At present, the terahertz time-domain spectroscopic information of each component in multi-composition compounds detection is comprehensively combined. The lower resolution level of mixed spectra has posed many difficulties in signal analysis due to the overlapping characteristic information in the mixed ones. In this paper, to compare the performances of Nonnegative Matrix Factorization (NMF), Self-modeling Mixture Analysis (SMMA) and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) on complex systems, a binary mixture and a ternary mixture are employed during THz-TDS testing. The position of the absorption peak (PK) and the correlation coefficient are used to evaluate the decomposition effects. The experimental results show that the component spectra resolved by MCR-ALS demonstrate good consistency with the sample components. Further, MCR-ALS presents excellent results in comparison with NMF and SMMA in terms of decomposing precision and computing speed. MCR-ALS thus appears a promising algorithm to resolve the THz multi-way data, and may be useful for many applications, such as medicine quality assurance and unknown component identification in the general area of terahertz science and technology.

目前，多组分化合物检测中各组分的太赫兹时域光谱信息已经得到了综合结合。由于混合频谱中的特征信息重叠，混合频谱的较低分辨率级别在信号分析中带来了许多困难。为了比较非负矩阵分解（NMF），自建模混合分析（SMMA）和多元曲线分辨率-交替最小二乘（MCR-ALS）在复杂系统上的性能，使用了二元混合和三元混合在THz-TDS测试中。吸收峰（PK）的位置和相关系数用于评估分解效果。实验结果表明，MCR-ALS解析的组分光谱显示出与样品组分的良好一致性。此外，在分解精度和计算速度方面，与NMF和SMMA相比，MCR-ALS表现出出色的结果。因此，MCR-ALS似乎是解决THz多路数据的有前途的算法，并且可能对许多应用有用，例如太赫兹科学和技术领域的药物质量保证和未知成分识别。