The selection of attributes from a group of candidates to be assessed through sensory analysis is an important step when planning sensory panels. When selecting attributes, it is desirable to reduce the list of those to be presented to panelists in order to avoid fatigue, however keeping the attributes that are relevant to the sensory characterization of samples. This paper presents a multivariate method for attribute selection in descriptive sensory panels, such as those used in the QDA (Quantitative Descriptive Analysis) and Spectrum protocols. The proposed method is implemented using Principal Component Analysis and Descriptive Analysis, and it is evaluated in a case study where beef cubes in stew, used as combat ration by the American Army, are characterized in sensory panels through the Spectrum method. The method significantly reduced the number of attributes to be considered in sensory panels, while yielding satisfactory accuracy in the classification of samples.
Attribute selection; Sensory evaluation; Multivariate analysis
