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OPTIMIZATION OF PRE-FRY MICROWAVE DRYING OF FRENCH FRIES USING RESPONSE SURFACE METHODOLOGY AND GENETIC ALGORITHMS M. HASHEMI SHAHRAKI 1 , A.M. ZIAIIFAR, S.M. KASHANINEJAD 1 and M. GHORBANI Department of Food Science and Technology, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran 1 Corresponding 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- Brnc ˇic ´ 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 1 Journal of Food Processing and Preservation •• (2012) ••–•• © 2012 Wiley Periodicals, Inc.
Transcript of OPTIMIZATION OF PRE-FRY MICROWAVE DRYING OF FRENCH …
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
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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,
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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
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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
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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.
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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.
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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.
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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
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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.
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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.
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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
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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.
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FIG. 9. EXPERIMENTAL VALUES VERSUS PREDICTED OIL CONTENT VALUES (A) Experimental values versus predicted values by RSM model, (B) Experimental values versus predicted values by GA-optimized model.
FIG. 10. EXPERIMENTAL VALUES VERSUS PREDICTED MOISTURE CONTENT VALUES (A) Experimental values versus predicted values by RSM model, (B) Experimental values versus predicted values by GA-optimized model.
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OPTIMIZATION OF PRE-FRY MICROWAVE DRYING M. HASHEMI SHAHRAKI ET AL.
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 Wiley Periodicals, Inc.
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 AL.
2 Journal of Food Processing and Preservation •• (2012) ••–•• © 2012 Wiley Periodicals, Inc.
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
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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 AL.
4 Journal of Food Processing and Preservation •• (2012) ••–•• © 2012 Wiley Periodicals, Inc.
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.
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T A
N D
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6 Journal of Food Processing and Preservation •• (2012) ••–•• © 2012 Wiley Periodicals, Inc.
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
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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 AL.
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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
R RE
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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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.
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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.
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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.
OPTIMIZATION OF PRE-FRY MICROWAVE DRYING M. HASHEMI SHAHRAKI ET AL.
12 Journal of Food Processing and Preservation •• (2012) ••–•• © 2012 Wiley Periodicals, Inc.
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
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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.
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