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ISSN: 2157-7110
Journal of Food Processing & Technology
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Optimization of Osmotic Dehydration Process of Pineapple by Response Surface Methodology

Sridevi M and Genitha Er TR*

Department of Food Process Engineering, Sam Higginbottom Institute of Agriculture, Technology and Sciences India

*Corresponding Author:
Genitha Er TR
Department of Food Process Engineering
Sam Higginbottom Institute of Agriculture
Technology and Sciences (SHIATS)
Allahabad - 211007, UP, India
E-mail: [email protected]

Received date: May 22, 2012; Accepted date: July 24, 2012; Published date: July 28, 2012

Citation: Sridevi M, Genitha Er TR (2012) Optimization of Osmotic Dehydration Process of Pineapple by Response Surface Methodology. J Food Process Technol 3:173. doi:10.4172/2157-7110.1000173

Copyright: © 2012 Sridevi M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Response Surface Methodology was used for quantitative investigation of water loss, solid gain & weight reduction during osmotic dehydration of pineapple in sugar syrup. Effects of temperature (30, 35, 40, 45 & 50°C), processing time (30, 60, 90, 120 & 150 min), sugar concentration (40, 45, 50, 55 & 60°B) and sample to solution ratio 1:10 (constant) on osmotic dehydration of pineapple were estimated. Quadratic regression equations describing the effects of these factors on the water loss, solid gain & weight reduction were developed. It was found that effects of temperature and sugar concentrations were more significant on the water loss than processing time. For solid gain, processing time & sugar concentration were the most significant factors. The osmotic dehydration process was optimized for water loss, solid gain & weight reduction. The optimum conditions were found to be temperature 38.2°C, processing time 128.7 min and sugar concentration 44.05°B. At these optimum values, water loss, solid gain & weight reduction were found to be 30.0921%, 13.3634% & 20.3772% respectively.

Keywords

Optimization; Osmotic dehydration; Pineapple; Sugar solution; Response surface methodology

Introduction

In most tropical developing countries, the natural abundance of tropical fresh fruits often leads to a surplus with regard to the local requirements. Unfortunately, the excess of these fruits is not always fully used, once a limited variety and quantity of tropical fruit products are produced and commercialized.

The relatively short shelf life of fresh fruit after harvest is one of the main factors that demonstrate the necessity of developing an efficient and cheap preservation process. Also, the growing search for products with similar sensory and nutritional properties to fresh fruits, such as minimally processed fruits and vegetables, and for products enriched with some compounds, such as functional foods, also stimulates the food industry to look for new food preservation techniques.

One of the techniques being widely studied is osmotic dehydration. The process involves removal of water by immersing them in concentrated aqueous solutions mainly sugar, salt and spices. In osmotic dehydration, three types of counter-current mass transfer occur: (i) water flows from the product to the solution, (ii) a solute transfer from solution to the product and (iii) a leaching out of the product’s own solutes (sugars, organic acids, minerals, vitamins, etc.) [1-3].

Besides reducing the drying time, osmotic dehydration is used to treat fresh produce before further processing to improve sensory, functional and even nutritional properties. It has been proven to improve the texture characteristics of thawed fruits and vegetables [4,5], decreases structural collapse [6,7], and retain natural colour as well as volatile compounds during subsequent drying [8]. Water content reduction and sugar gain during osmotic dehydration have been observed to have some cryoprotectant effects on colour and texture in several frozen fruits [4].The two most important advantages for its use as pretreatment in a complementary process are: quality improvement and energy saving [1].

Response surface methodology (RSM) is widely used in food industries. In RSM, several factors are simultaneously varied. The multivariate approach reduces the number of experiments, improves statistical interpretation possibilities, and evaluates the relative significance of several affecting factors even in the presence of complex interactions. It is employed for multiple regression analysis using quantitative data obtained from properly designed experiments to solve multivariate equations simultaneously.

The objectives of this study are to study the effects of sugar concentration, temperature and processing time over the weight reduction, water loss and solid gain in the osmotic dehydration of pine apple and to determine the optimum operating conditions (Temperature, Immersion Time, Sugar Concentration) that maximizes water loss and weight reduction and minimizes the solid gain by response surface methodology.

Materials and Methods

Raw materials

Fresh, good quality Kew variety Pineapple (ripe) were procured from the local market Allahabad on daily basis prior to each set of experiment.

Experimental design and statistical analysis

Response surface methodology (RSM) was used to estimate the main effects of osmotic dehydration process on water loss (WL) and Solid gain (SG) in pineapple. A central composite design was used in temperature (30, 35, 40, 45 & 50°C), processing time (30, 60, 90, 120 & 150 min), sugar concentration (40, 45, 50, 55 & 60°B) & sample to solution ratio kept constant (1:10) being the independent variables Table 1. For the generated 30 experiments, RSM was applied to the experimental data using design expert 6.0.10.

    Range and levels
Symbol Independent Variables -2 -1 0 +1 +2
X1 Temperature oC 30 35 40 45 50
X2 Immersion time (min) 30 60 90 120 150
X3 Sugar concentration oB 40 45 50 55 60

Table 1: Process variables and their levels of experimental design.

Osmotic dehydration process

The pineapple was peeled and cut into 15 mm3 cubes. The prepared samples were subjected to osmotic dehydration according to the experimental design shown in Table 2. The temperature was controlled using a constant temperature water bath. The ratio of sample to solution was maintained at 1:10 in order to ensure concentration of the osmotic solution did not change significantly during the experiment. The samples were withdrawn, rinsed quickly in water, blotted gently with a tissue paper in order to remove adhering water and then dried in a tray drier at 70°C for 18 h. In each of the experiment fresh osmotic syrup was used.

  Actual Values Coded values
Design X1 X2 X3 X1 X2 X3
1 40 90 50 0 0 0
2 45 60 45 1 -1 -1
3 35 60 55 -1 -1 1
4 40 90 50 0 0 0
5 45 60 45 1 -1 -1
6 40 90 60 0 0 2
7 45 60 55 1 -1 1
8 35 120 45 -1 1 -1
9 50 90 50 2 0 0
10 45 120 45 1 1 -1
11 40 90 40 0 0 -2
12 30 90 50 -2 0 0
13 40 150 50 0 2 0
14 35 120 55 -1 1 1
15 40 90 60 0 0 2
16 35 60 45 -1 -1 -1
17 45 120 55 1 1 1
18 35 120 45 -1 1 -1
19 40 90 40 0 0 -2
20 45 120 45 1 1 -1
21 40 30 50 0 -2 0
22 35 60 45 -1 -1 -1
23 40 30 50 0 -2 0
24 35 60 55 -1 -1 1
25 35 120 55 -1 1 1
26 40 150 50 0 2 0
27 45 60 55 1 -1 1
28 45 120 55 1 1 1
29 50 90 50 2 0 0
30 30 90 50 -2 0 0

Table 2: Observed values of dependent variables for different runs of optimization experiments.

Experimental design for optimization of osmotic dehydration of pineapple

Response Surface Methodology was applied to the experimental data using a commercial statistical package (Design expert, trial version 6.0.10) for the generation of response surface plots and optimization process variables. The experiments were conducted according to Central Composite Rotatable Design (CCRD) [9]. Five levels of each variable were chosen for study, including 2 centre points and 2 axial points. A factorial study was used to study the effects of temperature (X1), processing time (X2) sugar concentration (X3) of the pine apple cubes.

image   (1)

image   (2)

image        (3)

In order to follow adequately the osmotic dehydration kinetics, individual analysis for each sample were carried out and from these weight reductions (WR), solid gain (SG) and water loss (WL) data were obtained according to the expressions.

image      (4)

WL = {(Wo−Wt ) +(St−So)}×100 /Wo    (5)

image    (6)

where Mo-initial mass of sample (g), M-mass of sample after dehydration (g), Wo is the initial weight taken for osmotic dehydration at any time (g), So is the initial dry matter (g), St is the dry matter of after osmotic dehydration for any time (g).

Results and Discussion

Fitting models

Experiments were performed according to the CCD experimental design given in Table 2 in order to search for the optimum combinations of parameters for the osmotic dehydration of pineapple cubes. The Model F-Value of 6.27, 11.35 & 9.91 for WL, WR & SG respectively implies the model is significant. There is only a 0.03% chance that a “Model F-Value” this large could occur due to noise. Values of “Prob>F” less than 0.0500 indicates model terms are significant. In this case X2, X3, X22 are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. The “Lack of Fit F-Value” of 5.78, 17.32 & 1.52 for WL, WR & SG respectively implies the lack of fit is significant. There is only a 0.36% chance that a “Lack of Fit F-Value” this large could occur due to noise. The goodness of fit of the model is checked by determination coefficient (R2). The coefficient of determination was calculated to be 0.7382, 0.7627 & 0.8169 for WL, WR & SG respetively . The R2 value is always between 0 and 1, and a value >0.75 indicates aptness of the model. For the good statistical model, R2 value should be close to 1. The adjusted R2 value corrects the R2 value for the sample size and for the no. of terms in the model and the sample size is not very large, the adjusted R2 may be noticeably smaller than the R2. The adjusted R2 value is 0.6204, 0.7627 & 0.7345 for WL, WR & SG respectively. The predicted R2 is WL 0.4315, WR 0.6312 & SG-0.6081 are in reasonable agreement with the adjusted R2. Values of CV is also as low as WL 9.81%, WR-18.54% & SG 18.57% indicate that the deviations between experimental & predicted values are low. Adequate measures the signal to noise ratio. A ratio greater than 4 is desirable. In this work the ratio is found to be 8.447, 10.088 & 9.014 for WL, WR & SG respectively indicates an adequate signal.

The experimental results are analyzed through RSM to obtain in empirical model for the best response. The mathematical expression of relationship to the response with variables are shown below.

 Y1=30.83+1.30X1+2.83 X2+ 2.08 X3-0.27 X12-1.76 X22–0.021 X32+0.9 X1X2-0.19 X1X3-0.86 X2 X3      (7)

 Y2=18.49+1.84 X1+4.99 X2+1.34 X3–1.95 X12–0.56 X22+0.13 X32–1.11 X1X2- 0.062 X1X3+0.9 X2 X3     (8)

 Y3=14.60+3.30 X1+1.24 X2+2.23 X3+0.1 X12 –1.27 X22 + 0.5 X32 + 0.93 X1 X2+ 1.3 X1X3–1.4 X2 X3   (9)

where Y1, Y2 and Y3 are water loss (%), weight reduction (%) & solid gain (%) respectively, and X1, X2 & X3 are the coded values of the test variables, temperature (°C), processing time (min) & sugar concentration (°B).

The standard scores were fitted to a quadratic polynomial regression model for predicting individual Y responses by employing at least square technique (Wanasaundara and Shahidi, 1966). The second order polynomial equation was fitted to the experimental data of each dependent variable as given. The model proposed to each response of Y was

image    (10)

where β0, βi, βij are intercepts, quadratic regression coefficient terms. Xi and Xj are independent variables. The model permitted evaluation of quadratic terms of the independent variables on the dependent variable. The response surface and contour plot were generated for different interactions of any two independent variables, where holding the value of third variables as constant at central level. The optimization of the process was aimed at finding the optimum values of independent variables.

The result for multiple linear regressions conducted for the second order response surface model are given in Table 3-5. The significance of each coefficient was determined by Student’s t-value and smaller the p-value, the more significant is the corresponding coefficient. Values of ”Prob>F” less than 0.0500 indicate model terms are significant. In this case, X1, X2, X3, X1 X2 and X1 X3 for WL X1, X2, X3, X32 and X2 X3 for WR and X1, X2,X1, X12, X32, X1 X2 and X1 X3 for SG are significant model terms. Values greater than 0.10 indicate the model terms are not significant. This implies that the linear are more significant than the other factors.

  Coded values Experimental % Predicted %
Design X1 X2 X3 WL WR SG WL WR SG
1 0 0 0 21.91 8.57 7.15 23.07 7.67 8.00
2 1 -1 -1 20.88 8.07 8.81 23.07 7.67 8.00
3 -1 -1 1 24.01 11.52 12.48 22.95 13.69 10.13
4 0 0 0 23.55 10.71 12.84 22.95 13.69 10.13
5 1 -1 -1 26.93 16.16 11.78 28.64 18.06 11.42
6 0 0 2 27.97 16.86 11.11 28.64 18.06 11.42
7 1 -1 1 31.44 17.92 13.51 32.12 19.64 17.28
8 -1 1 -1 26.44 14.15 20.29 32.12 19.64 17.28
9 2 0 0 29.91 10.17 13.73 28.56 8.68 12.65
10 1 1 -1 28.91 10.97 13.48 28.56 8.68 12.65
11 0 0 -2 26.44 14.15 20.29 29.22 14.45 19.99
12 -2 0 0 29.72 14.41 20.80 29.22 14.45 19.99
13 0 2 0 28.66 23.37 9.29 30.70 22.68 10.46
14 -1 1 1 26.43 23.70 7.73 30.70 22.68 10.46
15 0 0 2 34.15 23.30 22.81 34.96 24.01 21.52
16 -1 -1 -1 34.46 19.99 21.42 34.96 24.01 21.52
17 1 1 1 29.24 7.91 10.32 28.32 9.90 9.33
18 -1 1 -1 31.31 8.05 10.09 28.31 9.90 9.33
19 0 0 -2 36.56 20.83 20.82 31.80 16.08 20.43
20 1 1 -1 29.72 20.04 16.67 31.80 16.08 20.43
21 0 -2 0 20.89 7.91 5.99 21.10 8.52 8.93
22 -1 -1 -1 20.42 7.71 7.66 21.10 8.52 8.93
23 0 -2 0 33.17 28.26 15.91 30.61 25.30 13.10
24 -1 -1 1 35.52 28.63 12.89 30.61 25.30 13.10
25 -1 1 1 30.53 20.74 13.80 27.26 16.59 12.27
26 0 2 0 27.65 19.21 8.74 27.26 16.59 12.27
27 1 -1 1 34.94 19.20 15.75 34.27 21.11 19.76
28 1 1 1 36.23 21.12 24.15 34.27 21.11 19.76
29 2 0 0 30.75 15.85 14.89 30.83 18.49 14.60
30 -2 0 0 29.78 20.30 14.58 30.83 18.49 14.60

Table 3: Experimental condition and observed respond values of CCD.

Source Coefficient of estimation Sum of squares DF Mean squares f-value p-value
prob>f
Model 30.83 455.26 9 50.58 6.27 0.0003*
X1 1.03 29.19 1 29.19 3.62 0.0717
X2 2.83 218.24 1 218.24 27.04 <0.0001
X3 2.08 118.54 1 118.54 14.69 0.0010
X12 -0.27 0.90 1 0.90 0.11 0.7422
X12 -1.76 37.42 1 37.42 4.64 0.0437
X12 -0.021 5.559E-003 1 5.559E-003 6.886E-004 0.9793
X1 X2 0.90 12.96 1 12.96 1.61 0.2197
X1 X3 0.19 0.60 1 0.60 0.075 0.7875
X2 X3 -0.86 11.75 1 11.75 1.46 0.2418
Residual   161.44 20 8.07    
Lack of Fit   106.27 5 21.25 5.78 0.0036*
Pure error   55.17 15 3.68    
Corrected Total   616.69 29      
Std. Dev   2.84        
R2   0.7382        
Adj R2   0.6204        
Pred. R2   0.4315        
Adeq. Precision   8.447        
CV%   9.81        

Table 4: ANOVA for response surface quadratic model for osmotic dehydration of pineapple- water loss.

Source Coefficient of estimation Sum of squares DF Mean squares f-value p-value
prob>f
Model 18.49 935.87 9 103.99 11.35 <0.0001*
X1 1.84 92.16 1 92.16 10.06 0.0048
X2 4.99 680.11 1 680.11 74.26 <0.0002
X3 1.34 49.34 1 49.34 5.39 0.0310
X12 -1.95 45.85 1 48.85 5.01 0.0368
X12 -0.56 3.80 1 3.80 0.42 0.5266
X12 0.13 0.19 1 0.19 0.021 0.8869
X1 X2 -1.11 19.68 1 19.68 2.15 0.1583
X1 X3 -0.062 0.062 1 0.062 6.811E-003 0.9350
X2 X3 0.90 13.06 1 13.06 1.43 0.2464
Residual   183.17 20 9.16    
Lack of Fit   156.13 5 31.23 17.32 <0.0001*
Pure error   27.04 15 1.80    
Corrected Total   1119.04 29      
Std. Dev   3.03        
R2   0.8363        
Adj R2   0.7627        
Pred. R2   0.6312        
Adeq. Precision   10.088        
CV%   18.54        

Table 5: ANOVA for response surface quadratic model for osmotic dehydration of pineapple- Weight Reduction (WR).

Response surfaces and contour plots

Response surface plots as a function of two factors at a time, maintaining all other factors at fixed levels are more helpful in understanding both the main and the interaction effects of these two factors. These plots can be easily obtained by calculating from the model, the values taken by one factor where the second varies with constraint of a given Y value. The response surface curves were plotted to understand the interaction of the variables and to determine the optimum level of each variable for maximum response. The response surface curves shown in (Figure 1-9). The nature of the response surface curves shows the interaction between the variables. The elliptical shape of the curve indicates good interaction of the two variables and circular shape indicates no intercation between the variables. From fiqures it was observed that the elliptical nature of the contour in 3D–response surface graphs depict the mutual interaction between every two variables. There was a relative significant interaction between every two variables, and there was a maximum predicted yield indicated by the surface confined in the smallest ellipse in the contour diagram.

food-processing-technology-temperature-processing

Figure 1: 3D plot of combined effect of temperature and processing time on water loss.

food-processing-technology-sugar-concentration

Figure 2: 3D plot of combined effect of temperature and sugar concentration on water loss.

food-processing-technology-water-loss

Figure 3: 3d plot of combined effect of sugar concentration and processing time on water loss.

food-processing-technology-water-reduction

Figure 4: 3D plot of combined effect of temperature and sugar concentration on water reduction.

food-processing-technology-concentration-processing

Figure 5: 3D plot of combined effect of sugar concentration and processing time on water reduction.

food-processing-technology-plot-combined

Figure 6: 3D plot of combined effect of temperature and processing time on water reduction.

food-processing-technology-processing-time

Figure 7: 3D plot of combined effect of temperature and processing time on solid gain.

food-processing-technology-sugar-concentration

Figure 8: 3D plot of combined effect of temperature and sugar concentration on solid gain.

food-processing-technology-processing-time-solid

Figure 9: 3D plot of combined effect of sugar concentration and processing time on solid gain.s

Water loss: From the Table 3 the magnitude of P and F values indicates the maximum positive contribution of all the three variables namely temperature, processing time & sugar concentration on the water loss during osmotic dehydration. It implies increased water loss with increase in all the process variables. Further, the interaction X1-X2 and X1-X3 has positive effect and X2-X3 has negative effect on water loss. This is due to rise on fruit membrane permeability caused by higher temperatures promotes swelling and plasticization of cell membrane, favouring mass transfer [10,11]. Higher temperatures promote faster water loss through swelling and plasticizing of cell membranes as well as the better water transfer characteristics on the product surface due to lower viscosity of the osmotic medium [12,13].

Weight reduction: From Table 4 the magnitude of P and F-values indicates the maximum positive contribution of all the three variables, temperature, processing time & sugar concentration on the weight reduction during osmotic dehydration. It implies weight reduction increased with increase in all the three variables. Further the interaction of X1-X2 and X1-X3 has negative effect and X2-X3 has positive effect on weight reduction. Weight reduction increased with increase in immersion time. Similar results were found by Jokie et al. [14]. He investigated the influence of time on the osmotic dehydration of sugar beet; results showed that weight reduction was linearly effected by immersion time.

Solid gain: From the Table 6 the magnitude of P and F values indicate the maximum positive contribution of all the three variables namely temperature, processing time & sugar concentration on the solid gain. Further the interaction X1-X2 and X1-X3 has positive effect and X2-X3 has negative effect on solid gain during osmotic dehydration. The experimental values for water loss & solid gain under different treatment conditions showed that water removal was always higher than the osmotic agent uptake, in agreement with the results of other workers [10].

Source Coefficient of estimation Sum of squares DF Mean squares f-value p-value
prob>f
Model 14.60 602.54 9 66.95 9.91 <0.0001*
X1 3.3 297.15 1 297.15 44.00 <0.0001
X2 1.24 41.91 1 41.91 6.21 0.0216
X3 2.23 135.27 1 135.27 20.03 0.0002
X12 0.10 0.12 1 0.12 0.018 0.8947
X12 -1.27 19.40 1 19.40 2.87 0.1056
X12 0.50 3.04 1 3.04 0.45 0.5098
X1 X2 0.93 13.87 1 13.87 2.05 0.1673
X1 X3 1.30 27.09 1 27.09 4.01 0.0589
X2 X3 -1.40 31.53 1 31.53 4.67 0.0430
Residual   135.06 20 6.75    
Lack of Fit   43.35 5 9.07 1.52 0.2433 NS
Pure error   89.71 15 5.98    
Corrected Total   737.60 29      
Std. Dev   2.60        
R2   0.8169        
Adj R2   0.7345        
Pred. R2   0.6081        
Adeq. Precision   9.014        
CV%   18.57        

Table 6: ANOVA for response surface quadratic model for osmotic dehydration of pineapple- Solid Gain (SG).

Optimum condition for osmotic dehydration

Optimum conditions for osmotic dehydration of pineapple was determined to obtain maximum water loss and weight reduction and minimum solid gain. Second order polynomial models obtained in this study were utilised for each response to determine the specified optimum conditions. The sequential quadratic programming in Design-Expert D×6 6.0.10 is used to solve the second degree polynomial regression equation 7, 8, 9. The optimum values obtained by substituting the respective coded values of variables are 38.2°C, 125.7 min, 44.05°B. At this point, water loss, weight reduction and solid gain was calculated as 30.0921 (g/100g initial sample), 20.3772 (g/100g initial sample) & 13.3634 (g/100g initial sample).

Conclusion

It can be concluded from this study that solution temperature and sugar concentration were the most pronounced factors affecting solid gain and water loss of pineapple cubes during osmotic dehydration followed by immersion time. Results obtained evident that osmotic dehydration using sucrose solution was able to improve the quality of hot air drying of pineapple cubes in term of the colour, texture, aroma, appearance as well as overall acceptability. The regression equations obtained in this study were successfully used to predict optimum conditions for the maximum water loss & weight reduction and minimum solid gain and physical properties of dried pineapple cubes.

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