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Rapid and non-destructive identification of Anopheles gambiae and Anopheles arabiensis mosquito species using Raman spectroscopy via machine learning classification models

Crossref DOI link: https://doi.org/10.1186/s12936-023-04777-y

Published Online: 2023-11-08

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Omucheni, Dickson L.

Kaduki, Kenneth A.

Mukabana, Wolfgang R.
Funding

Funding for this research was provided by:

Swedish International Development Cooperation Agency (SIDA), through the International Science Programme (ISP), Uppsala University. (IPPS KEN:04, IPPS KEN:04, IPPS KEN:04)
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Text and Data Mining valid from 2023-11-08

Version of Record valid from 2023-11-08
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Article History

Received: 26 July 2023

Accepted: 31 October 2023

First Online: 8 November 2023

Declarations

:

: This declaration is not applicable.

: The authors declare that they have no competing interests.

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