Journal of Software Engineering and Applications, 2011, 4, 259-267
doi:10.4236/jsea.2011.44029 Published Online April 2011 (http://www.SciRP.org/journal/jsea)
259
Modeling and Simulation of High Power
Ultrasonic Process in Preparation of Stable
Oil-in-Water Emulsion
Javad Sargolzaei, Mohamad Taghi Hamed Mosavian, Attieh Hassani
Department of Chemical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
Received March 12th, 2011; revised April 13th, 2011; accepted April 15th, 2011.
ABSTRACT
The aims of this research are to study application of high power ultrasound in preparation of stable oil-in-water emul-
sion. The effect of pH, ionic strength, pectin, Guar gum, lecithin, egg yolk, and xanthan gum as well as the time of so-
nication, temperature and viscosity of oil-water mixture on the sp ecific surface area and size o f droplets, and creaming
index of the emulsion samples was investigated. The experimental data were analyzed with Taguchi method and opti-
mum conditions were determined. In addition, an adaptive neuro-fuzzy inference system (ANFIS) was employed to
modeling and categorizes the properties of the resulted emulsion. The results showed that increasing sonication time
narrowed the range of droplets size distribution. Pectin and xanthan enhanced the stability of emulsion, although they
had different impacts on the emulsion stability when used individually or together. Guar gum improved the viscosity of
the continuous phase. Emulsions stabilized by egg yolk were found to be stable to droplet flocculation at pH 3 and at
relatively low salt concentrations.
Keywords: Ul tr asound Process, Stable Oil, Egg Yolk, Emulsion, ANFIS, FIS
1. Introduction
Several products in food industry are oil-in-water emul-
sions that contain an aqueous medium with uniformly
dispersed small lipid droplets such as ice cream, marga-
rine, butter, milk, beverages, cream, dressings, dips, sau-
ces, and desserts. [1-4]. These foodstuffs are thermody-
namically unstable. For that reason, it is of importance
for manufacturers to improve food emulsions stability for
having no or minimal changes in the structure or consis-
tency during storage. Size of droplets is the most impor-
tant parameter in emulsion stability. This has been stud-
ied for many years leading to development of new con-
cepts and technologies. It has long been known that ul-
trasound is capable of making fine emulsions [5-11]. The
disintegration effect of ultrasound is due to the bubbles
collapsing at the interface of two immiscible liquids dis-
rupting one phase into another.
In this work our goal was to investigate the effect of
pH, ionic strength, pectin, Guar gum, egg yolk, xanthan
gum as well as the time of sonication, viscosity of oil-
water mixture and temperature on the specific surface
area and size of droplets, and creaming index of the
emulsion samples and create a prediction model for the
emulsion properties by fuzzy modeling for employing in
food industry to produce emulsion with elevated qu ality.
1.1. Adaptive Neuro-Fuzzy Inference System
(ANFIS) Theory
Studies of fuzzy neural networks that combine both ad-
vantages of the fuzzy systems and the learning ability of
the neural networks have been carried out. These techni-
ques can alleviate the matter of fuzzy modeling by learn-
ing ability of neural networks and have been reported
since around the beginning of 1990s. Fuzzy neural net-
works can be applied not only for simple pattern classi-
fication but also for meaningful fuzzy if-then rules crea-
tion; therefore, they can be put into practice for various
applications. In the early stage of fuzzy neural network
researches, Lin et al. [12,13] proposed one of the current
prima models that decide the initial fuzzy model by Ko-
honen’s self-organizing algorithm [14] and carry out pa-
rameter adjustment by back propagation algorithm. Also
as a representative example, Jang et al. proposed ANFIS
[15] in 1993. ANFIS applies a neural network in deter-

Modeling and Simulation of High Power Ultrasonic Process in Preparation of Stable Oil-in-Water Emulsion 260
mination of the shape of membership functions and rule
extraction. However, since it needs to divide the input
data space in advance, accuracy of the system depends
much on the achievement of this pre-processing. Wang
reported an approach to acquire fuzzy rules by dividing
input space [16]. These techniques, however, do not con-
sider the output data space, so the obtained rules should
not be always reasonable. Since the architecture and be-
havior of ANFIS are very applicable [17], it has been
adopted as a basic component for interpretation resear-
ches [17,18].
Normalization of inputs leads to avoidance of numeri-
cal overflows due to very large or very small weights [2].
Therefore, data are normalized by the following rela-
tionship:

min
max min
1
N
UL L
VV
VVV

 
 (1)
Where VN is the normalized value of V, the Vmax and Vmin
are th e ma x i mu m a n d mi n i mu m v alues of V, respectively.
From experience, the authors have found that a better fit
will be achieved if U  and
L
 (small margins) are
kept at a value of 0.05 [19].
2. Materials and methods
2.1. Materials
Pectin extracted from citrus peel, Xanthan Gum extracted
from Xanthamonas campestris, Lecithin from soybean
and Guar gum were purchased from Sigma Chemical
Company, Germany.
Analytical grade sodium chloride (NaCl) and acetic
acid glacial (CH3COOH) was supplied from the Merck
Chemical Company, Germany.
Sunflower oil and egg were obtained from local mar-
ket. Deionized water was used for the preparation of all
solutions.
2.2. Preparation of Emulsion Mixture
Oil phase was prepared by dispersing different propor-
tions of lecithin or egg yolk, xanthan, pectin, guar gum
and NaCl in different amount of sun flower oil and mix-
ed for 30 minutes by a magnetic stirrer (Model VELP
SCIENTIFICA s.r.l, Europe). The oil phase mixture was
then mixed with distilled water adjusted to pH 3 or 4 by
glacial acetic acid. Table 1 shows proportion of each
constituent in emulsion mixture for various formulas
used in the experiment.
2.3. Ultrasonic emulsification
The aqueous and oil phase were premixed together in a
beaker using a magnetic stirrer. 5 ml aliquot of the mix-
ture was introduced into a round bottom glass. Sonication
was carried out using a Dr. Hielscher ultrasonic proces-
sor (Model UP 200 H, Germany), with the operating
frequency of 24 KHz and power output of 460 W. A ta-
pered titanium sonotrode of 3 mm in diameter was used
for sonication. The tip of sonotrode was placed 1 cm be-
low the surface of mixture. All samples were sonified in
0 and 10˚C for 60, 120, 180 and 240 seconds in thermos-
tated water bath. All sonicated experiments were con-
ducted in triplicates. One of the three samples was used
to measure creaming and the other two were used for
particle size measurement.
2.4. Particle Size Measurement
Drop size distributions of samples were measured imme-
diately after sonication and also after one month storage
at 4˚C using Fritsch laser diffraction analyzer (Model
Analysette 22, Germany).
2.5. Viscosity Measurement
Viscosity was measured using Brooke Field DV-II (at 80
RPM) at 27˚C. The types of spindles used were S62, S63
and S64.
2.6. Creaming Measurement
Ten ml of each emulsion were transferred into a test tube,
and then stored for 1month at refrigerator temperature
(4˚C). During storage a number of emulsions separated
into an optically opaque ‘cream’ layer at the top and a
transparent (or turbid) ‘serum’ layer at the bottom. Crea-
ming index was calculated by dividing the total height of
cream to the total height of the column of emulsion.
3. Statistical Analysis
Taguchi L32 orthogonal array design was used to inves-
tigate the effect of pH, sonication time, temperature and
concentration of lecithin, egg yolk, guar gum, xanthan,
pectin and sodium chloride on droplet size distribution,
creaming and viscosity of samples. Table 1 shows the
L32 orthogonal array design used in this study.
4. ANFIS Design
The ANFIS information errors used for process are
shown in Table 2. There are 0.5, 1.25, 0.5, 0.15 values
for ranges of influence, squash factor, accept ratio, and
reject ratio. Also the optimum method is back propaga-
tion. The best threshold of the generation ANFIS is 0.05
for Egg yolk and Lecithin process simulations.
ANFIS architecture for multi input and single output
(for example, viscosity) data is shown in Figure 1.
In this work, the applications of ANFIS for prediction
of viscosity, creaming, arithmetic mean diameter, and
specific surface area were tested at different operation
conditions. For each variable, the optimum values of

Modeling and Simulation of High Power Ultrasonic Process in Preparation of Stable Oil-in-Water Emulsion 261
Table 1. Taguchi L-32 orthogonal array (egg yolk).
Experiment number Factors
oil Egg yolk (gr) Xanthan (gr)Pectin (gr)Guargum (gr)NaCl (gr)Time (s) pH Temperature (˚C)
1 40 6 0.1 0.1 0.1 0.0 60 3 0
2 40 6 0.1 0.2 0.1 0.4 120 3 0
3 40 8 0.2 0.1 0.2 0.8 180 3 0
4 40 8 0.2 0.2 0.2 1.2 240 3 0
5 40 10 0.3 0.4 0.4 0.0 180 3 0
6 40 10 0.3 0.3 0.4 0.4 240 3 0
7 40 12 0.4 0.4 0.3 0.8 60 3 0
8 40 12 0.4 0.3 0.3 1.2 120 3 0
9 50 6 0.2 0.3 0.4 0.8 60 3 10
10 50 6 0.2 0.4 0.4 1.2 120 3 10
11 50 8 0.1 0.3 0.3 0.0 180 3 10
12 50 8 0.1 0.4 0.3 0.4 240 3 10
13 50 10 0.4 0.2 0.1 0.8 180 3 10
14 50 10 0.4 0.1 0.1 1.2 240 3 10
15 50 12 0.3 0.2 0.2 0.0 60 3 10
16 50 12 0.3 0.1 0.2 0.4 120 3 10
17 60 6 0.3 0.1 0.3 0.8 240 4 0
18 60 6 0.3 0.2 0.3 1.2 180 4 0
19 60 8 0.4 0.1 0.4 0.0 120 4 0
20 60 8 0.4 0.2 0.4 0.4 60 4 0
21 60 10 0.1 0.4 0.2 0.8 120 4 0
22 60 10 0.1 0.3 0.2 1.2 60 4 0
23 60 12 0.2 0.4 0.1 0.0 240 4 0
24 60 12 0.2 0.3 0.1 0.4 180 4 0
25 80 6 0.4 0.3 0.2 0.0 240 4 10
26 80 6 0.4 0.4 0.2 0.4 180 4 10
27 80 8 0.3 0.3 0.1 0.8 120 4 10
28 80 8 0.3 0.3 0.1 1.2 60 4 10
29 80 10 0.2 0.2 0.3 0.0 120 4 10
30 80 10 0.2 0.1 0.3 0.4 60 4 10
31 80 12 0.1 0.2 0.4 0.8 240 4 10
32 80 12 0.1 0.1 0.4 1.2 180 4 10

Modeling and Simulation of High Power Ultrasonic Process in Preparation of Stable Oil-in-Water Emulsion 262
Figure 1. ANFIS Architecture of a multi-input-single-output with 30 rules in Egg yolk process.
Table 2. ANFIS information for design of network in Egg
yolk with viscosity output.
Number of nodes: 612
Number of linear parameters: 300
Number of nonlinear par am eters: 540
Total number of parameter s : 84 0
Number of training data pairs: 49
Number of checking data pairs : 15
Number of f uzzy rules: 30
epoch (N optimum 100 000) were attained by minimiza-
tion of root mean square error (RMSE) and SSE. It can
be concluded that there is an optimum modeling using
test data. Since it provides the minimum degrees of free-
dom sustained by testing data points. Figure 2 shows the
best ANFIS is with 100 000 epochs. For all output values,
ANFIS is run with this epoch number. Figure 3 shows
training data for viscosity of egg.
5. Results and discussion
5.1. Droplet Size Distribution
Figure 4 graphically show FIS outputs of experimental
factors on arithmetic mean diameter. It is quite obvious
that continuing from upper to lower levels of some factors
(oil, pectin, guar gum, time, pH, temperature) the drop
Figure 2. ANFIS training RMSE for viscosity of Egg yolk
with variables input data.
Figure 3. Shows training RMSE achieve with ANFIS for
viscosity of egg.

Modeling and Simulation of High Power Ultrasonic Process in Preparation of Stable Oil-in-Water Emulsion 263
lets’ diameter decreases. Regarding some factors (egg,
xanthan, NaCl), by going from upper to lower levels,
droplets’ diameter increases. This figu re shows the high-
er level of oil is better for forming small mean diameter.
If emulsion contains small amount of oil, aggregation
phenomena will happen. In this case, other ingredient of
emulsion such as emulsifiers and surfactant close each
other and we have a result of particles with large mean
diameter, which is not a desirable result. Oil droplet cau-
sed distance between particles and prevented flocculation
and had the strongest effect on specific surface area after
one month. Also higher level of pH is better for smaller
mean diameter after sonication and after 1 month. Time
of sonication had strongest effect on specific surface area
in fresh emulsion. The lower level of egg yolk is better
for a smaller mean diameter as an emulsifier in conjunc-
tion with surfactant such as xanthan. When the concen-
tration of egg yolk and xanthan increased the protein of
egg yolk started aggregate. Therefore according to Fig-
ure 4 small amount of egg yolk is better for a minimum
particle size.
We observed protein denaturation at 10˚C in some
sample because proteins in eggs will unfold and aggre-
gate on heating above th eir thermal denaturation temper-
ature, which influences the stability and rheological pro-
perties of emulsions which is also observed by other re-
searchers [20-22].
Emulsions stabilized by egg yolk were found to be
stable to droplet flocculation at pH 3 at relatively low salt
concentrations, but unstable to flocculation at pH 3 at
high salt concentration s. A great d eal of time is n ecessary
for breaking bridging bound that may happen during so-
nication.
Figures 4 and 5 also shows that the effect of factors on
mean diameter after one month matched earned data for
fresh emulsion. The higher level of oil, guar gum, pH,
time and temperature and the lower level of NaCl and
xanthan increased the specific surface area (Figure 5).
5.2. Viscosity
Acoording to Observation that obtained from FIS, we
found that the viscoelasticity of emulsion was the highest
at pH 3. The addition of medium level NaCl can also
improve the characteristics of the emulsion, but too much
NaCl may cause the egg yolk proteins to aggregate in the
aqueous phase of the emulsion rather than forming a
coating on the oil droplets [23].
In Figure 6, it was shown that the magnitude of the
viscosity increases with the increase of xanthan concen-
tration due to the interaction between emulsion droplets
and xanthan gum. The viscoelasticity an d stability again-
st creaming of the emulsion should be at highest when
made with a high level of egg yolk because in this way
the egg yolk elements form a strong film around the lipid
droplets to prevent coalescence.
5.3. Creaming Rate
Measurements of creaming index and observation of
phase separation of the emulsions indicated that they
were stable at a low level of pH because of adequate
electro static repulsion between droplets to prevail over
the attractive droplet-droplet interactions. Higher level of
sonication time resulted in smaller droplets, higher vis-
cosity and enhanced stability against creaming.
According to FIS outputs (Figure 7), using a small
amount of xanthan and medium level of egg yolk in-
creased the stability of emulsion against creamin g as well
as their structure by the formation of aggregates of larger
Figure 4. Arithmetic mean diameter after sonication and after 1 month for 32 samples.

Modeling and Simulation of High Power Ultrasonic Process in Preparation of Stable Oil-in-Water Emulsion 264
Figure 5. Specific surface area after sonication and after 1month for 32 samples.
Figure 6. Viscosity of 32 emulsion formulations.
Figure 7. Creaming of 32 emulsions formulations.

Modeling and Simulation of High Power Ultrasonic Process in Preparation of Stable Oil-in-Water Emulsion 265
size.
5.4. ANFIS Modeling Results
The output values of the model are classified into two
groups. The first group shows the predicted values when
using input patterns belonging to train the ANFIS net-
work, that is, near the training data set. These results al-
low checking the effectiveness of the model closer to th e
data set used for model. The second group represents the
predicted values that do not belong to the training data
set. These values will allow testing the model.
Figure 8 illustrates the best recall performances of
ANFIS, which shows that the system is well-trained to
model the actual viscosity, creaming, arithmetic mean
diameter, and specific surface area.
Evidently, all plots generated by ANFIS pass through
each and every training data point. All epochs in model-
ing are 100 000 .
It is studies that the Hybrid Learning approach is sup-
posed to converge better and faster than BP approach.
The results showed that there is an excellent agreement
between the ANFIS with desired data. Dynamic model-
ing of producing stable emulsion process performance is
very important for designing and better understanding of
the present process. In this paper, ANFIS was applied to
compare results. ANFIS approximation is able to accu-
rately capture the non-linear dynamics of conditions that
have not been used in the training process (testing data).
The results showed that there is an excellent agreement
between the checked data (not used in training) and mo-
deling data, with average errors very low. As shown in
Figure 9, there is agreement between the output ANFIS
behavior with testing data. Also these figure shows that
the ANFIS predicted values are a close match of the ac-
tual ones.
During the ANFIS training, the training set up foresaw
the analytical forms of prod and probor operators for the
connectors AND and OR, respectively, the min for the
IF-THEN implication, the max for the ELSE aggr egation,
and the defuzzification method Wtaver produced the
crisp output [24]. The whole procedure was implemented
on a Pentium IV 330 MHz, using Matlab 7.0 (Mathworks
Inc.). The ANFIS is the best manufacturer of FIS. Note
that the basic fuzzy inference system (FIS) can take ei-
ther fuzzy inputs or crisp inputs (which are viewed as
fuzzy singletons), but outputs it produces are almost al-
ways fuzzy sets. Sometimes it is necessary to have a
crisp output, especially in a situatio n where a fuzzy infe-
Figure 8. Comparison between values of desired and the best ANFIS for Egg yolk with training data.

Modeling and Simulation of High Power Ultrasonic Process in Preparation of Stable Oil-in-Water Emulsion 266
Figure 9. Comparison between values of desired and ANFIS (hybrid method) pr edicted of producing stable emulsion pr ocess
for viscosity, Creaming, arithmetic mean diameter, and specific surface area.
rence s ys te m is u s ed a s a c on tr o ller . Th e r efor e, we n e ed a
method of defuzzification to extract a crisp value that best
represents a fuzzy set. Defuzzification refers to the way a
crisp value is extracted from a fuzzy set as a representa-
tive value. In general, there are several methods for defu-
zzifying a fuzzy set: Centroid of area (COA), Bisector of
area (BOA), Mean of maximum (MOM), Smallest of
maximum (SOM), Largest of maximum (LOM), and
weighted average (Wtaver). These defuzzification opera-
tions are not easily subject to rigorous mathematical ana-
lysis, so most of the studies are based on experimental
results [24]. Since each rule has a crisp output, the over-
all output is obtained via weighted average (Wtaver), thus
avoiding the time consuming process of defuzzification
required in a Mamdani model. In practice, the Wtaver
operator is sometimes replaced with the weighted sum
(Wsum) operator to reduce computation further, especial-
ly in the training of a fuzzy inference system.
6. Conclusions
The diameter, specific surface area of droplet, viscosity
and creaming index of emulsion are strongly affected by
the amount of oil. lower level of oil is better for stability
and viscosity of emulsion. The existence of guar gum had
supportive effect on oil behavior and contributes desira-
ble texture. Xanthan was found to have synergic effect
on pectin in stabilizing the emulsion especially at pH 4
for specific surface area and mean diameter and pH 3 for
viscosity and creaming index. The presence of salt was
shown to have positive effect on the viscosity and stabil-
ity through lowering creaming index. Using high tem-
perature was good for mean and specific surface area and
low temperature preferable for smaller creaming index.
High level of sonication time has a preference for egg
yolk stabilized emulsion. The results of Particle size
measurement showed larger specific surface area and
smaller arithmetic mean diameter after 1 month. High

Modeling and Simulation of High Power Ultrasonic Process in Preparation of Stable Oil-in-Water Emulsion 267
level of sonication time has a preference for egg yolk
stabilized emulsion.
The current study proves that ANFIS is a technique
that can be used capably to predict the food properties. It
is consider that this technique can be useful to predict
many other peroperties and parameters in food industry.
7. Acknowledgments
The authors would like to thank the Ferdowsi University
of Mashhad for supporting this research (P. No. 9085, 15
November 200 9) .
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