Abstracts - Association for Chemoreception Sciences

study was to develop a paradigm for testing declarative memory
processes with regard to odors. Therefore, we implemented
three memory tasks in E-prime 2.0 presentation software. With
help of an odor-place associative memory task we investigated
memory of odor-place combinations. During an odor item
recognition task the subjects were asked if they had experienced
several odors during the previous task. As a control task for the
first task a picture-place associative memory task was utilized.
Each of those tasks contained two phases – an encoding phase (1
block) and a retrieval phase (4 blocks) – and were applied to 17
healthy subjects. The results suggest that the subjects were able
to learn during the encoding as well as during the retrieval phase.
We found higher odor-place associative, odor recognition, and
picture-place associative memory performance scores at the end
compared to the beginning of the retrieval phase. We thus claim
that our paradigm renders useful during the investigation of
olfactory memory processes and the influence of external factors
on those processes in humans. We here present a fully automated
paradigm, which can be utilized during future functional imaging
studies with the goal to shed light onto the neural network of
human olfactory memory processes. Acknowledgements: This
research was funded by a startup grant from the Medical Faculty
of RWTH Aachen University.
groups of at least 10 Ss on over a dozen such materials.
We used the CPL protocols that have given stable results and
required no normalization. The outcome fell in line with the
data for the hundreds of materials, with neither more nor less
variance. It adds evidence that the LSER has universal relevance
to predict potency. Since the solvation-relevant parameters
used for prediction exist for thousands of materials, the LSER
may afford more accessibility and accuracy than values in
compilations. Acknowledgements: Supported by International
Flavors and Fragrances.
#P130 POSTER SESSION III:
TRIGEMINAL; HUMAN OLFACTORY
PSYCHOPHYSICS; TASTE PERIPHERY
Categorical Dimensions of Human Odor Descriptor Space
Revealed by Non-Negative Matrix Factorization
Jason B Castro 1 , Arvind Ramanathan 2 , Chakra S Chennubhotla 3
1
Bates College, Department of Psychology, Program in Neuroscience
Lewiston, ME, USA, 2 Oak Ridge National Laboratory Oak Ridge, TN,
USA, 3 University of Pittsburgh, Department of Computational and
Systems Biology Pittsburgh, PA, USA
#P129 POSTER SESSION III:
TRIGEMINAL; HUMAN OLFACTORY
PSYCHOPHYSICS; TASTE PERIPHERY
Fragrance Materials Extend the Range of a Model for
Odor Potency
William S Cain 1 , Roland Schmidt 1 , Jorge E Cometto-Muñiz 1 ,
Sang Park 1 , Craig B Warren 1 , Michael H Abraham 2 , Matthias Tabert 3
1
Chemosensory Perception Lab (CPL) La Jolla, CA, USA, 2 University
College London / Chemistry London, United Kingdom, 3 International
Flavors & Fragrances Union Beach, NJ, USA
Olfactory research offers few trustworthy odor thresholds.
Bad for the field, the situation sets nonexperts in need of the
information adrift. A rule of thumb says to choose the lowest
threshold of a set because studies with the best stimulus control
and some measure of concentration normally yield the lowest
values. Nagata et al. over years produced the largest set of
thresholds gathered with coherent methodology and analytical
verification. They lie below most others. Abraham et al. used a
well-established linear solvation energy relationship (LSER) to
describe the set. To strengthen the position, they incorporated
sets from the CPL and Hellman. Data obtained via olfactometer
in recent years from the CPL needed no normalization with the
Nagata set, whereas those from Hellman and earlier squeezebottle
data from the CPL did. The LSER accommodated these
via indicator variables (factors) to bring the data into line and
described potency for 353 odorants. Although the equation left
about 25% of variance unaccounted for, it offers the strongest
statement yet for prediction. Until now, no sets included
fragrance materials, such as patchouli oil or vanillin, known for
potency. Without them, one could argue that the equation might
describe local rather than universal rules. We accordingly ran
Recent studies using Principal Components Analysis (PCA)
support low-dimensional models of odor space, in which one or
two dimensions – with hedonic valence featuring prominently –
explain most odor variability. Here we use non-negative matrix
factorization (NMF) – a nonlinear optimization method - to
discover an alternative, reduced-dimensional representation
of the Dravnieks odor database(144 odors x 146 descriptors).
We first divided the dataset into training and testing halves,
and found that RMSD testing error attained a minimum for
subspace choice of 25, motivating this as an upper bound for
odor perceptual space dimensionality. More parsimonious
representations were found by comparing reconstruction errors
(fraction of unexplained variance) of NMF with reconstruction
errors of PCA on scrambled data (PCAsd). For subspace
sizes > 10, NMF error was indistinguishable from PCAsd
error, indicating no gain in retaining more than 10 perceptual
dimensions. As is typical of NMF basis sets, the 10 odor
dimensions we obtain are sparse (only a small subset of the
146 descriptors apply), and categorical (represent a positive
valued quality). Moreover, these 10 dimensions were nearorthogonal,
with a mean angle of 73 degrees between all pairs
of basis vectors. Investigating the distribution of odors in this
10-dimensional space, we find marked clustering, with each
odor clearly defined by its membership in a single dimension, to
the exclusion of others. Members of each cluster have notable
structural homology, which we quantified as correlations among
physiochemical descriptors of odors in each cluster. In sum, we
describe a representation of odor perceptual space consisting of
10 discretely occupied dimensions that apply categorically. We
propose that this may help elucidate the natural axes of olfaction.
Acknowledgements: NIH GM086238 (CSC)
POSTER PRESENTATIONS
Abstracts are printed as submitted by the author(s).
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