Predictive Modeling and Optimization of Extrusion Cooking Process for Color Characteristics and Consumer Acceptability of Fortified Rice Snacks
Journal Title: IOSR Journal of Environmental Science, Toxicology and Food Technology (IOSR-JESTFT) - Year 2019, Vol 13, Issue 1
Abstract
Production of extruded snacks from different blends of low grade milled rice and cowpea flours was conducted and extrusion parameters were optimized using response surface methodology (RSM) and desirability function. A 3-factor, 5-levels central composite rotatable design was employed to determine the effect of the process parameters, namely temperature (100-140oC), feed moisture (15-25%) and cowpea composition (8-24%) on colour indices and consumer acceptability. Total of 20 extrusion experiments were performed and data fitted to a second-order polynomial equation through regression analysis. Results showed satisfactory fit of the data with coefficient of determination (R2 ) values of 0.9660, 0.9840 and 0.9520 for L*, a* and b* colour characteristics respectively and non-significant (p>0.05) lack-of-fit test. The optimum conditions of extrusion conditions were: temperature 120oC, 20% moisture and 24% feed cowpea composition which corresponds to optimal L* =16.16, a* = 2.06 and b* = 22.16 recorded from combined desirability function of 0.9940. The acceptability test indicated that the snack produced at the optimal temperature (120 oC) containing the least cowpea composition (2.6%) and extruded at 20% moisture content was highly rated 9.88 on a 15 point scale. Principal component analysis of colour and sensory data shows that the first five principal components (PCs) contributed up to 94.9% of the total variations observed, with PC1 contributing 30%, PC2 responsible for 23.8%, PC3 provides 17.3%, while PC4 and PC5 contributing 15.5 and 8.1% respectively. Thus demonstrating adequacy of RSM model and desirability function to predict optimal process conditions and consumer acceptability.
Authors and Affiliations
Danbaba N, Nkama I, Badau M. H, Idakwo P. Y
Keywords
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- EP ID EP443102
- DOI 10.9790/2402-1301013343.
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