Data mining-based method for identifying discriminant attributes in sensory profiling

Selection of attributes from a group of candidates to be assessed through sensory analysis is an important issue when planning sensory panels. In attribute selection it is desirable to reduce the list of those to be presented to panelists to avoid fatigue, minimize costs and save time. In some applications the goal is to keep attributes that are relevant and non-redundant in the sensory characterization of products. In this paper, however, we are interested in keeping attributes that best discriminate between products. For that we present a data mining-based method for attribute selection in descriptive sensory panels, such as those used in the Quantitative Descriptive Analysis. The proposed method is implemented using Principal Component Analysis and the k-Nearest Neighbor classification technique, in conjunction with Pareto Optimal analysis. Objectives are (i) to identity the set of attributes that best discriminate samples analyzed in the panel, and (ii) to indicate the group of panelists that provide consistent evaluations. The method is illustrated through a case study where beef cubes in stew, used as combat ration by the American Army, are characterized in sensory panels using the Spectrum protocol.