Identification of Three Medically Important Mosquito Species Using Raman Spectroscopy

Abstract

Accurate identification of disease vector insects is crucial when collecting epidemiological data. Traditionally, mosquitoes that transmit diseases like malaria, yellow fever, chikungunya, and dengue fever have been identified by looking at their external morphological features at different life cycle stages. This process is tedious and labour intensive.In this paper, the potential of Raman spectroscopy in combination with Linear and Quadratic Discriminant Analysis to classify three mosquito species, namely: Aedes aegypti, Anopheles gambiae and Culex quinquefasciatus, was explored. The classification was based on the mosquitoes’ cuticular melanin. The three mosquito species represented two subfamilies of medically important mosquitoes, i.e. the Anophelinae and the Culicinae. The housefly (Musca domestica) was included as a ‘control’ group to assess the discrimination ability of the classifiers. This study is the first to use Raman spectroscopy to classify mosquitoes. Fresh mosquitoes were anaesthetized with chloroform, and a dispersive Raman microscope was used to capture spectra from their legs. Broad melanin peaks centred around 1400 cm-1, 1590 cm-1, and 2060 cm-1 dominated the spectra. Variance Threshold (VT) and Principal Component Analysis (PCA) were used for feature selection and feature extraction respectively from the preprocessed data. The extracted features were then used to train and test Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) classifiers.The VT/PCA/QDA classification model performed better than VT/PCA/LDA. VT/PCA/QDA achieved an overall accuracy of 94%, sensitivity of 87% and 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 groundwork for future development of models for discriminating morphologically indistinguishable insect species.

Rapid and Non-destructive Identification of Anopheles gambiae and Anopheles arabiensis Mosquitoes Species Using Raman Spectroscopy via Machine Learning Classification Models

“…The main peaks of interest are labeled. These peaks coincide well with our previously reported data on mosquito discrimination and classi cation (32). Previous studies have shown that performing Raman spectroscopy on cuticles of some insects such as bumble bees (36), wasps (37), spiders (38) and skins of Lacertids (39) produced spectra dominated by peaks attributable to melanin pigment.…”

“…Femurs and tibiae were chosen for their relative ease of being focused under the microscope and because they did not contain signals emanating from other conditions such as food consumed by the mosquitoes and gonotrophic status. Furthermore, mosquito legs have been successfully used to classify mosquitoes (25,27,32). Rayleigh and Raman scattered photons (indicated as green and red arrows respectively pointing away from the sample) in Fig.…”

“…From the extensively reviewed literature, no studies currently exist that have used Raman spectroscopy as a tool to distinguish these two species. We recently demonstrated the capability of Raman spectroscopy to discriminate and classify mosquito genera (32). Cuticular melanin was found to be a potential biomarker for discrimination.…”