Abstract

Globally, hepatitis C virus (HCV) infection is the leading cause for liver related morbidity and mortality. Mortality rate linked with HCV can be effectively lowered by prompt and early diagnosis. In this research work, we aimed to evaluate the potential of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy to discriminate healthy sera samples from HCV infected freeze-dried sera samples in order to develop a new and quick reagent free diagnostic tool. Clear variation was observed between the ATR-FTIR spectra of 87 HCV infected and 83 healthy freeze-dried sera samples. Major variation was observed in the two spectral regions i.e., 3500–2800 and 1800–900 cm−1. Multivariate classification algorithms including principal component analysis (PCA) and linear discriminant analysis (LDA) were used. PCA scatter plot of first two PCs showed 97% variation between the two sample classes. When the four PCs were projected to the LDA algorithm for developing PCA-LDA diagnostic model it discriminated the HCV and healthy classes with 100% accuracy. This study provides an initial insight on the potential to use ATR-FTIR spectroscopy in conjugation with multivariate data classification tools for effective HCV diagnosis.

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