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Optimization of the contents of Arabic gum, xanthan gum and orange oil affecting turbidity, average particle size, polydispersity index and density in orange beverage emulsion
ARTICLE IN PRESS
FOOD
HYDROCOLLOIDS Food Hydrocolloids 22 (2008) 1212–1223 www.elsevier.com/locate/foodhyd
Optimization of the contents of Arabic gum, xanthan gum and orange oil affecting turbidity, average particle size, polydispersity index and density in orange beverage emulsion Hamed Mirhosseinia, Chin Ping Tana,, Nazimah S.A. Hamidb, Salmah Yusofc a
Department of Food Technology, Faculty of Food Science and Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia b Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia c Department of Food Technology, Faculty of Science and Technology, University Science Islam Malaysia, 71800 Nilai, Negeri Sembilan, Malaysia Received 17 January 2007; accepted 27 June 2007
Abstract This paper focuses on the development of an effective methodology to determine the optimum levels of three independent variables leading to (a) maximize turbidity, (b) minimize polydispersity index (PDI) and (c) obtain the target value for average particle size and density of orange beverage emulsion. A three-factor central composite design (CCD) was employed to determine the effect of Arabic gum content (7–13% w/w), xanthan gum content (0.1–0.3% w/w) and orange oil content (6–10% w/w). The emulsion properties studied as response variables were: turbidity (Y1), average particle size (Y2), PDI (Y3) and density (Y4). The response surface analysis was carried out to create efficient empirical models for predicting the changes of response variables. In general, analysis of variance (ANOVA) showed high coefficients of determination values (R2) in the range of 0.922–0.975 for the response surface models, thus ensuring a satisfactory adjustment of the polynomial regression models with the experimental data. The results of regression analysis indicated that more than 92% the response variation could be explained by the models. The results also indicated that the linear term of xanthan gum was the most significant (po0.05) variable affecting the overall responses. The multiple optimization results showed that the overall optimum region with high total desirability (D ¼ 0.92) was found to be at the combined level of 13.88% w/w Arabic gum content, 0.27% w/w xanthan gum content and 11.27% w/w orange oil content. Under the optimum condition, the corresponding predicted response values for turbidity, average particle size, PDI and density of the desirable orange beverage emulsion were 129.55, 988, 0.261 and 1.03, respectively. For validation of the models, the experimental values were compared with predicted values to check the adequacy of the models. The experimental values were found to be in agreement with those predicted, thus indicating suitability of the models employed using response surface methodology (RSM) for optimizing the physical properties of the orange beverage emulsion. r 2007 Elsevier Ltd. All rights reserved. Keywords: Orange beverage emulsion; Turbidity; Average particle size; Polydispersity index; Central composite design; Multiple optimization; Validation; Response surface methodology
1. Introduction The term ‘‘beverage emulsion’’ is used to describe a group of products that have similar composition, preparation and physicochemical properties, for example, fruit drinks, punches and sodas (Tan, 1997). In soft drinks, the beverage emulsion may provide flavor, color and cloudy appearance for the beverage, or just the cloudiness. In fact, Corresponding author. Tel.: +603 89468418; fax: +603 89423552.
E-mail address: [email protected] (C.P. Tan). 0268-005X/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodhyd.2007.06.011
beverage emulsions are either flavor emulsions to provide the beverage with flavor, cloudiness and color as in certain formulae or cloud emulsions to provide only cloudiness (Tan, 1997). Beverage emulsions are oil-in-water (o/w) emulsions that are prepared in a concentrated form and then diluted several hundred times in sugar/acid solution prior to consumption in order to produce the finished beverage, either carbonated or non-carbonated (Tse & Reineccius, 1995). Several studies have been mainly carried out on physical stability and rheological properties of the beverage emulsion (Buffo & Reineccius, 2002; Buffo,
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Reineccius, & Oehlert, 2001; Chanamai & McClements, 2000, 2001a, 2001b; Taherian, Fustier, & Ramaswamy, 2006; Tan, 1997; Tse & Reineccius, 1995). In an emulsion-based product, turbidity, particle size, polydispersity index (PDI) and density are key characteristics, as they contribute to physical stability and rheological properties of the finished products. The perceived quality of emulsion-based food products, such as cream, salad dressings, sauces and beverages, is strongly influenced by their stability, rheology and appearance. Although the stability of an emulsion is very critical for various industrial processes and emulsion-based products, the evaluation of emulsion stability is not easy. Particle size of fat globules (oil phase) and their size distribution play a predominant role in deciding the stability of emulsion and emulsion-based products with precisely controlled particle size exhibit better emulsion stability (McClements, 1999). The stability of an emulsion to gravitational separation can be enhanced by reducing the droplet size (McClements, 1999). As a rule, large globules tend to coalesce faster than the small ones (Bergenstahl & Claesson, 1990). Emulsion stability is a measure of the rate at which an emulsion creams, flocculates or coalesces. The rate of these changes can be measured by determining the size and distribution of the oil droplets in emulsion. Stoke’s law states that the velocity at which a droplet moves is proportional to the square of the droplet size radius. A decrease in its average globule diameter by a factor of two may decrease the coalescence rate by a factor of 10–100 (Bergenstahl & Claesson, 1990). Particle size is a key characteristic, as it contributes to the physical stability and organoleptic properties of the beverage emulsion. This is especially true for beverage emulsions, which have to be stable in both concentrated and diluted forms. For beverage emulsions, the determination of average particle size and its distribution could serve two proposes: first for the estimation of the overall quality of the emulsion concentrates and second for the prediction of emulsion stability in the finished product. For example, a beverage with a droplet size of 0.1 mm diameter will travel upward 100 times slower in the bottle than a droplet size of 1.0 mm diameter (Chanamai & McClements, 2000). Several investigations have been carried out in order to determine the effect of droplet size on the quality of emulsion (Bhandari, Dumoulin, Richard, Noleau, & Lebert, 1992; Brenner, Henderson, & Bergentsen, 1976; Chiewchan, Phungamngoen, & Siriwattanayothin, 2006). All these investigators have reported the significant effect of particle size on the stability of emulsions. Hence, obtaining an emulsion with a uniform small droplet size becomes essential to achieve a stable emulsion system. One of the most important quality attributes of foods is their appearance (Clydesdale, 1993; Hutchings, 1999). The appearance of a food emulsion depends on the way that it scatters and absorbs light waves in the visible region of the electromagnetic spectrum (Kerker, 1969). Scattering largely determines the turbidity, cloudiness, opacity or
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lightness of an emulsion, whereas absorption determines its ‘chromaticitiness’ (McClements, 2002). The degree of scattering by an emulsion depends on the concentration, average droplet size, particle size distribution and refractive index of any particles present (Kerker, 1969; Van de Hulst, 1957). The turbidity of an emulsion containing very fine and polydispersed particles is a complex function of mean droplet size, concentration and particle size distribution. Therefore, the variation in turbidity indicates the change in particle size, dispersity and concentration. In an unstable emulsion, particle size and concentration change with time due to coagulation and coalescence of particles. The turbidity of suspensions containing very coarse particles varies inversely with particle size. The turbidity method has been used for determining the size of very coarse particles in emulsion (Bagchi & Vold, 1975; Hiemenz & Vold, 1966). Turbidity measurement has also been used to determine emulsion stability (Song, Jho, & Kim, 2000). A technique utilizing the spectral absorbance at several wavelengths has been proposed to allow the determination of emulsion stability within a relatively short period of time (Frenkel, Shwartz, & Garti, 1982). RSM is an empirical modeling approach for determining the relationship between various process parameters and responses with the various desired criteria and searching the significance of these process parameters on the coupled responses (Myers & Montgomery, 1995). The basic theoretical and fundamental aspects of response surface methodology (RSM) were originally described by Box and Wilson (1951). The main advantage of RSM is to reduce the number of experimental trials needed to evaluate multiple parameters and their interactions. Therefore, it is less laborious and time-consuming than other approaches required to optimize a process (Giovanni, 1983). RSM has been successfully used to model food ingredients (Gallagher, O’Brien, Scannell, & Arendt, 2003; Shelke, Dick, Holm, & Loo, 1990; Vaisey-Genser, Ylimaki, & Johnston, 1987), optimize food process variables (Senanayake & Shahidi, 1999, 2002; Telez-Luis, Moldes, Alonso, & Vazquez, 2003), optimize extraction condition of phenolic compounds from berries (Cacace & Mazza, 2003a, 2003b) and evening primrose meal (Wettasinghe & Shahidi, 1999), as well as extraction condition of anthocyanins from black currants (Cacace & Mazza, 2003a) and sunflower hull (Gao & Mazza, 1996). The ability of food manufacturers to formulate emulsion-based products with desirable and reproducible appearances depends on the knowledge of the relationship between their optical properties and their composition and microstructure (Chanamai & McClements, 2001a, 2001b; Dickinson, 1994; McClements, 1999; Phillips, McGiff, Barbano, & Lawless, 1995a, 1995b). There are various factors and parameters that affect emulsion properties, including mixing and homogenizing conditions, the proportion of emulsion components, environmental conditions and so on. The objective of this study was to determine the optimum levels of emulsion components
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(namely the Arabic gum, xanthan gum and orange oil in the emulsion formulation) as independent variables leading to (1) the highest turbidity, (2) target value for average particle size and density and (3) the least PDI by using RSM. The beverage emulsion with these properties would be considered as an optimum beverage emulsion that can provide the desirable emulsion-based product especially in terms of appearance and stability. The response variables were modeled as a function of three independent variables to provide information regarding the optimum proportion of emulsion components. It should be noted that the other critical parameters such as zeta potential, physical stability, viscosity, flow behavior and pH have also been investigated by the same optimization procedure in another study. 2. Materials and methods
The Arabic gum solution was kept overnight at room temperature for full hydratation (Buffo et al., 2001; Tse & Reineccius, 1995). To prepare the water phase, xanthan gum solution was prepared separately by dissolving xanthan gum in deionized water and then mixed with Arabic gum solution by using a high-speed blender. While mixing the water phase, the cold-pressed orange oil was gradually added into the water phase to provide an initial coarse emulsion. Fine emulsification (i.e. an average droplet size of 1 mm and narrow particle size distribution) was achieved by subjecting the pre-emulsions to prehomogenization using the high-shear blender (Silverson L4R, Buckinghamshire, UK) for 1 min and then passed through a high-pressure homogenizer (APV, Crawley, UK), for three passes (30, 28 and 25 MPa). 2.3. Analytical methods
2.1. Materials Arabic gum was provided by Colloides Naturels International Co. (Rouen, France). Xanthan gum was donated by CP Kelco (Chicago, USA). Citric acid, sodium benzoate and potassium sorbate (X95%) were purchased from Fisher Scientific (Pittsburgh, PA). Valencia cold-pressed orange oil was provided by Danisco (Cultor, Aarhus, Denmark). 2.2. Preparation of orange beverage emulsion As shown in Table 1, 20 orange beverage emulsions composed of gum Arabic (7–13% w/w), xanthan gum (0.1–0.3% w/w), orange oil (6–10% w/w), sodium benzoate (0.1% w/w), potassium sorbate (0.1% w/w), citric acid (0.4% w/w) and deionized water were prepared for the optimization procedure based on a central composite design (CCD). No weighting agent and vegetable oil were added. A representative orange beverage emulsion is usually composed of two phases: a water phase and an oil phase. To prepare the water phase, sodium benzoate, potassium sorbate and citric acid were dispersed in deionized water (60 1C) using a high-shear blender (Waring blender 32BL80, New Hartford, USA). While mixing the mixture, Arabic gum was gradually added to the deionized water (60 1C) and mixed for 3 min to facilitate hydration.
Table 1 Levels of independent variables established according to the central composite design (CCD) Variable
Independent variable levels
Independent variables
Low
Center
High
Axial (a)
Axial (+a)
Arabic gum content (% w/w) Xanthan gum content (% w/w) Orange oil content (% w/w)
7 0.1 6
10 0.2 8
13 0.3 10
5.101 0.037 4.734
14.899 0.363 11.266
2.3.1. Turbidity The turbidity of diluted emulsions (1:1000) was determined using a portable turbidimeter (Model 2100P, Hach Company, Loveland, CO, USA). The results were reported in nephelometric turbidity units (NTU). Accuracy of the instrument was found to be approximately 70.1 NTU with stray light less than or equal to 0.1 NTU. The accuracy value is essential especially for small and diluted emulsions with high surfactant content. The measurement of turbidity was carried out in triplicate. 2.3.2. Average particle size and PDI Mean particle size and size distribution of beverage emulsions were determined by integrated light scattering using a Malvern series ZEN 3500 (size and zeta potential nano-particle sizer ZS, Malvern Instruments Ltd., Malvern, Worcester, UK). The measurement of droplet size was done immediately after sample preparation. To avoid multiple scattering effects, the emulsions were diluted (1:100; approximately 250 ml) with deionized water prior to analysis, and then directly placed into the module. A laser beam was directed through the diluted samples, scattered by the droplets in a characteristic pattern dependent on their size and detected by an array of photodiodes located behind the cuvette. The instrument used the method of photon correlation spectroscopy (PCS) to measure particle size in constant random thermal, or Brownian, motion. The particle diameter range and number of photon counts per second [kilo Count per second (kCps)] were evaluated at room temperature (Taherian et al., 2006). The PDI was calculated as the best fit between the measured scattered pattern and that predicted by light scattering theory. All measurements were repeated in triplicate for each sample. 2.3.3. Density The density of beverage emulsion concentrates was measured at room temperature using hydrometers (CMS Hydrometers, Curtis Matheson Scientific, New York, NY)
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of appropriate range (Buffo et al., 2001). The measurement was carried out in triplicate. The average of measurement values obtained was taken as a response value for fitting the model and data analysis.
equations (Mason, Gunst, & Hess, 2003; Montgomery, Runger, & Hubele, 2001; Vining, 2003).
2.4. Experimental design
Regression analysis and analysis of variance (ANOVA) were conducted for (1) determining regression coefficients and statistical significance of model terms and (2) fitting the mathematical models to the experimental data, aiming at an overall optimal region for the response variables. Multiple regression coefficients were determined by employing the least-squares technique (Myers & Montgomery, 1995) to predict linear and quadratic polynomial models for the response variables studied. The behavior of the response surface was investigated for the response function (Yi) using the regression polynomial equation. The generalized polynomial model proposed for predicting the response variables is given as
The effect of three independent variables, x1 (Arabic gum content), x2 (xanthan gum content) and x3 (orange oil content), on four response variables (Y1Y4, namely turbidity, average particle size, PDI and density) was evaluated by using the RSM. A CCD was employed (1) to study the main and combined effects of main emulsion components on the physical emulsion properties studied, (2) to create models between the variables and (3) to determine the effect of these variables to optimize the proportion of emulsion components in terms of the response variables studied leading to the desirable goals. Therefore, 20 treatments were assigned based on the second-order CCD with three independent variables at five levels of each variable. Independent variable ranges studied were: gum Arabic (7–13% w/w), xanthan gum (0.1–0.3% w/w) and orange oil (6–10% w/w). Experiments were randomized in order to minimize the effects of unexplained variability in the actual responses due to extraneous factors. The center point was repeated six times to calculate the repeatability of the method (Montgomery, 2001). The matrix of the CCD including the values corresponding to the levels of factors and treatments is shown in Tables 1 and 2. As shown in Tables 1 and 2, the arrangement of CCD presented was in such a way that it allowed the development of the appropriate empirical
Table 2 Matrix of the central composite design (CCD) Treatment runs
Blocks
Arabic gum
Xanthan gum
Orange oil
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 3 3
7.000 7.000 13.000 10.000 13.000 10.000 7.000 7.000 13.000 10.000 10.000 13.000 10.000 14.899 10.000 10.000 10.000 5.101 10.000 10.000
0.100 0.300 0.300 0.200 0.100 0.200 0.100 0.300 0.100 0.200 0.200 0.300 0.200 0.200 0.200 0.200 0.037 0.200 0.200 0.363
6.000 10.000 6.000 8.000 10.000 8.000 10.000 6.000 6.000 8.000 8.000 10.000 8.000 8.000 4.734 11.266 8.000 8.000 8.000 8.000
(C) (C)
(C) (C) (C)
(C)
(C), Center point.
2.5. Statistical analyses
Y i ¼ b0 þ b1 x1 þ b2 x2 þ b3 x3 þ b11 x21 þ b22 x22 þ b33 x23 þ b12 x1 x2 þ b13 x1 x3 þ b23 x2 x3
ð1Þ
where Yi is predicted response, b0 is offset term, b1, b2 and b3 are the regression coefficients for linear effect terms, b11, b22 and b33 are quadratic effects and b12, b13 and b23 are interaction effects. In this model, x1, x2 and x3 are the independent variables. The ANOVA results presented the effect and regression coefficients of individual linear, quadratic and interaction terms that were individually determined. The significance of the equation parameters for each response variable was also assessed by F-ratio at a probability (p) of 0.05. The adequacy of the models was determined using model analysis, lack-of-fit test and coefficient of determination (R2) analysis as outlined by the previous studies (Lee, Ye, Landen, & Eitenmiller, 2000; Weng, Liu, & Lin, 2001). Joglekar and May (1987) suggested that for a good fit of a model, R2 should be at least 0.80. All results were expressed as the mean values of three independent trials. The experimental design matrix, data analysis and optimization procedure were performed using the Minitab v. 13.2 statistical package (Minitab Inc., PA, USA). 2.6. Optimization procedure After generating the polynomial regression equations relating the responses to the independent variables studied, the optimization procedure was performed to obtain the optimal levels of three factors (x1, x2 and x3). Numerical optimization was also carried out to determine the exact optimum level of independent variables leading to the desirable orange beverage emulsion in terms of the response variables. The desired goal for each variable was chosen. Different weights were assigned to each goal to adjust the shape of its particular desirability function for simultaneous optimization of the multiple responses. The optimal condition that depended on the independent
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variables was obtained using the predicted equations determined by RSM. Furthermore, a graphical technique was used in order to deduce workable optimum conditions (Floros & Chinnan, 1988; Giovanni, 1983). Therefore, the fitted regression models were expressed as surface plots in order to visualize the relationship between the response and experimental levels of each factor and to deduce the optimum conditions. For graphical optimization, the threedimensional (3D) plots were plotted by keeping one variable constant at the center point and varying the other two variables within the experimental range. 2.7. Verification of models The adequacy of response surface models for predicting the optimum response values was verified by conducting experiments under the recommended optimum conditions. The experimental and predicted values of the responses were compared in order to check the validity of models. 3. Results and discussion 3.1. Preliminary study The initial preliminary experiments were conducted (1) to study the influence of mixing and homogenizing conditions (speed, time and pressure) on the particle size and distribution, and then (2) to establish the best preparation condition leading to narrow size distribution and target average particle size (approximately 1 mm). The results showed that fine emulsification was achieved by mixing the emulsion for 1 min and then passing it through
the high-pressure homogenizer, three passes at 300, 280 and 250 bar (data not shown). 3.2. Fitting the response surface models In the present work, multiple regression analyses were carried out using response surface analysis (1) to fit mathematical models to the experimental data aiming at an optimal region for the response variables studied and (2) to define the relationship between three independent variables and the criteria of five response variables as presented in Table 3. The response surface analysis allowed the development of an empirical relationship where each response variable (Yi) was assessed as a function of Arabic gum content (x1), xanthan gum content (x2) and orange oil content (x3) and predicted as the sum of constant (b0), three first-order effects (linear terms in x1, x2 and x3), three interaction effects (interactive terms in x1x2, x1x3 and x2x3) and three second-order effects (quadratic terms in x21, x22 and x23). The results obtained were then analyzed by ANOVA to assess the ‘‘goodness of fit’’. Only terms found statistically significant (po0.05) were included in the reduced model. As shown in Eqs. (2)–(5), the models obtained for predicting the response variables explained the main quadratic and interaction effects of factors affecting the response variables. The estimated regression coefficients of the polynomial response surface models along with the corresponding R2 values and lack of fit tests are given in Table 4. The significance of each term was determined using the F-ratio and p-value as presented in Table 5. It was found that the most significant variable affecting the variation of response variables studied was
Table 3 Central composite design: independent (Xi) and response variables (Yj) (mean7SD) Run
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Block
1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 3 3
Independent variable (% w/w)
Response variable
Arabic gum (x1)
Xanthan gum (x2)
Orange oil (x3)
Turbidity (NTU) (Y1)
Average droplet size (nm) (Y2)
Polydispersity index (Y3)
Density (g/cm3) (Y4)
7.000 7.000 13.000 10.000 13.000 10.000 7.000 7.000 13.000 10.000 10.000 13.000 10.000 14.899 10.000 10.000 10.000 5.101 10.000 10.000
0.100 0.300 0.300 0.200 0.100 0.200 0.100 0.300 0.100 0.200 0.200 0.300 0.200 0.200 0.200 0.200 0.037 0.200 0.200 0.363
6.000 10.000 6.000 8.000 10.00 8.000 10.00 6.000 6.000 8.000 8.000 10.000 8.000 8.000 4.734 11.266 8.000 8.000 8.000 8.000
16.570.78 73.570.71 39.072.12 46.572.12 48.571.41 45.071.41 52.572.12 22.573.54 26.572.12 68.071.41 64.571.41 127.071.41 61.071.41 54.070.00 34.070.00 90.070.78 55.071.41 15.071.41 48.070.00 81.070.00
1062719.7 1038736.6 1270723.5 100177.0 923721.5 93576.4 959710.4 1270710.8 1180716.2 924738.4 98873.2 96775.6 1019724.0 105279.2 153075.5 97377.0 880727.5 110078.9 987726.0 1010711.5
0.76270.028 0.34070.003 0.76570.023 0.47270.034 0.37270.015 0.60670.023 0.32970.022 0.87070.005 0.81470.018 0.52470.011 0.45970.011 0.30270.033 0.58370.039 0.53570.022 0.87670.015 0.31570.005 0.58470.027 0.63470.019 0.47070.009 0.42670.020
1.01770.005 1.00970.010 1.03670.003 1.02370.001 1.02870.005 1.02370.004 1.00870.008 1.01770.010 1.03670.007 1.02470.003 1.02270.005 1.03070.000 1.02270.006 1.03770.002 1.02770.007 1.02270.005 1.02370.000 1.01370.009 1.02370.003 1.02470.004
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Table 4 Regression coefficients, R2, adjusted R2 probability values and lack of fit for five variables Regression coefficient
Turbidity (NTUs, Y1)
Average particle size (nm, Y2)
Polydispersity index (Y3)
Density (g/cm3, Y4)
b0 b1 b2 b3 b21 b22 b23 b12 b13 b23 R2 R2 (adj) Regression (p-value) Lack of fit (F-value) Lack of fit (p-value)
37.4 11.0 753.8 8.6 0.8 506.2 0.7 26.7 0.5 50.6 0.975 0.929 0.000a 2.25 0.261b
2741 12 2857 451 3 2175 29 64 5 109 0.968 0.909 0.001a 0.44 0.743b
2.3985 0.0536 0.1290 0.2780 0.0024 0.8062 0.0101 – – – 0.922 0.852 0.000a 0.62 0.719b
1.00806 0.00294 0.00371 0.00191 – – – – – – 0.973 0.962 0.000a 3.74 0.153b
bi: The estimated regression coefficient for the main linear effects; bii: the estimated regression coefficient for the quadratic effects; bij: the estimated regression coefficient for the interaction effects (1: Arabic gum; 2: xanthan gum; 3: orange oil). a Significant (po0.05). b Not significant (p40.05).
Table 5 ANOVA and regression coefficients of the first- and second-order polynomial regression models Variables
Main effects x2
x1 Turbidity (Y1) p-Value F-ratio
Quadratic effects x3
x21
x22
Interaction effects x23
x1x2
x1x3
x2x3
0.167 2.478
0.004 19.999
0.748 0.114
0.030 8.043
0.104 3.663
0.671 0.199
0.018 10.298
0.293 1.329
0.007 16.492
Average droplet size (Y2) p-Value 0.726 F-ratio 0.135
0.012 12.596
0.010 13.543
0.073 4.700
0.136 2.965
0.008 14.884
0.159 2.579
0.056 5.579
0.116 3.375
Polydispersity index (Y3) p-Value 0.357 F-ratio 0.941
0.901 0.016
0.268 1.395
0.401 0.776
0.752 0.106
0.510 0.471
– –
– –
– –
Density (Y4) p-Value F-ratio
0.423 0.687
0.000 43.507
– –
– –
– –
– –
– –
– –
0.000 388.72
Significant at po0.05.
the linear term of xanthan gum content. The results showed that the regression models for the response variables (Y1–Y4) were significant by the F-test at the 5% confidence level (po0.05). Analysis of variance also confirmed that the models were highly significant (po0.05) for all response variables (Table 4). The probability (p) values of all regression models were less than 0.003, which had no indication of lack of fit. The R2 values for these response variables were higher than 0.80 (0.922–0.975), thus ensuring a satisfactory fitness of the regression models to the experimental data. The R2 values for turbidity, average particle size, PDI and density
were 0.975, 0.968, 0.922 and 0.973, respectively. The response surface models could explain more than 90% of the variation in the response variables studied as function of main emulsion components. Therefore, the R2 values of the response models were high enough, thus indicating that the variability of all responses was explained well by the models. The lack of fit, which measures the fitness of models, resulted in no significant F-value (p40.05) in terms of the response variables studied, indicating that models were sufficiently accurate for predicting those response variations. The following response surface models (Eqs. (2)–(5)) were fitted to each of
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the response variables (Yi) three independent variables (x1, x2 and x3): Turbidity:
Turbidity
0:7x23 þ 26:7x1 x2 þ 0:5x1 x3 þ 50:6x2 x3 .
ð2Þ
Average particle size: Y 2 ¼ 2741 12x1 þ 2857x2 451x3 þ 3x21 2175x22 þ 29x33 64x1 x2 5x1 x3 109x2 x3 .
ð3Þ
PDI:
Turbidity value (NTU)
Y 1 ¼ 37:4 þ 11:0x1 753:8x2 þ 8:6x3 0:8x21 þ 506:2x22 100 90 80 70 60 50 40 30 20 10 5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w)
Y 3 ¼ 2:3985 0:0536x1 þ 0:1290x2 0:2780x3 þ 0:0024x21 0:8062x22 þ 0:0101x23 .
ð4Þ
0.1
an
nth
Xa
Hold values: Orange oil: 8.0
Density:
Turbidity
3.2.1. Turbidity The effect of three independent variables on the turbidity value was reported by the coefficient of the second-order polynomial regression equations (Tables 4 and 5). To aid visualize the effect of main emulsion components on the turbidity, the response surface for turbidity is shown in Fig. 1(a–c). As shown in Eq. (2), the second-order polynomial regression equation (full quadratic) was fitted for predicting the turbidity value. Table 5 clearly exhibited that turbidity was most significantly (po0.05) influenced by the linear effect of xanthan gum, followed by interaction terms of xanthan gum and orange oil. It was found that the quadratic term of Arabic gum had a significant (p40.05) negative effect on turbidity while showing a positive effect in the linear term (Table 4). These significant terms should be considered as the primary factors for determining the variation of turbidity. The optimum turbidity (the highest value, Y1 ¼ 161.15) was predicted to be obtained at 14.899% (w/w) Arabic gum, 0.339% (w/w) xanthan gum and 11.266% (w/w) orange oil using response surface plots and response optimizer. Fig. 1 shows the surface plot where the turbidity increased as the orange oil content increased. Therefore, the beverage emulsion with higher orange oil content resulted in higher turbidity.
Turbidity value (NTU)
(5)
100
50
0 5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w) Turbidity
150 100 50
0.0
3.2.2. Average particle size The results showed good repeatability between the replications of particle size measurements. The droplet radius of the diluted emulsions remained constant throughout the experiments, which exhibited no coalescence. The most significant (po0.05) factor affecting the average particle size was the quadratic term of orange oil, followed by the linear terms of orange oil and xanthan gum (Table 5). Interaction effects between factors were negatively related to the particle size value (Table 4). However, the interaction terms between factors were shown to be non-significant (p40.05) on average particle size (Table 5).
1 10. 1.5 9 . 5 5 w) 7.58.5 w/ 6 . 5 % 5 il ( 4.5 .5 o nge Ora
Hold values: Xanthan gum: 0.2
Turbidity value (NTU)
Y 4 ¼ 1:00806 þ 0:00294x1 þ 0:00371x2 0:00191x3 .
0.0
0.4 ) 0.3 w/w % ( gum
0.2
0.1 0.2 Xanthan gum
0.3 (% w/w)
0.4
1 10. 1.5 9 . 5 5 w) 7.58.5 w/ 6 . 5 % 5 il ( 4.5 .5 o nge Ora
Hold values: Arabic gum: 10.0 Fig. 1. Response surface plots for turbidity as a function of (a) Arabic gum and orange oil contents, (b) xanthan gum and orange oil contents and (c) Arabic gum and xanthan gum contents.
As shown in Table 4, the linear terms of Arabic gum and orange oil had negative effects on average particle size; conversely, the response value was positively influenced by the linear effect of xanthan gum (Table 4). This behavior
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may be explained by the positive effect of xanthan on the emulsion viscosity. Table 4 clearly exhibits that the quadratic terms of factors had reverse behavior as compared to their linear terms. To aid visualization, the response surface for average particle size is shown in
Droplet size value (nm)
Average droplet size
1150 1050 950 850 5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w)
0.2
0.1
0.0
an
h ant
0.3
0.4
% m(
)
w/w
gu
X
Hold values: Orange oil: 8.0
Droplet size value (nm)
Average droplet size
1600 1500 1400 1300 1200 1100 1000 900 5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w)
1 10. 1.5 9 .5 5 ) 7.58.5 6 w/w . 5.5 5 % ( 4.5 oil nge a r O
Hold values: Xanthan gum: 0.2
Droplet size value (nm)
Average droplet size
1600 1500 1400 1300 1200 1100 1000 900 800
1219
Fig. 2(a–c). The optimum average particle size was obtained at a combination of 14.807% (w/w) Arabic gum, 0.363% (w/w) xanthan gum and 8.347% (w/w) orange oil. 3.2.3. Polydispersity index The linear and quadratic effects of independent variables on the PDI showed the same effect as their effects on average particle size (Tables 4 and 5). As shown in Table 4, indicats that the linear terms of Arabic gum and orange oil were negatively influenced by the PDI, whereas their quadratic terms showed the opposite behavior. On the other hand, results showed that the PDI was positively affected by the linear term of xanthan, whereas the quadratic term of xanthan gum had a negative effect on the PDI (Table 4). The optimum PDI (the least value, Y3 ¼ 0.253) was predicted to be obtained by set level of 14.899% Arabic gum, 0.363% (w/w) xanthan gum and 11.266% (w/w) orange oil determined using response surface plots and response optimizer. Fig. 3 shows the surface plot for the PDI as a function of Arabic gum, xanthan gum and orange oil content. 3.2.4. Density The linear regression equation was fitted to predict the effect of independent variables on the variation of density leading to obtain the target density value. The quadratic and interaction effects were shown to be non-significant (p40.05) on density (Tables 5). The linear term of Arabic gum was the most significant (po0.05) factor for density, followed by the linear term of orange oil (Table 5). Therefore, these significant terms were the primary determining factors for predicting the density value. It was found that the linear terms of Arabic gum and xanthan gum showed positive effects on the density value (Table 4). Conversely, as expected, that the linear term of orange oil had a significant (po0.05) negative effect on density. This observation may be explained by the positive effect of water phase and the negative effect of oil phase on the density value. The optimum density (target value, Y4 ¼ 1.03) was obtained at 10.547% (w/w) Arabic gum, 0.363% (w/w) xanthan gum and 4.734% (w/w) orange oil. Fig. 4 shows the surface plot for density as a function of Arabic gum, xanthan gum and orange oil content. 3.3. Optimization procedure
0.0
0.1 0.2 0.3 Xanthan gum (% w/w)
1 10. 1.5 9 .5 5 ) 7.58.5 6 w/w 5.5 .5 (% l 4 i o 0.4 .5 nge Ora
Hold values: Arabic gum: 10.0 Fig. 2. Response surface plots for average particle size as a function of (a) Arabic gum and orange oil contents, (b) xanthan gum and orange oil contents and (c) Arabic gum and xanthan gum contents.
Numerical and graphical optimization procedures were carried out for predicting the exact optimum level of independent variables leading to the desirable response goals. First, an optimal treatment was found via response surface plotting of the data, but there had to be a compromise between the optimum ranges for the five responses: turbidity, average particle size, PDI and density. For the graphical interpretation of the independent variable interactions, the use of 3D plots of the regression model was highly recommended (Mason et al., 2003;
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Density
0.65 0.60 0.55 0.50 5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w)
0.0
0.1
0.2
han
0.3
0.4
%
( gum
) ww
Density value (gr/cm3)
Polydispersity value
Polydispersity
1.04 1.03 1.02 1.01 5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w)
nt Xa
Hold values: Orange oil: 8.0
1 1 1.5 9.5 0.5 8. ) 6 7.5 5 w/w 5.5 .5 % ( 4.5 oil nge a r O
Density value (gr/cm3)
Polydispersity value
5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w)
)
w/w
gu
X
1.04 1.03 1.02 1.01 1.00 5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w)
1 1 1.5 9.5 0.5 8 .5 ) 6 7.5 w/w 5 .5 % ( 4.5 .5 oil nge a r O
Hold values: Xanthan gum: 0.2
Hold values: Xanthan gum: 0.2
Density
1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
0.4
1 1 1.5 9.5 0.5 8. ) 6 7.5 5 w/w 5.5 .5 % ( 4.5 oil nge a r O
Hold values: Arabic gum: 10.0
Density value (gr/cm3)
Polydispersity
Polydispersity value
an
h ant
% m(
Density
1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2
0.2 0.3 Xanthan gum (% w/w)
0.0
0.4
Hold values: Orange oil: 8.0
Polydispersity
0.0
0.2
0.1
0.3
1.028
1.023
1.018 0.0
0.1
0.2 0.3 Xanthan gum (% w/w)
1 1 1.5 9.5 0.5 8 .5 ) 6 7.5 w/w 5.5 .5 % ( 4 l 0.4 .5 oi nge Ora
Hold values: Arabic gum: 10.0
Fig. 3. Response surface plots for polydispersity index as a function of (a) Arabic gum and orange oil contents, (b) xanthan gum and orange oil contents and (c) Arabic gum and xanthan gum contents.
Fig. 4. Response surface plots for density as a function of (a) Arabic gum and orange oil contents, (b) xanthan gum and orange oil contents and (c) Arabic gum and xanthan gum contents.
Montgomery, Runger, & Hubele, 2001; Vining, 2003). The response optimizer was also employed to confirm the optimum variable region. Therefore, the 3D surface plots were drawn to illustrate the interactive effects of the independent variables corresponded response variable. The
optimum condition was then determined by superimposing the plots of all the responses. Variables giving quadratic and interaction terms with the largest absolute coefficients and t-value in the fitted models were chosen for the axes of the response surface plots to account for curvature of the
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Table 6 Experimental and predicted values for the response variables based on the reduced models Run
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Turbiditya (Y1, NTUs)
Average article sizea (Y2, nm)
Polydispersity indexa (Y3)
Densitya (Y4, g/cm3)
(Y0)
(Yi)
(Y0–Yi)
(Y0)
(Yi)
(Y0–Yi)
(Y0)
(Yi)
(Y0–Yi)
(Y0)
(Yi)
(Y0–Yi)
16.5 73.5 39.0 46.5 48.5 45.0 52.5 22.5 26.5 68.0 64.5 127.0 61.0 54.0 34.0 90.0 55.0 15.0 48.0 81.0
15.8 67.1 40.5 50.9 43.7 50.9 54.1 18.9 36.0 66.3 66.3 119.4 60.9 53.6 25.5 96.2 41.2 19.3 60.9 80.5
0.7 6.4 1.5 4.4 4.8 5.9 1.6 3.6 9.6 1.7 1.8 7.6 0.1 0.4 8.5 6.2 13.8 4.3 12.9 0.5
1062 1038 1270 1001 923 935 959 1270 1180 924 988 967 1019 1052 1530 973 880 1100 987 1010
1158 983 1253 973 888 973 898 1263 1168 983 983 993 1004 1004 1485 1043 926 1004 1004 1082
96 55 17 28 35 38 61 7 12 59 5 26 15 48 45 70 46 96 17 72
0.762 0.340 0.765 0.472 0.372 0.606 0.329 0.870 0.814 0.524 0.459 0.302 0.583 0.535 0.876 0.315 0.584 0.634 0.470 0.426
0.762 0.344 0.762 0.553 0.344 0.553 0.341 0.758 0.758 0.550 0.550 0.341 0.553 0.553 0.894 0.212 0.553 0.553 0.553 0.553
0.000 0.004 0.003 0.081 0.028 0.053 0.012 0.112 0.056 0.026 0.091 0.039 0.030 0.018 0.018 0.103 0.031 0.081 0.083 0.127
1.017 1.009 1.036 1.023 1.028 1.023 1.008 1.017 1.036 1.024 1.022 1.030 1.022 1.037 1.027 1.022 1.023 1.013 1.023 1.024
1.017 1.011 1.034 1.023 1.028 1.023 1.011 1.017 1.035 1.023 1.023 1.029 1.024 1.038 1.029 1.019 1.024 1.009 1.024 1.024
0.000 0.002 0.001 0.000 0.000 0.000 0.003 0.000 0.001 0.001 0.001 0.001 0.002 0.001 0.002 0.003 0.001 0.004 0.001 0.000
(Y0): Experimental value; (Yi): predicted value; (Y0–Yi): residue. a No significant (p40.05) difference between experimental (Y0) and predicted value (Yi).
surfaces. The response surface plots were also drawn by imposing a constant value (i.e., the central points of the interval taken into consideration to one independent variable). A numerical optimization was also carried out for simultaneous multiple optimization of response variables and determining the overall optimal condition. The desired goals for each response variable were chosen. The responses were then analyzed jointly by conferring to them either the same importance or weight. The optimum set of three independent variables leading to the desired goals of response variables was obtained using the response optimizer. The emulsion would be considered an optimum product if the criteria applied for graphical optimization led to (a) maximize turbidity, (b) minimize PDI and (c) achieve the target value for average particle size and density. The optimization procedures showed the overall optimum region to be at 13.88% (w/w) Arabic gum, 0.27% (w/w) xanthan gum and 11.27% (w/w) orange oil by overlaying all the responses and response optimizers. Under the optimum condition, the corresponding predicted response values for turbidity, average particle size, PDI and density of the optimized orange beverage emulsion were 129.55, 988, 0.261 and 1.03, respectively.
tally predict the value of responses using the models. The observed experimental values and fitted values predicted by the response surface models are presented in Table 6. Good agreement must exist between the values calculated using the model equations and the experimental values at the points of interest. A two sample f-test was conducted in order to compare the experimental values of the responses with the predicted values calculated using the models. No significant (po0.05) difference was reported between the actual and the predicted values (test value). The experimental response values were found to be in agreement with the predicted ones. The high correlation coefficients (0.956–0.981) also confirmed that a close agreement between experimental data and predicted values calculated using the models was obtained. On the other hand, the orange beverage emulsion containing the optimum variable levels was practically prepared. The results indicated that the corresponding experimental values for turbidity, average particle size, PDI and density of the desirable orange beverage emulsion were 123.42, 1013, 0.278 and 1.026, respectively. Closeness between the experimental and predicted values under the optimum region indicated the suitability of the corresponding models.
3.4. Verification of models 4. Conclusions The adequacy of the model equations for predicting the optimum response values was tested using the recommended optimum conditions. This set of independent variables was determined to be optimum by an RSM optimization approach, which was also used to experimen-
The central composite design (CCD) was found to be a valuable tool for optimizing the proportion of beverage emulsion components leading to the desirable goals of response variables studied. Analysis of variance (ANOVA)
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showed a high overall coefficient of determination value (R2X0.915) for the regression models. Therefore, it was possible to develop the empirical equations for describing and predicting the variation of response variables. In general, the results indicated that the most significant (po0.05) factor affecting most of the response variables was the linear term of xanthan gum. The adjustment of xanthan content was found to be the most important variable to determine the changes in the response variable studied. Interaction effects between factors appeared in all fitted models except for the polydispersity index and density. Adjustment of the quadratic model with the experimental data was also found to be satisfactory in all regression equations except for the one fitted for density. However, the quadratic and interaction terms exhibited no significant (p40.05) effects in most cases. The optimization procedure indicated that the overall optimum region with high overall desirability (D ¼ 0.92) was obtained by setting the experiment at 13.88% (w/w) Arabic gum, 0.27% (w/w) xanthan gum and 11.27% (w/w) orange oil. The response fitted models were verified using the recommended optimum conditions. The experimental values were shown to be in agreement with those predicted, thus indicating adequacy of the fitted models. Acknowledgments The authors wish to thank Prof. Gary Reineccius and Dr. Ali Reza Taherian for their invaluable guidance and suggestions at the critical start of this project. The authors would like to express their appreciation to Dr. Boo Huey Chern for her help in this project. Sincere appreciation is also extended to Mr. Yap from Siber Hegner (M) Sdn. Bhd. for his assistance in zeta potential and particle size measurements. References Bagchi, P., & Vold, R. D. (1975). A simple method for determination of the average particle size of coarse suspensions from measurements of apparent specific turbidity. Journal of Colloid and Interface Science, 53, 194–201. Bergenstahl, B. A., & Claesson, P. M. (1990). In Friberg (Ed.), Surface forces in emulsions in food emulsions (2nd ed., pp. 41–55). NewYork: Marcel Dekker. Bhandari, B. R., Dumoulin, E. D., Richard, H. M. J., Noleau, I., & Lebert, A. (1992). Flavor encapsulation by spray drying: Application to citral and linalyl acetate. Journal of Food Science, 57, 217–221. Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, 13, 1–45. Brenner, J., Henderson, G. H., & Bergentsen, R. W. (1976). Process of encapsulating an oil and product produced thereby. US Patent 3-971-852. Buffo, R. A., & Reineccius, G. A. (2002). Modeling the rheology of concentrated beverage emulsions. Journal of Food Engineering, 51, 267–272. Buffo, R. A., Reineccius, G. A., & Oehlert, G. W. (2001). Factors affecting the emulsifying and rheological properties of gum acacia in beverage emulsions. Food Hydrocolloids, 15, 53–66. Cacace, J. E., & Mazza, G. (2003a). Optimization of extraction of anthocyanins from black currants with aqueous ethanol. Journal of Food Science, 68, 240–248.
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FOOD
HYDROCOLLOIDS Food Hydrocolloids 22 (2008) 1212–1223 www.elsevier.com/locate/foodhyd
Optimization of the contents of Arabic gum, xanthan gum and orange oil affecting turbidity, average particle size, polydispersity index and density in orange beverage emulsion Hamed Mirhosseinia, Chin Ping Tana,, Nazimah S.A. Hamidb, Salmah Yusofc a
Department of Food Technology, Faculty of Food Science and Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia b Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia c Department of Food Technology, Faculty of Science and Technology, University Science Islam Malaysia, 71800 Nilai, Negeri Sembilan, Malaysia Received 17 January 2007; accepted 27 June 2007
Abstract This paper focuses on the development of an effective methodology to determine the optimum levels of three independent variables leading to (a) maximize turbidity, (b) minimize polydispersity index (PDI) and (c) obtain the target value for average particle size and density of orange beverage emulsion. A three-factor central composite design (CCD) was employed to determine the effect of Arabic gum content (7–13% w/w), xanthan gum content (0.1–0.3% w/w) and orange oil content (6–10% w/w). The emulsion properties studied as response variables were: turbidity (Y1), average particle size (Y2), PDI (Y3) and density (Y4). The response surface analysis was carried out to create efficient empirical models for predicting the changes of response variables. In general, analysis of variance (ANOVA) showed high coefficients of determination values (R2) in the range of 0.922–0.975 for the response surface models, thus ensuring a satisfactory adjustment of the polynomial regression models with the experimental data. The results of regression analysis indicated that more than 92% the response variation could be explained by the models. The results also indicated that the linear term of xanthan gum was the most significant (po0.05) variable affecting the overall responses. The multiple optimization results showed that the overall optimum region with high total desirability (D ¼ 0.92) was found to be at the combined level of 13.88% w/w Arabic gum content, 0.27% w/w xanthan gum content and 11.27% w/w orange oil content. Under the optimum condition, the corresponding predicted response values for turbidity, average particle size, PDI and density of the desirable orange beverage emulsion were 129.55, 988, 0.261 and 1.03, respectively. For validation of the models, the experimental values were compared with predicted values to check the adequacy of the models. The experimental values were found to be in agreement with those predicted, thus indicating suitability of the models employed using response surface methodology (RSM) for optimizing the physical properties of the orange beverage emulsion. r 2007 Elsevier Ltd. All rights reserved. Keywords: Orange beverage emulsion; Turbidity; Average particle size; Polydispersity index; Central composite design; Multiple optimization; Validation; Response surface methodology
1. Introduction The term ‘‘beverage emulsion’’ is used to describe a group of products that have similar composition, preparation and physicochemical properties, for example, fruit drinks, punches and sodas (Tan, 1997). In soft drinks, the beverage emulsion may provide flavor, color and cloudy appearance for the beverage, or just the cloudiness. In fact, Corresponding author. Tel.: +603 89468418; fax: +603 89423552.
E-mail address: [email protected] (C.P. Tan). 0268-005X/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodhyd.2007.06.011
beverage emulsions are either flavor emulsions to provide the beverage with flavor, cloudiness and color as in certain formulae or cloud emulsions to provide only cloudiness (Tan, 1997). Beverage emulsions are oil-in-water (o/w) emulsions that are prepared in a concentrated form and then diluted several hundred times in sugar/acid solution prior to consumption in order to produce the finished beverage, either carbonated or non-carbonated (Tse & Reineccius, 1995). Several studies have been mainly carried out on physical stability and rheological properties of the beverage emulsion (Buffo & Reineccius, 2002; Buffo,
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Reineccius, & Oehlert, 2001; Chanamai & McClements, 2000, 2001a, 2001b; Taherian, Fustier, & Ramaswamy, 2006; Tan, 1997; Tse & Reineccius, 1995). In an emulsion-based product, turbidity, particle size, polydispersity index (PDI) and density are key characteristics, as they contribute to physical stability and rheological properties of the finished products. The perceived quality of emulsion-based food products, such as cream, salad dressings, sauces and beverages, is strongly influenced by their stability, rheology and appearance. Although the stability of an emulsion is very critical for various industrial processes and emulsion-based products, the evaluation of emulsion stability is not easy. Particle size of fat globules (oil phase) and their size distribution play a predominant role in deciding the stability of emulsion and emulsion-based products with precisely controlled particle size exhibit better emulsion stability (McClements, 1999). The stability of an emulsion to gravitational separation can be enhanced by reducing the droplet size (McClements, 1999). As a rule, large globules tend to coalesce faster than the small ones (Bergenstahl & Claesson, 1990). Emulsion stability is a measure of the rate at which an emulsion creams, flocculates or coalesces. The rate of these changes can be measured by determining the size and distribution of the oil droplets in emulsion. Stoke’s law states that the velocity at which a droplet moves is proportional to the square of the droplet size radius. A decrease in its average globule diameter by a factor of two may decrease the coalescence rate by a factor of 10–100 (Bergenstahl & Claesson, 1990). Particle size is a key characteristic, as it contributes to the physical stability and organoleptic properties of the beverage emulsion. This is especially true for beverage emulsions, which have to be stable in both concentrated and diluted forms. For beverage emulsions, the determination of average particle size and its distribution could serve two proposes: first for the estimation of the overall quality of the emulsion concentrates and second for the prediction of emulsion stability in the finished product. For example, a beverage with a droplet size of 0.1 mm diameter will travel upward 100 times slower in the bottle than a droplet size of 1.0 mm diameter (Chanamai & McClements, 2000). Several investigations have been carried out in order to determine the effect of droplet size on the quality of emulsion (Bhandari, Dumoulin, Richard, Noleau, & Lebert, 1992; Brenner, Henderson, & Bergentsen, 1976; Chiewchan, Phungamngoen, & Siriwattanayothin, 2006). All these investigators have reported the significant effect of particle size on the stability of emulsions. Hence, obtaining an emulsion with a uniform small droplet size becomes essential to achieve a stable emulsion system. One of the most important quality attributes of foods is their appearance (Clydesdale, 1993; Hutchings, 1999). The appearance of a food emulsion depends on the way that it scatters and absorbs light waves in the visible region of the electromagnetic spectrum (Kerker, 1969). Scattering largely determines the turbidity, cloudiness, opacity or
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lightness of an emulsion, whereas absorption determines its ‘chromaticitiness’ (McClements, 2002). The degree of scattering by an emulsion depends on the concentration, average droplet size, particle size distribution and refractive index of any particles present (Kerker, 1969; Van de Hulst, 1957). The turbidity of an emulsion containing very fine and polydispersed particles is a complex function of mean droplet size, concentration and particle size distribution. Therefore, the variation in turbidity indicates the change in particle size, dispersity and concentration. In an unstable emulsion, particle size and concentration change with time due to coagulation and coalescence of particles. The turbidity of suspensions containing very coarse particles varies inversely with particle size. The turbidity method has been used for determining the size of very coarse particles in emulsion (Bagchi & Vold, 1975; Hiemenz & Vold, 1966). Turbidity measurement has also been used to determine emulsion stability (Song, Jho, & Kim, 2000). A technique utilizing the spectral absorbance at several wavelengths has been proposed to allow the determination of emulsion stability within a relatively short period of time (Frenkel, Shwartz, & Garti, 1982). RSM is an empirical modeling approach for determining the relationship between various process parameters and responses with the various desired criteria and searching the significance of these process parameters on the coupled responses (Myers & Montgomery, 1995). The basic theoretical and fundamental aspects of response surface methodology (RSM) were originally described by Box and Wilson (1951). The main advantage of RSM is to reduce the number of experimental trials needed to evaluate multiple parameters and their interactions. Therefore, it is less laborious and time-consuming than other approaches required to optimize a process (Giovanni, 1983). RSM has been successfully used to model food ingredients (Gallagher, O’Brien, Scannell, & Arendt, 2003; Shelke, Dick, Holm, & Loo, 1990; Vaisey-Genser, Ylimaki, & Johnston, 1987), optimize food process variables (Senanayake & Shahidi, 1999, 2002; Telez-Luis, Moldes, Alonso, & Vazquez, 2003), optimize extraction condition of phenolic compounds from berries (Cacace & Mazza, 2003a, 2003b) and evening primrose meal (Wettasinghe & Shahidi, 1999), as well as extraction condition of anthocyanins from black currants (Cacace & Mazza, 2003a) and sunflower hull (Gao & Mazza, 1996). The ability of food manufacturers to formulate emulsion-based products with desirable and reproducible appearances depends on the knowledge of the relationship between their optical properties and their composition and microstructure (Chanamai & McClements, 2001a, 2001b; Dickinson, 1994; McClements, 1999; Phillips, McGiff, Barbano, & Lawless, 1995a, 1995b). There are various factors and parameters that affect emulsion properties, including mixing and homogenizing conditions, the proportion of emulsion components, environmental conditions and so on. The objective of this study was to determine the optimum levels of emulsion components
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(namely the Arabic gum, xanthan gum and orange oil in the emulsion formulation) as independent variables leading to (1) the highest turbidity, (2) target value for average particle size and density and (3) the least PDI by using RSM. The beverage emulsion with these properties would be considered as an optimum beverage emulsion that can provide the desirable emulsion-based product especially in terms of appearance and stability. The response variables were modeled as a function of three independent variables to provide information regarding the optimum proportion of emulsion components. It should be noted that the other critical parameters such as zeta potential, physical stability, viscosity, flow behavior and pH have also been investigated by the same optimization procedure in another study. 2. Materials and methods
The Arabic gum solution was kept overnight at room temperature for full hydratation (Buffo et al., 2001; Tse & Reineccius, 1995). To prepare the water phase, xanthan gum solution was prepared separately by dissolving xanthan gum in deionized water and then mixed with Arabic gum solution by using a high-speed blender. While mixing the water phase, the cold-pressed orange oil was gradually added into the water phase to provide an initial coarse emulsion. Fine emulsification (i.e. an average droplet size of 1 mm and narrow particle size distribution) was achieved by subjecting the pre-emulsions to prehomogenization using the high-shear blender (Silverson L4R, Buckinghamshire, UK) for 1 min and then passed through a high-pressure homogenizer (APV, Crawley, UK), for three passes (30, 28 and 25 MPa). 2.3. Analytical methods
2.1. Materials Arabic gum was provided by Colloides Naturels International Co. (Rouen, France). Xanthan gum was donated by CP Kelco (Chicago, USA). Citric acid, sodium benzoate and potassium sorbate (X95%) were purchased from Fisher Scientific (Pittsburgh, PA). Valencia cold-pressed orange oil was provided by Danisco (Cultor, Aarhus, Denmark). 2.2. Preparation of orange beverage emulsion As shown in Table 1, 20 orange beverage emulsions composed of gum Arabic (7–13% w/w), xanthan gum (0.1–0.3% w/w), orange oil (6–10% w/w), sodium benzoate (0.1% w/w), potassium sorbate (0.1% w/w), citric acid (0.4% w/w) and deionized water were prepared for the optimization procedure based on a central composite design (CCD). No weighting agent and vegetable oil were added. A representative orange beverage emulsion is usually composed of two phases: a water phase and an oil phase. To prepare the water phase, sodium benzoate, potassium sorbate and citric acid were dispersed in deionized water (60 1C) using a high-shear blender (Waring blender 32BL80, New Hartford, USA). While mixing the mixture, Arabic gum was gradually added to the deionized water (60 1C) and mixed for 3 min to facilitate hydration.
Table 1 Levels of independent variables established according to the central composite design (CCD) Variable
Independent variable levels
Independent variables
Low
Center
High
Axial (a)
Axial (+a)
Arabic gum content (% w/w) Xanthan gum content (% w/w) Orange oil content (% w/w)
7 0.1 6
10 0.2 8
13 0.3 10
5.101 0.037 4.734
14.899 0.363 11.266
2.3.1. Turbidity The turbidity of diluted emulsions (1:1000) was determined using a portable turbidimeter (Model 2100P, Hach Company, Loveland, CO, USA). The results were reported in nephelometric turbidity units (NTU). Accuracy of the instrument was found to be approximately 70.1 NTU with stray light less than or equal to 0.1 NTU. The accuracy value is essential especially for small and diluted emulsions with high surfactant content. The measurement of turbidity was carried out in triplicate. 2.3.2. Average particle size and PDI Mean particle size and size distribution of beverage emulsions were determined by integrated light scattering using a Malvern series ZEN 3500 (size and zeta potential nano-particle sizer ZS, Malvern Instruments Ltd., Malvern, Worcester, UK). The measurement of droplet size was done immediately after sample preparation. To avoid multiple scattering effects, the emulsions were diluted (1:100; approximately 250 ml) with deionized water prior to analysis, and then directly placed into the module. A laser beam was directed through the diluted samples, scattered by the droplets in a characteristic pattern dependent on their size and detected by an array of photodiodes located behind the cuvette. The instrument used the method of photon correlation spectroscopy (PCS) to measure particle size in constant random thermal, or Brownian, motion. The particle diameter range and number of photon counts per second [kilo Count per second (kCps)] were evaluated at room temperature (Taherian et al., 2006). The PDI was calculated as the best fit between the measured scattered pattern and that predicted by light scattering theory. All measurements were repeated in triplicate for each sample. 2.3.3. Density The density of beverage emulsion concentrates was measured at room temperature using hydrometers (CMS Hydrometers, Curtis Matheson Scientific, New York, NY)
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of appropriate range (Buffo et al., 2001). The measurement was carried out in triplicate. The average of measurement values obtained was taken as a response value for fitting the model and data analysis.
equations (Mason, Gunst, & Hess, 2003; Montgomery, Runger, & Hubele, 2001; Vining, 2003).
2.4. Experimental design
Regression analysis and analysis of variance (ANOVA) were conducted for (1) determining regression coefficients and statistical significance of model terms and (2) fitting the mathematical models to the experimental data, aiming at an overall optimal region for the response variables. Multiple regression coefficients were determined by employing the least-squares technique (Myers & Montgomery, 1995) to predict linear and quadratic polynomial models for the response variables studied. The behavior of the response surface was investigated for the response function (Yi) using the regression polynomial equation. The generalized polynomial model proposed for predicting the response variables is given as
The effect of three independent variables, x1 (Arabic gum content), x2 (xanthan gum content) and x3 (orange oil content), on four response variables (Y1Y4, namely turbidity, average particle size, PDI and density) was evaluated by using the RSM. A CCD was employed (1) to study the main and combined effects of main emulsion components on the physical emulsion properties studied, (2) to create models between the variables and (3) to determine the effect of these variables to optimize the proportion of emulsion components in terms of the response variables studied leading to the desirable goals. Therefore, 20 treatments were assigned based on the second-order CCD with three independent variables at five levels of each variable. Independent variable ranges studied were: gum Arabic (7–13% w/w), xanthan gum (0.1–0.3% w/w) and orange oil (6–10% w/w). Experiments were randomized in order to minimize the effects of unexplained variability in the actual responses due to extraneous factors. The center point was repeated six times to calculate the repeatability of the method (Montgomery, 2001). The matrix of the CCD including the values corresponding to the levels of factors and treatments is shown in Tables 1 and 2. As shown in Tables 1 and 2, the arrangement of CCD presented was in such a way that it allowed the development of the appropriate empirical
Table 2 Matrix of the central composite design (CCD) Treatment runs
Blocks
Arabic gum
Xanthan gum
Orange oil
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 3 3
7.000 7.000 13.000 10.000 13.000 10.000 7.000 7.000 13.000 10.000 10.000 13.000 10.000 14.899 10.000 10.000 10.000 5.101 10.000 10.000
0.100 0.300 0.300 0.200 0.100 0.200 0.100 0.300 0.100 0.200 0.200 0.300 0.200 0.200 0.200 0.200 0.037 0.200 0.200 0.363
6.000 10.000 6.000 8.000 10.000 8.000 10.000 6.000 6.000 8.000 8.000 10.000 8.000 8.000 4.734 11.266 8.000 8.000 8.000 8.000
(C) (C)
(C) (C) (C)
(C)
(C), Center point.
2.5. Statistical analyses
Y i ¼ b0 þ b1 x1 þ b2 x2 þ b3 x3 þ b11 x21 þ b22 x22 þ b33 x23 þ b12 x1 x2 þ b13 x1 x3 þ b23 x2 x3
ð1Þ
where Yi is predicted response, b0 is offset term, b1, b2 and b3 are the regression coefficients for linear effect terms, b11, b22 and b33 are quadratic effects and b12, b13 and b23 are interaction effects. In this model, x1, x2 and x3 are the independent variables. The ANOVA results presented the effect and regression coefficients of individual linear, quadratic and interaction terms that were individually determined. The significance of the equation parameters for each response variable was also assessed by F-ratio at a probability (p) of 0.05. The adequacy of the models was determined using model analysis, lack-of-fit test and coefficient of determination (R2) analysis as outlined by the previous studies (Lee, Ye, Landen, & Eitenmiller, 2000; Weng, Liu, & Lin, 2001). Joglekar and May (1987) suggested that for a good fit of a model, R2 should be at least 0.80. All results were expressed as the mean values of three independent trials. The experimental design matrix, data analysis and optimization procedure were performed using the Minitab v. 13.2 statistical package (Minitab Inc., PA, USA). 2.6. Optimization procedure After generating the polynomial regression equations relating the responses to the independent variables studied, the optimization procedure was performed to obtain the optimal levels of three factors (x1, x2 and x3). Numerical optimization was also carried out to determine the exact optimum level of independent variables leading to the desirable orange beverage emulsion in terms of the response variables. The desired goal for each variable was chosen. Different weights were assigned to each goal to adjust the shape of its particular desirability function for simultaneous optimization of the multiple responses. The optimal condition that depended on the independent
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variables was obtained using the predicted equations determined by RSM. Furthermore, a graphical technique was used in order to deduce workable optimum conditions (Floros & Chinnan, 1988; Giovanni, 1983). Therefore, the fitted regression models were expressed as surface plots in order to visualize the relationship between the response and experimental levels of each factor and to deduce the optimum conditions. For graphical optimization, the threedimensional (3D) plots were plotted by keeping one variable constant at the center point and varying the other two variables within the experimental range. 2.7. Verification of models The adequacy of response surface models for predicting the optimum response values was verified by conducting experiments under the recommended optimum conditions. The experimental and predicted values of the responses were compared in order to check the validity of models. 3. Results and discussion 3.1. Preliminary study The initial preliminary experiments were conducted (1) to study the influence of mixing and homogenizing conditions (speed, time and pressure) on the particle size and distribution, and then (2) to establish the best preparation condition leading to narrow size distribution and target average particle size (approximately 1 mm). The results showed that fine emulsification was achieved by mixing the emulsion for 1 min and then passing it through
the high-pressure homogenizer, three passes at 300, 280 and 250 bar (data not shown). 3.2. Fitting the response surface models In the present work, multiple regression analyses were carried out using response surface analysis (1) to fit mathematical models to the experimental data aiming at an optimal region for the response variables studied and (2) to define the relationship between three independent variables and the criteria of five response variables as presented in Table 3. The response surface analysis allowed the development of an empirical relationship where each response variable (Yi) was assessed as a function of Arabic gum content (x1), xanthan gum content (x2) and orange oil content (x3) and predicted as the sum of constant (b0), three first-order effects (linear terms in x1, x2 and x3), three interaction effects (interactive terms in x1x2, x1x3 and x2x3) and three second-order effects (quadratic terms in x21, x22 and x23). The results obtained were then analyzed by ANOVA to assess the ‘‘goodness of fit’’. Only terms found statistically significant (po0.05) were included in the reduced model. As shown in Eqs. (2)–(5), the models obtained for predicting the response variables explained the main quadratic and interaction effects of factors affecting the response variables. The estimated regression coefficients of the polynomial response surface models along with the corresponding R2 values and lack of fit tests are given in Table 4. The significance of each term was determined using the F-ratio and p-value as presented in Table 5. It was found that the most significant variable affecting the variation of response variables studied was
Table 3 Central composite design: independent (Xi) and response variables (Yj) (mean7SD) Run
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Block
1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 3 3
Independent variable (% w/w)
Response variable
Arabic gum (x1)
Xanthan gum (x2)
Orange oil (x3)
Turbidity (NTU) (Y1)
Average droplet size (nm) (Y2)
Polydispersity index (Y3)
Density (g/cm3) (Y4)
7.000 7.000 13.000 10.000 13.000 10.000 7.000 7.000 13.000 10.000 10.000 13.000 10.000 14.899 10.000 10.000 10.000 5.101 10.000 10.000
0.100 0.300 0.300 0.200 0.100 0.200 0.100 0.300 0.100 0.200 0.200 0.300 0.200 0.200 0.200 0.200 0.037 0.200 0.200 0.363
6.000 10.000 6.000 8.000 10.00 8.000 10.00 6.000 6.000 8.000 8.000 10.000 8.000 8.000 4.734 11.266 8.000 8.000 8.000 8.000
16.570.78 73.570.71 39.072.12 46.572.12 48.571.41 45.071.41 52.572.12 22.573.54 26.572.12 68.071.41 64.571.41 127.071.41 61.071.41 54.070.00 34.070.00 90.070.78 55.071.41 15.071.41 48.070.00 81.070.00
1062719.7 1038736.6 1270723.5 100177.0 923721.5 93576.4 959710.4 1270710.8 1180716.2 924738.4 98873.2 96775.6 1019724.0 105279.2 153075.5 97377.0 880727.5 110078.9 987726.0 1010711.5
0.76270.028 0.34070.003 0.76570.023 0.47270.034 0.37270.015 0.60670.023 0.32970.022 0.87070.005 0.81470.018 0.52470.011 0.45970.011 0.30270.033 0.58370.039 0.53570.022 0.87670.015 0.31570.005 0.58470.027 0.63470.019 0.47070.009 0.42670.020
1.01770.005 1.00970.010 1.03670.003 1.02370.001 1.02870.005 1.02370.004 1.00870.008 1.01770.010 1.03670.007 1.02470.003 1.02270.005 1.03070.000 1.02270.006 1.03770.002 1.02770.007 1.02270.005 1.02370.000 1.01370.009 1.02370.003 1.02470.004
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Table 4 Regression coefficients, R2, adjusted R2 probability values and lack of fit for five variables Regression coefficient
Turbidity (NTUs, Y1)
Average particle size (nm, Y2)
Polydispersity index (Y3)
Density (g/cm3, Y4)
b0 b1 b2 b3 b21 b22 b23 b12 b13 b23 R2 R2 (adj) Regression (p-value) Lack of fit (F-value) Lack of fit (p-value)
37.4 11.0 753.8 8.6 0.8 506.2 0.7 26.7 0.5 50.6 0.975 0.929 0.000a 2.25 0.261b
2741 12 2857 451 3 2175 29 64 5 109 0.968 0.909 0.001a 0.44 0.743b
2.3985 0.0536 0.1290 0.2780 0.0024 0.8062 0.0101 – – – 0.922 0.852 0.000a 0.62 0.719b
1.00806 0.00294 0.00371 0.00191 – – – – – – 0.973 0.962 0.000a 3.74 0.153b
bi: The estimated regression coefficient for the main linear effects; bii: the estimated regression coefficient for the quadratic effects; bij: the estimated regression coefficient for the interaction effects (1: Arabic gum; 2: xanthan gum; 3: orange oil). a Significant (po0.05). b Not significant (p40.05).
Table 5 ANOVA and regression coefficients of the first- and second-order polynomial regression models Variables
Main effects x2
x1 Turbidity (Y1) p-Value F-ratio
Quadratic effects x3
x21
x22
Interaction effects x23
x1x2
x1x3
x2x3
0.167 2.478
0.004 19.999
0.748 0.114
0.030 8.043
0.104 3.663
0.671 0.199
0.018 10.298
0.293 1.329
0.007 16.492
Average droplet size (Y2) p-Value 0.726 F-ratio 0.135
0.012 12.596
0.010 13.543
0.073 4.700
0.136 2.965
0.008 14.884
0.159 2.579
0.056 5.579
0.116 3.375
Polydispersity index (Y3) p-Value 0.357 F-ratio 0.941
0.901 0.016
0.268 1.395
0.401 0.776
0.752 0.106
0.510 0.471
– –
– –
– –
Density (Y4) p-Value F-ratio
0.423 0.687
0.000 43.507
– –
– –
– –
– –
– –
– –
0.000 388.72
Significant at po0.05.
the linear term of xanthan gum content. The results showed that the regression models for the response variables (Y1–Y4) were significant by the F-test at the 5% confidence level (po0.05). Analysis of variance also confirmed that the models were highly significant (po0.05) for all response variables (Table 4). The probability (p) values of all regression models were less than 0.003, which had no indication of lack of fit. The R2 values for these response variables were higher than 0.80 (0.922–0.975), thus ensuring a satisfactory fitness of the regression models to the experimental data. The R2 values for turbidity, average particle size, PDI and density
were 0.975, 0.968, 0.922 and 0.973, respectively. The response surface models could explain more than 90% of the variation in the response variables studied as function of main emulsion components. Therefore, the R2 values of the response models were high enough, thus indicating that the variability of all responses was explained well by the models. The lack of fit, which measures the fitness of models, resulted in no significant F-value (p40.05) in terms of the response variables studied, indicating that models were sufficiently accurate for predicting those response variations. The following response surface models (Eqs. (2)–(5)) were fitted to each of
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the response variables (Yi) three independent variables (x1, x2 and x3): Turbidity:
Turbidity
0:7x23 þ 26:7x1 x2 þ 0:5x1 x3 þ 50:6x2 x3 .
ð2Þ
Average particle size: Y 2 ¼ 2741 12x1 þ 2857x2 451x3 þ 3x21 2175x22 þ 29x33 64x1 x2 5x1 x3 109x2 x3 .
ð3Þ
PDI:
Turbidity value (NTU)
Y 1 ¼ 37:4 þ 11:0x1 753:8x2 þ 8:6x3 0:8x21 þ 506:2x22 100 90 80 70 60 50 40 30 20 10 5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w)
Y 3 ¼ 2:3985 0:0536x1 þ 0:1290x2 0:2780x3 þ 0:0024x21 0:8062x22 þ 0:0101x23 .
ð4Þ
0.1
an
nth
Xa
Hold values: Orange oil: 8.0
Density:
Turbidity
3.2.1. Turbidity The effect of three independent variables on the turbidity value was reported by the coefficient of the second-order polynomial regression equations (Tables 4 and 5). To aid visualize the effect of main emulsion components on the turbidity, the response surface for turbidity is shown in Fig. 1(a–c). As shown in Eq. (2), the second-order polynomial regression equation (full quadratic) was fitted for predicting the turbidity value. Table 5 clearly exhibited that turbidity was most significantly (po0.05) influenced by the linear effect of xanthan gum, followed by interaction terms of xanthan gum and orange oil. It was found that the quadratic term of Arabic gum had a significant (p40.05) negative effect on turbidity while showing a positive effect in the linear term (Table 4). These significant terms should be considered as the primary factors for determining the variation of turbidity. The optimum turbidity (the highest value, Y1 ¼ 161.15) was predicted to be obtained at 14.899% (w/w) Arabic gum, 0.339% (w/w) xanthan gum and 11.266% (w/w) orange oil using response surface plots and response optimizer. Fig. 1 shows the surface plot where the turbidity increased as the orange oil content increased. Therefore, the beverage emulsion with higher orange oil content resulted in higher turbidity.
Turbidity value (NTU)
(5)
100
50
0 5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w) Turbidity
150 100 50
0.0
3.2.2. Average particle size The results showed good repeatability between the replications of particle size measurements. The droplet radius of the diluted emulsions remained constant throughout the experiments, which exhibited no coalescence. The most significant (po0.05) factor affecting the average particle size was the quadratic term of orange oil, followed by the linear terms of orange oil and xanthan gum (Table 5). Interaction effects between factors were negatively related to the particle size value (Table 4). However, the interaction terms between factors were shown to be non-significant (p40.05) on average particle size (Table 5).
1 10. 1.5 9 . 5 5 w) 7.58.5 w/ 6 . 5 % 5 il ( 4.5 .5 o nge Ora
Hold values: Xanthan gum: 0.2
Turbidity value (NTU)
Y 4 ¼ 1:00806 þ 0:00294x1 þ 0:00371x2 0:00191x3 .
0.0
0.4 ) 0.3 w/w % ( gum
0.2
0.1 0.2 Xanthan gum
0.3 (% w/w)
0.4
1 10. 1.5 9 . 5 5 w) 7.58.5 w/ 6 . 5 % 5 il ( 4.5 .5 o nge Ora
Hold values: Arabic gum: 10.0 Fig. 1. Response surface plots for turbidity as a function of (a) Arabic gum and orange oil contents, (b) xanthan gum and orange oil contents and (c) Arabic gum and xanthan gum contents.
As shown in Table 4, the linear terms of Arabic gum and orange oil had negative effects on average particle size; conversely, the response value was positively influenced by the linear effect of xanthan gum (Table 4). This behavior
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may be explained by the positive effect of xanthan on the emulsion viscosity. Table 4 clearly exhibits that the quadratic terms of factors had reverse behavior as compared to their linear terms. To aid visualization, the response surface for average particle size is shown in
Droplet size value (nm)
Average droplet size
1150 1050 950 850 5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w)
0.2
0.1
0.0
an
h ant
0.3
0.4
% m(
)
w/w
gu
X
Hold values: Orange oil: 8.0
Droplet size value (nm)
Average droplet size
1600 1500 1400 1300 1200 1100 1000 900 5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w)
1 10. 1.5 9 .5 5 ) 7.58.5 6 w/w . 5.5 5 % ( 4.5 oil nge a r O
Hold values: Xanthan gum: 0.2
Droplet size value (nm)
Average droplet size
1600 1500 1400 1300 1200 1100 1000 900 800
1219
Fig. 2(a–c). The optimum average particle size was obtained at a combination of 14.807% (w/w) Arabic gum, 0.363% (w/w) xanthan gum and 8.347% (w/w) orange oil. 3.2.3. Polydispersity index The linear and quadratic effects of independent variables on the PDI showed the same effect as their effects on average particle size (Tables 4 and 5). As shown in Table 4, indicats that the linear terms of Arabic gum and orange oil were negatively influenced by the PDI, whereas their quadratic terms showed the opposite behavior. On the other hand, results showed that the PDI was positively affected by the linear term of xanthan, whereas the quadratic term of xanthan gum had a negative effect on the PDI (Table 4). The optimum PDI (the least value, Y3 ¼ 0.253) was predicted to be obtained by set level of 14.899% Arabic gum, 0.363% (w/w) xanthan gum and 11.266% (w/w) orange oil determined using response surface plots and response optimizer. Fig. 3 shows the surface plot for the PDI as a function of Arabic gum, xanthan gum and orange oil content. 3.2.4. Density The linear regression equation was fitted to predict the effect of independent variables on the variation of density leading to obtain the target density value. The quadratic and interaction effects were shown to be non-significant (p40.05) on density (Tables 5). The linear term of Arabic gum was the most significant (po0.05) factor for density, followed by the linear term of orange oil (Table 5). Therefore, these significant terms were the primary determining factors for predicting the density value. It was found that the linear terms of Arabic gum and xanthan gum showed positive effects on the density value (Table 4). Conversely, as expected, that the linear term of orange oil had a significant (po0.05) negative effect on density. This observation may be explained by the positive effect of water phase and the negative effect of oil phase on the density value. The optimum density (target value, Y4 ¼ 1.03) was obtained at 10.547% (w/w) Arabic gum, 0.363% (w/w) xanthan gum and 4.734% (w/w) orange oil. Fig. 4 shows the surface plot for density as a function of Arabic gum, xanthan gum and orange oil content. 3.3. Optimization procedure
0.0
0.1 0.2 0.3 Xanthan gum (% w/w)
1 10. 1.5 9 .5 5 ) 7.58.5 6 w/w 5.5 .5 (% l 4 i o 0.4 .5 nge Ora
Hold values: Arabic gum: 10.0 Fig. 2. Response surface plots for average particle size as a function of (a) Arabic gum and orange oil contents, (b) xanthan gum and orange oil contents and (c) Arabic gum and xanthan gum contents.
Numerical and graphical optimization procedures were carried out for predicting the exact optimum level of independent variables leading to the desirable response goals. First, an optimal treatment was found via response surface plotting of the data, but there had to be a compromise between the optimum ranges for the five responses: turbidity, average particle size, PDI and density. For the graphical interpretation of the independent variable interactions, the use of 3D plots of the regression model was highly recommended (Mason et al., 2003;
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Density
0.65 0.60 0.55 0.50 5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w)
0.0
0.1
0.2
han
0.3
0.4
%
( gum
) ww
Density value (gr/cm3)
Polydispersity value
Polydispersity
1.04 1.03 1.02 1.01 5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w)
nt Xa
Hold values: Orange oil: 8.0
1 1 1.5 9.5 0.5 8. ) 6 7.5 5 w/w 5.5 .5 % ( 4.5 oil nge a r O
Density value (gr/cm3)
Polydispersity value
5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w)
)
w/w
gu
X
1.04 1.03 1.02 1.01 1.00 5 6 7 8 9 10 11 12 13 14 15 Arabic gum (% w/w)
1 1 1.5 9.5 0.5 8 .5 ) 6 7.5 w/w 5 .5 % ( 4.5 .5 oil nge a r O
Hold values: Xanthan gum: 0.2
Hold values: Xanthan gum: 0.2
Density
1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
0.4
1 1 1.5 9.5 0.5 8. ) 6 7.5 5 w/w 5.5 .5 % ( 4.5 oil nge a r O
Hold values: Arabic gum: 10.0
Density value (gr/cm3)
Polydispersity
Polydispersity value
an
h ant
% m(
Density
1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2
0.2 0.3 Xanthan gum (% w/w)
0.0
0.4
Hold values: Orange oil: 8.0
Polydispersity
0.0
0.2
0.1
0.3
1.028
1.023
1.018 0.0
0.1
0.2 0.3 Xanthan gum (% w/w)
1 1 1.5 9.5 0.5 8 .5 ) 6 7.5 w/w 5.5 .5 % ( 4 l 0.4 .5 oi nge Ora
Hold values: Arabic gum: 10.0
Fig. 3. Response surface plots for polydispersity index as a function of (a) Arabic gum and orange oil contents, (b) xanthan gum and orange oil contents and (c) Arabic gum and xanthan gum contents.
Fig. 4. Response surface plots for density as a function of (a) Arabic gum and orange oil contents, (b) xanthan gum and orange oil contents and (c) Arabic gum and xanthan gum contents.
Montgomery, Runger, & Hubele, 2001; Vining, 2003). The response optimizer was also employed to confirm the optimum variable region. Therefore, the 3D surface plots were drawn to illustrate the interactive effects of the independent variables corresponded response variable. The
optimum condition was then determined by superimposing the plots of all the responses. Variables giving quadratic and interaction terms with the largest absolute coefficients and t-value in the fitted models were chosen for the axes of the response surface plots to account for curvature of the
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Table 6 Experimental and predicted values for the response variables based on the reduced models Run
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Turbiditya (Y1, NTUs)
Average article sizea (Y2, nm)
Polydispersity indexa (Y3)
Densitya (Y4, g/cm3)
(Y0)
(Yi)
(Y0–Yi)
(Y0)
(Yi)
(Y0–Yi)
(Y0)
(Yi)
(Y0–Yi)
(Y0)
(Yi)
(Y0–Yi)
16.5 73.5 39.0 46.5 48.5 45.0 52.5 22.5 26.5 68.0 64.5 127.0 61.0 54.0 34.0 90.0 55.0 15.0 48.0 81.0
15.8 67.1 40.5 50.9 43.7 50.9 54.1 18.9 36.0 66.3 66.3 119.4 60.9 53.6 25.5 96.2 41.2 19.3 60.9 80.5
0.7 6.4 1.5 4.4 4.8 5.9 1.6 3.6 9.6 1.7 1.8 7.6 0.1 0.4 8.5 6.2 13.8 4.3 12.9 0.5
1062 1038 1270 1001 923 935 959 1270 1180 924 988 967 1019 1052 1530 973 880 1100 987 1010
1158 983 1253 973 888 973 898 1263 1168 983 983 993 1004 1004 1485 1043 926 1004 1004 1082
96 55 17 28 35 38 61 7 12 59 5 26 15 48 45 70 46 96 17 72
0.762 0.340 0.765 0.472 0.372 0.606 0.329 0.870 0.814 0.524 0.459 0.302 0.583 0.535 0.876 0.315 0.584 0.634 0.470 0.426
0.762 0.344 0.762 0.553 0.344 0.553 0.341 0.758 0.758 0.550 0.550 0.341 0.553 0.553 0.894 0.212 0.553 0.553 0.553 0.553
0.000 0.004 0.003 0.081 0.028 0.053 0.012 0.112 0.056 0.026 0.091 0.039 0.030 0.018 0.018 0.103 0.031 0.081 0.083 0.127
1.017 1.009 1.036 1.023 1.028 1.023 1.008 1.017 1.036 1.024 1.022 1.030 1.022 1.037 1.027 1.022 1.023 1.013 1.023 1.024
1.017 1.011 1.034 1.023 1.028 1.023 1.011 1.017 1.035 1.023 1.023 1.029 1.024 1.038 1.029 1.019 1.024 1.009 1.024 1.024
0.000 0.002 0.001 0.000 0.000 0.000 0.003 0.000 0.001 0.001 0.001 0.001 0.002 0.001 0.002 0.003 0.001 0.004 0.001 0.000
(Y0): Experimental value; (Yi): predicted value; (Y0–Yi): residue. a No significant (p40.05) difference between experimental (Y0) and predicted value (Yi).
surfaces. The response surface plots were also drawn by imposing a constant value (i.e., the central points of the interval taken into consideration to one independent variable). A numerical optimization was also carried out for simultaneous multiple optimization of response variables and determining the overall optimal condition. The desired goals for each response variable were chosen. The responses were then analyzed jointly by conferring to them either the same importance or weight. The optimum set of three independent variables leading to the desired goals of response variables was obtained using the response optimizer. The emulsion would be considered an optimum product if the criteria applied for graphical optimization led to (a) maximize turbidity, (b) minimize PDI and (c) achieve the target value for average particle size and density. The optimization procedures showed the overall optimum region to be at 13.88% (w/w) Arabic gum, 0.27% (w/w) xanthan gum and 11.27% (w/w) orange oil by overlaying all the responses and response optimizers. Under the optimum condition, the corresponding predicted response values for turbidity, average particle size, PDI and density of the optimized orange beverage emulsion were 129.55, 988, 0.261 and 1.03, respectively.
tally predict the value of responses using the models. The observed experimental values and fitted values predicted by the response surface models are presented in Table 6. Good agreement must exist between the values calculated using the model equations and the experimental values at the points of interest. A two sample f-test was conducted in order to compare the experimental values of the responses with the predicted values calculated using the models. No significant (po0.05) difference was reported between the actual and the predicted values (test value). The experimental response values were found to be in agreement with the predicted ones. The high correlation coefficients (0.956–0.981) also confirmed that a close agreement between experimental data and predicted values calculated using the models was obtained. On the other hand, the orange beverage emulsion containing the optimum variable levels was practically prepared. The results indicated that the corresponding experimental values for turbidity, average particle size, PDI and density of the desirable orange beverage emulsion were 123.42, 1013, 0.278 and 1.026, respectively. Closeness between the experimental and predicted values under the optimum region indicated the suitability of the corresponding models.
3.4. Verification of models 4. Conclusions The adequacy of the model equations for predicting the optimum response values was tested using the recommended optimum conditions. This set of independent variables was determined to be optimum by an RSM optimization approach, which was also used to experimen-
The central composite design (CCD) was found to be a valuable tool for optimizing the proportion of beverage emulsion components leading to the desirable goals of response variables studied. Analysis of variance (ANOVA)
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showed a high overall coefficient of determination value (R2X0.915) for the regression models. Therefore, it was possible to develop the empirical equations for describing and predicting the variation of response variables. In general, the results indicated that the most significant (po0.05) factor affecting most of the response variables was the linear term of xanthan gum. The adjustment of xanthan content was found to be the most important variable to determine the changes in the response variable studied. Interaction effects between factors appeared in all fitted models except for the polydispersity index and density. Adjustment of the quadratic model with the experimental data was also found to be satisfactory in all regression equations except for the one fitted for density. However, the quadratic and interaction terms exhibited no significant (p40.05) effects in most cases. The optimization procedure indicated that the overall optimum region with high overall desirability (D ¼ 0.92) was obtained by setting the experiment at 13.88% (w/w) Arabic gum, 0.27% (w/w) xanthan gum and 11.27% (w/w) orange oil. The response fitted models were verified using the recommended optimum conditions. The experimental values were shown to be in agreement with those predicted, thus indicating adequacy of the fitted models. Acknowledgments The authors wish to thank Prof. Gary Reineccius and Dr. Ali Reza Taherian for their invaluable guidance and suggestions at the critical start of this project. The authors would like to express their appreciation to Dr. Boo Huey Chern for her help in this project. Sincere appreciation is also extended to Mr. Yap from Siber Hegner (M) Sdn. Bhd. for his assistance in zeta potential and particle size measurements. References Bagchi, P., & Vold, R. D. (1975). A simple method for determination of the average particle size of coarse suspensions from measurements of apparent specific turbidity. Journal of Colloid and Interface Science, 53, 194–201. Bergenstahl, B. A., & Claesson, P. M. (1990). In Friberg (Ed.), Surface forces in emulsions in food emulsions (2nd ed., pp. 41–55). NewYork: Marcel Dekker. Bhandari, B. R., Dumoulin, E. D., Richard, H. M. J., Noleau, I., & Lebert, A. (1992). Flavor encapsulation by spray drying: Application to citral and linalyl acetate. Journal of Food Science, 57, 217–221. Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, 13, 1–45. Brenner, J., Henderson, G. H., & Bergentsen, R. W. (1976). Process of encapsulating an oil and product produced thereby. US Patent 3-971-852. Buffo, R. A., & Reineccius, G. A. (2002). Modeling the rheology of concentrated beverage emulsions. Journal of Food Engineering, 51, 267–272. Buffo, R. A., Reineccius, G. A., & Oehlert, G. W. (2001). Factors affecting the emulsifying and rheological properties of gum acacia in beverage emulsions. Food Hydrocolloids, 15, 53–66. Cacace, J. E., & Mazza, G. (2003a). Optimization of extraction of anthocyanins from black currants with aqueous ethanol. Journal of Food Science, 68, 240–248.
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