bioRxivEcology
Rapid identification of mosquito species, sex and age by mass spectrometric analysis
I. Wagner, L. Grigoraki, P. Enevoldsen, M. Clarkson, S. Jones, J. L. Hurst, R. J. Beynon, H. Ranson
Abstract
BackgroundA rapid, accurate method to identify and to age grade mosquito populations would be a major advance in predicting the risk of pathogen transmission and evaluating the public health impact of vector control interventions. Whilst other chemometric methods show promise, current approaches rely on challenging morphological techniques or simple binary classifications that cannot identify the subset of the population old enough to be infectious. In this study, the ability of Rapid Evaporative Ionisation Mass Spectrometry (REIMS) to identify the species, sex and age of mosquitoes reared in the laboratory and derived from the wild was investigated. ResultsThe accuracy of REIMS in identifying morphologically identical species of the Anopheles gambiae complex exceeded 97 % using principal component/linear discriminant analysis (PC-LDA) and 84 % based on random forest analysis. Age separation into 3 different age categories (1 d, 5-6 d, 14-15 d.) was achieved with 99 % (PC-LDA) and 94 % (random forest) accuracy, after including three species and storing samples at room temperature for up to 11 days. When tested on wild mosquitoes from the UK, REIMS data could determine the species and age of the specimens with accuracies of 91 and 90 % respectively. ConclusionsThe accuracy of REIMS to resolve the species and age of Anopheles mosquitoes is comparable to that achieved by chemometric approaches. The processing time and ease of use represent significant advantages over current, dissection-based methods. Importantly the accuracy was maintained when using wild mosquitoes reared under differing environmental conditions, and when mosquitoes were stored frozen or desiccated. This high throughput approach thus has potential to conduct rapid, real- time monitoring of vector populations, providing entomological evidence of the impact of alternative interventions.
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Prediction of malaria mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning
Mario González‐Jiménez, Simon A. Babayan, … Klaas Wynne·Wellcome Open ResearchPeer-reviewed·May 1, 2019
- Rapid evaporative ionisation mass spectrometry (REIMS): A potential and rapid tool for the identification of insecticide resistance in mosquito larvae.
J. Morgan, J. E. Salcedo-Sora, … C. Strode·bioRxiv·Feb 10, 2022
- Rapid ageing and species identification of natural mosquitoes for malaria surveillance
D. J. Siria, R. Sanou, … F. Baldini·bioRxiv·Jun 11, 2020
- Identification of Three Medically Important Mosquito Species Using Raman Spectroscopy
Dickson L Omucheni, Kenneth Amiga Kaduki, Wolfgang Richard Mukabana·Journal of Raman SpectroscopyPeer-reviewed·Mar 29, 2023
- Towards Scalable Age-Grading of Aedes albopictus mosquito using Mid-Infrared Spectroscopy and Machine Learning
M. G. Foti, Martina Micocci, … Francesco Baldini·Scientific ReportsPeer-reviewed·Nov 18, 2025
- Pixel intensity of wing photos used to predict age of Anopheles gambiae caught during the RIMDAMAL II clinical trial
Greg Pugh, Emmanuel Sougue, … Brian D. Foy·bioRxiv·Feb 27, 2025