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

作者：Anzanello Michel J; Fogliatto Flavio S^*; Rossini Karina

来源：Food Quality and Preference, 2011, 22(1): 139-148.

DOI：10.1016/j.foodqual.2010.08.010

• Attribute selection
• Discriminant attributes
• Sample classification
• Data mining tools

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 illust

• 出版日期2011-1