Relating a sensor or instrument’s odor response to an animal’s perception of the odor remains an elusive goal of neuroscience, and for the neuromorphic approach to artificial olfaction. Several hurdles must be crossed before realizing this goal. Clearly, in the first stage of this process, correlations between odorant molecules and perceptions are only possible if the sensing instrument captures information about physiochemical properties (e.g., functional group, carbon chain-length) to which biological receptors have affinity.
A recent study by Raman and Gutierrez-Osuna92
has made an attempt to match chemical features with high level descriptors using infrared (IR) spectroscopy. Treating the IR absorption at a particular wavenumber as a “pseudosensor”, the authors created high-dimensional inputs that were subsequently reorganized into compact, spatial odor maps using a feature-clustering scheme that mimics the chemotopic convergence of receptor neurons onto the olfactory bulb (see Figure ). Cluster analysis of the generated IR odor maps revealed chemical groups with members that have similar perceptual characteristics, for example, fruity, nutty, and so forth. Further, the generated clusters match those obtained from a similar analysis of olfactory bulb odor maps36
obtained from rats for the same set of chemicals.
Figure 6 Artificial odor maps show how sensor responses can be linked to perceptions. Top: Chemicals associated with 10 different smell percepts predicted from their infrared absorption spectra were organized into these maps using the chemotopic convergence model (more ...)
An approach similar to that proposed by Raman and Gutierrez-Osuna92
has been independently proposed by Schumaker and Schneider.93
Here, odor maps were created for a larger set of odorants using 184 molecular descriptors. Given the spatial map of the odor, a panel of two-class naive Bayes classifiers, one for each odor quality (e.g., fruity vs nonfruity), predicted the overall percept.
In another effort, Mamlouk and colleagues analyzed the relationships between 851 chemicals based on 278 organoleptic odor descriptors (e.g., fruity, sweet, etc.) found in the Aldrich Flavor and Fragrances Catalog.94
Using multidimensional scaling and 2-D self-organized maps in series to reduce the dimensionality of the data, they created a 2-D topological map of the odor space. This work provides another approach to relate olfactory perception with the physiochemical properties of the molecules.