Abstract
Sensory analysis has an important impact on food production since its results can help the understanding of consumers’ perceptions about the products. Thus, many methods have been proposed and applied to quantify sensory attributes of food products. In this paper we proposed a methodology, using Kohonen's Self-Organizing Maps and K-means algorithm, to classify food samples through the responses, provided by human evaluators, for their attributes such as aroma, flavor, appearance and texture. Conducted experiments in sensory analysis to determine the acceptance of new gelatins produced from chicken feet and new wines produced from spares of Açaí and Cajá confirm that proposed methodology is suitable for the investigated purpose.
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