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Comparing molecular representations, e-nose signals, and other featurization, for learning to smell aroma molecules
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 choi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10420360/ https://www.ncbi.nlm.nih.gov/pubmed/37566580 http://dx.doi.org/10.1371/journal.pone.0289881 |
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author | Debnath, Tanoy Badreddine, Samy Kumari, Priyadarshini Spranger, Michael |
author_facet | Debnath, Tanoy Badreddine, Samy Kumari, Priyadarshini Spranger, Michael |
author_sort | Debnath, Tanoy |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10420360 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-104203602023-08-12 Comparing molecular representations, e-nose signals, and other featurization, for learning to smell aroma molecules Debnath, Tanoy Badreddine, Samy Kumari, Priyadarshini Spranger, Michael PLoS One Research Article 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. Public Library of Science 2023-08-11 /pmc/articles/PMC10420360/ /pubmed/37566580 http://dx.doi.org/10.1371/journal.pone.0289881 Text en © 2023 Debnath et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Debnath, Tanoy Badreddine, Samy Kumari, Priyadarshini Spranger, Michael Comparing molecular representations, e-nose signals, and other featurization, for learning to smell aroma molecules |
title | Comparing molecular representations, e-nose signals, and other featurization, for learning to smell aroma molecules |
title_full | Comparing molecular representations, e-nose signals, and other featurization, for learning to smell aroma molecules |
title_fullStr | Comparing molecular representations, e-nose signals, and other featurization, for learning to smell aroma molecules |
title_full_unstemmed | Comparing molecular representations, e-nose signals, and other featurization, for learning to smell aroma molecules |
title_short | Comparing molecular representations, e-nose signals, and other featurization, for learning to smell aroma molecules |
title_sort | comparing molecular representations, e-nose signals, and other featurization, for learning to smell aroma molecules |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10420360/ https://www.ncbi.nlm.nih.gov/pubmed/37566580 http://dx.doi.org/10.1371/journal.pone.0289881 |
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