Improvement of Odor Approximation Using Mass Spectrometry

Abstract

Odor approximation is a technique of creating a scent similar to a target scent by blending multiple odor components. This technique expands the range of odors that can be presented even if the number of odor components is limited. This is a key technology for both odor reproduction using an odor recorder and an olfactory display. As a set of odor components that can cover a wide range of smells is not yet revealed, we study a selection of odor components using an essential-oil and food-flavor mass-spectrum database. Basis vectors are extracted by the nonnegative matrix factorization (NMF) method, and then the nonnegative least-squares method is used to determine the recipe. To increase the approximation accuracy, two methods are proposed. One method is to increase the contributions of the samples with less frequent occurrence. The other method is to set appropriate initial values of the basis vectors in the NMF method using clustering analysis. The accuracy of the odor approximation is increased using these methods.