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

doi:10.1016/j.aca.2019.04.055

	A fast progressive spectrum denoising combined with partial least squares algorithm and its application in online Fourier transform infrared quantitative analysis
	Zhang, Genwei 1; Peng, Silong2,3 ; Cao, Shuya 1; Zhao, Jiang 1; Xie, Qiong2,3 ; Han, Quanjie2,3 ; Wu, Yifan2,3 ; Huang, Qibin 1
刊名	ANALYTICA CHIMICA ACTA
	2019-10-03
卷号	1074 页码:62-68
关键词	Fourier transform infrared spectroscopy Progressive spectrum denoising Augmented Lagrange method Partial least squares Quantitative analysis
ISSN号	0003-2670
DOI	10.1016/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.
资助项目	National Natural Science Foundation of China[61571438] ; National Natural Science Foundation of China[61601104]
WOS关键词	SPECTROSCOPY
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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/24416]
专题	中国科学院自动化研究所
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.