基于集成特征的拉曼光谱谱库匹配方法
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摘要
基于谱库的匹配识别是应用拉曼光谱进行物质成分鉴别的关键,会直接影响匹配结果的准确性。在谱库匹配中,尤其是针对混合物的光谱,利用单一的匹配特征无法全面反映被测样本光谱与谱库光谱的相似性,光谱匹配识别时需要综合考虑多种匹配特征。采用逻辑回归数学模型融合谱峰匹配系数、非负最小二乘匹配系数以及夹角余弦匹配系数,提出了一种新的光谱集成匹配方法。该方法既考虑了被测样品的谱峰信息,又考虑了其全谱信息。基于20种氨基酸混合物拉曼光谱谱库匹配的实验结果表明:所提光谱集成匹配方法具有更低的误判率。
Library-based matching recognition is the key to the application of Raman spectroscopy for material composition identification,which directly affects the accuracy of matching results.For the library matching,especially for the mixture spectrum,the single matching feature can not fully reflect the similarity between the measured sample spectrum and the spectral spectrum in the library.The spectral matching needs to comprehensively consider multiple matching features.In this paper,a new spectral integration matching method is proposed by using the logistic regression mathematical model to fuse the peak matching coefficient,the non-negative least squares matching coefficient and the cosine matching coefficient.The new method takes into account both the spectral peak information and full spectrum information of the sample.The experiment results based on the Raman spectroscopy library matching of 20 kinds of amino acid mixtures show that the spectral integration matching method has lower false positive rate.
Library-based matching recognition is the key to the application of Raman spectroscopy for material composition identification,which directly affects the accuracy of matching results.For the library matching,especially for the mixture spectrum,the single matching feature can not fully reflect the similarity between the measured sample spectrum and the spectral spectrum in the library.The spectral matching needs to comprehensively consider multiple matching features.In this paper,a new spectral integration matching method is proposed by using the logistic regression mathematical model to fuse the peak matching coefficient,the non-negative least squares matching coefficient and the cosine matching coefficient.The new method takes into account both the spectral peak information and full spectrum information of the sample.The experiment results based on the Raman spectroscopy library matching of 20 kinds of amino acid mixtures show that the spectral integration matching method has lower false positive rate.
引文
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[3]Fang X Q,Peng Y K,Li Y Y,et al.Rapid and quantitative detection method of sodium benzoate in carbonated beverage based on surface-enhanced Raman spectroscopy[J].Acta Optica Sinica,2017,37(9):0930001.房晓倩,彭彦昆,李永玉,等.基于表面增强拉曼光谱快速定量检测碳酸饮料中苯甲酸钠的方法[J].光学学报,2017,37(9):0930001.
[4]Blanco M,Romero M A.Near-infrared libraries in the pharmaceutical industry:a solution for identity confirmation[J].The Analyst,2001,126(12):2212-2217.
[5]Chen Z P,Li L M,Jin J W,et al.Quantitative analysis of powder mixtures by Raman spectrometry:the influence of particle size and its correction[J].Analytical Chemistry,2012,84(9):4088-4094.
[6]Sato-BerrúR Y,Medina-Valtierra J,MedinaGutiérrez C,et al.Quantitative NIR-Raman analysis of methyl-parathion pesticide microdroplets on aluminium substrates[J].Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy,2004,60(10):2231-2234.
[7]Zhou W H,Ying Y B,Xie L J.Spectral database systems:a review[J].Applied Spectroscopy Reviews,2012,47(8):654-670.
[8]Zhang Z M,Chen X Q,Lu H M,et al.Mixture analysis using reverse searching and non-negative least squares[J].Chemometrics and Intelligent Laboratory Systems,2014,137:10-20.
[9]Gawinkowski S,Kamińska A,Roliński T,et al.Anew algorithm for identification of components in a mixture:application to Raman spectra of solid amino acids[J].The Analyst,2014,139(22):5755-5764.
[10]Peng Y,Zhang Z M,Lu H M,et al.Identification of colorants in food by surface-enhanced Raman spectroscopy and wavelet-based reverse search[J].Journal of Instrumental Analysis,2017,36(5):627-632.彭颖,张志敏,卢红梅,等.基于小波-反向搜索及表面增强拉曼的食品中色素的光谱定性分析[J].分析测试学报,2017,36(5):627-632.
[11]Kong X B,Shu N,Tao J B,et al.A new spectral similarity measure based on multiple features integration[J].Spectroscopy and Spectral Analysis,2011,31(8):2166-2170.孔祥兵,舒宁,陶建斌,等.一种基于多特征融合的新型光谱相似性测度[J].光谱学与光谱分析,2011,31(8):2166-2170.
[12]Chu X L,Li J Y,Chen P,et al.Algorithms,strategies and application progress of spectral searching methods[J].Chinese Journal of Analytical Chemistry,2014,42(9):1379-1386.褚小立,李敬岩,陈瀑,等.分子光谱自动检索算法、策略与应用进展[J].分析化学,2014,42(9):1379-1386.
[13]Zhang Z M,Tong X,Peng Y,et al.Multiscale peak detection in wavelet space[J].The Analyst,2015,140(23):7955-7964.
[14]Liu M H,Dong Z R,Xin G F,et al.Discrimination method of Raman spectral peaks based on Voigt function fitting[J].Chinese Journal of Lasers,2017,44(5):0511003.刘铭晖,董作人,辛国锋,等.基于Voigt函数拟合的拉曼光谱谱峰判别方法[J].中国激光,2017,44(5):0511003.
[15]Lawson C L,Hanson R J.Solving least squares problems[M].[S.l]:Society for Industrial and Applied Mathematics,1995:160-165.
[16]Eilers P H C.A perfect smoother[J].Analytical Chemistry,2003,75(14):3631-3636.
[17]Cai Y Y,Yang C H,Xu D G,et al.Baseline correction for Raman spectra using penalized spline smoothing based on vector transformation[J].Analytical Methods,2018,10(28):3525-3533.