Optimization of a Chocolate Peanut Spread Using Response Surface Methodology (Rsm)

Abstract

Response surface methodology was used to optimize formulations of chocolate peanut spread. Thiq-six formulations with varying levels of peanut (25-90%). chocolate (570%) and sugar (555%) were processed using a three-component constrained simplex lattice design. The processing variable, roast (light, medium, dark) was also included in the design. Response variables, measured with consumers (n = 60) participating in the test, were spreadability, overall acceptability. appearance, color, flavor, sweetness and texture/mouthfeel, using a 9-point hedonic scale. Regression analysis was performed and models were built for each significant (p < 0.01) response variable. Contour plots for each attribute, at each level of roast, were generated and superimposed to determine areas of overlap. Optimum formulations (consumer acceptance rating of 2 6.0 for all attributes) for chocolate peanut spread were all combinations of 29-65% peanut, 9-41 % chocolate, and 17-36% sugar, adding up to loo%, at a medium roast. Verification of two formulations indicated no difference between predicted and observed values.

New chestnut-based chips optimization: Effects of ingredients

“…The chips made with the optimized level of ingredients were prepared and evaluated by trained judges. The results of the paired t-test (p 0.05) indicate that there are no significant differences between predicted and observed values for different attributes validating the optimization results, according to Chu and Resurreccion (2004), Holt, Resurrecion, and McWatters (1992), Nath and Chattopadhyay (2007) and Seog et al (2008).…”

“…Experimental design points were determined by the extreme vertices method (Chu & Resurreccion, 2004). The minimum number of points needed to estimate the parameters for the quadratic model can be determined through the following equation:…”

“…RSM was successfully applied to optimize the onion biscuit baking process and many polynomial models developed by RSM were highly effective at describing the relationship between the studied factors and the response (Seog, Kim, & Lee, 2008). This method was also used to optimize formulations of chocolate peanut spread (Chu & Resurreccion, 2004), malted milk beverage (Singh, Tiwari, Singh, Tyagi, & Arora, 2008), extruded snack food (Thakur & Saxena, 2000), reduced calorie tropical mixed fruits jam (Abdullah & Cheng, 2001), robusta coffee (Mendes, de Menezes, Aparecida, & da Silva, 2001), pasta from rice brokens (Raina, Singh, Bawa, & Saxena, 2005). Castro et al (2003) used a complex constrained simplex method to optimize a ternary mixture of protein ingredients used to prepare a milk drink.…”

Combining Cluster Analysis, Surface Response Methodology and JAR Scales to Increase Consumer Input in Optimizing Acceptability of a High‐Protein Soy Dessert

“…Several statistical techniques have been developed for consumer‐orientated food product optimization (Gacula ). Among them, response surface methodology (RSM) has been successfully applied to obtain the best possible formulation that maximizes consumer acceptance given a fixed set of ingredients in different type of foods (Chu and Resurrección ; Gan et al . ; Villegas et al .…”

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

“…In a recent study by Chu and Resurreccion (2004), a chocolate peanut spread was developed and optimized using consumer‐acceptance studies. The major ingredients in a chocolate peanut spread – peanut (P), chocolate (C) and sugar (S) – were combined to obtain a spreadable product using proportions of ingredients in a mixture design.…”