Odor Impression Prediction from Mass Spectra.
The sense of smell arises from the perception of odors from chemicals. However, the relationship between the impression of odor and the numerous physicochemical parameters has yet to be understood owing to its complexity. As such, there is no established general method for predicting the impression of odor of a chemical only from its physicochemical properties. In this study, we designed a novel predictive model based on an artificial neural network with a deep structure for predicting odor impression utilizing the mass spectra of chemicals, and we conducted a series of computational analyses to evaluate its performance. Feature vectors extracted from the original high-dimensional space using two autoencoders equipped with both input and output layers in the model are used to build a mapping function from the feature space of mass spectra to the feature space of sensory data. The results of predictions obtained by the proposed new method have notable accuracy (R≅0.76) in comparison with a conventional method (R≅0.61).
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Kanagawa, Japan.
Yuji
http://orcid.org/0000-0002-4193-5588
Y
Nozaki
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Kanagawa, Japan.|Precision and Intelligence Laboratory, Tokyo Institute of Technology, Kanagawa, Japan.
Takamichi
T
Nakamoto
D000465:Algorithms; D013058:Mass Spectrometry / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009812:Odorants / Q000032:analysis*; D025341:Principal Component Analysis; D015203:Reproducibility of Results