Hepatitis C virus (HCV) infection is one of the leading causes of morbidity and mortality worldwide. Mortality linked with HCV infection can be lowered with effective and prompt diagnosis in early stages of infection. In this study potential of Raman spectroscopy to differentiate between healthy and HCV infected serum samples was investigated. Clear differences were observed in the Raman spectra of HCV infected and healthy sera samples. Using the analysis of variance (ANOVA) and t-test (p < 0.001) on Raman spectra of diseased and healthy samples, we observed eleven unique Raman bands at 676, 825, 853, 936, 1029, 1105, 1155, 1305, 1620, 1654 and 1757 cm-1 associated with only HCV infected sera and have not been reported in earlier studies. In addition, six Raman bands at 556, 585, 716, 815, 1273 and 1142 cm-1were observed in healthy sera only. Three Raman bands at 1330, 1526 and 1572 cm-1 were observed in both type of samples but their intensity was drastically reduced in diseased samples. Various multivariate analysis techniques were employed to demonstrate the robustness of the results. We employed multivariate and unsupervised principal component analysis (PCA) in conjunction with supervised classification linear discriminant analysis (LDA), using ten-fold jackknife cross-validation, in order to develop effective diagnostic algorithm technique (PCA-LDA). Our PCA-LDA model yielded sufficient sensitivity and specificity i.e. correctly identified all infected samples included in this study. Ours results indicate that these unique Raman bands have the potential to be used as biomarkers for optical diagnosis of HCV infection.
Keywords: ANOVA; HCV infection; PCA-LDA; Raman spectroscopy; t-Test.
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