Machine Learning in Human Olfactory Research
Open accessAbstract
The complexity of the human sense of smell is increasingly reflected in complex and high-dimensional data, which opens opportunities for data-driven approaches that complement hypothesis-driven research. Contemporary developments in computational and data science, with its currently most popular implementation as machine learning, facilitate complex data-driven research approaches. The use of machine learning in human olfactory research included major approaches comprising 1) the study of the physiology of pattern-based odor detection and recognition processes, 2) pattern recognition in olfactory phenotypes, 3) the development of complex disease biomarkers including olfactory features, 4) odor prediction from physico-chemical properties of volatile molecules, and 5) knowledge discovery in publicly available big databases. A limited set of unsupervised and supervised machine-learned methods has been used in these projects, however, the increasing use of contemporary methods of computational science is reflected in a growing number of reports employing machine learning for human olfactory research. This review provides key concepts of machine learning and summarizes current applications on human olfactory data.
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Use of machine learning algorithms facilitates automated design of VOC sensors, eliminating the need for arduous and time-consuming design processes.
“First, the use of machine learning algorithms facilitates the automated design of volatile organic compound sensors, thereby eliminating the need for arduous and time-consuming design processes.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Lotsch et al. 2019
AI-assisted design systems efficiently handle critical and repetitive optimization procedures, leading to more effective and efficient design outcomes.
“AI-assisted design systems efficiently handle these tasks, leading to more effective and efficient design outcomes.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Lotsch et al. 2019
AI algorithms are becoming increasingly important for VOC gas detection because of their benefits for VOC sensors.
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From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Lotsch et al. 2019
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- Authors
- Lötsch, J., Kringel, D., & Hummel, T.
- Affiliations
- Goethe University Frankfurt (DE)
- Klinik und Poliklinik für Hals-, Nasen- und Ohrenheilkunde (DE)
- Corresponding author
- Lötsch, Jörn
- Venue
- Chemical Senses
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-10-27
- Volume
- 44
- Issue
- 1
- Pages
- 11-22
- ISSN
- 0379-864X (ISSN-L)
- 1464-3553
- PMC
- PMC6295796
- OpenAlex
- W2898511110
- Cited by
- 71
- References
- 138
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 11 · 2023: 9 · 2022: 9 · 2021: 11 · 2020: 9 · 2019: 6
- FWCI
- 1.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Chemical Senses (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion, other-oa)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- Landesoffensive zur Entwicklung wissenschaftlich-ökonomischer Exzellenz
- LOEWE-Zentrum für Translationale Medizin und Pharmakologie
- Deutsche Forschungsgemeinschaft (DFG HU 441/18-1, DFG Lo 612/10–1)
- European Union Seventh Framework Programme (FP7/2007 - 2013, 602919)
- Topics
- Life Sciences › Neuroscience › Sensory Systems › Olfactory and Sensory Function Studies
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Chemical Sensor Technologies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- machine learning, human olfaction, odor detection, odor recognition, olfactory biomarkers, odor prediction, volatile compounds, physicochemical properties, pattern recognition
- MeSH terms
- Machine Learning, Humans, Odorants, Smell, Biomarkers, Databases, Factual, Volatile Organic Compounds, Electronic Nose
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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