Highly efficient synthesis of an emerging lipophilic antioxidant: 2-ethylhexyl ferulate

Article abstract of DOI:10.3390/molecules21040478

Ferulic acid in ester form has shown a stronger ability in ameliorating certain pathological conditions and inhibiting lipid oxidation. In present study, a solvent-free and reduced pressure evaporation system was developed for lipase-catalyzed synthesis o

Full text of DOI:10.3390/molecules21040478

molecules
Article
Highly Efficient Synthesis of an Emerging Lipophilic
Antioxidant: 2-Ethylhexyl Ferulate
Kuo-Chuan Huang 1, Ying Li 2, Chia-Hung Kuo 3, Yawo-Kuo Twu 1 and Chwen-Jen Shieh 2,
*
1
Department of BioIndustry Technology, Da-Yeh University, 168 University Road, Dacun, Changhua 51591,
Taiwan; freedomlove0216@gmail.com (K.-C.H.); poly2001@mail.dyu.edu.tw (Y.-K.T.)
Biotechnology Center, National Chung Hsing University, 250 Kuokuang Road, Taichung 40227, Taiwan;
quatrefoil47@yahoo.com.tw
Department of Seafood Science, National Kaohsiung Marine University, 142 Haijhuan Road, Nanzih District,
Kaohsiung 81157, Taiwan; chkuo@webmail.nkmu.edu.tw
2
3
*
Correspondence: cjshieh@nchu.edu.tw; Tel.: +886-4-2284-0450 (ext. 5121); Fax: +886-4-2286-1905
Academic Editors: Jean-Marc Sabatier and Derek J. McPhee
Received: 7 March 2016; Accepted: 7 April 2016; Published: 12 April 2016
Abstract: Ferulic acid in ester form has shown a stronger ability in ameliorating certain pathological
conditions and inhibiting lipid oxidation. In present study, a solvent-free and reduced pressure
evaporation system was developed for lipase-catalyzed synthesis of 2-ethylhexyl ferulate (2-EF) from
ferulic acid and 2-ethylhexanol. A Box-Behnken design with response surface methodology (RSM)
and artificial neural network (ANN) was selected to model and optimize the process. Based on
the yields of 2-EF, reaction temperature was shown to be the most important process factor on the
molar conversion among all variables. The residual values and the coefficient of determination (R2)
calculated from the design data indicated that ANN was better than RSM in data fitting. Overall,
the present lipase-catalyzed approach for 2-EF synthesis at low reaction temperature in a reduced
pressure evaporation system shows high 2-EF production efficiency. Notably, this approach can
reduce the enzyme denaturation and ferulic acid oxidation that usually occur during long-term
biosynthetic operations at high temperature.
Keywords: 2-ethylhexyl ferulate; lipase; reduced pressure evaporation system; response surface
methodology; artificial neural network
1. Introduction
Ferulic acid (FA) belongs to the family of phenolic acids and is very abundant in grains, fruits
and vegetables. Studies have shown that FA displays excellent antioxidant [
activities [ ] and potential health benefits against cardiovascular problems, inflammatory diseases
and cancer [ ]. However, the solubility of FA in hydrophobic solvents is low, thus limiting its
1], ultraviolet-absorbing
2,3
4
utilization in the pharmaceutical, cosmetic and food industries. In order to increase utilization
of FA, lipophilization is needed, where enzymatic esterification with alcohols has proved to be a
promising approach [
5
–8]. In recent years, many studies have report that the enzymatic synthesis of
hydrophobic derivatives of FA can increase its oil-solubility [
9–
11], and some of them have been applied
for preparation of emulsion-based creams due to their anti-inflammatory activity [12]. FA esters, the
hydrophobic derivatives of FA, have also demonstrated other bioactivities such as antioxidant [13],
anticonvulsant [14], synergistic photobactericidal effects [15], and antithrombotic activities [16], as well
as inhibition of the atherogenic effects of leptin [17]. Interestingly, FA esters possess better inhibitory
effect on lipid oxidation than FA [18].
Enzymatic biosynthesis of these compounds has attracted much attention in recent years, and
it may be a better approach in comparison to conventional chemical processes [19–23]. It has been
Molecules 2016, 21, 478; doi:10.3390/molecules21040478
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reported that continuous synthesis of alkyl ferulate by immobilized lipase shows great conversion
˝
rates (approximately 90%) at 90 C after two days [21]. It should be kept in mind that FA is susceptible
to oxidation under high temperatures. Novozym®435 had been used in the lipase-catalyzed synthesis
of ferulate esters, but the yield was only 17% after several days [22]. Generally, solvent-free reaction
systems are applied in the lipase-catalyzed synthesis of ferulate esters to decrease the reaction time
and increase the yield [20,23]. The solvent-free system is a simple mixture of substrates, offering
advantages including maximization of substrate concentration, greater volumetric production, and
cost savings in reactor design and product separation. In addition, the boiling point of the component
liquids is lower in a reduced pressure evaporation system than that in the atmosphere at the same
temperature, suggesting that the byproducts with relative low vapor pressure physical characteristics
are more prone to be eliminated in the reduced pressure system. This may increase the efficiency of
the lipase-catalyzed reaction. Therefore, a reduced pressure evaporation system combined with a
solvent-free system may be an excellent strategy to overcome the problems of long reaction time and
low yields of FA esters synthesis. So far, the literature regarding the lipase-catalyzed esterification
of ferulic acid in a reduced pressure evaporation system is still limited. Notably, there have been no
reports regarding lipase-catalyzed synthesis of 2-ethylhexylferulate (2-EF), a common FA ester with
great inhibitory effect on lipid oxidation, which has been widely used as the antioxidant in the food
and cosmetic industries.
Recently, response surface methodology (RSM) and an artificial neural network (ANN) approach
have been applied for optimization and process modeling. The development of an optimum enzymatic
synthesis procedure to improve the yield conversion to reduce production costs would be attractive for
manufacturers and consumers. RSM and ANN have been successfully applied to model and optimize
extraction of lignans [24], curdlan production [25], and biodiesel production [26]. In this study, a
reduced pressure evaporation system was used to synthesize 2-ethylhexyl ferulate (2-EF) under solvent
free conditions. RSM and ANN were employed to investigate the effects of different reaction variables
(reaction time, reaction temperature, and enzyme amount) on the response (yield %), and to obtain the
optimal conditions to solve the problems of long reaction time and low yield of FA esters.
2. Results and Discussion
The present study applied RSM and ANN for experimental design of enzymatic synthesis of 2-EF
in a solvent-free system. The molar ratio of FA and 2-ethylhexanol (the reactants) was 1:129, and the
independent parameters selected for the experimental design were: reaction time (X1), enzyme amount
(X2), and reaction temperature (X3). Their ranges and levels are given in Table 1.
2.1. RSM Model
The RSM Box-Behnken procedure was employed to fit the second-order polynomial equation
to the experimental data (Table 1). Among the various treatments, the highest molar conversion
˝
˘
(98.9%
˘
2.5%) was observed in treatment 12 (reaction temperature of 80 C, enzyme amount of
1500 PLU and reaction time of 16 h), and the lowest molar conversion (42.7%
3.3%) was observed in
treatment 10 (reaction temperature of 60 ˝C, enzyme amount of 1000 PLU and reaction time of 8 h).
The second-order polynomial equation obtained was as follows:
Y “ ´968.26667 ` 3.60052X1 ` 26.28208X2 ` 0.039783X3 ´ 0.027812X1X2 ` 0.0003X1X3 ` 0.0004X2X3
(1)
2
2
2
´0.028971X1 ´ 0.17154X2 ´ 0.0000305167X3
The analysis of variance (ANOVA), represented in Table 2, indicates that this second-order
polynomial model was highly significant and adequate to represent the actual relationship between
the response (molar conversion) and the significant variables with a very small p value (<0.001) and
a satisfactory coefficient of determination (R2 = 0.9852). The ANOVA results of responses reveal an
insignificant “lack of fit” for p > 0.05. Therefore, these models were adequate for prediction within
the range of variables employed. Furthermore, the ANOVA results indicate that the linear term of

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2
2
X1 and X3 and the square term of X2 and X3 had significant (p < 0.05) influence on molar conversion.
The interaction terms had less influence (p > 0.05).
Table 1. Box-Behnken design and observed experimental data for 3-level-3-factor response
surface analysis.
Experimental Variables
a
Treatment No.
Observed Molar Conversion (%)
X1 (h)
X2 (PLU)
X3 (˝C)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
16
16
16
8
24
8
24
16
8
16
16
24
8
24
a
500
1000
1000
1000
500
80
70
70
70
70
70
70
70
60
60
60
80
60
80
80
85.7 ˘ 3.4
92.4 ˘ 3.1
92.0 ˘ 2.8
91.7 ˘ 2.3
68.7 ˘ 4.1
82.3 ˘ 3.2
80.4 ˘ 2.1
98.8 ˘ 1.6
39.8 ˘ 2.7
42.7 ˘ 3.3
44.8 ˘ 4.2
98.9 ˘ 2.5
64.2 ˘ 2.1
86.3 ˘ 2.8
98.7 ˘ 1.9
500
1500
1500
500
1000
1500
1500
1000
1000
1000
The treatments were run in a random order.
Table 2. ANOVA for synthetic variables pertaining to response percent molar conversion.
Source
Sum of Squares
Degree of Freedom
Prob > F a
Model
Linear
X1
X2
X3
22.50
9
0.0005
1.87
0.85
14.72
1
1
1
0.0033
0.0614
<0.0001
Interaction
X1X2
X1X3
X2X3
Square
0.01
0.17
0.02
1
1
1
0.7297
0.1762
0.5973
2
0.02
0.79
4.33
15.20
0.99
16.19
a
1
1
1
3
2
5
0.6545
0.0188
0.0005
0.09
X12
X22
X3
Lack of fit
Pure error
Total error
Prob > F: level of significance.
The effect of varying reaction temperature and enzyme amount on transesterification efficiency
at a constant reaction time of 16 h was shown in Figure 1A. With the lowest reaction temperature
(60 ˝C) and enzyme amount (500 PLU), a 2-EF molar conversion of 39.8% was obtained. Low molar
conversions are also found in a previous report on lipase-catalyzed synthesis of ferulate esters at the
temperature of 60 ˝C, enzyme amount of 100 mg, and time of 240 h [22]. The low molar conversion
of ferulate esters can be improved by using a reduced pressure evaporation system. In the reduced
pressure evaporation system, when the enzyme amount was increased to 1500 PLU and the reaction
temperature was increased to 80 ˝C, the molar conversion of 2-EF was increased to 98.9 (Figure 1A).
The great increase in molar conversion can result from the impact of solvent-free and reduced pressure
evaporation system which increases the reaction rate of lipase-catalyzed biosynthesis by elevating the

Molecules 2016, 21, 478
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substrate concentration and eliminating byproducts with low vapor pressure physical characteristics.
Figure 1B shows the effects of reaction time and reaction temperature on 2-EF biosynthesis at a
constant enzyme amount (1000 PLU). At the lowest reaction time (8 h) and reaction temperature
(60 ˝C), the molar conversion was 42.7%
˘
3.3%. Figure 1C shows the effects of reaction time and
enzyme amount on 2-EF biosynthesis at a constant reaction temperature. In this regard, enzyme
amount had an insignificant effect on molar conversion, while reaction time had only a partial effect
on molar conversion. Taken together, the molar conversion significantly increased when the reaction
temperature and reaction time increased. As the ANOVA results shown in Table 2 indicate, reaction
temperature and reaction time are important factors in Novozym®435-catalyzed reactions, which
is in accordance with our previous works [27]. However, it has been reported that higher reaction
temperature might decrease the lipase activity [21]. Thus, a reduced pressure evaporation system can
be a good solution for lipase-catalyzed synthesis of FA esters in order to obtain a higher conversion at
a lower reaction temperature.
Figure 1. Response surface plot for the relationships between molar conversion and experimental
variables. Response surface plot showing the relationships between molar conversion of 2-ethylhexyl
ferulate and reaction parameters: (
A) reaction temperature and enzyme amount; (B) reaction
temperature and reaction time and (C) reaction time and enzyme amount.
2.2. ANN Model
ANN is a promising modeling methodology for non-linear multivariate process/reaction [28].
In this study, the data generated from the Box-Behnken design (Table 1) were employed to generate
an ANN model for the enzymatic 2-EF synthesis. The proposed ANN consisted of three layers
in the present work: an input layer with four neurons (reaction time, reaction temperature, and
enzyme amount), a hidden layer with several neurons and an output layer containing one output
neuron (molar conversion). The feed-forward neural network was trained by using incremental
back propagation (IBP), batch backpropagation (BBP) and Levenberg-Marquardt algorithm (LM)
or genetic algorithm (GA), respectively. All neurons from the hidden layer and output layer
were calculated by a sigmoid transfer function. Different numbers of neurons, from three to six,

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were examined. The various architectures for ANN models were developed using NeuralPower
software (Version 2.5, CPC-X Software, USA; available from Internet: http://www.brothersoft.com/
neuralpower-download-21356.html), and the best one was selected based on the maximization of the
R2 value and the minimization of the RMSE value.
All results from the test were evaluated statistically based on the RMSE values. According to
Figure 2, the best performance network was trained using a LM algorithm with a sigmoid transfer
function, which exhibited a lower and stable RMSE. It is noted that the curve of the RMSE levels off at
five hidden neurons. Therefore, the best ANN model in this study is determined to be a multilayer
feed forward connection, trained by a LM algorithm using a sigmoid transfer function that consists of
a 3–6–1 topology, as shown in Figure 3. This learning was acquired with RMSE = 0.79, R2 = 0.99.
Figure 2. Learning curves of ANN model at different hidden neurons. The training set uses sigmoid
transfer function in the hidden layer and output layer based on increment back propagation (IBP),
batch back propagation (BBP), Levenberg-Marquardt algorithm (LM) or genetic algorithm (GA).
Figure 3. The optimal architecture of multilayer feed forward neural network. The network contains
three inputs (reaction temperature, reaction time and enzyme amount), one hidden layer with
six nodes and one output (molar conversion of 2-ethylhexyl ferulate). W means weight; b means
basic input/output system (BIOS).
According to Figure 4, the reaction time and reaction temperature displayed great impact on the
conversion with a relative importance of approximate 39% and 46%, respectively. These results are in
accordance with the ones obtained with RSM.

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Figure 4. Importance of experimental variables on lipase-catalyzed synthesis of 2-ethylhexyl ferulate.
2.3. Comparison between RSM and ANN and Optimization for Experimental Condition
The distribution of the residual values obtained from the experimental data of Box-Behnken
design experiments minus the related computed RSM and ANN data are shown in Figure 5. It was
observed that the deviations of residual values showed a relatively small variation in the ANN model
when compared with the RSM model. This suggested that ANN was better than RSM in data fitting.
RSM (R2 = 0.98)
ANN (R2 = 0.99)
6
4
2
0
-2
-4
0
2
4
6
8
10
12
14
16
Experimental run
Figure 5. The distribution of residual values for each experimental run of Box-Behnken design
experiments employing the RSM and ANN models. Coefficient of determination (R2) calculated
from the design data for RSM and ANN models were 0.98 and 0.99, respectively. Residual values:
predicted values minus actual values.
In order to validate and test the extrapolative capability of both the ANN and RSM models,
a completely new set of five experiments was conducted on an experimental range which does not
belong to the design data sets. The experimental and predicted values of the response for both the
ANN and RSM models are given in Table 3. The performance of the newly constructed ANN and RSM
models were statistically measured, in terms of AAD and RMSE values. The RMSEs for RSM and ANN
were found as 7.322 and 5.152. These results indicate that the RSM prediction has a greater deviation
than the ANN prediction. The AAD for RSM and ANN was found to be 10.046 and 9.430. From the
results, it was observed that predictive power of ANN was found to be more powerful than RSM.

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Table 3. Validation and comparison of RSM- and ANN-modeling biocatalysis.
Independent Variable
Molar Conversion (%)
ANN Predicted
Run
X1 (h)
X2 (PLU) X3 (˝C) Experimental RSM Predicted
1
2
3
4
5
14
15
10
9
1250
750
850
1400
950
75
65
75
65
60
93.2
76.8
74.6
65.4
51.7
98.8
71.2
90
65.1
56.9
95.0
73.5
85.3
62.2
56.3
20
AAD
RMSE
10.046
7.322
9.430
5.152
Ridge max analysis can be used to determine optimum operating conditions by computing the
estimated ridge of the maximum response for increasing the radius from the center of the original
design. As shown in Table 4, the optimum condition for lipase-catalyzed synthesis of 2-EF determined
˝
by the ridge max analysis of RSM was reaction time of 23 h, reaction temperature of 71 C and
enzyme amount of 1422 PLU. A verification experiment performed at this condition obtained a molar
conversion of 99.43%. Notably, higher molar conversion (99.74) was observed when the experiment
was performed using the ANN-modeled optimal conditions (Table 4).
Table 4. The optimal trial obtained from RSM and ANN models.
Optimal Condition
Relative Conversion (%)
Experimental Value Predicted Value
Model
X1 (h)
X2 (PLU)
X3 (˝C)
RSM
ANN
23
24
1422
1485
71
78
99.43
99.74
102.74
99.82
2.4. Evaluation of Enzyme Reusability
To examine the enzyme reusability, the ability of reused immobilized lipase for 2-EF synthesis was
investigated under optimum conditions (Figure 6). After reaction, the mixture was filtered through a
0.45 µm membrane. The immobilized lipase was then recovered from the membrane, washed three
times with fresh reaction media and dried before reuse in the next batch. After the immobilized
lipase was reused five times, the molar conversion of 2-EF still remained at about 90.4%. This result
showed that the immobilized lipase remained stable through long-term 2-EF exposure. Thus, the result
demonstrated that the lipase could be effectively applied for 2-EF synthesis in the reduced pressure
evaporation system and that the stability was high enough to permit reuse.
120
100
80
60
40
20
0
1
2
3
4
5
Number of reuses
Figure 6. Reusability of Novozym®435 in the synthesis of 2-ethylhexyl ferulate performed under
optimum conditions.

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3. Experimental Section
3.1. Materials
Immobilized lipase Novozym®435 (10,000 U/g; propyl laurate units (PLU)) from Candida
antarctica B (EC3.1.1.3) supported on a macroporous acrylic resin was purchased from NovoNor disk
Bioindustrials Inc. (Copenhagen, Denmark). Ferulic acid (FA), 2-ethylhexanol, 2-methyl-2-butanol,
methanol and acetic acid were purchased from the Sigma Chemical Co. (St. Louis, MO, USA). A 4 Å
molecular sieve was purchased from Davison Chemical (Baltimore, MD, USA). 2-ethylhexyl ferulate
(2-EF) was purchased from Shanghai Richem International Co., Ltd. (Shanghai, China). All the other
reagents and chemicals, unless otherwise noted, were of analytical grade.
3.2. Enzymatic 2-ethylhexyl Ferulate Synthesis
FA was dissolved in 2-ethylhexanol (1 mL) to the concentration of 0.05 M in the fingernails
emissions glass flask that contained Novozym®435 of a rotary evaporator (EYELA N-1100, Tokyo
Rikakikai Co., Ltd., Bohemia, NY, USA), which operated at 80 rpm and 560 torr under different
experimental conditions. The esterification of FA with 2-EF catalyzed by Novozym®435 is represented
in Scheme 1.
Scheme 1. Enzymatic synthesis of 2-ethylhexyl ferulate.
After incubation, liquid samples were withdrawn from the reaction mixture for analysis of
2-EF by high-performance liquid chromatography (HPLC). The sample was diluted with hexanol/
2-methyl-2-butanol (1:150), and was injected (30
µL) into a HPLC (Hitachi L-7400; Tokyo, Japan)
equipped with UV detector and Thermo C18 column (250 mm
ˆ
4.6 mm, Agilent, Santa Clara, CA,
USA). Separations were carried out by a gradient elution with 0.1% acetic acid and methanol, the flow
rate was set to 1.0 mL/min, and 2-EF was detected under UV light at 325 nm as shown in Figure 7.
Calibration curves were prepared from the FA and 2-EF standards based on the peak areas. The molar
conversion was defined as (mmol of 2-EF production per mmol of initial FA) ˆ 100%.
(2)
(1)
Figure 7. HPLC chromatogram of ferulic acid and its ester form. (1) ferulic acid; (2) 2-ethylhexyl ferulate.

Molecules 2016, 21, 478
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3.3. Box-Behnken Design
A Box-Behnken design for 15 experimental runs was employed in present study. To avoid bias, the
15 runs were performed in a totally random order. The variables and their levels selected for the study
of 2-EF biosynthesis were: reaction temperature (60 ˝C–80 ˝C), enzyme amount (500–1500 PLU) and
reaction time (8–24 h), which were coded as shown in Table 5. Table 1 shows the independent factors
(xi), levels and experimental design. Each experimental point was carried out in duplicate. Statistical
parameters mentioned in present study including coefficient of determination (R2), root-mean-square
error (RMSE), and absolute average deviation (AAD) were calculated by following Equations (1) to (3):
`
˘
ř
2
n
Ypre ´ Yexp
i“1
R2 “ 1 ´
(1)
`
˘
ř
2
n
Ym ´ Yexp
i“1
where Ypre is the predicted 2-EF yield (by either RSM or ANN), Yexp is the observed 2-EF yield and Ym
is the average 2-EF yield.
d
`
˘
ř
2
n
Ypre ´ Yexp
i“1
RMSE “
(2)
n
where Ypre and Yexp are the predicted and experimental response, respectively, and n is the number of
experiments.
«
ff
ˇ
ˇ
ˇ
`
˘
ř
n
ˇ
Yexp ´ Ypre {Ypre
i“1
AAD “
ˆ 100
(3)
n
where Ypre and Yexp are the predicted and experimental response, respectively, and n is the number
of experiments.
Table 5. Coding of experimental parameters and related levels.
Coded Levels
Symbol
Parameters
´1
8
0
1
Reaction time (h)
Enzyme amount (PLU) a
Reaction temperature (˝C)
a
X1
X2
X3
16
1000
70
24
1500
80
500
60
PLU means propyl laurate units.
4. Conclusions
The present study demonstrates for the first time that immobilized C. antarctica lipase
(Novozym®435) is able to synthesize 2-ethylhexyl ferulate (2-EF). A Box-Behnken design with RSM
and ANN was selected to model and optimize the biocatalysis. Both RSM and ANN models for the
2-EF synthesis were built, and ANN was better than RSM in data fitting and prediction. The optimum
˝
synthesis condition modeled by ANN was in a reaction time of 24 h, reaction temperature of 78 C and
enzyme amount of 1485 PLU. The conversion of 2-EF under this optimum condition was verified to be
99.74%. The reduced pressure evaporation system combined with solvent free reaction conditions can
decrease the reaction time and reaction temperature for ferulic acid ester synthesis.
Acknowledgments: The authors are very grateful to the Ministry of Science and Technology of Taiwan, ROC for
supporting this research (Grants No. MOST 104-2221-E-005-061-MY3 and MOST 104-2218-E-022-001-MY2).
Author Contributions: Yawo-Kuo Twu and Chwen-Jen Shieh conceived and designed the experiments;
Kuo-Chuan Huang performed the experiments and analyzed the data; Kuo-Chuan Huang, Ying Li and
Chwen-Jen Shieh wrote the paper. Jer-An Lin and Chia-Hung Kuo assisted paper revision.
Conflicts of Interest: The authors declare no conflict of interest.

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Sample Availability: Samples of 2-ethylhexyl ferulate are available from the authors.
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2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access
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