Exploring near-infrared spectroscopy ability to predict the age and species of Anopheles gambiae sensu lato mosquitoes from different environmental conditions in Burkina Faso
Author(s)
Type
Journal Article
Abstract
Background
Near infrared spectroscopy (NIRS) has shown ability in previous studies to predict age and species of laboratory-reared and wild mosquitoes with moderate to high accuracy. To validate the technique as a routine tool, it is necessary to assess NIRS accuracy on these variables under different environmental conditions susceptible to affect the mosquito cuticle and interfere with the machine accuracy. This study investigated the influence of environmental conditions on NIRS accuracy to determine the age and species of Anopheles gambiae sensu lato (s.l.).
Methods
Environmental conditions of three important seasonal periods in Burkina Faso covering the onset, the peak and the end of the rainy season were mimicked in the laboratory using incubators. Emerged An. gambiae s.s. and An. coluzzii from laboratory colonies were reared in each period using temperature and relative humidity for predicting mosquito species by NIRS. Wild An. gambiae s.l. (n = 3788) were caught during the 3 different periods described above and analysed by NIRS to predict Anopheles species. Furthermore, first generation of wild Anopheles (n = 1014) was used to assess NIRS ability to classify mosquito age in each environmental condition. All data analysis were performed using a binomial logistic regression model.
Results
NIRS discriminated between laboratory-reared Anopheles with 83% of accuracy independently of any environmental condition. Similar trend was found in wild-caught Anopheles. NIRS accuracies varied slightly in laboratory Anopheles (77–85%) and more strongly in their field counterparts (67–84%). In both cases, models developed from the season of interest were more accurate than models trained with insectary conditions or from a different period of the year, indicating temperature and humidity can impact NIRS accuracy. Models derived from laboratory-mosquitoes reared under fluctuating environmental conditions predicted field-derived mosquito species with a low accuracy (59%). Models trained on varying conditions reliably classified age into two categories (< 9 days or ≥ 9 days, 79–84% accuracy).
Conclusion
NIRS was able to predict An. gambiae s.l. species and classified age into two categories under different environmental conditions with modest accuracy. Models trained using wild mosquitoes from one season could predict species in wild mosquitoes from a different season, though with slightly lower accuracy.
Near infrared spectroscopy (NIRS) has shown ability in previous studies to predict age and species of laboratory-reared and wild mosquitoes with moderate to high accuracy. To validate the technique as a routine tool, it is necessary to assess NIRS accuracy on these variables under different environmental conditions susceptible to affect the mosquito cuticle and interfere with the machine accuracy. This study investigated the influence of environmental conditions on NIRS accuracy to determine the age and species of Anopheles gambiae sensu lato (s.l.).
Methods
Environmental conditions of three important seasonal periods in Burkina Faso covering the onset, the peak and the end of the rainy season were mimicked in the laboratory using incubators. Emerged An. gambiae s.s. and An. coluzzii from laboratory colonies were reared in each period using temperature and relative humidity for predicting mosquito species by NIRS. Wild An. gambiae s.l. (n = 3788) were caught during the 3 different periods described above and analysed by NIRS to predict Anopheles species. Furthermore, first generation of wild Anopheles (n = 1014) was used to assess NIRS ability to classify mosquito age in each environmental condition. All data analysis were performed using a binomial logistic regression model.
Results
NIRS discriminated between laboratory-reared Anopheles with 83% of accuracy independently of any environmental condition. Similar trend was found in wild-caught Anopheles. NIRS accuracies varied slightly in laboratory Anopheles (77–85%) and more strongly in their field counterparts (67–84%). In both cases, models developed from the season of interest were more accurate than models trained with insectary conditions or from a different period of the year, indicating temperature and humidity can impact NIRS accuracy. Models derived from laboratory-mosquitoes reared under fluctuating environmental conditions predicted field-derived mosquito species with a low accuracy (59%). Models trained on varying conditions reliably classified age into two categories (< 9 days or ≥ 9 days, 79–84% accuracy).
Conclusion
NIRS was able to predict An. gambiae s.l. species and classified age into two categories under different environmental conditions with modest accuracy. Models trained using wild mosquitoes from one season could predict species in wild mosquitoes from a different season, though with slightly lower accuracy.
Date Issued
2025-09-30
Date Acceptance
2025-08-28
Citation
Malaria Journal, 2025, 24
ISSN
1475-2875
Publisher
BMC
Start Page
306
Journal / Book Title
Malaria Journal
Volume
24
Copyright Statement
© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/41029677
PII: 10.1186/s12936-025-05554-9
Subjects
Anopheles
Age and species determination
Environmental conditions
Near infrared spectroscopy
Relative humidity
Temperature
Animals
Anopheles
Burkina Faso
Spectroscopy, Near-Infrared
Mosquito Vectors
Female
Seasons
Publication Status
Published
Coverage Spatial
England
Article Number
306
Date Publish Online
2025-09-30