中药材太赫兹数据预处理方法的研究
Research on Preprocessing Methods of Terahertz Data for Traditional Chinese Medicine

随着太赫兹技术的迅速发展和国家对中药行业的支持使其在中药材品质鉴别中成为了热点，但太赫兹光谱数据易受噪声干扰增加了药材鉴别的不确定性，因此需探索出适合中药材太赫兹光谱数据的预处理方法。本研究以当归为例，比较了多种不同预处理方式。同时研究了不同参数对SG平滑和小波变换效果的影响。结果显示，SG + 小波的组合能够有效去除噪声，且具有高效稳定性，是一种可标准化的处理方法；SG + MSC + 小波的组合处理效果最佳，但流程相对复杂，适用于高标准场景。本研究为中药材太赫兹光谱数据的预处理提供了有效方案。
With the rapid development of terahertz technology and the national support for the Traditional Chinese Medicine (TCM) industry, it has become a hotspot for identifying the quality of TCM. However, terahertz spectral data is easily affected by noise, which increases the uncertainty of herb identification. Therefore, it is necessary to explore preprocessing methods suitable for TCM terahertz spectral data. In this study, Angelica sinensis was used as an example to compare various preprocessing methods. The effects of different parameters on Savitzky-Golay (SG) smoothing and wavelet transform were also studied. The results show that the combination of SG and wavelet transform can effectively remove noise with high efficiency and stability, making it a standardizable processing method. The combination of SG, Multiplicative Scatter Correction (MSC), and wavelet transform yields the best results, but the process is relatively complex, making it suitable for high-standard scenarios. This study provides an effective solution for preprocessing TCM terahertz spectral data.