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Age-grading and species identification of male mosquito Anopheles gambiae s.l. using mid-infrared spectroscopy and machine learning

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Abstract

Most malaria vector control strategies target female Anopheles mosquitoes that transmit parasites, but emerging approaches such as gene drive, sterile insect technique, or Wolbachia require the release of males. Successful deployment depends on reliable knowledge of the species composition and age structure of male populations to overcome potential barriers to mating. Although approaches including near-infrared spectroscopy have been explored for male species identification and age grading, these methods remain constrained by limited validation, scalability, and accuracy under operational field conditions. Here we tested mid-infrared spectroscopy (MIRS) coupled with machine learning (ML) as a rapid and cost-effective approach for classifying species and age of male mosquitoes within the Anopheles gambiae complex. We used male mosquitoes from two laboratory-reared colonies in Burkina Faso (Anopheles coluzzii and An. gambiae) to develop a MIRS-ML model for species and age group (1–4, 5–10, 11–17 days) prediction under controlled conditions. This model was tested against a genetic and environmentally variable dataset consisting of male offspring obtained from gravid or blood fed Anopheles coluzzii and An. gambiae females collected from houses from two villages, Vallée du Kou and Soumousso and reared to adulthood in a semi-field system. The MIRS spectra from 2,120 males, representing both species, all age groups and both laboratory and semi-field backgrounds, were analysed using an extreme gradient boosting (XGBoost) algorithm to assess the ability to correctly predict age group and species. The XGBoost model classified mosquito species (86%) and age groups (85%) accurately in laboratory data, but performance declined under semi-field conditions (64% for species, 50% for age), reflecting environmental variability. Incorporating semi-field samples through transfer learning improved accuracy to 73% for species and 70% for age, underscoring both the limits of laboratory-only models and the value of transfer learning for enhancing generalisability in field settings. Our results demonstrate that mid-infrared spectroscopy with supervised machine learning (MIRS-ML) holds potential as a rapid tool for identifying the species and age group of cryptic male malaria vectors and represent one of the first applications of this approach to male Anopheles gambiae s.l. evaluated under semi-field conditions. However, before the approach can be used, larger datasets are needed to improve the classification algorithms and validate them for prediction in field populations.

Data availability

Software name: Python 3.12 , OPUS: [https://github.com/magonji/opus-dei](https:/github.com/magonji/opus-dei), Codes and data can be accessed at: [https://github.com/MwangaEP/Male-age-species](https:/github.com/MwangaEP/Male-age-species).

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Acknowledgements

We are grateful to Medical Research Council GCRF Infections Foundation Awards to A.D., F.B., F.O.O., H.M.F. and K.W, whose previous work on female mosquitoes inspired us to study male mosquitoes using MIRS. Authors thank the villagers who accepted to conduct the mosquito collection in their houses.

Funding

The current study is supported by ANTI-VeC grant (AV/TCA/002) to Roger Sanou, Abdoulaye Diabté at Institut de Recherche en Sciences de la Santé / Centre Muraz and Simon A. Babayan at University of Glasgow.

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Author notes

  1. Francesco Baldini and Abdoulaye Diabaté supervised this work equally.

Authors and Affiliations

  1. Institut de Recherche en Sciences de la Santé (IRSS)/ Centre Muraz, BP 545, Bobo-Dioulasso, Burkina Faso

    Roger Sanou, Bazoumana B. D. Sow, Hamidou Maïga, François Wiminga, Roch K. Dabiré & Abdoulaye Diabaté

  2. Environmental Health & Ecological Sciences Department, Ifakara Health Institute, Off Mlabani Passage, PO Box 53, Ifakara, Tanzania

    Emmanuel P. Mwanga, Doreen Josen Siria, Heather M. Ferguson, Fredros O. Okumu & Francesco Baldini

  3. Université Nazi BONI de Bobo-Dioulasso, PO 1091, Bobo-Dioulasso, Burkina Faso

    Roger Sanou, Bazoumana B. D. Sow & Adrien Marie Gaston Belem

  4. School of Biodiversity One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK

    Doreen Josen Siria, Heather M. Ferguson, Simon A. Babayan, Fredros O. Okumu & Francesco Baldini

  5. School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK

    Klaas Wynne & Mario González-Jiménez

Authors

  1. Roger Sanou
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  2. Emmanuel P. Mwanga
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  3. Bazoumana B. D. Sow
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  4. Hamidou Maïga
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  5. François Wiminga
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  7. Adrien Marie Gaston Belem
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  8. Roch K. Dabiré
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  9. Klaas Wynne
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  10. Heather M. Ferguson
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  11. Mario González-Jiménez
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  12. Simon A. Babayan
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  13. Fredros O. Okumu
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  14. Francesco Baldini
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  15. Abdoulaye Diabaté
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Contributions

Conceptualization: R.S., F.B., H.M.F., F.O.O., S.A.B.,D.J.S., A.M.G.B., R.K.D., K.W., M.G.J. and A.D. R.S., F.B., H.M.F., E.P.M, H.M. and M.G.J. wrote the main manuscript. Data analysis: E.P.M., B.B.S. and M.G.J. Mosquito collection and MIRS-spectra measurements: F.W. All authors reviewed the manuscript.

Corresponding authors

Correspondence to
Roger Sanou or Francesco Baldini.

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The authors declare no competing interests.

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Sanou, R., Mwanga, E.P., Sow, B.B.D. et al. Age-grading and species identification of male mosquito Anopheles gambiae s.l. using mid-infrared spectroscopy and machine learning.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-46306-2

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Keywords


  • Anopheles gambiae s.l.
  • Species
  • Age
  • Identification
  • Malaria
  • Mid-infrared spectroscopy
  • Machine learning

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