Making sense of taste-test results is difficult. MIT researchers using ‘genetic programming’ to crossbreed algorithms 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.
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.
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