LAUSR

Hepatitis B is an infectious disease cause by the hepatitis B virus (HBV). In recent years, HBV‐DNA level clinically gets more attention for its detailed information than other serological markers. Unfortunately, common clinical method for HBV‐DNA level detection is limited for its hours consuming. This study combined infrared spectroscopy with machine learning to investigate the feasibility of near‐infrared (NIR) and mid‐infrared (MIR) spectra for rapid detection of HBV‐DNA level. Based on partial least squares‐discriminant analysis (PLS‐DA) modeling method, the optimal NIR and MIR models and traditional data fusion models were constructed, respectively. Considering inequal weight between interval and point data in machine learning, interval‐point data fusion method was used to compare with other traditional date fusion methods. The results of the study illustrate that interval‐point data fusion of NIR and MIR spectra combined with PLS‐DA modeling can provide a rapid method for HBV‐DNA level detection.