Exploring near-infrared spectroscopy ability to predict the age and species of An. gambiae mosquitoes from different environmental conditions in Burkina Faso

Background Near infrared spectroscopy (NIRS) showed ability to predict some important entomological parameters in laboratory-reared and wild mosquitoes with moderate to high accuracy. Before validating the technique as a routine tool, it is necessary to assess NIRS accuracy on these variables under different environmental conditions similar to natural setting, as temperature and humidity could impact the mosquito cuticle and interfere with the machine prediction. This study aims to investigate the influence of environmental conditions on NIRS accuracy to determine age and species of An. gambiae 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 and An. coluzzii from laboratory colony were reared in each environmental condition and analysed by NIRS to predict mosquito species. Wild An. gambiae s.l. were caught during the 3 different periods described above and analysed by NIRS to compare the two results. Furthermore, first generation of Anopheles was used to assess NIRS ability to determine mosquito age in each environmental condition. 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 prediction accuracies varied slightly in laboratory Anopheles (77% − 85%) and more strongly in their field counterparts (67% − 84%). In both cases, prediction models developed from the season of interest were a little 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 smaller datasets and varying conditions were 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.