Diagnosis of Hepatitis C Virus Infection in human serum using ELISA and Raman Spectroscopy

Abstract

In this study we have presented the optical detection of Hepatitis C virus and molecular changes in human serum through partial least square regression vectors obtained from their Raman spectra. 140 samples tested through enzyme-linked immunosorbent assay and PCR for confirmation were used to create a model by utilizing spectral variations in the Raman spectra of the positive and control groups. Regression coefficients of this model were obtained and analyzed. The regression vector yielded by this model is utilized to predict hepatitis C in unknown samples. This model has been evaluated by a cross-validation method, which yielded a correlation coefficient of 0.91. Moreover, 30 unknown samples were screened for hepatitis C infection using this model to test its performance. From these calculations, accuracy, specificity and sensitivity of this model were determined to be 86.67%, 93.75% and 78.57% respectively. The value of area under the ROC curve was found to be about 1 which shows that the model is accurate.