Determining species of Anopheles gambiae s.l. in Burkina Faso using near-infrared spectroscopy

Abstract

Background: The ability to differentiate accurately Anopheles gambiae s.l. species is critical for malaria vector surveillance and control. Traditional methods for species identification, such as morphological analysis and polymerase chain reaction (PCR) are labor-intensive, time-consuming, and reliant on specialized laboratory infrastructure. In recent years, near-infrared spectroscopy (NIRS) has emerged as a promising alternative, offering a rapid, non-destructive, and cost-effective approach for species determination.

Methods: This study explores the use of NIRS to differentiate An. gambiae, An. coluzzii and An. arabiensis that are morphologically sibling mosquito species and the major malaria vectors in Burkina Faso. The methodology involves collecting near-infrared absorbance data from individual mosquito and applying machine-learning algorithms to classify the samples based on their spectral profiles. Thus, laboratory-reared mosquitoes or wild-caught ones from constant and varying ages were included in this study.

Results: Using laboratory-reared Anopheles gambiae s.l., NIRS average accuracy in classifying mosquito species of constant age was 93% while the analysis involving mosquitoes of varying ages, the accuracy falls to 59%. In addition to the laboratory data, analysis was conducted on wild Anopheles mosquito data of constant or varying age with respectively an average accuracy of 73% and 83%.

Conclusion: The study demonstrated that NIRS can determine Anopheles gambiae species with high accuracy, though it varied depending on experimental conditions such as the mosquitoes age.