Effect of combination of three texture‐improving ingredients on textural properties of emulsified sausage‐containing salted egg white

Abstract Response surface methodology based on Box–Behnken was used to assess the effects of three kinds of texture‐improving ingredients, namely, mixed starch (MS) (6%–8%) of sweet potato starch and glutinous rice flour, k‐carrageenan (CG) (0.4%–0.6%), and konjac flour (KF) (0.8%–1.2%), on the firmness, elasticity, and water holding capacity (WHC) of emulsified sausage (ES) made from pork and salted egg white (SEW). The three kinds of texture‐improving ingredients individually presented different effects on firmness, elasticity, and WHC. Their synergistic effects were significant. The three response models obtained by ANOVA were suitable to predict firmness, elasticity, and WHC. These models can also be used to design formulations for different types of sausage with different firmness and elasticity. The combination of MS (7.36%), CG (0.60%), and KF (1.20%) can produce SEW‐containing ES with remarkable firmness (224.04 g), elasticity (8.62), and WHC (8.41).

from the meat. Lean meat and fat were separately chopped into granulate in meat chopper. Uncooked salted egg was broken, and its egg white was separated from yolk. The chopped lean meat, fat, and egg white were separately packaged and stored at −20°C. For the konjac gel preparation, KF (Qiteng Trading Co., Ltd., Chengdu, China) and CG were mixed according to the required ratio. Water with a mass that is 20-fold that of KF and CG mass was added to the mixture. Afterward, the mixture was kept at room temperature for approximately 15 min to allow the two ingredients to absorb water and swell. Subsequently, the mixture was cooled at 4°C. MS was prepared by mixing sweet potato starch with glutinous rice flour at the mass proportion of 1:1. Approximately, 0.4% of sodium carbonate on the bases of total mass of sausage was mixed with the MS.

| ES preparation
On the basis of the designed scheme (Table 1), the frozen lean meat and fat granulate, and egg white were thawed at 4°C prior to sausage preparation. The lean meat granulate was first minced into like mud or paste. Then the starch, swelled KF and CG, and fat granulate were added and mixed with the lean meat mud. The mixed materials were further minced for about 1 min. Finally, the SEW and some flavoring ingredients, including sodium carbonate, were added and minced for 30 s. The batter was stuffed into collagen casings of 2 mm diameter.
The sausage sections were vacuum packed and cooked for 40 min in water bath at 80°C and subsequently for 15 min at 90°C. These sections were cooled to ambient temperature in tap water after heating and stored at 4°C overnight for analysis the following day. The lean meat and fat contents in the sausage were at a proportion of 7:3.

| Textural analysis and sensory evaluation
Sausage samples were cut into segments of 1 cm in height according to Lu, Luo, Li, & Li,2014); firmness of the samples was measured by P/50 probe of TA-XT PLUS textural analyzer (SMS Co., UK). Each sample was TA B L E 1 Box-Behnken design matrix and result data compressed to half of the original height. Sausage elasticity was sensory evaluated by chewing and touching with fingers of 10 trained assessors.
The sausages were cut into round pieces with about 5 mm thickness prior to elasticity evaluation. The assessors scored samples from 1 to 10, which corresponded to the lowest and highest elasticities, respectively.

| WHC determination
The sausage was cut into round slices with 10 mm height. Each slice was weighed, placed on dried filter paper, and covered with the same paper. Approximately, 1 kg of sausage slices was placed on the top of paper for 10 min at room temperature. The sample was weighed when the weights and filters were removed.
Evaluation was repeated three times for each sample. WHC was computed using Equation (1): where M 0 is the sample mass before pressing, and M 1 is the sample mass after pressing.

Response surface methodology (RSM) based on Box-Behnken in
Design-Expert 8.05 software (State-Ease Inc.) was used to design the experimental scheme and assess the effects of MS (6%, 7%, 8%), CG (0.4%, 0.5%, 0.6%), and KF (0.8%, 1.0%, 1.2%) on the firmness, elasticity, and WHC of ES made from pork and SEW. A series of 17 individual experiments was conducted and result of the three textural properties of sausage was analyzed. The low, middle, and high levels of each variable factor are designated as −1, 0, and 1, respectively. These three variables and their respective ranges were selected on the basis of literature and supporting information from our preliminary experiments.
The designed experimental scheme and result are shown in Table 1.

| Response models of experimental factors to textural properties
The designed experimental scheme and evaluated data of each point for firmness, elasticity, and WHC are shown in Table 1 where A, B, and C were coded terms for the three TIIs that were selected, that is, MS, CG, and KF, respectively. A positive sign in front of the terms indicated synergistic effect, whereas negative sign indicated antagonistic effect. The obtained results were analyzed by ANOVA to assess the goodness of fit.

| ANOVA
According to the ANOVA results shown in Table 2, the F values were sufficiently high. Furthermore, the values of Prob > F less than 0.05 indicated that the model terms were significant. The lack of fit values of the three models was not significantly relative to the pure error.
The goodness of fit of the models was evaluated by the determination coefficient (R 2 ), adjusted determination coefficient (Adj R 2 ), and predicted determination coefficient (Pred R 2 , data not shown). High R 2 , Adj R 2 , and Pred R 2 for all the analyzed properties ( Table 2) also revealed that the models were statistically significant. All adequate precision values were higher than 4, which indicated adequate signals. These models could be used to navigate the design space. property are presented in Figure 1. Both the figures in Figure 1 and the correlation coefficients in Table 2 showed a high consistency and correlation of predicted values from the three models with actual values from the experiment.

| Interaction effect of tiis on the three textural properties
The effect of interaction between any two of the three factors on each textural property could be observed in the 3D response (2) Y 1 =166.62 + 6.97A + 17.52B + 21.92C − 6.39AB +6.70AC − 14.44BC − 12.99A 2 + 27.88B 2 ; (3) Y 2 = 8.28 + 0.46B + 0.74C + 0.35BC − 0.32A 2 − 0.55B 2 − 0.64C 2 ; (4) surface plots generated by RSM and are shown in Figure 2. Evidently, the firmness increased with the increasing in CG and KF amount ( Figure 2; firmness). The interaction effect between MS and KF on firmness was positive, whereas the interaction effect between MS and CG or CG and KF produced negative effect. The most remarkable firmness (224.46 g) was observed at the combination of MS of 7.0%, CG of 6.0%, and KF of 1.2% (Table 1). The same factor condition also produced the highest elasticity (8.65) (Table 1) (Table 1).

| Optimization and verification of results from models
The optimum conditions for the three variables, namely, MS, CG, and KF, were obtained using the numerical optimization feature of