Comportamento de textura de géis formados por amido de arroz e isolado proteico de soro: Concentrações e velocidades de teste

cagro Ciência e Agrotecnologia Ciênc. agrotec. 1413-7054 1981-1829 Editora da Universidade Federal de Lavras RESUMO Géis fabricados que envolvem o uso de hidrocolóides estão ganhando polpularidade como alimentos de confeitaria/conveniência. O amido é geralmente combinado com um hidrocolóide (proteínas ou polissacarideos), particularmente na indústria de alimentos, uma vez que os amidos nativos geralmente não têm propriedades ideais para a preparação de produtos alimentares. Por conseguinte, os estudos de textura de misturas de amido-proteína podem proporcionar uma nova abordagem na produção de produtos alimentares à base de amido, sendo assim um atributo acrítico que precisa ser cuidadosamente ajustado ao gosto do consumidor. Este trabalho investigou as propriedades reológicas de textura de géis mistos de diferentes concentrações de amido de arroz (15%, 17,5% e 20%) e isolado proteico de soro (0%, 3% e 6%) com diferentes velocidades de teste (0,05, 5,0 e 10,0 mm/s) utilizando um delineamento experimental por Box-Behnken. As amostras foram submetidas a testes de compressão uniaxial com deformação de 80% para determinar os seguintes parâmetros reológicos: módulo de Young, tensão de fratura, deformação da fratura, energia recuperável e aparente viscosidade elongacional biaxial. Géis com maior concentração de amido de arroz e que foram submetidos a velocidades de teste mais altas mostraram-se mais rígidos e resistentes, enquanto que a concentração de isoldado proteico de soro teve pouca influência nessas propriedades. Os géis mostraram uma energia recuperável mais alta quando a velocidade de teste foi maior, e a viscosidade elongacional biaxial aparente também foi influenciada por este fator, exibindo propriedades diferentes dependendo da concentração de amido de arroz e da velocidade de teste. INTRODUCTION Rice starch (RS) is the basic food of almost half of the population worldwide; therefore, it has great global, social, and political economic importance (Tan; Sun; Corke, 2002). Approximately 90% of the total dry weight of the polished rice grain is composed of starch, which is the main plant polysaccharide that occurs naturally in the form of granules in vegetable cells (Singh et al., 2003). RS is hypoallergenic, white in color, and has a smooth texture when it is in the form of a gel (Viturawong; Achayuthakan; Suphantharika, 2008). However, gels with starch alone have a tendency to synerese and retrograde when subjected to cooking, shear stress, and cooling, which can lead to gels with an increased hardness and the formation of undesirable gels (Rosell; Yokoyama; Shoemaker, 2011). Starch is commonly combined with a hydrocolloid, particularly in the food industry, since native starches generally do not have ideal properties for the preparation of food products. Therefore, these combinations are used to modify the rheological and texture properties in order to improve humidity retention, control water mobility, and maintain general product quality during food processing and storage (Samutsri; Suphantharika, 2012). Whey and starch mixtures have received attention from researchers because whey proteins can form gels under certain temperatures and salt and protein concentrations, offering new possibilities when they are heated in the presence of starch dispersions. Thus, a continuous phase of mixed gels can be made by these protein and starch polymers, forming a network (Considine et al., 2011). Important functional properties of whey protein products include their ability to form heat-induced gels as well as to provide texture and water retention properties for food (Frydenberg et al., 2016). Food gels containing RS are increasingly becoming an option for the manufacture of gluten-free products, also known as gluten free, mainly for the growing market of people looking for a healthier diet and also for those who have some food restrictions such as those with celiac disease or diabetes. Substitution of ingredients or reformulation of existing food products may result in poor product quality due to changes in the textural characteristics, which depend on the concentrations of the components (Çakir et al., 2012). In addition, the fracture behavior is an important quality feature for product development (Sharma; Bhattacharya, 2014). Texture is one of the most important sensory attributes that determines the quality and acceptance of food by consumers (Çakir et al., 2012). During processing and use, food products are subjected to stress, including normal stress; therefore, compression tests are used to determine important properties of these foods. Compression is an efficient method to evaluate food texture, mainly because of its similarity to the chewing action (Sharma; Bhattacharya, 2014). Properties such as Young’s modulus, true stress, and true strain can be found using this type of test (de Jong; Van de Velde, 2007). Some studies using uniaxial compression with large deformations of gels have been reported, including those of mixed gels of carrageenan protein (Çakir et al., 2012), gels with hydrocolloids (gellan gum and agar) (Sharma; Bhattacharya, 2014), gels with proteins (de Jong; Van Vliet; de Jongh, 2015), gelatinous gels with soy protein isolate (Ersch et al., 2015), and gels with pea protein (Munialo; Van Der Linden; de Jongh, 2014). In order to understand the macroscopic properties of mixed gels, it is important to identify the gelation process that is responsible for gel formation (de Jong; Van de Velde, 2007). Heat gelation is a method commonly used to obtain a gel (de Jong; Van Vliet; de Jongh, 2015). Due to the increased consumption of gluten-free products, which include RS, and the limited amount of research in this area, the aims of this study were to evaluate the texture properties of mixed gels containing RS and whey protein isolate (WPI) and to elucidate how they are influenced by crosshead velocity. Important parameters such as Young’s modulus, fracture stress, strain, recoverable energy, biaxial extensional rate, and apparent biaxial elongational viscosity were determined. MATERIAL AND METHODS Material RS was purchased from Sigma Aldrich (St. Louis, MO, USA) and WPI was from Glanbia Nutritionals (Fitchburg, WI, USA). Ultrapure water with a conductivity of 0.05 μS/cm (Master System P & D, Gehaka, São Paulo, Brazil) was used in all tests. Methods Preparation of the gels The gels were prepared by thermal gelation (Kong; Kasapis; Bao, 2015). Adequate amounts of RS, WPI, and water were weighed (Shimadzu, AY220, Japan) and mixed with a magnetic stirrer for 10 min. Next, the sample was placed in a thermostatic bath at 90 °C for 30 min, under constant manual stirring for 9 min to avoid the formation of lumps (Marfil; Anhê; Telis, 2012). The RS concentrations were 15.0%, 17.5%, and 20% (w/w), whereas those of the WPI were 0.0%, 3.0%, and 6.0% (w/w). The samples were covered with plastic film in order to prevent dehydration and were kept refrigerated at 4 °C for 24 h. Compression test A TA XTplus texture analyzer (Stable Micro Systems, Godalming, UK) was used to carry out the tests. The samples had a cubic shape, with an edge of 1.5 cm, and were lubricated with vegetable oil in order to avoid loss of moisture and to reduce friction. The gels were uniaxially compressed to 80% of the initial height using a 75-mm-diameter (P/75) cylindrical plate. The pretest velocity was 1 mm/s; the compression velocities ranged according to the experimental design (0.05, 5.0, and 10.0 mm/s) and the post-test velocity was 10 mm/s for determination of the texture parameters (Sharma; Bhattacharya, 2014). The tests were performed twice. The compression characteristics can be expressed in terms of true strain (ε) and true stress (σ) according to Equations 1 and 2, respectively (Sharma and Bhattacharya, 2014): ε = l n ( h 0 h 0 − Δ h ) (1) σ = F ( h 0 − Δ h ) A h 0 (2) where h 0 is the initial height of the sample, F is the force during compression, Δh is the variation of the height of the sample during the compression test, and A is the cross-sectional area of the sample. The Young’s modulus (E) was calculated from the linear slope of the stress-strain curve up to 5% deformation (Çakir et al., 2012) and described by Equation 3 (Bourne, 1982): E = F ⋅ L 0 A ⋅ Δ L (3) where F is the force value applied at 5% deformation, L 0 is the initial height, A is the cross-sectional area of the sample at tension, and ΔL is the variation of the sample height during the compression test. The recoverable energy (RE) of the material was calculated according to Equation 4 (de Jong; Van Vliet; de Jongh, 2015): R E = W d W c × 100 % (4) where W c is the work required to compress the sample as determined by the positive area of the stress-strain curve, and W d is the work released by the sample as determined by the negative area of the stress-strain curve. For calculation of the biaxial extensional rate (ɛ. r ) and the apparent biaxial elongational viscosity (η b ), Equations 5 and 6, respectively, were applied (Ramires-Wong et al., 1996): ε r = ν ( t ) 2 ⋅ h ( t ) (5) η b = F ( t ) ⋅ h ( t ) A ⋅ h 0 ⋅ ε r (6) where v(t) is the compression velocity used in the test, h(t) is the height of the sample at time t, F(t) is the force applied to the sample at time t, A is the cross-sectional area of the sample at tension, and h 0 is the initial height of the sample. Experimental design and statistical analysis The Box-Behnken (Ba-Abbad et al., 2015) experimental design with three variables and three levels was used to study the combined effect of RS (15-20%), WPI (0-6%), and crosshead velocity (0.05-10 mm/s) on the following texture parameters: strain, fracture stress, Young’s modulus, and recoverable energy. The variables and their levels are presented in Table 1. The design consisted of 4 combinations of factor levels plus 1 central point and 2 replicates, totaling 26 experimental units. A second-degree polynomial equation (Equation 7) was used to fit the experimental data (Bayraktar, 2001; Jha et al., 2013; Sadeghi et al., 2014): Y = β 0 + β 1 x 1 + β 2 x 2 + β 3 x 3 + β 12 x 1 x 2 + β 13 x 1 x 3 + β 23 x 2 x 3 + β 11 x 1 2 + β 22 x 2 2 + β 33 x 3 2 (7) where Y is the predicted response associated with each independent variable; x 1 , x 2 , and x 3 represent the independent variables; β 0 is the constant; β 1 , β 2 , and β 3 are the linear coefficients; β 12 , β 13 , and β 23 are the interaction coefficients between the three factors; and β 11 , β 22 , and β 33 are the quadratic coefficients. Data analysis was performed using Statistical Analysis System, version 9.0 (SAS® Institute Inc., Cary, NC, USA). Analysis of variance and regression analysis with significance of 10% were performed. RESULTS AND DISCUSSION From the data obtained, force-deformation curves were made. The response variables, including strain (Equation 1), fracture stress (Equation 2), Young’s modulus (Equation 3), and recoverable energy (Equation 4), are shown in Table 1. Table 1: Experiment planning and data for modeling texture characterization. RUN Coded values Real values Young Stress Strain* Rec. Energy* RS WPI VEL RS WPI VEL (kPa)* (kN)* 1 -1 -1 0 15 0 5.00 10.09 1.59 1.82 10.94 2 -1 1 0 15 6 5.00 8.72 2.95 1.54 7.68 3 1 -1 0 20 0 5.00 12.37 3.25 1.94 8.02 4 1 1 0 20 6 5.00 11.99 3.87 1.84 4.15 5 0 -1 -1 17.5 0 0.05 8.58 2.35 1.64 0.74 6 0 -1 1 17.5 0 10.00 9.63 2.44 1.78 13.17 7 0 1 -1 17.5 6 0.05 9.15 2.20 1.97 0.51 8 0 1 1 17.5 6 10.00 12.05 5.13 1.66 7.67 9 -1 0 -1 15 3 0.05 7.19 1.81 1.80 0.61 10 1 0 -1 20 3 0.05 10.50 2.37 1.97 0.55 11 -1 0 1 15 3 10.00 8.64 2.60 1.56 8.75 12 1 0 1 20 3 10.00 13.09 4.46 1.81 6.73 13 0 0 0 17.5 3 5.00 11.17 3.88 1.66 8.32 * average. Young’s modulus The Young’s modulus (E), which is a measure of the stiffness of a material (Munialo; Van Der Linden; de Jongh, 2014; Munialo et al., 2015), was calculated from the slope of the linear region of the stess-strain curve up to 5% of deformation (Çakir et al., 2012). Equation 8 after statistical analysis of the data shows the empirical relationship between the Young’s modulus and the variables studied. E = − 2419.47 + 666.01 R S + 200.65 V E L (8) where RS, WPI, and VEL indicate the RS concentration, WPI concentration, and test velocity, respectively. The lack of adjustment (P = 0.725) and the ratio of lack of fit to pure error (0.444), presented in Table 2, showed that the proposed model is adequate to describe the data. R² value (91.14%) of model showed a very good correlation between the experimental and predicted values. The significance of each term was obtained according to the Pareto principle, which states that among the many factors influencing the response, only a few are responsible for most of the impact (Sadeghi et al., 2014). The P values are described in Table 2 and determine the significance of each factor, i.e., the smaller the magnitude of the P value, the more significant the corresponding term (Bayraktar, 2001). Following this principle, the RS concentration was the most significant term for Young’s modulus determination, followed by the test velocity. The linear model was the best one that fit the studied response. Table 2: Terms of the quadratic model with their significance and analysis of variance (ANOVA) for the Young’s module. Source Master Model DF SS MS F Pr > F RS 1 4.4E+07 4.4E+07 9.692 0.007 VEL 1 1.6E+07 1.6E+07 3.484 0.080 Model 9 7E+07 7793494 1.703 0.169 (Linear) 3 6.1E+07 2E+07 4.420 0.019 (Quadratic) 3 6601196 2200399 0.481 0.700 Error 16 7.3E+07 4576514 (Lack of fit) 3 6811990 2270663 0.444 0.725 (Pure Error) 13 6.6E+07 5108633 Total 25 1.4E+12 As shown in the contour plot in Figure 1, the Young’s modulus ranged from 7.5 kPa for RS-WPI gel 15-3 with a velocity of 0.05 mm/s to 12.9 kPa for RS-WPI gel 20-3 with a velocity of 10 mm/s. The stiffness of the gel increased as the velocity and RS concentration increased, making it more resistant. While at a low velocity and a low RS concentration, a weaker gel was produced. This finding can be explained by the fact that as the RS concentration increases, the amount of water in the medium decreases; therefore, the granules cannot swell enough and become more rigid (Ersch et al., 2015). Similar behaviors regarding the rigidities of gellan and xanthan gum gels (de Jong; Van de Velde, 2007), gelatin and soy protein isolate gels (Ersch et al., 2015), pea protein gels (Munialo et al., 2015), and whey protein gels (Munialo et al., 2016) have been reported in previous studies. Young’s modulus should not vary for an ideal solid with different compression velocities under experimental conditions, but gels are viscoelastic products and may behave differently (Sharma; Bhattacharya, 2014). An increasing stiffness of gels with an increasing polysaccharide concentration may be related to an increased local polysaccharide concentration and an improved interaction between aggregated proteins, thus leading to increased gel stiffness, whereby higher forces are required for deformation (Munialo et al., 2016). Figure 1: Contour plot showing the influence of RS and velocity on Young’s modulus. Strain The fracture strain indicates the brittleness of a gel (Munialo; Van Der Linden; de Jongh, 2014; Munialo et al., 2016). It was observed that no variables contributed significantly to gel deformation, since P > 0.05 for all parameters, indicating that the gels were equally brittle (Table 3). Previous studies also have reported that strain does not depend on the polysaccharide/protein concentration (Wang et al., 2014; Munialo et al., 2015; Munialo et al., 2016). Table 3: Terms of the quadratic model with their significance and analysis of variance (ANOVA) for the strain. Source Master Model DF SS MS F Pr > F RS 1 0.179358 0.179358 2795269 0.113982 WPI 1 0.008569 0.008569 0.133542 0.719574 VEL 1 0.081039 0.081039 1262984 0.277666 RS*RS 1 0.023794 0.023794 0.370822 0.551105 RS*WPI 1 0.018426 0.018426 0.287165 0.599414 RS*VEL 1 0.003946 0.003946 0.061496 0.807302 WPI*WPI 1 0.01196 0.01196 0.186388 0.671702 WPI*VEL 1 0.103744 0.103744 1616829 0.221708 VEL*VEL 1 0.012413 0.012413 0.193451 0.665943 Model 9 0.422574 0.046953 0.73175 0.675543 (Linear) 3 0.268966 0.089655 1397265 0.279938 (Quadratic) 3 0.027492 0.009164 0.142821 0.932778 (Cross Product) 3 0.126115 0.042038 0.655163 0.591351 Error 16 1026638 0.064165 (Lack of fit) 3 0.096042 0.032014 0.44722 0.723432 (Pure Error) 13 0.930596 0.071584 Total 25 1449212 Fracture stress The fracture stress (FS) indicates the brittleness of a gel (Munialo; Van Der Linden; de Jongh, 2014), and the relationship between fracture stress and the studied variables can be described by Equation 9 after statistical analysis of the data: F S = − 1974.88 + 249.95 R S − 50.87 W P I + 5.41 V E L + 47.66 W P I × V E L (9) The lack of adjustment was not significant (P = 0.99), and the pure error (0.03) presented in Table 4 showed that the proposed model (Equation 7) adequately described the data. R² value (99.66%) of model showed a very good correlation between the experimental and predicted values. The first-order effect of velocity was the most significant term to determine fracture stress, followed by the RS and WPI concentrations, and then the cross-product between the WPI concentration and test velocity, respectively. Table 4: Quadratic model terms with their significance and analysis of variance (ANOVA) for fracture stress. Source Master Model DF SS MS F Pr > F RS 1 6247693 6247693 7261531 0.015943 WPI 1 5124697 5124697 5956302 0.026672 VEL 1 8721872 8721872 101372 0.00577 WPI*VEL 1 4048662 4048662 4705655 0.045473 Model 9 27148607 3016512 3506013 0.013958 (Linear) 3 20094262 6698087 7785012 0.00199 (Quadratic) 3 1889857 629952.5 0.732177 0.54782 Error 16 13766118 860382.4 (Lack of fit) 3 93065.39 31021.8 0.029495 0.992841 (Pure Error) 13 13673052 1051773 Total 25 40914725 The contour curves represent a combination of two variables, with a third variable maintained at mid-level (Bayraktar, 2001; Sadeghi et al., 2014). Figure 2a shows fracture stress as a function of RS and WPI concentrations. The gel had a higher fracture stress when it had higher RS and WPI concentrations, but the WPI concentration had less of an influence on the resistance of the material. Figure 2b shows the fracture stress as a function of the RS concentration and crosshead velocity. It can be observed that the fracture stress of the gel increases with an increasing RS concentration and crosshead velocity, making it more resistant to a higher RS ​​concentration ​​and compression velocity. Figure 2c shows the fracture stress as a function of the WPI concentration and crosshead velocity. For a low test velocity and a low WPI concentration, there was not much variation in the resistance of the material. This parameter was influenced when the material was subjected to a high test velocity and a high WPI concentration. Figure 2: Contour curves showing the influence of different variables on the fracture stress of the gels: (a) RS and WPI, (b) RS and velocity and (c) WPI and velocity. Wang et al. (2014) have studied the texture properties of agar gels and concluded that the fracture stress increases with an increasing agar concentration at all compression velocities studied and that the dependence of the fracture concentration is stronger at a faster compression velocity. Munialo et al. (2016) also have reported that the fracture stress increases as the pectin concentration increases. Recoverable energy Recoverable energy represents the energy that is stored elastically during deformation. Equation 10, after statistical analysis of the data, shows the empirical relationship between the recoverable energy (RE) and the variables studied: R E = 9.57 − 0.43 R S − 0.54 W P I + 2.06 V E L − 0.12 V E L 2 (10) P value for lack of fit (0.309617) and the ratio of lack of fit to pure error (1.321913) showed that the proposed model adequately described the data (Table 5). R² value (97.21%) of model showed a very good correlation between the experimental and predicted values. The first-order effect of velocity was the most significant term in the determination of the recoverable energy of the gel, followed by the second order of the velocity and the first order of the WPI and RS concentrations, respectively. The gels that were submitted to higher test velocities were the ones with the highest recoverable energy, around 10%; while at lower velocities, their recovery was practically null. Table 5: Terms of the quadratic model with their significance and analysis of variance (ANOVA) for recoverable energy. Source Master Model DF SS MS F Pr > F RS 1 1817817 1817817 5573265 0.031262 WPI 1 413758 413758 1268545 0.002602 VEL 1 2875934 2875934 8817357 0.0001 VEL*VEL 1 4583273 4583273 1405191 0.001753 Model 9 4257915 4731017 1450488 0.0001 (Linear) 3 3471474 1157158 3547743 0.0001 (Quadratic) 3 626749 2089163 6405188 0.004677 Error 16 5218678 3261674 (Lack of fit) 3 1219865 4066217 1321913 0.309617 (Pure Error) 13 3998813 307601 Total 25 4779783 Figure 3a shows the recoverable energy as a function of the RS and WPI concentrations. The energy increased with a decrease in the RS and WPI concentrations. Figures 3b and 3c represent the recoverable energy as a function of the test velocity with the RS and WPI concentrations, respectively. Both curves had the same behavior: as the test velocity increased, the recoverable energy of the gel increased; the maximum recoverable energy values for RS and WPI were 10.1% and 10.7%, respectively. For all situations, it was observed that the gels without protein were the ones that had a higher recoverable energy because the gels with a greater amount of water retained within in the network tended to be more elastic, thus affording a higher recoverable energy (Munialo; Van Der Linden; de Jongh, 2014). When a material is subjected to a high compression velocity, there is insufficient time for the total breakdown of its structure; therefore, there is more recovery and a higher recoverable energy is presented (Sharma; Bhattacharya, 2014). Figure 3: Contour curves showing the influence of different variables on the recoverable energy of the gels: (a) RS and WPI, (b) RS and velocity and (c) WPI and velocity. Recoverable energy also can be analyzed by force-strain curves. The force-strain curves for one of the RS-WPI gels at test velocities of 0.01, 1, and 10 mm/s are shown in Figures 4a, 4b, and 4c, respectively. It was observed that as the test velocity during the compression increased, the recovery of the sample increased, as indicated by the smaller negative area in the graph during the decompression (Figure 4c). The inverse behavior was observed at a lower velocity, where there was a greater energy loss and less sample restructuring (Figure 4a). The gel fractures at the point where the curve stops growing and begins to decay. In some cases, it is not possible to identify the breakpoint of a gel. Similar curves have been reported in the literature to be related to the behavior of agar gel (Sharma; Bhattacharya, 2014). Figure 4: Force-deformation curves for velocities of (a) 0.01 mm/s, (b) 1 mm/s and (c) 10 mm/s. Apparent biaxial elongational viscosity The apparent biaxial elongational viscosity (𝜂b) versus the biaxial extensional rate (𝜀̇r) for mixed gels of RS and WPI at different test velocities (0.05, 0.5, 0.1, 1, 5, and 10 mm/s) is shown in Figure 5. The graphs for all RS-WPI concentrations are similar, varying only in magnitude. The increase of ε̇r causes a decrease of the η b value, meaning that a decrease in gel strength occurs when the compression test velocity increases (Sharma; Bhattacharya, 2014). Two regions can be observed in the graphs: one with a sharp increase of the curve until a peak viscosity is reached, and another region with a slight decay until a constant viscosity is maintained, which looks like a Newtonian region, where the viscosity is constant, independent of the biaxial extensional rate (Ramires-Wong et al., 1996). At high test velocities, there was an increase in the viscosity until a peak value was reached, followed by a prolonged nonlinear decay. The pattern of the gel curves is much more complex when compared to other types of mass (Sharma; Bhattacharya, 2014). Figure 5: Apparent biaxial elongational viscosity versus biaxial extensional rate for mixed RS-WPI gels at () 0.05, () 0.1, () 0.5, () 1, () 5 e () 10 mm/s in different concentrations: (a) 15-0, (b) 15-3, (c) 15-6, (d) 17.5-0, (e) 17.5-3, (f) 17.5-6, (g) 20-0, (h) 20-3 e (i) 20-6. CONCLUSIONS In the present study, three independent variables (RS concentration, WPI concentration, and test velocity) were studied. Box-Behnken experimental design was applied and proved to be effective in determining the dependent variables. The RS concentration and velocity had a more significant impact on the strength and stiffness of the gel. Mean while, the WPI concentration had little influence on the rigidity of the gel. Gel deformation was not dependent on any independent variables, showing that the gels had the same brittle behavior. 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