Рубрики: ERRATUM
Аннотация и ключевые слова
Аннотация (русский):
The Editorial Office of Foods and Raw Materials would like to report an error in the published paper ‘Optimisation of functional sausage formulation with konjac and inulin: using D-Optimal mixture design’. Foods and Raw Materials, 2019, vol. 7, no. 1, pp. 177–184. DOI: The affiliation of Mojtaba Jafari should be changed from ‘Food Sciences and Technology Department, National Nutrition and Food Technology Research Institute, Faculty of Nutrition Sciences and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran’ to ‘Department of Food Science and Technology, Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran’. We apologise to the author for any inconvenience caused by this mistake. The change does not affect the scientific results. The manuscript will be updated and the original will remain available on the article webpage.

Ключевые слова:
Inulin, konjac, sausage, functional, formulation
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In recent years, unhealthy food habits and stressful
life style have significantly increased the risk of serious
health disorders such as obesity, cancer, high blood
cholesterol, and coronary heart diseases. This has created
and increased demand for new health products with
enhanced nutritional value. As a result, a number of research
have been conducted in order to develop foods
that are designed to improve digestive system health.
One of these approaches is the development of functional
foods using probiotics or prebiotics. Prebiotics can improve
the host health by stimulating the growth of beneficial
bacteria in gastrointestinal tract [12]. Along with
the nutritional value of a functional product, its structural
properties, such as water holding capacity (WHC) and
sensory characteristics, and effective cost should be taken
into consideration [23].
Inulin is a dietary fibre that has been approved by
WHO as a safe prebiotic. It is a well-known and successful
food ingredient in meat industry due to its unique
ability to enhance both taste and texture in various processed
meat products through binding water, forming gel
and mimics the oral tactile sensation of fat. The effectiveness
of inulin has been approved in many investigations
in a wide range of processed meat products such as
scalded sausages, canned meat products, meat balls, liver
pâté, and fermented sausages [19].
Konjac glucomannan, a neutral polysaccharide
made from the tuber Amorphophallus konjac, is another
prebiotic that is known for its important technological
properties and its ability to improve health. USDA
recently accepted the use of konjac as a binder in meat
and poultry products. Studies suggested konjac has
the ability to lower serum cholesterol, serum triglyceride,
glucose, bile acid levels and laxative effect as well
(Yang et al., 2017).
Some investigations reported that appropriate
amounts of konjac in the diet could help prevent diabetes
Safaei F. et al. Foods and Raw Materials, 2019, vol. 7, no. 1, pp. 177–184
and aid gradual weight loss. Several studies used konjac
as a fat substitute, emulsifier, and gelling and thickening
agent in various meat products, such as low-fat frankfurter,
bologna sausage, hot dogs, pepperoni, and summer
sausage [10]. Usually, the use of konjac in large amounts
decreases the firmness of meat products, and its combination
with other ingredients such as inulin, starch or carrageenan,
could moderate undesirable effect.
Mixture design methodology is a new method to determine
an effect of each ingredient in the formulation
of processed meat products and demonstrate the result of
ingredient interactions by applying reduced numbers of
experimental trials [1].
It should be noted that this is the first investigation on
effects of inulin and konjac on the physical and sensory
properties of functional sausage. Hence, the objective of
this research is to determine the influence of adding inulin,
konjac, starch, and their mixtures on properties of sausages
using the D-optimal mixture design and develop the
optimal formulation to produce a high quality sausage.
1. Experimental design. To determine the optimum
proportions of the prebiotic sausage formulation, we
used Design-Expert (7.1.5) software. D-optimal design
was used with three components: konjac (K), inulin (I)
and starch (S). The experimental design and the amounts
of the relevant ingredients used are shown in Table 1.
The component ranges were as follows: 0 < K < 0.5;
0 < I < 5; and 0 < S < 5. Design-Expert software designed
13 samples. Effects of inulin, starch, and konjac
on properties of sausage were evaluated, and optimum
combination was determined. For optimization, depending
on the influence of each factor; the combination of
factors that led to the best responses was determined.
2. Sausage preparation. We prepared minced meat
for sausage according to a basic formulation. The minced
meat consisted of 55% lean beef meat with fat content
of about 12.8 ± 1%, 10% soybean oil, 2.2% wheat flour,
1.5% sodium chloride, 0.35% sodium polyphosphate,
0.012% sodium nitrate, 0.02% ascorbic acid, 0.2% red
pepper, 0.2% ginger, 0.1% savory, 0.2% garlic powder,
and 17.418% water. All the ingredients were mixed in a
3,000 RPM cutter (Talsa Bowel cutter 15, Spain). Then
13 sausage samples were produced (5 kg each). Each
sample contained 4,750 g of the minced meat and various
proportions of konjac, inulin, and starch treatment
(Table 1). The sum of starch, inulin, and konjac in each
sample was 5%. Sausages were stuffed into polyamide
casings and cooked in a steam oven at 80°C for 60 min
until reaching an internal temperature of 72 ± 3°C. Inulin
(Inulin Frutafit TEX®) and konjac flour was obtained
from Roosendaal (the Netherlands) and Shandong (China),
3. Physical properties
3.1. Water holding capacity (WHC). The WHC of
the sausages was measured using the method described
by Asgharzadeh et al. and Méndez-Zamora et al. [5, 18].
About 0.3 g of sausage was placed between two filtre
papers and then placed between two 12×12 cm plates.
Four kg force was applied for 20 min. The released liquids
in the paper were considered as meat-free water.
WHC was calculated using Eqs. (2) and (3). The experiment
was performed in triplicate for each sample.
% of free water = [(Iw-Fw)/Iw]×100, (2)
WHC = 100 – % of free water, (3)
where Iw is the initial weight of the sample (0.3 g) and
Fw is the final weight.
3.2. Cooking yield. A slice of raw sausage 3 mm in
thickness was cooked on a hot plate at 160°C for 2 min
according to the procedure described by Amini et al.
[4]. Cooking yield was calculated using the initial and
final weights and expressed in g/100 g the initial sample
weight. Three replicates were carried out for each sample.
3.3. Frying loss. Frying loss was determined based
on the procedure described by Bengtsson et al. with
some modification [6]. Sliced cooked sausages, 1 cm
in thickness, deep fat fried in a fryer (moulinex, DR5),
maintained at 174°C, for 2 min until the center temperature
reached 72–73°C and then left to cool at room temperature.
The frying loss was calculated by weighing the
samples before and after frying. The test was done in
triplicate for each sample.
4. Texture profile analysis (TPA). Texture profile
analysis (TPA) was evaluated using an Instron
M350-10CT (500 N load cell, England, Rochdale). The
textural parameters were determined according the Procedure
described by Bourne [7]. Textural measurements
included hardness and cohesiveness.
5. Colour. Four samples from each formulation were
used to evaluate internal colour (cross-section) of the
sausages. For that, we used 2 cm cross-sections of recently
cut sausage. The colour values of the samples were
determined using a Chromo meter (CR-400, Minolta Co,
Konica, Japan) with D65, 2° observer to objectively measure
CIE Lab values (L* relative lightness, a* relative
redness and b* relative yellowness). Colorimeter calibrated
with white standard plate (L* = 94, a* = 0.3158,
b* = 0.3322). The calculated results were expressed with
mean value of these measurements.
Table 1. Sausage samples with konjac (K), inulin (I), and
starch (S) in a three component constrained D-optimal mixture
Samples Ingredients, %
1 0.5 0 4.5
2 0.375 1.188 3.438
3 0 0 5
4 0 2.5 2.5
5 0.5 0 4.5
6 0.5 4.5 0
7 0 0 5
8 0 5 0
9 0.125 3.688 1.188
10 0.375 3.438 1.188
11 0.5 2.25 2.25
12 0.25 4.75 0
13 0.125 1.188 3.688
Safaei F. et al. Foods and Raw Materials, 2019, vol. 7, no. 1, pp. 177–184
6. Sensory evaluation. Sensory analyse was performed
according to the international standards (ISO,
1985) in the sensory laboratory at the National Nutrition
and Food Technology Research Institute (NNFTRI). Private
stands under white fluorescent lights were prepared
for each panelist. Samples of each formulation were presented
randomly for panelists. Tap water was available to
clear the taste between samples. 15 panelists, 7 men and 8
women, comprising of postgraduate students of food science
and technology were asked to evaluate characteristics
using a 9-point hedonic scale. The age of the panelists
ranged from 20 to 40 years old. The panelists were trained
with two training sessions in the product and terminology.
Overall acceptability of the samples was scored as follows:
1 (extremely dislike) to 9 (extremely like).
7. Statistical and data analysis. Three equation
models were fitted to each of the responses (Y) with the
independent variables:
Linear model: Y= b1X1 + b2X2 + b3X3;
Quadratic model: Y= b1X1 + b2X2 + b3X3 +
+ b12X1X2 + b13X1X3 + b23X2X3; and
Cubic model: Y= b1X1 + b2X2 + b3X3 + b12X1X2 +
+ b13X1X3 + b23X2X3+ b123X1X2X3,
where X1is konjac, X2 is inulin, X3 is starch, and b is the
regression coefficients calculated from the experimental
data by multiple regression.
All parametric tests were performed in triplicate
for each experiment and all the data demonstrated the
mean and SD (standard deviation). The physicochemical
and textural properties were studied using one-way
ANOVA independently, and Duncan test was employed
to determine differences between the experimental
groups (p < 0.05). Sensory evaluation was analyzed by
the same software using Mann–Whitney U test. Correlation
analyses were conducted by using the Pearson correlation
model where p < 0.05 was taken as significance.
Fitting for the optimal model. The optimal model
was fitted according to low standard deviation, low predicted
sum of squares and high R-squared. P-values of
the acceptable model were lower than 0.05.
For frying loss, cooking yield, hardness and
overall acceptability, linear was found the best
model. For cohesiveness, a* and b* quadratic was adequately
fitted. The model which best matched to water
holding capacity and L* were modified special cubic and
special cubic, respectively.
Water Holding Capacity (WHC). According to the
regression coefficients in Table 3, all three components
increased WHC, however konjac had the greatest effect.
Interestingly, the mixtures of inulin, starch, and konjac
showed a substantial effect on increasing the WHC of
sausages. This result is well correlated with results illustrated
in Table 2, where samples no. 2 and 11 demonstrated
the highest WHC.
The results revealed that, although adding inulin
to the formulation of sausage could enhance WHC,
the higher levels of inulin (more than 2.5%) decreased
the WHC significantly. Sample no. 8 (contained
5% inulin) demonstrated the least WHC. The synergetic
effect of konjac and inulin in absorbing water
is in agree with the study of Mendez-Zamora et al. He
involved inulin and pectin in the formulation of frankfurter
sausages and showed that the addition of 15%
inulin and pectin improved WHC [18]. Studies performed
by [9] showed that konjac blend usually had
been used as multi-ingredient fat replacer in meat products.
In addition, incorporation of konjac blend with
carrageenan and starch in low fat bologna increased
WHC, produced more stable gel matrix with higher cooking
yield and more acceptable texture. López-López
et al. (described the type of fibers and quantity of their
polysaccharides are the factors that influence water
holding capacity of product [17]. They mentioned
large particles create open structures that enhance the
properties of hydration. Álvarez and Barbut also investigated
the effect of beta-glucan (BG), inulin,
and their mixture on the emulsion stability, and concluded
combination of BG and inulin compensa-
Table 2. Cooking and sensory characteristics of experimental sausage samples
Frying loss Overall acceptability
Hardness Cohesiveness
L* a* b*
1 63.78 ± 0.19 95.09 ± 0.16 21.05 ± 0.21 5.18 ± 0.11 22.63 ± 0.24 0.69 ± 0.01 39.84 ± 0.08 10.98 ± 0.09 15.56 ± 0.16
2 73.08 ± 0.21 93.58 ± 0.14 19.44 ± 0.17 5.57 ± 0.10 23.47 ± 0.21 0.59 ± 0.00 37.56 ± 0.08 8.39 ± 0.11 13.24 ± 0.19
3 59.37 ± 0.14 90.38 ± 0.14 17.59 ± 0.14 5.80 ± 0.10 21.10 ± 0.23 0.66 ± 0.01 38.01 ± 0.07 11.64 ± 0.08 16.19 ± 0.17
4 51.5 ± 0.22 89.76 ± 0.23 16.04 ± 0.12 6.27 ± 0.07 23.71 ± 0.24 0.62 ± 0.00 38.31 ± 0.08 12.27 ± 0.13 16.62 ± 0.16
5 63.77 ± 0.16 95.09 ± 0.16 21.03 ± 0.18 5.16 ± 0.09 22.63 ± 0.24 0.68 ± 0.00 39.83 ± 0.07 10.96 ± 0.07 15.59 ± 0.19
6 68.36 ± 0.28 93.86 ± 0.15 18.29 ± 0.21 6.06 ± 0.10 27.31 ± 0.22 0.62 ± 0.01 40.56 ± 0.07 10.58 ± 0.12 16.56 ± 0.19
7 59.37 ± 0.14 90.4 ± 0.16 17.62 ± 0.18 5.78 ± 0.07 21.09 ± 0.21 0.65 ± 0.00 38.01 ± 0.07 11.64 ± 0.09 16.19 ± 0.16
8 36.01 ± 0.22 89.07 ± 0.22 14.52 ± 0.15 6.83 ± 0.15 26.29 ± 0.21 0.71 ± 0.01 40.58 ± 0.07 18.25 ± 0.07 21.28 ± 0.18
9 54.16 ± 0.23 90.57 ± 0.17 16.20 ± 0.19 6.38 ± 0.12 25.33 ± 0.24 0.60 ± 0.00 38.88 ± 0.07 11.88 ± 0.14 15.97 ± 0.19
10 72.10 ± 0.17 92.97 ± 0.15 18.07 ± 0.19 6.05 ± 0.15 25.84 ± 0.25 0.58 ± 0.02 38.11 ± 0.07 8.98 ± 0.12 14.18 ± 0.18
11 82.96 ± 0.20 94.48 ± 0.15 19.62 ± 0.14 5.64 ± 0.14 25.00 ± 0.25 0.60 ± 0.02 36.78 ± 0.08 8.61 ± 0.14 14.34 ± 0.15
12 52.84 ± 0.14 91.49 ± 0.22 16.42 ± 0.21 6.46 ± 0.15 26.82 ± 0.24 0.61 ± 0.00 40.66 ± 0.06 12.51 ± 0.13 16.64 ± 0.14
13 62.25 ± 0.12 91.23 ± 0.14 17.71 ± 0.14 5.91 ± 0.15 22.74 ± 0.26 0.6 ± 0.010 37.82 ± 0.07 9.44 ± 0.12 13.93 ± 0.19
Safaei F. et al. Foods and Raw Materials, 2019, vol. 7, no. 1, pp. 177–184
ted undesirable effect of fat reduction by increasing
WHC [3]. Liu et al. also prepared konjac-egg white protein
gels and determined that konjac could significantly
improve the water retention capacity [15].
Cooking yield and frying loss. The results in Table 3
revealed that konjac with its positive coefficient had significantly
(p < 0.05) increased cooking yield, while inulin
and starch with their negative coefficient decreased this
parameter in the product. The samples no. 1 and 5, which
contained highest amount of konjac (0.5% konjac, 4.5%
starch, and 0% inulin), showed the highest cooking yield
and frying loss. As one can see in Table 1, two pair sam-
Table 3. Regression coefficients and correlation for the adjusted model to experimental data in D-optimal mixtures design for
physical properties, textural parameters, color parameters, and sensory analysis
Parameter K I S KI KS IS KIS Pred-R2
WHC 103.49 36.00 59.37 284.30 – 15.27 682.63 0.9997
Cooking yield 137.18 89.06 90.40 – – – – 0.9951
Frying loss 10.40 2.90 3.51 – – – – 0.9939
Overall acceptability –0.01 6.99 5.99 – – – – 0.9579
Hardness 36.44 26.31 21.10 – – – – 0.9908
Cohesiveness 16.15 0.70 0.65 –18.13 –16.89 –0.23 – 0.8950
L* 6.44 40.58 38.01 37.70 55.36 –3.95 –129.30 0.9961
a* 631.78 18.26 11.64 –767.03 –696.53 –10.72 – 0.9981
b* 783.11 21.27 16.20 –899.03 –859.00 –8.41 – 0.9923
Fig. 1. Contour plots for effectы of konjac (A), inulin (B)б and starch (C) on water holding capacity (WHC), cooking yield, frying
loss, hardness, cohesivenessб and overall acceptability of prebiotic sausage.
Safaei F. et al. Foods and Raw Materials, 2019, vol. 7, no. 1, pp. 177–184
ples are similar: one pair is samples no. 1 and 5, another
one is the samples no. 3 and 7. These compositions were
defined by Design Expert software to check the repeatability
of findings. As it was expected similar compositions
have displayed comparable results for cooking and
sensory characteristics in Table 2, indicating the repeatability
of findings.
Several studies suggested to use konjac in combination
with other hydrocolloids. Emir et al. discussed that
weak junction zones in konjac made it susceptible to heat,
and its interaction with other hydrocolloids caused tights
junction which made it resistant to cooking or frying [11].
According to regression coefficients (Table 3), inulin
caused decrease in cooking and frying loss. The sample
no. 8 (with the maximum level of inulin) displayed the
least cooking yield and frying loss. This data is similar
to that of Afshari et al. who indicated although inulin is
able to increase WHC and decrease frying loss, but, at
higher amounts, it reduced the moisture retention and
cooking yield probably due to its porous structures and
inability to form a tight gel [1].
Texture profile analysis (TPA). As the hardness
analysis showed, konjac had a strong effect on the hardness
of sausage, inulin also increased it, while starch, on
the contrary, reduced the hardness of the product.
Several studies indicated that the addition of
konjac into the food matrix increased the hardness
of products but it depends to many factors
that should be taken into consideration. These
factors are the molecular weight and particularly the
type of konjac (flour or as hydrolyzed), pH of a food
system, presence of salts, and an amount of incorpora-
Fig. 2. Contour plots for effectы of konjac (A), inulin (B), and starch (C) on lightness (L*), redness (a*), and yellowness (b*)
of prebiotic sausage.
ted konjac and other food ingredients, specially gelling
agents. All these studies are in agree that increase
in amount of konjac increase the hardness that may not
be accepted by consumers. Hu et al. reported that konjac
glucomannan (KGM) affected functional properties
of egg white protein and increased hardness,
chewiness, and springiness of the gel samples at a certain
concentration [13]. The investigation conducted
by Emir et al. indicated that the bigger molecular
weight of KGM caused the highest hardness and closely
the lowest springiness, which had negative effect on
the choice of panelists [11]. Akesowan reported that
increasing of NaCl resulted increase in links between
konjac/κ-carrageenan and konjac/gellan, leading to
the increment in the hardness of the produced gel [2].
Purwandari et al. used konjac a noodle formulation.
They found that the hardness and adhesiveness
of noodle significantly increased (p < 0.05) and became
three times harder than standard Chinese or Japanese
wheat noodle [21]. The researchers also indicated
that an increase in proportion of water in pregelatinised
flour led to increased harness in konjac noodle.
Several studies also determined that the use of powdered
inulin resulted in higher moisture loss during cooking.
This can affect the texture of a product and increased
hardness of burgers, frankfurter sausages and
dry-fermented chicken sausages. [1, 18 and 19]. These results
are in a good agreement with the results of this study.
Another texture parameter related to meat products is
cohesiveness. Adhesiveness and cohesiveness are parameters
that play an important role in handling of sau182
Safaei F. et al. Foods and Raw Materials, 2019, vol. 7, no. 1, pp. 177–184
sages, particularly for the slicing of these products. If
products are too adhesive or cohesive, they become undesirably
sticky, and it cannot be easily to cut [20]. In the
current investigation konjac showed a significant positive
effect on cohesiveness of probiotic sausage. On the
contrary, combination of konjac with inulin or starch reduced
the undesirable effects of using konjac alone and
maintained the appropriate adhesiveness and cohesiveness
of sausages with improved textural properties. The
study [9] documented that when konjac was used as a
multi ingredient in the formulation of meat products, unwanted
hardness and cohesiveness decreased significantly.
The researchers suggested to incorporate konjac in
combination with other hydrocolloids Purwandari et al.
also confirmed that konjac had a substantial effect on the
increase of adhesiveness and cohesiveness of noodle:
konjac noodle was about ten times more cohesive and
sticky than wheat flour noodle [21].
Colour. As shown in Table 3 and Fig. 2, all three
component separately increased the lightness of the
product, while their combination in special cubic model
caused decrease in L* value in the sausages. The results
indicated that inulin was meaningfully (p < 0.05) more effective
in enhancing the lightness compared to starch and
konjac. The samples no. 6 and 8, which containing the
highest amount of inulin, illustrated the most lightness.
According to Table 3, konjac demonstrated a significant
(p < 0.05) positive effect on a* and b* values causing
more reddishbrown product, while its combination
with starch or inulin decreased a* and b*. Trespalacios
and Pla reported that if when myoglobin and fat content
was maintained constant, the color of formulated products
was mostly influenced by many factors, including
additive ingredients [24]. In the present study, as the
protein and fat content was invariable, the color was influenced
mainly by mixing ingredients. Amini et al. mentioned
konjac led konjac to a more reddishebrown of a
product by its susceptibility to Maillard browning [4].
Jiménez-Colmenero et al. also indicated that the addition
of konjac in frankfurter sausage caused decrease
(p < 0.05) in lightness (L*) and an increase (p < 0.05) of
yellowness (b*), compared to other samples [14]. The results
of another investigation, conducted by Ruiz-Capillas
et al., are in agreement with the present study that
konjac gel affected color parameter of sausages through
decrease in L* and increase in yellowness (a*) [22]. Delgado-
Pando et al. observed less red, paler (p < 0.05), and
yellower pâtés as a result of adding konjac [10].
Sensory analysis. The experimental results obtained
from the regression coefficient values of overall acceptability
(Table 3 and Fig. 1) displayed that increase
in the proportion of konjac had a significant (p < 0.05)
negative effect on the overall acceptability of the product.
Inulin showed a positive effect on the acceptability
of the product, while starch was not significantly effective.
Formulation 12 (contained 0.25; 4.75; and 0%
konjac, inulin and starch, respectively) and formulation
9 (0.125; 3.68; and 1.18%) demonstrated the highest
overall acceptability score.
Results obtained by sensory analysis highlighted
that adding konjac in the amount of up to 0.2% could
improve the appearance of sausage. On the contrary,
increase in the amount of konjac (more than 0.2%) decreased
the overall acceptability significantly (p < 0.05).
The results emphasized that hardness and cohesiveness
are the factors that significantly influence overall acceptability.
Increase in proportion of konjac (more than
0.2%) can make the sausage harder and more cohesive
than standard sausage which may not be acceptable by
consumers. In the other words, consumers would not accept
a product with extreme hardness or cohesiveness.
Another explanation is that high amount of konjac probably
enhances its typical fishy taste/odours. These findings
are in a good agreement with the results of Purwandari
et al. who reported that the addition of konjac glucomannan
could improve sensory perception of wheat noodle,
while a high level of this ingredient reduced preference,
since noodle became too sticky [21]. Lin et al. also observed
that 1% konjac in reduced-fat frankfurter sausages
led to higher scores of sensory overall acceptability [15].
Fig. 3. Desirable plot for optimum formulation.
Safaei F. et al. Foods and Raw Materials, 2019, vol. 7, no. 1, pp. 177–184
Liu et al. also assumed that functional properties
of food products could be controlled by adding small
amounts of KGM without causing undesirable sensory
changes [16].
On the contrary, inulin showed positive coefficient on
overall acceptability of sausages and an increase in portion
of inulin improved the product flavour. Menegas et
al. represented incorporation of inulin (maximum level
of 7.5%) in reduced-fat sausages made the product more
favorable and acceptable by consumers [19].
Mixture proportion optimization and desirability
The optimisation was done in order to access the optimal
amount of each component that had an excessive
effect on quality properties of the sausages. The predicted
values of the responses are shown in Fig. 3. Our
aims were to maximize overall acceptability and cooking
yield of the sausages, minimise frying loss, and, at the
same time, to maintain WHC, hardness, and cohesiveness
within normal range. Having all these criteria taken into
consideration, we found that optimal amounts of inulin,
starch, and konjak were 2.09; 2.76; and 0.146%, respectively
(Fig. 3). The selected mixture achieved 0.858 desirability
score. As the desirability value between 0.8 and
1.0 is recognized as acceptable and excellent product, the
formulation with 0.858 desirability value was selected as
optimal formulation that could provide valuable nutritional
and technological properties.
In conclusion, the development of functional foods
opens up new possibilities for the food industry and
consumers. The development of healthier sausage with
prebiotics inulin and konjac is a promising direction of
research. The physicochemical and sensory characteristics
of the prebiotic sausages are conditioned by the formulation.
The study demonstrated that the sausage contained
0.146; 2.09; and 2.76% konjac, inulin, and starch, respectively,
has high quality and sensorial properties.
The authors declare that there is no conflict of interests.
We are thankful to Tehran Meat Products Company
for their help in sausage manufacturing.
This research was financially supported by National
Nutrition and Food Technology Research Institute
(NNFTRI) of Iran.

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