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LWT - Food Science and Technology 44 (011) 1658e1665 Contents lists available at ScienceDirect LWT - Food Science and Technology journal homepage: www.elsevier.com/locate/lwt Effect of processing conditions on physicochemical properties of sodium caseinate-stabilized astaxanthin nanodispersions Navideh Anarjan, Hamed Mirhosseini, Badlishah Sham Baharin, Chin Ping Tan * Department of Food Technology, Faculty of Food Science and Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia article info abstract Article history: Received 1 February 010 Received in revised form 1 December 010 Accepted 17 January 011 Keywords: Astaxanthin Sodium caseinate Nanodispersions Emulsificationeevaporation RSM The aim of the present study was to investigate the preparation of sodium caseinate-stabilized astaxanthin nanodispersions as potential active ingredients for food formulations in order to optimize processing conditions. Nanodispersions containing astaxanthin were prepared by an emulsificationeevaporation processing technique. The influence of the processing conditions, namely, the pressure of the highpressure homogenizer (0e90 MPa), the number of passes through the homogenizer (0e4) and the evaporation temperature (16e66 C) on the physicochemical properties of the prepared astaxanthin nanodispersions were evaluated using a three-factor central composite design. Average particle size, polydispersity index (PDI) and astaxanthin loss in the prepared nanodispersions were considered as response variables. The multiple-response optimization predicted that using three passes through the high-pressure homogenizer at 30 MPa for the preparation of the astaxanthin nanoemulsion and then removing the organic phase (solvent) from the system by evaporation at 5 C provided astaxanthin nanodispersions with optimum physicochemical properties. Ó 011 Elsevier Ltd. All rights reserved. 1. Introduction Astaxanthin, the main carotenoid pigment found in many seafoods including salmon, trout, red sea bream, shrimp, lobster and fish eggs, is closely related to other well-known carotenoids and shares many of the metabolic and physiological functions attributed to carotenoids. Furthermore, the presence of the hydroxyl and keto groups on the ionone rings at each end of the astaxanthin molecule explains some of its unique features, such as its ability to be esterified, a higher anti-oxidant activity and a more polar configuration than other carotenoids (Jyonouchi, Sun, & Gross, 1995; Lorenz & Cysewski, 000). In many of the aquatic animals in which it is found, astaxanthin has several essential biological functions including protection of essential polyunsaturated fatty acids against oxidation, protection against UV light effects, immune response, pigmentation, communication, reproductive behavior and improved reproduction (Lorenz & Cysewski, 000). The bioactivity of astaxanthin points to its potential to target several human health conditions, where its antioxidant, UV-light protecting, anti-inflammatory and other properties could play a beneficial role in many health problems (Guerin, Huntley, & Olaizola, 003). Thus, it has important applications in the * Corresponding author. Tel.: þ603 89468418; fax: þ603 894355. E-mail address: tancp@putra.upm.edu.my (C.P. Tan). nutraceutical, cosmetics, food and feed industries. However, it is water insoluble, as are many other carotenoids, and thus has a low bioavailability and uptake in the body (Ribeiro, Rico, Badolato, & Schubert, 005). A strategy to solve the availability problems of these bioactive compounds involves the preparation of their nanodispersed forms, which have interesting properties such as impressive increases in solubility, improvements in biological absorption and the modification of optical, electro-optical and other physical properties which are achievable only with particle sizes in the middle- or lower-nanometer range (50e500 nm). A number of techniques have been developed to prepare nanodispersions, such as emulsificationeevaporation, solvent displacement, emulsification-diffusion and precipitation (Horn & Rieger, 001). The emulsificationeevaporation method consists of dissolution of the active compound in a lipophilic solvent and formation of an oil-in-water (O/W) emulsion by emulsifying the active-compound solution with an aqueous phase containing an emulsifier. Converting the emulsion into a nanodispersion is then carried out by evaporation of the solvent. Precipitation or crystallization of the active compound takes place in the O/W emulsion droplets during evaporation when the solubility limit is exceeded (Chu, Ichikawa, Kanafusa, & Nakajima, 007; Horn & Rieger, 001; Sjostrom, Bergenstahl, Lindberg, & Rasmuson, 1994; Tan & Nakajima, 005). Proteins, especially milk proteins, are good emulsifiers and hence are utilized as ingredients in a wide range of formulated food emulsions. During emulsification, proteins with 003-6438/$ e see front matter Ó 011 Elsevier Ltd. All rights reserved. doi:10.1016/j.lwt.011.01.013

N. Anarjan et al. / LWT - Food Science and Technology 44 (011) 1658e1665 1659 amphiphilic structures have been reported to diffuse and adsorb at the oil-water (O/W) interface, lowering the interfacial tension. Proteins also form a protective interfacial membrane and/or generate repulsive forces between droplets, due to a combination of electrostatic interactions and hydrophobic interactions when the ph is not close to the isoelectric point of the protein; these effects protect the droplets from coalescence (Saito, Yin, Kobayashi, & Nakajima, 006). Caseins are important milk proteins and have a number of advantages over other proteins in emulsions. Sodium caseinate (SC) has a better solubility in water and is more thermally stable than other proteins, presumably because the relatively flexible casein molecules do not undergo drastic heat-induced conformational changes like the globular whey proteins (Srinivasan, Singh, & Munro, 00). SC contains a soluble mixture of surface-active caseins that adsorb rapidly at the oil-water interface during emulsification and stabilize the dispersions by a combination of electrostatic repulsion and steric stabilization (Dickinson, Semenova, & Antipova, 1998). SC is also highly effective at protecting emulsified oils from oxidation, owing to its unique ironchelating property and the ability to produce thick interfacial layers around the droplets (Hu, McClements, & Decker, 003). Response-surface methodology (RSM) is an empirical modeling approach for determining the relationship between various process parameters and responses with the various desired criteria and determining the significance of these process parameters on the coupled responses (Myers & Montgomery, 00). The main advantage of RSM is the reduction in 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). The objective of this study was to determine the optimum primary processing conditions, namely, the pressure and number of passes in a high-pressure homogenizer and the temperature of the evaporator, as independent variables leading to (1) the smallest particle size, () the lowest polydispersity index (PDI) (3) the least astaxanthin loss during processing by using RSM. The nanodispersions with these properties would be considered as optimum sodium caseinate-stabilized astaxanthin nanodispersions that can be used in different aqueous food and cosmetic formulations.. Material and methods.1. Materials Astaxanthin (purity > 90g/100g) was purchased from Kailu Ever Brilliance Biotechnology Co., Ltd. (Beijing, China). Sodium caseinate, analytical and HPLC-grade dichloromethane, methanol and acetonitrile were purchased from Fisher Scientific (Leicestershire, UK)... Preparation of astaxanthin nanodispersions Sodium caseinate (1g/100g) was dissolved in deionized water. This aqueous solution was magnetically stirred for 5 h before the organic phase (1g/100g astaxanthin in dichloromethane) was added. The ratio of organic phase to aqueous phase was 1:9 by weight. The pre-mix was homogenized using a conventional homogenizer (Silverson L4R, Buckinghamshire, UK) at 5000 rpm for 5 min to produce a coarse O/W emulsion, immediately followed by the high-pressure homogenizer (APV, Crawley, UK), using different numbers of passes at different pressures. The organic phase of the prepared nanoemulsions was then evaporated using a rotary evaporator (Eyela NE-1001, Tokya Rikakikai Co. Ltd, Tokyo, Japan) at different temperatures at 5 kpa and 100 rpm. Based on the three-factor central composite design (CCD), 0 SC-stabilized astaxanthin nanodispersions were prepared under various homogenization conditions including the pressure (0e90 MPa) in, and number of passes (0e4) through, the high-pressure homogenizer and the evaporation temperature (16e66 C), to determine and optimize the effects of these processing parameters on average particle size, PDI and astaxanthin concentration (Tables 1 and ). Each dispersion sample was then characterized for particle size, size distribution or PDI and astaxanthin concentration..3. Analysis of particle size and its polydispersity (PDI) The dispersions were characterized in terms of particle size and size distribution. Particle-size analysis measurements were performed using a ZetaSizer Nano ZS, Model ZEN 1600 (Malvern Instrument Ltd, Malvern, UK.) The particle size of the prepared astaxanthin nanodispersions was described by the volumeweighted mean diameter (D 4,3 ). By definition, the volumeweighted mean islet volume is the mean islet volume considering the droplet weights as proportional to their volumes. This parameter can be estimated without assumptions regarding the shape of the droplets and provides unbiased information on three-dimensional size, in contrast to the commonly used two-dimensional estimates of mean droplet profile area. The PDI is a measure of the width of the distribution ranging from 0 (monodispersion) to 1 (broad distribution) (Cheong, Tan, Man, & Misran, 008). The measurements were reported as averages of three individual injections, with two readings made per injection..4. Determination of astaxanthin content.4.1. Sample preparation for astaxanthin determination In order to measure the astaxanthin content of the nanodispersions, the sample preparation procedures were modified from Cheong et al. (008). The reverse-phase solid-phase extraction C18 cartridges (Varian, Harbor City, CA, USA) were conditioned by washing with 1 ml of methanol and then with ml of deionized water prior to use. 1 ml sample was applied to the conditioned Bond Elut C18 cartridge. The cartridge was then washed twice with 4 ml deionized water followed by elution with acetone, then ml of eluate was further filtered with a membrane filter (0.47 mm). An aliquot (0 ml) of filtrate was injected into the HPLC..4.. HPLC - astaxanthin analysis Astaxanthin was assayed by HPLC, using a UV-Vis detector. HPLC separation was carried out with a JASCO liquid chromatograph system (JASCO International Co., Ltd., Kyoto, Japan), equipped with a Jusco PU-1580 pump, a Jasco UV 1570 UV-Vis detector, Jasco LG 1580-04 Quaternary Gradient unit, Jasco DG 1580-54 four-line degasser and a Nova-Pak Ò C18 (3.9 300 mm) Waters HPLC Column. Quantitative measurement of astaxanthin was done at 480 nm (Yuan & Chen, 1998). HPLC separation was done using an isocratic mobile phase consisting of 85 ml methanol, 5 ml dichloromethane, 5 ml acetonitrile and 5 ml water per 100 ml of mixture. The calibration of peak area versus astaxanthin concentration was linear in the concentration range of 0.05e1 g/l Table 1 Levels of independent variables established according to the central composite design (CCD). Variable Independent variable levels Independent Variables Low Center High a þa Pressure of High-pressure homogenizer (MPa) 0 55 90 33 77 Number of passes in high-pressure homogenizer 0 4 1 3 Temperature of evaporator ( C) 16 41 66 5 56

1660 N. Anarjan et al. / LWT - Food Science and Technology 44 (011) 1658e1665 Table Matrix of the central composite design (CCD). Treatment runs Blocks Temperature of evaporator ( C) Pressure of High pressure homogenizer (MPa) 1 5 77 1 56 77 3 3 56 33 1 4 5 33 3 5 (C) 41 55 6 (C) 41 55 7 1 5 33 1 8 1 5 77 3 9 (C) 1 41 55 10 (C) 1 41 55 11 1 56 33 3 1 1 56 77 1 13 3 41 90 14 (C) 3 41 55 15 3 16 55 16 (C) 3 41 55 17 3 41 0 18 3 66 55 19 3 41 55 4 0 3 41 0 0 (C), center point. (R ¼ 0.997, n ¼ 6). Injections were done in duplicate for each samples and standards. All results were reported in mg/l..5. Transmission-electron microscopy (TEM) analysis The nanodispersions were also observed by TEM for microstructure and particle-size distribution. The sample was prepared using the conventional negative-staining method. TEM images were then taken using an electron microscope (Hitachi H e 7100, Nissei Sangyo, Tokyo, Japan) operating at 100 kv..6. Experimental design and statistical analysis Number of cycle of high-pressure homogenizer The effects of three independent variables, namely, x 1, evaporation temperature, x, pressure in the high-pressure homogenizer and x 3, the number of passes through the high-pressure homogenizer on three response variables (Y 1 Y 3 ), namely, average particle size, PDI and astaxanthin concentration, respectively, were evaluated using the RSM. In the present study, circumscribed central composite design (CCD) was employed as the original form of the central composite design to (1) study the main quadratic and interaction effects of these independent variables on the response variables, () create empirical models between the variables and (3) optimize the processing conditions to produce the nanodispersions with the most desirable physicochemical properties in terms of the response variables studied. Twenty treatments were prepared based on the CCD, with three independent variables at five levels of each variable, involving eight cube (factorial) points, six axial points and six center points. The advantage of the CCD here was to simultaneously study the main and interaction effects of three independent variables on the response variables studied. The CCD contains an imbedded factorial or fractional factorial design with center points that is augmented with a group of star points, allowing the estimation of curvature. The star points are at some distance a from the center based on the properties desired for the design and the number of factors in the design. The precise value of a and the number of center points used in the design depend on certain properties desired for the design and the number of factors involved (Mirhosseini, Tan, Hamid, & Yusof, 008). Experiments were randomized in order to minimize the effects of unexplained inconsistency in the actual responses as a result of unrelated factors. The center point was repeated six times to calculate the repeatability of the method (Montgomery, 001). In the present study, the use of a blocked design with orthogonal blocking allowed the estimation of individual and interaction-factor effects independently of the block effects. Blocks were assumed to have no impact on the nature and shape of the response surface (Mirhosseini & Tan, 009). As shown in Table, the arrangement of the constructed CCD was in a way that allowed the development of the appropriate empirical equations (Montgomery, 001). Analysis of variance (ANOVA) and regression-surface analysis were conducted to determine the statistical significance of model terms and fit a regression relationship relating the experimental data to the independent variables. The generalized response-surface model for describing the variation in response variables is given below: Y ¼ b 0 þ X b i x i þ X b ii x i þ X b ij x i x j (1) where Y is the response value predicted by the model, b 0 is an offset value, and b i, b ii and b ij are the main (linear), quadratic and interaction regression coefficients, respectively. The adequacy of the models was determined using model analysis, i.e., coefficient of determination (R ) analysis (Mirhosseini, Tan, Hamid, Yusof, & Chern, 009). The corresponding variables were considered more significant (p < 0.05) as the absolute t value became larger and the p-value became smaller (Atkinson & Donev, 199). The terms found statistically non-significant (p > 0.05) were dropped from the initial models and the experimental data were refitted only to significant (p < 0.05) independent variable effects in order to obtain the final reduced model. It should be noted that some variables were retained in the reduced model despite non-significance. For example, non-significant linear terms were kept in the model if a quadratic or interaction term containing this variable was significant (p < 0.05) (Mirhosseini et al., 009). All obtained correlations are valid only within the selected range of variables. The experimental design matrix, data analysis and optimization procedure were performed using the Minitab v. 14 statistical package (Minitab Inc., PA, USA)..7. Optimization and validation procedure In this study, a collective level of emulsification and evaporation variables (as input variable settings) was required to provide astaxanthin nanodispersions with desirable physicochemical properties (i.e., the lowest average particle size, PDI and astaxanthin loss or highest astaxanthin concentration). Each of the processing variables was important in determining the quality of the finished product, while the optimal settings of design variables for one response (i.e., an individual optimum region) might be far from the optimal area for another response. Thus, those properties should be considered together for a multiple optimization process. For multiple numerical optimizations, the response optimizer was applied by using the Minitab software for determining the exact optimum level of independent variables leading to individual and overall response goals. The response optimizer allowed us to compromise among the various responses, and helps identify the combination of input variable settings that jointly optimized a single response or a set of responses. This numerical response optimization allowed us to interactively change the input variable settings and perform sensitive analyses, possibly improving upon the initial solution. For method validation, the experimental data were compared with predicted values in order to verify the adequacy of the final reduced models. Close agreement and no significant (p > 0.05) difference must exist between the experimental and predicted values (Mirhosseini et al., 009).

N. Anarjan et al. / LWT - Food Science and Technology 44 (011) 1658e1665 1661 Table 3 Regression coefficients, R, adjusted R and probability values for the final reduced models. Regression coefficient * 3. Results and discussion Average particle size (nm) 3.1. Fitting the response-surface equations Polydindex (PDI) Astaxanthin loss (g/100g) b 0 139.993 0.44681 16.3608 b 1 1.366 0.004777 0.0066 b 0.68 0.00443 0.871 b 3 6.84 0.09308 3.8466 b 1 0.010 0.000059 e b 0.007 0.000044 0.004 b 3 3.700 0.006657 e b 1 0.010 e 0.0046 b 13 e e e b 3 0.198 e 0.094 R 0.959 0.840 0.985 R (adj) 0.930 0.74 0.978 P-value (regression) 0.000 ** 0.001 ** 0.000 ** F-value (regression) 3.46 8.85 143.5 *b 0 is a constant, b i, b ii and b ij are the linear, quadratic and interaction coefficients of the quadratic polynomial equation, respectively. 1: Temperature of evaporator : Pressure of High pressure homogenizer; 3: Number of passes of high-pressure homogenizer. **Significant (p < 0.05). The estimated regression coefficients for the response variables, along with the corresponding R, R (adj), F- and p-values of the regressions, are given in Table 3. Each response (Y i ) was assessed as a function of main, quadratic and interaction effects of pressure and number of passes of the high-pressure homogenizer and the evaporation temperature. The individual significance F-values and p-values of the independent variables are shown in Table 4. The results indicated that the response-surface models with high coefficients of determination (R > 0.84) were significantly (p > 0.05) fitted for all response variables studied (Table 3). Thus, more than 84 percent of the variability of the physicochemical dispersion properties could be explained by the RSM models as a nonlinear function of the main processing conditions. As shown in Table 4, the emulsification variables had the most significant (p < 0.05) effect on the responses as compared to evaporation. The main and quadratic effects of pressure and number of passes were retained in all reduced models, while the quadratic effect of evaporation temperature was shown to be significant (p < 0.05) only in the reduced model fitted for the PDI of prepared nanodispersions. As shown clearly in Table 3 and 4, the interaction effects of pressure with other two independent variables had significant (p < 0.05) effects on average particle size and astaxanthin loss. The interaction of number of passes with evaporation temperature had no significant effect on any studied response, so it was omitted in all reduced models fitted for responses. As stated by Montgomery, the polynomial regression models and recommended optimum region may be significant (p < 0.05) only in the studied independent variable ranges. Thus, it may not be true beyond the ranges of the factors, and the fitted models cannot be extrapolated beyond these ranges. 3.. Average particle size As shown in Table 4, all the main and quadratic effects of the independent variables as well as their interaction effects (except the interaction of number of passes with evaporation temperature) were significant (p < 0.05) for the average particle size (Y 1 ), and the variation of average particle size was significantly (p < 0.05) explained by a second-order regression equation (R ¼ 0.959; Table 3). The results indicated that emulsification variables had negative single effects and positive quadratic effects on the average particle size of the prepared nanodispersions, meaning that the effect of these variables on average particle size is different at various levels of these factors. So according to the fitted equation for average particle size, at low pressures and number of passes, increasing these two factors caused the average particle size of nanodispersions to decrease, but further increases of these two factors caused the average particle size to increase. Therefore, there were optima for these two factors to produce the nanodispersions with minimum particle size. The evaporation temperature also had different effects on particle size at different levels. The positive main and negative quadratic effects of this factor on particle size showed that, at low temperatures, increasing the temperature caused an increase in average particle size, but it affected this response inversely at higher levels. As shown in Table 4, the main effect of temperature on average particle size was more significant than its quadratic effects. Thus, temperature affected the average particle size strongly at low temperatures. Generally, the influence of emulsification conditions on average particle size was more significant (lower p-values) than evaporation temperature. It was also shown that the interactions of pressure with two other factors were significant (p < 0.05) on the variation in average particle size. As shown in Fig. 1 (a), at low pressures, increasing the number of passes caused the particle size to decrease, but increasing the number of passes increased particle size at high pressures. These patterns were also seen in the effect of pressure on mean particle size with different numbers of passes. Therefore, at a lower number of passes, increasing the pressure decreased the particle size, but at a higher number of passes, it had the reverse effect. Fig. 1 (b) shows that the combined effects of pressure and evaporating temperature on particle size depended on the levels of these two factors. For example, at low evaporation temperatures, increasing the pressure caused particle size to decrease, while, at high temperatures and low pressures, increasing the pressure reduced the particle size up to a point, but further increases in applied emulsification pressure caused the particle size to increase. Different effects of evaporation temperature were seen also at different levels of pressure, as shown in Fig. 1 (b); at low Table 4 The significance probability (p-value, F-ratio) of regression coefficients in final reduced second-order polynomial models. Variables Main effects Quadratic effects Interacted effects x 1 x x 3 x 1 x x 3 x 1 x x 1 x 3 x x 3 Average particle size (Y 1,nm) P-value 0.001 * 0.013 * 0.000 * 0.010 * 0.001 * 0.000 * 0.004 * e 0.000 * F-ratio 0.91 8.738 117.874 9.610 0.995 33.154 13.191 e 9.900 PDI (Y ) p-value 0.006 * 0.001 * 0.047 * 0.006 * 0.000 * 0.048 * e e e F-ratio 11.370 19.98 16.999 11.689 5.371 4.93 e e e Astaxanthin loss (g/100g) p-value 0.935 0.000 * 0.007 * e 0.04 * e 0.031 * e 0.000 * F-ratio 0.004 35.403 10.04 e 5.090 e 5.963 e 17.156 *Significant (p < 0.05).

166 N. Anarjan et al. / LWT - Food Science and Technology 44 (011) 1658e1665 Fig. 1. Response surface plots for average particle size and astaxanthin loss percentage as function of significant (p < 0.05) interaction effects between preparation variables. pressures, the average particle size of the nanodispersions was increased with increasing evaporation temperature, but at high pressures it decreased with an increase in temperature. The decrease in particle size due to the increase in pressure has been reported in most previous studies (Cheong et al., 008; Tan & Nakajima, 005; Yuan, Gao, Mao, & Zhao, 008). This effect of pressure on particle size was due to the fact that, under these processing conditions, the intensity of the high shear forces and the turbulence and/or cavitation produced during the homogenization process determined particle size. As the pressure of the homogenizer was increased, the size of the dispersed droplets decreased as a result of the various forces induced in the high-pressure homogenizer (Tan & Nakajima, 005). However, a maximum limit of homogenization efficiency for the high-pressure homogenizer has been observed in other studies, at which a minimum dispersion particle size was achieved under given conditions. According to the results obtained by Kanafusa, Chu, and Nakajima (007), there was little change seen in mean particle size when the emulsification pressure was further increased, although the droplet diameter showed some tendency to increase at high pressures. The production of nanoemulsions with large particle size due to high pressures in the emulsification step was also confirmed in the work of Floury, Desrumaux, and Lardières (000). Part of this effect may be due to a limitation on the emulsifier occurring in the prepared organic-in-water emulsions, perhaps protein denaturation (Tan & Nakajima, 005). Therefore, despite producing very small particles, they may immediately re-coalescence, causing a low emulsification efficiency in severe emulsification conditions due either to protein denaturation or insufficient residence time in the homogenizer to allow protein adsorption on the entire available droplet surface before dropletedroplet collisions occurred. This recoalescence likely caused the formation and growth of the broad size-distribution tails skewed toward large particle size. The shear forces acting on the products in the homogenizer play an important role on the size of the particles. The impact forces acting on the droplets as a result of collisions might be sufficient to cause disruption of the interfacial membranes (McClements, 005). Mohan and Narsimhan (1997) found that higher turbulent intensity could lead to higher coalescence efficiency as well as a higher rate of collision between drops due to the larger turbulent force, thus resulting in a higher coalescence rate. The same observations were shown for different effects of the number of passes due to their different levels. As Tan and Nakajima (005) and Cheong et al. (008) reported, increasing the number of passes up to three caused the particle size of prepared nanodispersions to decrease, but more passes did not yield a further reduction in particle size. According to Floury et al. (000), increasing the number of passes at high applied homogenization pressure caused the average particle size of nanodispersions to increase. As previously mentioned, this increase in average particle size from increasing the number of passes at high pressures may have occurred due to the growth of the broad tail in the distribution toward large particle sizes. Temperature can also influence the particle size of an emulsion because it could affect the viscosity and interfacial tension between the phases as well as the molecular structure of the emulsifier (Yuan et al., 008). Converting the nanoemulsion into nanodispersion was carried out by evaporation of the solvent from the system. Precipitation or crystallization of astaxanthin took place in the O/W emulsion droplets during evaporation when the solubility limit was exceeded. It seems that the effect of temperature in the evaporation stage on particle size is due to its influence on the precipitation or crystallization rate that may control the size and shape of particles. The individual optimum conditions yielding minimum average particle size (Y 1 ¼ 110.85 nm) were predicted to be 30 MPa of applied pressure and three passes in the highpressure homogenizer followed by evaporation at 16 C.

N. Anarjan et al. / LWT - Food Science and Technology 44 (011) 1658e1665 1663 3.3. Polydispersity index (PDI) The results (Tables 3 and 4) showed an acceptable R (0.840) for the fitted final reduced model, confirming that the variation of PDI (Y ) could be accurately explained as a function of all the main and quadratic effects of pressure and number of passes in the highpressure homogenizer and evaporation temperature, meaning that all studied independent variables had significant (p < 0.05) effects on the average PDI (Table 3). None of the interaction effects had significant effects on the PDI, so all were omitted in the final reduced model. As shown in Table 3, the influences of homogenization conditions (pressure and number of passes) on the PDI were more significant than evaporation temperature. The regression equation also demonstrated that all coefficients of the main-effect variables (b 1, b, b 3 ) were negative, but all coefficients of quadratic effects of the variables were positive (b 11, b, b 33 ). Thus, the effects of the studied processing factors on the PDI were, as for particle size (Y 1 ), different at different levels for these variables. According to final regression equation for PDI, at low values of the variables, their increase caused the PDI to decrease, but at high values they had the inverse effect, so as the values increased, the PDI also increased. Thus, there were optimum values for all three variables to produce the astaxanthin nanodispersions with the most desirable (minimum) PDI. In several previous studies, increasing the pressure of the high-pressure homogenizer was observed to cause an increase in the PDI of prepared nanodispersions, and also increasing the number of passes, up to three, caused the PDI to decrease significantly (Cheong et al., 008; Tan & Nakajima, 005). However, the results of Floury et al. (000) showed a different effect of the number of passes on the PDI at high pressures. Chu et al. (007) also reported that the PDI was increased as the pressure of homogenizer decreased, and at high emulsification pressures, increasing the number of passes also caused an increase in the PDI of the nanodispersions produced. A high energy input can be applied to emulsion systems by recycling it several times through the homogenizer or using high pressures for emulsification or by the use of high evaporation temperatures. These high energy inputs during emulsification and evaporation can generally improve the characteristics of emulsions and decrease the PDI. However, a very high energy input can affect the efficiency of the emulsifier, leading to a higher coalescence rate after homogenization (Chu et al., 007). Our results confirmed these two different effects of the studied variables on the PDI. The individual optimum condition indicated that the minimum PDI (Y ¼ 0.14) was predicted to be obtained by homogenization at 50 MPa pressure with two passes and an evaporation temperature of 41 C. This combination indicated that values near the middle range of the variables led to the best PDI. 3.4. Astaxanthin loss The concentration of astaxanthin in each prepared sample, as mentioned, was measured using high-performance liquid chromatography (HPLC) and the percentage of astaxanthin loss was calculated and considered as a third response variable (Y 3 ) investigated in this study. The astaxanthin loss (g/100g of freshly added astaxanthin) was calculated as: Astaxanthin loss ðg=100g of freshly added astaxanthinþ h. ¼ ðc * C C *i 100 () where C * is the mean content of astaxanthin in the freshly prepared coarse emulsion (by Silverson L4R), which was 797 mg/l by our prehomogenization conditions, and C is the astaxanthin content of samples after the evaporation step. Thus, in the calculated astaxanthin loss, all losses that occurred during the high-pressure homogenization and evaporation steps were considered. As shown in Tables 3 and 4, the variation in total astaxanthin loss was significantly (p < 0.05) well-fitted by a second-order nonlinear regression equation (R ¼ 0.985). Both emulsification factors (pressure and number of passes) had significant (p < 0.05) main effects on the variation of astaxanthin loss, so they were retained in the final reduced model. The evaporation temperature was also retained despite its main effect being insignificant (p > 0.05) because its interaction effect with pressure was significant (p < 0.05), as was the interaction of pressure with number of passes in the model for variation of astaxanthin loss. Among the quadratic effects, only that of pressure was significant (p < 0.05) for variation of astaxanthin loss. The astaxanthin loss during the preparation should be kept to a minimum in both emulsification and evaporation steps. As shown in Fig. 1 (c and d), at all temperatures and numbers of passes through the homogenizer, the increasing of pressure caused the astaxanthin loss to increase, and the increasing of temperature also increased astaxanthin loss at all pressures, while the number of passes had different effects on astaxanthin loss at different pressures. At low pressures, increasing the number of passes increased the astaxanthin loss, but increasing the number of passes at high pressures decreased the astaxanthin loss. Most of our results were in agreement with those of previous researchers (Cheong et al., 008; Tan & Nakajima, 005) in that increasing the pressure and number of passes caused increased astaxanthin loss. Increased astaxanthin loss due to increased pressure, number of passes and temperature was expected due to the high sensitivity of astaxanthin molecules. Severe processing conditions may have caused some chemical changes in astaxanthin structure, degrading it to another compound, resulting in a reduction in astaxanthin concentration. The presence of heat, light and oxygen during the processing steps were the most likely causes for astaxanthin loss and decreased astaxanthin concentration in the prepared nanodispersions. However, the decreasing in astaxanthin loss with an increasing the number of passes at high emulsification pressures observed in our case may be related to the relatively large particle sizes of the nanodispersions produced in these conditions. As stated by Tan and Nakajima (Tan & Nakajima, 005), the degree of degradation of carotenoids in the nanodispersions was increased by decreasing the mean particle size. Thus, because of the relatively large particle size produced at high pressures and a high number of passes, the reduction of astaxanthin concentration would be decreased compared to smaller particle sizes. The results (Tables 3 and 4) also illustrated that the main effect of pressure on astaxanthin loss was more significant (high F-value) than its quadratic effect. Therefore, in spite of the negative quadratic effect of pressure on astaxanthin loss, a decreasing loss with increasing pressure was not seen in our studied parameter region. The individual optimization verified that an astaxanthin nanodispersion prepared at 0 MPa and one pass through the homogenizer and evaporated at 16 C had the highest astaxanthin content and the least astaxanthin loss during the processing steps (.86 g/100g). As clearly shown, the lowest levels of pressure and number of passes as well as the lowest evaporation temperature resulted in the least astaxanthin loss (Y 3 ). 3.5. Optimization procedure for predicting the processing conditions to produce the most desirable astaxanthin nanodispersions For the graphical interpretation, three-dimensional (3D) response-surface plotting was highly recommended (Mason, Gunst, & Hess, 1989). Thus, the fitted polynomial equation was expressed as a response-surface plot in order to visualize the relationship between the response and experimental levels of the independent variables. The overall optimum condition was then determined by superimposing all response-surface plots. A numerical optimization

1664 N. Anarjan et al. / LWT - Food Science and Technology 44 (011) 1658e1665 was also carried out using the response optimizer in the Minitab software for determining the exact optimum levels of the independent variables leading to the overall optimum condition (Mirhosseini et al., 009). The numerical optimization results indicated that the overall optimum region was predicted to be at the combined level of 30 MPa of pressure, three passes through the homogenizer and a 5 C evaporation temperature. The corresponding response values for average particle size, PDI and percentage of astaxanthin loss predicted under the recommended optimum conditions were 116.59, 0.48 and 15.45, respectively. 3.6. Verification of the reduced models The sufficiency of the response-surface equations was checked by the comparison of experimental and fitted values predicted by the response-regression models. The experimental and predicted values are given in Table 5. No significant (p > 0.05) differences were found between these values. The agreement between the experimental and predicted values confirmed the adequacy of the corresponding response-surface models employed for describing the variation of physicochemical properties as functions of emulsification and evaporation conditions. Four astaxanthin nanodispersions were then prepared according to the predicted optimum levels of pressure, number of passes and evaporation temperature and the nanodispersion properties were evaluated. The results indicated that the corresponding experimental values for average particle size, PDI and astaxanthin loss of the prepared nanodispersions using the optimum operation conditions were 117.31 5.65, 0.41 0.009 and 14.96 1.88, respectively. The t-tests were also performed for comparison of the experimental and predicted values. No significant differences (p > 0.05) were seen between these values, indicating the suitability of the corresponding models (Mirhosseini et al., 009). 3.7. TEM analysis A representative TEM image of the optimum astaxanthin nanodispersion is presented in Fig.. The TEM image shows that Table 5 Experimental and predicted values for response variables based on final reduced models. Run Average particle size * (Y 1,nm) Polydispersity index * (Y ) Astaxanthin loss * (Y 3, g/100g) Y 0 Y i Y 0 e Y i ** Y 0 Y i Y 0 e Y i ** Y 0 Y i Y 0 e Y i ** 1 135.36 135.38 0.0 0.87 0.81 0.006 39.90 38.6 0.01 133.86 134.37 0.51 0.6 0.75 0.015 39.8 43.0 0.53 3 137.0 138.11 1.09 0.7 0.55 0.017 18.07 17.93 0.61 4 115.04 117.41.37 0.6 0.51 0.009 17.14 14.48 0.37 5 16.50 16.70 0.0 0.15 0.4 0.009 30.03 9.81.38 6 16.08 16.70 0.6 0.16 0.4 0.008 30.0 9.81 0.78 7 17.30 130.3.93 0.44 0.55 0.011 16.15 13.01.81 8 136. 139.99 3.77 0.73 0.75 0.00 3.6 31.80 1.41 9 17.86 16.70 1.16 0.16 0.3 0.007 9.8 9.81 0.06 10 16.54 16.70 0.16 0.15 0.3 0.008 31.5 9.81 1.41 11 13.15 15.9.14 0.7 0.49 0.01 0.49 19.41 1.56 1 18.65 19.76 1.11 0.86 0.80 0.006 44.99 49.84.18 13 14.80 141.03 1.77 0.84 0.77 0.007 4.07 46.51.73 14 16.70 16.70 0.00 0.17 0.03 0.014 30.45 9.81 1.5 15 14.17 119.98 4.0 0.4 0.39 0.003 7.40 3.30 0.4 16 16.81 16.70 0.11 0.16 0.03 0.013 30.84 9.81 0.05 17 13.96 130.06.90 0.3 0.37 0.014 7.90 7.19.68 18 1.58 11.31 1.8 0.7 0.41 0.014 34.81 36.33 1.05 19 139.1 136.47.65 0. 0.5 0.003 7.67 7.14 0.09 0 16.74 161.91 0.83 0.64 0.69 0.005 4.44 3.7 0.54 *No significant (p > 0.05) difference between experimental (Y 0 ) and predicted (Y i ) values. **Y 0 e Y i : Residue. Fig.. Transmission-electron micrographs of astaxanthin nanodispersion prepared in optimum processing condition (30 MPa, 3 number of passes in high-pressure homogenizer and 5 C in evaporator). most of the astaxanthin particles displayed a spherical morphology. The observations closely corresponded with the results observed in the dynamic light-scattering particle-size analysis. 4. Conclusion The central composite design was found to be a valuable tool for optimizing the processing conditions leading to the most desirable physicochemical properties evaluated in the prepared proteinstabilized astaxanthin nanodispersions. Analysis of variance (ANOVA) showed high overall coefficient of determination values (R ¼ 0.840) for the regression models. Therefore, it was possible to develop empirical equations for describing and predicting the variation of the response variables (Mirhosseini et al., 009). The results indicated that the emulsification factors were more significant (p < 0.05) in affecting all three response variables than the evaporation factor. Therefore, the emulsification step can be considered to be the most important stage governing the final characteristics of nanodispersions. The quadratic effect of pressure also had a significant (p < 0.05) effect on the all studied responses. Interaction effects between the pressure of the high-pressure homogenizer and two other studied independent variables also had significant (p < 0.05) effects on the fitted models, except for the PDI. The optimization procedure indicated that the overall optimum processing region for producing the most desirable nanodispersion was obtained by using the high-pressure homogenizer at 30 MPa with three passes followed by evaporation at 5 C. The experimental values were shown to be in good agreement with the predicted values, thus indicating the adequacy of the fitted models. Acknowledgments Financial support of this work by the Ministry of Higher Education through Fundamental Research Grant Scheme (0-11- 08-0619FR) is gratefully acknowledged.

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