ANALYSIS OF A SYSTEM OF DESCRIPTION OF ODORS BY MEANS OF 4 DIFFERENT MULTIVARIATE STATISTICAL-METHODS

In order to analyze the relationships among 32 descriptors of odors (notes), similarity coefficients were calculated using a data bank of 628 odoriferous products. The simple examination of the similarity matrix (32,32) has shown notes selectively and strongly associated (e.g. camphoraceous - piney and musky - powdery) and others less selectively associated (e.g. floral, green and herbaceous). This analysis was completed by four multivariate statistical methods. Non-linear mapping (NLM) proved to be more efficient than principal coordinates analysis for planar representation of olfactory notes, and has given results similar to those previously obtained using other data and other methods (similar disposition of notes around the central note 'floral'). Furthermore, the ascending hierarchical taxonomy and the minimal spanning tree were coherent with the NLM representation. These three methods complete each other and constitute a convenient system to analyze odor descriptions.