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Food Hydrocolloids 44 (2015) 40e48

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Food Hydrocolloids

journal homepage: www.elsevier .com/locate/ foodhyd

Nano-emulsification of orange peel essential oil using sonication andnative gums

Adel Mirmajidi Hashtjin, Soleiman Abbasi*

Food Colloids and Rheology Lab., Department of Food Science & Technology, Faculty of Agriculture, Tarbiat Modares University, P. O. Box 14115-336, Tehran,Iran

a r t i c l e i n f o

Article history:Received 20 January 2014Accepted 28 August 2014Available online 17 September 2014

Keywords:NanoemulsionsSonicationOrange peel essential oilRheology

* Corresponding author. Tel.: þ98 21 48292321; faxE-mail address: sabbasifood@modares.ac.ir (S. Abb

http://dx.doi.org/10.1016/j.foodhyd.2014.08.0170268-005X/© 2014 Elsevier Ltd. All rights reserved.

a b s t r a c t

Essential oils are widely used in food and pharmaceutical industries where they encounter major con-cerns more likely insolubility and instability. Therefore, using the response surface methodology, theinfluence of ultrasonication conditions as well as native gums on the mean droplets diameter (Z-averagevalue), polydispersity index (PDI), and viscosity of the orange peel essential oil (OPEO) nanoemulsionswere evaluated. In addition, the flow behavior and stability of selected nanoemulsions was assessedduring storage at different temperatures. Results showed that the optimum conditions for producingOPEO nanoemulsions (12.68 nm) were determined as 94% (sonication amplitude), 138 s (sonication time)and 37 �C (process temperature). Moreover, the soluble fraction of Persian and tragacanth gums at lowconcentration showed significant effect on stability, particle size, and rheology. In addition, the flowbehavior of produced nanoemulsions was Newtonian, and the effect of storage conditions (time andtemperature) on the Z-average value was highly significant (P < 0.0001).

© 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Nowadays, due to increased level of knowledge, most con-sumers prefer foods without synthetic chemicals. Accordingly, theuse of natural aromatic compounds and flavors such as essentialoils in food products is of utmost importance (Teixeira, Andrade,Farina, & Rocha-Leao, 2004). Orange peel essential oil (OPEO) isamong the most common important essential oils used in food,cosmetics, and pharmaceutical industries. The OPEO normally in-cludes limonene (94%), myrcene (2%), linalool (0.5%), octanal (0.4%),decanal (0.4%), neral (0.1%), geraniol (0.1%) and some others(Ashurst, 1999). Moreover, the most constitutional components ofOPEO such as aldehydes (octanal, decanal), alcohols (linalool), es-ters and terpenoids (limonene) are low molecular weight com-pounds (Fisher & Scott, 1997), making the essential oil morevolatile. In addition, most of these compounds are highly lipophilic,therefore they have lower solubility in aqueous and aqueous sugar-containing beverages due to lower hydrogen bonding (Ashurst,1999).

: þ98 21 48292200.asi).

The popularity of OPEO is due to its pleasant aromatic scent aswell as facilitating its acceptance by the individuals to benefit fromits therapeutic properties (Duke, Bogenschutz-Godwin, Cellier, &Duke, 2002).

Due to their particular aromas and low costs, the use of suchcompounds has always been considered by food industries, buttheir applications have been associated with problems such as lackof compatibility and solubility inmost food environments, volatilityand instability during processing and storage. Therefore, findingsomemethods to increase the stability of these essential oils in foodenvironments as well as its controlled release when needed is aturning point in the production, trade and use of aromatic com-pounds and flavors in foodstuffs and other non-food products. Inthis regard, the emulsion technology seems to be one of the mostessential processes to enhance their solubility, nanocapsulation,and protection (Peter & Given, 2009).

Nanoemulsions are a group of emulsions that have droplet sizesmostly smaller than 100 nm, and have been used for many indus-trial applications due to high kinetic stability, low viscosity, andhigh transparency, which are not formed spontaneously(McClements, 2005; Solans, Izquierdo, Nolla, Azemar, & Garcia-Celma, 2005).

In emulsion systems, properties such as stability, rheology,appearance, color, and texture depend on the size of emulsion

A.M. Hashtjin, S. Abbasi / Food Hydrocolloids 44 (2015) 40e48 41

droplets and particle size distribution (Gutierrez et al., 2008;Tadros, Izquierdo, Esquena, & Solans, 2004). The characteristics ofemulsions are also dependent upon the techniques used to preparethe emulsion and emulsification process. There are also manytechniques for producing nanoemulsions, each of which with itsown advantages and disadvantages generating emulsions withdifferent properties (Gutierrez et al., 2008; Qian & McClements,2011; Silva, Cerqueira, & Vicente, 2012; Tadros et al., 2004).

As mentioned earlier, nanoemulsions are unbalanced systemswhich are not formed spontaneously. Therefore, some kinds ofenergy (mechanical or chemical) are required for their formation(Gutierrez et al., 2008; Silva et al., 2012; Solans et al., 2005; Tadroset al., 2004). As a result, their optimization would be achievablethrough optimizing the composition and variables related to thepreparation methods and conditions. In addition, the ultimateobjective in optimizing is often to reach the minimum droplet size,minimum polydispersity, and maximum stability (Gutierrez et al.,2008; Tadros et al., 2004).

In the food industry, nanoemulsions are usually produced usinghigh-energy emulsification methods, such as high pressure ho-mogenization, microfluidization, and high intensity ultrasonication(Rao & McClements, 2011a). Among these, the use of ultra-sonication to produce nanoemulsions is a recent development (Li&Chiang, 2012). Lower energy and surfactant consumption, smallerdroplet size, lower polydispersity, and higher stability of nano-emulsions are among the major advantages of this technique overthe other methods (Kentish et al., 2008; Li & Chiang, 2012; Tadroset al., 2004). It should be noted that in the production of nano-emulsions using ultrasonication, some variables such as amplitude,sonication time, as well as temperature can be effective upon thecharacteristics of nanoemulsions (Kaltsa, Michon, Yanniotis, &Mandala, 2013; Kentish et al., 2008; Li & Chiang, 2012; MirmajidiHashtjin & Abbasi, 2014).

Moreover, the type and concentration of the components ofemulsions play significant roles in determining their characteris-tics. Therefore, to improve the physical and rheological propertiesand stability of nanoemulsions, selecting the type of compoundsand their proportions in the formulation is an important matter.Water, oil phase and emulsifier form the basic structure of nano-emulsions (Silva et al., 2012). In this regard, the type and concen-tration of emulsifier are the most important factors affectingnanoemulsion systems. There are a number of diverse emulsifierssuch as Spans and Tweens which just play the role of emulsifier(Marie, Perrier-Cornet, & Gervais, 2002), and some such as gums,modified starches and milk proteins which have both emulsifyingand stabilizing roles (Dickinson, 2009; Mohan & Narsimhan, 1997).

On the other hand, because of the willingness and propensity ofconsumers to reduce food additives in formulated food products,recently the feasibility of replacing additives with natural in-gredients has become especially important. In this regard, it hasalready been reported that native gums (e.g. gum tragacanth andPersian gum) can function as emulsifier and emulsion stabilizer(Abbasi & Mohammadi, 2013; Abbasi & Rahimi, 2014; Azarikia &Abbasi, 2010), therefore, they might be able to improve someproperties of nanoemulsion systems as well.

Creaming, flocculation and coalescence are the most commonexamples of emulsion instability during storage. It is known thatthe presence of hydrocolloids in an emulsion strongly influenceemulsion stability (Garti & Leser, 2001; Kaltsa et al., 2013). Mean-while, the presence of an appropriate amount of hydrocolloids inthe emulsions can lead to a reduction in the size of the droplets.This is possibly due to covering more interfacial area, higher rate ofsurface coverage and lower rate of droplet collisions because of theincrease in emulsifier concentration and continuous phase viscos-ity. All these reasons will lead to a lower re-coalescence and

consequently, smaller emulsion droplet size (Jafari, He, & Bhandari,2007; Qian & McClements, 2011). Preliminary results obtainedduring course of this investigation showed that a proper concen-tration of soluble phases of gum tragacanth and Persian gumindividually and in combined forms had a favorable effect oncertain characteristics such as reducing the mean droplet size andPDI. Consequently, the aims of this study were to investigate theeffect of different sonication conditions as well as the presence ofnative gums on some physical and rheological properties of OPEOnanoemulsions using response surface methodology (RSM).

2. Materials and methods

2.1. Materials

Natural orange peel essential oil (OPEO), without any purifica-tion, was supplied by a local manufacturer (Giah Essance, Gorgan,Iran). Tween 80 was purchased from Merck Chemicals Company(Darmstadt, Germany). Persian gum (PG) and gum tragacanth (GT)were bought from a local herbal store. Sodium azide (analyticalgrade) was purchased from Sigma Chemicals Company (St. Louis,USA). Deionized water (Electrical resistivity¼ 18 MU$cm) was usedfor the production of nanoemulsions.

2.2. Preparation of gum tragacanth and Persian gum powder

Gum tragacanth ribbons (GT) as well as Persian gum hunks (PG)were pulverized using domestic grinder (Moulinex, France), andafter passing through a number of sieves, the powders (meshsize < 60) were collected and kept in a closed container (Abbasi &Mohammadi, 2013; Azarikia & Abbasi, 2010).

2.3. Preparation of PG and GT dispersions

In order to prepare dispersions, powders (PG 3, GT 0.5% w/w)were weighted (Tecator, Swiss) and gradually added to a beakercontaining deionized water on a magnetic stirrer (Heidolph-MR3001, Germany) to obtain uniform dispersions. Sodium azide(0.004 wt %) was added to the dispersions as an antimicrobialagent. Afterwards, to assure fully hydration, the dispersions wereincubated in water bath (Kottermann, Germany) for 30 min at50 �C, and were then kept for 24 h at ambient temperature (Abbasi& Mohammadi, 2013; Azarikia & Abbasi, 2010).

2.4. Separation of soluble and insoluble fractions of PG and GT

At this step, the dispersions were transferred to 50 ml plastictubes. Then, the soluble and insoluble fractions were separated bycentrifugation (Sigma, model K 30-3, Germany) at 20,000 g for20 min at 25 �C. Next, the fractions were manually separated, andafter weighting the phases, the amount of their dry matter wasdetermined by oven (EHRET, model TKL 4105, Germany) at a tem-perature of 105�C (Abbasi & Mohammadi, 2013; Azarikia & Abbasi,2010).

2.5. Preparation of nanoemulsions

The oil-in-water (O/W) nanoemulsions were prepared usingOPEO (1% w/w), as the oil phase, and mixture of Tween 80 (2% w/w), combined soluble fractions of PG (SFPG) and GT (SFGT) (0.25%w/w) and deionized water (96.75% w/w), as the aqueous phase. Sothat, formulation of all samples was as follows: 1% OPEO þ 2%Tween 80 þ 0.25% gum (SFPG:SFGT, with a mixing ratio of 75:25).All emulsions were prepared through a two-stage process. At first,the oil and aqueous phases (total of 100 g) were placed in a glass

Table 1Uncoded and coded independent variables used in RSM design.

Symbol Independent variable Coded levels

�1.68 �1 0 1 1.68

X1 Amplitude (%) 69.86 76 85 94 100.14X2 Time (s) 89.73 102 120 138 150.27X3 Temperature (�C) 4.82 13 25 37 45.18

Table 2Effect of different ultrasonication conditions (time, amplitude, temperature) onenergy consumption, viscosity, Z-average, and PDI of nanoemulsion (1 wt% OPEO,2 wt% Tween 80 and 0.25 wt% gum (SFPG:SFGT, ratio of 75:25)).

Runa Independent variableb Responsec

X1 X2 X3 Y1 Y2 Y3 Y4

1 76 138 37 13,193 2.22 17.24 0.5452 85 89.73 25 9672 2.05 34.75 0.6073 85 150.27 25 16,289 1.81 20.28 0.4754 94 138 37 16,981 2.10 12.68 0.4395 76 138 13 12,529 2.19 22.71 0.5496 94 102 13 12,703 2.19 20.48 0.5127 94 138 13 16,471 2.06 15.33 0.4778 76 102 37 9787 2.38 22.14 0.6119 69.86 120 25 10,534 2.02 26.24 0.57010 85 120 25 12,755 1.91 26.08 0.54411 85 120 25 12,819 1.90 22.65 0.54912 85 120 45.18 12,837 2.28 13.76 0.47113 85 120 25 12,991 1.90 22.10 0.54714 85 120 25 13,065 1.89 21.31 0.53815 85 120 25 12,954 1.92 21.59 0.55316 85 120 25 12,972 1.92 23.03 0.57617 94 102 37 12,204 2.36 19.16 0.57118 76 102 13 9588 2.29 25.62 0.62019 100.14 120 25 15,753 1.83 18.83 0.48620 85 120 4.82 13,210 2.14 20.58 0.553

a In runs 9 and 19, amplitude of sonication was considered 70 and 100%,respectively. Moreover, in runs 12 and 20, process temperature was considered 45and 5 �C, respectively.

b X1, sonication amplitude (%); X2, sonication time (s); X3, process temperature(�C).

c Y1, energy input (J); Y2, viscosity (mPa$s); Y3, Z-average (nm); Y4, PDI.

A.M. Hashtjin, S. Abbasi / Food Hydrocolloids 44 (2015) 40e4842

beaker and mixed at room temperature (z25 �C) using a magneticstirrer (700 rpm for 15 min). Afterwards, they were sonicated(Sonicator 4000, 20 kHz, high gain cylindrical titanium sonotrodeof 19.1 mm in diameter, Misonix, Inc, New York) in a double-walledcylindrical glass cell where the sonotrode was immersed 1 cmbelow the surface of the emulsion. The sonicationwas carried out atvarious amplitudes (70e100%) for different times (90e150 s)controlled by the software of the device. The total input energy (J)was also recorded for every treatment. The sonication was carriedout at various temperatures that were kept constant at intendedtemperature (5e45 �C) throughout sonication by circulatingcoolant fluid through the jacket of chamber. In order to control thetemperature during the sonication process, a double-walled cylin-drical glass container (inner diameter 65, depth 90 mm) wasdesigned. Due to the continuous circulation of the antifreeze(propylene glycol) liquid through the jacket and the embeddedspiral coil, the temperature of premix was kept constant (with anaccuracy of ±1 �C) at intended temperature throughout sonication(Mirmajidi Hashtjin & Abbasi, 2014).

2.6. Droplet size and size distribution measurements

The mean droplet diameter (Z-average) and polydispersity in-dex (PDI) of OPEO nanoemulsions were determined by dynamiclight scattering (Nano-ZS90, Malvern Instruments, Worcestershire,UK) using a Zetasizer (Tang, Manickam, Wei, & Nashiru, 2012).

2.7. Rheological measurements

The rheological properties of nanoemulsions were determinedat 25 �C with a Brookfield rheometer (LV DV-ІІІ Ultra, BrookfieldEngineering Laboratories Inc., MA, USA), equipped with anenhanced UL Adapter. In order to determine the shear stress andviscosity as functions of shear rate, as well as the flow behavior ofnanoemulsions, the shear rate (0.01e171.2 s�1 in 5 min and171.2e0.01 s�1 within next 5 min) was changed. The data werefitted with the Newtonian model using linear regressions. Mean-while, their time-dependency (constant shear rate of 91.73 s�1,temperature of 25 �C, 2 min) were assessed (Abbasi&Mohammadi,2013; Barnes, 2000; Bourne, 2002; Mirmajidi Hashtjin & Abbasi,2014).

2.8. Stability at different storage temperature

For this purpose, some 20 g of nanoemulsions were transferredinto a 25 ml glass bottles, tightly sealed with plastic caps, wrappedwith aluminum foil, and stored at 5, 25 and 45 �C for 12 weeks.Then, their stability was assessed by measuring the mean dropletdiameter and size distribution at 4 week intervals.

2.9. Experimental design

Response surface methodology (RSM) was used to evaluate theeffects of independent variables [sonication amplitude 76e94%(X1), sonication time 102e138 s (X2), and process temperature13e37 �C (X3) ] as well as their interactions on responses [energyinput during ultrasonication (Y1), viscosity (Y2), Z-average (Y3) andPDI (Y4)] of OPEO nanoemulsions. The coded and uncoded inde-pendent variables used in the RSM design are shown in Table 1. Theexperiments were designed according to the central compositedesign (CCD) using a 23 factorial and star design with six centralpoints as shown in Table 2. A second-order polynomial equationwas used to express the responses as a function of the independentvariables as follows:

Yi ¼ a0 þ a1X1 þ a2X2 þ a3X3 þ a11X21 þ a22X

22 þ a33X

23

þ a12X1X2 þ a13X1X3 þ a23X2X3 (1)

where a0 is a constant, ai, aii and aij are the linear, quadratic andinteractive coefficients, respectively. The coefficients of theresponse surface equation were determined using Design-Expert7.1.1 software.

2.10. Statistical analysis

The analysis of variance (ANOVA), regression coefficient calcu-lation, performance stepwise procedure to simplify the models andgenerating of three-dimensional surface plots were carried outusing Design-Expert 7.1.1 software. The significance of the equationparameters for each responsewas assessed by F-value at (P) of 0.05.In addition, the adequacy of the models was determined usingmodel analysis, lack-of-fit test and coefficient of determination (R2)analysis.

3. Results and discussion

3.1. Fitting the models

The total energy input during ultrasonication, viscosity, Z-average and polydispersity index of the OPEO nanoemulsions are

Table 4ANOVA of the regression coefficients of the fitted quadratic equations for the Z-average (Y3) and polydispersity index (Y4) of the nanoemulsions (1 wt% OPEO, 2 wt%Tween 80 and 0.25 wt% gum (SFPG:SFGT, ratio of 75:25)).

Variable Z-average (Y3) Polydispersity index (Y4)

Regressioncoefficient

F-value P-value Regressioncoefficient

F-value P-value

a0 22.89 0.54Lineara1 �2.38 13.93 0.0039 �0.034 28.26 <0.0001a2 �3.21 25.24 0.0005 �0.039 35.81 <0.0001a3 �1.79 7.84 0.0188 �0.009512 2.18 0.1589Interactiona12 �0.48 0.33 0.5794 e e e

a13 0.62 0.56 0.4724 e e e

a23 �0.41 0.25 0.6294 e e e

Quadratica11 �0.72 1.33 0.2754 e e e

a22 1.04 2.83 0.1237 e e e

a33 �2.61 17.71 0.0018 e e e

Model 7.91 0.0017 22.08 <0.0001Lack of fit 2.70 0.1495 4.29 0.0600R2 0.88 0.81

A.M. Hashtjin, S. Abbasi / Food Hydrocolloids 44 (2015) 40e48 43

shown in Table 2. The experimental data was used to compute thecoefficients of the quadratic polynomial equations, and the derivedequations (Eqs. (2)e(5)) were used to predict the values ofdependent variables of the nanoemulsions. According to Tables 3and 4, analysis of variance (ANOVA) showed that the coefficientof multiple determinations (R2) of the models for the dependentvariables was 0.99, 0.80, 0.88 and 0.81, respectively. This indicatesthat the quadratic polynomial models were only adequate todescribe and predict the influence of the independent variables onthe energy input and Z-average. The estimated regression co-efficients of the polynomial response surfacemodels alongwith thecorresponding R2 values and lack of fit tests are also shown inTables 3 and 4. The significance of each term was evaluated usingthe F-value and P-value where a small P-value and a large F-valuewould indicate a more significant effect on the respective inde-pendent variables. Thus, during ultrasonication the variables withthe largest effect on the energy input were the linear terms ofsonication time and amplitude (P < 0.0001). The interaction be-tween amplitude and sonication time as well as sonication timeand process temperature also had a significant effect (P < 0.05) onthe energy input. Furthermore, none of the quadratic terms had asignificant effect (P > 0.05) on the energy input (Table 3). In terms ofthe viscosity (Table 3), the linear term of sonication time (P < 0.05)and the quadratic term of process temperature had a significanteffect (P < 0.001).

For the Z-average of the nanoemulsions (Table 4), the variableshaving the largest effect on this response were the linear terms ofsonication time (P < 0.001), amplitude (P < 0.01) and processtemperature (P < 0.05), followed by the quadratic term of processtemperature (P < 0.01), while the effect of the other terms wasinsignificant (P > 0.05).

Regarding the polydispersity index (Table 4), only the linearterms of amplitude and sonication time had a significant effect(P < 0.0001), whereas the effect of the other termswas insignificant(P > 0.05).

Y1 ¼ 12965:35þ 1613:79X1 þ 1905:30X2 þ 18:06X3

þ 274:75X1X2 þ 184:25X2X3 (2)

Y2 ¼ 1:97� 0:077X2 þ 0:041X3 þ 0:14X23 (3)

Table 3ANOVA of the regression coefficients of the fitted quadratic equations for the energyinput (Y1), viscosity (Y2) of the nanoemulsions (1 wt% OPEO, 2 wt% Tween 80 and0.25% gum (SFPG:SFGT, ratio of 75:25)).

Variable Energy input (Y1) Viscosity (Y2)

Regressioncoefficient

F-value P-value Regressioncoefficient

F-value P-value

a0 12,930.02 1.9Lineara1 1613.79 914.98 <0.0001 �0.050 2.82 0.1242a2 1905.30 1275.40 <0.0001 �0.077 6.58 0.0282a3 18.06 0.11 0.7419 0.041 1.89 0.1988Interactiona12 274.75 15.54 0.0028 �0.016 0.17 0.6880a13 �106.50 2.33 0.1575 0.011 0.082 0.7806a23 184.25 6.99 0.0246 �0.024 0.37 0.5591Quadratica11 50.60 0.95 0.3529 0.048 2.65 0.1347a22 �7.03 0.018 0.8950 0.049 2.85 0.1224a33 8.17 0.025 0.8781 0.15 25.68 0.0005

Model 246.26 <0.0001 4.45 0.0144Lack of fit 4.79 0.0553 167.54 <0.0001R2 0.99 0.80

Y3¼22:30�2:38X1�3:21X2�1:79X3þ1:12X22�2:54X2

3 (4)

Y4 ¼ 0:54� 0:034X1 � 0:039X2 (5)

3.2. Analysis of response surfaces

In order to recognize the effect of the independent variables onthe dependent ones, surface response plots of the quadratic poly-nomial models were generated by varying two of the independentvariables, within the experimental range, while holding anotherone constant at the central point.

3.2.1. Energy inputThe effect of changes in sonication time and amplitude at con-

stant temperature (25 �C) on the amount of consumed energy isshown in Fig. 1a. As can be seen, with increasing sonication timeand amplitude, the consumed energy was increased and itsmaximum value was achieved at 94% (amplitude) and 138 s (son-ication period).

The effect of amplitude and temperature at a constant time(120 s) on the energy consumption is shown in Fig. 1b. It can be seenthat the impact of amplitude was much higher than temperature.Moreover, at constant amplitude (85%), the effect of process time onenergy consumption was much higher than temperature (Fig. 1c).

3.2.2. ViscosityThree-dimensional response surface plots of viscosity are shown

in Fig. 2. As can be observed, the effect of independent variables onviscosity had a downward trend up to the central point, after whichthe trend is changed. In other words, around the central point,minimum viscosity is reached. Meanwhile, in comparison to othervariables, the effect of the quadratic term of process temperaturehas been significantly high (P < 0.001).

3.2.3. Z-averageThe effect of sonication time and amplitude at constant tem-

perature (25 �C) on Z-average value of nanoemulsions is shown inFig. 3a. As can be seen, with increasing intensity and duration ofsonication, Z-average value is decreased, and its minimum wasreached at 94% (sonication amplitude) and 138 s (sonication time).The droplet disruption, as the major mechanism of emulsion

Fig. 1. Response surface plots of the energy input during ultrasonication as a functionof a) time and amplitude, b) temperature and amplitude, c) temperature and time.

Fig. 2. Response surface plots of the viscosity of the nanoemulsions (1 wt% OPEO, 2 wt% Tween 80 and 0.25 wt% gum (SFPG:SFGT, ratio of 75:25)) as a function of a) time andamplitude, b) temperature and amplitude, c) temperature and time.

A.M. Hashtjin, S. Abbasi / Food Hydrocolloids 44 (2015) 40e4844

formation, is depended on the type and quantity of applied shearforce (Kentish et al., 2008; Li& Chiang, 2012; Tadros et al., 2004). Asa consequence, in this study with increasing the intensity oramplitude of sonication (up to 94%), the size of emulsion droplets

significantly decreased (P < 0.01). However, higher intensities maylead to an increased rate of droplet coalescence (Li & Chiang, 2012).

On the other hand, in an O/Wemulsion system, sonication time,owing to thermodynamic equilibrium, has paramount effect uponthe adsorption rate of surfactant to the surface of the droplets and

Fig. 3. Response surface plots of the Z-average of the nanoemulsions (1 wt% OPEO,2 wt% Tween 80 and 0.25 wt% gum (SFPG:SFGT, ratio of 75:25)) as a function of a) timeand amplitude, b) temperature and amplitude, c) temperature and time.

A.M. Hashtjin, S. Abbasi / Food Hydrocolloids 44 (2015) 40e48 45

size distribution of newly formed droplets. Therefore, longer son-ication (above 138 s) may lead to formation of larger dropletsmainly because of coalescence (Li & Chiang, 2012).

The effect of amplitude and temperature in the fixed time(120 s) on the Z-average value of nanoemulsions is also shown inFig. 3b. As can be seen, by increasing the temperature (up to 25 �C),the Z-average is increased, whereas it is decreased at higher tem-peratures. These findings mean that in constant temperature

Fig. 4. Response surface plots of the polydispersity of the nanoemulsions (1 wt% OPEO,2 wt% Tween 80 and 0.25 wt% gum (SFPG:SFGT, ratio of 75:25)) as a function of a) timeand amplitude, b) temperature and amplitude, c) temperature and time.

A.M. Hashtjin, S. Abbasi / Food Hydrocolloids 44 (2015) 40e4846

(25 �C), increasing the intensity of sonication can result in smallerparticle size. Furthermore, our results showed that the Z-averagevalue of nanoemulsions was minimum at 37 �C. Thus, it appearsthat the temperature can be very effective on the emulsion dropletsize because of its impact on viscosity and interfacial tension of oiland water phases (Yuan, Gao, Mao, & Zhao, 2008).

Fig. 3c also shows the effect of sonication time and temperature(constant sonication intensity of 85%) on the Z-average value. Atconstant temperature (25 �C), increasing sonication time can resultin smaller particle sizes.

3.2.4. PolydispersityThree-dimensional response surface plots for polydispersity

index variable are shown in Fig. 4. As can be seen, with increasing

Fig. 5. Flow curves a) shear stress/shear rate, b) viscosity/shear rate and c) viscosity/time o75:25)) prepared by sonication (94%, 138 s, 37 �C).

intensity and duration of sonication, PDI is decreased. Meanwhile,in comparison to process temperature, the effect of other variableshas been significantly higher (P < 0.0001).

3.3. Optimization of conditions for producing nanoemulsions

One of the main objectives of the present study was to deter-mine the optimal values of the independent variables to generatenanoemulsions with minimum droplet size and PDI. Hence, nu-merical optimization method was used to achieve the optimumprocess conditions. In this method, the optimization goals werespecified, the levels of the responses (Z-average and PDI) and in-dependent variables were adjusted, and the best responses wereobtained using fine tuning technique. The optimum range of

f nanoemulsions (1 wt% OPEO, 2 wt% Tween 80 and 0.25 wt% gum (SFPG:SFGT, ratio of

Table 5ANOVA of the storage time and temperature for the Z-average and PDI value ofnanoemulsions (1wt% OPEO, 2 wt% Tween 80 and 0.25wt% gum (SFPG:SFGT, ratio of75:25)) prepared by sonication (94%, 138 s, 37 �C).

Source Z-average PDI

F-value P-value F-value P-value

Time 290.066 <0.0001 10.924 0.001Temperature 1288 <0.0001 110.254 <0.0001Time*Temperature 269.462 <0.0001 13.772 <0.0001Model 460.290 <0.0001 30.538 <0.0001

R2 0.998 0.966

A.M. Hashtjin, S. Abbasi / Food Hydrocolloids 44 (2015) 40e48 47

independent variables including sonication amplitude, sonicationtime and process temperature to achieve the best-predicted valuesof Z-average (~13 nm) and PDI (0.457) were 94%, 138 s, and 37 �C,respectively. It should be noted that the desirability value of thepredicted zone was equivalent to 0.934 to 0.942. On the other hand,according toTable 2, the Z-average and PDI value of nanoemulsions,produced under the optimum conditions, was equivalent to12.68 nm and 0.439, respectively. Thus, this indicates that a verygood correlation existed between the practical results and esti-mated values of models.

3.4. Flow behavior

Based on our rheological measurements, the Newtonianmodel was the most appropriate model to determine the flow

Fig. 6. Effect of storage (time and temperature) on a) Z-average and b) PDI of nanoemulsionsby sonication (94%, 138 s, 37 �C).

behavior of nanoemulsions (Fig. 5a). As can be seen, there is alinear relationship between shear rate and shear stress. Inaddition, in the laminar flow range, the viscosity is independentof shear rate, which confirms its Newtonian behavior (Fig. 5b).Meanwhile, no hysteresis loop was observed in shear stress vs.shear rate curves (Barnes, 2000; Bourne, 2002). Fig. 5c alsoshows that in constant shear rate, viscosity remains constantdespite increased shear time. It should be noted that suchbehavior was observed in all the nanoemulsions even after threemonths of storage at 5, 25 and 45 �C.

3.5. Storage stability

Colloidal dispersions and emulsions normally need to be stored atdifferent temperatures for long time for commercial and industrialapplications. Accordingly, in order to evaluate the effect of storagetime and temperature on the stability, nanoemulsions (including 1%OPEO, 2% Tween 80 and 0.25 wt% gum (SFPG:SFGT, ratio of 75:25))were produced at optimum emulsification conditions (sonicationamplitude 94%, sonication time 138 s, and temperature 37 �C). Then,they were kept at 5, 25 and 45 �C for 12 weeks, and their meandroplet size, particle size distribution and the flow behavior wereevaluated at 4-week intervals. It should be noted that the dropletdiameter and size distribution are important parameters affectingemulsion stability (Cheong, Tan, CheMan,&Misran, 2008). Based onstatistical analysis (Table 5), the effect of storage time, storage tem-perature and their interaction on the Z-average value was significant(P < 0.0001). Regarding the PDI, the storage period showed a

(1 wt% OPEO, 2 wt% Tween 80 and 0.25 wt% gum (SFPG:SFGT, ratio of 75:25)) prepared

A.M. Hashtjin, S. Abbasi / Food Hydrocolloids 44 (2015) 40e4848

significant effect (P ¼ 0.001) where the storage temperature andtheir interaction was more effective (P < 0.0001).

The influence of storage on Z-average and PDI is shown in Fig. 6.As can be seen, the Z-average value of the samples stored at 5 and25 �C within the first 4 weeks showed a downward trend, afterwhich it was increased with a much lower slope. It seems that thedownward trend of Z-average during the first 4 weeks is due to agradual reduction in kinetic energy barrier in the system, subse-quent to which the samples gradually reach kinetic equilibrium. Inthe systems including oil, water and surfactant at ambient tem-perature, there is a kinetic energy barrier preventing these systemsfrom reaching the highest kinetic equilibrium status (Rao &McClements, 2011a). Reduction of this barrier over time probablyenhances the kinetic equilibrium of the system. The upward trendof Z-average after the first 4 weeks is likely due to droplets collisionand phenomena such as coalescence and Ostwald ripening, whichsubsequently lead to an increase in the size of the droplets (Rao &McClements, 2011a). In contrast, at 45 �C the OPEO nanoemulsionswere highly unstable and faced with an increasing trend in theaverage size of the droplets. This trend could be due to the collisionrate and coalescence of droplets at higher temperatures, whichsubsequently lead to an increase in emulsion droplet size (Rao &McClements, 2011b). The difference which was observed on theZ-average value at 5 and 25 �C can be likely related to higherdroplets collision rate at higher temperatures (Li & Chiang, 2012).That is why the Z-average value at 25 �C was slightly higher thanone at 5 �C.

In addition, during the first 8 weeks of storage (at 25 and 45 �C),the PDI showed a downward and then an almost steady trend(Fig. 6b). It appears that during this storage period, the nano-emulsion system has progressed towards more kinetically balancedue to the interactionsoccurring in the system. It is needs tobenotedthat during the first 4 weeks of storage at 5 �C, unlike trend wasclearly seen. But during the following weeks, it seems that lowtemperature has decreased the coalescence rate and the subsequentincrease in kinetic equilibrium led to the further decrease of PDI.

4. Conclusions

The results of this study showed that the soluble fraction ofPersian and tragacanth gums (in combination) along with emulsi-fier (Tween 80) could be used in the structure of stable nano-emulsions. Moreover, the application of response surface methodin finding optimum conditions of ultrasonic emulsification processto produce OPEO nanoemulsions is a very effective and reliablemethod (Desirability ¼ 0.942). According to our findings, the son-ication amplitude, sonication time and their interaction as well asthe interaction of sonication time and process temperature wereeffective on energy consumption during emulsification process.Regarding the Z-average value, the sonication time, sonicationamplitude and process temperature were effective. Meanwhile, theeffect of sonication amplitude and time on PDI was significant.Furthermore, the optimum condition for production of nano-emulsion (1% OPEO, 2% Tween 80 and 0.25% gum (SFPG:SFGT,weight ratio of 75:25) Z-average 13 nm) was equivalent to 94%,sonication amplitude, 138 s, sonication time, and 37 �C, tempera-ture. In addition, the nanoemulsions showed Newtonian behaviorand were physically stable at 5 and 25 �C over three months ofstorage. These results elucidate the potential capability of ultra-sonic technique for production of nano-scale emulsions of essentialoils with reasonably long term kinetic stability which can be used infood and pharmaceutical products.

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