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A fast progressive spectrum denoising combined with partial least squares algorithm and its application in online Fourier transform infrared quantitative analysis

文献类型：期刊论文

作者Zhang, Genwei1; Peng, Silong2,3; Cao, Shuya1; Zhao, Jiang1; Xie, Qiong2,3; Han, Quanjie2,3; Wu, Yifan2,3; Huang, Qibin1
刊名ANALYTICA CHIMICA ACTA
出版日期2019-10-03
卷号1074页码:62-68
ISSN号0003-2670
关键词Fourier transform infrared spectroscopy Progressive spectrum denoising Augmented Lagrange method Partial least squares Quantitative analysis
DOI10.1016/j.aca.2019.04.055
通讯作者Cao, Shuya(caoshuya@163.com) ; Huang, Qibin(fhxw108@sohu.com)
英文摘要Fourier transform infrared (FTIR) spectroscopy is an important method in analytical chemistry. A material can be qualitatively and quantitatively analyzed from its FTIR spectrum. Spectrum denoising is commonly performed before online FTIR quantitative analysis. The average method requires a long time to collect spectra, which weakens real-time online analysis. The Savitzky-Golay smoothing method makes peaks smoother with the increase of window width, causing useful information to be lost. The sparse representation method is a common denoising method, that is used to reconstruct spectrum. However, for the randomness of noise, we can't achieve the sparse representation of noise. Traditional sparse representation algorithms only perform denoising once, and the noise can not be removed completely. FTIR spectrum denoising should therefore be performed in a progressive way. However, it is difficult to determine to what degree of denoising is required. Here, a fast progressive spectrum denoising combined with partial least squares method was developed for online FTIR quantitative analysis. Two real sample data sets were used to test the performance of the proposed method. The experimental results indicated that the progressive spectrum denoising method combined with the partial least squares method performed markedly better than other methods in terms of root mean squared error of prediction and coefficient of determination in the FTIR quantitative analysis. (C) 2019 Elsevier B.V. All rights reserved.
WOS关键词SPECTROSCOPY
资助项目National Natural Science Foundation of China[61571438] ; National Natural Science Foundation of China[61601104]
WOS研究方向Chemistry
语种英语
出版者ELSEVIER SCIENCE BV
WOS记录号WOS:000469775600006
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/24416]  
专题自动化研究所_智能制造技术与系统研究中心_多维数据分析团队
通讯作者Cao, Shuya; Huang, Qibin
作者单位1.State Key Lab NBC Protect Civilian, Beijing 102205, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Genwei,Peng, Silong,Cao, Shuya,et al. A fast progressive spectrum denoising combined with partial least squares algorithm and its application in online Fourier transform infrared quantitative analysis[J]. ANALYTICA CHIMICA ACTA,2019,1074:62-68.
APA Zhang, Genwei.,Peng, Silong.,Cao, Shuya.,Zhao, Jiang.,Xie, Qiong.,...&Huang, Qibin.(2019).A fast progressive spectrum denoising combined with partial least squares algorithm and its application in online Fourier transform infrared quantitative analysis.ANALYTICA CHIMICA ACTA,1074,62-68.
MLA Zhang, Genwei,et al."A fast progressive spectrum denoising combined with partial least squares algorithm and its application in online Fourier transform infrared quantitative analysis".ANALYTICA CHIMICA ACTA 1074(2019):62-68.

入库方式： OAI收割

来源：自动化研究所

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