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Physicochemical, functional and sensory properties
of mellorine enriched with different vegetable juices
and TOPSIS approach to determine optimum
juice concentration
Gokturk Ozturk
a
, Mahmut Dogan
b,
n
, Omer Said Toker
c
a
Ahi Evran University, Kaman Vocational College, Food Technology Programme, 40300 Kirsehir, Turkey
b
Erciyes University, Engineering Faculty, Food Engineering Department, 38039 Kayseri, Turkey
c
Yildiz Technical University, Faculty of Chemical and Metallurgical Engineering, Food Engineering Department,
34210 İstanbul, Turkey
article info
Article history:
Received 4 September 2013
Received in revised form
3 March 2014
Accepted 1 May 2014
Keywords:
Mellorine
Rheology
Functional properties
Power-law model
Exponential model
Multi-criteria decision technique
abstract
In this study different concentrations (2.5%, 5%, 10%) of beetroot, red cabbage and broccoli
juices were added to mellorine to increase its bio-functional properties. Some physicochemical
(brix, pH) and bio-functional properties (total phenolic and flavanoid content and DPPH activity)
of the juices were determined and total phenolic content of broccoli, beetroot, and red cabbage
juices were found to be 419.8, 570.6 and 1131.9 mg/L, respectively. The rheological, physico-
chemical properties of mellorine mixes and functional and sensory properties of mellorine
enrinched with vegetable juices in different concentrations were investigated. All mixes
showed shear thinning behavior. The apparent viscosity and consistency index values (K)
decreased with increase in vegetable juice concentration. Total phenolic, total flavonoid and
DPPH radical scavenging activity increased with increasing all vegetable juice concentration
added to the formulation. Regarding sensory properties, among the samples containing
vegetable juice, broccoli juice containing sample in concentration of 5% had the highest scores
considering colour and appearance, body and consistency and taste and colour properties.
TOPSIS (Technique for order preference by similarity to ideal solution) was performed to
determine optimum vegetable juice type and concentration regarding bio-functional and
sensory properties. According to TOPSIS, the mellorine including 10% red cabbage juice was
found as the best sample when considering determined conditions.
&2014 Published by Elsevier Ltd.
1. Introduction
Mellorine, mainly composed of milk, vegetable oil, sugar,
emulsifier and stabiliser, is one of the ice cream products or
frozen desserts (Clarke, 2004) and it has a complex structure
similar to the dairy ice cream (Goff, 2002). Unlike dairy ice
cream, in mellorine formulation, all or some proportion of
dairy fat is substituted with vegetable based oils (Clarke, 2004;
Keeney, 2012). Using vegetable oils in the production of
mellorine does not negatively influence sensory profiles of
mellorine, even they contribute to a positive effect on human
nutrition since they contain remarkable amount of
http://dx.doi.org/10.1016/j.fbio.2014.05.001
2212-4292/&2014 Published by Elsevier Ltd.
n
Corresponding author. Tel.: þ90 352 437 4937; fax: þ90 352 437 5784.
E-mail address: dogan@erciyes.edu.tr (M. Dogan).
Food Bioscience 7 (2014) 45–55
unsaturated fatty acids (Anonymous, 2013;Hyvönen, Linna,
Tuorila, & Dijksterhuis, 2003;Nadeem, Abdullah, & Ellahi,
2010). Mellorine is consumed by people of all age throughout
worldwide as an alternative product to ice cream (Karasu,
Doğan, Toker,& Doğan, 2014); therefore, increasing function-
ality of the product is important for human health since
mellorine is poor in terms of natural phytochemicals such as
phenolics (O’Connell & Fox, 2001). Bio-functional properties of
mellorine or dairy ice cream can be improved by adding
biologically active compounds or substances containing these
compounds to ice cream formulation. For this reason, in recent
years, a variety of researches has been conducted to fortify ice
cream formulation with phenolic compounds by adding some
fruits (Karaman et al., 2014;Sun-Waterhouse, Edmonds,
Wadhwa, & Wibisono, 2011), fruit pulp (El–Samahy,Youssef,&
Moussa–Ayoub, 2009), herbal tea (Karaman & Kayacier, 2012)
and some phenolics (Sagdic, Ozturk, Cankurt, & Tornuk, 2012)to
ice cream mix in different concentrations.
Fruits and vegetables are rich in phenolic compounds
which contribute to colour and taste of the product (Blasa,
Gennari, Angelino, & Ninfali, 2010). Furthermore, plants
contain a variety of antioxidants such as phenolics and
flavonoids, which have a protective effect against some
diseases, for instance cardiovascular diseases and some types
of cancer caused by free radicals, especially reactive oxygen
species (Fraga, 2010;Keller, 2009). Broccoli and red cabbage are
among Cruciferous vegetables, which have attracted much inter-
est in recent years due to a number of compounds with high
antioxidant activities, such as phenolics, predominantly kaemp-
ferol and hydroxycinnamic acids derivates, and cyanidin deri-
vates, respectively (Chun, Smith, Sakagawa, & Lee, 2004;
Heimler, Vignolini, Dini, Vincieri, & Romani, 2006;Wu and
Prior, 2005). They are also a good source of glucosinolates,
known as sulphur-containing substances which have cancer-
protective properties. The sulphur-containing substances have
been studied to understand their functional specifications in
cancer research in vitro and vivo studies (Higdon, Delage,
Williams, & Dashwood, 2007;Podsedek, 2007). One of these
researches, Boivin et al. (2009), studied the antiproliferative and
antioxidant activities of common vegetables, and those vegeta-
bles were divided into four groups (little, intermediate, high, and
very high) according to their effects on certain types of cancer-
ous tumour cells. According to this classification beetroot,
broccoli and red cabbage were classified in high group; therefore
usage of these vegetables for improving functionality of the
product is beneficial for human health.
Increasing the bio-functional properties of the product
alone is not sufficient for the acceptability of the product
(Gurmeric, Dogan, Toker, Senyigit, & Ersoz, 2013). Therefore,
sensorial analysis was performed to determine consumer’s
acceptance or rejection of a new product. Although sensory
analysis is useful for determination of the formulation of the
product, it is very difficult to interpret the results since as one
sample might be preferred regarding one sensory property
(such as taste), the other sample might be preferred con-
sidering the other sensory property (such as odor). Obtaining
one score from different sensory properties, which might be
carried out by multi-criteria decision techniques, is facilita-
tive for interpretation or decision. Multi-criteria decision
techniques can deal with decision problems considering a
number of decision criteria simultaneously (Pohekar &
Ramachandran, 2004). They can be used for the evaluation
of alternatives based on the determined criteria by using a
number of qualitative and/or quantitative criteria (Ozcan,
Celebi, & Esnaf, 2011). One of the multi-criteria decision
techniques is the TOPSIS (technique for order preference by
similarity to ideal solution) which provides a decision hier-
archy and requires pairwise comparison between criteria
(Balli & Korukoglu, 2009). According to the TOPSIS method,
the best alternative is nearest to the positive ideal solution
and farthest from the negative ideal solution (Benitez, Martin,
& Roman, 2007;Lin, Wang, Chen, & Chang, 2008). Although
there have been many studies about the application of multi-
criteria decision making techniques in different areas, we
have found only two studies, one of them is related to
application of different multi-criteria decision techniques on
sensory properties of the prebiotic pudding sample (Gurmeric
et al., 2013) and the other one is about combination of
sensory properties and bioactive properties of persimmon
enriched ice cream with TOPSIS method (Karaman et al.,
2014), about this subject in the food bioscience field.
The aim of this study was to determine how different
vegetable juices at different concentrations affect the bio-
functional, rheological and some physicochemical properties
of mellorine mix, and to determine the optimum concentra-
tion by the TOPSIS technique considering bioactive and
sensorial features.
2. Material and methods
2.1. Material
Skimmed milk powder, vegetable oil (sunflower oil), sugar,
potable water, broccoli, red cabbage and red beetroot were
purchased from a local market in Kayseri, emulsifier (mono-
and di-glyceride) was obtained from Safiye Cikrikcioglu Voca-
tional College, in Erciyes University and xanthan gum was
obtained from Sigma. Methanol, sodium carbonate, Folin-
Ciocalteau reagent, sodium nitrite, aluminium chloride and
sodium hydroxide were obtained from Merck Co. and DPPH
was obtained from Sigma Co.
2.2. Preparation of mellorine
Broccoli, red cabbage and red beetroot were washed and then
pressed to prepare their juices after they were cut small parts.
Vegetable juices pasteurised at 90 1C for 1 min with magnetic
stirrer prior adding to mellorine mix. The mellorine mix
(basic mix) was prepared according to method described by
Karaman and Kayacier (2012) with some modifications. The
mix formulations contained 14% sugar, 11% skimmed milk
powder, 7% vegetable oil, 0.3% emulsifier and 0.2% xanthan
gum. Ingredients were added to the drinking water in the
following order: vegetable oil at 30 1C, skimmed milk powder
at 40 1C, sugar at 50 1C, dry mixture (remained sugarþ
emülsifierþxanthan gum) at 70 1C. The mixture obtained
was heated to 85 1C and held for 30 s at this temperature
for pasteurisation. The pasteurised mix was cooled to 4 1C
and then aged for 22 h at 4 1C. Pasteurised vegetable juices
Food Bioscience 7 (2014) 45–5546
were added to the aged mix at concentrations of 2.5, 5 and
10% (w/w). All experiments were done in duplicate. The
mellorine including vegetable juices was semi-frozen using
a ice cream maker (Simac II Gelataio GC 5000). After the
freezing process, which took exactly 16 min, the semi-frozen
samples were packaged. The frozen mellorine samples were
hardened by a batch freezer and stored at 18 1C for 24 h.
2.3. Rheological measurements
The rheological properties of the mixes were determined
using a controlled rheometer (Thermo-Haake, RheoStress 1,
Germany) with a temperature control unit (Haake, Karlsruhe
K15 Germany). The measurements were carried out using a
cone-plate configuration (cone diameter 35 mm, angle 41, gap
size 0.140 mm) in the shear rate range of 0.1–100 s
1
at 20 1C.
The rheological parameters of the mixes were calculated
using RheoWin Data Manager (RheoWin Pro V. 2.96, Haake,
Karlsruhe, Germany) based on the Power law model
σ¼K_γnð1Þ
in which σis shear stress (Pa), Kis consistency coefficient
(Pa s
n
), γis shear rate (1/s), and nis flow behaviour index
(dimensionless).
The apparent viscosity of the mixes (η
50
) represents the
shear rate in the mouth (Bourne, 2002). To determine the
effect of vegetable juice concentration on apparent viscosity
at shear rate of 50 s
1
, the following equations were used
η50 ¼η1ðCa1Þð2Þ
η50 ¼η2expða2CÞð3Þ
where η
1
and η
2
is constant for concentration effect (Pa s), a
1
and a
2
are constant, Cis concentration.
2.4. Physicochemical analysis
The total solids, pH, ash, colour, overrun and melting rate of the
samples were determined. The samples were dried at 105 1Cfor
4 h in a drying oven (Memmert, Germany) (AOAC, 1990). The pH
values were determined by a pH meter (Inolab Terminal Level 3,
Germany) until a constant value was observed on the screen.
The dry ash procedure was performed at 55 1Cinanashfurnace
(Protherm, Turkey) without black residual after it was dried at
105 1Cfor3hintheoven(Kurt, 1990). The colour values of the
mix samples were measured with colourimeter (Lovibond RT
Series Reflectance Tintometer, England) calibrated with a white
and black area. Overrun was calculated according to the follow-
ing equation (Arbuckle, 1986)
The hardened samples (approximately 40 g) were placed
on a wire mesh over a glass beaker and allowed to melt in the
oven at 25 1C. The melting rate of the samples was calculated
according to the proportion of the dripped weight to initial
weight of the samples.
2.5. Bioactivity analysis
2.5.1. Extraction
Ten grams of each sample was weighed and put into a 100 mL
bottle. The sample was diluted to 1:5 with 80% methanol.
This mixture was left at room temperature for 15 h for
extraction. The extracts were centrifuged at 13,000 rpm for
10 min, and the supernatant was filtered through a 0.22 mm
microfilter into a 15 mL falcon tube. By following this proce-
dure, extracts were obtained for analysis of total phenolic and
flavonoid content and DPPH activity.
2.5.2. Total phenolic content
The amount of total phenolics in the samples was determined
according to the method described by Sun, Powers, and Tang
(2007) with some modifications. 1.5 mL of Folin-Ciocalteu
reagent (1:10 v/v, diluted with distilled water) was added to
0.2 mL extract of the sample. After 5 min, 1.5 mL of 2% (w/v) of
sodium carbonate was added and then the absorbance of all
samples was measured at 750 nm using a UV–vis spectrophot-
ometer (Agilent 8453, Germany) after incubating at room tem-
perature for 30 min. Gallic acid was used as a standard.
2.5.3. Total flavonoid content
Total flavonoid analysis was performed according to the
aluminium chloride colourimetric method described by
Zhishen, Mengcheng, and Jianming (1999). 4 mL of distilled
water was added to 1 mL of the extract. 0.3 mL of 5% NaNO
2
(w/v) was added to the test tube before adding 0.3 mL of 10%
AlCl
3
(w/v) at 5th min. After 2 mL of NaOH (1M) was added to
the test tube at 11th min, the total volume was completed to
10 mL with distilled water. The absorbance of the samples
was measured at 510 nm using the UV–vis spectrophotometer
against the prepared blank and observed data were expressed
as mg catechin equivalent.
2.5.4. DPPH radical scavenging activity
DPPH radical scavenging activity (RSA) was determined
according to the method described by Faller and Fialho
(2009). After 0.1 mL of the filtrate was mixed in with 3.9 mL
of 0.1 mM DPPH solution (in 80% methanol), the mixture was
covered with aluminium foil and incubated at room tem-
perature in the dark place for 30 min. The absorbance of the
samples was measured at 517 nm using the UV–vis spectro-
photometer. The antioxidant capacity of the samples was
calculated using the following equation
%RSA ¼1absorbance of sample at 517 nm
absorbance of control at 517 nm
ð5Þ
2.5.5. Sensory evaluation
Twenty eight panelists were selected from academic staff or
graduate students of the Food Engineering Department at
Overrunð%Þ¼ weight of the mix weight of the same volume of the sample
weight of the same volume of the sample
100 ð4Þ
Food Bioscience 7 (2014) 45–55 47
Erciyes University, Kayseri. Panelists cleaned their palates
with potable water after analysing each sample. Before
sensory analyses, the panelists were informed about the
aim and requirements of the sensorial analyses. The colour
and appearance, taste and odour, and consistency of the ice
cream samples were evaluated by the panelists. Panelists
were cleaned their palate before proceeding the next sample.
Five-point hedonic scale was used for the sensory evaluation
of the samples (1: extremely dislike, 2: dislike, 3: not too bad,
4: like, 5: extremely like).
2.6. Application of TOPSIS method
The hierarchy of TOPSIS for decision is shown in Fig. 1. The
TOPSIS method is composed of six steps (Balli & Korukoglu,
2009).
Step 1. The decision matrix is normalised by the following
equation
xij ¼aij
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
∑
m
k¼1
a2
kj
sk¼1;2;3…;i;…;k;i¼1;2;…ð6Þ
where x
ij
is the normalised value and a
ij
is the real value of
the criteria.
Step 2. The weighted normalised decision matrix is
formed (weight of each criteria as presented in Fig. 1, deter-
mined based on opinion of the staff and graduate students
(totally 15 person) of the Food Engineering Department in
Erciyes University (average value was calculated for each
criterion)) using Eq. (7).
vij ¼xij wij ð7Þ
where v
ij
is the weighted normalised value and w
ij
is the
weight of each criteria.
Decision
Appearance
(0.25)
Consistency
(0.2)
Bioactivity
Sensory Properties
K
M1
M2
M3
P1
P2
P3
B1
B2
B3
DPPH
(0.125)
Taste
(0.3)
Phenolic
(0.125)
Fig. 1 –The decision hierarchy of the determination of vegetable juice concentration added to mellorine based on the sensorial
and bioactivity properties (B: Broccoli, M: Red cabbage, P: Beetroot, 1: 2.5%, 2: 5%, 3: 10%). (For interpretation of the references
to color in this figure legend, the reader is referred to the web version of this article.)
Food Bioscience 7 (2014) 45–5548
Step 3. The positive and negative ideal solutions are
determined.
A
n
¼{v
1
n
,v
2
n
,
v
3
n
…,v
n
n
} (maximum values)
A
¼{v
1
,v
2
,
v
3
…,v
n
} (minimum values)
Step 4. The distance of each alternative from the positive
and negative ideal solution is calculated according to the
following equations
dn
i¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ðvij vn
jÞ2
qð8Þ
d
i¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ðvij v
jÞ
q2ð9Þ
where dn
iand d
i
is the distance of alternative from positive
and negative ideal solution, respectively.
Step 5. The closeness coefficient of each alternative (C)is
obtained using Eq. (10).
C¼d
i
dn
iþd
i
ð10Þ
Step 6. The ranking of the alternatives is determined based
on the Cvalues. The alternative with the highest Cvalue is
selected as the best alternative.
2.7. Statistical analysis
The statistical analysis of the samples was performed by the
SPSS Statistics 17.0.1 programme. Differences between the
samples were determined by Duncan’s test (Ural & Kilic,
2006).
Fig. 2 –Shear rate versus shear stress curves of the mellorine samples,. (K: Control mix, P: Samples containing beetroot juice,
M: Samples containing red cabbage juice, B: Samples containing broccoli juice, 1: 2.5%, 2: 5%, 3: 10%).
Table 1 –Rheological parameters of the Power law model determined for the mellorine mixes.
Sample K(Pa s
n
)nR
2
η
50
(Pa s)
K 1.62670.022
a
0.30670.017
a
0.998 0.09770.003
a
P1 1.41170.064
cb
0.30470.017
a
0.999 0.08770.002
bc
P2 1.34470.034
cd
0.30770.011
a
0.999 0.08470.001
cd
P3 1.23370.049
e
0.30970.009
a
0.999 0.07870.002
ef
M1 1.42970.093
cb
0.30370.013
a
0.999 0.08670.004
bcd
M2 1.38070.039
c
0.29970.009
a
0.999 0.08370.001
cde
M3 1.20970.059
e
0.30770.008
a
0.999 0.07670.002
f
B1 1.48170.066
b
0.29970.006
a
0.999 0.08670.004
bcd
B2 1.38470.039
cb
0.29270.003
a
0.999 0.08370.001
cde
B3 1.27270.028
de
0.30870.011
a
0.999 0.07670.002
f
Different letters in the same column indicate significant differences (Po0.05), (K: Control mix, P: Samples containing beetroot juice, M: Samples
containing red cabbage juice, B: Samples containing broccoli juice, 1: 2.5%, 2: 5%, 3: 10%).
Food Bioscience 7 (2014) 45–55 49
3. Results and discussion
3.1. Rheological properties
The shear stress versus shear rate data of the mellorine
samples including different vegetable juices in different
concentrations are shown in Fig. 2. As seen, the apparent
viscosity of the samples decreased with shear rate, indicating
the shear thinning behaviour of the mellorine mix samples.
Shear thinning behavior of ice cream mixes was reported in
different studies (Dogan, Kayacier, Toker, Yilmaz, & Karaman,
2013a;Toker et al., 2013a;Toker, Yılmaz, Karaman, Doğan, &
Kayacıer, 2012a). Usage of vegetable oil instead of milk fat did
not influence the flow behavior of the sample. Dickinson and
Stainsby (1982) reported that the shear thinning behaviour of
the ice cream mix is related with the complex involvement of
partially broken-down micellar casein at the droplet surface.
The rheological parameters (consistency coefficient (K), flow
behaviour index (n) and apparent viscosity values (η
50
)) of the
mellorine samples including different vegetable juices at
different concentrations are summarized in Table 1. It can
be seen that the Ostwald de Waele model (also known as
Power law model) described well the steady shear flow
behaviour of the mellorine samples (R
2
Z0.998), which was
agreement with the previous studies (Dogan et al., 2013a;
Karaman & Kayacier, 2012;Toker et al., 2012a,2013a).
The consistency coefficient and apparent viscosity values
of the mixes decreased with increase in the vegetable juice
concentration while there were no significant changes in the
flow behaviour index (n)(P40.05). The results reported for
trend between Kand nor η
50
and nwere consistent with
previous studies (Dogan, Toker, & Goksel, 2011;Dogan, Toker,
Aktar & Goksel, 2013b;Goksel et al., 2013;Toker, Dogan,
Canyılmaz, Ersöz, & Kaya, 2013b;Toker, Dogan, & Goksel,
2012b). The consistency coefficient of the samples varied
between 1.209 and 1.626 Pa s
n
, which decreased with increas-
ing fruit juice concentration in the mix samples. The nvalues
of the samples changed between 0.292 and 0.309, thus also
indicating shear thinning behaviour of the mellorine sam-
ples. The results of our study were similar to the findings of
Karaman and Kayacier (2012),Aime, Arntfield, Malcolmson,
and Ryland (2001), and Dogan and Kayacier (2007).
The apparent viscosity value of mixes at 20 1C ranged
between 0.076 and 0.097 Pa s. The η
50
value of mix samples
was significantly affected by the addition of vegetable juices
(Po0.05), but no significant difference was found among the
vegetable juice type and concentration (P40.05). Karaman
and Kayacier (2012) investigated the rheological and physi-
cochemical properties of ice cream mix enriched with some
tea, and they reported that the η
50
value of the ice cream mix
and mix enriched with black tea brewed at 40 1C was found to
be 0.91 Pa s and 1.13 Pa s at 20 1C, respectively. In addition, η
50
value of ice cream mixes, prepared with dairy cream, were
determined as higher (0.467–1.950 Pa s) than that of the mixes
found in this study. The raw materials used in ice cream
production, such as fat type, sweetener and stabiliser/emul-
sifier, and their concentrations affect the viscosity of ice
cream mix (Junior & Lannes, 2011;Bahramparvar & Tehrani,
2011). Lower η
50
value of mellorine when compared with ice
cream mixes might have explained by the fact that viscosity
of vegetable oil found in the formulation is lower than that of
the dairy fat. Yalcin, Toker, and Dogan (2012) reported that
viscosity of oils decreased with increase in polyunsaturated
fatty acid composition of the oils. Because of the high water
content in the vegetables juices, the water content of the
samples increased with vegetable juice concentration, which
caused a decrease in the viscosity values of the mellorine
mixes. El–Samahy et al. (2009) reported that the viscosity of
ice cream enriched with cactus pulp increased as concentra-
tion of cactus pulp increased, whereas Hwang, Shyu, and Hsu
(2009) found that the viscosity of ice cream containing grape
wine lees increased by increasing of grape wine lees concen-
tration from 50 g/kg to 150 g/kg.
The relationship between vegetable juice concentration
and the η
50
values was determined using power-law and
exponential models. As shown in Table 2, the effect of
vegetable concentration on the η
50
of the samples was
explained better by the exponential model for the mellorine
containing beetroot juice and red cabbage juice, while the
power-law model explained better the relation between juice
concentration and η
50
value of mellorine containing broccoli
juice. In the study of Dogan et al. (2013a), effect of gum
concentration on the η
50
values of the ice cream samples was
better described by the exponential model (R
2
¼0.980).
3.2. The physicochemical characteristics
Brix value of the broccoli, beetroot and red cabbage juices was
found to be 5.60, 6.67 and 9.91, respectively. pH value of these
juices was determined as 7.09, 6.69 and 6.98, respectively.
Some physicochemical properties of the mellorine mixes
with different concentrations of vegetable juices are shown
in Table 3. The effect of vegetable juice type and
Table 2 –Effect of the type and concentration of vegetable juices on the apparent viscosity of mixes at 20 1C.
Sample Power-law model Exponential model
η
50
¼η
1
(C
a1
)η
50
¼η
2
exp (a
2
C)
η
1
a
1
R
2
η
2
a
2
R
2
P 0.093973 0.077749 0.979 0.090279 0.014586 0.999
M 0.093919 0.087612 0.970 0.089846 0.016591 0.999
B 0.095156 0.068159 0.964 0.091243 0.011591 0.895
P: Samples including beetroot juice, M: Samples including red cabbage juice, B: Samples including broccoli juice.
Food Bioscience 7 (2014) 45–5550
concentration on pH, dry matter and ash content of the
mellorine was found as statistically significant (Po0.05). The
dry matter, pH values and ash content of the mixes varied
between 30.60–33.72%, 7.48–7.68, 0.334–0.394%, respectively.
The dry matter and pH values of the mellorine mixes
decreased with increasing vegetable juice concentration
while its ash content increased.
The overrun values of ice cream samples ranged from
29.90% to 33.62%. Vegetable juice type and concentration
significantly influenced the overrun values of the samples
(Po0.05). Overrun, which is a measure of increase in volume,
influences some characteristics of ice cream, such as melting
down and hardness (Sofjan & Hartel, 2004). The overrun of
mellorine decreased as vegetable juices concentration
increased, since water content of the ice cream samples
increased with addition of vegetable juice. Hwang et al.
(2009) observed that the overrun of samples decreased as
grape wine lees were added to the ice cream. Similar results
considering overrun values were also reported by Sun-
Waterhouse et al. (2011) and El–Samahy et al. (2009).
The melting rate of the ice cream samples was determined
as a function of time (45th min, 60th min, 75th min). The
addition of vegetable juice affected the melting rate, depend-
ing on the concentration of vegetable juice (Po0.05), but no
difference between the type of vegetable juices was found
(P40.05). Previous studies indicated that stabiliser/emulsi-
fiers had an important role in some of the desirable proper-
ties of ice cream and related products, such as melting
resistance and overrun (Guven, Karaca, & Kacar, 2003;
Keçeli & Konar, 2003;Moeenfard & Tehrani, 2008;Rezaei,
Khomeiri, Kashaninejad, & Aalami, 2011). A decrease in
overrun and an increase in melting rate in the mellorine
samples including vegetable juices could be explained by the
higher water content of the vegetable juices, resulting the
decaying of the stabiliser/emulsifier system (Bahramparvar &
Tehrani, 2011;Lal, O’Connor, & Eyres, 2006). The possible
reason for the higher melting rate of the mellorine containing
vegetable juices could be explained by the effect of overrun
on melting properties (Sofjan & Hartel, 2004). The authors
revealed that resistance to melting in the ice cream with
higher overrun was better than with lower overrun Table 4.
3.3. Bioactive properties of the mellorine samples
Phenolic content of the broccoli, red beetroot and red cabbage
juices was determined to be 420, 571 and 1132 mg/L, respec-
tively. Red cabbage juice was found as the juice one had the
highest phenolic content. Flavanoid content of these juices
Table 3 –Physicochemical properties of the mellorine mixes with different concentrations of vegetable juices.
Sample pH Dry matter
x
Ash
x
La b
K 7.6870.01
a
33.7270.17
a
0.33470.002
f
65.2370.11
a
1.5370.02
d
2.5570.05
e
P1 7.6170.01
d
32.7470.36
b
0.34370.006
e
56.6170.11
e
9.9870.08
c
1.8070.12
f
P2 7.5570.03
e
31.9470.30
c,d
0.35570.007
d
52.1870.14
f
14.9870.10
b
2.5970.10
e
P3 7.4870.01
f
30.8170.35
e
0.39470.007
a
46.3370.65
g
19.9770.62
a
4.3270.07
d
M1 7.6570.01
b
32.5670.02
b
0.34470.005
e
56.4570.59
e
4.9370.15
h
7.0770.29
g
M2 7.6170.00
d
31.8670.14
d
0.35270.005
d
52.3270.39
f
6.3270.11
i
10.5970.11
h
M3 7.5670.00
e
30.7570.19
e
0.36270.005
c
45.7470.68
h
6.3470.14
i
14.7070.09
i
B1 7.6370.01
c
32.8070.11
b
0.34570.003
e
64.6570.20
b
2.3270.06
e
4.6670.10
c
B2 7.6170.01
d
32.1970.22
c
0.35370.006
d
63.9170.23
c
2.9270.02
f
6.2870.13
b
B3 7.5570.00
e
30.6070.12
e
0.38070.002
b
62.5670.29
d
3.9170.07
g
9.1370.03
a
Different letters in the same column are statistically significant by Duncan’s test at 0.05 level of significance, (K: Control mix, P: Samples
containing beetroot juice, M: Samples containing red cabbage juice, B: Samples, containing broccoli juice, 1: 2.5%, 2: 5%, 3: 10%).
x
Expressed as a percentage.
Table 4 –Physical properties of the mellorine with different concentrations of vegetable juices.
Sample Overrun
x
Melting rate
x
45th min 60th min 75th min
K 33.6270.63
a
11.3870.85
e
42.2270.92
e
74.7670.48
e
P1 32.7070.13
b
14.5170.32
d
45.1470.44
d
79.4070.34
c,d
P2 30.7071.16
c,d
15.4970.41
c
46.2871.22
c,d
80.7670.34
b
P3 29.9070.69
d
18.3770.14
a,b
48.6170.42
a,b
83.4670.42
a
M1 32.5370.46
b
14.4370.24
d
44.9670.45
d
78.8670.69
c,d
M2 31.2770.52
c
15.4870.32
c
45.3770.40
d
79.7970.52
b,c
M3 30.0170.46
d
17.8870.15
b
48.3270.24
b
83.1770.99
a
B1 32.4370.29
b
14.2170.13
d
45.0670.61
d
78.9070.32
d
B2 31.1970.55
c
15.2570.07
c
47.5471.30
b,c
79.9170.49
b,c
B3 30.0170.96
d
18.5770.29
a
49.6570.57
a
82.9870.88
a
Different letters in the same column are statistically significant by Duncan’s test at 0.05 level of significance (K: Control mix, P: Samples
containing beetroot juice, M: Samples containing red cabbage juice, B: Samples containing broccoli juice, 1: 2.5%, 2: 5%, 3: 10%).
x
Expressed as a percentage.
Food Bioscience 7 (2014) 45–55 51
was found as 249, 453 and 327 mg/L, respectively. As seen red
beetroot was the richest sample regarding flavanoid content.
As expected, strong correlation was observed between phe-
nolic content and DPPH activity which found to be 53.76%,
60.23% and 85.30% for broccoli, red beetroot and red cabbage
juices, respectively. The total phenolic and total flavonoid
content and antioxidant capacity (DPPH) of the mellorine
mixes are shown in Table 5. It can be seen that, vegetable
juice type and concentration significantly affected the func-
tional properties of mellorine (Po0.05). Total phenolic content
of the samples ranged from 40.022 to 202.776 mg/L. It was
determined that the K (control sample) had the lowest total
phenolic content and the M3 had the highest. As expected, ice
cream samples increasing red cabbage juice had the highest
phenolic content, followed by the samples including red beet-
root juice and broccoli juice, respectively. As seen in Table 5,the
total flavonoid content of the samples changed between 31.196
and 92.930 mg/L and it was seen that the P3 had the highest
total flavonoid quantity. DPPH, which is a measure of antiox-
idant capacity, was affected by the addition of vegetable juice to
mellorine, and an increase in the antioxidant capacity was
observed as the concentration of vegetable juice rose from 2.5%
to 10%. While the DPPH of the K sample (11.79%) was found to
belowestamongthesamples,theM3hadthehighestanti-
oxidant capacity (67.54%).
Hwang et al. (2009) reported that incorporation of grape
wine lees, a waste product in the production of grape wine, to
ice cream resulted in an increase in amount of phenolic
compounds because it was rich in terms of phenolics, and the
phenolic content of ice cream enriched with grape wine lees
was found as 1.52 mg/mL. Karaman and Kayacier (2012)
reported that phenolic content of ice cream enriched with
herbal teas increased up to 415.2 mg/kg with tea addition.
They emphasized that ice cream could be fortified by material
which is abundant in terms of phenolics. Similar result was
reported by Sagdic et al. (2012).
3.4. Sensory attributes
The sensory scores of the mellorine samples are illustrated in
Table 6. As seen, adding vegetable juice to mellorine caused a
decrease in sensory scores. Control sample had the highest
sensory scores regarding all of the properties evaluated by
sensory analyses. As seen from the table, color and appear-
ance scores of the mellorine samples enriched with vegetable
juices were found as close to control sample, which might be
explained by the fact that coloring compounds found in the
vegetable juices improved attarctiveness of the ice cream.
Among the vegetable juice containing samples, the B2 sample
had the highest scores considering colour and appearance,
body and consistency and taste and colour properties. Body
and consistency scores of the ice cream decreased with
addition of vegetable juices, thus increasing water content
of the mellorine mix samples, which might result in icy
structure, negatively affected body and consistency of the
samples. Body and consistency of ice cream samples includ-
ing vegetable juices could be improved by decreasing water
amount added during production of ice cream.
Vegetable juice addition also caused a decrease in taste
and odour scores of the ice cream. As known, ice cream is a
sweet product and addition of vegetable juice decreased
sweetness of the product; therefore, mellorine enriched with
vegetable juice had lower taste and odour scores.
3.5. Combination of biological activity and sensory scores
In the present study, rheological characteristics of the mellorine
mix samples and physicochemical (dry matter, ash content, pH
and color values), bio-functional (total phenolic content, flava-
noid content and DPPH activity) and sensory properties of the
mellorine were determined. As seen from the results, physico-
chemical properties were slightly influenced by the juice addi-
tion. Rheological analyses were performed in mellorine mix
samples. In addition to the rheological analyses, in this part of
the study, we focused on sensory and bio-functional properties
of the mellorine samples enriched with different juices in
different concentrations. The combination of the bio-functional
and sensory properties of the samples is important for the
determination of the best sample since awareness of consumers
about the consumption of healthy food is growing. Decision
making is very difficult because there are six different results
(total phenolic, DPPH, flavanoid, colour and appearance, taste
Table 5 –Some bio-functional properties of the mellorine with vegetable juice.
Sample Total phenolic
x
Total flavonoid
y
DPPH
z
K 40.02270.594
h
31.19670.815
g
11.7970.32
j
P1 59.92870.738
f
47.79971.964
e
18.6671.04
g
P2 70.35370.898
e
64.83072.340
c
21.3370.23
f
P3 101.87471.199
c
92.93072.681
a
31.1870.37
d
M1 88.30570.591
d
40.72173.182
f
32.3571.53
c
M2 127.70570.998
b
57.32572.300
d
46.5970.35
b
M3 202.77672.363
a
90.78374.794
a
67.5470.11
a
B1 58.04070.964
g
38.47976.278
f
13.9471.67
i
B2 69.82171.721
e
50.11672.936
e
16.4770.19
h
B3 88.13571.723
d
69.08574.644
b
27.6070.12
e
Different letters in the same column are statistically significant by Duncan’s test at 0.05 level of significance, (K: Control mix, P: Samples
containing beetroot juice, M: Samples containing red cabbage juice, B: Samples, containing broccoli juice, 1: 2.5%, 2: 5%, 3: 10%).
x
Expressed as mg gallic acid equivalent/L.
y
Expressed as mg catechin equivalent/L.
z
Expressed as a percentage.
Food Bioscience 7 (2014) 45–5552
and odour, consistency). While one sample is better when
considering one criterion, another sample is better based on
different criteria. Therefore, a comparison of the alternatives or
the samples is very difficult. In order to ease comparison, the
TOPSIS method was applied. As seen from Fig. 1,thereareten
alternatives and five criteria. Initially, the importance of the
criteria is determined by considering different opinions obtained
from students and academicians. Fig. 1 also shows the ratio of
the criteria. For example, while the importance of the total
phenolic content was 12.5%, that of taste was 30% in decision
making. Table 7 shows the normalised and weighted normalised
matrices which are formed as mentioned in Section 2 by using
real values. After obtaining the weighted normalised matrices,
the positive and negative ideal solutions of each criterion were
determined (Table 8). The distance of each alternative from the
negative and positive ideal solution was calculated by using Eqs.
(8)and(9). Table 9 shows the distance values and closeness
coefficient of each sample. As seen from the Cvalues, the M3
samplewasselectedasthebestsample based on the determined
criteria. This result was interesting because the sensory scores of
that sample were very low when compared with the other
samples. However, the total phenolic and DPPH activity of that
sample was very high, which is the reason why the M3 sample
Table 6 –Sensory scores of the mellorine samples.
Sample Colour and appearance Body and consistency Taste and odour
K 4.8670.38
a
4.5770.54
a
4.5770.54
a
P1 3.7170.76
b
3.7170.76
ab
3.5770.79
bc
P2 4.0070.82
b
3.5771.13
b
3.7170.95
abc
P3 4.0070.58
b
3.2970.76
b
3.2970.76
bc
M1 3.7170.49
b
3.4370.79
b
2.7170.49
c
M2 3.7170.95
b
3.5770.54
b
3.0070.82
c
M3 4.1470.90
ab
3.1470.70
b
3.1471.35
bc
B1 4.4370.54
ab
3.7170.76
ab
3.4370.54
bc
B2 4.5770.54
ab
3.8670.69
ab
4.1470.70
ab
B3 4.2970.76
ab
3.4370.98
b
3.0071.00
c
Different letters in the same column are statistically significant by Duncan’s test at 0.05 level of significance, (K: Control mix, P: Samples
containing beetroot juice, M: Samples containing red cabbage, juice, B: Samples containing broccoli juice, 1: 2.5%, 2: 5%, 3: 10%).
Table 7 –Normalised and weighted normalised decision matrix.
Alternatives Appearance Consistency Taste Phenolic DPPH
Normalised K 0.3695 0.3962 0.4132 0.1255 0.1128
P1 0.2821 0.3217 0.3228 0.1879 0.1786
P2 0.3041 0.3095 0.3354 0.2205 0.2041
P3 0.3041 0.2853 0.2975 0.3193 0.2984
M1 0.2821 0.2974 0.2450 0.2768 0.3096
M2 0.2821 0.3095 0.2712 0.4003 0.4458
M3 0.3148 0.2723 0.2839 0.6357 0.6463
B1 0.3368 0.3217 0.3101 0.1819 0.1334
B2 0.3475 0.3347 0.3743 0.2189 0.1576
B3 0.3262 0.2974 0.2712 0.2763 0.2641
Weighted normalised K 0.0924 0.0792 0.1240 0.0157 0.0141
P1 0.0705 0.0643 0.0968 0.0235 0.0223
P2 0.0760 0.0619 0.1006 0.0276 0.0255
P3 0.0760 0.0571 0.0892 0.0399 0.0373
M1 0.0705 0.0595 0.0735 0.0346 0.0387
M2 0.0705 0.0619 0.0814 0.0500 0.0557
M3 0.0787 0.0545 0.0852 0.0795 0.0808
B1 0.0842 0.0643 0.0930 0.0227 0.0167
B2 0.0869 0.0669 0.1123 0.0274 0.0197
B3 0.0815 0.0595 0.0814 0.0345 0.0330
(K: Control mix, P: Samples containing beetroot juice, M: Samples containing red cabbage juice, B: Samples containing broccoli juice, 1: 2.5%,
2: 5%, 3: 10%).
Table 8 –Positive (A
n
) and negative ideal solution (A
) for
the criteria.
Criteria A
n
A
Appearance and colour 0.0924 0.0705
Body and consistency 0.0792 0.0545
Taste and odour 0.1240 0.0735
Total phenolic 0.0795 0.0157
DPPH 0.0808 0.0141
Food Bioscience 7 (2014) 45–55 53
was the best sample. Determination of weight of criteria is very
important for decision. The ranking of the samples changed as
the weight of criteria is changed. The difference between C
values of the unenriched and enriched samples might be
decreased by increasing of weight of sensory scores. However,
as the importance of the bioactive properties is increased, the
differences between the Cvalues of the samples might be
increased; therefore, assigning a weight of criteria will eventually
affect the final decision. According to the results, it was seen that
TOPSIS can be successfully used in food industry to ease
comparison and decision.
4. Conclusion
The addition of vegetable juices to the mellorine mix
decreased the apparent viscosity, dry matter and overrun,
however, the ash content and melting rate increased. All
mixes had a pseudoplastic flow behaviour. The bio-functional
properties, such as phenolic, flavonoid and DPPH, of the
mellorine containing vegetable juices were significantly
affected by increasing the vegetable juice concentration.
Mellorine with 10% red cabbage juice (M3) was higher than
the other samples in terms of the phenolic quantity and
DPPH while control (K) was the lowest. In order to compare
the samples easily, the TOPSIS method was used considering
the bioactivity and sensory properties. In the determined
conditions, M3 sample was found as the best sample. Accord-
ing to the results of this study, it is observed that the use of
TOPSIS or similar techniques is possible in the food industry
area in order to facilitate decision making or comparison.
Acknowledgments
The authors would like to thank the Erciyes University
Research Project Unit (Project No. FBY-10-3094) for financial
support of this work.
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