# Deadliest Animals with the Thinnest Wings: Near-Infrared Properties of Tropical Mosquitoes

## TL;DR

This study explores the near-infrared properties of tropical mosquitoes to improve remote monitoring for disease transmission control.

## Contribution

The paper introduces a novel method for measuring mosquito features using spectral reflectance models and near-infrared imaging.

## Key findings

- Mosquito wings are as thin as 174 nm, the thinnest reported to date.
- Spectral models achieved adjusted R² values exceeding 95% for reflectance prediction.
- Mosquito body orientation has minimal impact on shortwave infrared spectra.

## Abstract

Tropical mosquitoes transmit diseases like malaria, yellow fever, and Zika. Classifying mosquitoes by species, sex, age, and gravidity offers vital insights for assessing transmission risk and effective mitigations. Photonic monitoring for mosquito classification can be used in distributed sensors or lidars on longer ranges. However, a reflectance model and its parameters are lacking in the current literature. This study investigates mosquitoes of different species, sexes, age groups, and gravidity states, and reports metric pathlengths of wing chitin, body melanin, and water. We use hyperspectral push-broom imaging and laser multiplexing with a rotation stage to measure near-infrared spectra from different angles and develop simple models for spectral reflectance, including wing thickness and equivalent absorption path lengths for melanin and water. We demonstrate wing thickness of 174 (±1) nm – the thinnest wings reported to our knowledge. Water and melanin pathlengths are determined with ∼10 µm precision, and spectral models achieve adjusted R² values exceeding 95%. While mosquito aspect angle impacts the optical cross-section, it alters shortwave infrared spectra minimally (∼2%). These results demonstrate the potential for remote retrieval of micro- and nanoscopic mosquito features using spectral sensors and lidars irrespective of insect body orientation. Improved specificity of vector monitoring can be foreseen.

Graphical abstractThis is a visual representation of the abstract.

This is a visual representation of the abstract.