CASIA OpenIR
A fast progressive spectrum denoising combined with partial least squares algorithm and its application in online Fourier transform infrared quantitative analysis
Zhang, Genwei1; Peng, Silong2,3; Cao, Shuya1; Zhao, Jiang1; Xie, Qiong2,3; Han, Quanjie2,3; Wu, Yifan2,3; Huang, Qibin1
Source PublicationANALYTICA CHIMICA ACTA
ISSN0003-2670
2019-10-03
Volume1074Pages:62-68
Corresponding AuthorCao, Shuya(caoshuya@163.com) ; Huang, Qibin(fhxw108@sohu.com)
AbstractFourier 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.
KeywordFourier transform infrared spectroscopy Progressive spectrum denoising Augmented Lagrange method Partial least squares Quantitative analysis
DOI10.1016/j.aca.2019.04.055
WOS KeywordSPECTROSCOPY
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61571438] ; National Natural Science Foundation of China[61601104]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaChemistry
WOS SubjectChemistry, Analytical
WOS IDWOS:000469775600006
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24417
Collection中国科学院自动化研究所
Corresponding AuthorCao, Shuya; Huang, Qibin
Affiliation1.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
Recommended Citation
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.
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