Optimization of the Aqueous Extraction of Virgin Coconut Oil by Response Surface Methodology

Abstract

Virgin coconut oil is a functional food which provides health benefits. It is different from other cooking oils in that it has medium-chain fatty acids rather than long-chain fatty acids. The traditional aqueous extraction of the oil is very flexible that can be applied in a medium to large-scale industry. The study was carried out to optimize the aqueous extraction of virgin coconut oil using response surface methodology. The most influencing three factors were used for this experiment, which are the coconut milk-to-water percentage, fermentation and refrigeration time. The optimization study showed that the method can produce the best yield with quality by using coconut milk (73.8%), fermented (14.1 h) and refrigerated time (20.5 h). Coconut milk percentage and fermentation time significantly affected the response of extraction yield (p ≤ 0.01). The aqueous extraction can be used commercially for the production of virgin coconut oil as the method is environmental friendly.

Full text

Food Sci. Technol. Res., 19 (5), 729–737, 2013
Optimization of the Aqueous Extraction of Virgin Coconut Oil by Response Surface
Methodology
Nor Faadila M. idrus1, Noor A. Febrianto1, Wahidu ZZaman1,2, Tang E. Cuang1 and Tajul A. yang1*
1 School of Industrial Technology, Food Technology Division, Universiti Sains Malaysia, Penang 11800, Malaysia
2 Department of Food Engineering and Tea Technology, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
Received October 10, 2012; Accepted May 20, 2013
Virgin coconut oil is a functional food which provides health benets. It is different from other cook-
ing oils in that it has medium-chain fatty acids rather than long-chain fatty acids. The traditional aqueous
extraction of the oil is very exible that can be applied in a medium to large-scale industry. The study was
carried out to optimize the aqueous extraction of virgin coconut oil using response surface methodology.
The most inuencing three factors were used for this experiment, which are the coconut milk-to-water
percentage, fermentation and refrigeration time. The optimization study showed that the method can pro-
duce the best yield with quality by using coconut milk (73.8%), fermented (14.1 h) and refrigerated time
(20.5 h). Coconut milk percentage and fermentation time signicantly affected the response of extraction
yield (p ≤ 0.01). The aqueous extraction can be used commercially for the production of virgin coconut oil
as the method is environmental friendly.
Keywords: virgin coconut oil, aqueous extraction, optimization, response surface methodology
*To whom correspondence should be addressed.
E-mail: taris@usm.my
Introduction
Today, the trend of oil consumption has shifted from the
use of puried oil into natural oil. The development of health
issues and the increasing awareness of chemical free product
are several reasons that initiate this phenomenon. This condi-
tion has raised the growing market of less-proceeded oil such
as virgin olive oil, virgin sunower seed oil and virgin coco-
nut oil (VCO). Currently, VCO is used as directly-consumed
supplement due to its nutritious components such as high
content of medium fatty acid and shows good digestibility
(Cheman et al., 1997). Hypocholesterolemic and antioxidant
effect of some unsaponiable components, such as vitamins,
polyphenols, sterols is effective in reducing the lipid levels
and lipid peroxidation (Anderson et al., 2001; Khor et al.,
1998; Nicolosi et al., 2001). It is also reported that consump-
tion of coconut oil can lower body-fat deposition and reduce
blood clotting tendency (Bruce-Fife, 2001). Thus, the less
processed VCO is preferred because it neglects the common
process of rening, bleaching and deodorizing that could re-
move the useful minor constituent (McWilliams, 2001).
Unlike the common coconut oil that produced from co-
pra, which generally had been long stored, and currently
produced at medium and large industries using specific
equipment whereas VCO is usually obtained through wet
processing (aqueous) from fresh coconut, which is possible
to be applied at small and even at home kitchen scale. Ma-
rina et al. (2009) had described several methods of VCO ex-
traction, via traditional wet extraction; chilling, freezing and
thawing techniques; fermentation by microorganism culture
and enzymatic extraction.
The simple process and no specific equipment needed
is the advantage of traditional aqueous extraction compared
to other extraction methods (Mechanical pressing, solvent
extraction, etc.). Traditional wet extraction can be carried out
using water to extract oil from the pulp of fresh grated coco-
nut. This process involved the step of squeezing the grated
coconut flesh to obtain coconut milk and then followed by
separation of coconut milk into oil and non-oil product. In
common practice, the destabilization of coconut milk emul-
sion to separate the oil from the mixture (Marina et al., 2009)
can be done in several ways. Suhardiyono (1988) mentioned
that it can be done by cooking the coconut milk for 3 − 4 h
to release the water through evaporation, whereas Marina

n. F. m. idrus
et al.
Yield (%) = [weight of extracted oil (g)/
weight of coconut milk (g)] × 100 Eq. 1
Acid value and free fatty acid analysis The acid value
(AV) was determined by the AOAC ofcial method Cd 3a-
63 (AOAC, 2000). A known weight of the oil sample (2.0
g) was dissolved in 50 mL of neutralized methanol and then
titrated with KOH (0.1 mol/L). The free fatty acids (FFA)
concentration was determined by titration followed by the
AOCS method Ca 5a-40 (AOCS, 1998) in each sample. In
short, the oil was weighed and poured into a ask, along with
95% neutralized ethanol and a phenolphthalein pH-indicator.
The mixture was titrated against a sodium hydroxide solution
until a permanent pink colour persisted for at least 30 s. The
FFA concentration (% w/w) was calculated as lauric-acid-
based oil. The sample was titrated in triplicate.
Peroxide value The peroxide value (POV) was deter-
mined using AOAC ofcial method 965.33 (AOAC, 2000).
Thirty milliliters of chloroform/acetic acid 3:2 (v/v) was
used to dissolve a known weight of oil sample (5.0 g) then 0.5
mL freshly prepared saturated KI solution was added and the
mixture and the vortexed for exactly 1 min. Thirty milliliters
distilled water and starch indicator (0.5 mL, 1%) were added
and then titrated with sodium thiosulfate (0.1 mol L−1).
K232 and K270 specific extinction coefficients K232 and
K270 were determined in triplicate by the spectrophotometric
method according European Union commission Regulation
EEC 2568/91 (EC, 1991). About 0.50 ± 0.05 g of sample was
weighed in 50 mL volumetric flask, which was previously
wrapped with aluminum foil. Then, top up to a nal volume
of 50 mL with cylohexane (1% (w/v) solution). After shak-
ing for a while, the absorption at 232 nm and 270 nm were
measured using UV-1601 PC, UV-visible spectrophotometer
(Shimadzu Corp., Kyoto, Japan). The K232 and K270 specic
extinction coefcients were carried out in triplicate for each
sample.
Color Color of the samples was determined using a
spectrophotometer (CM-3500d, Osaka, Japan-Spectra Magic
3.61 software). The parameters measured in this analysis
were L*, a*, b*, C* and h. L is brightness (L = 100 is the
brightest and L = 0 is the darkest). a* represents red (+) and
green (−) while b* represent yellow (+) and blue (−). C* is
the degree of saturated color while hue, h is the tendency of
color towards certain color. The center of these coordinates
is a chromatic; as a* and b* values increase. The color mea-
surement was carried out in triplicate for each sample.
Statistical analysis A Box-Behnken design of response
surface methodology (RSM) was employed to collect the
data by three factors and three levels of variable combina-
tions. The experiment design and the result of the experi-
et al. (2009) stated that it can be done through creaming by
gravitational force, flocculation and coalescence process.
However, in the simpler way as several small scales used, it
can be carried out by simply letting stand the coconut milk
for several hours until the oil is separated. Seow and Gwee
(1997) also described that the chilling and freezing followed
by thawing process also can be done in the attempt to break
the protein stabilized oil-in-water emulsion.
According to Suhardiyono (1998), letting stand method
is less favorable because it produces lower yield compared
to another aqueous extraction. However, the oil produced by
this method is preferred by consumers because of the aroma
and taste. In addition, it is desirable due to simpler process
that can be carried out at home by anyone to produce their
own virgin coconut oil. This study was carried out to opti-
mize the traditional aqueous extraction using response sur-
face methodology. The optimization was aimed to optimize
the proper composition of coconut milk and fermentation
time (letting stand) and chilling process (refrigerated time)
for small-scale industry and even home kitchen-scale appli-
cation.
Materials and Methods
Raw samples Coconut milk, the raw material was
bought from a market in Sungai Petani, Kedah. The coconut
milk was transferred into small containers and stored at −18
± 1 ℃ until used.
Extraction of virgin coconut oil Fresh coconut milk in
the containers was added with water. The water-to-coconut
percentages were 0:1, 1:1, and 2:1 respectively. Then, fer-
mentation was done by leaving them to stand for 12, 24 and
36 h. Fermentation process was carried out with the presence
of air, substance (coconut milk), and water. Letting stand the
mixture causing reaction between coconut milk and water
make the fermentation process happen. After the fermenta-
tion, the samples were placed into refrigerator (4℃ ± 1℃)
for 12, 24 and 36 h. The samples were taken out from the
chiller immediately as the refrigeration time had reached.
Subsequently, after the storage of samples in the refrigerator,
the 2 layers were frozen. So, the butterfat portion was taken
manually and put into a container by using a spoon followed
by thawing for 4 h at room temperature. After the thawing
process, the sample’s weights were determined and recorded.
In this experiment, the separation was assisted by using cen-
trifuge force at the speed of 822 g for 30 min at room tem-
perature. The pure oil obtained from centrifugation was put
into a container wrapped with aluminum foil and stored in
the freezer (−18 ± 1℃) until further analysis.
Extraction yield was expressed as the percentage of oil in
the “Eq. 1”.
730

Optimization of the Aqueous Extraction of Virgin Coconut Oil
fatty acid value, acid value, peroxide value, K232, and K270
the sum of squares of the sequential model were analyzed
together with the corresponding fitting of the explanatory
models. The sequential model of sum of squares analyses
indicated that adding terms up to quadratic signicantly im-
proved the model (p < 0.05) (Table 2) with the exception of
K270 (0.0602). Koocheki et al. (2009) mentioned that coef-
cients of determination (R2), adj-R2 and coefcients of varia-
tions (CV) also indicate the adequacy of the model, which R2
should not be less than 80% to ensure the suitability of the
model. In this study, the R2 values for extraction yield, FFA,
AV, POV, K232 and K270 were 0.951, 0.954, 0.953, 0.979, 0.983
and 0.967, respectively (Table 3).
All responses have shown the adj-R2 values higher than
0.80 to ensure the model adequacy, whereas the coefcient
of variation of all the responses were lower than 10%. Coef-
cient of variation (CV) measures the dispersion of a prob-
ability distribution, which high CV will indicate high varia-
tion in the mean value and sufcient response model cannot
be developed. Thus, CV should not be higher than 10%
(Koocheki et al., 2009). Moreover, the adequacy of the qua-
dratic model was convinced with the non-signicant effect of
lack of ts.
Yield Coconut milk percentage, fermentation time and
their quadratic effects signicantly affected the response of
extraction yield (p ≤ 0.01). The quadratic effect of refrigera-
tion time and the interaction effect of coconut milk percent-
age and refrigeration time affected the experimental results
less signicantly (p ≤ 0.05) (Table 3). The regression equa-
ment performed using a combination of the variables for
six response functions (yield, FFA, AV, POV, K232 and K270)
is shown in Table 1. The data were analyzed by multiple
regressions to fit the quadratic equations to the dependent
variables. Statistical analysis was performed using RSM soft-
ware, Design-Expert 8 (Stat-Ease Inc, Minneapolis, USA).
Analysis of variance (ANOVA) was performed to evaluate
the adequacy of the generated mathematical models. In this
study, Box-Behnken statistical screening design was used to
optimize and evaluate main effects, interaction effects, and
quadratic effects of variables.
The nonlinear computer-generated quadratic model is
given as:
Y = b0 + b1X1 + b2X2 + b3X3 + b12X1X2 + b13X1X3
+ b23X2X3 + b11X1
2 + b22X2
2 + b33X3
2
Where Y is the measured response associated with each fac-
tor level combination; b0 is an intercept; b1 to b33 are the re-
gression coefcients; and X1, X2, and X3 are the independent
variables.
Numerical optimization technique of the Design-Expert
software was used for simultaneous optimization of the
multiple responses. The desired goals for each variables and
response were chosen. All the independents variables were
kept within range while the responses were either maximized
or minimized.
Results and Discussion
Model tting The variation of the extraction yield, free
Table 1. Experimental design and analysis result in coded (x) and actual level (X) of variables.
Expt.
No.
% Coconut milk Fermentation time Refrigeration time
Yield
(%)
FFA
(%)
AV
(mgKOH/
g oil)
POV
(meq/
kg oil)
K232 K270
Coded
Level
x1
Actual
Level
(X1, %)
Coded
Level
x2
Actual
Level
(X2, h)
Coded
Level
x3
Actual
Level
(X3, h)
1 − 50.00 −1 12.00 0 24.00 24.14 0.50 0.95 0.50 1.13 0.030
2 1 50.00 0 24.00 −1 12.00 28.37 0.57 1.08 0.65 1.17 0.029
3 − 50.00 0 24.00 1 36.00 27.29 0.62 1.18 0.40 1.15 0.050
4 1 50.00 1 36.00 0 24.00 27.60 0.64 1.21 0.25 1.20 0.055
5 − 75.00 −1 12.00 −1 12.00 28.57 0.38 0.73 1.00 1.14 0.035
6 1 75.00 −1 12.00 1 36.00 28.70 0.52 0.98 0.40 1.10 0.038
7 − 75.00 0 24.00 0 24.00 29.70 0.45 0.86 0.55 1.15 0.034
8 1 75.00 0 24.00 0 24.00 29.30 0.47 0.89 0.60 1.14 0.034
9 0 75.00 0 24.00 0 24.00 29.16 0.47 0.89 0.60 1.14 0.035
10 0 75.00 0 24.00 0 24.00 29.89 0.45 0.86 0.60 1.14 0.038
11 0 75.00 0 24.00 0 24.00 29.85 0.43 0.82 0.65 1.15 0.036
12 0 75.00 1 36.00 −1 12.00 30.23 0.49 0.92 0.55 1.20 0.031
13 0 75.00 1 36.00 1 36.00 30.12 0.47 0.89 0.45 1.16 0.061
14 0 100.00 −1 12.00 0 24.00 25.75 0.55 1.05 0.50 1.13 0.028
15 0 100.00 0 24.00 −1 12.00 28.24 0.57 1.08 1.00 1.17 0.025
16 0 100.00 0 24.00 1 36.00 30.27 0.67 1.27 0.30 1.12 0.041
17 0 100.00 1 36.00 0 24.00 28.96 0.65 1.24 0.40 1.17 0.038
731

n. F. m. idrus
et al.
of water added for the extraction of oil. Che Man et al. (1997)
reported that adding less water contributed to a higher pro-
portion of oil, but if the coconut content is too high or water
content is too low, yield will be decreasing.
Generally, the longer fermentation time will give higher
yield. Waisundara et al. (2006) evaluated the effect of micro-
bial lipase that may have been produced due to the growth
of microorganism during storage at room temperature. The
enzymes will degrade the polysaccharides and improve the
yields. Suitable environmental condition such as acidic en-
vironment, room temperature and the presence of catalysts
(iron) will optimize the lipase reaction (Ibrahim, 1994). Pre-
cipitation of soluble protein in the interfacial lm occurred as
a result of lactic acid formation during fermentation. Lactic
acid destabilized the protein and caused water to be released
(Suhardiyono, 1992). Besides, the separation of a mixture of
tion for the response functions in the actual level of this vari-
able is:
Y1 = 10.8325 + 0.4315X1 + 0.47802X2
− 0.003064X1
2 − 0.007309X2
2 + 0.00609X3
2
The signicant quadratic effect of coconut milk percent-
age and fermentation time indicates that yield extraction
increased with the increase in coconut milk content and
fermentation time, and decreased at certain range (Fig 1).
For coconut milk percentage, increasing coconut content
or decreasing water content will increase the yield. As the
percentage of water added was increased, the oil content de-
creased. Higher proportions of water increased the dilution
effect and therefore decreased the oil yield in coconut cream
(Che Man et al., 1997). Banzon et al. (1990) found that the
composition of coconut cream is largely based on the amount
Table 3. Condensed ANOVA table for yield (Y1), FFA (Y2), AV (Y3), POV (Y4), K232 (Y5) and K270 (Y6).
Source of
variations
Y1Y2Y3Y4Y5Y6
Sum of
square p-value Sum of
square p-value Sum of
square p-value Sum of
square p-value Sum of
square p-value Sum of
square p-value
Model Quadratic Quadratic Quadratic Quadratic Quadratic Quadratic
Constant 41.8900 0.0008 0.1100 0.0007 0.3900 0.0007 0.6500 <0.0001 0.0100 <0.0001 0.0014 0.0002
X14.2300 0.0076 0.0015 0.2002 0.0061 0.1835 0.0200 0.0168 0.0003 0.0083 0.0001 0.0038
(F)X211.8800 0.0004 0.0110 0.0062 0.0380 0.0078 0.0700 0.0006 0.0062 <0.0001 0.0004 0.0002
(R)X30.1200 0.5565 0.0091 0.0104 0.0330 0.0111 0.3400 <0.0001 0.0026 <0.0001 0.0007 <0.0001
X1X20.0160 0.8284 0.0004 0.4907 0.0012 0.5279 0.0057 0.1419 0.0002 0.0369 0.0000 0.0260
X1X32.4200 0.0265 0.0006 0.3936 0.0020 0.4215 0.0510 0.0016 0.0004 0.0071 0.0000 0.3794
X2X30.0140 0.8351 0.0064 0.0227 0.0200 0.0326 0.0630 0.0009 0.0000 0.6361 0.0002 0.0015
X1
215.4400 0.0002 0.0790 <0.0001 0.2900 <0.0001 0.0420 0.0027 0.0004 0.0065 0.0000 0.3415
X2
24.6600 0.0060 0.0001 0.6808 0.0007 0.6546 0.0320 0.0054 0.0004 0.2385 0.0000 0.0254
X323.2400 0.0142 0.0012 0.2516 0.0033 0.3118 0.0320 0.0054 0.0000 0.9454 0.0000 0.1379
Lack of Fit 1.7200 0.0718 0.0042 0.0777 0.0160 0.0514 0.0094 0.1985 0.0000 0.7066 0.0000 0.0877
R20.951 0.954 0.959 0.979 0.983 0.967
Adj-R20.888 0.896 0.892 0.951 0.960 0.924
CV 1.940 5.250 5.300 8.200 0.440 7.100
Table 2. Sequential model sum of square of yield (Y1), FFA (Y2), AV (Y3), POV (Y4), K232 (Y5) and K270 (Y6).
Source Yield FFA AV POV K232 K270
Sum of
square
p-value
>F-value
Sum of
square
p-value
>F-value
Sum of
square
p-value
>F-value
Sum of
square
p-value
>F-value
Sum of
square
p-value
>F-value
Sum of
square
p-value
>F-value
Mean 13901.89 − 4.6600 − 16.8000 − 5.2000 − 22.51 − 0.0240 −
Linear 16.23 0.1027 0.0220 0.4199 0.0760 0.4294 0.4300 0.0030 0.0099 <0.0001 0.0011 0.0004
Interaction 2.45 0.8097 0.0073 0.8344 0.0230 0.8640 0.1200 0.0635 0.0005 0.1072 0.0002 0.0127
Quadratic 23.21 0.0004 0.0810 0.0001 0.2900 0.0001 0.1000 0.0014 0.0004 0.0174 0.0000 0.0602
Cubic 1.72 0.0718 0.0042 0.0777 0.0160 0.0514 0.0094 0.1985 0.0000 0.8395 0.0000 0.0877
Residual 0.44 − 0.0011 − 0.0033 − 0.0005 − 0.0001 − 0.0000 −
Total 13945.94 − 4.78 − 17.21 − 5.87 − 22.52 − 0.025 −
732

Optimization of the Aqueous Extraction of Virgin Coconut Oil
tions in the actual level of this variable are:
Y2 = 1.4 + 0.00142X2 + 0.000218X1
2
Y3 = 2.675 + 0.002577X2 + 0.0004168X1
2
FFA and AV are dependent on coconut milk percentage
and decreased with the increase in coconut milk percentage
but an inection point occurred at certain range (Fig 2). High
coconut milk content with low water content has a negative
effect to FFA and AV value until the inection point where
it becomes contrariwise. This is because hydrolysis liberates
the FFA from the parent oil and thus releasing the FFA that
are responsible for the rancid aroma (Freeland-Graves and
Peckham, 1996). Hydrolytic rancidity is a result of a break-
ing of the glyceride molecule at the ester linkage, forming
fatty acids which give out undesirable odor (Brien, 1998).
The presence of large amounts of water in the coconut milk
promotes faster microbial growth and the enzyme will react
more actively. The higher the moisture content in the oil
water and protein-fat will be more complete if the fermen-
tation time is longer, so the yield will be higher. However,
the lipase is impaired after the reaction reached a maximum
level (Frazier and Westhoff, 1994).
In this experiment, refrigeration was utilized to separate
the oily layer and non-oily layer of solution and the longer
refrigeration time decreased the yield (Fig 1). The decreasing
yield as the increase of refrigeration time currently correlated
with the percentage of coconut milk to water used. At higher
proportion of water used, the tendency of yield is decreasing
as the longer refrigeration time, vice versa, at lower propor-
tion of it the yield is increasing as the longer refrigeration
time.
FFA and AV From Table 3, the variation in fermenta-
tion time and the quadratic effect of coconut milk percentage
show the most signicant effect (p ≤ 0.01) on the acid value,
whereas the linear effect of refrigeration time and its interac-
tion with fermentation time were less signicant (p ≤ 0.05).
The regression equation for the FFA and AV response func-
Fig. 1. Left: Response surface for the effect of coconut milk percentage and fermentation time on the yield of VCO at constant refrigeration
time 24 h. Right: Response surface for the effect of refrigeration time and fermentation time on the yield of VCO at constant coconut milk
percentage 50%
Fig. 2. Left: Response surface for the effect of coconut milk percentage and refrigeration time on the free fatty acids of VCO at constant
fermentation time 24 h. Right: Response surface for the effect of coconut milk percentage and fermentation time on the acid value of VCO
at constant refrigeration time 24 h.
733

n. F. m. idrus
et al.
bination of high coconut milk percentage and longer time of
refrigeration produce lower POV; whereas the use of longer
fermentation time also could produce lower POV.
K232 and K270 specific extinction coefficients K232 is a
spectrophotometric measure of conjugated dienes, which is
used along with the peroxide index to evaluate the primary
oxidation of the oil. K270 is used to evaluate, spectrophoto-
metrically, the conjugated trienes and indicate the state of
secondary oxidation of the oil (Torres, 2006). The presence
and formation of conjugated dienes and trienes would pre-
dispose the oil for further oxidative changes (Subramanian et
al., 1998).
From Table 3, the K232 response analysis showed that it
was signicantly affected by fermentation time, its quadratic
effect, its interaction with refrigeration time, the variation in
refrigeration time and fermentation time (p ≤ 0.01). The in-
teraction effect of coconut milk percentage and fermentation
time affected the experimental results less signicantly (p ≤
0.05). The regression equation for the response functions in
the actual level of this variable is:
Y5 = 1.15013 − 0.001242X1 + 0.002687X2 + 0.0006187X3
− 0.00002166X1X2 – 0.00003166X1X3 + 0.00001508X1
2
Increase in coconut milk percentage increased the value
of K232 at short refrigeration time, but increased the value of
K232 at long refrigeration time (Fig 4). There are not so much
changes of K232 value at low refrigeration time compared to
high refrigeration time. Oxidation process increased with
increasing moisture content (Akinoso et al., 2010). At high
refrigeration time, increasing coconut milk percentage (de-
creasing moisture content) will decrease the value of K232. At
short refrigeration time, K232 did not change much with the
coconut milk percentage. This might be due to initial storage
in the refrigerator which the oxidation rate is still low.
The signicant linear effect of refrigeration time percent-
age indicates that K232 value decreased with the increase in
refrigeration time. Alonso et al. (2005) mentioned that lipid
oxidation occurs fairly slowly at low temperature. The effect
of decreasing in K232 value is more obvious if the refrigera-
tion time is longer. Due to the separation of water from but-
ter fat phase is more complete, therefore moisture content in
the oil is lower, giving lower oxidation rates. Theoretically,
coconut oil should show a low rate of oxidation because it
contains low levels of unsaturated fatty acids (Che Man,
1997).
As shown in Table 3, coconut milk percentage, refrigera-
tion time, fermentation time, interaction effect of refrigera-
tion time and coconut milk percentage signicantly affected
the response of K270 (p ≤ 0.01). The interaction effect of
coconut milk percentage between fermentation time, and
resulted in the higher percentage of FFA (Che Man et al.,
1997).
Increases in refrigeration time affect the increase in FFA
and AV (Fig 2). Cancel (1979) reported that the amount of
fatty acid produced will be decreased if higher temperature
involved in the extraction. Types and activities of enzymes
also will be affected, such as enzyme lipase which brings
about an increased release of fatty acids from oil during
processing at lower temperature (Chen and Diosady, 2003).
The FFA of the samples increased constantly with increase in
storage time. This might be due the production of microbial
lipase during storage (Waisundara et al., 2006).
The significant linear effect of fermentation time indi-
cates that FFA and AV increased with the increase in fermen-
tation time. Longer fermentation time gives higher FFA and
AV. The lipase released by the microorganism during fer-
mentation will catalyze the action of moisture to hydrolyze
the triglycerides (Brien, 1998).
POV POV generally indicates the extent to which an oil
sample has undergone primary oxidation (Raghavendra and
Raghavarao, 2011). In this experiment, the value varied from
0.25 − 1.00 meq/kg oil. This result still in agreement with
the result presented by Raghavendra and Raghavarao (2011)
which found that the peroxide value of VCO was around
0.81 ± 0.02, whereas commercial VCO was 0.94 ± 0.02. We
found that POV was significantly affected by almost all of
the linear, interaction and quadratic effect of coconut milk
percentage, fermentation time and refrigeration time (p <
0.05) except the interaction of coconut milk percentage and
fermentation time (p = 0.1419). The regression equation for
the response functions in the actual level of this variable is:
Y4 = 0.2 + 0.032X1 − 0.00885X2 − 0.039X3 − 0.000375X1X3
– 0.000868X2X3 − 0.00016X1
2 − 0.000607X2
2 − 0.000607X3
2
Coconut oil is mainly consisted by saturated fatty acid
that more resistant to oxidation due to it less reactivity
over influence of light, heat and oxidation (Francis, 1999).
Raghavendra and Raghavarao (2011) mentioned that virgin
coconut oil contain 43 − 53% lauric acid, 16 − 21% myristic
acid, 7.5 − 10% palmitic acid, 5 − 10% caprylic acid, and
4.5% − 5.8% capric acid as major constituent. In spontane-
ous oxidation, radical oxygen will attack the unsaturated
bonds in unsaturated fatty acid to produce hydroxyperoxide
(Ketaren, 1986). Raghavendra and Raghavarao (2011) also
mentioned that VCO was also consisted of 5 − 10% oleic
acid (C18:1) and 1.0 − 2.5% linoleic acid (C18:2) which sus-
ceptible to be oxidized. Moreover, during fermentation and
refrigeration in his experiment, the container containing a
mixture of coconut milk and water was left open-air and
unprotected from ambient light. According to Fig 3, the com-
734

Optimization of the Aqueous Extraction of Virgin Coconut Oil
tion and refrigeration, container containing mixture of coco-
nut milk and water is left open-air and is not wrapped with
aluminum foil. So with the presence of light, a significant
increase of K270 was noticed to be faster at the beginning and
then progressively decreased because of the small permeabil-
ity of the means (Vekiari et al., 2007). Caponio et al. (2005)
reported that oils kept under light contained products of sec-
ondary oxidation.
CIE- L*a*b* Color From the result in Table 4, hue
angle of all samples are in the range of 95 − 97°, which is the
area within 90° and 180° is represented by greenish-yellow
color. But due to low C* value, indicating low color inten-
sity, thus the color is clear or water-like color. Meanwhile,
hue angle of reference coconut oil was 109.69°, which is in-
dicated by light-greenish color. But for the same reason, low
C* value giving water-like color. The total color difference
(ΔE) values of all VCO samples were just in the range of 1.56
− 2.31, meaning that the color of the samples is quite similar
to the reference oils, consumers hardly differentiate them.
fermentation time between refrigeration time and quadratic
effect of fermentation time affected the experimental results
less signicantly (p ≤ 0.05). The regression equation for the
response functions in the actual level of this variable is:
Y6 = 0.024875 + 0.000558X1 – 0.00085X2 – 0.000808X3
− 0.0000125X1X2 + 0.00004687X2X3 + 0.00002552X2
2
K270 value is dependent on coconut milk percentage and
decrease with the increase in coconut milk percentage (Fig
4). The increase in oxidation ensured by the increase in K270
value due to increasing number of compounds resulting from
peroxide degradation (Vekiari et al., 2007). The result showed
that rate of secondary oxidation was too low. This can be
proven that virgin coconut oil produced is very stable against
oxidation due to the high degree of saturated fatty acid.
The signicant linear effect of refrigeration time shows
that K270 value increased with the increase in refrigeration
time (Fig 4). Increase in fermentation time also induced a
partially exponential increase in K270 value. During fermenta-
Fig. 3. Left: Response surface for the effect of coconut milk percentage and fermentation time on the peroxide value of VCO at constant re-
frigeration time 24 h. Right: Response surface for the effect of coconut milk percentage and refrigeration time on the peroxide value of VCO
at constant fermentation time 24 h.
Fig. 4. Left: Response surface for the effect of coconut milk percentage and fermentation time on the K232 of VCO at constant refrigeration
time 24 h. Right: Response surface for the effect of coconut milk percentage and refrigeration time on the K270 of VCO at constant refrigera-
tion time 24 h.
735

n. F. m. idrus
et al.
Acknowledgments We gratefully acknowledge the Fellowship
Scheme of the Institute of Postgraduate Studies, Universiti Sains
Malaysia (Grant No. 1001/PTEKIND/814141) and anonymous ref-
erees for comment and constructive advice in improving this manu-
script.
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Table 4. Experimental result for the response function for
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Expt.
No. L*a*b*C*Hue ∆E
1 98.92 −0.31 3.05 3.07 95.74 1.95
2 99.37 −0.29 2.85 2.86 95.81 2.28
3 99.12 −0.29 2.84 2.85 95.83 2.05
4 99.29 −0.30 2.82 2.84 96.07 2.20
5 99.45 −0.30 2.97 2.98 95.71 2.39
6 98.40 −0.30 3.11 3.13 95.50 1.56
7 99.02 −0.31 3.01 3.03 95.82 2.02
8 99.01 −0.31 3.06 3.08 95.78 2.03
9 99.00 −0.31 2.99 3.01 95.86 1.99
10 98.95 −0.31 2.96 2.98 95.88 1.94
11 99.01 −0.30 3.03 3.04 95.65 2.02
12 99.43 −0.30 2.99 3.00 95.67 2.38
13 99.10 −0.32 2.99 3.01 96.10 2.08
14 98.91 −0.30 3.05 3.06 95.56 1.94
15 99.40 −0.30 2.83 2.85 95.98 2.31
16 99.10 −0.30 2.84 2.85 95.94 2.03
17 99.16 −0.33 2.97 2.99 96.28 2.13
Ref. 99.74 −0.78 2.18 2.32 109.69 −
*Values are expressed as means at triplicate.
736

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