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自参考交错子矩阵法确定混合物的化学组分数
王 苗, 汪婉萍, 邵利民*
中国科学技术大学化学系,合肥 230026
摘要:
本文提出SRISM方法解决这个问题,方法基于化学信息和噪声之间的频率差异. 首先,对原始波谱数据矩阵进行间隔采样,获得两个交错子矩阵. 原始化学信息在交错子矩阵中仍被完全采样,但是噪声并非如此. 然后,对两个交错子矩阵分别进行主成分分析,得到两组主成分. 最后,将两组主成分配对比较,从而获得原始波谱数据矩阵的化学组分数. 通过处理模拟数据和实验数据,SRISM方法能够有效克服信号重叠、微量组分和噪声等干扰因素,获得正确的化学组分数. SRISM方法具有原理完备、计算效率高、自动进行等优点.
关键词:  化学组分数,双线性矩阵,交错子矩阵,主成分分析
DOI:10.1063/1674-0068/31/cjcp1805115
分类号:
基金项目:
Using Self-referencing Interlaced Submatrices to Determine the Number of Chemical Species in a Mixture
Miao Wang, Wan-ping Wang, Li-min Shao*
Department of Chemistry, University of Science and Technology of China, Hefei 230026, China
Abstract:
Determining the number of chemical species is the first step in analyses of a chemical or biological system. A novel method is proposed to address this issue by taking advantage of frequency differences between chemical information and noise. Two interlaced submatrices were obtained by downsampling an original data spectra matrix in an interlacing manner. The two interlaced submatrices contained similar chemical information but different noise levels. The number of relevant chemical species was determined through pairwise comparisons of principal components obtained by principal component analysis of the two interlaced submatrices. The proposed method, referred to as SRISM, uses two self-referencing interlaced submatrices to make the determination. SRISM was able to selectively distinguish relevant chemical species from various types of interference factors such as signal overlapping, minor components and noise in simulated datasets. Its performance was further validated using experimental datasets that contained high-levels of instrument aberrations, signal overlapping and collinearity. SRISM was also applied to infrared spectral data obtained from atmospheric monitoring. It has great potential for overcoming various types of interference factor. This method is mathematically rigorous, computationally efficient, and readily automated.
Key words:  Number of chemical species, Bilinear two-way data matrix, Interlaced submatrix, Principal component analysis