Towards scalable age-grading of Aedes albopictus mosquito using mid-infrared spectroscopy and machine learning.

ABSTRACT: The age structure and dynamics of mosquito populations are crucial for understanding their ability to spread diseases and assessing the effectiveness of anti-mosquito control measures. However, available methods to age-grade mosquito populations are labour-intensive and imprecise, particularly for Aedes species. We investigated the potential of Mid-Infrared Spectroscopy (MIRS) combined with Supervised Machine Learning (ML) to rapidly and accurately predict the age of adult females and males of the arbovirus vector, Aedes albopictus. First, we demonstrated the ability of MIRS-ML to age male and female mosquitoes reared under laboratory conditions. Second, we optimised the model with adults emerged from wild collected eggs reared under natural conditions in a semi-field facility, to expose t

SUBMITTER: Foti M 

PROVIDER: S-EPMC12627515 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature