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Formulation of a soy-coffee beverage by response surface methodology and internal preference mapping

Felberg, Ilana, Deliza, Rosires, Farah, Adriana, Calado, Eronica, Donangelo, Carmen Marino
Journal of sensory studies 2010 v.25 no.s1 pp. 226-242
analysis of variance, attitudes and opinions, cluster analysis, coffee beans, consumer acceptance, consumer information, ingredients, instant coffee, least squares, powders, response surface methodology, risk, soymilk, sugars
Coffee consumers (n = 60) tasted and rated samples of a new soy-coffee beverage made from instant coffee, soymilk powder and sugar. Ingredient concentrations (independent variables) varied according to a 2³central composite design for overall degree of acceptance. Data were analyzed by analysis of variance (ANOVA), least square difference and response surface methodology, followed by internal preference mapping (IPM) with cluster analysis. ANOVA from the consumers' acceptance data revealed that samples differed significantly (P [less-than or equal to] 0.05). Although soymilk content did not influence significantly the consumers' acceptance in the tested range, IPM with cluster analysis indicated that at least part of the acceptance differences was based on the soy beverage consumption habit. The final beverage formulation was evaluated cold and hot for overall acceptability (9-point structured hedonic scale) by 112 coffee consumers and the cold beverage reached a good acceptability mean score (6.2) among the participants. The consumption of soy products has been reported to reduce the risk of several diseases and a number of recent studies have found beneficial health properties attributed to coffee. Considering the current consumer trend for healthier alternatives in food products, we decided to combine the health benefits of these two important Brazilian commodities in a functional beverage. In order to optimize the formulation and maximize sensory acceptance, we performed consumers' tests using response surface methodology. Internal preference mapping and cluster analyses were also applied to provide information on the variability of the consumer individual opinions and segment them in groups of similar preference criteria.