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

Autor: Debnath T; Sony AI, SONY Corporation, Tokyo, Japan., Badreddine S; Sony AI, SONY Corporation, Tokyo, Japan., Kumari P; Sony AI, SONY Corporation, Tokyo, Japan., Spranger M; Sony AI, SONY Corporation, Tokyo, Japan.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Aug 11; Vol. 18 (8), pp. e0289881. Date of Electronic Publication: 2023 Aug 11 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0289881
Abstrakt: 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 our experiments using a QCM (Quartz Crystal Microbalance) type smell sensor module (Aroma Coder®V2 Set). We compare these smell features with different baseline features to evaluate the cluster composition, considering the frequencies of the top odor descriptors carried by the aroma molecules. Experimental results suggest a statistically significant better performance of the QCM type smell sensor module compared with other baseline features with F1 evaluation metric.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Debnath et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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