FTIR-based molecular fingerprinting for the rapid classification of dengue and chikungunya from human sera using machine learning: an observational study.

Background: Dengue and chikungunya are arboviral diseases transmitted by Methods: We conducted an observational study to evaluate the diagnostic potential of Fourier Transform Infrared (FTIR) and Raman micro-spectroscopy combined with machine learning for the classification of dengue and chikungunya from human serum. Serum samples from confirmed dengue (N = 142), chikungunya (N = 120), and healthy controls (N = 40) were analysed. Vibrational spectra were acquired using FTIR and Raman techniques, followed by spectral deconvolution and machine learning-based classification using Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) models. Findings: FTIR analysis revealed distinctive group-specific vibrational signatures, particularly in the Amide I and III regions, where dengue-infected sera exhibited a marked increase in β-sheet content and loss of α-helical structures. Raman spectroscopy further identified differences in nucleic acid backbone vibrations and protein conformations. The SVM, RF, and NN models, trained on FTIR data, achieved near-perfect classification (AUC = 1.000; CA-score ≥0.989), outperforming traditional diagnostic methods. Additionally, t-SNE and Silhouette analyses demonstrated superior clustering performance with FTIR, with clear separation of Chikungunya samples (average Silhouette score 0.385) compared to Raman, where clustering was less distinct. Interpretation: Vibrational spectroscopy, particularly FTIR integrated with machine learning, offers a robust, rapid, and scalable diagnostic platform for distinguishing arboviral infections in regions with high co-infection rates. By capturing host biomolecular changes directly from serum, this method minimizes cross-reactivity and enhances diagnostic speed compared to ELISA and RT-PCR. Its deployment in point-of-care settings could significantly improve arboviral surveillance and clinical management, especially in resource-limited regions. Funding: This study was funded by the Department of Health Research- Indian Council of Medical Research (DHR-ICMR) Grant-In-Aid grant number GIA/2020/000346 and CoEs Phase II, IIT/SRIC/IDK-PHASE-II/2024/01.