Gluten-free yeast bread optimization with response surface methodology - Nicomedes, Christal Elgie Caballero (College of Home Economics, University of the Philippines Diliman, 2013)
To help address the need for gluten-free (GF) food items for sufferers of celiac disease and other gluten-related disorders, numerical and graphical optimization processes were employed through RSM in the development of sweet potato (var. Murasaki-29) starch-based GF yeast bread. Based on preliminary studies, the test levels for the two input variables - xanthan gum and water - ranged from 0 to 2% and 67.5 to 87.5% , respectively, while the rest of the formulation was held constant. The response variables were evaluated through descriptive sensory analysis and physicochemical tests, then those significantly affected by variations in the input variables were identified for numerical and graphical optimization. Analysis of the mathematical models representing the relationship between responses and input variables showed that water significantly affected springiness (p < 0.0500), moistness (p < 0.0001), stickiness (p < 0.0500), firmness (p < 0.0001) and crumbliness (p < 0.0001) whereas the primary order-effect of xanthan gum influenced whiteness (p < 0.0500) and height (p < 0.0001). The quadratic term of xanthan gum significantly affected percent gas cells (p < 0.0500) while the interaction of the input variables had significant effect on yeast odor (p < 0.0500). Through numerical optimization, the best combination of xanthan gum and water was found to be 1.52% and 84.57% (starch weight basis), respectively. Based on graphical optimization, the operable region was found to be beyond the selected design space for the study. Discrepancies between predicted and actual responses for the aforementioned variables, except percent gas cells, may be attributed to the poor discriminating ability of panelists across samples as indicated by high p- and low MSE-values.