Machine learning predicts non-preferred and preferred vertebrate hosts of Tsetse flies (Glossina spp.) based on skin volatile emission profiles

dc.contributor.authorOrubuloye, Olabimpe Y.
dc.contributor.authorTchouassi, David P.
dc.contributor.authorYusuf, Abdullahi Ahmed
dc.contributor.authorPirk, Christian Walter Werner
dc.contributor.authorMasiga, Daniel K.
dc.contributor.authorKariuki, Edward
dc.contributor.authorTorto, Baldwyn
dc.contributor.emailorubuloye.olabimpe@tuks.co.za
dc.date.accessioned2025-09-17T10:35:17Z
dc.date.available2025-09-17T10:35:17Z
dc.date.issued2025-04
dc.descriptionDATA AVAILABILITY : The original dataset generated during this study is available from the corresponding authors upon reasonable request.
dc.description.abstractTsetse fly vectors of African trypanosomosis preferentially feed on certain vertebrates largely determined by olfactory cues they emit. Previously, we established that three skin-derived ketones including 6-methyl-5-hepten-2-one, acetophenone and geranyl acetone accounted for avoidance of zebra by tsetse flies. Here, we tested the hypothesis that these three ketones serve as biomarkers for tsetse flies to distinguish between non-preferred- and preferred-vertebrate hosts. We used coupled gas chromatography/mass spectrometry to analyze and compare the skin volatile emissions of two non-preferred- (waterbuck and zebra) and four preferred- (buffalo, donkey, horse, warthog) vertebrate hosts in two wildlife parks in Kenya. We detected a total of 96 volatile organic compounds (VOCs) in the skin emissions composed mainly of aldehydes, ketones, alcohols, phenols and alkanes, which varied with the vertebrate host. Using random forest analysis, we found a weak correlation between the three skin-odor repellent ketones and non-preferred and preferred vertebrate hosts. However, we found that the three repellent ketones plus skin background odors may be more sensitive chemical signals for tsetse flies to discriminate vertebrate hosts. These results contribute to understanding tsetse fly vertebrate host preferences in their natural habitat across geographic scales.
dc.description.departmentZoology and Entomology
dc.description.librarianhj2025
dc.description.sdgSDG-15: Life on land
dc.description.sponsorshipThe European Union’s Integrated Biological Control Applied Research Programme – tsetse repellent component (EUIBCARP tsetse); UK’s Department for International Development (DFID); the Swedish International Development Cooperation Agency (Sida); the Swiss Agency for Development and Cooperation (SDC); the Australian Centre for International Agricultural Research; the Norwegian Agency for Development Cooperation; the German Federal Ministry for Economic Cooperation and Development; and the Government of the Republic of Kenya. OYO was supported by a German Academic Exchange Service (DAAD) in-region postgraduate scholarship for a PhD degree in Entomology at the University of Pretoria, South Africa. Open access funding was provided by the University of Pretoria, South Africa.
dc.description.urihttps://link.springer.com/journal/10886
dc.identifier.citationOrubuloye, O.Y., Tchouassi, D.P., Yusuf, A.A. et al. Machine Learning Predicts Non-Preferred and Preferred Vertebrate Hosts of Tsetse Flies (Glossina spp.) Based on Skin Volatile Emission Profiles. Journal of Chemical Ecology 51, 30 (2025). https://doi.org/10.1007/s10886-025-01582-6.
dc.identifier.issn0098-0331 (print)
dc.identifier.issn1573-1561 (print)
dc.identifier.other10.1007/s10886-025-01582-6
dc.identifier.urihttp://hdl.handle.net/2263/104357
dc.language.isoen
dc.publisherSpringer
dc.rights© The Author(s) 2025. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License.
dc.subjectAfrican trypanosomosis
dc.subjectOlfaction
dc.subjectGenus Glossina
dc.subjectSkin odors
dc.subjectWildlife
dc.subjectGas chromatography/mass spectrometry
dc.subjectVolatile organic compound (VOC)
dc.titleMachine learning predicts non-preferred and preferred vertebrate hosts of Tsetse flies (Glossina spp.) based on skin volatile emission profiles
dc.typeArticle

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