PREDICTION OF SOME PHYSICAL PROPERTIES OF OSMODEHYDRATED CARROT CUBES USING RESPONSE SURFACE...

jfpp_436 1041..1063

PREDICTION OF SOME PHYSICAL PROPERTIES OFOSMODEHYDRATED CARROT CUBES USING RESPONSE

SURFACE METHODOLOGY

MINA KARGOZARI, SOHRAB MOINI and ZAHRA EMAM-DJOMEH1

Transfer Phenomena Laboratory (TPL)Department of Food ScienceTechnology and Engineering

Faculty of Biosystem EngineeringAgricultural Campus of the University of Tehran

Karaj 31587-11167, Iran

Accepted for Publication April 16, 2009

ABSTRACT

In this study, response surface methodology was used to optimize theprocessing conditions and predict some physical properties of carrot cubestreated by osmotic dehydration. Temperature (30–50C), processing time (15–360 min), glucose syrup (30–50% w/w) and NaCl (5–15% w/w) concentra-tions were the factors investigated. Experiments were designed according tothe central composite rotatable design. For each response, second-order poly-nomial models were developed using multiple linear regression analysis. Withrespect to texture firmness, density, rehydration capacity and color changes,both linear and quadratic effects were significant. Analysis of variancewas performed to check the adequacy and accuracy of the fitted models. Theoptimal conditions for firmness, density and rehydration capacity andminimum color difference correspond to a temperature of 50C, 43.13%glucose syrup, 5% salt and a processing time of 221.82 min; these parametersyield firmness, density and rehydration capacity values of 38.06, 1.20 and79.98%, respectively.

PRACTICAL APPLICATIONS

Response surface methodology is a statistical tool owning significantadvantages in optimization and controlling of food process. Because thequality attributes are the most effective parameters influencing consumerchoice, optimizing these parameters have a great practical and economical

1 Corresponding author. TEL: +98-218-879-6165; FAX: +98-218-865-0886; EMAIL: [email protected]

Journal of Food Processing and Preservation 34 (2010) 1041–1063.DOI: 10.1111/j.1745-4549.2009.00436.x 1041© 2010 Wiley Periodicals, Inc.

importance. Osmotic dehydration is a suitable way to produce the shelf-stableproducts or partially dehydrated foods ready to place in other complementaryprocesses such as air-drying, freezing and others. The osmotically dehydratedcarrots can be added directly into soups, stews or can be suited to a broad rangeof food formulations including instant soups, snack seasoning and so on.

INTRODUCTION

Carrot (Daucus carota L.) is known for its nutrient contents, includingcarotene and carotenoids, and they also contain appreciable amounts of vita-mins (B1, B2, B6 and B12) and minerals. Various methods are used to extendshelf life, including fermenting, pickling, canning, cold storage and freeze-drying (Gupta et al. 2007). Among these methods, freeze-drying producesthe highest quality of food products but is the most expensive preservationmethod. Therefore, there is a need for a simpler and less expensive alternative.This new method must be more energy efficient and less capital intensive andsuitable for use both in regions far away from production facilities and duringthe off-peak season. Osmotic dehydration is a method that meets these criteria(Shi and Maguer 2002).

A variety of studies have been carried out on the use of osmotic processesto obtain several different kinds of fruit products or food ingredients, includingminimally processed or intermediate moisture fruits (Alzamora et al. 1997).Osmotic processes have been used as a pretreatment for air-drying (Alvarezet al. 1995; Nieto et al. 1998) or freezing (Pinnavaia et al. 1988; Sormani et al.1999; Maestrelli et al. 2001). In addition to mass fluxes in the tissue, structuralchanges, such as cell deformation and breakage, are associated with dehydra-tion. These processes cause changes in the macroscopic properties of thesample, such as optical and mechanical properties, product appearance andtexture (Torreggiani and Bertolo 2001a,b; Talens et al. 2002; Chiralt andTalens 2005).

Data on bulk and particle density as a function of moisture content inbiological tissues have been reported by several authors (Karathanos et al.1996; McMinn and Magee 1997; Askari et al. 2006). Most of these studiesconcern air-drying processes, where volume is reduced via water removalduring the early stages of drying. Very few studies have been carried out onosmotic dehydration processes (Barat et al. 1998; Nieto et al. 2004).

Most of the dehydrated products are rehydrated before either consump-tion or further processing. Rehydration is a complex process composed of twosimultaneous processes: the absorption of water by dried tissue and the leach-ing of soluble solids (McMinn and Magee 1997; Lewicki 1998; Amami et al.2007). Previous studies have shown that the type of pretreatment affects

1042 M. KARGOZARI, S. MOINI and Z. EMAM-DJOMEH

rehydration characteristics (Rastogi et al. 2004). Taiwo et al. (2002) con-ducted studies on the rehydration behavior of osmotically dehydrated applesamples. Dried carrots are usually used in the formulation of dried soups, andthese kinds of meals are prepared by adding hot water. So, the capacity ofrehydration of their ingredients is a very important quality parameter (Taiwoet al. 2002; Amami et al. 2007).

Generally speaking, color affects consumer acceptance of a product,often accounting for 40% of the criteria for acceptance (Baardseth et al. 1988).Changes during dehydration that significantly affect color include tempera-ture, moisture and the storage history of the product (Falade et al. 2007).

Response surface methodology (RSM) is an important tool in both theprocessing and product improvement phases. RSM is an experimental designand optimization technique used to determine the relationship between aresponse and its independent variables. RSM is typically used to map aresponse surface over a particular region of interest, optimize the response orselect operating conditions to achieve target specifications or customerrequirements (Myers and Montgomery 1995; Khuri and Cornell 1996; CheMan et al. 1997; Rhee et al. 2004; Eren and Kaymak-Ertekin 2007). Mostresearchers had used RSM technique to optimize the solute gain and water loss(WL) during osmotic dehydration; using RSM, solute gain and WL werepredicted successfully (Burhan Uddin et al. 2004; Fermin and Corzo 2005;Azoubel and da Silva 2008; Singh et al. 2008).

The main objectives of this study were (1) to investigate the effects oftemperature, processing time, glucose syrup and salt concentrations on texturefirmness, density, rehydration balance and color properties of osmodehydratedcarrot cubes; (2) to present a model that predicts these factors as a function ofthe process variables; and (3) to find the optimum operating conditions thatmaximize firmness, density, rehydration balance as well as minimize the colorfriction.

MATERIALS AND METHODS

Materials

Fresh, well-graded carrots were washed and peeled manually. A manuallyoperated vegetable dicer was used to prepare carrot cubes of dimensions1 cm ¥ 1 cm ¥ 1 cm. The cubes were washed with fresh water to remove thecarrot fines adhered to the surface of the vegetable. The initial moisture contentof the fresh carrot cubes varied from 86 to 90% (wet basis).

Glucose syrup and NaCl (Dextrose equivalent = 42) were purchasedfrom Glucosan Co. (Ghazvin, Iran) and Merck Co. (Stutgart, Germany),respectively.

1043OSMODEHYDRATED CARROT CUBES

Osmotic Dehydration Procedure

Because a mixture of solutes is more effective at mass transfer than asingle solute, a mixed solution composed of NaCl and glucose syrup was usedas the osmotic solution. Glucose syrup and NaCl (salt) concentrations were inthe range of 30–50% (w/w) and 0–15% (w/w), respectively (Emam-Djomehet al. 2001; Rastogi et al. 2004; Gupta et al. 2007; Kargozari et al. 2007). Theeffect of temperature was also investigated. Experiments were conducted in atemperature range between 30 and 50C. Osmotic dehydration was carriedout in a batch system. Flasks containing osmotic solution and carrot cubeswere placed in a temperature- and agitation-controlled incubator shaker(Thermoshake-Gerthart, Germany). To prevent evaporation of the osmoticsolution, Erlenmeyer flasks were covered with plastic wrap during the experi-ments. The agitation was necessary in order to improve mass transfer andprevent the formation of a dilute solution film around the samples. Besides,agitation ensured that uniform concentration and temperature profiles wereformed. For each experiment, a 10/1 (w/w) solution/sample ratio and a con-stant agitation speed of 450 rpm were used. No blanching was done beforeosmosis because blanching has been reported to be detrimental to the osmoticdehydration process due to the loss of semipermeability of the cell membrane(Ponting 1973) and reduction of b-carotene within the carrots (Bao and Chang1994).

At each sampling time (120, 180, 240 min), the carrot cubes wereremoved from the osmotic medium, drained and gently blotted with filterpaper to remove adhering solution (Kargozari et al. 2007).

Analytical Methods

Measurements were performed in triplicate on fresh samples and thenafter the osmosis process. Moisture content was determined gravimetrically bydrying in a vacuum oven at 70C for 24 h (AOAC 1995). The WL and solidsgain (SG) of samples during the osmosis process were calculated using thefollowing relationships:

WLw w

w=

−×w w0

0

100 (1)

SGw w

w=

−×s s0

0

100 (2)

where ww and ww0 are, respectively, moisture content of sample after andbefore osmotic dehydration. wS0 and wS stand for solids content before and


after dehydration time. w0 is the initial weight of sample (Emam-Djomeh et al.2001).

In order to evaluate the texture of the samples, the firmness of osmode-hydrated carrot cubes was determined by a texture analyzer (Hounsfield-H5KS, Haslemere, Surrey, U.K.). The maximum incurred force wasconsidered as firmness of the sample tissue. The tests were conducted with aload cell (50 N) and the rate of crosshead was adjusted to 50 mm per minute(Moreira et al. 2007).

The bulk density of osmodehydrated carrot cubes was calculated accord-ing to the following equation:

ρb = m v (3)

where rb is the density (g/cm3), m stands for mass (g) and v is the volume ofthe sample (cm3), which was determined by immersing the cubes in n-hepteneas a reference liquid and assessing the displaced liquid (Nieto et al. 2004).

To determine the rehydration content, samples were dried in an airdryer (Armfield, London, U.K.) at T = 60 � 1.0C, relative humidity = 80%humidity and air velocity = 1 m/s until a final moisture content of 10% wasachieved. Figure 1 shows that the water removal of samples reached a constantlimit (about 65%) after 240 min and longer time periods had no water removaleffects on the cubes.

Dehydrated carrot samples were then rehydrated in water at 80 � 1.0C.The ratio of the volume of carrot cubes to the rehydration medium (water)

was maintained at 1:25. The samples were weighed and subjected to rehydra-tion for up to 10 min. The moisture content of the carrot samples after rehy-dration was estimated as per the AOAC (1995) procedure. The moisturecontent and solid content after rehydration was expressed in terms of kg ofwater or solids/kg of dry solids, respectively. All experiments were performedin triplicate, and average values were reported.

Commission Internationale d’Eclairage color parameters of osmosedcarrot cubes were determined. A Konika Minolta colorimeter (Chroma meterCR-400, Tokyo, Japan) was used to take measurement at three different posi-tions on each sample. The background was a white sheet provided by KonikaMinolta and samples must fill the entire apparatus frame which had a 2 ¥ 2-cm2 surface. Tristimulus values of L*, a* and b* were recorded directly fromthe colorimeter, where L* value represents the lightness, a* represents rednessand b* represents the yellowness. Color intensity (DE) was calculated usingthe following equation (Prothon et al. 2001; Falade et al. 2007):

ΔE L L a a b b= − + − + −( * ) ( * ) ( * )02

02

02* * * (4)


a

b

c

0

10

20

30

40

50

60

70

80

0 50 100 150 200 250 300

WL in 5% salt, 50% syrupconc.m temp. 30°C



Wat

er L

oss

(%)

0

10

20

30

40

50

60

70

80

0 50 100 150 200 250 300




Wat

er L

oss

(%)

Wat

er L

oss

(%)

Time (min)

Time (min)

Time (min)

0

10

20

30

40

50

60

70

80

0 50 100 150 200 250 300




FIG. 1. EFFECT OF DIFFERENT CONDITIONS ON WATER LOSS (WL) OF CARROT CUBESDURING OSMOTIC DEHYDRATION: EFFECT OF TEMPERATURE (a), SALT

CONCENTRATION (b), AND GLUCOSE SYRUP CONCENTRATION (c)


A small section was cut from the inner part of fresh osmodehydrated andair-dried carrot cubes. The samples were analyzed with a scanning electronmicroscope (SEM) (XL30, Philips, Amsterdam, the Netherlands).

Experimental Design and Statistical Analysis

RSM was used to estimate the main effects of the process variables ontissue firmness, density, rehydration balance and color difference after carrotswere osmotically dehydrated. Treatment time (x1), temperature (x2), salt con-centration (x3) and glucose syrup concentration (x4) were selected as indepen-dent variables by means of literature survey and preliminary experiments.The experimental domain was established by taking into account industrialpractice. A central composite rotatable design (CCRD) was used to selectthe experimental parameters. The design included 31 experiments and wasadopted by adding seven (n0) central points and eight (l = 2) axial points to 24

full factorial designs.The center runs provide a means for estimating the experimental error and

a measure of lack of fit. The axial points were added to the factorial design toestimate curvature of the model. Coded values corresponding to the naturalvalues of each variable and CCRD are shown in Table 1.

The second-order polynomial model presented in Eq. (5) was fitted to thedata. Four models of the following form were developed to relate fourresponses (Y) such as firmness, density, rehydration balance and color differ-ence to four process variables (x):

Y kk k ki i kii i kij i jj iiii

= + + + == +===∑∑∑∑β β χ β χ β χ χ0

2

1

4

1

3

1

4

1

4

1 2 3, , ,, ,4( ) (5)

where bk0, bki, bkii and bkij are constant regression coefficients; x is the codedindependent variable. The mathematical models were evaluated for eachresponse by means of multiple linear regression analysis. Modeling wasstarted with a quadratic model including linear, squared and interaction terms.The significant terms in the model were found by analysis of variance(ANOVA) for each response. Significance was judged by determining theprobability level that the F-statistic calculated from the data is less than 5%.The model adequacies were checked by R2, adjusted R2, predicted R2 andprediction error sum of squares (PRESS) (Myers and Montgomery 1995). Agood model will have a large predicted R2 and a low PRESS. After modelfitting was performed, residual analysis was conducted to validate the assump-tions used in the ANOVA (results not shown). This analysis included calcu-lating case statistics to identify outliers and examining diagnostic plots such asnormal probability plots and residual plots.


Maximization and minimization of the polynomials thus fitted were per-formed by the desirability function method, and the fitted responses weremapped with Minitab Version 15 software (Minitab, State College, PA).

Optimization

Several response variables describing the quality characteristics and per-formance of the systems such as fermentation, agitation, extraction and so onwere optimized. Some of these variables were maximized, and some were

TABLE 1.CENTRAL COMPOSITE ROTATABLE DESIGN WITH THE EXPERIMENTAL VALUES OF

THE RESPONSE VARIABLES

Runorder

Time(min)

Temperature(C)

Saltconcentration(%)

Glucoseconcentration(%)

Firmness(n)

Density(g/cm3)

Rehydration(%)

ColorChange(%)

1 240 40 10 40 37.1 1.1414 61.5 6.222 240 30 5 30 45.02 1.1424 54.47 11.373 240 50 15 50 14.22 1.3105 59.05 8.054 120 50 5 30 38.21 1.1342 53.43 8.675 120 50 15 50 15.35 1.3 70.64 4.556 180 40 10 50 25.87 1.1923 64.6 10.57 120 50 5 50 39.55 1.11 77.14 7.968 120 30 15 30 21.9 1.1471 35.69 9.19 240 50 5 50 38.03 1.1818 78.81 4.76

10 240 50 15 30 26.08 1.13 43.61 4.5711 180 40 10 40 36.23 1.1733 56.59 6.9312 240 50 5 30 35.69 1.1412 74.79 10.0413 120 30 5 30 48.98 1.1343 32.15 10.1114 240 30 5 50 43.77 1.1454 59.09 8.9215 180 50 10 40 30.11 1.224 53.57 12.4216 120 50 15 30 28.12 1.1697 30.58 11.8417 180 40 10 40 35.58 1.1622 57.36 10.1818 120 30 5 50 44.88 1.0941 48.41 9.8719 180 40 10 40 33.69 1.1425 59.11 8.3820 180 40 10 40 37.7 1.1555 57.02 8.6821 180 40 15 40 26.9 1.2461 44.53 7.1522 180 40 10 40 34.98 1.1558 58.07 11.123 180 40 10 30 32.22 1.1359 40.38 10.1124 180 30 10 40 39.22 1.2167 61.72 6.4125 180 40 10 40 32.68 1.1636 59.57 9.4726 180 40 5 40 35.69 1.2021 62.58 9.2727 120 30 15 50 27.7 1.2624 46.51 7.0528 180 40 10 40 35.32 1.1621 57.69 10.7229 240 30 15 30 23.13 1.1914 38.36 5.4630 240 30 15 50 26.11 1.277 40.45 6.0431 120 40 10 40 35.4 1.1413 50.26 4.84


minimized. In many cases, these responses compete, i.e., improving oneresponse may have an opposite effect on another, which further complicatesthe situation. Several approaches have been used to solve this problem. Oneapproach uses a constrained optimization procedure. The second methodsuperimposes the contour diagrams of the different response variables,whereas the third approach solves the problem of multiple responses throughthe use of a desirability function that combines all the responses into onemeasurement.

Depending on whether a particular response Yi is to be maximized orminimized, different desirability functions di(Yi) can be used (Derringer andSuich 1980). Li, Ui and Ti represent the lower, upper and target values, respec-tively, that are desired for response Yi, with Li, Ti, and U variables.

If a response is to be maximized, then its individual function with theexponent s determines the importance of hitting the target value. For s = 1, thedesirability function increases linearly towards Ti, which denotes a largeresponse value; for s < 1, the function is convex; and for s > 1, the function isconcave:

d yy x L

T L

y x L

L y x T

y xi i

i i

i i

s i i

i i i

i

ˆˆ

ˆ

ˆ

ˆ

( ) =( ) −

−⎛⎝⎜

⎞⎠⎟

( ) <≤ ( ) ≤

( )

0

1>>

⎧

⎨⎪⎪

⎩⎪⎪ Ti

(6)

If a response is to be minimized, then its individual desirability functionTi denotes a small value for the response:

d yy x U

T U

y x T

T y x U

y xi i

i i

i i

s i i

i i i

i

ˆˆ

ˆ

ˆ

ˆ

( ) =( ) −

−⎛⎝⎜

⎞⎠⎟

( ) <≤ ( ) ≤

( )

1

0>>

⎧

⎨⎪⎪

⎩⎪⎪ Ui

(7)

After desirability values are computed for each response variable, theyare combined into a single desirability index, D, by calculating their geometricmean. Further refinements to the weighting can be applied by assigning arange of numbers (from 1 to 5) to the importance of optimizing each responsevariable. The final desirability index then is computed as follows:

D d d d d dv v vnv v

iv

i

ni i i i i i= × × × ×( ) ∑ = ⎛

⎝⎜⎞⎠⎟=

∏1 2 3

1

1

. . . , (8)


where ui is a number indicating the relative importance of the i th response,which might typically be an integer in the range of 1 to 5, with 5 indicating thegreatest importance and 1 indicating the least.

In the present study, desirability functions were developed for the criteriaof maximum tissue firmness, density, rehydration balance and minimum colordifference.

RESULTS AND DISCUSSION

Fitting Models and Contour Plots

Different measurements of osmotic dehydration are shown in Table 1.ANOVA was conducted to determine the effect of process variables on eachresponse. Table 2 shows which process variables had a statistically significant

TABLE 2.ANALYSIS OF VARIANCE RESULTS REPRESENTING LINEAR, QUADRATICAND INTERACTION TERMS OF EACH VARIABLE AND COEFFICIENT FOR

THE PREDICTION MODEL

Source df Firmness(n)

Density(g/cm3)

Rehydration(%)

Color change(%)

Coef. P value Coef. P value Coef. P value Coef. P value

Model 14 34.331 <0.0001 1.171 <0.0001 57.036 <0.0001 9.016 <0.0001X1 1 -0.608 0.487 0.009 0.066 3.629 0.013 -0.475 0.370X2 1 -3.075 0.002 0.005 0.302 6.932 <0.0001 -0.082 0.876X3 1 -8.906 <0.0001 0.042 <0.0001 -7.303 <0.0001 -0.953 0.083X4 1 -1.326 0.140 0.030 <0.0001 7.847 <0.0001 -0.754 0.163X1

2 1 2.897 0.216 -0.044 0.003 -0.129 0.970 -3.096 0.037X2

2 1 1.312 0.568 0.035 0.012 1.636 0.638 0.789 0.569X3

2 1 -2.058 0.374 0.039 0.006 -2.454 0.483 -0.416 0.763X4

2 1 -4.308 0.073 -0.021 0.113 -3.519 0.318 1.679 0.234X1X2 1 -0.111 0.904 -0.004 0.404 -0.321 0.818 -0.079 0.887X1X3 1 0.349 0.705 0.007 0.195 -3.624 0.018 -0.431 0.442X1X4 1 0.121 0.895 0.008 0.129 -4.042 0.010 0.413 0.460X2X3 1 1.006 0.283 -0.001 0.819 -2.949 0.048 0.637 0.261X2X4 1 -1.524 0.112 0.010 0.058 3.090 0.039 -0.352 0.528X3X4 1 -0.886 0.342 0.033 <0.0001 1.237 0.381 0.212 0.703

Lack of fitness 10 0.013 0.016 0.000 0.0028 0.098R2 0.895 0.927 0.895 0.505Adj-R2 0.804 0.864 0.803 0.073CV 3.622 0.982 2.524 2.32

Xi, linear; Xi2, quadratic and XiXj: interaction of variables (1: time, 2: temperature, 3: salt concentration

and 4: Glucose syrup concentration).


effect on tissue firmness, density, rehydration balance and color difference atP < 0.05. At a 5% confidence level, all the process variables (time [X1], tem-perature [X2], salt concentration [X3] and glucose syrup concentration [X4])were statistically significant for rehydration balance. Conversely, none of thedependent variables affected color difference. Lack of color difference meansthat osmotic dehydration, time, temperature and solute concentration did notsignificantly affect sample color.

Multiple linear regression analysis of the experimental data yieldedsecond-order polynomial models that were used to predict firmness, density,rehydration balance and minimum color difference.

Some regression models were found to be statistically significant at the95% confidence level. Statistical significance of linear, quadratic and interac-tion of effects calculated for each response are shown in Table 2. Linear,quadratic and interaction of effects of the dependent variables are also shownin the models below. The effects that were not significant (P > 0.05) wereremoved from the models without damaging the model hierarchy.

R2, adjusted R2 and the coefficient of variation (CV) were calculated tocheck the model adequacy. A high proportion of variability (R2 > 0.89) in theresponse models can be explained successfully by the models (Table 2).However, a large R2 value does not always imply that the regression model isgood. Adding a variable to the model will always increase R2, regardless ofwhether the additional variable is statistically significant or not. Thus, it ispreferred to use an adjusted R2 to evaluate the model adequacy, and this valueshould be over 80%. Table 2 shows that R and adjusted R2 values for themodels did not differ dramatically, indicating that nonsignificant terms havenot been included in the model. The CVs, which indicate the relative disper-sion of the experimental points from the predictions of the SOP models, werefound to be 2.46, 0.98, 2.52 and 0.16% for firmness, density, rehydrationbalance and minimum color difference.

Model adequacy checks may be conducted by removing effects that arenot significant (P > 0.05) and then by considering the PRESS and predictedR2. A low PRESS and predicted R2 comparable with a fitted R2 imply that themodel as fitted is adequate for prediction. Predicted R2 measures the amountof variation in new data. Generally, a number closer to one is preferred,and the PRESS is a measure of how well the model fits each point in thedesign.

Firmness = − −34 3306 3 0750 8 90612 3. . .X X (9)

Density = + + − + +1 1711 0 04159 0 0304 0 0436 0 035390 0

3 4 12

22. . . . .

.X X X X

3391 0 033332

3 4X X X+ . (10)


Rehydration capacity = + + − +57 0356 3 6289 6 9317 7 30287

1 2 3. . . ..

X X X88467 3 6237 4 0425

2 9487 3 09004 1 3 1 4

2 3 2 4

X X X X XX X X X

− − −+. .

. . (11)

The regression equation coefficients for each proposed model for eachresponse are given in Table 2. To visualize the combined effects of the twofactors on the response, the response surface and contour plots were generatedfor each of the fitted models as a function of two variables while keeping othertwo variables constant. Figure 2 shows the effect of salt concentration andtemperature on carrot sample firmness. The higher the salt concentrationand the temperature of osmotic solution, the softer the tissue is. Monslave-Gonzalez et al. (1993) observed tissue softening during osmotic dehydrationof red delicious apple cylinders, which was attributed to pectin solubilizationand associated cell separation during soaking. The same result was describedby Del Valle et al. (1998) on studying apple tissue. The softened texture ofsamples could be due to the lower calcium content of the osmotically pre-treated samples. In fact, during the osmotic stage, leaching of calcium ions intothe osmotic solution is expected to take place. The lower concentration ofcalcium ions inside the tissue could in turn cause a softening of the tissueduring the drying steps due to the action of pectin methyl esterase, whichhydrolyzes pectin to pectic acid (Whitakers 1996).

It is clear that increasing the salt and glucose syrup concentrationsresults in a higher density (Fig. 3). This increase in density is because of thepenetration of salt and glucose syrup, which have greater density than carrot,into the sample tissue. Nieto et al. (2004) stated that the evolution of bulkdensity and volume of apple tissue throughout osmotic dehydration inglucose or sucrose aqueous solution were closely supported by microstruc-tural and ultrastructural changes. According to our results, there were slightdifferences in the changes of rb as a function of time during osmotic treat-ment (Fig. 3).

It is obvious that temperature (X2) and salt concentration (X3) had asignificant effect on the firmness of the samples. Density was affected by (X3)and glucose syrup concentration (X4). Figure 3a shows the effect of salt con-centration and temperature on rehydration balance of the samples. A decreasein salt concentration and an increase in solution temperature cause a higherrehydration balance. Salt ions form a thin layer on the surface of the samples,preventing water diffusion into the dehydrated carrots.

Figure 3b indicates that the rehydration balance increases by increasingthe glucose syrup concentration and the temperature of the solution. Thisresult is probably due to the hydrophilic properties of the glucose syrupmolecules.


15

20 10

30

40

30

50

40 550

firmness (N)

salt conc. (%)

temperature (°C)

time (min) 180glucose conc. (%) 40

hold values

Surface plot of firmness (N) vs salt conc. (%); temperature (°C)

temperature (°C)

salt

co

nc.

(%

)

5045403530

15.0

12.5

10.0

7.5

5.0

time (min) 180glucose conc. (%) 40

hold values

> – – – – < 25

25 3030 3535 4040 45

45

(N)firmness

Contour plot of firmness (N) vs salt conc. (%); temperature (°C)

FIG. 2. RESPONSE SURFACE AND CONTOUR PLOTS FOR FIRMNESS


501.15

40

1.20

1.25

5

1.30

10 3015

density (g/cm3)

glucose conc. (%)

salt conc. (%)

time (min) 180temperature (°C) 40

hold values

Surface plot of density (g/cm3) vs glucose conc. (%); salt conc. (%)

salt conc. (%)

glu

cose

co

nc.

(%

)

15.012.510.07.55.0

50

45

40

35

30


hold values

> – – – – < 1.12

1.12 1.161.16 1.201.20 1.241.24 1.28

1.28

(g/cm3)density

Contour plot of density (g/cm3) vs glucose conc. (%); salt conc. (%)

FIG. 3. RESPONSE SURFACE AND CONTOUR PLOTS FOR DENSITY


The same result was achieved when the simultaneous effect of salt andglucose syrup concentration was examined (Fig. 4c). The salt concentrationshould decrease and, at the same time, the glucose syrup concentration shouldincrease in order to reach higher rehydration balance.

Optimization

Optimum conditions to osmotically dehydrate carrots were determined toobtain maximum firmness, density and rehydration capacity and minimumcolor differences. The second-order polynomial models obtained in this studywere utilized for each response in order to determine optimum conditions.These regression models are valid only in the selected experimental domain.The operating parameter ranges were determined by considering economical,industrial and product quality constraints.

In this study, temperature, processing time, glucose syrup and saltconcentrations were selected in the range of 30–50C, 120–240 min, 30–50%and 0–15%, respectively. By applying the desirability function method, theoptimum values were found to be: 50C temperature, 221.82 min for time,43.13% for glucose syrup concentration and 5% for salt concentration. Thedesirability value of the optimum solution was 0.92%. At this point, firmness,density and rehydration capacity were calculated as 38.06, 1.20 and 79.98%,respectively.

Microstructure

It is important to note that the final decision of the optimal conditionsdepends on the costs, the product quality and the consumer expectations. It isobvious that lower process temperatures minimize cellular damage.

We studied the microstructure of carrot cubes using SEM. The SEMstudies showed that osmotic drying does not damage the cell wall. In Fig. 5,the structure of raw carrot (a) is compared with the air-dried carrot sample (b)and osmotically dehydrated carrot (c,d). Considerable cell rupture was seen inthe air-dried sample. A thickened cell wall can be observed, possibly due to theadsorption of glucose syrup to the components of the cell wall, cellulose,pectic substances or other polysaccharides, as suggested by Nieto et al. (1998).A decrease in cell size was also observed. After osmotic dehydration, a sig-nificant decrease in sample size was observed. The comparison of the osmoti-cally dehydrated samples (c,d) with the air-dried sample (b) shows the effectof osmotic treatment on the microstructure. However, the structure is alsoaffected by the osmotic treatment temperature. Thus, the cells show moreshrinkage at higher osmotic solution temperatures.


15

rehydration(%)

50

10

60

70

salt conc.3040 5

50temperature

hold valuestime 180glucose conc. 40

Surface plot of rehydration(%) vs salt conc.; temperature

temperature

salt

co

nc.

5045403530

15.0

12.5

10.0

7.5

5.0

hold valuestime 180glucose conc. 40

rehydration(%)

50 - 5555 - 6060 - 6565 - 70

> 70

< 4545 - 50

Contour plot of rehydration(%) vs salt conc., temperature

a

FIG. 4. RESPONSE SURFACE AND CONTOUR PLOTS FOR REHYDRATION (a–c)


50

40 40

50

60

30

70

40 3050

rehydration (%)

glucose conc. (%)

temperature (°C)

time (min) 180salt conc. (%) 10

hold values

Surface plot of rehydration (%) vs glucose conc. (%); temperature (°C)

temperature (°C)

glu

cose

co

nc.

(%

)

5045403530

50

45

40

35

30

time (min) 180salt conc. (%) 10

hold values

> – – – – – < 45

45 5050 5555 6060 6565 70

70

(%)rehydration

Contour plot of rehydration (%) vs glucose conc. (%); temperature (°C)

b

FIG. 4. CONTINUED


5020

40

30

5

40

10 3015

firmness (N)

glucose conc. (%)

salt conc. (%)


hold values

Surface plot of firmness (N) vs glucose conc. (%); salt conc. (%)

salt conc. (%)

glu

cose

co

nc.

(%

)

15.012.510.07.55.0

50

45

40

35

30


hold values

> – – – < 40

40 4848 5656 64

64

(%)rehydration

Contour plot of rehydration (%) vs glucose conc. (%); salt conc. (%)

c

FIG. 4. CONTINUED


CONCLUSION

The RSM was used to determine the optimum operating conditions yield-ing maximum firmness, density and rehydration capacity and minimum colordifference in osmotic dehydration of carrots. ANOVA has shown that all theprocess variables (time, temperature, salt concentration and glucose syrupconcentration) were statistically significant at a 5% confidence level for rehy-dration balance. Conversely, none of the dependent variables were effective oncolor change (DE). It was obvious that temperature and salt concentration hada significant effect on the firmness of the samples. Density was affected by saltconcentration and glucose syrup concentration. Second-order polynomialmodels were obtained to predict firmness, density and rehydration capacity.

The optimal conditions for firmness, density and rehydration capacity andminimum color difference correspond to a temperature of 50C, glucose syrupconcentration of 43.13%, a 5% salt concentration, time of 221.82 min, which

a

c

b

d

FIG. 5. SCANNING ELECTRON MICROSCOPY MICROGRAPHS OF RAW CARROT TISSUE(a), AIR-DRIED CARROT TISSUE (b) AND CARROT TISSUE AFTER OSMOTIC

DEHYDRATION IN 30C (c) AND 50C (d)


produced firmness, density and rehydration capacity values of 38.06, 1.20 and79.98%, respectively.

Study of the microstructure showed that the cellular structure after theosmotic dehydration is comparable with fresh carrot tissue. Air-drying causesa great rupture on the cell walls, whereas the solution temperature in theosmotic process plays a role in the shrinkage and collapse of the sample cells.In future studies, taste, shrinkage and freeze/thaw effects should also beinvestigated in order to achieve the desired final product specifications.

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PREDICTION OF SOME PHYSICAL PROPERTIES OF OSMODEHYDRATED CARROT CUBES USING RESPONSE SURFACE...

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