ABSTRACT.Eight samples of dried tomato soups were profiled by a trained panel, who also provided ratings of overall liking and liking for particular attributes on relative‐to‐ideal rating scales. A consumer panel tested a subset of four of the soups in home trials, assessing them for overall liking and for particular attributes on either hedonic or relative‐to‐ideal rating scales.The pattern of overall preferences differed between the trained and untrained panelists, demonstrating the inappropriateness of using trained panelists to provide measures of preference or acceptance. The conclusions regarding consumer preferences would differ depending on which rating scale was used; these differences disappeared when the relative‐to‐ideal ratings were converted into values of the same form as the hedonic ratings. The overall liking was best predicted by flavor rather than color or thickness.A principal components analysis (PCA) of the profile data compared well with a plot based on the trained panel preference data using MDPREF. The preference data were also fitted to the PCA dimensions using the PREMAP vector model, which gave a good fit for only six of the 15 trained panelists; the PREMAP ideal point model failed to show a better fit. In order to test these models adequately more than eight samples would need t