ORIGINAL PAPER

The Effect of Starch Concentration and Temperatureon Grape Molasses: Rheological and Textural Properties

Meryem Goksel & Mahmut Dogan & Omer Said Toker &

Seda Ozgen & Kemal Sarioglu & Rasim Alper Oral

Received: 17 June 2011 /Accepted: 12 October 2011 /Published online: 26 October 2011# Springer Science+Business Media, LLC 2011

Abstract Steady and dynamic shear properties of grapemolasses having varying levels of starch (5%, 7.5%,and 10%) have been determined at 60 °C, 70 °C, and80 °C. The consistency coefficient (K) and apparentviscosity (η50) increased with the starch concentration anddecreased with temperature. Similar behavior was alsoobserved in storage modulus (G′), loss modulus (G″),complex modulus (G*), and complex viscosity (η*). The5-hydroxymethylfurfural (HMF) content of mid-productmix decreased with the increase in the starch concentra-tion. Hardness, gumminess, and chewiness values in-creased with the starch concentration and temperature.The HMF content of the final product increased with theincrease in drying temperature and decreased as the starchconcentration increased.

Keywords Molasses–starch mixture . Drying temperature .

Starch concentration . Texture . Dynamic–steady rheology .

HMF

NomenclatureHMF 5-Hydroxymethylfurfural°C Centigrade degreeK Consistency index (pascal secondn)n Flow behavior indexσ Shear stress (pascal)

g Shear rate (per second)η50 Apparent viscosity at 50 s−1 (pascal second)η0 Constant for Arrhenius equation (pascal second)Ea Activation energy (kilojoules per mole)R Universal gas constant (kilojoules per

mole per kelvin)T Absolute temperature (kelvin)η1 Constant for concentration effect (pascal second)a Constant (per percent)η2 Constant for concentration effect (pascal second)η3 Constant for combined effect (pascal second)D1 Constant for combined effect (per percent)η4 Constant for combined effect (pascal second)N Number of the data pointsD2 Constant for combined effect (dimensionless)MPE Mean percentage errorMBE Mean bias errorRMSE Root mean square errorEF Modeling efficiencyχ2 Chi-squareηcexp,I Predicted apparent viscosity (pascal second)ηcexp ave Average of experimental values (pascal second)LVE Linear viscoelastic regionG′ Storage modulus (pascal)G″ Loss modulus (pascal)G* Complex modulus (pascal)η* Slope for complex viscosity (pascal second)ω Angular frequency (radians per second)K′ Intercept for complex viscosity (pascal second)K″ Intercept for loss modulus (pascal)K* Intercept for storage modulus (pascal)η′ Slope for storage modulus (dimensionless)η″ Slope for loss modulus (dimensionless)nu Number of the model parameters

M. Goksel :M. Dogan (*) :O. S. Toker : S. Ozgen :K. Sarioglu : R. A. OralDepartment of Food Engineering, Faculty of Engineering,Erciyes University,38039 Kayseri, Turkeye-mail: dogan@erciyes.edu.tr

Food Bioprocess Technol (2013) 6:259–271DOI 10.1007/s11947-011-0705-5

Introduction

Grape is produced widely in Turkey, and it is a nutrition aswell as source of income for people. On account of itshealth benefits and short shelf life, grapes are required to beprocessed into a form enabling long storage time withoutloss of nutritional value (Tangolar and Ergenoglu 1996;Eksi and Artık 1984). There are a variety of foods producedfrom grapes having different formulations and productionmethods throughout the country. Molasses (concentratedgrape juice), köfter (starch–grape juice gel), and pestil(grape leather) are among the most important productsespecially in the Middle Anatolia. Grape juice is concen-trated (up to 70–80% of soluble dry matter) to obtainmolasses (Bozkurt et al. 1999). It is a very important foodin human nutrition because molasses have high sugarcontent of monosaccharides such as glucose and fructose,mineral, and organic acids (Dogan 2007).

Köfter is one of the traditional food products in Turkey,and it is commonly produced from the grape molasses,water, and starch. Because of its high nutritional value, it ismostly consumed during winter in Turkey especially inMiddle Anatolia (Göksel and Doğan 2010). Characteriza-tion and determination of the rheological behavior of köftermix is important for numerous aspects of the production ofthe köfter, such as the optimization of the raw materials(concentration, type) and for the industrial processing. Therheological characteristics of the samples are also importantfor processes such as mixing, stirring, dispersing, extrusion,spinning, coating, injection molding, and spraying in foodprocessing (Fischer and Windhab 2011). Therefore, theviscosity and texture of food products are importantphysical properties (Tanglertpaibul and Rao 1987). Textureperceived as the senses of touch, sight, and hearing and theevaluation of the textural properties of the foods are drivenin the cause of mastication and are composed of thecombination of physical properties. Texture is a sensorialparameter (Szczesniak 2002) and is a fundamental featurethat affects the acceptability of the product. Texture ofköfter is one of the most important quality factors especiallyduring the long-term storage. No attempt has been made sofar to study the textural properties of köfter. Uslu et al.(2010) studied the effect of soapwort extract, and theredifferent starch ratios on physical, chemical, textural, andsensorial properties of Turkish delight. They reported thatsensory and textural properties of delight were significantlyaffected from the addition of starch. In addition to thetexture, safety of foods is an important factor affecting theacceptability of the product.

During the food processing, various thermal treatmentsare applied. Non-enzymatic browning is the phenomenaresulting from these treatments. Melanoidins and 5-hydroxymethylfurfural (HMF) are the final products of

non-enzymatic browning (Ibarz et al. 1999). Due to thetoxic effects of the HMF, it is regarded as a toxic substance.High concentration of the HMF is cytotoxic, irritating toeyes, upper respiratory tract, skin, and mucous membranes(Aldrich Chemical Company 1994). The high temperatureof drying causes the formation of more brown compounds(Buglione and Lozano 2002; Toribio and Lozano 1984).Due to the drying temperature and sugar content of theköfter, the HMF may be accepted as an important parameterfor the köfter quality. HMF is also produced from starchwhich is composed of glucose units linked together (Chunet al. 2010). Therefore, starch concentration in the köfterformulation is also important for HMF formation and HMFconcentration of the final product due to the effect of starchconcentration on the total sugar content of the formulation.To this respect, starch concentration is important forbalancing these two factors of HMF formation (HMFformation from starch or total sugar content) for diminishingthe HMF content of the sample. The optimization of sugarconcentration and the drying temperature are important forreducing the HMF content of the sample.

Variations in the process parameters including temperatureand the amount of starch determine the quality of köfter. Theaim of this study was to compare the effects of differentstarch concentrations and the drying temperature on physical,chemical, sensory, textural properties, and on the HMFcontent of the köfter samples as well as on the rheologicaland optical properties of the köfter mix.

Material and Methods

Materials

Corn starch (Bayrak Food Co, Kayseri, Turkey) and grapemolasses were obtained from a local market in the Kayseri,Turkey. The brand of the molasses was Gulsan. The molasseswere produced in 2011.

Preparation of Köfter

Generally, köfter is produced from grape molasses. Theflow sheet of traditional köfter production is given inFig. 1. Corn starch (5%, 7.5%, and 10% (w/w)) wasslowly added to prevent agglomeration into the molasses/water mixture (1/1) by continuous stirring with a magneticstirrer (Yelowline, Germany), heated to 85 °C, and heldfor 15 min. The final product (köfter) was obtained byspreading the mix immediately over a mold and allowed todry throughout 72 h at 25 °C, 35 °C, and 45 °C.Rheological measurements were carried out after the mixturewas cooled down to 80 °C, 70 °C, and 60 °C in a water bath(Memmert, Germany).

260 Food Bioprocess Technol (2013) 6:259–271

Physicochemical and HMF Analyses of the Samples

Moisture (oven drying at 70 °C) and ash content (AOAC2000) were determined. Total soluble solids (oBrix) of thesamples were measured in an automatic refractometer(Reichert AR, USA), and the results were reported at20 °C. pH (Hach, USA) and color (Lovibond ReflectanceTintometer, UK) were also determined.

Modified method of Guerra-Hernandez et al. (1992) wasperformed for HMF analysis. Sample (köfter and mixsample) solution was 10% (w/v). The solution wascentrifuged at 9000 rpm for 10 min. After that 1.5 mlsample of supernatants was collected the Eppendorf tubesand 0.15 ml Carrez I and II were added to the tubes. Thenthe tubes were centrifuged at 12000 rpm for 10 min. Thesupernatant was injected into an HPLC (Varian 9012Q)equipped with a diode array detector (Varian, Star 330). TheHPLC column was a Merck LiChrospher, RP-18, 5 mm,125×4 mm, fitted with a guard cartridge packed with thesame stationary phase (Merck, Milan). The HPLC con-ditions were the following: mobile phase, 95% water and5% acetonitrile; flow rate, 1 ml/min; injection volume,20 μl. All the solvents were of HPLC grade (Merck,Milan). The wavelength was 284 nm. HMF was identifiedby splitting the peak in caramels with a standard HMF(P>98% Sigma-Aldrich, Milan), and by comparison of the

spectra of the HMF standard with that of caramel samples.The amount of HMF was determined using an externalcalibration curve, measuring the signal at 284 nm (Ameuret al. 2006).

Rheological Measurements

Steady Shear Measurements

The rheological properties of the mixtures were determined bya controlled stress rheometer (Thermo-Haake, RheoStress 1,Germany) equipped with a temperature controlled unit(Haake, Karlsruhe K15, Germany). The measurements werecarried out in the shear rate range of 1–100 s−1 at 60 °C,70 °C, and 80 °C. Total 20 data points were recorded at 10-sintervals. Two repetitions were carried out, and the measure-ments were replicated six times in each repetition. Theapparent viscosity of the samples was determined asa function of the shear rate. The flow behaviors of thesamples were determined as Ostwald–de Waele model usingRheowin Data Manager (Rheowin Pro V. 2.96, Haake,Karlsruhe, Germany). The parameters of this model—consistency index (K) and flow behavior index (n)—werecalculated according to the following equation:

s ¼ Kgn ð1Þ

where σ is the shear stress (pascal), K is the consistencyindex (pascal secondn), γ is the shear rate (per second), and nis the flow behavior index (dimensionless).

The Effect of Temperature, Concentration, and CombinedEffect of Them on the Apparent Viscosity of Samples Theeffects of temperature, starch concentration, and theircombined effect on the apparent viscosity values weredetermined at shear rate of 50 s−1 (shear rate in mouth) Raoand Tattiyakul (1999). Temperature dependency of the η50(apparent viscosity) was described by the Arrhenius model(Rao et al. 1984).

h50 ¼ h0exp Ea=RTð Þ ð2ÞDependency of η50 on starch concentration was describedby power-law and exponential type model (Ibarz et al.1987).

h50 ¼ h1 Cað Þ ð3Þ

h50 ¼ h2exp b1Cð Þ ð4Þ

The Combined Effect of Temperature and Concentration Eq-uations 2–4 were combined to observe the effects oftemperature and concentration on the η50 values of the

(Kofter mix)

Molasses- Water Mixture (1:1)

Cutting With Different Shape

Drying

Spread On Mold

Heating With Stirrer (85 oC)

Adding Corn Starch

Fig. 1 Flow sheet ofgeneral köfter production

Food Bioprocess Technol (2013) 6:259–271 261

samples. When the equations were combined, followingequations were obtained.

h50 ¼ h3exp D1C þ Ea=RTð Þ ð5Þ

h50 ¼ h4CD2exp Ea=RTð Þ ð6Þ

The Performance of the Derived Models The differencesbetween experimental data and model estimates areimportant to determine the accuracy of the models. Forthis aim, R2, the mean percentage error (MPE), the meanbias error (MBE), the root mean square error (RMSE), themodeling efficiency (EF), and chi-square (χ2) were used.These parameters can be calculated as follows (Yılmazet al. 2011):

MPE ¼ 1

N

XN

i¼1

hC exp;i � hCpre;i� �

= hC exp;i

� �� 100 ð7Þ

MBE ¼ 1

N

XN

i¼1

hCpre;i � hC exp;i

� � ð8Þ

RMSE ¼ 1

N

XN

i¼1

hCpre;i�hC exp;i

� �2" #1=2

ð9Þ

EF ¼ 1�PN

i¼! hCpre;i � hCexp;i

� �2PN

i¼1 hC exp;i � hCexp;ave

� �2 ð10Þ

X2 ¼PN

i¼1 hCexp;i � hCpre;i� �2

N � nuð11Þ

where ηCexp,i is the experimental η50 (pascal second), ηCpre,i isthe predicted η50 (pascal second), ηCexp,ave is the average ofexperimental η50 (pascal second) values, N is the number ofdata points, and nu is the number of model parameters.

Dynamic Rheological Measurements

The viscoelastic properties of the samples were assessed byperforming a preliminary stress sweep test to identify thelinear viscoelastic region of the samples, and 1 Pa was usedin the frequency sweep test. A frequency sweep from 0.1 to10 Hz was performed by a dynamic oscillatory shearrheometer (RheoStress 1, Haake, Germany). In oscillatorytest, the overall response of the sample was known as

complex modulus (G*) (Gunasekaran and Ak 2000) andcomplex viscosity (η*) was calculated as:

G» ¼ G0ð Þ2 þ G00ð Þ2h i1=2

ð12Þ

h» ¼ G»=w ð13ÞIn addition, non–linear regression was applied to plots

of ω versus G″ and G′ to determine the magnitudes ofintercepts (K′, K″ and K*), slopes (η′, η″ and η*) and R2

was calculated according to the following equations (Yooand Rao 1996):

G0 ¼ K 0 wð Þn0 ð14Þ

G00 ¼ K 00 wð Þn0 0 ð15Þ

h» ¼ K» wð Þn»�1 ð16ÞAdditionally, the Cox–Merz rule was performed to

determine the correlations between the oscillatory shearparameters (η* and ω) and steady shear parameters (η and γ)(Rao and Tattiyakul 1999).

h» wð Þ ¼ h gð Þjw¼g ð17Þ

The Texture Analysis

The texture profile analysis (TPA) was performed using atexture analyzer (TAXT2i, Stable Micro Systems, Surrey,England). The samples were cut into cylinders of 20 mm inlength and 30mm in diameter, and then they were compressedunder a cylindrical probe (P/5) at a control force of 5 N. Thesamples were compressed twice at 1 mm/s, and the deforma-tion level was chosen as 25%. In TPA experiments, thesamples were generally subjected to 75–80% deformations. Inour study, a smaller level was selected because under largedeformation, the samples broke down and invalid parameterswere obtained. Pons and Fiszman (1996) recommended thedeformation level between 20% and 50% for starch gel foodsystem. The results are reported as the means of fourmeasurements. The textural parameters (hardness, adhesive-ness, springiness, cohesiveness, gumminess, chewiness, andresilience) were obtained from software.

Statistical Analysis

The analyses of variance were conducted to determine theeffect of variable, and Tukey multiple range test method

262 Food Bioprocess Technol (2013) 6:259–271

was used when difference in means is detected. Analyseswere performed using the Statistical Analysis Software(SAS) 8.0 (SAS Institute Inc., Cary, NC, USA).

Results and Discussion

Analyses of Grape Molasses–Starch Mixture

Physicochemical Characteristics and HMF Contentof the Samples

Table 1 shows the physicochemical characteristics of köftermixes. As expected, the Brix values increased with theincrease in starch concentration. No significant (p>0.05)difference was observed among the pH values of the starchcontaining samples. However, differences among the L, a,and b values of the köfter mixes were significant (p<0.05).

The HMF content of grape molasses was found to be4.92 ppm. Figure 2 shows the HMF content of the köftermix, which is prepared by heating water–molasses andstarch mixture at 85 °C over 10 min. The HMF content ofthe sample mix, which did not have starch, was 6.55 ppm.It is known that heat treatment causes increase in HMFcontent. Although HMF can form at low temperatures (Leeand Nagy 1990), it drastically increases as temperatures ofthermal treatments increase (Capuano and Fogliano 2011).The HMF content of the samples containing 5%, 7.5%, and10% starch was determined as 5.95, 5.26, and 5.13 ppm,respectively. Although the level of HMF tends to slightlyincrease with the decrease in starch concentration, thedifference was significant (p>0.05) containing between7.5% and 10% in starch samples. It is also known that HMFforms from dehydration of sugar under acidic conditionsduring thermal treatments applied to foods (Kroh 1994).Therefore, HMF content is directly proportional to sugarcontent. It may be expected that HMF content mayincrease with starch concentration. However, accordingto our results, HMF content did not increase. This may beresulted from:

1. Starch addition caused to decrease in water activityvalues of the samples. HMF formation is proportionalwith water activity because 1-mol HMF needs the

release of the 3-mol water (Ameur et al. 2007). Ameuret al. (2007) also found similar results.

2. The high density of H+ ions may facilitate and increasethe hydrolysis of starch to the smaller sugars mainlyglucose (Kunkes, et al. 2008). The pH of the medium inthis study (about 5.77) may prevent the degradation ofall of the starch found in the formulation.

3. HMF and sample amount increased simultaneously bythe starch addition. The concentration of the HMF inthe sample may decrease when considering these twopoints.

Rheological Analyses

Steady Shear Properties The shear stress–shear rate plots ofgrape molasses–starch mixture prepared with the addition ofdifferent levels of starch (0%, 5%, 7.5%, and 10%) at 60 °C,70 °C, and 80 °C are shown in Fig. 3. The apparent viscosityof the samples decreased with an increase in shear rate,indicating shear thinning behavior. The shear thinningbehavior of the molasses was reported in several studies(Kaur et al. 2002). The Ostwald–de Waele model welldescribed the flow behavior of köfter mix samples since thedetermination coefficients (R2) were considerably high.Table 2 shows the flow behavior index, the consistencycoefficient (K) values of the samples. The n and K valueschanged from 0.308 to 0.870 and 0.007 to 150.370 Pa sn. TheK values increased with the increase in starch concentrationand decreased with temperature. Lower values of K showed amore viscous nature due to the decrease in fluidity based onstarch concentration in the samples. The n values decreasedwith the increasing of starch, but there was no consistent trendof n affected by temperature. Similar results were obtainedby several researchers (Togrul and Arslan 2004; Sopade andFilibus 1995). And it was also reported that unlike the Kvalue, n was nearly independent of the concentration andtemperature (Hassan and Hobani 1998).

The Effect of Temperature on the Apparent Viscosity(η50) Temperature dependency of the apparent viscosity(η50) of the samples is described by the Arrhenius equation(Eq. 2, Rao and Tattiyakul 1999). For determination of theparameters of equation (η0, Ea), linear regression analysis

Table 1 Physicochemicalcharacteristics of köfter mixes

Letters in the same row showthe difference between thegroups, ± standard deviation

Mix samples 0% 5% 7.5% 10%

Brix% 36.48±0.01a 37.70±0.02b 38.60±0.01c 39.60±0.03d

pH 5.77±0.00a 5.77 ±0.00a 5.76±0.01a 5.77±0.00a

L 1.85±0.01a 4.66±0.03b 10.67±0.06c 13.53±0.03d

a 3.72±0.05a 3.84±0.04a 4.77±0.01b 6.48±0.00c

b 2.84±0.02a 4.73±0.06b 9.45±0.06c 15.73±0.01d

Food Bioprocess Technol (2013) 6:259–271 263

was applied to logarithmic form of this Eq. 2 (Table 2). Theapparent viscosity (η50) of the samples at temperatures of60 °C, 70 °C, and 80 °C are shown in Table 2. The η50values increased with decrease in temperature. Similarresults were obtained in previous studies (Ibarz et al.1994; Kaur et al. 2002; Khalil et al. 1989; Togrul andArslan 2004). When the temperature increases, the thermalenergy of the molecule increases with the increase ofintermolecular distances, causing reduction of intermolecularforces and viscosity decreases (Holdsworth 1971; Hassan andHobani 1998; Arslan et al. 2005).

The Effect of Starch Concentration on the ApparentViscosity (η50) As the starch concentration increased, theapparent viscosity increased as well. Kaur et al. (2002)found similar results when compared to our study. This isdue to the fact that more gelatinized granules were addedto the molasses by increasing the starch concentration(Abu-Jdayil et al. 2004). The variation of η50 values withthe starch concentration could be described by power-lawtype and exponential type models (Yılmaz et al. 2011).

Table 3 shows the parameters of these two models. Whenthese two models are compared, the power-law model (R2=0.988–0.999) seems to describe better than the exponentialmodel (0.959–0.998).

The Combined Effect of Temperature and Concentration onthe Apparent Viscosity (η50) It is very useful to obtain asingle equation describing the combined effect of temper-ature and concentration on η50 of the samples forengineering applications (Togrul and Arslan 2004). Equa-tions 2–4 combined to obtain Eqs. 5 and 6 for modeling ofthe combined effect of temperature and concentration onthe η50. Multiple linear regressions were applied to thelinearized forms of Eqs. 5 and 6 to obtain model constants.These constants and parameters indicating the performanceof the equations are detected in Table 4. The R2 values ofEq. 5 and 6 were 0.957 and 0.981, respectively. Highervalues of R2 and EF lower values of MPE, MBE, RMSE,and χ2 indicate better goodness of fit of the equation(Togrul and Arslan 2004). As it is seen from Table 4, MBEvalues of the models were lower than zero, indicating thatthe sum of the differences between the predicted andexperimental values was lower than zero (Eq. 8). Similarresults were found in the previous study (Yılmaz et al.2011). When the values of the two models are compared,Eq. 6, expressed as following, could be proposed to analyzethe η50 based on temperature and concentration.

h50 ¼ 2:7� 10�8 � C3:51 � exp 3; 893� 1=Tð Þð Þ ð18Þ

Figure 4 shows the effect of the starch concentration andthe temperature on the η50 values. These plots wereobtained using Eqs. 5 and 6 and experimental data. Whenthey were compared, it could also be seen that there was a

0 %5 %

7.5 % 10 %

0

1

2

3

4

5

6

7

1 2 3 4

HM

F c

once

ntra

tion

(ppm

)

Sample

Fig. 2 HMF content of the mix samples including different starchconcentrations

0

0.1

0.2

0.3

0 20 40 60 80 100

She

ar s

tres

s (P

a)

Shear rate (s-1)

a60 °C70 °C80 °C

0

15

30

45

60

75

90

0 20 40 60 80 100

She

ar s

tres

s (P

a)

Shear rate (s-1)

b60 °C70 °C80 °C

0

75

150

225

300

0 20 40 60 80 100

She

ar s

tres

s (P

a)

Shear rate (s-1)

c60 °C70 °C80 °C

0

150

300

450

600

750

0 20 40 60 80 100

She

ar s

tres

s (P

a)

Shear rate (s-1)

d60 °C70 °C80 °C

Fig. 3 Shear stress–shearrate plots for the köftermix including a 0%, b 5%,c 7.5%, and d 10% starchat different temperatures

264 Food Bioprocess Technol (2013) 6:259–271

good agreement between the model plots and experimentalplots.

The Dynamic Rheological Properties of the Samples Theviscoelastic properties of foods can be determined by dynamicrheological tests. The storage (elastic modulus) (G′) representsthe magnitude of energy stored in the material. On the otherhand, the loss (viscous) modulus (G″) means the energy thatis dissipated during sinusoidal deformation (Sopade et al.2004). While G′ estimates the solid behavior, G″ indicatesthe liquid or viscous behavior.

The dynamic rheological measurements were applied at1 Pa (in the linear region), which were determined after stresssweep test, which prevents the influence of non-linearitybetween stress and strain. By this means, the effect oftemperature, concentration, or other parameters on theviscoelastic parameters of the material was observed.

Figure 5 shows the changes in G′, G″, and η* (complexviscosity) as a function of frequency for grape molasses–starch mixtures. While the magnitudes of G′ and G″ increasewith increase in frequency representing viscoelastic behaviorof the samples, the decrease in η* values of the samples with

increasing frequency indicate shear thinning behavior of themix samples (Yılmaz et al. 2011). The dynamic rheologicaldata of ln (G′, G″) versus ln ω (radians per second) forsamples at 60 °C, 70 °C, and 80 °C were subjected to linearregression to determine the intercepts (K′, K″), slopes (n′, n″),and R2 values (Rao and Cooley 1992). These values arepresented in Table 5. It was seen from Table 6 that thesamples containing 7.5% and 10% starch exhibited weak gel-like structure because the slopes were positive (n′=0.233–0.275; n″=0.242–0.365) and the magnitudes of G′ (K′=264.26–1,734.10) were higher than those of G″ (K″=49.32–177.57). However, the sample containing 5% starch showedviscous behavior because the G″ values (K″=0.50–1.59) weregreater than the G′ values (K′=0.10–0.61). This result issimilar to what Rosalina and Bhattacharya (2002) found;studied starch concentration was lower than 6%, and they didnot observe the gel structure. The magnitudes of K′ and K″increase with an increase in starch concentration and adecrease in temperature. The inverse proportion between thetemperature and K′, K″ values could be resulted from therequired cooling time. Longer time in the cooling period maycause strength in gel structure as well as the decrease in

Table 2 Ostwald–de Waele parameters and temperature-dependent parameters of köfter mix at different temperatures

Samples T (°C) Ostwald–de Waele parameters Temperature-dependent parameters

K (Pa sn) n R2 η50 (Pa s) η0 (Pa s) Ea (kJ mol−1) R2

Starch (%)

0 60 0.011±0.001j 0.694±0.012b 0.962 0.004±0.000g 1.27×10−11 53.44 0.972

70 0.009±0.000j 0.631±0.010c 0.946 0.002±0.000g

80 0.007±0.000j 0.665±0.002bc 0.949 0.001±0.000g

5 60 7.650±0.551g 0.506±0.016e 0.997 1.047±0.025f 4.89×10−9 53.20 0.975

70 3.670±0.092h 0.600±0.017d 0.999 0.738±0.047fg

80 0.600±0.035i 0.874±0.030a 0.999 0.350±0.042fg

7.5 60 44.340±1.213d 0.437±0.007g 0.999 3.411±0.039d 0.004 18.70 0.987

70 32.850±1.000e 0.391±0.010h 1.000 2.802±0.100de

80 28.630±0.120f 0.441±0.010f 0.999 2.323±0.005e

10 60 150.370±14.802a 0.305±0.007j 0.999 9.497±1.147a 0.001 25.05 0.999

70 113.770±5.766b 0.308±0.014j 0.998 7.237±0.220b

80 61.240±4.300c 0.386±0.025i 0.999 5.297±0.292c

Table 3 Effect of starchconcentration on apparentviscosity of köfter mixsamples at differenttemperatures

Temperature (°C) Power-law function Exponential function

η50=η1 (Ca) η50=η2 exp (a2C)

η1 (Pa s) a R2 η2 (Pa s) a2 R2

60 0.0062 3.1647 0.996 0.118 0.4412 0.998

70 0.0037 3.2996 0.999 0.081 0.4566 0.986

80 0.0005 4.0639 0.988 0.025 0.5575 0.959

Food Bioprocess Technol (2013) 6:259–271 265

temperature. Figure 5 also indicates the effects of temperatureon the magnitude of G* (complex modulus) for the samples.It is seen that G* decreased with increase in temperature.Such relation between G* and temperature was found in theprevious study (Yılmaz et al. 2011).

The Correlation Between Dynamic and Steady ShearProperties The Cox–Merz rule is used to determine thecorrelation between the steady shear and dynamic propertiesof the samples (Alves et al. 2010). In this model, the apparentviscosity, ηa, in steady shear and the complex viscosity, η*, indynamic shear were plotted against the shear rate and thefrequency, respectively (Eq. 17). This rule has been studiedfor many polymers, solution, and complex food systems (DaSilva and Rao 1992; Tiziani and Vodovotz 2005; Yaşar et al.2009; Yılmaz et al. 2011). Figure 6 shows that the magnitudesof η* are higher than those of η for each starch concentration,indicating that the Cox–Merz rule was not applicable. Thissimilar behavior was found in waxy maize starch dispersions(Chamberlain and Rao 1999; Da Silva et al. 1998). The failure

in such systems to follow Cox–Merz rule could be resultedfrom the formation of aggregation of polymer systems(Lapasin and Pricl 1995) and structure decay due to the effectof strain deformation applied to the system (Chamberlain andRao 1999; Yılmaz et al. 2011). The differences between thevalues of η* and η increased with increase in concentrationof starch, showing that the departures of the samples fromthe Cox–Merz rule were concentration dependent (Fig 4).

Analyses of the Köfter Samples

Physicochemical Properties

Table 6 shows the physicochemical characteristics of aproduct obtained from grape molasses–starch mixtures atdifferent drying temperatures and starch concentrations. Asexpected, percent moisture, pH, L, a, and b values of theproducts vary significantly (p<0.05). However, ash valueswere not statistically significantly different.

Table 4 Combined effect of concentration and temperature on the apparent viscosity

Equation Equation parameters Statistical test parameters

ηi Dj Ea (kJ/mol) R2 MPE MBE RMSE EF χ2

η3 (Pa s) D1 (%)−1

η50=η3 exp(D1C+Ea/RT) 7.3×10−7 0.485 32.32 0.957 1.531 −0.024 0.795 0.977 0.947

η4 (Pa s) D2

η50=η4 CD2 exp(Ea/RT) 2.7×10−8 3.510 32.32 0.981 2.679 −0.070 0.382 0.995 0.219

MPE mean percentage error, MBE mean bias error, RMSE root mean square error, EF modeling efficiency, χ2 chi-square

02468

1012

56

78

910

6065707580

n50

(P

a s)

Concentra

tion (%

)

Temperature (0C)

a

02468

1012

56

78

910

6065707580

n50

(P

a s)

Concentra

tion (%

)

Temperature (0C)

b

02468

10

56

78

910

6065707580

n50

(P

a s)

Concentra

tion (%

)

Temperature (0C)

c

Fig. 4 Effect of starchconcentration and temperatureon η50 values (apparent viscosityat shear rate 50 s−1)

266 Food Bioprocess Technol (2013) 6:259–271

The Effect of Starch Concentration and DryingTemperature on the Textural Propertiesof the Final Product

TPA parameters (hardness, adhesiveness, springiness, cohe-siveness, gumminess, chewiness, resilience) of the samplesprepared with different starch concentration (7.5% and 10%)and dried at different temperatures (25 °C, 35 °C, and 45 °C)are presented in Table 7. Hardness refers to the strength of the

gel structure under compression (Lau et al. 2000). Thehardness values of the samples increased with increase indrying temperature and starch concentration (p<0.05). This isin agreement with the fact that an increase in starchconcentration will results in a harder gel (Edwards et al.1998), due to the retrogradation of starch. It is known that thegel firmness is mainly caused by the starch gel retrograding,water syneresis, and amylopectin crystallization (Miles et al.1985). In addition, according to Ring and Stainsby (1982), gel

Table 5 Parameters of power-law models describing storage (G′), loss (G″), and complex viscosity (η*)

Samples T (°C) G′=K′ (ω)n′ G″=K″ (ω)n″ η*=K* (ω)n*−1

K′ n′ R2 K″ n″ R2 K* n* R2

Starch (%)

5 60 0.61±0.03 1.18±0.15 0.990 1.59±0.08 0.91±0.02 0.990 1.93±0.17 0.928±0.012 0.820

70 0.16±0.01 1.28±0.03 0.981 0.69±0.01 0.94±0.00 0.993 0.87±0.05 0.893±0.039 0.888

80 0.10±0.02 0.98±0.04 0.873 0.50±0.03 0.63±0.04 0.886 0.57±0.01 0.636±0.017 0.947

7.5 60 277.05±8.41 0.28±0.01 0.952 68.16±3.14 0.35±0.01 0.981 286.87±14.83 0.279±0.007 0.995

70 264.26±5.19 0.22±0.00 0.965 49.32±2.19 0.37±0.04 0.961 269.48±10.71 0.225±0.015 0.998

80 153.08±1.02 0.27±0.03 0.911 52.77±4.87 0.24±0.00 0.925 164.74±7.27 0.258±0.037 0.993

10 60 1,734.10±23.19 0.13±0.01 0.957 177.57±9.71 0.27±0.01 0.975 1,744.10±17.29 0.136±0.001 0.999

70 894.81±12.25 0.21±0.03 0.903 166.40±8.61 0.26±0.02 0.971 913.69±9.02 0.210±0.013 0.994

80 530.45±9.08 0.25±0.00 0.909 96.48±4.30 0.28±0.01 0.961 541.46±11.05 0.211±0.027 0.995

0

1000

2000

3000

4000

0 2 4 6 8 10

G*

(Pa)

Frequency (Hz)

10 % starch60 °C70 °C80 °C

0

250

500

750

1000

0 2 4 6 8 10

G*

(Pa)

Frequency (Hz)

7.5 % starch

70 °C60 °C80 °C

0

30

60

90

120

0 2 4 6 8 10

G*

(Pa)

Frequency (Hz)

5 % starch70 °C80 °C60 °C

-3

-2

-1

0

1

2

3

4

-1 0 1 2 3 4 5

In G

', G

'' (P

a),

(Pa

s)

ln (rad/s)

5% starchIn G'ln G''ln *

0

2

4

6

8

-1 0 1 2 3 4 5

ln G

', G

'' (P

a),

*(P

a s)

ln ( ) (rad/s)

7.5 % starch

ln *ln G'ln G''

0

3

6

9

-1 0 1 2 3 4 5

ln G

', G

'' (P

a),

*(P

a s)

ln ( ) (rad/s)

10 % starch

ln * ln G'ln G''

Fig. 5 The changes in G′,G″, and η* (complex viscosity)as a function of ω for köfter mixsamples including 5%, 7.5%,and 10% starch at 70 °C andchange of G* as a functionof frequency at differenttemperatures

Food Bioprocess Technol (2013) 6:259–271 267

formation is associated with amylose concentration and thedegree of water swelling. On the other hand, initialdevelopment in hardness is owing to amylose gelation andamylopectin recrystallization (Biliaderis and Izydorczyk1992). The hardness of samples also increased with dryingtemperature, due to the moisture migration from the samplesincreased with temperature.

Adhesiveness is the capability of the sample to becomesticky (Sariçoban et al. 2009). It depends on the combinedeffect of cohesive and adhesive forces, viscosity, and visco-elasticity of the sample (Adhikari et al. 2001; Huang et al.2007). The magnitudes of the adhesiveness of the molasses–starch mixtures were in the range of 0.22–0.84 Ns. There wasnot any correlation between the starch concentration, dryingtemperature, and adhesiveness of the samples. Teng et al.(2011) and Edwards et al. (1998) also reported that the geladhesiveness was not a function of the starch concentration.

Springiness means the ability of the gel or sample to springback physically after it has been deformed during the first

compression (Bourne 2002; Huang et al. 2007). Generally,starch concentration and drying temperature increased thespringiness value (p<0.05). Teng et al. (2011) also foundthat starch and sugar concentration increased the springinessof the gel.

Cohesiveness refers the degree of difficulty in breakingdown the gel’s internal structure (Sanderson 1990; Wolfet al. 1989). It is seen from Table 7 that cohesivenessshowed very similar behavior to springiness in terms ofstarch concentration and drying temperature. These resultswere compatible with the previous study (Teng et al.2011).

Chewiness is known as the quantity to simulate theenergy required for masticating a semi-solid sample to asteady state of swallowing (Huang et al. 2007). The effectof starch concentration and drying temperature on thechewiness of the samples is shown in Table 7. It showedthat with the increase of starch concentration and dryingtemperature, more energy was needed to masticate the

Table 6 Physicochemical characteristics of köfter products in different storage temperatures

Concentration T (°C) Moisture (%) Ash (%) pH L a b

5% 25 37.16±0.08a 2.30±0.00a 6.04±0.00b 5.09±0.14g 3.19±0.22c 5.47±0.32e

35 32.88±0.05d 2.32±0.00a 6.04±0.03b 5.55±0.12f 3.22±0.00c 5.66±0.64d

45 27.51±0.10g 2.30±0.01a 6.00±0.00c 4.08±0.09h 3.22±0.09c 4.45±0.26f

7.5% 25 31.33±0.09b 2.44±0.02a 6.05±0.02a 10.60±0.36c 4.73±0.03c 5.93±0.09d

35 29.67±0.02e 2.40±0.01a 6.00±0.01b 8.24±0.17e 3.29±0.08b 6.43±0.45c

45 26.39±0.01h 2.41±0.00a 5.96±0.03d 8.73±0.07d 4.77±0.17b 7.53±0.33b

10% 25 26.16±0.01c 2.90±0.04a 6.00±0.01c 12.45±0.51a 4.79±0.54b 6.30±0.09c

35 25.19±0.06f 2.87±0.06a 6.00±0.00c 11.59±0.57b 4.81±0.03b 7.55±0.05b

45 24.85±0.07i 2.90±0.02a 5.99±0.06e 12.63±0.63a 5.55±0.11a 8.53±0.03a

Letters in the same column shows the difference between the groups, ± standard deviation

0

1.4

2.8

4.2

5.6

7

0 0.5 1 1.5 2 2.5

ln η

; ln

η*

[Pa

s]

ln [s-1] ; ln [rad/s]

10 %

-0.1

0.1

0.3

0.5

0.7

0.9

1.1

0 0.5 1 1.5 2 2.5

ln η

, ln

η* [P

a s]

ln [s-1]; ln [rad/s]

5 %

0

1

2

3

4

5

6

0 0.5 1 1.5 2 2.5

ln η

; ln

η*

[Pa

s]

ln [s-1] ; ln [rad/s]

7.5 %Fig. 6 Comparison of complexand steady viscosities of theköfter mix samples containing5%, 7.5%, and 10% starch at70 °C (closed symbol steadyviscosity (η50), open symbolcomplex viscosity (η*))

268 Food Bioprocess Technol (2013) 6:259–271

sample on the whole. Temperature increase resulted indecrease in moisture content resulting in an increase inchewiness. Similar result was obtained in the study of Rahmanand Al-Farsi (2005).

Gumminess is calculated by the hardness and cohesivenessof product, a characteristic of semi-solid foods having a lowdegree of hardness and a high degree of cohesiveness (Bourne2002). The variables affecting hardness also affect magni-tudes of the gumminess. Increase in starch concentration andtemperature increase during drying made the samples muchgummier. Such result was also obtained in the previous study(Teng et al. 2011).

The Effect of Starch Concentration and DryingTemperature on the HMF Content

Figure 7 depicts the effect of starch concentration and dryingtemperature (köfter mix samples stayed for 3 days at 25 °C,35 °C, and 45 °C) on the HMF content of the product. HMFcontent changed between 5.77 and −7.84 ppm. The finalproduct had higher HMF content than the mixtures,indicating that HMF concentration increases with storage(Capuano and Fogliano 2011). The fact that HMF contentincreased with increase in drying temperature and decreasedwith an increase in starch concentration may arise fromseveral factors including:

& Increase of starch concentration may result in decrease inwater activity values of the sample. Such result wasobtained in a previous study (Duggan et al. 2008). Wateractivity may also affect the formation of the HMF.

& The amount of water evaporated during drying increaseswith increase in temperature. Therefore, HMF concentra-tion of the samples may increase by decrease in totalweight of the sample.

& The increase of starch concentration results in decrease insugar concentration. So this may cause decrease in HMFconcentration of the samples.

& Temperature increase may accelerate HMF formation(Lee and Nagy 1990).

So HMF content of the samples may be decreased by theincrease of the starch concentration or the decrease of thedrying temperature. However, the texture, which is a crucialfactor for consumer acceptability of the product, of thesample may be deformed by this way. In addition to texture,the taste of the sample may be adversely affected by theincrease in the starch concentration, which resulted in thedecrease in sugar content of the product. Therefore, thestarch concentration and the drying temperature may beadjusted simultaneously by considering the HMF contentand textural properties of the product. If the dryingtemperature of the sample decreases, the textural propertiesof the sample may not be the desired level due to the highmoisture content of the sample. This problem may beironed out the increasing of starch concentration forabsorption of water or increasing the aging time of theköfter mix. In the future, another study may carry on for theoptimization of the properties of the sample with low HMFcontent.

Conclusion

In this study, it was aimed to observe the effects oftemperature and starch concentration on the rheological,textural, HMF content of the mid-product (mix), and the

Table 7 Effect of starch concentration and drying temperature on the textural properties of köfter

Concentration T (°C) Hardness(N)

Adhesiveness(N s)

Springiness(mm)

Cohesiveness Gumminess(N)

Chewiness(N mm)

Resilience

7.5% 25 0.92±0.02a −0.44±0.01c 0.876±0.001b 0.790±0.092b 0.71±0.01a 0.62±0.11a 0.51±0.30a

35 2.41±0.00b −0.38±0.00c 0.924±0.072a 0.925±0.071a 2.23±0.01b 2.12±0.01b 0.73±0.74b

45 4.15±0.01cd −0.58±0.02b 0.932±0.030a 0.922±0.190a 3.83±0.01cd 3.57±0.03cd 0.78±0.02bc

10% 25 3.73±0.01c −0.22±0.00d 0.931±0.043a 0.914±0.131a 3.41±0.00c 3.19±0.02c 0.72±0.05b

35 4.46±0.02d −0.60±0.01b 0.929±0.061a 0.930±0.084a 4.15±0.06d 3.85±0.01d 0.74±0.15b

45 5.83±0.00e −0.84±0.03a 0.944±0.043a 0.936±0.215a 5.45±0.13e 5.08±0.02e 0.82±0.08c

Letters in the same row shows the difference between the groups, ± standard deviation, 5% of starch concentration in mix did not show texturalproperties.

5

6

7

8

9

20 25 30 35 40 45 50HM

F c

once

ntra

tion

(ppm

)

Temperature (oC)

5%7.50%10%

Fig. 7 Effect of drying temperature and starch concentration on theHMF content of the köfter samples

Food Bioprocess Technol (2013) 6:259–271 269

end product. These properties, especially textural proper-ties, directly affect the consumer acceptability of theproduct, and HMF content is related to health. All of theköfter mix samples were most fitted to Ostwald–de Waeleflow behavior. In addition, the viscoelastic properties of themixtures were determined by the frequency sweep test, inwhich G′ and G″ values were obtained as a function offrequency. While the starch concentration increased thesevalues, they decreased with increase in temperature like Kand η50 values. The relationship between η* and η was notdescribed by the Cox–Merz rule.

The textural properties of the köfter samples (endproduct) were also determined at different starch concen-tration and the drying temperature of the köfter mix. It wasobserved that both temperature and starch concentrationobviously affected the textural properties of the köfter,especially the magnitudes of the hardness, gumminess, andchewiness (p<0.05). Moreover, it was seen that HMFcontent decreased with increase in the starch concentration.The increment of the drying temperature of the mix samplesresulted in an increase in HMF content of the köfter samples.This study shows that product containing low HMF contentand having desired texture could be obtained by modulatingthe drying temperature and starch concentration of the köftermix samples.

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