1982
DOI: 10.1126/science.7134974
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Odor Quality: Semantically Generated Multidimensional Profiles Are Stable

Abstract: Odors of ten compounds were characterized by approximately 150 subjects who used a list of 146 descriptors. Duplicate profiles correlated highly (P less than .001) and consistently higher than profiles of different odors. Profiles also agreed with those obtained previously. Thus, profiles based on combined responses of many subjects are stable constructs.

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Cited by 128 publications
(103 citation statements)
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“…Here, rather than moving from chemistry to perception, we went from perception to chemistry and probed a large set of physicochemical descriptors using an unbiased statistical approach. In this, we have followed in the footsteps of Schiffman (1974Schiffman ( , 1977, Amoore (1963), Dravnieks (1982Dravnieks ( , 1984Dravnieks ( , 1985, and others, that together laid the groundwork for this approach between the early 1950s to late 1970s (Amoore, 1963;Laffort and Dravnieks, 1973;Schiffman, 1974). In this respect, the contribution of the current work is in observing that the perception of pleasantness corresponded to a physicochemical axis that was the best single discriminator of molecules that have a smell.…”
Section: Discussionmentioning
confidence: 97%
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“…Here, rather than moving from chemistry to perception, we went from perception to chemistry and probed a large set of physicochemical descriptors using an unbiased statistical approach. In this, we have followed in the footsteps of Schiffman (1974Schiffman ( , 1977, Amoore (1963), Dravnieks (1982Dravnieks ( , 1984Dravnieks ( , 1985, and others, that together laid the groundwork for this approach between the early 1950s to late 1970s (Amoore, 1963;Laffort and Dravnieks, 1973;Schiffman, 1974). In this respect, the contribution of the current work is in observing that the perception of pleasantness corresponded to a physicochemical axis that was the best single discriminator of molecules that have a smell.…”
Section: Discussionmentioning
confidence: 97%
“…To reduce the dimensionality of perceptual descriptors obtained previously by Dravnieks (1982Dravnieks ( , 1985 and of physicochemical descriptors obtained using Dragon software (Talete, Milan, Italy), we used the principal component (PC) function in the Statistics Toolbox of Matlab (Mathworks, Natick, MA). In brief, principal component analysis (PCA) takes a data set consisting of N points in an M-dimensional space [for example, 160 odorants in the 146-dimensional odor descriptor space, in the case of the Dravnieks' (1982Dravnieks' ( , 1985 data] and finds a rotation matrix which rotates the N points onto a new M-dimensional space with certain special properties. These are (1) that the new dimensions are orthogonal and (2) that the new dimensions, called principal components (e.g., PC1, PC2, .…”
Section: Methodsmentioning
confidence: 99%
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“…Previous studies implied that gross olfactory perception is similar across individuals (25), and to test whether this was true here as well, we calculated the mean rating along each descriptor for each odorant, and then calculated the correlation of each individual with the mean. Note that here we are not comparing fingerprints, but rather ratings along specific descriptors; this revealed that individuals are indeed similar to each other in their gross perception.…”
Section: Olfactory Fingerprints Were Based On Matrices Of Perceived Omentioning
confidence: 99%