基于盲源分离的有机物混合信号特征提取与解析

A systematic study on blind source analysis of complex organic mixture system based on independent component analysis (ICA) was carried out. By establishing a reasonable number of independent components, the selection of the number of independent components was optimized by the root mean square error between the reconstructed signal of the separation signal and the original signal, and variance contribution rate of principal components. The following studies have been completed by integrating three ICA algorithms: (1) The source analysis of the mixed mass spectrum signals of various environmental organic pollutants including nitrobenzene, among which the independent components extracted by Kernel ICA have a high correlation with the actual source signals, and the average value (standard deviation) of the substance R is 0.8697 (0.10), which can meet the requirements of qualitative identification; (2) The extraction of characteristic component information in the ultraviolet spectrum signal of Compound Paracetamol and Amantadine Hydrochloride, Kernel ICA is the most effective algorithm for extracting the main components of drugs. The aboved research work provides theoretical support for the construction of the optimal blind source analysis model of organic matter system, and provides an effective means for the source analysis of organic pollutants in actual environmental samples and the extraction of effective components of drugs.