The aim of the paper is to propose an alternative method to external preference mapping for the case of complex data where explanatory variables are organized in meaningful blocks. We propose an innovative method in the multiblock modeling framework, called multiblock Redundancy Analysis. The interest and relevance of this method is illustrated on the basis of a European consumer preference study for cold-smoked salmon. The study aims at explaining six homogeneous clusters of preference with explanatory parameters organized in five thematic blocks related to physico-chemical measurements, microbiological characterization, appearance attributes, odor/flavor characterization and texture descriptors. Overall indexes and graphical displays associated with different interpretation levels are proposed to sort the key drivers of preference by order of priority at the variables and at the block level. On the basis of these data, multiblock Redundancy Analysis is also compared to standard preference mapping in terms of model quality; the best model is here associated with the multiblock method.