No Job NameOPTIMIZATION OF PRE-FRY MICROWAVE DRYING OF FRENCH FRIES
USING RESPONSE SURFACE METHODOLOGY AND GENETIC ALGORITHMS M.
HASHEMI SHAHRAKI1, A.M. ZIAIIFAR, S.M. KASHANINEJAD1 and M.
GHORBANI
Department of Food Science and Technology, Gorgan University of
Agricultural Sciences and Natural Resources, Gorgan, Iran
1Corresponding author. TEL: +98-1321295; FAX: +98-0171-4426432;
EMAIL:
[email protected]
Accepted for Publication July 16, 2012
doi:10.1111/jfpp.12001
ABSTRACT
In this study, microwave pretreatment and frying conditions
optimized with respect to quality attributes (moisture content, oil
content, texture and color parameters) and tried to investigate the
efficiency of genetic algorithms (GA) for improving response
surface methodology (RSM) models. RSM technique was used to develop
models to respond to the microwave power (180, 360, 540 W),
microwave time (2, 3, 4 min), frying temperature (140, 160, 180C)
and frying time (2, 5, 8 min). Microwave pretreatment had a
significant effect on the oil and mois- ture contents, maximum
force of French fries. GA was used as optimization coef- ficients
of obtained models from RSM. It was revealed that GA-optimized
models have better fitness (but no significance) with the
experimental results than RSM models. The optimum pre-fry drying
condition observed was microwave pre-frying at 400–500 W for 3–4
min and frying at 180C for 6–6.5 min.
PRACTICAL APPLICATIONS
The optimum pre-fry drying condition observed was microwave
pre-frying at 400–500 W for 3–4 min and frying at 180C for 6–6.5
min.
INTRODUCTION
Deep-fat frying is a process of simultaneous heat and mass
transfer. Heat is transferred from the oil to the food, which
results in the evaporation of water from the food and absorption of
oil by the food (Krokida et al. 2000a,b). Deep frying is widely
used in an industrial as well as institutional preparation of foods
because the consumers prefer the taste, appearance and texture of
fried food products (Rimac- Brncic et al. 2004).
Texture, color and oil content are the main quality parameters of
fried products. A good-quality fried product from tubers must have
a crispy crust and a golden yellow color, which is the result of
Maillard reaction that depends on the content of superficial
reducing sugars, temperature and time of frying. Oil content has
been a main concern for food processors from an economic point of
view and also from the health aspect for the consumers (Olajide
Sobukola et al. 2009). Many factors were reported in literature as
important in oil uptake (Mellema 2003; Ziaiifar et al. 2008). Oil
absorption decreases in final product with increasing initial solid
content of tubers (Gamble et al. 1987b). So, by
reducing the initial water content of tubers with pre-drying, oil
absorption can be reduced (Gupta et al. 2000; Ngadi et al.
2009).
Gupta et al. (2000) investigated the effect of pre-fry drying
duration on the kinetics of moisture removal and oil uptake. Ngadi
et al. (2009) showed that pretreatment with microwave could
decrease the initial moisture content in the product and make less
amount of free moisture available for removal during frying
pretreatment with microwave had a significant influence on moisture
loss and oil uptake in the nuggets during deep-fat frying. In
general, the longer the time of pretreatment with the microwave,
the lower the average moisture and oil content in the
product.
Drying pretreatment has a significant effect on all quality
parameters of French fries like color changes (Krokida et al.
2001a) and textural property as a multi-parameter attribute usually
associated with mechanical, geometrical and acous- tic parameters
(Olajide Sobukola et al. 2009).
Response surface methodology (RSM) is a statistical method for
determining and simultaneously solving multi- variate equations. It
usually uses an experimental design such as central composite
rotatable design to fit a first- or
Journal of Food Processing and Preservation ISSN 1745-4549
1Journal of Food Processing and Preservation •• (2012) ••–•• © 2012
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second-order polynomial by a least significance technique. An
equation is used to describe how the test variables affect the
response, and to determine the interrelationship among the test
variables in the response. The contour plots can be used to study
the response surfaces and locate the optimal parameters (Olajide
Sobukola et al. 2009).
Genetic algorithms (GA) are powerful optimization tech- niques
based on the methods of evolution (Gen and Cheng 1997). GAs solve
optimization problems by simulating the biological evolutionary
process. GA optimization includes the generation of possible
solutions, application of selec- tion, crossover and mutation
operations, and evaluation of each solution over an objective
function (fitness function) until a certain stopping criterion is
met. If the search termi- nation criterion is not met, the GA
applies the selection, crossover and mutation operations repeatedly
to the current population, evaluates the fitness of each possible
solution and reproduces a new population. The same cycle continues
until the termination criterion is met.
The advantages for capability of GAs to solve complex problems are
that (1) coding of the parameter set, not the parameters
themselves; (2) working with population of points, not a single
point; (3) using objective function infor- mation, not derivatives
or the other auxiliary knowledge; and (4) using probabilistic
transition rules, not determinis- tic rules (Haupt and Haupt 1998;
Goldberg 2001). GAs are powerful and broadly applicable stochastic
search and opti- mization techniques that really work for many
problems that are very difficult to solve by conventional
techniques. Most engineering problems are optimization problems
subject to complex constraints (Holland 1992).
The capability of GAs to solve complex problems suggests that they
are valuable tools for food processing systems. Some work has been
done on determining the thermal dete- rioration of vitamin C in
bio-product processing like steril- ization, concentration, drying
etc.; optimal conditions for spray-dried whole milk powder
processing; temperature control strategy of a fed-batch reactor;
and semi-real-time optimization and control of fed-batch
fermentation system; optimization of extrusion process variables
(Kaminnski et al. 1996; Koc et al. 1999; Zuo and Wu 2000; Jaya
Shankar and Bandyopadhyay 2004).
GAs also have several limitations as follows: (1) GA is defining a
representation for the problem. The language used to specify
candidate solutions must be robust (Marczyk 2004). (2) The problem
of how to write the fitness function must be carefully considered
so that higher fitness is attainable and actually does equate to a
better solution for the given problem. (Devillers 1996; Marczyk
2004). (3) One type of problem that GAs have difficulty dealing
with are problems with “deceptive” fitness functions (Mitchell
1996), those where the locations of improved
points give misleading information about where the global optimum
is likely to be found. (4) One well-known problem that can occur
with a GA is known as premature convergence. If an individual that
is more fit than most of its competitors emerges early on in the
course of the run. (Forrest 1993). (5) Maintaining a population of
genetic structures leads to an increase in execution time, because
of the number of times the objective function must be evaluated
(Allen and Karjalainen 1999). In recent studies, the efficiency of
GA combined with RSM to optimize the process has not been studied;
hence, the objective of this work was to optimize the pre-frying
micro- wave drying and frying conditions of French fries with
respect to quality attributes like moisture content, oil content,
color and texture parameters, and try to investigate for efficiency
of GAs for improvement the RSM models.
MATERIALS AND METHODS
Materials
Potatoes (Ageria variety) were purchased from a local market in
Gorgan. They were stored in darkness at 8C at the research
facilities until processing. Special frying oil (Bahar Co., Ltd.,
Tehran, Iran) was the frying medium.
Processing Methods
Microwave Operation. Microwave pretreatment was performed using a
domestic microwave system (MC-2003 TR; LG Co., Ltd., Yeouido,
Korea). The microwave operation was done in 180, 360 and 540 Watt
for 2, 3 and 4 min according to RSM design.
Frying Operation. Frying was performed in a fryer (Uroumax, Co.,
Ltd., Beijing, China) filled with about 3 liter of frying oil. A
temperature controller (309100D; Graco, Co., Ltd., Minneapolis, MN)
was applied in order to control the operating temperature. The
frying experiments were done in 140, 160 and 180C for 2, 5 and 8
min according to RSM design. After frying, potato strips drained
until exces- sive oils were separated.
Moisture content was determined by drying the samples to constant
weight at 105 1C (AOAC 1995). Oil content of fried potato strips
was determined by Soxhlet method using a solvent (ether petroleum)
extractor (AOAC 1990).
Analysis
Texture. The textural analysis of the French fries were per- formed
using a TA.XT2i (Stable Microsystems, Godalming,
OPTIMIZATION OF PRE-FRY MICROWAVE DRYING M. HASHEMI SHAHRAKI ET
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U.K.) that interfaced with a data processor Texture Expert version
1.0 (Systems 1995). The probe is especially designed for French
fries, and measures the resistance to penetration on 10 strips
simultaneously. Each strip is punctured with two probes (2 mm in
diameter) at 2 cm from each end. A crosshead speed of 1.7 mm/s and
a probe depth of 15 mm were selected, and the measurements were
carried out on the strips after frying. Four parameters were chosen
as texture indicators: the hardness to penetrate the upper crust
(F1), the force to go through the lower crust (F2), the initial
slope (S), which represents rigidity, and the area under the curve
(A), which represents the work to penetrate both crusts plus the
internal compression (Bunger et al. 2003).
Color. Colorimeter Development. The color of produced French fries
was measured usoffing a image processing method, for this purpose a
colorimeter machine was devel- oped as follows.
A digital camera with a resolution of 3,000 ¥ 4,000 pixels, which
is equivalent to 12 megapixel was adjusted on 35 cm on top of the
samples in a box (with 60 ¥ 60 cm length and width) which all of
its inside walls were covered with dark cloth for prevent light
scattering. For sample capturing, the lightness of box inside were
adjusted to 6,500°k by use of four fluorescent lamps (60 cm in
length with 18 W power). The standard illuminant in capturing
medium was adjusted by a Color temperature meter (KCM-3100, Kenko,
Tokyo, Japan). The angle between the camera lens axis and the
lighting sources was around 45°. The setting of the camera is shown
in Table 1. The obtained pictures were directly transferred to a
Pentium IV computer and were saved in JPEG format without
compression.
Image Processing Method. Image processing was done using Image J
(Ver.1.44 Trial; Wayne Rasband, National Institutes of Health,
Bethesda, MD) software as follow: (1) The noise of captured picture
was reduced by use of Noise Despeckle of Process menu. (2) The
color space of pictures was converted from RGB to CIELab by use of
Converter Space Color of Plugins menu under 6,500 k illumination.
(3) For each of color parameters (L*a*b*) the software gives
separate pictures and by use of Measure Stack from
Stacks menu the minimum, maximum and mean of each color parameters
of samples can be obtained from the Results window. The schematic
of the colorimeter machine was showed in Fig. 1. (4) Calibration of
colorimeter performance: for this purpose 24 colorful tiles with
qualified specification was used and the value of color parameters
obtained from colorful tiles bye colorimeter machine were fitted
against the standard color value of colorful tiles.
Optimization Procedure Using RSM
RSM was used to investigate the main effects of process variables
on the oil content (Y1), moisture content (Y2), lightness (Y3),
redness (Y4), yellowness (Y5) and maximum force (Y6), during the
microwave pre-frying drying and frying of French fries. Microwave
power (X1), microwave time (X2), frying temperature (X3) and frying
time (X4) (Table 2) were selected as independent variables. Process
variable ranges were determined by means of preliminary experiments
(Allen and Karjalainen 1999). Three levels of each of the
independent variable were chosen for the study; thus, 30
combinations including six replicates of the center point were
performed in random order, based on a central composite
experimental design for four factors.
Mathematical models were evaluated for each response by means of
multiple regression analysis. The modeling was started with a
quadratic model including linear, squared and interaction terms.
Significant terms in the model for each response were found by
analysis of variance (ANOVA)
TABLE 1. SETTING OF THE CAMERA
Flash Off Zoom On ISO velocity 100 White balance Fluorescence H
Aperture AV F/2.6 Macro On Shutter speed 1/10 s
ISO, International Standards Organization.
FIG. 1. THE GENERAL METHODOLOGY TO CONVERT RGB IMAGES INTO L*A*B*
UNITS
TABLE 2. CODED INDEPENDENT VARIABLES IN THE PROCESS
Factor Name Unit Min Mean Max
X1 Microwave power Watt 180 360 540 X2 Microwave time min 2 3 4 X3
Frying temperature C 140 160 180 X4 Frying time min 2 5 8
M. HASHEMI SHAHRAKI ET AL. OPTIMIZATION OF PRE-FRY MICROWAVE
DRYING
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and significance was judged by the F-statistic calculated from the
data (Mitchell 1996).
During optimization of condition of microwave and frying processes,
several response variables describing the quality characteristics
and performance measures of the systems are usually to be
optimized. Some of these variables are to be maximized while some
are to be minimized. In many cases, these responses are competing,
i.e., improving one response may have an opposite effect on another
one, which further complicates the situation. Several approaches
have been used to handle this problem. One approach uses a
constrained optimization procedure, the second is to super- impose
the contour diagrams of the different response vari- ables, and the
third approach is to solve the problem of multiple responses
through the use of a desirability function combining all responses
into one measurement (Mitchell 1996).
Desirability is an objective function that ranges from zero outside
of the limits to one at the goal. The numerical opti- mization
finds a point that maximizes the desirability func- tion. The
characteristics of a goal may be altered by adjusting the weight or
importance. For several responses and factors, all goals get
combined into one desirability function. The desirability value is
completely dependent on how closely the lower and upper limits are
set relative to the actual optimum. The goal of optimization is to
find a good set of conditions that will meet all the goals, not to
get to a desirability value of 1.0. Desirability is simply a
mathemati- cal method to find the optimum (Forrest 1993).
Myers and Montgomery (1995) describe a multiple response method
called desirability. The method makes use of an objective function,
D(X), called the desirability func- tion. It reflects the desirable
ranges for each response (di). The desirable ranges are from zero
to one (least to most desirable, respectively). The simultaneous
objective function is a geometric mean of all transformed responses
(Eq. 1):
D d d dn n= × × ×( )1 2 1
… (1)
Where n is the number of responses in the measure. If any of the
responses or factors falls outside their desirability range, the
overall function becomes zero.
The RSM was applied to the experimental data using a commercial
statistical package, Design-Expert version 8.0.6 Trial (Statease
Inc., Minneapolis, MN).
RESULTS AND DISCUSSION
Moisture and Oil Content
As expected, Fig. 2A–F shows that increase in microwave power,
microwave time, frying temperature and time decrease the moisture
content of French fries. All of four
factors had a significant effect (P < 0.05) on the moisture
content of French fries. As shown in Table 3 (ANOVA results)
microwave power was most effective factor in mois- ture content. A
quadratic model (R2 = 95.54) described the effect of tested factors
(microwave power, microwave time, frying time and frying
temperature) and their interaction on moisture content. The model
and their coefficients showed in Eq. (2). As the temperature of
frying increases, moisture content decreases and solid content
increases resulting in lower oil content. Similar results have been
reported (Gamble et al. 1987b; Gupta et al. 2000; Krokida et al.
2001a; Olajide Sobukola et al. 2009).
Moisture content X X X
= + ( ) − ( ) −
25 43514 0 025318 1 2 04125 2 0 1008
. . . . 33 0 781795 0 00152 1 2
1 56 06 1 3 0 00025 ( ) + ( ) − ( )( )
+ ( )( ) − . .
X X
0 00257 3 4
0 000293 3 0 05308 4
2 2
2 2
( ) + ( ) + ( ) − ( ) (2)
where X1, microwave power; X2, microwave time; X3, frying temp; X4,
frying time.
Variations of oil content of French fries under effect of different
studied factors were shown in Fig. 3A–F. With increase in microwave
power, microwave time the initial moisture of potato decreased
therefore led to decrease oil content in final product (Ngadi et
al. 2009). Increase in frying temperature and decrease frying time
had a signifi- cant effect on the oil content of French fries (P
< 0.05). The ANOVA results showed in Table 3. As shown in Table
3 microwave power was most effective factor in oil content and
frying time had least effect on oil content of French fries. A
quadratic model (R2 = 95.51) described the effect of factors and
their interaction on oil content. The model and their coefficients
showed in Eq. (3).
Oil content X X X3
= + ( ) + ( ) + ( ) +
2 12587 0 002721 1 2 087332 2 0 13114
. . . . 00 488107 4 0 00254 1 2
3 6 05 1 3 1 39 05 1 . .
. . X X X
X X
0 00077 3 4 4 8
( ) + ( )( ) − ( )( ) − ( )( ) +
. .
2 2
2 2
. . (3)
where X1 is the microwave power; X2 is the microwave time; X3 is
the frying temp; and X4 is the frying time.
Color Changes in French Fries
The color of fried products is one of the most significant quality
factors of acceptance. As shown in Fig. 4A–F, with the increase in
microwave power, microwave time, frying temperature and time, the
L* value of French fries decreases. The effect of frying time and
temperature was more than the effect of microwave power and time.
As
OPTIMIZATION OF PRE-FRY MICROWAVE DRYING M. HASHEMI SHAHRAKI ET
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FIG. 2. VARIATION OF MOISTURE CONTENT AGAINST DIFFERENT STUDIED
FACTOR (A) Interaction effect of microwave power (W) and microwave
time (min) on Moisture content. (B) Interaction effect of Microwave
power (W) and frying temperature (°C) on Moisture content. (C)
Interaction effect of microwave power (W) and frying time (min) on
Moisture content. (D) Interaction effect of microwave time (min)
and frying temperature (°C) on Moisture content. (E) Interaction
effect of microwave time (min) and frying time (min) on Moisture
content. (F) Interaction effect of frying time (min) and frying
temperature (°C) on Moisture content of produced French fries,
other variables are constant at mean values.
M. HASHEMI SHAHRAKI ET AL. OPTIMIZATION OF PRE-FRY MICROWAVE
DRYING
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TA B
LE 3.
A N
A LY
SI S
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VA RI
A N
C EF
O R
RE SP
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SE SU
RF A
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Q U
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EL FO
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OPTIMIZATION OF PRE-FRY MICROWAVE DRYING M. HASHEMI SHAHRAKI ET
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6 Journal of Food Processing and Preservation •• (2012) ••–•• ©
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FIG. 3. VARIATION OF OIL CONTENT AGAINST DIFFERENT STUDIED FACTOR
(A) Interaction effect of microwave power (W) and microwave time
(min) on oil content. (B) Interaction effect of microwave power (W)
and frying temperature (°C) on oil content. (C) Interaction effect
of microwave power (W) and frying time (min) on oil content. (D)
Interaction effect of microwave time (min) and frying temperature
(°C) on oil content. (E) Interaction effect of microwave time (min)
and frying time (min) on oil content. (F) Interaction effect of
frying time (min) and frying temperature (°C) on oil content of
produced French fries, other variables are constant at mean
values.
M. HASHEMI SHAHRAKI ET AL. OPTIMIZATION OF PRE-FRY MICROWAVE
DRYING
7Journal of Food Processing and Preservation •• (2012) ••–•• © 2012
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FIG. 4. VARIATION OF LIGHTNESS AGAINST DIFFERENT STUDIED FACTOR (A)
Interaction effect of microwave power (W) and microwave time (min)
on Lightness. (B) Interaction effect of microwave power (W) and
frying temperature (°C) on Lightness. (C) Interaction effect of
microwave power (W) and frying time (min) on Lightness. (D)
Interaction effect of microwave time (min) and frying temperature
(°C) on Lightness. (E) Interaction effect of microwave time (min)
and frying time (min) on Lightness. (F) Interaction effect of
frying time (min) and frying temperature (°C) on Lightness of
produced French fries, other variables are constant at mean
values.
OPTIMIZATION OF PRE-FRY MICROWAVE DRYING M. HASHEMI SHAHRAKI ET
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shown in Table 4, frying temperature was the most effective factor
in L* value variation. A linear model (R2 = 95.19) described the
effect of factors and their interaction with the L* value. The
model was shown in Eq. (4). Luminosity color component (L*)
decreases with increasing frying tempera- ture and time because the
potato slices get darker. The higher the frying temperature, the
darker the potato slices because nonenzymatic browning reactions
are highly tem- perature dependent. A similar trend for L* value
for frying of potato strips and potato slices has been found
(Bunger et al. 2003).
The chromatic color component a* value increases with frying time
and frying temperature as a result of the forma- tion of compounds
from the Maillard non-enzymatic reac- tion (Fig. 5A–F). The
chromatic component b* increases with frying time and shows the
same trend of a*; their values tend to increase faster as the
frying temperature increases. These results suggest that the
redness and yellow- ness of potato slices increases during frying,
and are coinci- dent with those obtained by other researchers for
potato chips and French fries (Krokida et al. 2001b; Pedreschi et
al. 2007; Olajide Sobukola et al. 2009). Changes in a* followed
from the linear model (R2 = 98.94) that with increase in frying
temperature and time increase a* value of French fries (Eq.
5).
As shown in Fig. 6A–F with increase in frying tempera- ture and
time, the b* value of French fries increases. The linear model (R2
= 99.66) describe the effect of factors and their interaction on b*
value (Eq. 6). The ANOVA results for a* and b* are shown in Table
4.
Lightness X X X
= − ( ) − ( ) − ( ) − 67 23141 0 00045 1 0 07722 2
0 03231 3 0 1 . . . . . 22426 4X( ) (4)
where X1 is the microwave power; X2 is the microwave time; X3 is
the frying temp; and X4 is the frying time.
Redness E- X X X
= + ( ) + ( ) + ( ) + 0 602963 9 88 05 1 0 015 2
0 018889 3 0 246 . . .
. . 8852 4X( ) (5)
where X1 is the microwave power; X2 is the microwave time; X3 is
the frying temp; and X4 is the frying time.
Yellowness X X X
0 09225 3 1 4 . . .
. . 993889 4X( ) (6)
where X1 is the microwave power; X2 is the microwave time; X3, is
the frying temp; and X4 is the frying time.
Texture
The effect of factors on Fmax as quality parameter shown in Fig.
7A–F. Fmax of produced French fries increased TA
B LE
4. A
N A
LY SI
S O
F VA
RI A
N C
E FO
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SP O
N SE
SU RF
A C
E LI
N EA
R M
O D
EL FO
R RE
SP O
N SE
L* A
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DRYING
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considerably by increase of microwave power, microwave time, Frying
time and temperature (P < 0.05). The results of ANOVA were shown
in Table 3. Frying time had most significant effect on sample
texture (Fmax). As can be seen,
quadratic model with high correlation coefficient (R2 = 94.97)
selected as the best model to describe Fmax of samples against the
studied parameters. Model is shown in Eq. (7).
FIG. 5. VARIATION OF REDNESS AGAINST DIFFERENT STUDIED FACTOR (A)
Interaction effect of microwave power (W) and microwave time (min)
on redness. (B) Interaction effect of microwave power (W) and
frying temperature (°C) on redness. (C) Interaction effect of
microwave power (W) and frying time (min) on redness. (D)
Interaction effect of microwave time (min) and frying temperature
(°C) on redness. (E) Interaction effect of microwave time (min) and
frying time (min) on redness. (F) Interaction effect of frying time
(min) and frying temperature (°C) on redness of produced French
fries, other variables are constant at mean values.
OPTIMIZATION OF PRE-FRY MICROWAVE DRYING M. HASHEMI SHAHRAKI ET
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FIG. 6. VARIATION OF YELLOWNESS AGAINST DIFFERENT STUDIED FACTOR
(A) Interaction effect of microwave power (W) and microwave time
(min) on yellowness. (B) Interaction effect of microwave power (W)
and frying temperature (°C) on yellowness. (C) Interaction effect
of microwave power (W) and frying time (min) on yellowness. (D)
Interaction effect of microwave time (min) and frying temperature
(°C) on yellowness. (E) Interaction effect of microwave time (min)
and frying time (min) on yellowness. (F) Interaction effect of
frying time (min) and frying temperature (°C) on yellowness of
produced French fries, other variables are constant at mean
values.
M. HASHEMI SHAHRAKI ET AL. OPTIMIZATION OF PRE-FRY MICROWAVE
DRYING
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FIG. 7. VARIATION OF FMax AGAINST DIFFERENT STUDIED FACTOR (A)
Interaction effect of microwave power (W) and microwave time (min)
on FMax. (B) Interaction effect of microwave power (W) and frying
temperature (°C) on FMax. (C) Interaction effect of microwave power
(W) and frying time (min) on FMax. (D) Interaction effect of
microwave time (min) and frying temperature (°C) on FMax. (E)
Interaction effect of microwave time (min) and frying time (min) on
FMax. (F) Interaction effect of frying time (min) and frying
temperature (°C) on FMax of produced French fries, other variables
are constant at mean values.
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FMax X X X= − ( ) + ( ) − ( ) +
18 60395 0 01551 1 0 653516 2 0 09364 3 0 10804
. . . . . 88 4 0 00056 1 2 8 4 05 1 3
1 16 05 1 4 0 X X X E- X X
E- X X ( ) − ( )( ) − ( )( )
. .003713 2 3
0 182917 2 4 0 00348 3 4 5 9 05 1
X X
( )( ) + ( )( ) − ( )( ) + (. . . )) + ( ) + ( ) + ( )
2
2 2 20 895965 2 0 001077 3 0 071774 4. . .X X X (7)
where X1 is the microwave power; X2 is the microwave time; X3 is
the frying temp; and X4 is the frying time.
Optimization
A summary of the optimization information and range of the factor
that was used for optimization was shown in Table 5. Minimum oil
content, moisture content and Fmax, and maximum L*, a* and b* were
considered. The
optimization was done in 43 solutions (not shown). The selected
value of four factors and the best results are shown in Fig.
8.
Lowest desirability was obtained for Fmax (0.2905) and the highest
desirability was obtained for lightness (0.9422). The best obtained
desirability for moisture content, oil content, redness and
yellowness were 0.6281, 0.7785, 0.8223 and 0.7825,
respectively.
GA Optimization
GA optimization algorithm did in 500 generation. Maximum and
minimum of the limiting point set ranges from input cell estimates.
Maximize R-squared considered as target of optimization process.
Best solutions obtained are shown in Table 6. Optimized
coefficients are shown in Table 7.
FIG. 8. SELECTED VALUE OF FOUR FACTORS AND BEST RESULTS
TABLE 5. SUMMARY OF THE OPTIMIZATION INFORMATION AND RANGE OF THE
FACTOR THAT USED FOR OPTIMIZATION
Name Goal Lower limit
Upper weight Importance
A – microwave power Is in range 180 540 1 1 3 B – microwave time Is
in range 2 4 1 1 3 C – frying temp Is in range 140 180 1 1 3 D –
frying time Is in range 2 8 1 1 3 Oil content Minimize 10.19 15.94
1 1 5 Moisture content Minimize 7.12 17.17 1 1 4 L Maximize 59.98
62.38 1 1 4 a Maximize 3.71 6.1 1 1 3 b Maximize 22.6 36.12 1 1 2
Fmax Minimize 18.16 32.88 1 1 5
M. HASHEMI SHAHRAKI ET AL. OPTIMIZATION OF PRE-FRY MICROWAVE
DRYING
13Journal of Food Processing and Preservation •• (2012) ••–•• ©
2012 Wiley Periodicals, Inc.
Evaluation Models
The results of fitness for RSM model and GA-optimized RSM model
with experimental moisture content and oil content are shown in
Figs. 9 and 10. RSM and GA- optimized models had good fitness with
experimental results. The experimental results with GA-optimized
models had a higher correlation, but this improvement was not
considered. Although GA optimization results showed a little
improvement in the prediction of the actual experi- ment than
RSM-predicted results; however. GA optimiza- tion could not
increase the accuracy and precision of the RSM prediction models
significantly. Our results demon- strated improvement in modeling
data was not considering and RSM was enough to optimization of
processes.
CONCLUSIONS
The main purpose of using RSM in this study was to optimize
microwave and frying conditions of French fries
with respect to quality attributes. Oil content of pretreated
samples was significantly reduced. For dried samples, lower initial
moisture content before frying reduced of oil absorption. A linear
model described the effect of factors on variation of color
parameters. Lightness of French fries was observed to decrease
significantly as a result of both processes. Redness and yellowness
parameters increased significantly because of browning reactions
that take place during frying of samples, while microwave condition
had no significant effect. Textural property (Fmax) of samples
decreased considerably as the studied factors increased. This study
suggests that French fries with acceptable quality attributes can
be obtained by microwave pre-frying at 400–500 W for 3–4 min and
frying at 180C for 6–7 min. Using GA optimization did not improve
obtained models significantly. The digital imaging method allows
measurements and analyses of the color of food surfaces that are
adequate for food engineering research.
TABLE 6. COMPARISON OF R-SQUARED AFTER AND BEFORE GA
OPTIMIZATION
Model Oil content
Moisture content Fmax L a b
Quadratic Quadratic Quadratic Linear Linear Linear
Before GA optimization 0.9551 0.9554 0.9497 0.9519 0.9894 0.9966
After GA optimization 0.9968 0.9946 0.9948 0.9686 0.9984
0.9981
GA, genetic algorithms.
Oil content Moisture content Fmax L a b
Before After Before After Before After Before After Before After
Before After
a 2.12587 2.089349 25.43514 24.60912 18.60395 19.15146 67.23141
68.13588 0.602963 0.626687 6.548333 6.870785 b 0.002721 0.002808
0.025318 0.025942 -0.01551 -0.0162 -0.00045 -0.00045 9.88E-05
9.58E-05 0.000358 0.000351 c 2.087332 2.046977 -2.04125 -2.01468
0.653516 0.655284 -0.07722 -0.07599 0.015 0.014706 0.095 0.096778 d
0.13114 0.125178 -0.1008 -0.09712 -0.09364 -0.09153 -0.03231
-0.03327 0.018889 0.018296 0.09225 0.095643 e 0.488107 0.479494
0.781795 0.752609 0.108048 0.106341 -0.12426 -0.12837 0.246852
0.246574 1.493889 1.531552 f -0.00254 -0.00245 -0.00152 -0.00154
-0.00056 -0.00055 g -3.6E-05 -3.6E-05 1.56E-06 1.54E-06 -8.4E-05
-8.6E-05 h 1.39E-05 1.37E-05 -0.00025 -0.00026 1.16E-05 1.14E-05 i
0.004688 0.004578 -0.00191 -0.00193 -0.03713 -0.03541 j -0.03458
-0.03367 -0.04229 -0.0413 0.182917 0.18926 k -0.00077 -0.00077
-0.00257 -0.00257 -0.00348 -0.0036 l 4.83E-06 5.02E-06 -4.3E-05
-4.4E-05 5.9E-05 5.82E-05 m -0.41842 -0.4184 0.362281 0.362152
0.895965 0.867488 n -0.00045 -0.00047 0.000293 0.000307 0.001077
0.001029 o -0.01982 -0.02059 -0.05308 -0.05358 0.071774
0.07309
Response = a + b (X1) + c (X2) + d (X3) + e (X4) + f (X1)(X2) + g
(X1)(X3) + h (X1)(X4) + i (X2)(X3) + j (X2)(X4) + k (X3)(X4) + l
(X1)2 + m (X2)2 + n (X3)2
+ o (X4)2. GA, genetic algorithms.
OPTIMIZATION OF PRE-FRY MICROWAVE DRYING M. HASHEMI SHAHRAKI ET
AL.
14 Journal of Food Processing and Preservation •• (2012) ••–•• ©
2012 Wiley Periodicals, Inc.
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