Abstract
Arbovirus and malaria infections affect more than half of the world's population causing major financial and physical burden. Current diagnostic tools such as microscopy, molecular and serological techniques are technically demanding, costly, or time consuming. Near-infrared spectroscopy has recently been demonstrated as a potential diagnostic tool for malaria and Dengue virus and as a screening tool for disease vectors. However, pathogen specific absorption peaks that allow detection of these infections are yet to be described. In this study, we identified unique visible and near-infrared peaks from existing laboratory strains of four major arboviruses including Barmah Forest virus, Dengue virus, Ross River virus, Sindbis virus and Plasmodium falciparum. Secondly, to determine the diagnostic ability of these peaks, we developed machine learning algorithms using artificial neural networks to differentiate arboviruses from media in which they were grown. Signature peaks for BFV were identified within the visible region at 410, 430, 562 and 588 nm and the near-infrared region at, 946, 958, 1130, 1154 and 1780 nm. DENV related peaks were seen at 410nm within the visible region and 1130 nm within the near-infrared region. Signature peaks for Ross River virus were observed within the visible region at 410 and 430 nm and within the near-infrared region at 1130 and 1780 nm, while Sindbis virus had a prominent peak at 410 nm within the visible region. Peaks at 514, 528, 547, 561, 582, and 595 nm and peaks at 1388, 1432, 1681, 1700, 1721, 1882, 1905, 2245, 2278, 2300 nm were unique for P. falciparum. Near-infrared spectroscopy predictive sensitivity defined as the ability to predict an arbovirus as an infection was 90% (n=20) for Barmah Forest virus, 100% (n=10) for Ross River virus and 97.5% (n=40) for Dengue virus, while infection specificity defined as the ability to predict media as not-infected was 100% (n=10). Our findings indicate that spectral signatures obtained by near-infrared spectroscopy are potential biomarkers for diagnosis of arboviruses and malaria.