Ability of near-infrared spectroscopy and chemometrics to predict the age of mosquitoes reared under different conditions

Abstract

Background: Practical, field-ready age-grading tools for mosquito vectors of disease are urgently needed because of the impact that daily survival has on vectorial capacity. Previous studies have shown that near-infrared spectroscopy (NIRS), in combination with chemometrics and predictive modeling, can forecast the age of laboratory-reared mosquitoes with moderate to high accuracy. However, it remains it is unclear whether the technique has utility for identifying shifts in the age structure of wild-caught mosquitoes or whether models derived from the laboratory or semi-field mosquitoes can be applied to mosquitoes reared under different environments. Methods: NIRS spectral data from adult female Aedes albopictus mosquitoes reared in the laboratory (2, 5, 8, 12 and 15 days old) and in semi-field cages populated by wild-caught pupae (resulting in adults of 1, 7 and 14 days old). Spectral data collected from mosquitoes were used to determine if models derived from laboratory material using partial least squares (PLS) regression for the development of predictive models could be effectively applied to mosquitoes from more natural semi-field environments. Results: Models trained on spectra from laboratory-reared material were able to predict the age of other laboratory-reared mosquitoes with moderate accuracy and successfully differentiated all day 2 and 15 mosquitoes. Models derived with laboratory mosquitoes could not differentiate between semi-field age groups, with age predictions relatively indistinguishable for day 1-14. Pre-processing of spectral data and improving the PLS regression framework to avoid overfitting can increase accuracy, but predictions of mosquitoes reared in different environments remained poor. Principle component analysis confirms substantial spectral variations between laboratory and semi-field mosquitoes despite both being derived from the same island population. Conclusions: Model trained on laboratory mosquitoes were able to predict ages of laboratory mosquitoes with good sensitivity and specificity, however it was unable to predict age class of semi-field mosquitoes. This study suggests that laboratory-reared mosquitoes do not capture enough environmental variation to accurately predict the age of the same species reared under different conditions. Further research is needed to explore alternative pre-processing methods and machine learning techniques, and to understand factors that affect absorbance in mosquitoes before field application using NIRS.