Archiv: Forschung / archive: research
|Zurück / back|
While considerable effort has been spent trying to relate the chemical structure of odorants to odor perception, very little is yet understood about the larger organization of human odor perception. Instead, both the perfume industry and olfactory researchers rely on pure data catalogues such as Dravnieks' Atlas of Odor Character Profiles or other compilations which describe the odors elicited by a particular chemical, e.g. Hexyl Butyrate, using a set of odor descriptors, e.g. fruity, sweet and pineapple.
Over the last several years the relationship between the odor descriptors found in these data sets has been examined in an attempt to determine whether they might reveal an underlying structure in human odor perception. Initial efforts using an information theoretic approach suggested that, in fact, there might be an overall organization to the space of human olfactory perception.
In this project we extend this effort using a combination of several mapping techniques. The structures revealed could provide scientists a more rigorous way to select odorants for human psychophysical experiments as well as a more solid foundation for systems, cellular and molecular studies of the biology of olfaction.
We designed a special semi-metric measure that appeared to be most
sufficient for olfactory data. Using this measure we were able to
extract dissimilarity information out of the data.
We used Multidimensional Scaling (MDS) to extract information for a reasonable map from this odor dissimilarity matrix. A dimension of about 32 appeared to be a good estimation in terms of the trade-off between a high and statistically stable stress relaxation and the highest sustainable dimensional reduction.
To visualize the (still) high dimensional results of MDS on a low dimensional map, we used a two-dimensional self-organizing mapping, which preserves the general topology of the odor data, i.e. if two points are close-by on the map, they should be close-by in the high dimensional input space as well.
In this project, we try to express relationships between odors using techniques which generate a solid topology conserving map. The approximation via MDS provides strong quantitative support for the long held belief that olfactory perception space is high dimensional. The resulting maps also allow us to order odor perception in a way not previously possible.
We believe that these maps can provide a new foundation for studies of the olfactory system that is related to the structure of olfactory perception rather than strictly based on the structure of chemical compounds. Many efforts to construct an understanding of olfactory perception based primarily on structural differences in chemical compounds have failed. Using the techniques described here, relationships between single odors can be quantified in a fundamentally new and more rigorous way. New questions can also be asked about the molecular, cellular and systems biology underlying olfactory perception.