Sensory Profiling and Characterization of Chocolate Peanut Spread Using Response Surface Methodology

Response surface methodology was used to profile and characterize formulations of chocolate peanut spread. A constrained mixture design for 36 different formulations with varying peanut (P), chocolate (C) and sugar (S) was used. A processing variable, roast (R), was included where peanuts were roasted to light, medium and dark levels. A descriptive panel (n = 10) identified and rated 24 attributes, using 150‐mm unstructured line scales. Regression analysis was performed and models were reduced. Models having R2 > 0.70 were selected for prediction. Contour maps were constructed to: (1) visualize the effects of mixture components and roasting level and (2) characterize optimum formulations at light, medium and dark, which were determined as (30–49% P, 23–40% C and 21–31% S); (29–65% P, 0.9–41% C and 17–36% S) and (27–56% P, 19–45% C and 18–35% S), respectively, adding up to 100% of the mixture. Analyses of optimum and nonoptimum formulations and significant differences were not found between predicted and observed values for most attributes.