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

CASIA OpenIR > 智能制造技术与系统研究中心 > 多维数据分析（彭思龙）-技术团队

	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
发表期刊	ANALYTICA CHIMICA ACTA
ISSN	0003-2670
	2019-10-03
卷号	1074 页码:62-68
通讯作者	Cao, Shuya(caoshuya@163.com) ; Huang, Qibin(fhxw108@sohu.com)
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.
关键词	Fourier transform infrared spectroscopy Progressive spectrum denoising Augmented Lagrange method Partial least squares Quantitative analysis
DOI	10.1016/j.aca.2019.04.055
关键词[WOS]	SPECTROSCOPY
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61601104] ; National Natural Science Foundation of China[61571438] ; National Natural Science Foundation of China[61601104] ; National Natural Science Foundation of China[61571438] ; National Natural Science Foundation of China[61571438] ; National Natural Science Foundation of China[61601104]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Chemistry
WOS类目	Chemistry, Analytical
WOS记录号	WOS:000469775600006
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/24417
专题	智能制造技术与系统研究中心_多维数据分析（彭思龙）-技术团队
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.