PRINCIPAL COMPONENTS AND CLUSTER ANALYSIS FOR DESCRIPTIVE SENSORY ASSESSMENT OF INSTANT COFFEE

Abstract

ABSTRACTThe relationships among 13 aroma, flavor, mouthfeel and appearance variables for 18 soluble coffees were analyzed using flavor profiling. Three‐way ANOVA showed significant main effects for coffees and judges in all attributes. The data were submitted to principal component analyses (PCA) and cluster analysis (CA). Two sequential PCA were performed. The first PCA showed that flavor, bitterness and duration were the most important descriptors positively correlated with the first PC, while the variation in appearance properties dominated the second PC, negatively correlated with these attributes. Five attributes were eliminated and a subset of 8 variables was submitted to a second PCA. The meaning of the first two PC remained unchanged and, as expected, the total variation explained by the first four PC increased. Frequency of positive and negative judgments in both PC allowed to separate coffees into four categories.Confirming the choice of the variables, the CA revealed similar distribution of coffees into four clusters. Aroma, flavor and mouthfeel attributes seemed to play a more important role in the determination of clusters than the appearance variables.