Image: Christine Daniloff |
The
design of aromas is a multibillion-dollar business. The big flavor companies
spend tens of millions of dollars every year on research and development,
including a lot of consumer testing.
But
making sense of taste-test results is difficult. Subjects’ preferences can vary
so widely that no clear consensus may emerge. Collecting enough data about each
subject would allow flavor companies to filter out some of the inconsistencies,
but after about 40 flavor samples, subjects tend to suffer “smell fatigue,” and
their discriminations become unreliable. So companies are stuck making
decisions on the basis of too little data, much of it contradictory.
One
of the biggest flavor companies in the world has turned to researchers in Massachusetts
Institute of Technology’s (MIT’s) Computer Science and Artificial Intelligence
Laboratory (CSAIL) for help. To analyze taste-test results, the CSAIL
researchers are using genetic programming, in which mathematical models compete
with each other to fit the available data and then cross-pollinate to produce
models that are more accurate still.
The
Swiss flavor company Givaudan asked CSAIL principal research scientist Una-May
O’Reilly, postdoc Kalyan Veeramachaneni, and the University of Antwerp’s
Ekaterina Vladislavleva to help interpret the results of tests in which 69
subjects evaluated 36 different combinations of seven basic flavors, assigning
each a score according to its olfactory appeal.
For
each subject, O’Reilly and her colleagues randomly generate mathematical
functions that predict scores according to the concentrations of different
flavors. Each function is assessed according to two criteria: accuracy and
simplicity. A function that, for example, predicts a subject’s preferences
fairly accurately using a single factor—say, concentration of butter—could
prove more useful than one that yields a slightly more accurate prediction but
requires a complicated mathematical manipulation of all seven variables.
After
all the functions have been assessed, those that provide poor predictions are
winnowed out. Elements of the survivors are randomly recombined to produce a
new generation of functions; those are then evaluated for accuracy and simplicity.
The whole process is repeated about 30 times, until it converges on a set of
functions that accord well with the preferences of a single subject.
Because
O’Reilly and her colleagues’ method produces profiles of individual test
subjects’ tastes, it can sort them into distinct groups. It could be, for
instance, that test subjects tend to have strong preferences for either
cinnamon or nutmeg but not both. By marketing one product to cinnamon lovers
and another to nutmeg lovers, a company could do much better than by marketing
one product to both. “For every one of these 36 flavors, someone hated it and
someone liked it,” O’Reilly says. “If you try to identify a flavor that the
whole panel likes, you end up settling for a little bit less.”
O’Reilly
and her colleagues haven’t had an opportunity to empirically determine whether
their models correctly predict subjects’ responses to new flavors. So to try to
establish their model’s accuracy, they instead built another model. First, they
developed a set of mathematical functions that represent subjects’ true taste
preferences. Then they showed that, given the limitations of particular test
designs, their algorithms could still divine those preferences. Although they
developed the model purely to validate their approach, O’Reilly says, flavor
researchers were intrigued by the possibility of using it to develop more
accurate and efficient test protocols.
“People
have been playing with these [evolutionary] techniques for decades,” says Lee
Spector, a professor of computer science at Hampshire College
and editor-in-chief of Genetic Programming and Evolvable Machines, where
the MIT researchers’ latest paper appears. “One of the reasons that they
haven’t made a big splash until recently is that people haven’t really figured
out, I think, where they can pay off big.” Taste preference, Spector says, “is
a pretty brilliant area in which to apply the evolutionary methods—and it looks
as though they’re working, also, so that’s exciting.”
