Quantitative detection of hepatocyte mixture based on terahertz time-domain spectroscopy using spectral image analysis methods.

In recent years, terahertz (THz) technology has received widespread attention and has been leveraged to make breakthroughs in the field of bio-detection. However, studies on its application in mixtures have not yet been extensively conducted. Traditional one-dimensional (1D) spectral feature extraction methods are inefficient in terms of sensitivity and overall performance owing to spectral overlapping and distortions of a mixture. Thus, we adopted the Gramian angular field (GAF) method to map THz 1D spectra to two-dimensional (2D) images using correlation information between sequences. Image features of hepatocyte mixtures with different ratios were extracted using histogram of oriented gradients (HOGs) and gray level histograms (GLHs). A support vector regression (SVR) model was established for quantitative analysis. The method was more stable and accurate than principal component analysis (PCA) method, and RMSE and R values reached 0.072 and 0.932, respectively. This study enriches the algorithms of THz detection by combining the advantages of data upscaling and image processing, which is of great significance for the application of THz technology toward mixed-system detection.