Split-plot Central Composite Experimental Design Method for Optimization of Cake Height to Achieve desired Texture

Abstract

In many industrial experimental situations, the levels of certain factors under investigation are much harder to change than others due to time and/or cost constraints. An appropriate approach to such situations is to restrict the randomization of the hard-to-change (HTC) factors, which leads to a split-plot structure. This work designs and conducts a split-plot central composite experiment for optimizing cake height using oven temperature(Factor A) as the HTC factor, amount of flour (B), baking powder (C), and amount of milk (D) as the easy-to-change (ETC) factors. A second-order split-plot central composite design (CCD) model was fit to the generated data and analyzed using generalized least squares (GLS). A stationary point, which gives optimum cake height, was then determined. The results show that main effects of oven temperature, flour, baking powder, and milk were highly significant on the cake height (P<0.01) . Their quadratic effects were also significant except that of the flour. The flower/baking powder interaction effect was significant. The fitted model accounted for about 95% of the total variability in the cake height data. The observed optimum cake height was ̂y=11.047cm at a stationary point: A=250 oC, B=180.75gm, C=6.75gm, and D=194.06gm . This study has established the potentials of response surface experiments in optimizing products in food industries.