The bearcat, an animal from Southeast Asia, marks its territory with
popcorn-smelling urine.
Researchers,
i.e. professional piss-sniffers, used gas chromatography-mass spectrometry to
identify the compound 2-acetyl-1-pyrroline in the bearcat’s urine. This is the
same compound that gives popcorn its smell. It forms when heat drives a
reaction between the sugars and amino acids in the kernel. A similar thing
happens when bread is toasted and when rice is cooked.
The researchers say that with the bearcat, the compound 2-AP is probably
created when the animal’s urine combines with microorganisms living on its skin
and fur. These microorganisms break down the urine in the same way human-armpit
microorganisms turn our sweat into “body odor.”
Although popcorn is an unusual example of body odor, for sure, it is
considered one of the ten
primary
categories of smell. It should be noted that this very recent method for
classifying odors is pretty loose, meaning the categories are not rigidly
defined, and by viewing
this
chart, one can see the slight alterations that are almost as acceptable. Researchers
used statistical analysis to condense
the categories into the following list: Fragrant, Woody/resinous, Fruity
(non-citrus), Chemical, Minty/peppermint, Sweet, Popcorn, Lemon, Pungent,
Decayed.
Notice here the persistent difficulty in organizing smells – one
category requires its own disambiguation (fruity non-citrus), and one isn’t
even a smell (sweet).
The organization of smells is an arduous task, and although a handful
of people have tried, none have been successful. Using statistical semantic
analysis is a relatively new approach. They began with a standard catalogue of
odors, taken from the Andrew Dravniek's 1985 Atlas of Odor Character Profiles
(144 odors, I believe). Upon this, they do a form of statistical analysis
called non-negative matrix factorization (NMF). NMF is a
dimensionality-reduction technique, which makes it handy for categorizing the
perceptual space of smell. It has to do
with confusing things like normalization and consensual matrices, but all we
need to know here is that a huge network of smell descriptors are matched
against each other to measure their similarity, both to each other, and to a
baseline. Each descriptor is then given a kind of similarity score. The
descriptors that are the most similar to others are then called the primary
categories.
A hypothetical corpus of all possible smells is a multidimensional
thing that has never been (and perhaps can never be) reduced to a small set of
categories like colors or musical notes. Statistical methods such as NMF reveal
that smells are not evenly spread throughout odor-space, meaning that they are
not equally different from each other. Instead, they form clusters of
similarity, albeit a very loose clustering. As the scientists note in their
paper, “Because NMF is an iterative optimization algorithm, it may not converge
to the same solution each time it is run (with random initial conditions).”
-source
This method, which provides not answers but approximations, turns out
to be quite appropriate. The odor lexicon is an ephemeral thing, like smell
itself. It is a precognitive perception which bypasses the language centers of
our brain, yielding an unpredictable set of descriptors that will change with
the verbalizing person. I just wonder – in cultures where they don’t have popcorn, what
would they call bearcat piss?
Notes:
phys.org, April 2016
Castro JB, Ramanathan A, & Chennubhotla CS (2013). Categorical
dimensions of human odor descriptor space revealed by non-negative matrix
factorization. PloS one, 8 (9) PMID:
24058466.
