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.