Effects of sample preservation methods and duration of storage on the performance of mid-infrared spectroscopy for predicting the age of malaria vectors

Mgaya, J. N., Siria, D. J., Makala, F. E., Mgando, J. P., Vianney, J.-M., Mwanga, E. P. and Okumu, F. O. (2022) Effects of sample preservation methods and duration of storage on the performance of mid-infrared spectroscopy for predicting the age of malaria vectors. Parasites and Vectors, 15, 281. (doi: 10.1186/s13071-022-05396-3) (PMID:35933384) (PMCID:PMC9356448)

Abstract

Background: Monitoring the biological attributes of mosquitoes is critical for understanding pathogen transmission and estimating the impacts of vector control interventions on the survival of vector species. Infrared spectroscopy and machine learning techniques are increasingly being tested for this purpose and have been proven to accurately predict the age, species, blood-meal sources, and pathogen infections in Anopheles and Aedes mosquitoes. However, as these techniques are still in early-stage implementation, there are no standardized procedures for handling samples prior to the infrared scanning. This study investigated the effects of different preservation methods and storage duration on the performance of mid-infrared spectroscopy for age-grading females of the malaria vector, Anopheles arabiensis. Methods: Laboratory-reared An. arabiensis (N = 3681) were collected at 5 and 17 days post-emergence, killed with ethanol, and then preserved using silica desiccant at 5 °C, freezing at − 20 °C, or absolute ethanol at room temperature. For each preservation method, the mosquitoes were divided into three groups, stored for 1, 4, or 8 weeks, and then scanned using a mid-infrared spectrometer. Supervised machine learning classifiers were trained with the infrared spectra, and the support vector machine (SVM) emerged as the best model for predicting the mosquito ages. Results: The model trained using silica-preserved mosquitoes achieved 95% accuracy when predicting the ages of other silica-preserved mosquitoes, but declined to 72% and 66% when age-classifying mosquitoes preserved using ethanol and freezing, respectively. Prediction accuracies of models trained on samples preserved in ethanol and freezing also reduced when these models were applied to samples preserved by other methods. Similarly, models trained on 1-week stored samples had declining accuracies of 97%, 83%, and 72% when predicting the ages of mosquitoes stored for 1, 4, or 8 weeks respectively. Conclusions: When using mid-infrared spectroscopy and supervised machine learning to age-grade mosquitoes, the highest accuracies are achieved when the training and test samples are preserved in the same way and stored for similar durations. However, when the test and training samples were handled differently, the classification accuracies declined significantly. Protocols for infrared-based entomological studies should therefore emphasize standardized sample-handling procedures and possibly additional statistical procedures such as transfer learning for greater accuracy.

• Item Type: Articles
• Additional Information: Funding: The study was fnancially supported by the Ifakara Health Institute Training unit together with the Wellcome Trust International Masters Fellowships in Tropical Medicine and Hygiene, grant no. WT214643/Z/18/Z, to EPM and Bill and Melinda Gates Foundation, grant no. INV-002138 to FOO, at Ifakara Health Institute.
• Keywords: Research, Malaria, Vector control, Sample handling, An.arabiensis, Age-grading, Machine learning and infrared spectroscopy
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Okumu, Professor Fredros
• Authors: Mgaya, J. N., Siria, D. J., Makala, F. E., Mgando, J. P., Vianney, J.-M., Mwanga, E. P., and Okumu, F. O.
• College/School: College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
• Journal Name: Parasites and Vectors
• Publisher: BioMed Central
• ISSN: 1756-3305
• ISSN (Online): 1756-3305
• Copyright Holders: Copyright © The Author(s) 2022
• First Published: First published in Parasites and Vectors 15: 281
• Publisher Policy: Reproduced under a Creative Commons licence