ORIGINAL PAPER

Multi-Objective Optimization of Osmotic–UltrasonicPretreatments and Hot-Air Drying of Quince Using ResponseSurface Methodology

Mohammad Noshad & Mohebbat Mohebbi &Fakhri Shahidi & Seyed Ali Mortazavi

Received: 26 August 2010 /Accepted: 4 April 2011 /Published online: 23 June 2011# Springer Science+Business Media, LLC 2011

Abstract In this study, application of a multi-objectiveoptimization technique based on response surfacemethodologyhas been presented. Quince slices were dehydrated usingosmotic dehydration with sucrose solutions at differentconcentration (40 and 60 Brix), processing time (1, 1.5, and2 h), and ultrasonication time (0, 15, and 30 min) were thefactors investigated with respect to water loss, solid gain, andweight reduction. Response surface methodology was used todetermine the optimum processing conditions that yieldmaximum water loss and weight reduction and minimum solidgain during osmotic dehydration of quinces. Dehydratedquince slices at optimized osmo-ultrasound condition werethen subjected to air-drying at 60 and 80 °C. Rehydration ratio,shrinkage, and moisture content of dried samples wereregarded as responses to the non-thermal and air-dryingconditions. Multi-objective optimization led to obtaining thebest condition for production of dried quince slices with lowestmoisture content, and shrinkage.

Keywords Air-drying . Optimization . Quince . Responsesurface methodology . Ultrasonic–osmotic dehydration

Introduction

Quince (Cydonia oblonga Mill) is a member of pomes fruitfamily; 83.8% water and 15.3% carbohydrates (wet basis)are the main constituents of quince. Minor ingredients ofquince are proteins (0.4%, wet basis) and fats (0.1%, wet

basis). It is presumed to be a good source of fiber,potassium, and vitamin C. The mean of the last 10 years’(1998–2008) world production of quince is estimated to be510,000 t (FAO 2010).

Fruit drying is a well-known process mostly used forpreservation of fruits. Dried quince can also be used asingredient of traditional Iranian food such as quinceKhoresh and chowder. The traditional way, which is oftenused in Iran for quince drying, is to spread it into the directsunlight. In such traditional drying methods, seriousdecreases of nutritive and sensorial values are possible,damaging mainly the flavor, color, and nutrients of theproduct (Lenart 1996; Lin et al. 1998). Therefore, it isessential to find better ways for drying quince.

Since air-drying is a simultaneous heat and mass transferprocess, which results in phase change, it is an energyintensive and consequently expensive process (Barbanti et al.1994). To reduce initial water content or to modify the fruittissue structure and decreasing drying time, different pre-treatment have been applied by researchers.

Osmotic dehydration is the most reported pretreatmentused prior to air-drying (Antonio et al. 2008; Pani et al.2008; Lombard et al. 2008; Fernandes et al. 2008; Azoubelet al. 2009; Bchir et al. 2011; Sosa et al. 2011). Amongemergent new technologies, ultrasonication is an encourag-ing process, which is commonly regarded as a non-thermalprocess; such process is favorable because of reduction infood degradation. No liquid phase change will occur in thisprocess for water removal (Povey and Mason 1998;Fernandes et al. 2008).

The ultrasonic pretreatment includes the immersion ofthe fruit in water or in a hypertonic aqueous solution towhich ultrasound is applied. Ultrasonic waves in frequencyrange of 20–100 kHz can cause rapid series of alternativecompressions and expansions in a similar way to a sponge

M. Noshad (*) :M. Mohebbi : F. Shahidi : S. Ali MortazaviDepartment of Food Science and Technology, FerdowsiUniversity of Mashhad,Mashhad, Irane-mail: mo.noshad@gmail.com

Food Bioprocess Technol (2012) 5:2098–2110DOI 10.1007/s11947-011-0577-8

when it is squeezed and released repeatedly (sponge effect).In addition, ultrasound produces cavitations, which may behelpful to remove strongly attached moisture. The spongeeffect caused by ultrasound application may be responsiblefor the creation of microscopic channels in porousmaterials, such as fruits, that reduces the diffusion boundarylayer and increases the convective mass transfer in thefoodstuff (Tarleton 1992; Tarleton and Wakeman 1998; Dela Fuente-Blanco et al. 2006). This process can be carriedout at ambient temperature, and no heating is required;therefore, the probability of food degradation will decrease(Povey and Mason 1998). If distilled water is used as theliquid medium, the ultrasonic treatment will not incorporatesoluble solids into the fruit changing its natural taste(Fernandes et al. 2008).

Optimization has been used in food engineering for theefficient operation of processing systems and unit processesyielding a highly acceptable product. Response surfacemethodology (RSM) is the process, which combinesmathematics with statistics. In multifactor experiments,RSM can be used to comprehensively examine variousparameters with minimum experimental times and deter-mine the most relevant factors and their influence ranges aswell as interactions among the factors. This experimentalstrategy has been widely used in the development of foodprocesses (Emadzadeh et al. 2011; Corzo et al. 2008;Chakraborty et al. 2007; Yao et al. 2007; Sharma andPrasad 2006; Varnalis et al. 2004).

In industrial processes, several response variables describequality characteristics of the final products; such exestuationcan be carried out with multi-objective optimization. One ofthe critical points in these procedures is the opposing nature ofdifferent variables in some statues. This fact makes optimiza-tion procedure a challenging issue in food industries, whichshould lead to a compromise between several objectives.Constrained optimization technique can overcome this prob-lem. On the other hand, superimposing the contour plot ofdifferent response variation and solving the problem ofmultiple responses by defining a multi-objective functionincluding all responses may be regarded to as an alternativestrategy (Eren and Kaymak-Ertekin 2007).

Making response variable dimensionless, deciding weight-ing coefficient, composing objective function and solution,and applying fuzzy similar precedable ratio method areseveral approaches for defining desirability functions in anoptimization problem (Erbay and Icier 2009; Shi et al. 2008).

For optimization or assessing multiple responses andinitiating logical mathematical model with easy graphicalexplanation of a process, RSM can be utilized (Fermin andCorzo 2005; Corzo and Gomez 2004; Madamba 2002;Garrote et al. 1993; Guillou and Floros 1993).

The main objective of this study was: (1) to investigatemain effects of process variables on the product quality

during ultrasound-osmotic dehydration pretreatment ofdrying quince; (2) to investigate main effects of processvariables on the product quality during hot air-drying ofquince; (3) to determine optimum process conditions fordrying of quince.

Material and Methods

Preparation of Samples

Fresh quinces (varieties of Nishabur) were purchased atlocal market in Mashhad, Iran. Ripe quinces were sorted,washed, and hand-peeled. The peeled quince samples werecut to obtain cylinders of the same dimensions (0.01 maverage in height and 0.026 m average diameter) and thenimmersed in 1% sodium Meta bisulphate solution for 5 minin order to prevent enzymatic browning reactions. Themoisture content was determined upon AOAC method931.04(AOAC 1990).

Ultrasonication

An experimental set of four quince samples was immersed indistilled water and submitted to ultrasonic waves for 0–30 min.The experiments with ultrasound treatment were carried out inseparate 250-ml Erlenmeyer flasks to avoid interferencebetween the samples and runs. The experiments were carriedout under ambient temperature (30 °C) in an ultrasonic bath(SCHAPER model Unique USC 25 kHz) without mechanicalagitation. The ultrasound frequency was 25 kHz, and the poweroutput of ultrasonic bath was 500 W. The temperature increasewas measured using a thermometer and was lower than 2 °Cafter 30 min of ultrasonic treatment. To determine the effect ofultrasound, the same experimental procedure was carried outwithout applying ultrasound. After removal from the distilledwater, the samples from each group were drained and blottedwith absorbent paper to remove the excess water. Weight andmoisture content were measured individually. At the end,samples were transferred to osmotic solution.

Osmotic Dehydration

Each experimental group consisting of three quincecylinders was immersed in the osmotic solution for 1, 1.5,or 2 h. The osmotic solution used in each experiment wasprepared by mixing food-grade sucrose with distilled waterto give a concentration of 40–60°Brix. The osmoticsolution to fruit ratio was maintained at 20:1 (weight basis).Experiments were performed with the same constantmagnetic agitation. The temperature was monitored by thethermocouple and heating plate of the rotary shaker andwas set at 50 °C. After removal from the solution, the

Food Bioprocess Technol (2012) 5:2098–2110 2099

dehydrated samples from each group were drained andblotted with absorbent paper to remove the excess solution.Weight and moisture content were measured individually.The concentration of the solution was monitored during theruns for determining the osmotic solution soluble solidscontent (°Brix) using a refract meter.

Weight and moisture content of the samples were used tocalculate the response variables of the experimentalplanning: weight reduction (WR), water loss (WL), andsolid gain (SG), according to the following equation:

WL ¼ mizi � mf zfmi

� 100 g=100 g fresh sampleð Þ ð1Þ

SG ¼ mf sf � misimi

� 100 g=100 g fresh sampleð Þ ð2Þ

WR ¼ WL� SG g=100 g fresh sampleð Þ ð3Þ

where mi and mf are the initial and final weight (g) of thesamples, respectively; zi and zf are the initial and final massfraction of water (g water/g sample), respectively; si and sfare the initial and final mass fraction of total solids (g totalsolids/g sample), respectively. All experiments were donein triplicate.

Air-Drying

Hot air-drying was performed in a laboratory drier (SoroushMedical Company) operating with air velocity of 1.5 m/s.Before each drying experiment, the drier was run withoutsample for about 0.5 h to set desired conditions. The quincesamples pretreated with osmotic dehydration and ultrasonicwere subjected to air-drying at 60–80 °C. Air-drying wascarried out for 4, 5, and 6 h. Finally, the moisture content ofdried quince was determined upon AOAC method 931.04(AOAC 1990). The experiments were conducted with fourreplications.

Determination of Shrinkage, Moisture Content,and Rehydration Ratio

Percentage of shrinkage was determined from the changesof the bulk volume of the quince slices using the liquiddisplacement method (Eq. 4) (Ko et al. 2008). In this study,toluene was used instead of water because it causedreduction of liquid absorption into the fruits.

Sh ¼ V0 � V

V0� 100 ð4Þ

Where V0 and V are initial and final volume of the sample.

The moisture content, based on dry basis, was deter-mined as follows (Eq. 5) (Ayensu 1997):

Xi ¼ Mi �Md

Mdð5Þ

Where Mi is weight in time i, Md is the weight of the driedproduct.

Rehydration ratio (RR) was evaluated by soaking knownweight (5–10 g) of each sample in a sufficient volume ofwater (approximately 30 times the weight of dried quince) in aglass beaker at 95 °C for 20 min. After soaking, the excesswater was removed with the help of absorbent paper, andsamples were weighed. The rehydration ratio was computedusing Eq. 6 (Alam et al. 2010; Johnson et al. 1998):

RR ¼ Wr

Wdð6Þ

Where, Wr is the drained weight of rehydrated sample (g) andWd is the weight of dried sample used for rehydration (g).

Experimental Design and Statistical Analysis

In the first step, the response surface methodology was usedto estimate the main effects of the process variables on WL,WR, and SG in quince slice. A Box–Behnken design wasused with sugar concentration (40–60%, w/w), osmoticdehydration (60–120 min), and ultrasonic (0-30 min) beingthe independent process variables (Table 1). This generated17 experiments with five replications at the center point(Table 2). The RSM was applied to the experimental datausing a commercial statistical package, Design-Expertversion6.02 (Stat ease Inc., Minneapolis, USA). Thefollowing polynomial model was fitted to the data:

Y ¼ b0 þ b1X1 þ b2X2 þ b3X3 þ b11X12 þ X22X2

2

þ b33X32 þ b12X1X2 þ b13X1X3 þ b23X2X3 ð7Þ

Where bn are constant regression coefficients; Y is theresponse (i.e., WL, SG, and WR); X1, X2, and X3 areultrasound time (min), osmosis time (min), and Brix (%),respectively.

Table 1 Range of different variables for osmotic dehydration processin coded and uncoded form

Coded values Uncoded values

Ultrasound min Osmosis time (min) Brix (%)

−1 0 60 40

0 15 90 50

1 30 120 60

2100 Food Bioprocess Technol (2012) 5:2098–2110

Mathematical models were evaluated for each responseby means of multiple regression analysis. The modelingwas started with a quadratic model including linear,squared, and interaction terms. Significant terms in themodel for each response were found by analysis of variance(ANOVA) and significance was judged by the F-statisticcalculated from the data (Eren and Kaymak-Ertekin 2007).After model fitting, residual analyses including the exam-ination of diagnostic plots and calculation of case statisticswere conducted to validate assumptions used in ANOVA.The software was used to fit response surfaces and optimizethe drying process.

The same methodology was used to estimate the maineffects of air-drying process on shrinkage (Sh), moisturecontent (Xw), and rehydration ratio (RR). A centralcomposite design was used with temperature (60–80 °C)

and processing time (240,300, and 360 min) being theindependent process variables (Table 3). For the generated13 experiments, RSM was applied to the experimental datausing design expert 6.02 (Table 4).

Experimental data were fitted to a second-order polyno-mial model and regression coefficients were obtained.

Results and Discussion

Multiple linear regression analysis of the experimentaldata yielded second-order polynomial models for pre-dicting WL, SG, and WR and shrinkage, moisturecontent, and rehydration ratio. ANOVA was conductedto determine significant effects of process variables oneach response and to fit second-order polynomial modelsto experimental data.

Regression equation coefficients of the proposed modelsand statistical significance of all main effects calculated foreach response were obtained, and nonsignificant effects (p>0.05) were stepped down from models without damaging themodel hierarchy (Tables 5 and 6). ANOVA also showed thatthe lack of fit was not significant for all response surfacemodels at 95% confidence level.

To visualize the combined effects of two factors on anyresponse, the response surface and contour plots weregenerated for each of fitted models as the function of twoindependent variables, while keeping the other variable atthe central value. Six different response surface plots(Figs. 1, 2, 3, 4, 5, 6) were illustrated by maintaining one

Table 3 Range of different variables for osmotic dehydration processin coded and un-coded form

Coded values Un-coded values

Time Temperature

−1.42 215 56

−1 240 60

0 300 70

1 360 80

1.42 384 84

Urtrasound (min) Osmosis time (min) Brix (%) WL SG WR

30.00 90.00 40.00 0.239 0.19 0.049

0.00 120.00 50.00 0.303 0.143 0.16

0.00 90.00 40.00 0.218 0.143 0.075

15.00 90.00 50.00 0.195 0.117 0.078

30.00 90.00 60.00 0.382 0.272 0.11

15.00 120.00 60.00 0.299 0.191 0.108

30.00 120.00 50.00 0.37 0.193 0.177

30.00 60.00 50.00 0.292 0.232 0.06

15.00 90.00 50.00 0.212 0.12 0.092

15.00 60.00 60.00 0.197 0.139 0.058

15.00 60.00 40.00 0.146 0.092 0.054

15.00 90.00 50.00 0.211 0.128 0.083

15.00 90.00 50.00 0.187 0.14 0.047

0.00 90.00 60.00 0.28 0.188 0.092

15.00 90.00 50.00 0.2 0.116 0.084

15.00 120.00 40.00 0.21 0.12 0.09

0.00 60.00 50.00 0.269 0.148 0.121

30.00 90.00 40.00 0.239 0.19 0.049

Table 2 Box–Behnken designand observed values ofresponse variables

Food Bioprocess Technol (2012) 5:2098–2110 2101

Table 5 ANOVA evaluation of linear, quadratic, and interaction terms for each response variable and coefficient of prediction models

source DF Water loss (WL) DF Solid gain (SG) DF Weight reduction (WR)

Coefficient Sum ofsquares

p Value Coefficient Sum ofsquares

p Value Coefficient Sum ofsquares

pValue

Model 6 0.20 0.067 <0.0001 3 0.12 0.033 0.0024 4 0.077 0.019 0.0125

X1 1 0.027 5.671×10−3

0.0012 1 0.033 8.778×10−3

0.0002

X2 1 0.035 9.660×10−3

0.0002 1 0.030 7.32×10−3

0.0024

X3 1 0.043 0.015 <0.0001 1 0.031 7.503×10−3

0.0004

X1X2

X1X3 1 0.020 1.640×10−3

0.0235

X2X3

X12 1 0.087 0.032 0.1709 1 0.059 0.014 <0.0001 1 0.028 3.38×

10−3

X22 1 0.020 1.731×

10−30.0262 1 0.024 2.5×

10−3

X32 1 -0.024 2.36×

10−3

Residual 10 0.0026 13 4.32×10−3

12 0.014

Lackof fit

6 0.0022 0.1445 9 3.86×10−3

0.098 8 0.013 0.1037

Pure error 4 4.3×10−3

4 4.33×10−3

4 1.22×10−3

Total 16 0.069 16 0.035 16

R2 0.9788 0.9333 0.8887

Adj-R2 0.9516 0.8476 0.7456

Pre-R2 0.7506 0.1093 0.0377

Adeqprecision

21.361 11.904 9.516

p<0.05 is significant at a=0.05; lack of fit is not significant at p>0.05

Time (min) Temperature (°C) Shrinkage Moisture content Rehydration ratio

360.00 80.00 0.575729 0.163024 1.34168

300.00 70.00 0.592431 0.224242 1.26975

300.00 70.00 0.589342 0.224852 1.22244

300.00 84.00 0.500319 0.200643 1.47389

300.00 70.00 0.565449 0.238994 1.22782

300.00 70.00 0.569431 0.248366 1.27891

240.00 60.00 0.42776 0.37418 1.24112

240.00 80.00 0.52813 0.241746 1.37264

300.00 56.00 0.556576 0.288235 1.27037

360.00 60.00 0.591715 0.260593 1.22156

300.00 70.00 0.577395 0.229329 1.21959

385.00 70.00 0.64517 0.191722 1.2966

215.00 70.00 0.523048 0.280444 1.13936

Table 4 Central compositedesign and observed values ofresponse variables

2102 Food Bioprocess Technol (2012) 5:2098–2110

of factors constant for each figure. These figures weretypical examples plotted for center points of constant factor.Effects of variables on responses were discussed byevaluation of these plots.

Model adequacy checking may be carried out steppingdown the effects that are not significant (p>0.05) andpredicted R2. A predicted-R2 comparable to fitted R2

implies that the fitted is adequate to predicting. Predicted-

Table 6 ANOVA evaluation of linear, quadratic, and interaction terms for each response variable and coefficient of prediction models

Source DF Shrinkage DF Moisture content DF Rehydretion ratio

Coefficient Sum ofsquares

p Value Coefficient Sum ofsquares

p Value Coefficient Sum ofsquares

pValue

Model 3 0.58 0.029 <0.0001 3 0.23 0.032 <0.0001 2 1.24 0.084 0.0035

X1 1 0.061 0.019 <0.0001 1 −0.047 0.013 <0.0001

X2 1 −0.053 0.017 <0.0001 1 0.076 0.035 0.0052

X1X2 1 −0.029 4.692×10−3

0.0033

X12

X22 1 −0.044 5.630×

10−30.0019 1 0.017 1.4×

10−30.1482 1 0.084 0.023 0.0162

Residual 9 3×10−3

9 1.33×10−3

10 0.027

Lackof fit

5 2.45×10−3

0.1262 5 9.05×10−3

0.4243 6 0.024 0.0798

Pureerror

4 5.65×10−4

4 4.29×10−4

4 8×10−3

Total 12 0.030 12 0.033 12 0.089

R2 0.904 0.9594 0.8404

Adj-R2 0.8827 0.9478 0.6769

Pre-R2 0.6 0.8946 −1.2059Adeqprecision

19.958 29.382 9.070

P value <0.05 is significant at a=0.05; lack of fit is not significant at p value >0.05

Fig. 1 Perturbation graphshowing the effect ofindependent variables on waterloss

Food Bioprocess Technol (2012) 5:2098–2110 2103

R2 measures the amount of variation in new ata explainedby model.

Water Loss

The effects of independent variables on the responses asrepresented in perturbation graphs (Fig. 1) reveals thatsucrose concentration and osmosis time increased waterloss. The elevated as well as increased levels of ultrasoundtime resulted into increased water loss response.

The magnitude of p values in Table 5 indicates that thelinear effects of all variables show positive effect on waterloss. The quadratic terms of ultrasound time and sucrose

concentration have positive effect on water loss. Theinteractive effect of x2x3 has positive effect on water loss.Relative magnitude β values (Table 5) indicate themaximum positive contribution of sugar concentration (β=0.043) followed by osmosis (β=0.035) and ultrasound time(β=0.027) on the water loss. These results indicate anincrease in water loss with an increase in sugar concentrationand osmosis time (Fig. 2). It also indicates the morepronounced effect of sucrose concentration compared toosmosis time on water loss during osmotic dehydration.The

0.09

0.13

0.17

0.21

0.25

SG

0

7.5

15

22.5

30

40

45

50

55

60

A: ultrasound

C: brix

Fig. 4 Response surface and contour plots for solid gain

0.141685

0.181358

0.22103

0.260703

0.300375

w

l

60.00

75.00

90.00

105.00

120.00

40.00

45.00

50.00

55.00

60.00

B: osmosis time C: brix

Fig. 2 Response surface and contour plots for water loss

Fig. 3 Perturbation graphshowing the effect ofindependent variables on solidgain

2104 Food Bioprocess Technol (2012) 5:2098–2110

quadratic terms of ultrasound time and osmosis time havepositive effect on water loss. Further, the interactions ofimmersion time and sucrose concentration’ have positive andsignificant effect, whereas the interactions of “ultrasoundtime and immersion time” and “ultrasound time and sucroseconcentration” have no significant effect on water loss.

Therefore, the models as fitted provide an adequateapproximation to the true system.

The model as fitted (Table 5) corresponded to:

WL ¼ 0:2174� 0:01652� ultrasound time� 2:808

� 10�3 � osmosis timeþ 2:288� 10�3 � Brix

þ 3:848� 10�4 � ultrasound time2 þ 2:21� 10�5

� osmosis time2 þ 1:35� 10�3 � osmosis time

� Brix

Solid Gain

The effects of independent variables on the responses arerepresented in perturbation graphs (Fig. 3).The increase inthe level of ultrasound time and sugar concentration had anincreased solid gain response, while osmosis time had noeffect on solid gain response.

The linear effects of ultrasound time and sugarconcentration have positive effects on solid gain. Rela-tive magnitude β values (Table 5) indicate the maximumpositive contribution of ultrasound time (β=0.033) fol-lowed by sugar concentration (β=0.031), and osmosistime has no significant effect on solid gain (Fig. 4). It alsoindicates an increase in solid gain with an increase inultrasound time, and sugar concentration also indicates themore pronounced effect of sucrose concentration than

0.03

0.06

0.08

0.11

0.13

WR

60

75

90

105

120

40

45

50

55

60

B: osmosis

C: brix

Fig. 6 Response surface and contour plots for weight reduction

Fig. 5 Perturbation graphshowing the effect ofindependent variables on weightreduction

Food Bioprocess Technol (2012) 5:2098–2110 2105

ultrasound time on water loss during osmotic dehydration. Anonly just quadratic term of ultrasound time has positive effecton solid gain. The quadratic model developed in the uncodedform of process variables (at 5% level of significance) is:

SG ¼ 2:375� 10�3 � 5:67� 10�3 � ultrasound time

þ 3:062� 10�3 � sugar concentrationþ 2:635

� 10�4 � ultrasound time2

Weight Reduction

The effects of independent variables on the responses asrepresented in perturbation graphs (Fig. 5).The increase inthe level of osmosis time and sugar concentration had anincreased solid gain response, while ultrasound time hadhad a reduced weight reduction.

The magnitude of p values in Table 5 indicates onlyjust the linear effects positive contribution of osmosis timeon the weight reduction. Figure 6 indicates an increase inweight reduction with increase of osmosis time at120 min of process duration. The quadratic terms ofultrasound time and osmosis time have positive effect,and sucrose concentration has negative effect on weightreduction.

Weight reduction ¼ �0:247� 5:1� 10�3 � osmosis time

þ 1:26� 10�4 � ultrasound time2

þ 2:706� 10�5 � osmosis time2

� 2:364� 10�4

In the research, maximum WL and WR and minimumSG were regarded for developing a multi-objectivefunction.

Optimum condition for osmotic dehydration of quinceswas determined to obtain maximum water loss and weightreduction and minimum solid gain. Second-order polyno-mial models obtained in this study were utilized for eachresponse in order to determine the specified optimum

0.44

0.49

0.54

0.59

0.64

shr

inka

ge

240.00

270.00

300.00

330.00

360.00

60.00

65.00

70.00

75.00

80.00

A: time B: temperature

Fig. 8 Response surface and contour plots for shrinkage

Fig. 7 Perturbation graphshowing the effect ofindependent variables onshrinkage

2106 Food Bioprocess Technol (2012) 5:2098–2110

conditions. These regression models are valid only in theselected experimental domain. So, optimization criteriawere selected based on different parameters includingeconomical and product-quality-related attributes (Erenand Kaymak-Ertekin 2007).

By applying desirability function method, solution wasobtained for the optimum covering the criteria. At this point,27.25 min for ultrasound time, 120 min for osmosis time, and50.52% for sucrose concentration. Hot air-drying treatmentwas done at optimized osmo-ultrasound condition.

Shrinkage

The effects of independent variables on the responses arerepresented in perturbation graphs (Fig. 7). The increase oftime and temperature at process duration will increase theamount of shrinkage.

The magnitude of p values in Table 6 indicates thelinear effects positive contribution of time on shrinkage.Figure 8 indicates an increase in shrinkage with increaseof time and temperature at process duration. It alsoindicates that the shrinkage is low at the lowest time andtemperature. The interactive effect of x1x2 effect isnegative on shrinkage. So that, with increasing tempera-ture at process duration, shrinkage may be increased andthen decreased; in some cases, maximum shrinkage wasobserved in temperature range of 65 to 70 °C. Thequadratic terms of temperature has negative effect onshrinkage. Therefore, the models as fitted provide anadequate approximation to the true system. The model asfitted (Table 6) corresponded to:

Shrinkage ¼ �2:95þ 4:33� 10�3 � time� 4:396� 10�4

� temperature2 � 4:85� 10�5 � time

� temperature

Moisture Content

The effects of independent variables on the responses asrepresented in perturbation graphs (Fig. 9) reveals that time

0.15

0.20

0.25

0.30

0.35

m

oist

ure

cont

ent

240.00

270.00

300.00

330.00

360.00

60.00

65.00

70.00

75.00

80.00

A: time B: temperature

Fig. 10 Response surface and contour plots for moisture content

Fig. 9 Perturbation graphshowing the effect ofindependent variables onMoisture content

Food Bioprocess Technol (2012) 5:2098–2110 2107

and temperature showed the lowering effect with referenceto moisture content.

The linear effects of all variables in Table 6 shownegative effect on moisture content. Figure 10 indicatesdecrease in moisture content with increase of time andtemperature at process duration. The quadratic termstemperature has positive effect on moisture content. The

quadratic model developed in the uncoded form of processvariables (at 5% level of significance) is:

Moisture content ¼ 1:86� 7:81� time� 0:035

� temperatureþ 2:075� 10�4

� temperature2

Rehydration Ratio

The effects of independent variables on the responses arerepresented in perturbation graphs (Fig. 11); the increase oftime and temperature at process duration will increase theamount of rehydration ratio.

The magnitude of p values in Table 6 indicates the linearpositive effect contribution of temperature on the rehydra-tion ratio. Figure 12 indicates increase in rehydration ratiowith increased temperature. The quadratic term of temper-ature has positive effect on rehydration ratio. The quadraticmodel developed in the uncoded form of process variables(at 5% level of significance) is:

Rehydration ratio ¼ 4:82� 0:11� temperatureþ 8:37

� 10�4 � temperature2

Optimum conditions for air-drying of quinces weredetermined to obtain minimum shrinkage and moisturecontent and maximum rehydration ratio. Second-order

Fig. 11 Perturbation graphshowing the effect ofindependent variables onrehydration ratio

1.22

1.26

1.31

1.35

1.40

reh

ydra

tion

capa

city

60.00

65.00

70.00

75.00

80.00

240.00

270.00

300.00

330.00

360.00

B: temperature A: time

Fig. 12 Response surface and contour plots for rehydration ratio

2108 Food Bioprocess Technol (2012) 5:2098–2110

polynomial models obtained in this study were utilized foreach response in order to determine the specified optimumconditions. Since the regression models are only valid inthe selected domain, a few economical and quality-relatedconstraints were considered for determining operatingregion (Eren and Kaymak-Ertekin 2007).

By applying desirability function method, one solutionwas obtained for the optimum covering the criteria; at thispoint, 297.86 min for time and 80 °C for temperature.

Conclusion

Response surface method was used to determine theoptimum operating conditions that yield maximum waterloss, weight reduction, and rehydration ratio and minimumsolid gain, shrinkage, and moisture content in drying ofquince. The second-order polynomial models for all theresponse variables were found to be statistically significant.

The optimal conditions for maximum water loss andweight reduction and minimum solid gain, were27.25 min for ultrasound time, 120 min for osmosistime, and 50.52% for sucrose concentration in order toobtain water loss of 34.68 (g/100 g fresh sample), solidgain of 18.66 (g/100 g fresh sample), and weightreduction of 16.21(g/100 g fresh sample) and optimalconditions for maximum rehydration ratio and minimummoisture content and shrinkage, were 297.86 min fortime, and 80 °C for temperature in order to obtainshrinkage of 52.84%, moisture content of 0.1971, andrehydration ratio of 1.451. The model equation for theresponse variables predicted values under the identifiedoptimum conditions, which were experimentally verified tobe in general agreement with the model.

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