Optimization of a chocolate-flavored, peanut–soy beverage using response surface methodology (RSM) as applied to consumer acceptability data

Optimization of a chocolate-flavored, peanut–soy beverage was done using response surface methodology (RSM). Twenty-eight beverage formulations were processed by mixing three basic ingredients: peanut (X1=30.6 g/100 g–58.7 g/100 g), soy (X2=28.3 g/100 g–43.5 g/100 g), and chocolate syrup (X3=13.0 g/100 g–25.9 g/100 g). The proportions of these ingredients were obtained using a three component, constrained mixture design where the source of soy was either flour (SF) or protein isolate (SPI). Consumer acceptability was measured in terms of nine response variables by 41 consumers using a 9-point hedonic scale. Parameter estimates were determined by performing regression analysis with no intercept option. l-pseudo-components were introduced to get equivalent second degree models further used to generate contour plots. The regions of maximum consumer acceptability [hedonic rating ⩾5.0 since the control (commercial chocolate milk) ratings were 6.0–7.0] were identified and marked on these contour plots for each sensory response. Superimposition of contour plots corresponding to each response variable resulted in optimum regions having consumer acceptability ratings ⩾5.0. Optimum formulations were all the combinations of X1: 34.1 g/100 g–45.5 g/100 g, X2: 31.2 g/100 g–42.9 g/100 g, and X3: 22.4 g/100 g–24.1 g/100 g for SF-based; and X1: 35.8 g/100 g–47.6 g/100 g, X2: 31.2 g/100 g–43.5 g/100 g, and X3: 18.3 g/100 g–23.6 g/100 g for SPI-based beverage formulations.