I have really met my match. In the world of smell, where
language is a game more than a utility, there is one group of researchers who
have finally said
f*** it. They
took all the words out, smashed all the molecules together, presented a bunch
of people with their sniff panel, and recorded the responses. (This study is
from 2013, but still worth writing about, since this is pretty important point
in smell science.)
And it worked. They found that we don't smell molecules; we
smell mixtures of molecules. In their words: "The algorithm that worked
best was one that treats the odor-mixture as a single value, rather than a
bunch of values reflecting each of its components."
They also found that "Pleasantness is the primary odor
dimension in human olfactory perception," but we already knew that.
The Study
They use mixtures of 1 to 43 different components, making
each 191 mixture-pairs in total, each having 1433 physiochemical descriptors
(via the Dragon dataset), and gave them to 48 people.
The Findings: Pairwise Distance Model for Predicting
Odorant-Mixture Similarity
They pit the molecular mixtures against each other and have
people rate their similarity.
Again, "We found that the mean pairwise Euclidean
distance over all the descriptors of all mono-molecular components comprising
any two mixtures was a poor predictor of perceptual similarity between the two
mixtures." But it gets even better, because apparently they're saying that
the weak predicting capacity is because the data is screwed up by the
monomolecules' comparisons to themselves! Maybe I'm wrong here, but I think
they're saying people gave different
ratings for the same pairs of molecules at different times in the test, and
those ratings changed so much from time to time, that they make the model no
good.
The Findings: Angle Distance Model for Predicting Odorant-Mixture
Similarity
This is the meat of the study. They came up with a
statistical regime to turn the odor mixtures into a perceptual whole, that way
it could be manipulated as if they it was an individual odor.
Just about every smell-science experiment like this will use
odors that have been very carefully isolated – single molecules with single
names (let's not kid anyone here, any particular molecule can have a dozen
different names, from the local vernacular to the formalized IUPAC
designation). The point is that molecules are typically isolated. Because
science likes that. Lumping molecules together is messy. But that's also how we
interface odors in the real world.
So they made this study more like the real world. They take
all the physiochemical components of each molecule, add them together, and
divide by the norm. That makes the mixture-odor as if it were a single odor,
with a single vector (a single point in multi-dimensional odor-space). And this
was the model that worked.
If you take its higher logical plateau, you end up with
Olfactory White, one of
the most mindbending osmological facts you'll ever comprehend: if you add
enough molecules together, it doesn't make "brown paint" like colors
do, it makes the mixture smell like nothing. Sure enough, these researchers
found that the more components you add to the mixture, the closer the mixture
gets to every other mixture (approaching 30 components).
A very important note here is that these odor mixtures were
made to be all the same intensity. (This was done in the Olfactory White study
as well.) Odors can have very different perceived intensities, and it's more
than unlikely that this would ever happen in the wild. I can't help but get
into some quick industrial hygiene here: the
odor detection threshold
for Ammonia is 50ppm; Acetone 100ppm, Trimethyl Amine (rotten fish) 0.0002ppm,
Hydrogen Sulfide (rotten eggs) 0.005ppm.
Another extension of this study is maybe not so logical, but
certainly an important point in smell science: we can't identify individual
components of a mixture of only 4 components. You think you know what peanut
butter smells like. And pineapples, and cinnamon. But if you add one more thing
to that mixture, they all fall away, losing their identify on your great
epithelial equalizer.
Conclusion
"The olfactory system treats odorant-mixtures as
unitary synthetic objects, and not as an analytical combination of
components."
Limitations
Being that they have three clear limitations, they should be
included here:
1. The mixtures were
intensity-normalized. This is not natural, because perceived odor-intensity
changes drastically across odors. See mention above.
2. The odorants represent only a
limited portion of olfactory perceptual space (not much we can do about that,
since it's kind of infinite).
3. Many physicochemical features
such as boiling point or vapor pressure remain unrepresented (they narrowed
down the features from ~4,000 to 25).
Post Script
If you don’t know what the word steganography means, you do know:
camouflage. Think about it – you don’t like broccoli? Blend it with enough
other smells and you won’t even notice!
Olfactory White also its own name, and it’s called Laurax.
Notes
Semantic free approach to structure-odor prediction, general
perceptual primaries rather than individual odorant primaries:
Predicting odor perceptual
similarity from odor structure.
Snitz K, Yablonka A, Weiss T,
Frumin I, Khan RM, Sobel N
PLoS Comput Biol. 2013;
9(9):e1003184.
Olfactory White:
Weiss T, Snitz K, Yablonka A,
Khan RM, Gafsou D, et al. (2012) Perceptual convergence of multi-component
mixtures in olfaction implies an olfactory white. Proc Natl Acad Sci USA 109:
19959–19964.
Odor Thresholds:
Gregory Leonardos , David
Kendall & Nancy Barnard (1969) Odor Threshold Determinations of 53 Odorant
Chemicals, Journal of the Air Pollution Control Association, 19:2, 91-95, DOI:
10.1080/00022470.1969.10466465
