Kinetic study on the enzymatic esterification of octanoic acid and hexanol by immobilized Candida antarctica lipase B

Article abstract of DOI:10.1016/j.molcatb.2014.09.011

This study investigates reaction kinetics of the esterification of octanoic acid and hexanol into hexyloctanoate, catalyzed by an immobilized Candida antarctica lipase (Novozym435). The product is consid-ered natural and used as a fresh vegetable and fruity flavour additive in food, cosmetic and pharmaceuticalproducts. The reaction is performed in n-decane as the solvent, to improve enzyme stability and toincrease the reaction yield. The influence of substrate concentration on hexyl octanoate synthesis isinvestigated over a wide range up to 2 M. The observed bi-substrate inhibition pattern follows a Ping-Pong bi-bi mechanism with dead-end inhibition by both substrates and, based on the proposed model, thekinetic constants of the esterification reaction are estimated. These parameters are verified to be intrin-sic neither external nor internal mass transfer resistances are significant for the examined reactionsystem and are essential to extend analysis to a large-scale process and for a wide range of operatingconditions. The progress of the reaction is also observed and the kinetic model is validated by fittingexperimental progress curves with two different concentrations of biocatalyst. Effects of biphasicity ofthe reaction system, inhibition by the ester produced and the influence of the reverse reaction have beenalso evaluated.

Full text of DOI:10.1016/j.molcatb.2014.09.011

Journal of Molecular Catalysis B: Enzymatic 110 (2014) 64–71
Contents lists available at ScienceDirect
Journal of Molecular Catalysis B: Enzymatic
journal homepage: www.elsevier.com/locate/molcatb
Kinetic study on the enzymatic esterification of octanoic acid and
hexanol by immobilized Candida antarctica lipase B
Catia Giovanna Loprestoa,∗, Vincenza Calabròa, John M. Woodleyb, Pär Tufvessonb
a Department of Informatics, Modeling, Electronics and System Engineering, University of Calabria, 87036 Rende, CS, Italy
b Department of Chemical and Biochemical Engineering, Technical University of Denmark, 2800 Lyngby, Denmark
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 24 April 2014
Received in revised form 1 September 2014
Accepted 20 September 2014
Available online 30 September 2014
This study investigates reaction kinetics of the esterification of octanoic acid and hexanol into hexyl
octanoate, catalyzed by an immobilized Candida antarctica lipase (Novozym® 435). The product is consid-
ered natural and used as a fresh vegetable and fruity flavour additive in food, cosmetic and pharmaceutical
products. The reaction is performed in n-decane as the solvent, to improve enzyme stability and to
increase the reaction yield. The influence of substrate concentration on hexyl octanoate synthesis is
investigated over a wide range up to 2 M. The observed bi-substrate inhibition pattern follows a Ping-
Pong bi-bi mechanism with dead-end inhibition by both substrates and, based on the proposed model, the
kinetic constants of the esterification reaction are estimated. These parameters are verified to be intrin-
sic – neither external nor internal mass transfer resistances are significant for the examined reaction
system – and are essential to extend analysis to a large-scale process and for a wide range of operating
conditions. The progress of the reaction is also observed and the kinetic model is validated by fitting
experimental progress curves with two different concentrations of biocatalyst. Effects of biphasicity of
the reaction system, inhibition by the ester produced and the influence of the reverse reaction have been
also evaluated.
Keywords:
Kinetics
Hexyl octanoate
Lipase Novozym
Ping-Pong bi-bi
Enzymatic esterification
© 2014 Elsevier B.V. All rights reserved.
1. Introduction
post-treatments because of the poor selectivity and undesirable
side reactions [3]. It has been shown that biocatalysis can offer sig-
Esters of carboxylic acids and alcohols have a great commer-
cial importance because of their applications in several industrial
fields. In particular, short-chain aliphatic esters are mainly used
as flavours and fragrance compounds in the food, cosmetic and
pharmaceutical industries. Their fruity notes make them suitable
as additives in fruit juices, cheeses, baked goods, candies, bever-
ages and ice creams [1]. Likewise, esters produced from long-chain
fatty acids and short-chain alcohols are used in the food, cosmetic
and pharmaceutical industries; in addition, they have applications
in detergent industries [2], as plasticizers for PVC, solvents, diesel
additives, water-resisting agents, lubricants and in hydraulic fluids
[3,4].
nificant environmental savings due to a decreased use of hazardous
chemicals and more efficient use of resources [5,6]. Consequently,
owing to the need to develop methods for the production of such
esters from cheaper and more available raw materials and to the
growing demand for “green” and natural products, biocatalysis is
an attractive alternative for the synthesis of esters from natural
precursors.
Biocatalysis has achieved important improvements in the appli-
cation of lipases – E.C.3.1.1.3 – as catalysts to hydrolyse or
synthesize esters [7–10]. Lipases are conventionally defined as
enzymes which catalyze the triacylglycerol hydrolysis to glycerol
and free fatty acids [11], but at low water content, the reverse
reaction of esterification is thermodynamically favoured [12]. This
reversal of their original hydrolytic activity is usually obtained by
using them in organic solvents at low water activity or in solvent-
free media composed of the mixture of substrates while removing
the water formed. Synthesis of esters in the presence of various
organic solvents is well documented and many studies have inves-
tigated the influence of different solvents [11,13–20]. It should be
noted that water must be present at sufficient concentration to acti-
vate the biocatalyst, but not too much in order to avoid hydrolysis
Traditionally, esters are prepared by chemical synthesis
with inorganic catalysts
– alkali metals, mineral acids, ion-
exchange resins, Lewis acids, metals and their oxides – requiring
∗
Corresponding author at: Via P. Bucci, Lab. of Transport Phenomena and Biotech-
nology, Building 42A, University of Calabria, 87036 Rende, CS,
Italy. Tel.: +39 09846671.
E-mail address: catialopresto@gmail.com (C.G. Lopresto).
http://dx.doi.org/10.1016/j.molcatb.2014.09.011
1381-1177/© 2014 Elsevier B.V. All rights reserved.

C.G. Lopresto et al. / Journal of Molecular Catalysis B: Enzymatic 110 (2014) 64–71
65
lipase Novozym® 435, mainly used in production of aromatic esters
[40–44], in transesterification of oils to fatty acid methyl or ethyl
esters (FAME/FAEE) [45,46] and in esterification of free fatty acids in
pretreatments of waste cooking oils [47,48]. Recently, Novozym®
435 was also compared to other commercial lipase preparations
in the synthesis of flavour esters and it showed to be more suit-
able than other biocatalysts in most cases [49]. The product of the
reaction between octanoic acid and hexanol is hexyl octanoate,
which has a special relevance in food processing as a natural flavour
enhancer.
The aim of this study is an experimental investigation of the
lipase-catalyzed esterification kinetics and the suggestion of a
mechanism in order to determine the kinetic constants. The study
is organized as follows: firstly, the effect of substrate concentra-
tions on the initial reaction rate has been assessed by initial-rate
measurements, pointing out the possible inhibitory effects of both
substrates; then, the esterification kinetics has been modelled by
the Ping-Pong bi-bi mechanism and the kinetic parameters have
been evaluated; finally, the kinetic model was validated by reaction
tests carried out for long times, by taking in account also the influ-
ence of the biphasicity, the inhibitory effect of the ester produced
and the reverse reaction.
[21,22]. Much research has been conducted in this field and has it
been extensively reviewed by for example Gandhi [23], Yahya et al.
[22], Paiva et al. [12] and Hou and Shimada [24].
The main obstacle to the wider application of lipase is the bio-
catalyst cost. As a means of reducing it, lipase immobilization on
an inert support can be advantageous to allow the easy recycling of
the biocatalyst. However, the cost of the biocatalyst depends on a
number of factors, for instance scale of production [25]. As of now,
lipases are finding greater use in industry such as for biodiesel pro-
duction which will certainly help to bring the costs down and open
the door for other applications using the same biocatalyst.
In order to make the most efficient use of the catalyst, iden-
tifying the optimal conditions to perform the ester synthesis is
important. To do so it is helpful to evaluate the reaction kinetics
and the constants describing the kinetic behaviour. Several the-
oretical models have been proposed to explain lipase-catalyzed
esterification in organic solvents. Kinetic studies on processes using
immobilized enzymes as catalysts were relatively scarce before
2000 and have become important only in the last decade. Paiva et al.
[12] reported an overview of the possible kinetics and mechanisms
of reactions catalyzed by immobilized lipase. Enzymatic models
that are based on the application of simple Michaelis–Menten
kinetics are valid for most simple enzymatic reactions. Never-
theless, when a two-substrate two-product reaction occurs, the
general scheme “bi-bi” has to be taken in account. This scheme
includes two possible mechanisms: Ping-Pong (non-sequential)
and ternary complex (sequential). The latter type can be ordered
or random [11,26]. The Ping-Pong bi-bi mechanism for lipase-
catalyzed esterification and transesterification reactions in organic
media is the generally accepted model [2,13,14,17,27–31], where
the reaction is thought to occur via an acyl-enzyme. When enzy-
matic systems are used, it is often necessary to consider inhibition
effects of one or both the substrates. These phenomena are the
result of inactive binary or ternary complexes between the enzyme
and other substrates in the medium, competing with the main sub-
strate. With regard to esterification, some reports have not found
any inhibition phenomena [1,28,29,32]. Other studies have iden-
tified an inhibition by only alcohol [13,14,31,33–35] or only acid
[11,16,19]. A few publications have dealt with the inhibition by
both substrates; this effect has been found in reactions between
short-chain acid and short-chain alcohol such as isovaleric acid and
ethanol [17], butyric acid and isoamyl alcohol [30]; long-chain acid
and short-chain alcohol such as oleic acid and n-butanol [2] and
oleic acid and ethanol [32]; short-chain acid and long-chain alco-
hol such as butanoic acid and oleyl alcohol [2]; long-chain acid and
long-chain alcohol such as oleic acid and cetyl alcohol [36] and oleic
acid and oleyl alcohol [2].
2. Material and methods
2.1. Enzyme and chemicals
The commercial immobilized lipase Novozym® 435 from Can-
dida antarctica lipase B on a macroporous acrylic resin (Lewatit OC
1600), a kind gift from Novozymes A/S (Denmark), was used for
the hexanol–octanoic acid esterification. The specific surface area
´
˚
2
is 95.50 m /g, the average pore diametre is 179.2 A and it has a nom-
inal activity of 7000 PLU/g. One propyl laurate unit (PLU) is defined
as the number of ␮mol of n-propyl laurate obtained in the standard
test corresponding to the esterification of lauric acid with n-propyl
alcohol, after 15 min at atmospheric pressure [50]. All chemicals
were of analytical reagent grade purchased from Merck Schuchardt
OHG (Germany). Substrates were used without any pre-treatment.
2.2. Esterification reaction
Esterification is a bi-substrate bi-product reaction A + B ↔ P +
Q (A: octanoic acid, B: hexanol, P: hexyl octanoate, Q: water). This
reaction was carried out in n-decane as solvent, because it has the
logarithm of the octanol/water partition coefficient (log P) of 5.6,
which gives the suitable conditions for a high reaction yield [51].
Batch reactions were performed in glass vials of 4 ml
In spite of several kinetic studies, the information to perform
an appropriate analysis for a later industrial scale up continues to
be quite limited. Also, most of the studies deal with ester synthesis
from very short- or long-chain substrates, or to investigate differ-
ent immobilization techniques, or to obtain qualitative information
on effects of different solvents, lengths of alcohol and acid chains,
additions of water and lipase origins.
with
a
working volume of 2 ml containing different
substrate–concentration mixtures in n-decane. The vials were
placed in a thermo-shaker (HLC Bovenden, Germany) and sub-
strates and solvent were loaded. The reaction mixture was
incubated at 35 ◦C with shaking (600 rpm). The biocatalyst was
added to initiate the reaction.
The topic of the current paper, the esterification of octanoic acid
and hexanol has not received much attention. The product of the
reaction between octanoic acid and hexanol is hexyl octanoate,
which has a special relevance in food processing as a natural flavour
enhancer incorporated in a wide range of aromas such as apple,
banana, cider, grape and melon [37]. This reaction has been stud-
ied using cutinase [37] or free lipases in free-solvent systems [38]
and aqueous miniemulsion systems [39], but there are no reports
on immobilized lipase-catalyzed kinetics in organic solvents. For
this reason, the kinetic study of the enzymatic synthesis of a long-
chain ester has been rigorously carried out in the present work.
The reaction has been catalyzed by the commercial immobilized
The effect of substrate concentration on the reaction rate
was investigated by esterifying varying-initial concentrations of
octanoic acid (from 0.1 M to 2 M) and varying-initial concentrations
of hexanol (from 0.1 M to 2 M), with 2.5 g/l of biocatalyst. Samples
of 100 ␮l volume were taken at different times to study the change
in composition. The progress of esterification was monitored by
determining the ester concentration by HPLC. Low conversions
(<10%) were used to minimize the possible inhibition by products
(hexyl octanoate and water) and to consider the linear region of the
reaction rate. Initial reaction rates were determined from the slope
of the initial linear portions of the ester concentration versus time
plots.

66
C.G. Lopresto et al. / Journal of Molecular Catalysis B: Enzymatic 110 (2014) 64–71
Other tests were performed with ester addition together with
substrates at the start of the reaction to verify if ester (product) is
or not an inhibitor for the esterification.
Since esterification rate depends on amount of biocatalyst
[4,16,17,19,30,36,52], tests with equimolar substrates (0.2, 0.5,
0.75, 1, 2 M) were performed at 35 ◦C with two different amounts of
Novozym 435 (2.5 g/l and 5 g/l). In order to evaluate the relevance
of the reverse reaction (hydrolysis), tests with different concen-
trations (from 0.1 M to 2 M) of water and ester as substrates were
carried out in decane at 35 ◦C with 2.5 g/l of biocatalyst under stir-
ring (600 rpm). These experiments were carried out for longer times
in order to obtain the progress curves.
All experiments (reactions) were done in duplicates.
2.3. Analysis of samples
Aliquots of the reaction mixture were withdrawn periodi-
cally and centrifuged by an Eppendorf MiniSpinPlus centrifuge at
14,500 rpm for 30 s to remove catalyst residues. The ester concen-
tration was essayed by HPLC using the UltiMate 3000 Dionex (Luna
C18 column from Phenomenex, ID 4.6 mm, L 250 mm, particle size
5 ␮m) and the software Chromeleon®. The method of detection
was UV at 210 nm. The compounds were eluted by gradient elu-
tion with an eluant system of methanol and water, both containing
0.05% of acetic acid, starting at 85% methanol for 7 min, followed by
90% methanol for 3 min and ending with 100% methanol for 10 min
[53]. The flow rate of mobile phase was 1 ml/min.
Fig. 1. Hanes plot with constant acid concentration. ꢀA = 0.1 M; ꢁ A = 0.15 M; -ꢂ-
A = 0.2 M; ᭹ A = 0.35 M; ♦ A = 0.5 M; ꢃ A = 0.75 M; ꢀ A = 1 M; ꢀ A = 2 M.
2.4. Kinetic constant determination
The initial reaction rates obtained at various acid and alco-
hol concentrations were fitted to Michaelis–Menten kinetics with
Ping-Pong bi-bi mechanism by a nonlinear regression method
(NLR) with the aid of the software Microsoft Excel 2010. The val-
ues of kinetic constants were computed by the minimization of
ꢁ
ꢀ
the
i ln 1 + MSRi2, where MSR is the mean square residual
Fig. 2. Hanes plot with constant alcohol concentration. ꢀB = 0.1 M; ꢁ B = 0.15 M; -ꢂ-
B = 0.2 M; ᭹ B = 0.35 M; ♦ B = 0.5 M; ꢃ B = 0.75 M; ꢀ B = 1 M; ꢀ B = 2 M.
between theoretical and experimental data.
3. Results and discussion
evident at low alcohol concentrations, because when A/B ratios
were high it needs to consider not only the competitive inhibition
by A but also the effect of the lower pH of the reaction mixture.
3.1. Effect of substrates
Substrate effects on reaction rate were investigated, maintain-
ing one substrate at constant concentration while varying the
concentration of the other reactant.
3.2. Effect of ester addition on esterification reaction
In order to investigate the presence of inhibition by the ester
produced, hexyl octanoate at different concentrations was added to
the substrates (octanoic acid + hexanol) at the start of the reaction.
The initial rate was evaluated and compared with data obtained
under the same operating conditions, but without initial addition
of ester. Results showed that ester did not have any significant
inhibitory effect on the esterification. For example, comparison
between initial rates obtained with initial addition of ester (0.2 M)
and without addition of ester is evident in Fig. 3 for different sub-
strate concentrations.
The concentration of ester versus time was plotted for each
experimental condition. A linear trend was observed for the first
6–10 min and its slope – divided by the concentration of biocata-
lyst loaded – gave the specific initial rate for each test. Then, initial
reaction rates were plotted versus the concentration of the variable
substrate for each group. The inhibition by both alcohol and acid
can be seen clearly in Figs. 6 and 7 (Section 4). Here, the decrease
of initial reaction rate at increasing alcohol and acid concentra-
tion was evident for every acid concentration and for low alcohol
concentrations, respectively.
Also Martinelle and Hult [14] detected no inhibition caused by
the ester (ethyl octanoate) on Candida antarctica lipase B immobi-
lized on polypropylene.
The maximum initial rate was reached when the A/B ratio was
between 2:0.75 and 2:1.
Therefore, a bi-substrate inhibition has been observed in this
work. In both cases, inhibition was due to the formation of dead-end
or non-productive complexes between substrates and enzyme. This
statement is supported by the Hanes plots (see Figs. 1 and 2). When
acid concentration was constant (Fig. 6), its increase led to increas-
ing maximum reaction rates and to inhibitory effects occurring at
higher alcohol concentrations. Instead, when alcohol concentration
was constant (Fig. 7), the inhibitory effect of the acid was more
3.3. External and internal mass-transfer limitations
Since the reaction has been catalyzed by an immobilized lipase
on a porous support, it needs to be checked if kinetic constants
were intrinsic or affected by diffusional internal and external mass-
transfer phenomena.

C.G. Lopresto et al. / Journal of Molecular Catalysis B: Enzymatic 110 (2014) 64–71
67
Fig. 4. Reaction mechanism of enzymatic esterification with inhibition by substrates
(A = Acid, B = Alcohol, P = Ester, Q = Water, E = Enzyme, Ac = Acyl group).
4. Kinetic modeling – determination of kinetic parameters
Experimental data allow qualitative aspects of the reaction to
be highlighted, however in order to be used for design, predic-
tion and to find the reaction mechanism, it must be expressed in
mathematical terms by the kinetic equation. In this work, the most
accepted Ping-Pong bi-bi model [12,56] with inhibition by both the
substrates was used and fitted to the experimental data. The reac-
tion mechanism is illustrated in Fig. 4, where both substrates form
dead-end complexes (E-B; E.Ac-A) with enzyme forms.
Eq. (5) describing this model is non-linear with five parameters:
maximum reaction rate Vmax, Michaelis constant of the acid KmA
and of the alcohol KmB, inhibition constant of the acid KiA and of the
alcohol KiB [57,58].
Fig. 3. Initial rate with and without initial addition of ester. ꢀ A = 0.2 M, E = 0 M; ꢂ
A = 0.75 M, E = 0 M; ꢁ A = 2 M, E = 0 M; ♦ A = 0.2 M, E = 0.2 M; ꢀ A = 0.75 M, E = 0.2 M; ꢃ
A = 2 M, E = 0.2 M.
With regards to external mass transfer, diffusivity coefficients
of octanoic acid and hexanol in n-decane (D01,2) were estimated to
be 1.89 × 10−6 cm2/s and 2.08 × 10−6 cm2/s, respectively, using the
Wilke and Chang [54] correlation (Eq. (1)):
T · M0.5
D01,2 = 7.4 × 10−8
[=] (cm2/s)
(1)
2
Vmax[A][B]
ꢁ2V10.6
ꢄ
ꢅ
ꢄ
ꢅ
v =
(5)
[A][B] + KmA[B] 1 + ([B]/KiB
)
+ KmB[A] 1 + ([A]/KiA
)
where subscripts 1 and 2 are referred to diffusing species and sol-
vent, respectively; T (K) is the operating temperature; ꢁ2 (cP) is
the viscosity of solvent; V1 (cm3/mol) is the molar volume of dif-
fusing species at boiling point under normal conditions. With the
conservative hypothesis of Sherwood’s number (kL · dp/D01,2) equal
to 2 – valid for non-agitated systems – the mass transfer coeffi-
cient kL was calculated as 9.43 × 10−5 cm2/s for octanoid acid and
1.04 × 10−4 cm2/s for hexanol. In addition, diffusion time tD and
characteristic reaction time tr were evaluated by Eqs. (2) and (3)
[16,30].
ꢁ
ꢀ
The five parameters that minimize
i ln 1 + MSRi2 are
reported in Table 1, with a mean standard deviation less than 8%.
Michaelis constants combine the kinetic constants reported in
Fig. 4, according to the Eqs. (6) and (7):
(k−1 + k2)k4
KmA
=
=
(6)
(7)
k1(k2 + k4)
(k−3 + k4)k2
KmB
k3(k2 + k4)
D01,2
Since KmA is higher than KmB, biocatalyst has a greater affinity
for the alcohol than for the acid. Moreover, the KiB is greater than
KiA, meaning that octanoic acid has a greater inhibitory effect than
hexanol.
The graphic illustration of obtained Ping-Pong bi-bi model is
depicted in Fig. 5.
Comparisons of the experimental and calculated – through the
model – initial rates using parameters from Table 1 are shown in
Figs. 6 and 7. The model fits the data very well.
tD
=
(2)
(3)
2
kL
1
tr
v(S0)
S0
=
In Eq. (3), the highest observed initial reaction rate was
considered as v(S0), according to a conservative criterion. The
characteristic reaction time (73 min) was much greater than the
diffusion times (3.5 min for octanoic acid and 3.2 min for hexanol).
Consequently, the reaction was the rate determining step and this
assessment is supported by a safe approach in computation of reac-
tion time and diffusion time.
Thiele’s observable modulus was evaluated by Eq. (4) [55] to
assess the magnitude of possible diffusional limitations inside bio-
catalyst pores:
5. Validation of the kinetic model at long times
5.1. Conversion profiles at long times and effect of biocatalyst
amount
A set of tests were carried out for long times (up to 6 h) with
a molar A:B ratio of 1:1 and two different amounts of biocatalyst
loaded before starting the reaction. As the substrate concentrations
increased, reaction was slower because of inhibitory effects and
the possible reversibility of the reaction. This is evident in plots of
conversion versus time in Figs. 8 and 9.
Reaction was very fast and when acid and alcohol concentra-
tions were low (0.2 M), a conversion of 90% was reached within 1 h
and 2 h with 5 g/l and 2.5 g/l of biocatalyst, respectively.
ꢂ
ꢃ
2
Vp
v0
˚ =
(4)
DeffS0 Ap
where v0 is the maximum initial reaction rate observed in our tests;
S0 is the limiting-substrate concentration corresponding to v0; Deff
is the effective diffusion coefficient for substrate; Vp and Ap denote
the particle volume and external surface area, respectively.
Thiele’s modulus was equal to 0.0323 and 0.0293 when effective
diffusion coefficient of acid and alcohol were considered, respec-
tively. Since in both cases Thiele’s modulus was lower than 0.3,
internal mass-transfer limitation was negligible [55].
Comparing initial data for the same substrate concentrations, it
can be observed that there was a linear relationship, in line with
what other authors have found. As expected, conversion rate with

68
C.G. Lopresto et al. / Journal of Molecular Catalysis B: Enzymatic 110 (2014) 64–71
Fig. 5. Illustration of obtained Ping-Pong bi-bi model.
Fig. 6. Experimental and calculated initial rates with constant octanoic acid con-
centration. Experimental data: ꢀ A = 0.1 M; ꢁ A = 0.15 M; ꢂ A = 0.2 M; ᭹ A = 0.35 M;
♦ A = 0.5 M; ꢃ A = 0.75 M; ꢀ A = 1 M; ꢀ A = 2 M. Model simulation: – A = 0.1 M; * * *
A = 0.15 M; – – – A = 0.2 M; — A = 0.35 M; – · – · A = 0.5 M; − − − A = 0.75 M; – · – ·
A = 1 M; – ·· – ·· A = 2 M.
Fig. 7. Experimental and calculated initial rates with constant hexanol concen-
tration. Experimental data: ꢀ B = 0.1 M; ꢁ B = 0.15 M; ꢂ B = 0.2 M; ᭹ B = 0.35 M; ♦
B = 0.5 M; ꢃ B = 0.75 M; ꢀ B = 1 M; ꢀ B = 2 M. Model simulation: – B = 0.1 M; * * *
B = 0.15 M; – – – B = 0.2 M; — B = 0.35 M;– · – · B = 0.5 M; – – – B = 0.75 M; – · – ·
B = 1 M; – ·· – ·· B = 2 M.
5 g/l of biocatalyst (Fig. 9) was twice of the conversion with 2.5 g/l
(Fig. 8), and also the final conversion was the same, i.e. this does not
depend on initial enzyme amount. This was further confirmed in
Fig. 10, where initial rate was plotted versus catalyst concentration
at different substrate concentrations.
5.2. Fitting progress curves
The proposed Eq. (5) with kinetic parameters in Table 1 was
integrated and used to fit experimental data in the progress curves.
Even if the mean standard deviation over all experiments was
low (5.1% and 5.5% when 2.5 g/l and 5 g/l of biocatalyst were
used respectively), the model fit the experimental data for the
first 45 min better (mean standard deviation of 2.64% and 4.10%),
whereas the experimental product concentration after 45 min is
slower than that expected (mean standard deviation of 7.77% and
6.48%). This may be due to the observed agglomeration of biocata-
lyst particles, that reduces the active sites exposed to the substrates.
Agglomeration has been reported by Foresti and Ferreira [59] for
Fig. 8. Plot of conversion versus time with different substrate concentrations and
2.5 g/l of biocatalyst. -ꢂ- A = B = 0.2 M; ♦ A = B = 0.5 M; ꢃ A = B = 0.75 M; ꢀ A = B = 1 M;
ꢀ A = B = 2 M.
Table 1
Kinetic constants of Ping-Pong bi-bi model.
Parameter
Value
Vmax [ꢂmol/ min ·mgcat
]
KmA [mol/l]
KmB [mol/l]
KiA [mol/l]
KiB [mol/l]
264.4
17.09
1.205
0.173
0.355

C.G. Lopresto et al. / Journal of Molecular Catalysis B: Enzymatic 110 (2014) 64–71
69
Different hypothesis were considered and verified to better
understand the behaviour of the reaction system at long times:
- as water was produced, its increased concentration led to a sec-
ond phase where a certain amount of the substrate hexanol was
solubilized;
- the enzyme was inactivate by the acid;
- the reverse reaction has to be taken in account.
5.3. Effect of the biphasic reaction medium
Thermodynamic calculations revealed that only when substrate
concentration was 0.75 M, 1 M and 2 M, the concentration of the
water produced during the esterification led to a biphasic reaction
medium after more than 1 h. Instead, when substrate concentra-
tion was 0.2 M and 0.5 M, the highest concentration of water – at
the end of the reaction – was less than its solubility limit in the reac-
tion mixture. Foresti et al. [60] presented a biphasic model, which
explicitly considered the presence of a second phase in ethyl oleate
synthesis by an immobilized Candida antarctica B. This approach
was considered also in esterification of octanoic acid and hexanol
in this work. Chemical reaction and mass transfer between liq-
uid phases (organic phase and aqueous phase) were considered,
but the biphasic approach was not found to give a better fitting
at long times. The traditional monophasic approach and the new
biphasic approach represented experimental data with the same
fitting. Therefore, the presence of water as second liquid phase did
not influence the behaviour at long times in our operating condi-
tions, probably because the volume of water produced was very
small in relation to the organic phase volume. In fact, the highest
concentration of water was 2 M – if reaction with A = B = 2 M would
be complete – which corresponds to 36 ␮l of water in 2 ml of reac-
tion volume (0.018, v/v). The amount of hexanol solubilized in this
low volume of water was negligible and the aqueous phase was not
enough to influence the progress curves.
Fig. 9. Plot of conversion versus time with different substrate concentrations and
5 g/l of biocatalyst. -ꢂ- A = B = 0.2 M; ♦ A = B = 0.5 M; ꢃ A = B = 0.75 M; ꢀ A = B = 1 M; ꢀ
A = B = 2 M.
5.4. Enzyme denaturation by substrates
Fig. 10. Plot of initial rate versus catalyst concentration at different substrate
concentrations. ꢂ A = B = 0.2 M; ♦ A = B = 0.5 M; ꢃ A = B = 0.75 M; ꢀ A = B = 1 M; ꢀ
A = B = 2 M.
Enzyme denaturation by substrates has been included in the
kinetic model by Bezbradica et al. [18,31] in free-solvent systems.
Nevertheless, in our operating conditions the presence of the sol-
vent provided a sheltering effect and no enzyme denaturation took
place, insomuch as the model expressed in Eq. (5) with enzyme
inactivation by acid expressed in Eq. (8) had the best fitting with
experimental data when the constant of enzyme deactivation kD
was zero-value.
an immobilized Candida antarctica lipase B. Moreover, equilibrium
concentrations of ester was lower than the model which leads to
100% conversions.
It can also be observed that the model fitting was very
good when substrate concentrations were low, but the fit
decreased with increasing substrate concentrations (except with
substrate concentration of 2 M and 2.5 g/l of biocatalyst), as shown
in Table 2:
d[E]
dt
−
= kD[E][A]
(8)
5.5. Reverse reaction
Table 2
The reverse reaction of esterification (hydrolysis) was not
experimentally relevant in our operating conditions. Since the
esterification reaction reached a final conversion of more than
90%, an analysis of hydrolysis at initial rate would be inaccurate.
Ester concentration was monitored for long times and its varia-
tion had standard deviations less than 3% depending probably on
experimental errors when samples are prepared to be analyzed.
Consequently, any change in ester concentration is comparable
with the experimental error and it is not possible to evaluate any
reverse reaction constant accurately.
Mean standard deviation percentage for tests at long times.
A = B [M]
Biocatalyst concentration [g/l]
Mean stand. dev. [%]
0.2
2.5
5
2.5
5
2.5
5
2.5
5
1.31
0.99
2.95
4.26
4.83
5.86
6.16
6.43
2.63
9.68
0.5
0.75
1
2
2.5
5
Nevertheless, the Ping-Pong bi-bi model expressed in Eq. (5) can
be modified in to Eq. (9) [57] to take into account the contribution

70
C.G. Lopresto et al. / Journal of Molecular Catalysis B: Enzymatic 110 (2014) 64–71
Table 3
Initial octanoic acid, alcohol and decane concentrations.
Octanoic acid [M]
Hexanol [M]
Decane [M]
0.2
0.5
0.75
1
0.2
0.5
0.75
1
4.8
4.4
4.0
3.7
2.2
2
2
of the reverse reaction.
Vf Vr([A][b] − ([P][Q]/Keq))
ꢄ
ꢅ
ꢄ
ꢅ
v =
Vr[A][B] + VrKmA[B] 1 + ([B]/KiB
)
+ VrKmB[A] 1 + ([A]/KiA
)
Vf KmQ [P]
Vf KmP[Q]
Vf KmQ [A][P]
Vf [P][Q]
VrKmA[B][Q]
+
+
+
+
+
Keq
Keq
KeqKa
Keq
Kq
(9)
Fig. 11. Comparison of experimental and reversible model prediction in progress
curves with 2.5 g/l of biocatalyst and initial substrate concentrations of 0.2 M, 0.5 M,
0.75 M and 1 M. Experimental data: ꢂ A = B = 0.2 M; ♦ A = B = 0.5 M; ꢃ A = B = 0.75 M;
ꢀ A = B = 1 M. Model simulation: – A = B = 0.2 M; * * * A = B = 0.5 M; – – – A = B = 0.75 M;
– · – · A = B = 1 M.
The parameters introduced with this reversible model are the
reverse maximal velocity Vr, the equilibrium constant Keq, the
Michaelis constants for the products KmP and KmQ, the dissociation
constant for acid substrate KA(= (k−1/k1)) and the dissociation con-
stant for water KQ (= (k4/k−4)). Vf is the forward maximal reaction
and it is the same of Vmax in Eq. (5).
Since it was not possible to accurately make hydrolysis analysis
at initial rates, the equilibrium constant and the kinetic constants of
esterification have been estimated by fitting Eq. (9) with progress
curves of esterification (whose equivalent conversion profiles are
showed in Figs. 8 and 9) by a non-linear regression method. The
best fitted kinetic parameters led to a very good fitting when ini-
tial substrate concentrations were 0.2 M, 0.5 M, 0.75 M and 1 M,
whereas a different behaviour was observed when substrate con-
centration was increased from 1 M to 2 M (data not shown). This
can be due to the different polarity of the reaction medium. In
fact, increasing substrate concentrations correspond to decreasing
solvent (decane) concentration, as shown in Table 3. When both
substrate concentrations were 0.2–1 M, solvent concentration was
higher than substrate concentration. Instead, when both substrate
concentration was 2 M, decane concentration was comparable to
substrate concentration. This led to a change in polarity of the reac-
tion system which made the proposed kinetic model inaccurate in
all range 0.2–2 M.
Therefore, the non-linear regression method was applied with-
out taking into account data at 2 M substrate concentration. The
reversible model – with equilibrium and kinetic constants reported
in Table 4 – fit very well the experimental data (see Figs. 11 and 12).
Mean standard deviation was 0.76% and 1.05% with 2.5 g/l and 5 g/l
of biocatalyst, respectively, whereas maximum standard deviation
was 4.45% and 4.20% with 2.5 g/l and 5 g/l of biocatalyst, respec-
tively.
Fig. 12. Comparison of experimental and reversible model prediction in progress
curves with 5 g/l of biocatalyst and initial substrate concentrations of 0.2 M, 0.5 M,
0.75 M and 1 M. Experimental data: ꢂ A = B = 0.2 M; ♦ A = B = 0.5 M; ꢃ A = B = 0.75 M;
ꢀ A = B = 1 M. Model simulation: – A = B = 0.2 M; * * * A = B = 0.5 M; - - - A = B = 0.75 M;
– · – · A = B = 1 M.
0
f
/R) (1/298.15)−(1/T)
Keq(T) = Keq(298.15 K) · e(ꢀH
(11)
(
)
The equilibrium constant at 35 ◦C (operating temperature) cal-
culated by Eqs. (10) and (11) is low and equal to 2.1 × 10−3, so
hydrolysis is thermodynamically favoured. Nevertheless, the ester-
ification in organic solvents can be shifted towards the product side
by decreasing the concentration of water, one of the products, in
the reaction mixture [62]. As a consequence, the equilibrium con-
stant of esterification carried out in decane was higher (equal to
438.6, Table 4) and esterification was favoured.
The thermodynamic constant Keq should depend only on reac-
tion and operating temperature and it can be calculated from
thermodynamic information on standard Gibbs free energy and
enthalpy of formation of octanoic acid, hexanol, hexyl octanoate
and water [61], according to Eqs. (10) and (11):
0
f
Keq(298.15 K) = e−(ꢀG
(10)
/R·298.15)
Table 4
Kinetic parameters of reversible Ping-Pong bi-bi model (substrate concentration 0.2–1 M).
Parameter Vf [ꢂmol/ min ·mgcat
Value 264.4
]
Vr [ꢂmol/ min ·mgcat
]
Keq [/] KmA [mol/l] KmB [mol/l] KmP [mol/l] KmQ [mol/l] KA [mol/l] KQ [mol/l] KiA [mol/l] KiB [mol/l]
438.6 17.09 1.205 0.187 5.856 1.094 3.068 0.173 0.355
3.509

C.G. Lopresto et al. / Journal of Molecular Catalysis B: Enzymatic 110 (2014) 64–71
71
6. Conclusion
[18] D. Bezbradica, D. Mijin, S. Siler-Marinkovic, Z. Knezevic, J. Mol. Catal. B: Enzym.
38 (2006) 11.
[19] R. Ben Salah, H. Ghamghui, N. Miled, H. Mejdoub, Y. Gargouri, J. Biosci. Bioeng.
103 (2007) 368.
[20] T. Raghavendra, D. Sayania, D. Madamwar, J. Mol. Catal. B: Enzym. 63 (2010)
31.
[21] A. Marty, W. Chulalaksananukul, R.M. Willemot, J.S. Condoret, Biotechnol. Bio-
eng. 39 (1992) 273.
[22] A.R.M. Yahya, W.A. Anderson, M. Moo-young, Enzyme Microb. Technol. 23
(1998) 438.
[23] N.N. Gandhi, J. Am. Oil Chem. Soc. 74 (1997) 621.
[24] C.T. Hou, Y. Shimada, Encycl. Microbiol. (2009) 385.
[25] P. Tufvesson, J. Lima-Ramos, M. Nordblad, J.M. Woodley, Org. Process Res. Dev.
15 (2011) 266.
[26] A.G. Marangoni, Enzyme Kinetics, A Modern Approach, John Wiley & Sons, New
Jersey, 2003.
The enzymatic hexanol–octanoic acid esterification was carried
out by immobilized lipase to produce a natural flavour compound.
The aim of the investigation was to better understand the behaviour
of the reactive system through an analysis of the reaction kinet-
ics. In particular, effects of substrate concentration and biocatalyst
amount have been investigated. In addition, experimental data
were used to evaluate kinetic constants with a view to a scaled-up
process. The reaction was found to be inhibited by both the sub-
strates and the proposed mathematical model – Ping-Pong bi-bi
– showed good agreement with the experimental data. In addi-
tion, a linear reaction rate increase with biocatalyst amount was
observed. The progress of the reaction was investigated towards
equilibrium and the contribution of the reverse reaction was con-
sidered. Conversion of 90% was reached within only one hour
when both substrate concentrations were 0.2 M and biocatalyst
amount was 5 g/l. In all other experimental conditions, almost com-
plete conversion of substrates was obtained within four hours. The
results indicate that the model developed is adequate to describe
the kinetics of the lipase-catalyzed esterification of octanoic acid
and hexanol up to both substrate concentration 1 M. Extrapolations
over this concentration would be inaccurate because of changes in
polarity and a different model or different kinetic parameters have
to be taken into account.
[27] W. Chulalaksananukul, J.S. Condoret, P. Delorme, R.M. Willemot, FEBS Lett. 276
(1990) 181.
[28] Y.P. Yong, B. Al-Duri, J. Chem. Technol. Biotechnol. 65 (1996) 239.
[29] S. Basheer, U. Cogan, M. Nakajima, JAOCS 75 (1998) 1785.
[30] S. Hari Krishna, N.G. Karanth, Biochim. Biophys. Acta 1547 (2001) 262.
ˇ
[31] D. Bezbradica, D. Mijin, S. Siler-Marinkovic´, Z. Knezˇevic´, J. Mol. Catal. B: Enzym.
45 (2007) 97.
[32] A.C. Oliveira, M.F. Rosa, M.R. Aires-Barros, J.M.S. Cabral, J. Mol. Catal. B: Enzym.
11 (2001) 999.
[33] A.C. Oliveira, M.F. Rosa, J.M.S. Cabral, M.R. Aires-Barros, J. Mol. Catal. B: Enzym.
5 (1998) 29.
[34] S. Ramamurthi, A.R. McCurdy, JAOCS 71 (1994) 927.
[35] G. Sandoval, J.S. Condoret, P. Monsan, A. Marty, Biotechnol. Bioeng. 78 (2002)
313.
[36] T. Garcia, A. Coteron, M. Martinez, J. Aracil, Chem. Eng. Sci. 55 (2000) 1411.
[37] D.P.C. de Barros, P. Fernandes, J.M.S. Cabral, L.P. Fonseca, Catal. Today 173 (2011)
95.
Finally, it has been confirmed that reaction was carried out in
conditions of kinetic-limited regime and neither external nor inter-
nal diffusional limitations took place.
[38] N.N. Gandhi, K.D. Mukherjee, J. Agric. Food Chem. 48 (2000) 566.
[39] D.P.C. de Barros, L.P. Fonseca, J.M.S. Cabral, E.M. Aschenbrenner, C.K. Weiss, K.
Landfester, Biotechnol. Bioeng. 106 (2010) 507.
[40] M.D.R. Diaz, J.M. Gómez, B. Díaz-Suelto, A. García-Sanz, Eng. Life Sci. 10 (2010)
171.
[41] A.B. Martins, N.G. Graebin, A.S.G. Lorenzoni, R. Fernandez-Lafuente, M.A.Z.
Ayub, R.C. Rodrigues, Process Biochem. 46 (2011) 2311.
[42] C.-H. Kuo, S.-H. Chiang, H.-Y. Ju, Y.-M. Chen, M.-Y. Liao, Y.-C. Liu, C.-J. Shieh, J.
Sci. Food Agric. 92 (2012) 2141.
[43] P. Lozano, J.M. Bernal, A. Navarro, Green Chem. 14 (2012) 2953.
[44] V.V. Kuperkar, V.G. Lade, A. Prakash, V.K. Rathod, J. Mol. Catal. B: Enzym. 99
(2014) 143.
[45] N. Sangaletti, R. Briones, C. Beltran, M. Cea, R. Navia, 2nd Int. Work. Advances
Sci. Technol. Nat. Resour., Pucón, Chile, 2010, pp. 20.
[46] C. José, G.B. Austic, R.D. Bonetto, R.M. Burton, L.E. Briand, Catal. Today 213 (2013)
73.
[47] K.F. Haigh, S.Z. Abidin, G.T. Vladisavljevic´, B. Saha, Fuel 111 (2013) 186.
[48] K.F. Haigh, G.T. Vladisavljevic´, J.C. Reynolds, Z. Nagy, B. Saha, Chem. Eng. Res.
Des. 92 (2014) 713.
[49] A.B. Martins, A.M. da Silva, M.F. Schein, C. Garcia-Galan, M.A. Záchia Ayub, R.
Fernandez-Lafuente, R.C. Rodrigues, J. Mol. Catal. B: Enzym. 105 (2014) 18.
[50] Novo, Novozyme 435 – Novo Enzymes Technical Report, Bagsværd, 1995.
[51] A.M. Klibanov, Nature 409 (2001) 241.
[52] M. Karra-Châabouni, H. Ghamgui, S. Bezzine, A. Rekik, Y. Gargouri, Process
Biochem. 41 (2006) 1692.
Acknowledgments
We are very grateful to all researchers of the Department of
Chemical and Biochemical Engineering (Technical University of
Denmark), where this experimental study has been carried out.
Special thanks to Dr Watson Neto for support in the experimental
set-up and analysis.
References
[1] P. Mahapatra, A. Kumari, V. Kumar Garlapati, R. Banerjee, A. Nag, J. Mol. Catal.
B: Enzym. 60 (2009) 57.
[2] A. Zaidi, J.L. Gainer, G. Carta, A. Mrani, T. Kadiri, Y. Belarbi, A. Mir, J. Biotechnol.
93 (2002) 209.
[3] M.S. Shintre, R.S. Ghadge, S.B. Sawant, J. Chem. Technol. Biotechnol. 77 (2002)
1114.
[4] H. Ghamgui, M. Karra-Chaâbouni, Y. Gargouri, Enzyme Microb. Technol. 35
(2004) 355.
[53] P. Tufvesson, A. Annerling, R. Hatti-Kaul, D. Adlercreutz, Biotechnol. Bioeng. 97
(2007) 447.
[5] O. Thum, K.M. Oxenbøll, SOFW J. 134 (2008) 44.
[6] D. Adlercreutz, P. Tufvesson, A. Karlsson, R. Hatti-Kaul, Ind. Biotechnol. 6 (2010)
204.
[7] N. Verma, S. Thakur, A.K. Bhatt, Int. Res. J. Biol. Sci. 1 (2012) 88.
[8] A. Salihu, Z. Alam, Sci. Res. Essays 7 (2012) 2667.
[54] C.R. Wilke, P. Chang, AIChE J. 1 (1955) 264.
[55] J.E. Bailey, D.F. Ollis, Biochemical Engineering Fundamentals, 2nd ed., McGraw
Hill Book Company, Singapore, 1986.
[56] N.N. Gandhi, N.S. Patil, S.B. Sawant, J.B. Joshi, P.P. Wangikar, D. Mukesh, Catal.
Rev. Sci. Eng. 42 (2000) 439.
[57] I.H. Segel, Enzyme Kinetics –Behavior and Analysis of Rapid Equilibrium and
Steady-State Enzyme Systems, Wiley-Interscience Publication, 1993.
[58] H. Bisswanger, Enzyme Kinetics –Principles and Methods, Wiley-VCH, Wein-
heim, 2002.
[59] M.L. Foresti, M.L. Ferreira, Catal. Today 107–108 (2005) 23.
[60] M.L. Foresti, M. Pedernera, M.L. Ferreira, V. Bucalá, Appl. Catal. A: Gen. 334
(2008) 65.
[61] R.H. Perry, D.W. Green, Perry’s Chemical Engineers’ Handbook, 7th ed., McGraw
Hill, United States of America, 1997.
[9] P.-Y. Stergiou, A. Foukis, M. Filippou, M. Koukouritaki, M. Parapouli, L.G.
Theodorou, E. Hatziloukas, A. Afendra, A. Pandey, E.M. Papamichael, Biotechnol.
Adv. 31 (2013) 1846.
[10] K.P. Dhake, D.D. Thakare, B.M. Bhanage, Flavour Fragr. J. 28 (2013) 71.
[11] N.A. Serri, A.H. Kamaruddin, W.S. Long, Bioprocess Biosyst. Eng. 29 (2006) 253.
[12] A.L. Paiva, V.M. Balcão, F.X. Malcata, Enzyme Microb. Technol. 27 (2000) 187.
[13] M. Goto, N. Kamiya, M. Miyata, F. Nakashio, Biotechnol. Prog. 10 (1994) 263.
[14] M. Martinelle, K. Hult, Biochim. Biophys. Acta 1251 (1995) 191.
[15] A.E.M. Janssen, B.J. Sjursnes, A.V. Vakurov, P.J. Halling, Enzyme Microb. Technol.
24 (1999) 463.
[16] G.D. Yadav, P.S. Lathi, J. Mol. Catal. B: Enzym. 27 (2004) 113.
[17] G.V. Chowdary, S.G. Prapulla, Indian J. Chem. 44B (2005) 2322.
[62] T. Kobayashi, W. Furutani, S. Adachi, R. Matsuno, J. Mol. Catal. B: Enzym. 24–25
(2003) 61.