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 R2 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.

Study on the detection method of biological characteristics of hepatoma cells based on terahertz time-domain spectroscopy.

Liver cancer usually has a high degree of malignancy and its early symptoms are hidden, therefore, it is of significant research value to develop early-stage detection methods of liver cancer for pathological screening. In this paper, a biometric detection method for living human hepatocytes based on terahertz time-domain spectroscopy was proposed. The difference in terahertz response between normal and cancer cells was analyzed, including five characteristic parameters in the response, namely refractive index, absorption coefficient, dielectric constant, dielectric loss and dielectric loss tangent. Based on class separability and variable correlation, absorption coefficient and dielectric loss were selected to better characterize cellular properties. Maximum information coefficient and principal component analysis were employed for feature extraction, and a cell classification model of support vector machine was constructed. The results showed that the algorithm based on parameter feature fusion can achieve an accuracy of 91.6% for human hepatoma cell lines and one normal cell line. This work provides a promising solution for the qualitative evaluation of living cells in liquid environment.