen-Jen Shieh

Received: 28 September 2010 / Revised: 15 June 2011 / Accepted: 26 July 2011 / Published online: 13 August 2011

� AOCS 2011

Abstract Propylene glycol mono- (PGM) and diesters

(PGD) are widely used as emulsifiers in food and phar-

maceutical industry. Solvent engineering was applied to

determine the optimum solvent mixtures for the lipase-

catalyzed synthesis of 1,3-PGM and 1,3-PGD. After 24 h

reaction, the results showed that the molar production of

1,3-PGM was 75% under pure 2M2B (2-methyl-2-butanol)

system, whereas 1,3-PGD was preferred to produce in

binary mixture system (n-hexane: octane 1:1) with 55% of

molar production. Furthermore, the reaction parameters

that affect esterification of 1,3-PGD using oleic acid as acyl

donor in optimum cosolvent environment were evaluated

by response surface methodology (RSM). The reaction

temperature and reaction time were the most important

parameters. Based on a ridge max analysis, the optimum

conditions for 1,3-PGD synthesis were predicted to consist

of a reaction time of 40.6 min, a temperature of 59 �C,

an enzyme amount of 70.4%, a substrate molar ratio

(1,3-propylene glycol/oleic acid) of 1:2.7 and an enzyme

pretreatment pH of 6.4 on percentage of molar production

of 1,3-PGD of 43.3 ± 4.2%.

Keywords Ester � Lipase � Mixture design �Optimization � Propylene glycol � RSM

Introduction

Specifically structured derivatives, such as propylene gly-

col (propanediol; PDO) mono- (PGM) and diester (PGD)

are widely used as emulsifiers and cosmetic formulations,

such as: skin conditioning agents and viscosity increasing

agents in the food and pharmaceutical industries [1].

However, the synthesis of propylene glycol esters by

chemical method usually results in a complex mixture,

dark by-products, off-flavors, toxicity and low purity at

high reaction temperature [2–4]. The lipases-mediating

biosynthetic reactions in organic media for preparing

value-added structured glycerides as natural products, offer

several advantages such as milder operating conditions,

high molar conversion, cleaner products, and reduced

waste production [5, 6].

A number of parameters have been screened and eval-

uated the effects on the lipase-catalyzed conversion of

PGM and PGD including temperature, enzyme pretreat-

ment pH, organic solvents, reaction time, etc [7, 8]. The

first element to be considered is the organic solvent

selection according to the mutual solubility between reac-

tants and organic solvents, or that the product around the

enzyme will cause enzyme inhibition or side reaction to

occur. That means, if the polarity of reactants is high, it

indicates that the product will easily dissolve in water, so

H.-C. Chen, C.-H. Kuo contributed equally to this work.

H.-C. Chen � W.-C. Tsai

Department and Graduate Program of Bioindustry Technology,

Dayeh University, Chang-Hua 515, Taiwan

C.-H. Kuo � Y.-L. Chung � C.-J. Shieh (&)

Biotechnology Center, National Chung Hsing University,

Taichung 402, Taiwan

e-mail: cjshieh@nchu.edu.tw

W.-D. Chiang

Department of Food Science, Tunghai University, Taichung 402,

Taiwan, ROC

C.-M. J. Chang � Y.-C. Liu

Department of Chemical Engineering, National Chung Hsing

University, Taichung 402, Taiwan

123

J Am Oil Chem Soc (2012) 89:231–241

DOI 10.1007/s11746-011-1914-9

the water-miscible solvents (log P B 2) must be the best

choice for the enzymatic reaction. Laane et al. [9] reported

the major principle for the most suitable organic solvents

selection, called partition coefficient (log P), a polarity

index of organic solvents, which can be calculated by the

equation that is defined as P = [organic solvent]octanol/

[organic solvent]water. As the log P value C4, it indicates

that this kind of solvent is extremely unlikely to dissolve in

water and will not grab the essential water from the pro-

tein’s surface to benefit the enzymatic reaction. In addition,

the log P value between 2 and 4 means the polarity of

solvents is medium with a lower hydrophobicity. The log

P value of high polarity solvents (water-miscible and

hydrophilic solvents) is less than 2 and this kind of solvents

can easily grab the essential water from the protein’s sur-

face to inactive the enzymatic reaction. Therefore, the

composition of the mixed organic solvent would be another

important factor distinct in regard to the alteration of

substrate specificity and affinity of substrates for lipase-

catalyzed reactions [10–12].

For further industrial applications, it is important to design

a simple and efficient enzymatic biosynthesis system from

large experimental variables and to obtain the optimum pro-

duction of valuable products with appropriate solvent for-

mulation in a short time period and minimum trial. A number

of functional statistical models, such as response surface

methodology (RSM), central composite rotatable design

(CCRD) and mixture design, have been successfully applied

to investigate their possible interactions and to optimize var-

ious valuable esters production by lipase [13–15].

The present work focuses on obtaining a fundamental

understanding of the solvents polarity effects on regioselec-

tivity of lipase by using a simplex centroid design. Three kinds

of organic solvents with different reactant solubility properties

accompanied by varied log P values were selected in this

study; they are 2M2B (2-methyl-2-butanol; log P = 1.5),

hexane (log P = 3.5), and octane (log P = 4.5), respectively.

Triangular contour plots were employed to achieve the opti-

mum solvent mixtures for further optimum biosynthesis pro-

cedure development of PGM and PGD by RSM; it can be used

to better understand the relationships between the selected

factors, including reaction time, temperature, enzyme amount,

substrate molar ratio, and enzyme pretreatment pH on the

response (percentage of molar production of 1,3-propylene

glycol ester (PGE)).

Experimental Procedures

Materials

Novozym� 435 (from presently named Candida antarc-

tica) supported on macroporous acrylic resin was pur-

chased from Novo Nordisk Bioindustrials, Inc. (Bagsvaerd,

Denmark). Oleic acid (99% pure), 1,3-propylene glycol

(99% pure), 1(3)-monooleoyl-rac-glycerol, 1,3-dioleoyl

glycerol and 2M2B (2-methyl-2-butanol) were purchased

from Sigma Chemical Co. (St. Louis, MO, USA). Octane

and n-hexane were obtained from Merck Chemical Co.

(Darmstadt, Germany). All other chemicals were of ana-

lytical reagent grade.

Experimental Design

A three-variable simplex centroid design was employed, in

which the total number of points was 2q - 1, q being equal

to the number of variables, i.e., three in this study resulted in

23 - 1 total number of points as shown in Table 1 [16, 17].

Table 1 Three-variable

simplex centroid design,

experimental data, and

predicted data for mixture

response surface analysis

Mixture design results in seven

formulations

2M2B 2-methyl-2-butanol;

1,3-PGM 1,3-propylene glycol

monoester; 1,3-PGD1,3-propylene glycol diester;

PG propropylene glycola Unreacted PG (%) was

calculated by comparing the

amount of PG input and the

amount of products madeb Underline type indicates

predicted values from model

listing in Table 2

Formula number Components of solvent (%) Yield (%) Unreacteda

2M2B n-hexane Octane 1,3-PGM 1,3-PGD PG (%)

1 100 0 0 79.32 7.02 13.66

72.63b 7.03 20.34

2 0 100 0 63.30 36.71 0

60.39 39.61 0

3 0 0 100 55.74 37.84 6.42

55.88 38.10 6.02

4 50 50 0 75.15 13.58 11.27

77.61 12.24 10.15

5 50 0 50 63.74 16.78 19.48

65.18 14.82 20.00

6 0 50 50 39.67 56.51 3.82

46.83 49.20 3.97

7 33.3 33.3 33.3 67.75 20.85 11.40

68.10 19.74 12.16

232 J Am Oil Chem Soc (2012) 89:231–241

123

The percentage of composite blends of solvent is presented

at levels ranging from 0 to 100%. Two replications were

conducted in this study.

A 5-level-5-factor CCRD was employed in this study,

requiring 32 experiments [18, 19]. The fractional factorial

design consisted of 16 factorial points, 10 axial points (two

axial points on the axis of each design variable at a distance

of 2 from the design center), and 6 center points. The

variables and their levels selected for the study of 1,3-PGD

synthesis were: reaction time (10–50 min); temperature

(25–65 �C); enzyme amount (20–100%), (by weight of

1,3-propylene glycol; 0.0046 to 0.0182 g); substrate molar

ratio (1,3-propylene glycol/oleic acid; 1:2–1:4); and

enzyme pretreatment pH (pH 5–9). Table 3 shows the

independent factors (xi), levels and experimental design in

terms of coded and uncoded.

Immobilized Lipase Pretreated under Different

pH Value

Novozym� 435 was soaked in the different pH value of

0.1 M phosphate buffer solution (pH 5–9) stirred in an

orbital shaking water bath (200 rpm) for 24 h, then dehy-

drated by a freeze dryer (Eyela FD-1000, Japan) for 24 h.

The water content of lipase was measured with a Karl-

Fischer analyzer (Mettler-Toledo DL31, USA). The initial

water content of lipase was 2.1%.

Esterification Method

Typical enzymatic synthesis of 1,3-PGD was carried out

in closed screw-capped test tubes containing 100 mM

1,3-propylene glycol and 200 mM oleic acid, 0.23 g (100%

1,3-propylene glycol, w/w) of Novozym� 435 and 3 mL

organic solvent mixtures containing variant components

as shown in Table 1. All reactions were incubated in an

orbital shaking water bath (200 rpm) at a reaction tem-

perature of 55 �C for 24 h.

After the optimum cosolvent environment (n-hexane

and 2M2B, 1:1, v/v) was established by simplex centroid

design and triangular contour plots. Each modified bio-

synthesis reactions contained 100 mM 1,3-propylene gly-

col, diverse molar ratios of oleic acid and 3 mL optimum

cosolvent environment, followed by different amounts of

pretreated Novozym� 435 with different pH values and

incubated under various reaction temperatures and reaction

times as shown in Table 3.

Determination of 1,3-PGE

Immobilized enzyme and residual water was removed by

passing the reaction media through an anhydrous sodium

sulfate column. All samples were analyzed by injecting a

20-lL aliquot into a Hewlett-Packard 1100 high-performance

liquid chromatograph (Avondale, PA, USA) equipped with an

ultraviolet detector at 210 nm and a C18 reversed-phase

analytical Spherisorb ODS-2 column (5 lM, 250 9 4.6 mm)

(Macherey-Nagel, Duren, Germany) thermostated at a reac-

tion temperature of 45 �C. Elution was carried out using

acetone and acetonitrile (50:50, vol/vol), a flow rate of

1 mL/min held for 5 min and then increased to 1.5 mL/min.

The total running time was 12 min. The molar conversion was

defined as (mmol of product (PGM or PGD)/mmol of initial

PG) 9 100%. PGM and PGD concentrations were calcu-

lated using response factors derived from calibration curves of

1(3)-monooleoyl-rac-glycerol and 1,3-dioleoyl glycerol,

respectively.

Statistical Analysis

The Statistical Analysis System (SAS) was employed to

analyze the experimental data listed in Table 1 [20].

Multiple regression analysis (Proc Reg) of this package

was employed to fit a quadratic canonical polynomial

model described as follows [16]:

Y ¼ b1X1 þ b2X2 þ b3X3 þ b1b2X1X2 þ b1b3X1X3

þ b2b3X2X3 ð1Þ

where Y is a predicted dependent variable (each yield of

acylglycerides); b1, b2, b3, b1b2, b1b3, b2b3 are the cor-

responding parameter estimates for each linear and cross-

product term produced for the prediction models; X1 is the

component of 2M2B; X2 is the component of n-hexane; and

X3 is the component of octane.

Another experimental data (Table 3) were analyzed by

the response surface regression (RSREG) procedure to fit

the following second-order polynomial Eq. 2:

Y ¼ bk0 þX5

i¼1

bkixi þX5

i¼1

bkiix2i þ

X4

i¼1

X5

j¼iþ1

bkijxixj ð2Þ

where Y is response (percentage of molar production of

1,3-PGE); bk0, bki, bkii, and bkij are constant coefficients

and xi the uncoded independent variables. The RIDGE

MAX analysis was used to compute the estimated ridge of

maximum response for increasing radii from the center of

the original design.

Results and Discussion

Optimum Solvent Constituents for Specific Selectivity

of Lipase

The enzyme utilized in this study, called Novozym� 435, is

an immobilized preparation of a thermostable lipase. The

J Am Oil Chem Soc (2012) 89:231–241 233

123

positional specificity of Novozym� 435 depends on the

reactants. In some reactions it shows 1,3-positional speci-

ficity whereas in other reactions the lipase functions as a

non-positional specific lipase. Several studies have shown

that the activity and specificity of hydrolases in organic

solvents is highly dependent on the nature of the solvent

[21, 22]. To get a better understanding of solvent polarity

effects on reaction selectivity and productivity, a system-

atically statistical methodology, mixture RSM, able to

evaluate the prediction of variant propylene glycol ester

distribution has been employed in our study. Three organic

phases, 2M2B, hexane and octane, accompanied different

polarity (log P value) of 1.5, 3.5 and 4.5, were selected for

optimum solvent system strategy development.

The experimental and predicted product distributions at

equilibrium in Novozym� 435 catalyzed esterification of

1,3-propylene glycol are presented in Table 1. Among the

various treatments, the molar production of 1,3-PGD was

only 7% in pure 2M2B (treatment no.1), but 57% higher

molar production (treatment no.6) in binary mixture system

containing n-hexane and octane (1:1; v/v) represented that

a positive correlation between the solubility of hydropho-

bic substrates (oleic acid and 1,3-propylene glycol) in

binary mixture system containing n-hexane (log P = 3.5)

and octane (log P = 4.5), and the product yields obtained

in a lipase-catalyzed esterification reaction. Based on the

results of multiple regressions, the estimates of parameters

and the coefficient of determination (R2) for the prediction

models for percentage yield of all products are shown in

Table 2. With very satisfactory R2 value ([0.99) indicated

that the quadratic canonical polynomial equations were

highly significant and adequate to represent the actual

relationship and the response (percent yield) and significant

variables. The prediction models were employed to gen-

erate triangular response surface and contour plots for

further applicable evaluation.

The triangular contour and response surface plots of

percentage of molar production of 1,3-PGM and 1,3-PGD

in lipase-catalyzed esterification of 1,3-propylene glycol

are shown in Fig. 1. The contour and response surface

behavior of the 1,3-PGM (Fig. 1A and a) revealed that the

percentage of molar production of 1,3-PGM were 75, 60

and 35% under pure 2M2B, n-hexane and octane biosyn-

thetic systems, respectively. These indicated that the lipase

tends to be more favorable toward a polar solvent system

with low log P value for higher monoester production

activity that might be caused by specific interaction

between the enzyme and 2M2B. However, only 10% of

1,3-PGD (Fig. 1B and b) molar production was observed in

the pure 2M2B system, but a 4-fold higher diester pro-

duction was achieved under a pure n-hexane and octane

system with high log P value. More interestingly, since the

lipase catalyzed esterification in a solvent mixture con-

taining 50% n-hexane and 50% octane, the 1,3-PGD pro-

duction increased to 55%. It indicated that the hydrophobic

co-solvent system might cause the protein conformational

change to maintain a high stable status to enhance the

specificity of lipase toward 1,3-PGD production. In com-

parison with previous works, our results are in agreement

with a former observation that suggested various physico-

chemical effects on enzyme molecules, especially lipase,

differ depending upon the kinds of organic solvents [13,

15, 23]. The yields of PGMDHA and PGMEPA with fatty

acid and propylene glycol as substrate in hexane/tert-butyl

alcohol = 9:1 (log P 3.5/0.8) by lipase-catalyzed, were 47

and 49 mM [10]. Esterification reaction of undecanoic acid

and 1,3 propanediol or 1,2 propanediol, using catalyzed

PS-30, in solvent log P 1.2–5.0 or log P 1.4–4.5. The yields

were 18–84% and 12–52% [11]. Monoglyceride synthesis

by lipase-catalysis esterification in hexane/2M2B system

obtained 94% yield [24]. These results were same as ours .

The result indicated the hydrophobic co-solvent system

may influence a high stable status to enhance the specificity

of lipase toward the 1,3-PGD production. Apparently, the

combination of solvent engineering and mixture RSM

created another powerful tool to simplify the production

process with an appropriate biosynthetic solvent phase to

increase the yield of diverse valuable products for further

industrial applications.

RSM Basis of Optimum Enzymatic Synthesis

of 1,3-PGD

As a result of unapparent solvent polarity effects on lipase-

catalyzed of 1,3-PGM, we decided to utilize the 5-level-5-

factor CCRD and RSM, a useful statistical technique,

for further optimum biosynthesis of 1,3-PGD investiga-

tion. With minimum experimental frequency and time

consumption, we clearly realized the inter-relationships

between reaction parameters and responses and could

obtain the molar production process to benefit food man-

ufacturers and processors.

Table 2 Parameter estimates for variables used in predicted models

for glycerolysis products

Solvent 1,3-PGM 1,3-PGD

2M2B (x1) 0.075 1.003

n-hexane (x2) -0.179 1.253

Octane (x3) -85.072 107.697

2M2B 9 n-hexane (x1x2) 0.027 -0.030

2M2B 9 octane (x1x3) 1.264 -1.753

n-hexane 9 octane (x2x3) 4.244 -3.987

R2 0.994 0.992

2M2B 2-methyl-2-butanol; 1,3-PGM 1,3-propylene glycol monoester;

1,3-PGD 1,3-propylene glycol diester; PG propropylene glycol

234 J Am Oil Chem Soc (2012) 89:231–241

123

Profiles of Esterification

The selection of reaction time range needs to be extre-

mely precise in the study of CCRD, otherwise, the opti-

mal conditions of biosynthesis cannot be found inside the

experimental region through the analyses of statistics and

contour plots. Figure 2 shows the time course for the

esterification of 1,3-propylene glycol with oleic acid by

Novozym� 435 at a reaction temperature of 55 �C. The

percentage of molar production of 1,3-PGD increased up

to 50% at a reaction time of 50 min, therefore, the

reaction duration was set up from 0 to 50 min in this

study.

Statistical Model Fitting

The major objective of this paper is the development and

evaluation of a statistical approach to better understand the

Fig. 1 Contour and response surface plots using predicted model for lipase-catalyzed acylglycerols in a mixture system (n-hexane:octane 1:1):

Aa 1,3-propylene glycol monoester (1,3-PGM); Bb 1,3-propylene glycol diester (1,3-PGD)

Reaction time (min)

0 20 40 60 80 100 120

Mol

ar p

rodu

ctio

n (%

)

0

10

20

30

40

50

60

70

1,3-PGD1,3-PGM

Fig. 2 Time course of 1,3-propylene glycol monoester (1,3-PGM)

and 1,3-propylene glycol diester (1,3-PGD) in a batch conversion

yield of Novozym� 435. The reaction was carried out at a reaction

temperature of 55 �C, a substrate molar ratio of 1,3-propylene glycol/

oleic acid of 1:2, an enzyme amount of 100% (wt. of 1,3-propylene

glycol) in a mixture system of (n-hexane:octane 1:1)

J Am Oil Chem Soc (2012) 89:231–241 235

123

relationship between the variables and the responses (per-

centage of molar production) of a lipase-catalyzed esteri-

fication reaction. In this way, the process can be optimized

before the scaling-up procedure, in order to save work,

money and time consumption, allowing us to obtain an

economically valuable product with high quality and lower

costs. Compared with a one-factor-at-a-time design, which

has been adopted most often in the literature, RSM con-

jugated with 5-level-5-factor CCRD employed in this study

was more efficient in reducing the experimental runs

and time consumption for the optimized biosynthesis of

1,3-PGD under investigation.

The RSREG procedure was employed to fit the second-

order polynomial Eq. 2 to the experimental data—per-

centage of molar productions of 1,3-PGD (Table 3).

Among the various treatments, the highest percentage of

molar production (47.03%) was treatment #8 (a reaction

time of 40 min, a reaction temperature of 55 �C, a substrate

molar ratio (1,3-propylene glycol/ oleic acid) of 1:2.5, an

enzyme amount of 80% and an enzyme pretreatment pH of

6.0), and the lowest conversion (14.61%) was treatment

#19 (a reaction time of 30 min, a reaction temperature of

25 �C, a substrate molar ratio of 1:3, an enzyme amount of

60% and an enzyme pretreatment pH of 7.0). From the

Table 3 Central composite

rotatable second-order design,

experimental data, and

predicted values for five-level-

five-factor response surface

analysis

a The treatments were run in a

random orderb Numbers in parenthesis

represent actual experimental

amounts

Treatment Reaction

time

(min)

Reaction

temperature

(�C)

Enzyme amount

(% by wt. of 1,3-

propylene glycol)

Substrate molar

ratio (1,3-propylene

glycol/oleic acid)

Enzyme

pretreatment

pH

Yield

(%)

#a x1 x2 x3 x4 x5 Y

1 -1 (20)b -1 (35) -1 (40) -1 (1:2.5) 1 (8) 21.48

2 1 (40) -1 (35) -1 (40) -1 (1:2.5) -1 (6) 24.20

3 -1 (20) 1 (55) -1 (40) -1 (1:2.5) -1 (6) 25.56

4 1 (40) 1 (55) -1 (40) -1 (1:2.5) 1 (8) 30.00

5 -1 (20) -1 (35) 1 (80) -1 (1:2.5) -1 (6) 31.07

6 1 (40) -1 (35) 1 (80) -1 (1:2.5) 1 (8) 35.11

7 -1 (20) 1 (55) 1 (80) -1 (1:2.5) 1 (8) 34.98

8 1 (40) 1 (55) 1 (80) -1 (1:2.5) -1 (6) 47.03

9 -1 (20) -1 (35) -1 (40) 1 (1:3.5) -1 (6) 22.76

10 1 (40) -1 (35) -1 (40) 1 (1:3.5) 1 (8) 25.13

11 -1 (20) 1 (55) -1 (40) 1 (1:3.5) 1 (8) 26.94

12 1 (40) 1 (55) -1 (40) 1 (1:3.5) -1 (6) 34.60

13 -1 (20) -1 (35) 1 (80) 1 (1:3.5) 1 (8) 28.02

14 1 (40) -1 (35) 1 (80) 1 (1:3.5) -1 (6) 34.97

15 -1 (20) 1 (55) 1 (80) 1 (1:3.5) -1 (6) 23.73

16 1 (40) 1 (55) 1 (80) 1 (1:3.5) 1 (8) 33.31

17 -2 (10) 0 (45) 0 (60) 0 (1:3) 0 (7) 16.13

18 2 (50) 0 (45) 0 (60) 0 (1:3) 0 (7) 30.78

19 0 (30) -2 (25) 0 (60) 0 (1:3) 0 (7) 14.61

20 0 (30) 2 (65) 0 (60) 0 (1:3) 0 (7) 40.99

21 0 (30) 0 (45) -2 (20) 0 (1:3) 0 (7) 18.74

22 0 (30) 0 (45) 2 (100) 0 (1:3) 0 (7) 22.47

23 0 (30) 0 (45) 0 (60) -2 (1:2) 0 (7) 21.95

24 0 (30) 0 (45) 0 (60) 2 (1:4) 0 (7) 24.29

25 0 (30) 0 (45) 0 (60) 0 (1:3) -2 (5) 26.84

26 0 (30) 0 (45) 0 (60) 0 (1:3) 2 (9) 19.88

27 0 (30) 0 (45) 0 (60) 0 (1:3) 0 (7) 20.31

28 0 (30) 0 (45) 0 (60) 0 (1:3) 0 (7) 20.03

29 0 (30) 0 (45) 0 (60) 0 (1:3) 0 (7) 22.33

30 0 (30) 0 (45) 0 (60) 0 (1:3) 0 (7) 22.33

31 0 (30) 0 (45) 0 (60) 0 (1:3) 0 (7) 21.23

32 0 (30) 0 (45) 0 (60) 0 (1:3) 0 (7) 17.40

236 J Am Oil Chem Soc (2012) 89:231–241

123

SAS output of RSREG, the second-order polynomial

equation is given below:

Y ¼ 129:505� 0:425x1 � 1:394x2 þ 0:636x3

� 23:004x4 � 19:653x5 þ 0:017x21 þ 0:010x1x2

þ 0:028x22 þ 0:005x1x3 � 0:005x2x3 þ 0:003x2

3

þ 0:054x1x4 � 0:213x2x4 � 0:221x3x4

þ 6:467x24 � 0:166x5x1 � 0:021x2x5 � 0:009x3x5

þ 0:579x4x5 þ 1:678x25: ð3Þ

Analysis of variance (Table 4) indicated that the second-

order polynomial model was highly significant and ade-

quate to represent the actual relationship between the

response (percentage of molar production) and the signif-

icant variables with a very small p value (0.038) and a

satisfactory coefficient of determination (R2 = 0.811).

Furthermore, the overall effect of the five synthesis vari-

ables on the percentage of molar production of 1,3-PGD

was further analyzed by a joint test (Table 4). The results

revealed that the reaction time (x1) and reaction tempera-

ture (x2) were the most important parameters exerting a

statistically significant overall effect (p \ 0.1) on the

response of molar production of 1,3-PGD. Otherwise, the

most insignificant parameter, enzyme pretreatment pH,

observed in our study, will be constant at pH 7.0 (0-level)

in the subsequent discussions.

Reciprocal Effects on Various Parameters

Reaction time and reaction temperature were investigated

in the range of reaction time of 10–50 min and reaction

temperature of 25–65 �C. Figure 3a shows the effect of

varying reaction time and reaction temperature on ester-

ification efficiency at an enzyme amount of 60%, a sub-

strate molar ratio (1,3-propylene glycol/oleic acid) of 1:2

and an enzyme pretreatment pH of 7.0. With the highest

reaction temperature (65 �C) and highest reaction time

(50 min), the maximum percentage of molar production

(50%) of 1,3-PGD was obtained. Whereas, as the reaction

temperature decreased to a range from reaction tempera-

ture of 25–45 �C accompanying the short reaction time

(10–30 min), only 20–30% molar production remained.

This indicated that both of the reaction time and reaction

temperature are the most important parameters on bio-

synthesis of 1,3-PGD.

Figure 3b shows the effect of enzyme amount, reaction

temperature, and their mutual interaction on 1,3-PGD

synthesis at a reaction time of 30 min, a substrate molar

ratio (1,3-propylene glycol/oleic acid) of 1:3 and an

enzyme pretreatment pH of 7.0. At any given enzyme

amount ranging from 20 to 100%, there is no significant

effect on the molar production of 1,3-PGD, whereas, the

molar production was remarkably increased with a reaction

temperature increase. That might be because of variant

reaction temperature affecting the activity and stability of

the enzymes and thus the rates of reaction. Higher reaction

temperature (50–60 �C) may shift the position of the

equilibrium to the product side and increases the yield [25].

It also promotes collisions between enzyme and substrate

molecules to result in accelerated rates of reaction [26].

This contention was in support of our project to give an

explanation of the highest molar production of 1,3-PGD

(45%) observation at reaction temperature of 65 �C in this

study. During the initial reaction time, the reaction velocity

is limited by the enzyme amount; however, slight differ-

ences of molar productions with varying enzyme amounts

used in this study indicate that 20% of enzyme is sufficient

for enzyme-substrate complex formation and further

improving the total reaction velocity.

The effect of substrate molar ratio and reaction tem-

perature on esterification efficiency at a constant reaction

time of 30 min, an enzyme amount of 60% and a enzyme

pretreatment pH of 7.0 is shown in Fig. 3c. At any given

substrate molar ratio (1,3-propylene glycol/oleic acid;

1:2–1:4), an increase of the reaction temperature tends

toward higher yields of 1,3-PGD. As the reaction condition

followed by lowest substrate molar ratio (1:2) and highest

reaction temperature (65 �C), the maximal yield (50%) of

1,3-PGD was obtained. An increase in the substrate molar

ratio up from 2.0 to 4.0 resulted in a slightly reduced

esterification efficiency at higher reaction temperatures

([45 �C), whereas, a lower temperature seems to slightly

increase the molar production of 1,3-PGD. The lack of a

further increase in enzyme activity at oleic acid/1,3-pro-

pylene glycol ratios above 2.0 could be explained by a

transition from a second-order to a pseudo-first-order

reaction, where the reaction becomes independent of the

oleic acid concentration [27]. In addition, the acidity may

be increased due to the increase in the oleic acid to shift the

ionization state of residue in their active site that might

cause the enzyme inactivation by an unfavorable ionization

state conversion [28, 29].

Table 4 Analysis of variance for joint test for 1,3-propylene glycol

diester

Factor Degrees of

freedom

Sum of

squares

Prob [ Fa

Reaction time (X1) 6 427.79 0.09

Reaction temperature (X2) 6 593.98 0.04

Enzyme amount (X3) 6 315.22 0.19*

Substrate molar ratio (X4) 6 186.42 0.44*

Enzyme pretreatment pH (X5) 6 149.03 0.56*

a Prob [ F: level of significance

* Not significant at P = 0.1

J Am Oil Chem Soc (2012) 89:231–241 237

123

Figure 3d shows the effect of the enzyme pretreatment

under different pH values, reaction temperatures, and their

mutual interaction on the 1,3-PGD synthesis yield at a

reaction temperature of 45 �C, a reaction time of 30 min

and a substrate molar ratio of 1:3. Similar behavior showed

that the enzyme pretreatment pH (pH 5–9) has no signifi-

cant effects on the molar production of 1,3-PGD but higher

reaction temperature can reach a maximum yield (45%) at

any given enzyme pretreatment pH. In general, most

enzymes are only catalytically active at certain pH,

depending on their origin and the ionization state of resi-

dues in their active sites. Although lipases contain base,

neutral, and acid residues at their catalytic site, however,

they are only active in one particular ionization site, which

is called the optimal pH [28, 29]. Excessive acid or base

may cause enzyme denaturation and loss of its activity with

the specific ionization site destruction. In our observations,

we found that the Novozym� 435 possesses a broad range

of optimum pH from 5 to 9 with no significant effects on

esterification activity indicating it is highly tolerant of

extreme heat, is an acidic and basic biocatalyst and is in

widespread use for various industrial applications.

The relationships between reaction factors and response

can be better understood by examining the series of contour

plots (Fig. 4) generated from the predicted model Eq. 3 by

holding the the enzyme amount constant (40, 60, 80%) and

substrate molar ratio (1,3-propylene glycol/ oleic acid; 1:2,

1:2.5, 1:3). Figure 4a–c represent the same substrate molar

ratio (1:2); and A, D, and G represent the same enzyme

amount (40%). Such an application could be adopted to

study the synthesis variables simultaneously in a five-

dimensional space. Reaction time (x1) and reaction tem-

perature (x2) were important variables for 1,3-PGD

synthesis and considered as indicators of effectiveness and

economical performance. In general, all nine contour plots

in Fig. 4 exhibited the similar behavior in that predicted

molar production increased by the reaction time and

reaction temperature. The reaction with a enzyme amount

of 80% and a substrate molar ratio of 1:2 (Fig. 4c) was

suggested as the optimal condition for enzymatic

10

20

30

40

50

60

10

20

30

40

50

2535

4555M

ola

r p

rod

asu

ctio

n o

f 1,

3-P

GD

(%

)

Reatio

n tim

e (m

in)

Temperature ( C)°

0

10

20

30

40

50

25

35

45

55

65

2040

6080M

ola

r p

rod

uct

ion

of

1,3-

PG

D (

%)

Temper

ature

( C

)°

Enzyme amout (%)

(A) (B)

15

20

25

30

35

40

45

50

55

25

35

45

55

65

2.02.5

3.03.5

Mo

lar

pro

du

ctio

n o

f 1,

3-P

GD

(%

)

Temper

ature

( C

)°

Substrate molar ratio

0

10

20

30

40

50

60

25

35

45

5565

56

78

Mo

lar

pro

du

ctio

n o

f 1,

3-P

GD

(%

)

Tem

pera

ture

( C

)°

Enzyme Pretreatment pH

(C)(D)

Fig. 3 Response surface plots showing the relationships between

1,3-propylene glycol diester molar production and reaction parame-

ters in the mixture system (n-hexane:octane 1:1): a reaction time and

temperature; b enzyme amount and reaction temperature; c substrate

molar ratio and reaction temperature; d enzyme pretreatment pH and

reaction temperature

238 J Am Oil Chem Soc (2012) 89:231–241

123

biosynthesis of 1,3 PGD which represented higher pre-

dicted of molar production than the others in Fig. 4.

Attaining Optimum Circumstances

The optimum point was determined by ridge max analysis.

The method of ridge analysis computes the estimated ridge

of maximum response for increasing radii from the center

of the original design. The ridge max analysis (Table 5)

indicated that the maximum molar production of 1,3 PGD

was 43.3 ± 4.2% at a reaction time of 40.6 min, a reaction

temperature of 59 �C, a substrate molar ratio of 1:2.7, and

an enzyme amount of 70.4%. For economic reasons,

although it was suggested to utilize a high enzyme amount,

it is, however, not the most important factor (see Table 4)

and only slightly affects the 1,3-PGD production. There-

fore, if the enzyme amount is the primary consideration for

production cost reduction, the manufacturer can alterna-

tively decrease the enzyme amount with different reaction

time, temperature and substrate molar ratio based on the

results as shown in Fig. 4. Otherwise, due to the fact that

Novozym� 435 is an immobilized preparation of a ther-

mostable lipase, it can be reused to efficiently reduce the

cost of materials facilitating economic production.

253035404550556065

253035404550556065

253035404550556065

10 20 30 40 50

50

4040

4040

30

303030

2020

20

2020

20

10 20 30 40 50

6045 4545

4530 30

30

30

3010 20 30 40 50

10 20 30 40 50 10 20 30 40 50 10 20 30 40 50

10 20 30 40 50 10 20 30 40 50 10 20 30 40 50

60

6050 5050

5040 40

40

40

40

5040

40

30

303030

2020

20

20

2020

50

40

4040

30

30

30

3030

2020

20

20

30 50

5040 4040

40

40

3030

30

303030

40

4030 30

30

3020 2020

20

2020

20

50

40

40

4030

30

30

30

30

40

4030 3030

30

30

30

20 20

20

2020

20

Tem

per

ture

( C

)°

1:2

1:2.

51:

3S

ub

stra

te m

ola

r ra

tio

60 8040

(A) (B) (C)

(D) (E)(F)

(G)(H) (I)

50

4040

4040

30

303030

2020

20

2020

20

6045 4545

4530 30

30

30

30

60

6050 5050

5040 40

40

40

40

5040

40

30

303030

2020

20

20

2020

50

40

4040

30

30

30

3030

2020

20

20

30 50

5040 4040

40

40

3030

30

303030

40

4030 30

30

3020 2020

20

2020

20

50

40

40

4030

30

30

30

30

40

4030 3030

30

30

30

20 20

20

2020

20

(A) (B) (C)

(D) (E) (F)

(G) (H) (I)

Enzyme amount (%)

Reaction time (min)

Fig. 4 Contour plots of molar

production of 1,3-propylene

glycol diester using Novozym�

435 pretreated at pH 7.0.

The enzyme amount was by

weight of 1,3-propylene

glycol in mixture system

(n-hexane:octane 1:1). The

numbers inside the contour plots

indicate the percentage of molar

production under given reaction

conditions

Table 5 Estimated ridge of

maximum response for variable

percentage of molar production

Coded

radius

Estimated response

(% conversion)

Standard

error

X1

(min)

X2

(�C)

X3

(%)

X4 (1,3

PG/acid)

X5

(pH)

0.0 19.62 2.15 30.00 45.00 60.00 3.00 7.00

0.2 22.37 2.13 32.31 47.62 63.19 2.96 6.92

0.4 26.09 2.13 34.52 50.38 65.54 2.91 6.81

0.6 30.81 2.36 36.63 53.22 67.43 2.85 6.69

0.8 36.54 3.02 38.66 56.13 69.04 2.79 6.57

1.0 43.31 4.17 40.63 59.07 70.47 2.73 6.44

J Am Oil Chem Soc (2012) 89:231–241 239

123

Model Validation

The validity of the predicted model was examined by

experiments under the suggested optimum synthesis con-

ditions and 3 center points of CCRD (treatment #9, #18,

and #27). The predicted value of 1,3-PGD molar produc-

tion was 43.3% obtained by ridge max analysis. Under

optimum synthesis conditions, the actual values of 1,3-

PGM and 1,3-PGD were 50.2 ± 3.6 and 42.2 ± 2.6%,

respectively. A Chi-square test (P value = 0.971, df = 5)

indicated that the observed values were significantly the

same as the predicted values and the generated model

adequately predicted the percentage of molar production

[30]. Thus, the optimization of lipase-catalyzed synthesis

of 1,3-propylene glycol esters by Novozym� 435 was

successfully developed by CCRD and RSM.

Conclusions

The optimal organic media, including solvent mixtures, for

enzymatic synthesis of 1,3-PGD has been successfully iden-

tified and demonstrated in the present work which suggested a

binary solvent system (n-hexane: octane 1:1) with 55% of 1,3-

PGD molar production. For further industrial applications,

five parameters were chosen to optimize the synthesis of 1,3-

PGD, they were reaction time, temperature, substrate molar

ratio, enzyme amount and enzyme pretreatment pH, respec-

tively. The optimum molar production of 1,3-PGD (around

43%) was observed under a reaction time of 41 min, a reaction

temperature of 59 �C, a substrate molar ratio (1,3-propylene

glycol/ oleic acid) of 1:2.7, and an enzyme amount of 70.4%.

Novozym� 435 was also stable in the presence of heat and

organic solvent and is necessary for possible scale-up bio-

synthesis procedure.

Acknowledgments This research was supported by the National

Science Council (NSC 90-2313-B-212-005), Taiwan.

References

1. Parker NS (1987) Properties and functions of stabilizing agents in

food emulsions. Crit Rev Food Sci Nutr 25:300–315

2. Choudhury R, Basu R (1960) The preparation and purification of

monoglycerides. I. Glycerolysis of oils. J Am Oil Chem Soc

37:483–486

3. Lauridsen JB (1976) Food emulsifiers: surface activity, edibility,

manufacture, composition, and application. J Am Oil Chem Soc

53:400–407

4. Sonntag NOV (1982) Fat splitting, esterification, and intereste-

rification. In: Swern D (ed) Bailey’s industrial oil and fat prod-

ucts, New York, pp 134

5. Sreenivasan B (1978) Interesterification of fats. J Am Oil Chem

Soc 55:796–805

6. Macrae AR (1983) Lipase-catalyzed interesterification of oils and

fats. J Am Oil Chem Soc 60:291–294

7. Liu KJ, Chen ST, Shaw JF (1998) Lipase-catalyzed transesteri-

fication of propylene glycol with triglyceride in organic solvents.

J Agric Food Chem 46:3835–3838

8. Lee CH, Parkin KL (2001) Effects of water activity and immo-

bilization on fatty acid selectivity for esterification reactions

mediated by lipases. Biotechnol Bioeng 75:219–227

9. Laane C, Boeren S, Vos K, Veeger C (1987) Rules for optimi-

zation of biocatalysis in organic solvents. Biotechnol Bioeng

30:81–87

10. Liu KJ, Shaw JF (1995) Synthesis of propylene glycol monoes-

ters of docosahexaenoic acid and eicosapentaenoic acid by lipase-

catalyzed esterification in organic solvents. J Am Oil Chem Soc

72:1271–1274

11. Kuo SJ, Parkin KL (1996) Solvent polarity influences product

selectivity of lipase-mediated esterification reactions in micro-

aqueous media. J Am Oil Chem Soc 73:1427–1433

12. Rendon X, Lopez-Munguıa A, Castillo E (2001) Solvent engi-

neering applied to lipase-catalyzed glycerolysis of triolein. J Am

Oil Chem Soc 78:1061–1066

13. Shaw JF, Lo S (1994) Production of propylene glycol fatty acid

monoesters by lipase-catalyzed reactions in organic solvents.

J Am Oil Chem Soc 71:715–719

14. Garcıa R, Martınez M, Aracil J (2002) Enzymatic esterification of

an acid with an epoxide using an immobilized lipase from Mucormiehei as catalyst: optimization of the yield and isomeric excess

of ester by statistical analysis. J Ind Microbiol Biotechnol

28:173–179

15. Liao HF, Tsai WC, Chang SW, Shieh CJ (2003) Application

of solvent engineering to optimize lipase-catalyzed 1,3-di-

glyacylcerols by mixture response surface methodology. Bio-

technol Lett 25:1857–1861

16. Cornell JA (1983) Experiments with mixtures: a review. Tech-

nometrics 15:437–455

17. Shieh CJ, Akoh CC, Koehler PE (1996) Formulation and opti-

mization of sucrose polyester physical properties by mixture

response surface methodology. J Am Oil Chem Soc 73:455–460

18. Cochran WG, Cox GM (1992) Experimental designs. New York

19. Shieh JS, Lou YH (2000) Five-factor response surface optimi-

zation of the enzymatic synthesis of citronellyl butyrate by lipase

IM77 from Mucor miehei. J Am Oil Chem Soc 77:521–526

20. AS S (1990) SAS user’s guide. SAS Institute, Cary

21. Carrea G, Ottolina G, Riva S (1995) Role of solvent in the control

of enzyme selectivity in organic media. Trends Biotechnol 13:

63–70

22. Kazlauskas RJ, Bornscheuer UT (1998) Biotransformations with

Lipases. In: Rehm HJ, Reed G, Puhler A, Stadler P JW, Kelly DR

(eds) Biotechnology-Series, Weinheim, pp 37–191

23. Castillo E, Pezzotti F, Navarro A, Lopez-Munguıa A (2003)

Lipase-catalyzed synthesis of xylitol monoesters: solvent engi-

neering approach. J Biotechnol 102:251–259

24. Christophe BJ, Luc C, Edmundo C, Alain M (2001) Combining

solvent engineering and thermodynamic modeling to enhance

selectivity during monoglyceride synthesis by lipase-catalyzed

esterification. Enzyme Microb Technol 28:362–369

25. Chen HP, Hsiou KF, Wang KT (1995) Regioselectivity

enhancement by partial purification of lipase from Aspergillusniger. Biotechnol Lett 17:305–308

26. McGilvery RW, Goldstein GW (1983) Biochemistry: a functional

approach. Philadelphia, London, Toronto Mexico City and Tokyo

240 J Am Oil Chem Soc (2012) 89:231–241

123

27. Degn P, Zimmermann W (2001) Optimization of carbohydrate

fatty acid ester synthesis in organic media by a lipase from

Candida antarctica. Biotechnol Bioeng 74:483–491

28. Yamane T (1987) Enzyme technology for the lipids industry: an

engineering overview. J Am Oil Chem Soc 64:1657–1662

29. Malcata FX, Reyes HR, Garcia HS, Hill CG (1992) Kinetics and

mechanisms of reactions catalyzed by immobilized lipase.

Enzyme Microb Technol 14:426–446

30. Ott L (1988) An introduction to statistical methods and data

analysis. PWS-Kent Publishing, Boston

J Am Oil Chem Soc (2012) 89:231–241 241

123