Rapid and Nondestructive Sex Differentiation of <i>Aedes aegypti</i> Using Infrared Spectroscopy and Discriminant Analysis

<i>Aedes aegypti</i> (<i>A. aegypti</i>) mosquitoes are arthropods that transmit a set of viruses of great relevance to public health, such as Dengue, Zika, and Chikungunya, causing recurrent epidemics in tropical and subtropical regions. This fact raises the need for improvements and enhancements in the entomological surveillance of these species, preventing the occurrence of outbreaks. In this work, the potential of near-infrared (NIR) and mid-infrared (MIR) spectroscopy together with variable selection and supervised classification methods for the differentiation of <i>A. aegypti</i> mosquitoes according to their sex was analyzed. 66 mosquitoes were analyzed by NIR and 198 by MIR. Chemometric models of successive projection algorithm-linear discriminant analysis (SPA-LDA), genetic algorithm-linear discriminant analysis (GA-LDA), and partial least squares-discriminant analysis (PLS-DA) coupled with interval selection by the successive projections algorithm (iSPA-PLS-DA) were developed to build models capable of differentiating the spectra between males and females. The results demonstrated that sensitivity and specificity values achieved 93.94%, demonstrating that it is possible to perform the differentiation with both NIR and MIR spectroscopies. Based on sensitivity and specificity values, it was possible to note a better performance of the NIR models compared to the MIR models, with the NIR-GA-LDA model being more efficient than the NIR-SPA-LDA model, while NIR-iSPA-PLS-DA was highlighted with the best classification results. Furthermore, a cost and time analysis against gold standard techniques is also performed in this study, demonstrating the advantage of the tools described here. Finally, new studies should be developed so that in the near future it will be possible to count on portable tools based on spectroscopy in the field of entomological surveillance of these species, seeking to identify regions with a high probability of outbreaks in the future.