Identification of three medically important mosquito species using Raman spectroscopy

Accurate identification of disease vectors is crucial when collecting epidemiological data. In mosquitoes, which transmit diseases like malaria, yellow fever, chikungunya, and dengue fever, identification mainly relies on the observation of external morphological features at different life cycle stages. This process is tedious and labor-intensive. In this paper, the utility of Raman spectroscopy to discriminate and classify three mosquito species, namely, Aedes aegypti, Anopheles gambiae, and Culex quinquefasciatus, is presented. The three species were chosen to represent two subfamilies of medically important mosquitoes, that is, the Anophelinae and the Culicinae. The study is primarily a proof of concept on the potential of Raman spectroscopy in mosquito taxonomy. A dispersive Raman microscope was used to record spectra from the legs (femur and tibia) of fresh anesthetized laboratory-bred mosquitoes. Broad peaks centered around 1400, 1590, and 2060 cm(-1) dominated the spectra. These peaks, attributed to cuticular melanin, were important in mosquito discrimination. Variance threshold (VT) and principal component analysis (PCA) were used for feature selection and feature extraction, respectively. The extracted features were then used to train and test linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) classifiers. VT/PCA/QDA achieved an overall accuracy of 94%, a sensitivity of 87%, and a specificity of 96%, whereas VT/PCA/LDA attained an accuracy of 85%, a sensitivity of 69%, and a specificity of 90%. The success of these relatively simple classification models on Raman spectroscopy data lays the ground for future development of machine learning models that may include discrimination of cryptic species.

Automated summary

This study demonstrates the use of Raman spectroscopy for the identification and classification of three mosquito species, providing a foundation for further development of machine learning models in mosquito taxonomy.