Pasting and Texture Properties of Commercial Plant Proteins and Its Mixtures

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Therefore, the aim of this work was to study the pasting and texture properties of commercial proteins commonly used in food products (pea, lentil, fava bean, rice and soybean) and its binary and ternary mixtures. The pasting properties were studied by Rapid Visco Analyser (RVA) and the texture by Texture Profile Analysis (TPA) method using a texturometer. Results showed that protein mixtures exhibit distinct behaviors when compared to single proteins. Single lentil and soy protein presented the highest final viscosity (847 and 806 cP, respectively) whilst the rice the lowest final viscosity (10 cP). Related to texture, faba bean and soy exhibited the highest gel hardness (1.52 and 1.50 N, respectively). For binary and ternary mixtures, in general, the viscosity and texture profiles parameters decreased. Rice-containing mixtures showed the lowest final viscosity (30.5–62.0 cP), while lentil and faba bean mixtures had the highest final viscosities and gel strengths (579 cP and 1.77 N, respectively). From the ternary mixtures, samples containing lentil, fava bean, and rice displayed superior gel strength (0.9 N) due to a synergistic interaction. This work provides information about vegetable proteins and its mixtures that can be used for a better design of plant based food products. soy protein isolate rice protein isolate pulses proteins protein gelation Figures Figure 1 Figure 2 Figure 3 1. Introduction An increasing number of consumers are transitioning to a predominantly plant-based diet. On the other hand, soy and gluten proteins, generally used to produce meat analogous, are avoided due to its allergenicity leading to alternative proteins increasing popularity [ 1 ]. Using proteins from different sources can be a viable option both economically and good food amino acids balance and techno-functional properties. Among these legumes are: pea protein and emerging protein sources such as fava beans and lentils [ 2 , 3 ]. Plant proteins are generally deficient in essential amino acids compared to animal proteins [ 4 ]. To solve this problem, the combination of proteins is recommended [ 5 ], this is possible due to the wide variety of protein sources [ 6 ]. However, it is expected that proteins mixtures presents techno-functional properties different from pure compounds on food products application. Protein techno-functional properties are classified based on the mechanism of action, including hydration-related (solubility, water and oil holding capacity), protein structure and rheological characteristics (gelation and viscosity), and surface characteristics (foam and emulsion) [ 7 ]. Plant proteins gelation properties knowledge are essential in food applications where a semi-solid structure is required [ 1 ], for example, texturization to produce meat analogues (Lam et al., 2018) and 3D printing of foods [ 8 , 9 ]. A gel is a dispersed system characterized by a lack of fluidity and elastic deformability [ 10 ]. The mechanism of globular protein gelation is a complex process that involves partial denaturation of the protein molecule, gradual association or aggregation, and formation of a network [ 11 ]. The protein intrinsic composition determines the intramolecular networks formed during heating and cooling [ 12 ]. Mixtures of proteins may interact with each other during the gelation process to form a mixed network where the mechanical properties is governed by protein–protein interactions [ 13 ]. The effect of pea and rice [ 14 ], soy and gluten [ 15 ] protein mixtures showed that protein interaction affect the gel properties. This was attributed to changes in protein secondary structure, competition of different proteins for water and impairing in the protein groups involved in gel formation. Related to texturization, different mixture are reported to affect the mass flow behavior and structure of extrudates that depends on protein type and proportion between the components [ 16 , 17 ]. Understanding the synergistic behavior among the different protein structures during thermal treatment can help to better define optimum conditions for products and processes [ 18 ]. Besides, investigations into gelation and rheological properties of proteins mixtures for high-moisture meat analogues development are needed to address the incomplete understanding of their potential fibrous structure [ 19 ]. The Rapid Visco Analyzer (RVA) is generally used to study pasting properties. The rate of change in protein viscosity with respect to temperature measured by RVA can help to predict the type of networks formed during food processing, avoid flow problems [ 12 ], and is related to other protein properties, i.e. water absorption capacity and gelation [ 20 ]. RVA results can be used to study the performance of different proteins in applied products, for example, Woo et al. [ 19 ] showed that protein blends RVA results can be used to predict the structural attributes of high-moisture meat analogues. The use of RVA for proteins is relatively new and methods are still under development. The AACC official RVA method have been applied in some studies for proteins, i.e. run to 95°C, held for a period of time, and cooling to 50°C [ 2 ]. According to Stephani et al. (2015), the studies with protein ingredients, the RVA can be interesting due to its similarity to industrial processes (agitation and temperature), and can demonstrate the extent of the interaction between mixtures of components and the effect of changes in physical processing conditions on the proteins functionality. Another tool to understand the protein gel properties is the texture profile analysis (TPA). The TPA begins by compressing the sample and as the force increases, the gel responds by deforming. After the first deformation, the force is released and the gel partially recovers given information about the elasticity and recovery of the gel. The sample is compressed again and information about gumminess and chewiness can be assessed [ 21 ]. By this method the feelings and sensations during food consumption can be simulate [ 22 , 23 ]. As described previously, there is a necessity of utilizing a pool of proteins for its application in different products. This may be driven by complementing the amino acid profile, economic factors, or achieving better technological performance. Furthermore, the comprehension of the properties of pasting and gels texture of vegetable protein is essential to understand the technological performance of proteins in foods. Therefore, this work aimed to determine the pasting properties and gel texture of single vegetable proteins and their binary and ternary mixtures adjusted to pH 7. For this purpose, commercial pea, lentil, fava bean, rice, and soy proteins were used. 2. Material and Methods 2.1. Material The characteristics of proteins used in this work are described in Table 1 . All solutions were prepared with deionized water (Permution, Curitiba, Brazil). NaOH were purchased from Dinâmica (Indaiatuba, Brazil) and HCl from Synth (Diadema, Brazil). Table 1 – Moisture, pH and protein content of proteins used in this work Sample Moisture (%) pH Protein (% db a ) Pea Protein 1853 4.62 7.25 84.9 Lentil Protein 2550 b 6.92 7.85 65.5 Fava Bean Protein P360 5.56 6.23 70.9 Rice Protein PQ674 3.70 6.42 87.8 Soy Protein Isolate PQ262 6.72 7.43 90.42 a db: dry basis; b Starch content of lentil protein was 12.25% according to the manufacturer. 2.2. Methods 2.2.1 Experimental design The experimental mixture design was employed to study the effect of five variable components made up of vegetable proteins sources: Pea Protein (A); Lentil Protein (B), Fava Bean Protein (C), Rice Protein (D) and Soy Protein Isolate (E) on the response variables: viscosity and texture profile using a protein content of 10%, 5% and 3.33%, corresponding to 0.33; 0.50 and 1.0 proportions and totalizing 24 different combinations. 2.2.2 Viscosity Profiles by RVA The pasting properties was determined by viscosity parameters of protein isolates/concentrates using a Rapid Visco Analyser (RVA 4500, Perten Instruments, Hägersten, Sweden) coupled with a water bath at 25°C. It was used 10% of protein in water (w/w), and the suspension pH was adjusted to 7 using NaOH (40%) or HCl (15%). The protein samples were maintained under gentle agitation for 20 min before analysis. The Standard method 2 was employed as describe by Perten (2015). Samples were held at 50°C for 60 s (960 rpm/10 s and 160 rpm for the rest of the analysis), then heated to 95°C at ~ 9°C/min, held at 95°C for 5 min, cooled back to 50°C at ~ 5°C/min, and held at 50°C for 2 min. The curves were analyzed using TCW 3.15.1.255 software (Newport Scientifc, Warriewood, Australia). After the tests, the RVA paddle was removed from the container that was covered with polyvinylchloride (PVC) film and stored at 5°C overnight before the texture analysis. 2.2.3 Texture profile analysis (TPA) Texture profile analysis was performed on samples obtained after RVA analysis stored for 24 hours at 5°C, according to the methodology provided by AACC International Method 76-21.01. The samples were equilibrated at room temperature for 1h before analysis. Gel hardness was determined using a TA-XT2i texture analyzer (Stable Micro Systems, Godalming, Surrey, UK) at 25°C and the data was analysed by Texture Expert software. Conditions of the test were: pre-test and post-test speed of 2 mm/s; test speed of 0.8 mm/s; strain of 40%; trigger force of 0.049 N. The probe used was an AOAC cylindrical acrylic 25 mm high and the sample stored in the RVA canister was penetrated twice during the test. 2.2.4 Statistical Analysis Statistical analysis of the data was conducted using ANOVA and Tukey's test (p < 0.05) using the Statistica software (14.0.0.15, Tibco, Palo Alto, USA). 3. Results and discussion The results of this work will be discussed in separate sections comprising single proteins, binary and ternary mixtures pasting and gel properties. The investigation of pasting properties by RVA where the protein dispersions hydration, heating and low shear can be evaluated. The gel hardness was obtained by TPA. The texture of samples was also discussed in function of the TPA curve, that typically exhibits two distinct peaks corresponding to the initial compression and subsequent relaxation of the gel during related to the biting process. In addition, visual aspect and essential amino acids profile of different combinations are presented. 3.1. Vegetable proteins pasting and texture properties Figure 1 a and 1 b shows the pasting properties and TPA results of different vegetable proteins. It can be seen that proteins presented a distinct behavior due to its different intrinsic composition and structure. The viscosity of pea, lentil and faba bean increased with the heating and cooling. During the heating step, proteins denature increasing the water absorption and swelling in addition to the protein-protein hydrophobic interactions increasing resulting in higher suspension viscosity [ 15 , 25 ]. The behavior of pea and faba bean proteins RVA curves were similar, however after cooling and storage at 5°C, the faba bean protein gel was harder as observed in the TPA curve (Fig. 1 a) and by its visual aspect (Fig. 1 c). The viscosity result at 95°C (Table 2 ) shows the effect of heating on a protein viscosity and the final viscosity Indicates the ability of a sample to form a viscous paste after cooking and cooling [ 26 ]. Lentil (456.5 cP) and soy protein (359.0 cP) presented the highest values of final viscosity (Table 2 ). Starch content of lentil protein is 12.25% according to the manufacturer, therefore the highest lentil final viscosity (847.0 cP) may be due to the gelatinization of starch. Despite this, the gel force (Table 2 ) obtained by texturometer of lentil was smaller than those obtained for faba bean and soy. This can be attributed to the protein influence on starch viscosity due to changes on the swelling of starch granules [ 26 ]. Table 2 – Viscosity at 95°C and final viscosity obtained by RVA and gel force of pea, lentil, faba bean, rice and soy protein Protein Viscosity at 95°C (cP) Final Viscosity (cP) Gel force (N) Pea 97.0 ± 9.9 c 315.0 ± 2.8 b 0.75 ± 0.04 bc Lentil 456.5 ± 24.7 a 847.0 ± 19.8 a 1.00 ± 0.12 b Faba bean 80.0 ± 1.4 c 262.5 ± 12.0 b 1.52 ± 0.12ª Rice 13.5 ± 0.7 d 10.0 ± 0.0 c 0.60 ± 0.01 c Soy 359.0 ± 15.6 b 806.0 ± 97.6 a 1.50 ± 0.07ª Averages followed by the same letter do not differ significantly in the columns for the same parameter according to Tukey’s test (p < 0.05). None of samples showed a peak during heating on RVA curves that can be related to protein concentration. Different consistencies are obtained according to the protein concentration used, i.e. soft yogurt-like pastes are obtained below 12% of whey protein resulting in no peak in RVA curve [ 12 ]. The lowest viscosity was obtained for rice protein (13.5 cP) that did not form a gel as can be seen in the texture results and by its visual aspect (Fig. 1 c). This is expected for rice proteins due to its poor solubility [ 14 ]. Figure 1 d shows that rice protein is deficient in lysine and present a great amount of methionine and cysteine. This makes the rice a complement for pulses proteins that present the opposite behavior. Therefore, of the proteins studied, the rice protein is the most indicated to be used in pulses protein mixtures in order to balance the amino acids profile. Soy proteins are one of the most applied vegetable protein due to its excellent tech-functional properties, cost and essential amino acids profile [ 15 ]. In this work, differently from other proteins, soy presented a decrease on the viscosity with the increase on temperature and a viscosity rise during cooling step. However, different viscosity profiles for soy proteins can be found in literature [ 19 , 20 , 27 ]. Commercial soy proteins present a wide variation between them, showing differences in composition and level of protein denaturation [ 15 ]. The high initial viscosity is related to the water absorption and swelling upon hydration resulting in a high starting viscosity that decreases with the stirring and heating [ 28 ]. The viscosity reduction during heating is attributed to constant stirring diminishing protein-water interaction, protein aggregate dissociation, and the disruption of physical bonds. Conversely, increased viscosity during cooling is linked to reduced molecular mobility and strengthened attractive forces in protein aggregates through hydrogen bonding and hydrophobic interactions [ 19 , 29 ]. During the gelation process, proteins undergo complete melting and denaturation, resulting in irreversible interactions caused by the exposure of internal hydrophobic regions and sulfhydryl groups [ 19 ]. In this work, the samples obtained after RVA analysis result in a stable gel structure after cooling that can be evaluated by TPA (Fig. 1 b) in the RVA canister. When the sample is a semi solid, the measure is of back extrusion as compression forces the sample to flow through the annular gap formed between the plunger and the container [ 21 ]. The force-deformation curves provides a visual representation of how a protein gel behaves under controlled forces, helping in the quantification of gel textural force and optimize food formulations [ 30 ]. Faba bean (1.52 N) and soy (1.5 N) showed the higher gel hardness (Table 2 ). Soy protein is largely known by its gelation ability which makes it widely applied in different products such as tofu and meat analogues. Faba bean protein is an unconventional protein that presents great gelation ability forming dense and fine gel network structure at pH 7, along with the high stress and strain at fracture [ 31 ]. Pea and lentil protein showed an intermediate hardness value, 0.75 and 1.0 N, respectively. Pea protein has the ability to form soft gels that contribute to the development and production of beverages similar to dairy drinks, fermented products and curds [ 11 ] and lentil protein can be used in food texturization based on their good gel mechanical properties [ 32 ]. Rice formed a less stable suspension with lower hardness, and no adhesiveness (negative peak). This is expected since rice do not show a proper gelation due to its low solubility [ 33 ]. Figure 1 c illustrate the difference in the appearance of the gel obtained after RVA experiments and stored at 5°C. Samples presented different consistency that is related to changes in the structure formation as was also observed in the shape of the RVA and TPA curves. The samples that formed stable gels after texture analysis were those containing lentils, fava beans and soybeans proteins. Pea and rice protein dispersion showed phase separation and a more fluid appearance (Fig. 1 c). 3.2. Pasting and texture of vegetable proteins binary and ternary mixtures Figure 2 a and 2 b presents the RVA curves of pea, lentil, faba bean, rice and soy proteins binary mixtures. Binary mixtures presented a distinct behavior when compared to single proteins. The properties of gels formed by protein mixtures cannot necessarily be predicted by the gel behavior of single protein and new textures can be obtained [ 34 ]. This reflects the complex protein-protein and protein-water interactions, affected by temperature and time. In this study, in general, a decrease in the viscosity profile parameters was observed for binary mixtures when compared to single proteins. The protein functional properties, e.g. , swelling, solubility, gelation, and water holding capacity, are directly related to how proteins interact with water [ 35 ]. When protein-protein interaction occur, aggregation and phase separation can happen [ 36 ], this may lead to a decrease in the protein-water interaction decreasing the viscosity of the protein mixture. For example, Bainy et al. [ 15 ] showed that binary mixtures of soy protein and gluten SPI can experience a decrease in viscosity profile parameters due to soy protein impairing gluten proteins thiol/disulphide interchanges during thermal treatments. Differently from single proteins (Fig. 1 a and 1 b), some binary mixtures viscosity profiles curves (Fig. 2 a to 2 d) presented peaks, e.g. , pea-soy, lentil-soy and faba bean soy. Bainy et al. [ 15 ] found two peak viscosities for soy protein, one at 85°C and another during cooling, that were attributed to denaturation and non-covalent interactions, respectively. Therefore, peaks observed in this work may be attributed to protein interactions and protein coagulation. Mixtures containing rice protein showed the lowest viscosity values (10–64 cP) when compared to other binary mixtures as can be seen in Table 3 . The mixture of pea and rice resulted in a product with two heterogeneous phases: one liquid and other more compact, with a firmer gel (Fig. 3 e). This may be attributed to coagulation of proteins due to pea and rice interaction, in addition to the low solubility of these proteins in pH 7. Therefore, including rice in protein mixtures for application in products where it is necessary to increase protein content without change the fluidity of the product can be recommended. These mixtures also show a good essential amino acids balance since rice is rich in Met + Cys that is deficient in pea, lentil and faba bean (Table 3 f). Table 3 – Viscosity at 95°C and final viscosity obtained by RVA and gel force of pea, lentil, faba bean, rice and soy protein binary mixtures Protein Viscosity at 95°C (cP) Final Viscosity (cP) Hardness (N) Binary mixtures Pea-Lentil (PL) 79.5 ± 7.8 e 162.0 ± 18.4 d 0.26 ± 0.00 ef Pea-Faba bean (PF) 64.0 ± 2.8 ef 110.0 ± 2.8 de 0.44 ± 0.06 bcd Pea-Rice (PR) 27.5 ± 0.7 g 45.0 ± 2.8 f 0.32 ± 0.01 def Pea-Soy (PS) 212.0 ± 15.6 c 144.0 ± 18.4 d 0.25 ± 0.01 ef Lentil-Faba bean (LF) 167.0 ± 8.3 d 579.0 ± 7.1 b 1.77 ± 0.07 a Lentil-Rice (LR) 39.5 ± 0.7 fg 62.0 ± 2.8 ef 0.24 ± 0.00 ef Lentil-Soy (LS) 368.0 ± 1.4 a 675.5 ± 3.5 a 0.51 ± 0.03 bc Faba bean-Rice (FR) 27.0 ± 1.4 g 40.5 ± 0.7 f 0.37 ± 0.02 cde Faba bean-soy (FS) 291.0 ± 1.4 b 383.0 ± 32.5 c 0.56 ± 0.05 b Rice-Soy (RS) 53.5 ± 6.4 efg 30.5 ± 2.1 f 0.22 ± 0.00 f Ternary mixtures Pea-Lentil-Faba bean (PLF) 82.0 ± 2.8 c 146.0 ± 5.7 b 0.39 ± 0.02 c Pea-Lentil-Rice (PLR) 39.0 ± 0.0 ef 54.5 ± 3.5 cd 0.28 ± 0.00 b Pea-Lentil-Soy (PLS) 209.5 ± 4.9 a 243.5 ± 12.0 a 0.29 ± 0.01 c Pea-Faba bean-Rice (PLR) 30.5 ± 0.7 f 46.5 ± 0.7 d 0.31 ± 0.00 c Pea-Faba bean-Soy (PFS) 199.5 ± 13.4 ab 156.5 ± 10.3 b 0.29 ± 0.00 c Pea-Rice-Soy (PRS) 52.5 ± 0.7 de 37.0 ± 1.4 d 0.22 ± 0.00 d Lentil-Faba bean-Rice (LFR) 42.5 ± 2.1 def 78.5 ± 3.5 c 0.90 ± 0.02 a Lentil-Faba bean-Soy (LFFS) 185.0 ± 1.4 b 395.5 ± 26.2 a 0.32 ± 0.01 c Faba bean-Rice-Soy (FRS) 63.0 ± 5.7 cd 64.5 ± 6.4 d 0.24 ± 0.01 d Averages followed by the same letter do not differ significantly in the columns for the same parameter and same amount of protein from different sources according to Tukey’s test (p < 0.05). The highest value of final viscosity (579 cP) was observed for lentil and faba bean. The visual aspect of this samples (Fig. 2 e) was of a consistent and rigid gel, with a well-defined shape. Higher gel force was obtained when compared to single proteins showing good synergism for this mixture of proteins. Thus, lentil and faba bean protein is recommended for applications in products where harder gels are required such as meat analogues. However, this mixture is poor in Met + Cys and Trp and should not be consider in foods with the appeal of high protein content. A distinct behavior was also observed for TPA curves (Fig. 2 d and 2 c) for all samples. A decrease in force during the experiment occurred when compared to single proteins, except for LF mixture. When different proteins are combined in a mixture, molecular interactions occur that affect the structure and texture of the resulting gel changing the TPA curves. Despite the lower viscosity and gel force of PF, LS, FR, FS when compared to single proteins, the gel formation ability was maintained and these binary mixtures can be used as gelling agents (Fig. 2 e). The amino acids profile of binary mixtures was complete for samples containing rice (RS, FR, LR, and PR) and the PS mixture. From these mixtures, PS showed the highest viscosity along RVA analysis, however, the gel texture value was low. This behavior was also observed for other proteins and its mixtures showing that higher viscosity solutions do not consistently correlate with greater gel strength values. This observation highlights the intricate nature of protein interactions during gel formation and the importance of looking beyond viscosity alone when protein-based food products are formulated. Factors such as protein-protein interactions, hydration, and structural changes play pivotal roles in determining the final gel properties. Mixtures containing rice, despite present complete amino acids profile, need to be improved when applied to products where a high viscosity and gel hardness is required. On the other hand, such mixture can present advantages when applied in products such as beverages that requires a high protein content without increase the viscosity. The ternary mixtures of proteins RVA and TPA results are presented in Fig. 3 and Table 3 . As also observed for binary mixtures, lower viscosity and peak force are observed compared to single proteins. Results showed that other commercial proteins can decrease the initial viscosity of soy protein dispersions and mixture containing lentil generated higher viscosity and harder gels in ternary protein mixtures. Among the ternary mixtures studied, the combination of lentil, fava bean, and rice proteins (LFR) exhibited a visually stronger gel aspect, as depicted in Fig. 3 f. Notably, the LFR mixture stood out due to its higher gel force. Interestingly, rice protein alone typically has low viscosity and gel strength values. However, the synergistic interaction between rice, lentil, and fava bean proteins favored gel formation in the LFR mixture. As shown by Wang et al. [ 37 , 38 ] whey protein isolate and casein interaction with rice protein increase the rice solubility due to the formation of an hydrophilic particulate spheres promoted by mixture alkalinization followed by neutralization. Therefore, this blend represents an ideal balance of amino acids while maintaining favorable gelling properties. As observed, proteins mixtures can result better functional properties when appropriate sources are used. These mixtures can also present advantage in sensory properties. Vegetable proteins may not always provide a pleasant sensory experience for consumers, as highlighted by Kaale et al. [ 39 ] for lentil protein. Therefore, in protein mixtures, the overall acceptability can be improved while preserving the desirable gel-forming properties. The balance between sensory appeal, amino acids balance and functional performance is crucial for creating protein-based food products that resonate with consumers. 4. Conclusion This work aimed to determine the pasting properties and gel texture of single commercial vegetable proteins and their binary and ternary mixtures. In general, protein mixtures presented lower values of final viscosity and texture when compared to single proteins. Samples containing rice protein tend to form less viscous dispersions and after cooling no gel formation occurred. Mixtures where viscosity and gel force increase were those containing faba bean. Despite the lower viscosity and gel force of Pea-Faba, Lentil-Soy, Faba-Rice and Faba-Soy combinations, these mixtures maintain the gel formation ability and can be used as gelling agents. Results presented in this work shows that despite the need of include protein mixture in food products for essential amino acids balance, these mixtures affect the pasting properties and gel texture. Therefore, there is a need to study the impact of protein mixtures on the techno-functional properties of these ingredients to maintain the characteristics expected for food products in addition to the nutritional aspect of a formulation. The presented results shed light on their potential suitability of protein mixtures for various food formulations. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution Elaine Kaspchak: Conceptualization; Data curation; Formal analysis, original draft writing; Anna Paula Muntilha Guimarães Conceptualization; Data curation; Formal analysis; Elizabeth Harumi Nabeshima: Investigation; Methodology; Visualization; Writing - review & editing; Mitie Sônia Sadahira: Funding acquisition; Investigation; Project administration; Resources; Supervision, Writing - review & editing. Acknowledgments The authors acknowledge the State of São Paulo Research Foundation (Fapesp) for the financial support to carry out this project (grant number 2020/07015-7) and CNPq PIBIC/PIBITI-Ital. References K.K. Ma, M. Greis, J. Lu, A.A. Nolden, D.J. McClements, A.J. Kinchla, Foods. 11 , 1 (2022) D. Webb, Y. Li, S. Alavi, Trends Food Sci. Technol. 131 , 129 (2022) L. de Paiva, R. Gouvêa, T. Caldeira, de M.C. Lima Azevedo, I. Galdeano, J.R. Felberg, Lima, and C. Grassi Mellinger, Food Hydrocoll. 137, 108351 (2023) M. Lonnie, E. Hooker, J.M. Brunstrom, B.M. 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Kaale, M. Siddiq, S. Hooper, Legum Sci. 5 , 1 (2023) D. Shi, J.D. House, J.P.D. Wanasundara, M.T. Nickerson, Cereal Chem. 99 , 1013 (2022) A.N.A. Aryee, J.I. Boye, J. Food Process. Preserv. 41 , e12824 (2017) S.W. Han, K.M. Chee, S.J. Cho, Food Chem. 172 , 766 (2015) FAO, Dietary Protein Quality Evaluation in Human Nutrition: Report of an FAO Expert Consultation, 31 March-2 April, 2011, Auckland, New Zealand (Auckland, New Zealand, 2013) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4518581","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":315928901,"identity":"640a854f-66c2-4151-bec1-b6140b3e181d","order_by":0,"name":"Elaine Kaspchak","email":"data:image/png;base64,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","orcid":"","institution":"Instituto de Tecnologia de Alimentos (ITAL)","correspondingAuthor":true,"prefix":"","firstName":"Elaine","middleName":"","lastName":"Kaspchak","suffix":""},{"id":315928902,"identity":"e64fda3d-6e4c-477f-8c7e-6f72bb4a8c10","order_by":1,"name":"Anna Paula Muntilha","email":"","orcid":"","institution":"Instituto de Tecnologia de Alimentos (ITAL)","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"Paula","lastName":"Muntilha","suffix":""},{"id":315928903,"identity":"4c7d957a-ead9-4835-95cf-e61485d3d676","order_by":2,"name":"Elizabeth Harumi Nabeshima","email":"","orcid":"","institution":"Instituto de Tecnologia de Alimentos (ITAL)","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"Harumi","lastName":"Nabeshima","suffix":""},{"id":315928904,"identity":"3553bb82-6916-4bc3-af3e-8875a944305a","order_by":3,"name":"Mitie Sônia Sadahira","email":"","orcid":"","institution":"Instituto de Tecnologia de Alimentos (ITAL)","correspondingAuthor":false,"prefix":"","firstName":"Mitie","middleName":"Sônia","lastName":"Sadahira","suffix":""}],"badges":[],"createdAt":"2024-06-03 01:01:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4518581/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4518581/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58649130,"identity":"db9af9f9-25fc-44e4-bc61-381e18872195","added_by":"auto","created_at":"2024-06-19 09:35:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1144081,"visible":true,"origin":"","legend":"\u003cp\u003ePasting (a) and texture (b) profile curves of pea, lentil, faba bean, rice and soy proteins. The visual aspect of each sample after storage are presented in Figure c. Essential amino acids (mg/g of protein) reported by Shi et al. (2022), Aryee \u0026amp; Boye (2017) and Han et al. (2015) of each protein are presented in Figure d. The vertical line shows the reference of amino acids indicated by FAO (2013).\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4518581/v1/0b644a85f05a59695b41f2cc.png"},{"id":58649131,"identity":"e086cda6-8324-4d93-9470-29841ee06bb0","added_by":"auto","created_at":"2024-06-19 09:35:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1480209,"visible":true,"origin":"","legend":"\u003cp\u003ePasting (a, b) and texture (c, d) profile curves of pea, lentil, faba bean, rice and soy proteins binary mixtures. The visual aspect of each sample after storage are presented in Figure e. Essential amino acids (mg/g of protein) of mixtures were calculated based on the values of pure proteins reported by Shi et al. (2022), Aryee \u0026amp; Boye (2017) and Han et al. (2015) for each binary mixture of protein (Figure f). The vertical line shows the reference of amino acids indicated by FAO (2013). Where: Histidine (His), Isoleucine (Ille), Leucine (Leu), Lysine (Lys), Methionine + Cysteine (Met+Cys), Phenylalanine + Tyrosine (Phe+Tyr), Threonine (Thr), Tryptophan (Trp) and Valine (Val).\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4518581/v1/3fcf507542314e743251f6b4.png"},{"id":58649618,"identity":"2643b92d-c27a-4430-aee5-adff3682737a","added_by":"auto","created_at":"2024-06-19 09:43:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1475429,"visible":true,"origin":"","legend":"\u003cp\u003ePasting (a, b) and texture (c, d) profile curves of pea, lentil, faba bean, rice and soy proteins ternary mixtures. The visual aspect of each sample after storage are presented in Figure e. Essential amino acids (mg/g of protein) of mixtures were calculated based on the values of pure proteins reported by Shi et al. (2022), Aryee \u0026amp; Boye (2017) and Han et al. (2015) of each protein are presented in Figure f. The vertical line shows the reference of amino acids indicated by FAO (2013).\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4518581/v1/13e7ffa136738139ebedcfb2.png"},{"id":60506667,"identity":"bd5e56e9-85e9-48cc-b532-d72037afbb7d","added_by":"auto","created_at":"2024-07-17 13:39:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6034013,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4518581/v1/2cb3275a-0b49-4d6a-8860-9f9145598373.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePasting and Texture Properties of Commercial Plant Proteins and Its Mixtures\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAn increasing number of consumers are transitioning to a predominantly plant-based diet. On the other hand, soy and gluten proteins, generally used to produce meat analogous, are avoided due to its allergenicity leading to alternative proteins increasing popularity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Using proteins from different sources can be a viable option both economically and good food amino acids balance and techno-functional properties. Among these legumes are: pea protein and emerging protein sources such as fava beans and lentils [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePlant proteins are generally deficient in essential amino acids compared to animal proteins [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. To solve this problem, the combination of proteins is recommended [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], this is possible due to the wide variety of protein sources [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, it is expected that proteins mixtures presents techno-functional properties different from pure compounds on food products application. Protein techno-functional properties are classified based on the mechanism of action, including hydration-related (solubility, water and oil holding capacity), protein structure and rheological characteristics (gelation and viscosity), and surface characteristics (foam and emulsion) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePlant proteins gelation properties knowledge are essential in food applications where a semi-solid structure is required [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], for example, texturization to produce meat analogues (Lam et al., 2018) and 3D printing of foods [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A gel is a dispersed system characterized by a lack of fluidity and elastic deformability [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The mechanism of globular protein gelation is a complex process that involves partial denaturation of the protein molecule, gradual association or aggregation, and formation of a network [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The protein intrinsic composition determines the intramolecular networks formed during heating and cooling [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Mixtures of proteins may interact with each other during the gelation process to form a mixed network where the mechanical properties is governed by protein\u0026ndash;protein interactions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe effect of pea and rice [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], soy and gluten [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] protein mixtures showed that protein interaction affect the gel properties. This was attributed to changes in protein secondary structure, competition of different proteins for water and impairing in the protein groups involved in gel formation. Related to texturization, different mixture are reported to affect the mass flow behavior and structure of extrudates that depends on protein type and proportion between the components [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Understanding the synergistic behavior among the different protein structures during thermal treatment can help to better define optimum conditions for products and processes [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Besides, investigations into gelation and rheological properties of proteins mixtures for high-moisture meat analogues development are needed to address the incomplete understanding of their potential fibrous structure [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Rapid Visco Analyzer (RVA) is generally used to study pasting properties. The rate of change in protein viscosity with respect to temperature measured by RVA can help to predict the type of networks formed during food processing, avoid flow problems [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and is related to other protein properties, \u003cem\u003ei.e.\u003c/em\u003e water absorption capacity and gelation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. RVA results can be used to study the performance of different proteins in applied products, for example, Woo et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] showed that protein blends RVA results can be used to predict the structural attributes of high-moisture meat analogues. The use of RVA for proteins is relatively new and methods are still under development. The AACC official RVA method have been applied in some studies for proteins, \u003cem\u003ei.e.\u003c/em\u003e run to 95\u0026deg;C, held for a period of time, and cooling to 50\u0026deg;C [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to Stephani et al. (2015), the studies with protein ingredients, the RVA can be interesting due to its similarity to industrial processes (agitation and temperature), and can demonstrate the extent of the interaction between mixtures of components and the effect of changes in physical processing conditions on the proteins functionality.\u003c/p\u003e \u003cp\u003eAnother tool to understand the protein gel properties is the texture profile analysis (TPA). The TPA begins by compressing the sample and as the force increases, the gel responds by deforming. After the first deformation, the force is released and the gel partially recovers given information about the elasticity and recovery of the gel. The sample is compressed again and information about gumminess and chewiness can be assessed [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. By this method the feelings and sensations during food consumption can be simulate [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs described previously, there is a necessity of utilizing a pool of proteins for its application in different products. This may be driven by complementing the amino acid profile, economic factors, or achieving better technological performance. Furthermore, the comprehension of the properties of pasting and gels texture of vegetable protein is essential to understand the technological performance of proteins in foods. Therefore, this work aimed to determine the pasting properties and gel texture of single vegetable proteins and their binary and ternary mixtures adjusted to pH 7. For this purpose, commercial pea, lentil, fava bean, rice, and soy proteins were used.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Material\u003c/h2\u003e \u003cp\u003eThe characteristics of proteins used in this work are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All solutions were prepared with deionized water (Permution, Curitiba, Brazil). NaOH were purchased from Din\u0026acirc;mica (Indaiatuba, Brazil) and HCl from Synth (Diadema, Brazil).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Moisture, pH and protein content of proteins used in this work\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoisture (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtein (% db \u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePea Protein 1853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLentil Protein 2550 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFava Bean Protein P360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRice Protein PQ674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoy Protein Isolate PQ262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003e db: dry basis; \u003csup\u003eb\u003c/sup\u003e Starch content of lentil protein was 12.25% according to the manufacturer.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Methods\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Experimental design\u003c/h2\u003e \u003cp\u003eThe experimental mixture design was employed to study the effect of five variable components made up of vegetable proteins sources: Pea Protein (A); Lentil Protein (B), Fava Bean Protein (C), Rice Protein (D) and Soy Protein Isolate (E) on the response variables: viscosity and texture profile using a protein content of 10%, 5% and 3.33%, corresponding to 0.33; 0.50 and 1.0 proportions and totalizing 24 different combinations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Viscosity Profiles by RVA\u003c/h2\u003e \u003cp\u003eThe pasting properties was determined by viscosity parameters of protein isolates/concentrates using a Rapid Visco Analyser (RVA 4500, Perten Instruments, H\u0026auml;gersten, Sweden) coupled with a water bath at 25\u0026deg;C. It was used 10% of protein in water (w/w), and the suspension pH was adjusted to 7 using NaOH (40%) or HCl (15%). The protein samples were maintained under gentle agitation for 20 min before analysis. The Standard method 2 was employed as describe by Perten (2015). Samples were held at 50\u0026deg;C for 60 s (960 rpm/10 s and 160 rpm for the rest of the analysis), then heated to 95\u0026deg;C at ~\u0026thinsp;9\u0026deg;C/min, held at 95\u0026deg;C for 5 min, cooled back to 50\u0026deg;C at ~\u0026thinsp;5\u0026deg;C/min, and held at 50\u0026deg;C for 2 min. The curves were analyzed using TCW 3.15.1.255 software (Newport Scientifc, Warriewood, Australia). After the tests, the RVA paddle was removed from the container that was covered with polyvinylchloride (PVC) film and stored at 5\u0026deg;C overnight before the texture analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Texture profile analysis (TPA)\u003c/h2\u003e \u003cp\u003eTexture profile analysis was performed on samples obtained after RVA analysis stored for 24 hours at 5\u0026deg;C, according to the methodology provided by AACC International Method 76-21.01. The samples were equilibrated at room temperature for 1h before analysis. Gel hardness was determined using a TA-XT2i texture analyzer (Stable Micro Systems, Godalming, Surrey, UK) at 25\u0026deg;C and the data was analysed by Texture Expert software. Conditions of the test were: pre-test and post-test speed of 2 mm/s; test speed of 0.8 mm/s; strain of 40%; trigger force of 0.049 N. The probe used was an AOAC cylindrical acrylic 25 mm high and the sample stored in the RVA canister was penetrated twice during the test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis of the data was conducted using ANOVA and Tukey's test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) using the Statistica software (14.0.0.15, Tibco, Palo Alto, USA).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eThe results of this work will be discussed in separate sections comprising single proteins, binary and ternary mixtures pasting and gel properties. The investigation of pasting properties by RVA where the protein dispersions hydration, heating and low shear can be evaluated. The gel hardness was obtained by TPA. The texture of samples was also discussed in function of the TPA curve, that typically exhibits two distinct peaks corresponding to the initial compression and subsequent relaxation of the gel during related to the biting process. In addition, visual aspect and essential amino acids profile of different combinations are presented.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Vegetable proteins pasting and texture properties\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb shows the pasting properties and TPA results of different vegetable proteins. It can be seen that proteins presented a distinct behavior due to its different intrinsic composition and structure. The viscosity of pea, lentil and faba bean increased with the heating and cooling. During the heating step, proteins denature increasing the water absorption and swelling in addition to the protein-protein hydrophobic interactions increasing resulting in higher suspension viscosity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The behavior of pea and faba bean proteins RVA curves were similar, however after cooling and storage at 5\u0026deg;C, the faba bean protein gel was harder as observed in the TPA curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) and by its visual aspect (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe viscosity result at 95\u0026deg;C (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) shows the effect of heating on a protein viscosity and the final viscosity Indicates the ability of a sample to form a viscous paste after cooking and cooling [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Lentil (456.5 cP) and soy protein (359.0 cP) presented the highest values of final viscosity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Starch content of lentil protein is 12.25% according to the manufacturer, therefore the highest lentil final viscosity (847.0 cP) may be due to the gelatinization of starch. Despite this, the gel force (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) obtained by texturometer of lentil was smaller than those obtained for faba bean and soy. This can be attributed to the protein influence on starch viscosity due to changes on the swelling of starch granules [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Viscosity at 95\u0026deg;C and final viscosity obtained by RVA and gel force of pea, lentil, faba bean, rice and soy protein\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eViscosity at 95\u0026deg;C (cP)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFinal Viscosity (cP)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGel force (N)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e315.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLentil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e456.5\u0026thinsp;\u0026plusmn;\u0026thinsp;24.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e847.0\u0026thinsp;\u0026plusmn;\u0026thinsp;19.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaba bean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u0026ordf;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e359.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e806.0\u0026thinsp;\u0026plusmn;\u0026thinsp;97.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u0026ordf;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAverages followed by the same letter do not differ significantly in the columns for the same parameter according to Tukey\u0026rsquo;s test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNone of samples showed a peak during heating on RVA curves that can be related to protein concentration. Different consistencies are obtained according to the protein concentration used, \u003cem\u003ei.e.\u003c/em\u003e soft yogurt-like pastes are obtained below 12% of whey protein resulting in no peak in RVA curve [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe lowest viscosity was obtained for rice protein (13.5 cP) that did not form a gel as can be seen in the texture results and by its visual aspect (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). This is expected for rice proteins due to its poor solubility [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed shows that rice protein is deficient in lysine and present a great amount of methionine and cysteine. This makes the rice a complement for pulses proteins that present the opposite behavior. Therefore, of the proteins studied, the rice protein is the most indicated to be used in pulses protein mixtures in order to balance the amino acids profile.\u003c/p\u003e \u003cp\u003eSoy proteins are one of the most applied vegetable protein due to its excellent tech-functional properties, cost and essential amino acids profile [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In this work, differently from other proteins, soy presented a decrease on the viscosity with the increase on temperature and a viscosity rise during cooling step. However, different viscosity profiles for soy proteins can be found in literature [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Commercial soy proteins present a wide variation between them, showing differences in composition and level of protein denaturation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe high initial viscosity is related to the water absorption and swelling upon hydration resulting in a high starting viscosity that decreases with the stirring and heating [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The viscosity reduction during heating is attributed to constant stirring diminishing protein-water interaction, protein aggregate dissociation, and the disruption of physical bonds. Conversely, increased viscosity during cooling is linked to reduced molecular mobility and strengthened attractive forces in protein aggregates through hydrogen bonding and hydrophobic interactions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDuring the gelation process, proteins undergo complete melting and denaturation, resulting in irreversible interactions caused by the exposure of internal hydrophobic regions and sulfhydryl groups [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In this work, the samples obtained after RVA analysis result in a stable gel structure after cooling that can be evaluated by TPA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) in the RVA canister. When the sample is a semi solid, the measure is of back extrusion as compression forces the sample to flow through the annular gap formed between the plunger and the container [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The force-deformation curves provides a visual representation of how a protein gel behaves under controlled forces, helping in the quantification of gel textural force and optimize food formulations [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFaba bean (1.52 N) and soy (1.5 N) showed the higher gel hardness (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Soy protein is largely known by its gelation ability which makes it widely applied in different products such as tofu and meat analogues. Faba bean protein is an unconventional protein that presents great gelation ability forming dense and fine gel network structure at pH 7, along with the high stress and strain at fracture [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Pea and lentil protein showed an intermediate hardness value, 0.75 and 1.0 N, respectively. Pea protein has the ability to form soft gels that contribute to the development and production of beverages similar to dairy drinks, fermented products and curds [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and lentil protein can be used in food texturization based on their good gel mechanical properties [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Rice formed a less stable suspension with lower hardness, and no adhesiveness (negative peak). This is expected since rice do not show a proper gelation due to its low solubility [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec illustrate the difference in the appearance of the gel obtained after RVA experiments and stored at 5\u0026deg;C. Samples presented different consistency that is related to changes in the structure formation as was also observed in the shape of the RVA and TPA curves. The samples that formed stable gels after texture analysis were those containing lentils, fava beans and soybeans proteins. Pea and rice protein dispersion showed phase separation and a more fluid appearance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Pasting and texture of vegetable proteins binary and ternary mixtures\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb presents the RVA curves of pea, lentil, faba bean, rice and soy proteins binary mixtures. Binary mixtures presented a distinct behavior when compared to single proteins. The properties of gels formed by protein mixtures cannot necessarily be predicted by the gel behavior of single protein and new textures can be obtained [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This reflects the complex protein-protein and protein-water interactions, affected by temperature and time. In this study, in general, a decrease in the viscosity profile parameters was observed for binary mixtures when compared to single proteins.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe protein functional properties, \u003cem\u003ee.g.\u003c/em\u003e, swelling, solubility, gelation, and water holding capacity, are directly related to how proteins interact with water [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. When protein-protein interaction occur, aggregation and phase separation can happen [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], this may lead to a decrease in the protein-water interaction decreasing the viscosity of the protein mixture. For example, Bainy et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] showed that binary mixtures of soy protein and gluten SPI can experience a decrease in viscosity profile parameters due to soy protein impairing gluten proteins thiol/disulphide interchanges during thermal treatments.\u003c/p\u003e \u003cp\u003eDifferently from single proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), some binary mixtures viscosity profiles curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea to \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed) presented peaks, \u003cem\u003ee.g.\u003c/em\u003e, pea-soy, lentil-soy and faba bean soy. Bainy et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] found two peak viscosities for soy protein, one at 85\u0026deg;C and another during cooling, that were attributed to denaturation and non-covalent interactions, respectively. Therefore, peaks observed in this work may be attributed to protein interactions and protein coagulation.\u003c/p\u003e \u003cp\u003eMixtures containing rice protein showed the lowest viscosity values (10\u0026ndash;64 cP) when compared to other binary mixtures as can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The mixture of pea and rice resulted in a product with two heterogeneous phases: one liquid and other more compact, with a firmer gel (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). This may be attributed to coagulation of proteins due to pea and rice interaction, in addition to the low solubility of these proteins in pH 7. Therefore, including rice in protein mixtures for application in products where it is necessary to increase protein content without change the fluidity of the product can be recommended. These mixtures also show a good essential amino acids balance since rice is rich in Met\u0026thinsp;+\u0026thinsp;Cys that is deficient in pea, lentil and faba bean (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Viscosity at 95\u0026deg;C and final viscosity obtained by RVA and gel force of pea, lentil, faba bean, rice and soy protein binary mixtures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eViscosity\u003c/p\u003e \u003cp\u003eat 95\u0026deg;C (cP)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinal\u003c/p\u003e \u003cp\u003eViscosity (cP)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHardness (N)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBinary mixtures\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePea-Lentil (PL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.4\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePea-Faba bean (PF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePea-Rice (PR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003edef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePea-Soy (PS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.4\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLentil-Faba bean (LF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e579.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLentil-Rice (LR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003csup\u003efg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLentil-Soy (LS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e368.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e675.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaba bean-Rice (FR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaba bean-soy (FS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e291.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e383.0\u0026thinsp;\u0026plusmn;\u0026thinsp;32.5\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRice-Soy (RS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003csup\u003eefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTernary mixtures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePea-Lentil-Faba bean (PLF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePea-Lentil-Rice (PLR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePea-Lentil-Soy (PLS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e243.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePea-Faba bean-Rice (PLR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePea-Faba bean-Soy (PFS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePea-Rice-Soy (PRS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLentil-Faba bean-Rice (LFR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003csup\u003edef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLentil-Faba bean-Soy (LFFS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e395.5\u0026thinsp;\u0026plusmn;\u0026thinsp;26.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaba bean-Rice-Soy (FRS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAverages followed by the same letter do not differ significantly in the columns for the same parameter and same amount of protein from different sources according to Tukey\u0026rsquo;s test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe highest value of final viscosity (579 cP) was observed for lentil and faba bean. The visual aspect of this samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee) was of a consistent and rigid gel, with a well-defined shape. Higher gel force was obtained when compared to single proteins showing good synergism for this mixture of proteins. Thus, lentil and faba bean protein is recommended for applications in products where harder gels are required such as meat analogues. However, this mixture is poor in Met\u0026thinsp;+\u0026thinsp;Cys and Trp and should not be consider in foods with the appeal of high protein content.\u003c/p\u003e \u003cp\u003eA distinct behavior was also observed for TPA curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) for all samples. A decrease in force during the experiment occurred when compared to single proteins, except for LF mixture. When different proteins are combined in a mixture, molecular interactions occur that affect the structure and texture of the resulting gel changing the TPA curves. Despite the lower viscosity and gel force of PF, LS, FR, FS when compared to single proteins, the gel formation ability was maintained and these binary mixtures can be used as gelling agents (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003eThe amino acids profile of binary mixtures was complete for samples containing rice (RS, FR, LR, and PR) and the PS mixture. From these mixtures, PS showed the highest viscosity along RVA analysis, however, the gel texture value was low. This behavior was also observed for other proteins and its mixtures showing that higher viscosity solutions do not consistently correlate with greater gel strength values. This observation highlights the intricate nature of protein interactions during gel formation and the importance of looking beyond viscosity alone when protein-based food products are formulated. Factors such as protein-protein interactions, hydration, and structural changes play pivotal roles in determining the final gel properties.\u003c/p\u003e \u003cp\u003eMixtures containing rice, despite present complete amino acids profile, need to be improved when applied to products where a high viscosity and gel hardness is required. On the other hand, such mixture can present advantages when applied in products such as beverages that requires a high protein content without increase the viscosity.\u003c/p\u003e \u003cp\u003eThe ternary mixtures of proteins RVA and TPA results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As also observed for binary mixtures, lower viscosity and peak force are observed compared to single proteins. Results showed that other commercial proteins can decrease the initial viscosity of soy protein dispersions and mixture containing lentil generated higher viscosity and harder gels in ternary protein mixtures.\u003c/p\u003e \u003cp\u003eAmong the ternary mixtures studied, the combination of lentil, fava bean, and rice proteins (LFR) exhibited a visually stronger gel aspect, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef. Notably, the LFR mixture stood out due to its higher gel force. Interestingly, rice protein alone typically has low viscosity and gel strength values. However, the synergistic interaction between rice, lentil, and fava bean proteins favored gel formation in the LFR mixture. As shown by Wang et al. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] whey protein isolate and casein interaction with rice protein increase the rice solubility due to the formation of an hydrophilic particulate spheres promoted by mixture alkalinization followed by neutralization. Therefore, this blend represents an ideal balance of amino acids while maintaining favorable gelling properties.\u003c/p\u003e \u003cp\u003eAs observed, proteins mixtures can result better functional properties when appropriate sources are used. These mixtures can also present advantage in sensory properties. Vegetable proteins may not always provide a pleasant sensory experience for consumers, as highlighted by Kaale et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] for lentil protein. Therefore, in protein mixtures, the overall acceptability can be improved while preserving the desirable gel-forming properties. The balance between sensory appeal, amino acids balance and functional performance is crucial for creating protein-based food products that resonate with consumers.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis work aimed to determine the pasting properties and gel texture of single commercial vegetable proteins and their binary and ternary mixtures. In general, protein mixtures presented lower values of final viscosity and texture when compared to single proteins. Samples containing rice protein tend to form less viscous dispersions and after cooling no gel formation occurred. Mixtures where viscosity and gel force increase were those containing faba bean. Despite the lower viscosity and gel force of Pea-Faba, Lentil-Soy, Faba-Rice and Faba-Soy combinations, these mixtures maintain the gel formation ability and can be used as gelling agents. Results presented in this work shows that despite the need of include protein mixture in food products for essential amino acids balance, these mixtures affect the pasting properties and gel texture. Therefore, there is a need to study the impact of protein mixtures on the techno-functional properties of these ingredients to maintain the characteristics expected for food products in addition to the nutritional aspect of a formulation. The presented results shed light on their potential suitability of protein mixtures for various food formulations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eElaine Kaspchak: Conceptualization; Data curation; Formal analysis, original draft writing; Anna Paula Muntilha Guimar\u0026atilde;es Conceptualization; Data curation; Formal analysis; Elizabeth Harumi Nabeshima: Investigation; Methodology; Visualization; Writing - review \u0026amp; editing; Mitie S\u0026ocirc;nia Sadahira: Funding acquisition; Investigation; Project administration; Resources; Supervision, Writing - review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors acknowledge the State of S\u0026atilde;o Paulo Research Foundation (Fapesp) for the financial support to carry out this project (grant number 2020/07015-7) and CNPq PIBIC/PIBITI-Ital.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eK.K. 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Cho, Food Chem. \u003cb\u003e172\u003c/b\u003e, 766 (2015)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO, \u003cem\u003eDietary Protein Quality Evaluation in Human Nutrition: Report of an FAO Expert Consultation, 31 March-2 April, 2011, Auckland, New Zealand\u003c/em\u003e (Auckland, New Zealand, 2013)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"soy protein isolate, rice protein isolate, pulses proteins, protein gelation","lastPublishedDoi":"10.21203/rs.3.rs-4518581/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4518581/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProtein mixtures are usually applied in plant based products development in order to achieve amino acids balance and properly technological performance. Therefore, the aim of this work was to study the pasting and texture properties of commercial proteins commonly used in food products (pea, lentil, fava bean, rice and soybean) and its binary and ternary mixtures. The pasting properties were studied by Rapid Visco Analyser (RVA) and the texture by Texture Profile Analysis (TPA) method using a texturometer. Results showed that protein mixtures exhibit distinct behaviors when compared to single proteins. Single lentil and soy protein presented the highest final viscosity (847 and 806 cP, respectively) whilst the rice the lowest final viscosity (10 cP). Related to texture, faba bean and soy exhibited the highest gel hardness (1.52 and 1.50 N, respectively). For binary and ternary mixtures, in general, the viscosity and texture profiles parameters decreased. Rice-containing mixtures showed the lowest final viscosity (30.5\u0026ndash;62.0 cP), while lentil and faba bean mixtures had the highest final viscosities and gel strengths (579 cP and 1.77 N, respectively). From the ternary mixtures, samples containing lentil, fava bean, and rice displayed superior gel strength (0.9 N) due to a synergistic interaction. This work provides information about vegetable proteins and its mixtures that can be used for a better design of plant based food products.\u003c/p\u003e","manuscriptTitle":"Pasting and Texture Properties of Commercial Plant Proteins and Its Mixtures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-19 09:35:53","doi":"10.21203/rs.3.rs-4518581/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"be21533d-f910-4eae-adf1-6cb8cd8ea5f7","owner":[],"postedDate":"June 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-17T13:31:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-19 09:35:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4518581","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4518581","identity":"rs-4518581","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}