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Using self-referencing interlaced submatrices to determine the number of chemical species in a mixture
邵利民
Author NameAffiliationE-mail
邵利民 中国科学技术大学化学系 lshao@ustc.edu.cn 
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 problem 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 (PCs) obtained by principal component analysis (PCA) 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 factor 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
FundProject:
Using self-referencing interlaced submatrices to determine the number of chemical species in a mixture
邵利民
摘要:
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 problem 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 (PCs) obtained by principal component analysis (PCA) 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 factor 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.
关键词:  Number of chemical species, Bilinear two-way data matrix, Interlaced submatrix, Principal component analysis
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