In recent years, unhealthy food habits and stressful life style have significantly increased the risk of seri- ous health disorders such as obesity, cancer, high blood cholesterol, and coronary heart diseases. This has crea- ted and increased demand for new health products with enhanced nutritional value. As a result, a number of re- search 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 im- prove the host health by stimulating the growth of bene- ficial 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 suc-

cessful food ingredient in meat industry due to its unique ability to enhance both taste and texture in various pro- cessed meat products through binding water, forming gel and mimics the oral tactile sensation of fat. The effec- tiveness of inulin has been approved in many investiga- tions 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  technologi- cal 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 triglycer- ide, 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

Copyright © 2019, Safaei et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.

Table 1. Sausage samples with konjac (K), inulin (I),  and starch (S) in a three component constrained D-optimal mix- ture design

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 frankfu- rter, 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 combina- tion with other ingredients such as inulin, starch or carra- geenan, could moderate undesirable effect.

Mixture design methodology is a new method to de- termine 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 inu- lin, konjac, starch, and their mixtures on properties of sau- sages using the D-optimal mixture design and develop the optimal formulation to produce a high quality sausage.

STUDY OBJECTS AND METHODS

1. 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 de- signed 13 samples. Effects of inulin, starch, and konjac on properties of sausage were evaluated, and optimum combination was determined. For optimization, depend- ing 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 vari- ous 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. Inu- lin (Inulin Frutafit TEX®) and konjac flour was obtained from Roosendaal (the Netherlands) and Shandong (Chi- na), respectively.

3. Physical properties
   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 liq- uids in the paper were considered as meat-free water. WHC was calculated using Eqs. (2) and (3). The experi- ment 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. 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 tempera- ture reached 72–73°C and then left to cool at room tem- perature. 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 pro- file analysis (TPA) was evaluated using an Instron M350-10CT (500 N load cell, England, Rochdale). The textural parameters were determined according the Pro- cedure 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 recent- ly 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 mea- sure CIE Lab values (L* relative lightness, a* relative redness and b* relative yellowness). Colorimeter cali- brated with white standard plate (L* = 94, a* = 0.3158, b* = 0.3322). The calculated results were expressed with

mean value of these measurements.

6. Sensory evaluation. Sensory analyse was per- formed according to the international standards (ISO, 1985) in the sensory laboratory at the National Nutrition and Food Technology Research Institute (NNFTRI). Pri- vate stands under white fluorescent lights were prepared for each panelist. Samples of each formulation were pre- sented 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 scie- nce 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 terminolo- gy. Overall acceptability of the samples was scored as fol- lows: 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 physicoche- mical 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. Correla- tion analyses were conducted by using the Pearson cor- relation model where p < 0.05 was taken as significance.

RESULTS AND DISCUSSION

Fitting for the optimal model. The optimal model

was fitted according to low standard deviation, low pre- dicted 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 ade- quately 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 il- lustrated in Table 2, where samples no. 2 and 11 demon- strated the highest WHC.

The results revealed that,  although  adding  inu- lin 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 syner- getic 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 frank- furter sausages and showed that the addition of 15% inulin and pectin improved WHC [18]. Studies per- formed by [9] showed that konjac blend  usually  had been used as multi-ingredient fat replacer in meat prod- ucts. In addition, incorporation of konjac blend with carrageenan and starch in low fat bologna increased WHC, produced more stable gel matrix with higher co- oking yield and more acceptable texture. López-López et al. (described the type of fibers and quantity of their polysaccharides  are  the  factors   that   influence   wa- ter holding capacity of product [17]. They mentioned large particles create open structures that enhance the properties of hydration. Álvarez and Barbut also in- vestigated  the  effect  of  beta-glucan  (BG),   inulin, and their mixture on the emulsion stability, and con- cluded   combination   of   BG   and   inulin   compensa-

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

ted undesirable effect of fat reduction by increasing WHC [3]. Liu et al. also prepared konjac-egg white pro- tein 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 sig-

nificantly (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-

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.

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.

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 repeata- bility of findings. As it was expected similar composi- tions have displayed comparable results for cooking and sensory characteristics in Table 2, indicating the repeata- bility of findings.

Several studies suggested to use konjac in combina- tion 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 hard- ness 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   hard- ness  of  products  but  it  depends   to   many   fac- tors 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-

ted konjac and other food ingredients,  specially  gel- ling 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 ko- njac glucomannan (KGM) affected functional pro- perties of egg white protein and increased hardness, chewiness, and springiness of the gel samples at a cer- tain concentration [13]. The  investigation  conducted by Emir et al. indicated that the bigger molecular weight of KGM caused the highest hardness and close- ly 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   formula- tion. They found  that  the  hardness  and  adhesiveness of noodle significantly increased (p < 0.05) and be- came three times harder than standard Chinese or Ja- panese 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 pow- dered inulin resulted in higher moisture loss during co- oking. This can affect the texture of a product and in- creased  hardness  of  burgers,  frankfurter  sausages  and dry-fermented chicken sausages. [1, 18 and 19]. These re- sults are in a good agreement with the results of this study. Another texture parameter related to meat products is cohesiveness. Adhesiveness and cohesiveness are para-

meters that play an important role in handling of sau-

In recent years, unhealthy food habits and stressful life style have significantly increased the risk of seri- ous health disorders such as obesity, cancer, high blood cholesterol, and coronary heart diseases. This has crea- ted and increased demand for new health products with enhanced nutritional value. As a result, a number of re- search 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 im- prove the host health by stimulating the growth of bene- ficial 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 suc-

cessful food ingredient in meat industry due to its unique ability to enhance both taste and texture in various pro- cessed meat products through binding water, forming gel and mimics the oral tactile sensation of fat. The effec- tiveness of inulin has been approved in many investiga- tions 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  technologi- cal 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 triglycer- ide, 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

Copyright © 2019, Safaei et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.

Table 1. Sausage samples with konjac (K), inulin (I),  and starch (S) in a three component constrained D-optimal mix- ture design

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 frankfu- rter, 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 combina- tion with other ingredients such as inulin, starch or carra- geenan, could moderate undesirable effect.

Mixture design methodology is a new method to de- termine 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 inu- lin, konjac, starch, and their mixtures on properties of sau- sages using the D-optimal mixture design and develop the optimal formulation to produce a high quality sausage.

STUDY OBJECTS AND METHODS

1. 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 de- signed 13 samples. Effects of inulin, starch, and konjac on properties of sausage were evaluated, and optimum combination was determined. For optimization, depend- ing 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 vari- ous 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. Inu- lin (Inulin Frutafit TEX®) and konjac flour was obtained from Roosendaal (the Netherlands) and Shandong (Chi- na), respectively.

3. Physical properties
   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 liq- uids in the paper were considered as meat-free water. WHC was calculated using Eqs. (2) and (3). The experi- ment 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. 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 tempera- ture reached 72–73°C and then left to cool at room tem- perature. 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 pro- file analysis (TPA) was evaluated using an Instron M350-10CT (500 N load cell, England, Rochdale). The textural parameters were determined according the Pro- cedure 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 recent- ly 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 mea- sure CIE Lab values (L* relative lightness, a* relative redness and b* relative yellowness). Colorimeter cali- brated with white standard plate (L* = 94, a* = 0.3158, b* = 0.3322). The calculated results were expressed with

mean value of these measurements.

6. Sensory evaluation. Sensory analyse was per- formed according to the international standards (ISO, 1985) in the sensory laboratory at the National Nutrition and Food Technology Research Institute (NNFTRI). Pri- vate stands under white fluorescent lights were prepared for each panelist. Samples of each formulation were pre- sented 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 scie- nce 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 terminolo- gy. Overall acceptability of the samples was scored as fol- lows: 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 physicoche- mical 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. Correla- tion analyses were conducted by using the Pearson cor- relation model where p < 0.05 was taken as significance.

RESULTS AND DISCUSSION

Fitting for the optimal model. The optimal model

was fitted according to low standard deviation, low pre- dicted 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 ade- quately 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 il- lustrated in Table 2, where samples no. 2 and 11 demon- strated the highest WHC.

The results revealed that,  although  adding  inu- lin 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 syner- getic 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 frank- furter sausages and showed that the addition of 15% inulin and pectin improved WHC [18]. Studies per- formed by [9] showed that konjac blend  usually  had been used as multi-ingredient fat replacer in meat prod- ucts. In addition, incorporation of konjac blend with carrageenan and starch in low fat bologna increased WHC, produced more stable gel matrix with higher co- oking yield and more acceptable texture. López-López et al. (described the type of fibers and quantity of their polysaccharides  are  the  factors   that   influence   wa- ter holding capacity of product [17]. They mentioned large particles create open structures that enhance the properties of hydration. Álvarez and Barbut also in- vestigated  the  effect  of  beta-glucan  (BG),   inulin, and their mixture on the emulsion stability, and con- cluded   combination   of   BG   and   inulin   compensa-

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