scholarly journals Effects of Enzymatic Hydrolysis of Fava Bean Protein Isolate by Alcalase on the Physical and Oxidative Stability of Oil-in-Water Emulsions

2019 ◽  
Vol 67 (23) ◽  
pp. 6625-6632 ◽  
Author(s):  
Chang Liu ◽  
Mamata Bhattarai ◽  
Kirsi S. Mikkonen ◽  
Marina Heinonen
Author(s):  
Ng Pei Qi ◽  
Nor Hayati Ibrahim ◽  
Azlin Shafrina Hasim

Biopolymer interaction in oil-in-water (o/w) emulsions has been demonstrated to positively modify the emulsion physicochemical properties which lead to desirable stability. The present work focused on the effect of pea protein isolate (PPI), pectin, carboxymethyl cellulose (CMC) and their interaction on physicochemical properties and oxidative stability of o/w emulsions using a mixture design approach. The emulsions were prepared with 40 % sunflower oil stabilized with 1 % of PPI, pectin and CMC, respectively, as well as their mixtures according to a simplex-centroid design (10 points). The pH values for all emulsions were within acidic condition (3.22 to 4.66) and increased significantly (p<0.05) as the PPI-CMC level increased. Regression modelling revealed that ternary mixture of PPI-pectin-CMC had the strongest significant (p<0.05) synergism on 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity (85.06 to 91.17 %). Besides, interaction between PPI and CMC significantly (p<0.05) reduced the interfacial tension and at the same time thickened the interfacial membrane to provide the emulsion with desirable small droplet size (10.56 μm). This synergistic interaction effect also significantly (p<0.05) improved oxidative stability of the emulsion resulting in low total oxidation value (<7) due to decreased oxygen transportation rate across the thick interfacial membrane surrounding the emulsion droplets. Moreover, with high coefficients of determination (R2 > 96%) and insignificant lack of fit (p>0.05) of the fitted models, this study also proved that the mixture design with regression modelling was useful in elucidating PPI, CMC and pectin interactions and also able to empirically predict the responses to any blend of combination of the components.


2020 ◽  
Vol 101 ◽  
pp. 105517 ◽  
Author(s):  
Jiang-Ning Hu ◽  
Han Zheng ◽  
Xian-Xin Chen ◽  
Xiang Li ◽  
Yu Xu ◽  
...  

2017 ◽  
Vol 14 (1) ◽  
pp. 193-200
Author(s):  
Leila Rostammiry ◽  
Mohammad reza Saeidiasl ◽  
Reza Safar ◽  
Rohollah Javadian

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