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PREDICTION OF QUALITY PARAMETERSFOR SPRAY DRYEDPOMEGRANATE JUICE USING RESPONSE SURFACE METHODOLOGY

S. Yousefi1

, A. Amiri-Rigi2

and Z. Emam-Djomeh1

1Transfer Phenomena Laboratory (TPL), Department of Food Science, Technology and Engineering, Faculty of

Agricultural Engineering and Technology, Agricultural Campus of the University of Tehran, Karadj,

3158711167 Iran, Tel:+98 261-224 8804, E-mail: emamj@ut.ac.ir

2Department of Food Science and Technology, National Nutrition and Food Technology Research Institute,

Faculty of Nutrition Sciences and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran,

1981619573, Iran, Tel: +98 21-22360656, E-mail: at.amiri@yahoo.com

Abstract: Response surface methodology (RSM) comprises a collection of mathematical and statisticalprocedures to study the relationship between a number of factors and one or more responses. In this

study the effects of the carrier type and concentration, and the concentration of crystalline cellulose ina pomegranate juice spray drying process were investigated on five quality parameters. Qualityparameters of spray-dried pomegranate juice were predicted using RSM. Because of its structurednature, the RSM was useful for directly obtaining insight into the system and showed a good accuracyand predictive ability even with a limited number of experiments.

Keywords: Carrier, Spray drying, Pomegranate juice, Response surface methodology

http://mrd.mail.yahoo.com/compose?To=emamj%40ut.ac.irhttp://mrd.mail.yahoo.com/compose?To=emamj%40ut.ac.ir

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INTRODUCTION

Pomegranate ( punica granatum L.) is one of the

oldest known edible fruit as a potential source ofanthocyanins, ellagic acid, and phytostrogenicflavonoid, tannins and organic acids some of whichhave an antioxidant activity (Mousavineajad et al.,

2009). Considering that pomegranate is a seasonalfruit, many processes as drying are used to conservepomegranates or their juice. Drying materials with ahygroscopic nature such as pomegranate juice

powders cause many problems including sticking todryer walls, a decrease in the drying yield,agglomeration of particles, and low solubility.

Therefore, the use of carriers to assist the dryingprocedure, increase the drying yield, and improve thequality attributes of the product, appears to be crucial

(Bhandari et al., 1993). The quality of spray-driedfood products is difficult to control because of thecomplexity of the spray drying process, which isaffected by various parameters (Moreira et al., 2009).

This variety of interlinked parameters and propertiescombined with a general lack of fundamentalunderstanding of the drying process makes it clear

that predicting the quality of spray-dried foodproducts with an accurate mathematical model isquite important. Many researchers have investigatedthe suitability of different empirical models to predict

the changes in the quality parameters during differentdrying processes. These include kinetic-based models

and models based on response surface methodology(RSM) (Bas and Boyaci, 2007; Wadikar et al., 2008;

Beg et al., 2002).

In this study, response surface methodology isdeveloped to predict several quality parameters(drying yield, solubility, color change, totalanthocyanin content, and antioxidant activity) of

spray-dried pomegranate juice.

MATERIALS AND METHODS

Sample preparation

Pomegranates (cv. Malas) of the same age were

purchased from the local market in Saveh, Iran. Theskin was removed, and the fruit juice was extractedfrom the fleshy sacs using a hand-operated domesticpress. The obtained juice was stored at 4C overnight.Finally, the fresh juice was clarified using a spiral-wound ultrafiltration system with a molecular weightcut off equal to 40 KD (Osmonic, USA). The

clarified juice with 14.2% TSS was rapidly cooledand frozen at -25C and used for further experiments.

Spray drying

A Bchi mini spray dryer (Model B-191, Bchi

Laboratoriums-Technik, Flawil, Switzerland)equipped with two fluid atomizers was used in the

spray drying process. Spray drying was carried out atan aspirator rate of 70%, flow rate of 600 L/h,

pressure of 4.5 bar, and feed temperature of 20C.

The inlet air temperature was 130C for all

experiments. Once the juice total solids were

adjusted (12% w/w), the following substances wereadded: maltodextrin (DE=20, Merck, Germany),

Arabic gum (Merck, Germany), and waxy starch(Merck, Germany) at two concentration levels of 8and 12% (w/w). The solution was also treated withmicrocrystalline cellulose (Merck, Germany), whichwas used at concentrations of 0, 1.5, 3, and 4.5%(w/w). The powders produced were weighed, sealedin bottle and stored at 4 C in the dark.

Response Surface Methodology (RSM) modeling

A central composite rotatable experimental design(CCRD) was created with 20 runs, 8 cube points, 4

center points per cube, 6 axial points, 2 center points per axial, and =1. The independent variables

selected for the optimization were carrier type(maltodextrin, Arabic Gum, and waxy starch), carrierconcentration, and the concentration of crystallinecellulose. Regression analysis was performed on thedata obtained from the experiments.

The experiments were carried out in triplicate. Therelationship of the independent variables and theresponse was calculated by the second-order

polynomial Eq. (1).

(1)

Y is the predicted response; 0is a constant; iis thelinear coefficient; iiis the squared coefficient; ijisthe cross-product coefficient; and k is the number offactors (Bezerra et al., 2008).

The second-order polynomial coefficients werecalculated using the software package Minitab.

RESULTS AND DISCUSSION

An analysis of variance was conducted to determinethe significant effects of process variables on eachresponse in 20 different runs of the spray dryingprocess (data are not shown). The P-values were used

as a tool to check the significance of each of thecoefficients, which are necessary to understand thepattern of the mutual interactions between theindependent variables. Values of P less than 0.05

indicate that the model terms are significant. Processvariables were found to be statistically significant for

output data at P

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powder. Using the coefficients, the correspondingsecond-order response models (see Eq. 1) wereassembled for each response. For example:

Solubility (%) = 119.661 + 12.75X1 - 4.237X2 -

7.677X3 - 20.455X12 + 0.159X22 -0.202X32 +0.656X1X2 + 1.917X1X3 + 0.229X2X3

(2)

There was a better model fit for solubility, however apoorer fit and less significant variables were foundfor the antioxidant activity.

The optimal conditions for the three variables asobtained from the maximum point of the model were

identified as:

Type of gum: Arabic Gum, Concentration of Gum:16%, Concentration of Crystalline Cellulose: 3%. By

substituting levels of the factors into the regressionequation, the maximum predictable response for eachresponse (quality) parameter was calculated as 0.89,

68.87, 1.005, 593.36, and 62.89 for yield, solubility,color change, antioxidant activity, and totalanthocyanins, respectively. These were

experimentally verified using completely new (notused for training or optimization) data (Table 1).

Table 1. Predictive capacity of RSM for five modeloutputs.

Parameters MAE RMSE R2

Yield (%) 3.686 0.780 0.944

Solubility (%) 8.450 0.985 0.991

Color Change (-) 2.704 1.094 0.934

Antioxidant activity

(mol TE/mmol)22.897 2.002 0.701

Total AnthocyaninContent (mg/L)

13.905 1.604 0.860

CONCLUSIONS

In this work, the RSM methodology was applied in

the predicting of quality parameters of spray driedpomegranate juice. The RSM models were analyzedfor their modeling and predictive abilities and

showed to have a good accuracy and predictiveability. However, because of its structured nature, theRSM is useful for directly obtaining insight into thesystem (e.g., interactions between different

components). The final selected RSM model was

able to simultaneously predict the five outputparameters including drying yield, solubility, color

change, total anthocyanin content, and antioxidantactivity.

REFERENCES

Bas, D. & Boyaci, I. H. (2007). Modeling and

optimization II: Comparison of estimationcapabilities of response surface methodologywith artificial neural networks in a biochemicalreaction.Journal of Food Engineering, 78, 846-

854.

Beg, Q. K., Saxena, R. K. & Gupta, R. (2002).Kinetic constants determination for an alkalineprotease from Bacillus mojavensis usingresponse surface methodology. Biotechnology

and Bioengineering, 78 (3), 289-295.

Bezerra, M. A., Santelli, R. E., Oliveira, E. P.,Villar, L. S. & Escaleiraa, L. A. (2008).

Response surface methodology (RSM) as a toolfor optimization in analytical chemistry. Talanta,76, 965-977.

Bhandari, B. R., Senoussi, A., Dumoulin, E. D.& Lebert, A. (1993). Spray drying ofconcentrated fruit juices. Drying Technology, 11(5), 1081-1092.

Moreira, G. E .G., Costa, M. G. M., de Souza,A. C. R., de Brito, E. S., de Medeiros, M. F. D.& de Azeredo, H. M. C. (2009). Physical

properties of spray dried acerola pomace extractas affected by temperature and drying aids. LWT-Food Science and Technology, 42, 641-645.

Mousavinejad, G., Emam-Djomeh, Z., Rezaei,K. & Haddad Khodaparast, M. H. (2009).Identification and quantification of phenoliccompounds and their effects on antioxidantactivity in pomegranate juices of eight Iraniancultivars. Food Chemistry, 115, 1274-1278.

Wadikar, D. D., Majumdar, T. K., Nanjappa, C.,Premavalli, K. S. & Bawa, A. S. (2008).

Development of shelf stable pepper basedappetizers by response surface methodology(RSM). LWT-Food Science and Technology,41(8), 1400-1411.

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