ArticleIntegrative organismal biology (Oxford, England)2025
Classification of Odor-Derived Electroantennograms with Machine Learning.
Article in Integrative organismal biology (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Insects have a keen ability to detect numerous odors in their environment. These odors, known as volatile organic compounds (VOCs), provide the insect with information about food, predators, and mates that may be in the area and are detected by olfactory receptors expressed by sensory neurons on the antennae. When VOCs are transduced by the olfactory sensory neurons, the antennal electrical potential dynamically changes, causing a local field potential (LFP) response to occur. Research has used the LFP amplitude for determining VOC concentration, but only recently have antennal LFPs been posited to be able to be used for VOC discrimination and identification. To close this gap, we use the time-series response of the antenna to odors as well as principal components of these responses to capture the characteristics of the LFP response, including waveform dynamics, intensity, slope, and duration. We use antennae of the
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