Comparing molecular representations, e-nose signals, and other featurization, for learning to smell aroma molecules.

ABSTRACT: Recent research has attempted to predict our perception of odorants using Machine Learning models. The featurization of the olfactory stimuli usually represents the odorants using molecular structure parameters, molecular fingerprints, mass spectra, or e-nose signals. However, the impact of the choice of featurization on predictive performance remains poorly reported in direct comparative studies. This paper experiments with different sensory features for several olfactory perception tasks. We investigate the multilabel classification of aroma molecules in odor descriptors. We investigate single-label classification not only in fine-grained odor descriptors ('orange', 'waxy', etc.), but also in odor descriptor groups. We created a database of odor vectors for 114 aroma molecules to conduct

SUBMITTER: Debnath T 

PROVIDER: S-EPMC10420360 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature