Characterization and prediction of viral loads of Hepatitis B serum samples by using surface-enhanced Raman spectroscopy (SERS)

•Surface-enhanced Raman spectroscopy is employed to characterize the serum samples of clinically diagnosed of hepatitis B patients and healthy ones.•Silver nanoparticles are used as surface-enhanced Raman spectroscopy substrates.•Surface enhanced Raman spectral features are associated with the increasing viral loads of hepatitis b which can be employed for diagnostic purpose.•Principal components analysis is found helpful for the differentiation between surface enhanced Raman spectroscopy spectral data of clinically diagnosed patients of hepatitis B and healthy serum samples.•Partial Least Square Regression model developed with standard samples of known viral loads for predicting the viral loads of blind samples with good accuracy. Raman spectroscopy is a promising technique to analyze the body fluids for the purpose of non-invasive disease diagnosis. To develop a surface-enhanced Raman spectroscopy (SERS) based method for qualitative and quantitative analysis of hepatitis B viral (HBV) infection from blood serum samples. Clinically diagnosed hepatitis B virus (HBV) infected serum samples of patients of different levels of viral loads have been subjected for SERS analysis in comparison with the healthy ones by using silver nanoparticles (Ag NPs) based SERS substrates. The SERS measurements were performed on blood serum samples of 11 healthy and 32 clinically diagnosed HBV patients of different viral load levels of different exponentials including (101, 102 called as low level), (103, 104 called as medium level) and (105, 108 called as high level). Furthermore, multivariate data analysis techniques, Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were also performed on SERS spectral data. The SERS spectral features due to biochemical changes in HBV positive serum samples associated with the increasing viral loads were established which could be employed for HBV diagnostic purpose. PCA was found helpful for the differentiation between SERS spectral data of serum samples of different levels of HBV infection and healthy individuals. PLSR model developed with standard samples of known viral loads for predicting the viral loads of blind/unknown samples with 99% predicted accuracy. SERS can be employed for qualitative and quantitative analysis of HBV infection from blood serum samples. [Display omitted]