Comparison of Magnitude of Effect of Hydrocolloid on Functional Properties of Flours and Starches of Cassava And Maize Using Multinomial Logistic Regression


Authors : Virate J. Kiprop; Mary N. Omwamba; Symon M. Mahungu

Volume/Issue : Volume 7 - 2022, Issue 9 - September

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3earanQ

DOI : https://doi.org/10.5281/zenodo.7141253

Modification of starches and flours from underutilized food crops to obtain some specific desired functional properties is increasingly becoming necessary practice in food processing. The objective of this study was to compare the magnitude of effect of gum Arabic hydrocolloid substitution level on textural, physical and pasting properties between cassava and maize flours and starches. Pasting properties were determined using a Brabender viscograph, textural characteristics of the gels were analyzed using a texture analyzer while various methods were used to measure the physical characteristics of the pastes. Multinomial Logistic Regression (MRL) model was used to compare the effect of magnitude of gum Arabic functional properties on cassava versus maize. Maize starch had significantly highest level of firmness and consistency and also significantly lowest cohesiveness after gelling as compared to other materials. Maize flour and cassava starch did not differ significantly for all the textural properties. Each of the substitution levels did not have a significant difference on the onset pasting temperature in flours and starches for both cassava and maize. Cassava flour showed significant higher values of the selected physical properties as compared to other materials under study. From the results gum Arabic could be used to modify the various properties of flours and starches to achieve desirable qualities. The study recommends incorporation of this hydrocolloid into specific food products based on the aforementioned flours and starches.

Keywords : Functional properties, cassava flour and starch, maize flour and starch, gum Arabic, Multinomial Logistic Regression

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