Cao Yuqi, Guan Hanxiao, Qiu Weihang, Shen Liran, Liu Heng, Tian Liangfei, Hou Dibo, Zhang Guangxin
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China.
College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310000, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2025 Feb 5;326:125235. doi: 10.1016/j.saa.2024.125235. Epub 2024 Oct 1.
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.
近年来，太赫兹（THz）技术受到广泛关注，并已被用于在生物检测领域取得突破。然而，关于其在混合物中的应用研究尚未广泛开展。由于混合物的光谱重叠和失真，传统的一维（1D）光谱特征提取方法在灵敏度和整体性能方面效率低下。因此，我们采用格拉姆角场（GAF）方法，利用序列之间的相关信息将太赫兹1D光谱映射到二维（2D）图像。使用定向梯度直方图（HOG）和灰度直方图（GLH）提取不同比例肝细胞混合物的图像特征。建立了支持向量回归（SVR）模型进行定量分析。该方法比主成分分析（PCA）方法更稳定、准确，均方根误差（RMSE）和R值分别达到0.072和0.932。本研究结合了数据上采样和图像处理的优势，丰富了太赫兹检测算法，对太赫兹技术在混合系统检测中的应用具有重要意义。
