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Ion Mobility Spectrometry Spectrum Denoising and Baseline Correction Algorithm by Sparse Representation

文献类型：期刊论文

作者Zhang Gen-wei1,3; Peng Si-long2,4; Cao Shu-ya1,3; Zhao Jiang1,3; Yang Liu1,3; Yang Jie1,3; Yang Jun-chao1,3; Huang Qi-bin1,3
刊名SPECTROSCOPY AND SPECTRAL ANALYSIS
出版日期2020
卷号40期号:1页码:75-79
ISSN号1000-0593
关键词Ion mobility spectrometry Denoising Baseline correction Sparse representation Iteratively reweighted least squares
DOI10.3964/j.issn.1000-0593(2020)01-0075-05
通讯作者Cao Shu-ya(caoshuya@163.com) ; Huang Qi-bin(fhxw108@sohu.com)
英文摘要Ion mobility spectrometry (IMS) is a rapid, highly sensitive analytical method for the gaseous samples with low detection limit. It is widely used to detect chemical warfare agents, illegal drugs and explosives. Due to the ionization source, pumps, high voltage, electric field of drift tube, the circuit and the interference of environment, the accuracy of qualitative and quantitative analysis based on IMS technology is seriously influenced. The collected current is pA level, and original spectrum contains large noise and baseline drift which resulted in the fact that we failed to detect small signal. It is necessary to do pre-processing before qualitative and quantitative analysis. The traditional pre-processing methods of denoising and baseline correction used different algorithms and the performances of the algorithms were evaluated independently. In our article, a method simultaneously achieved denoising and baseline correction was proposed. In the sparse representation model, we chose l(1)-norm as the linear penalty and added a constraint of smoothness for the baseline. The regularization parameters lambda were used to adjust the scale of the penalty in the optimization. To solve the sparse representation model, a Gaussian dictionary was constructed to represent the shape of peak firstly, and iteratively reweighted least squares (IRLS) algorithm was used to adopted to solve it. To evaluate the performance of method proposed, three simulated data sets and one real sample data set were used. The simulated data sets were composed of Gaussian function, baseline (sinusoidal wave function, exponential function, linear function, respectively) and Gaussian noise. Meanwhile, we compared our method with asymmetric least squares (AsLS) baseline correction methods combined with Savitzky-Golay (SG) and wavelet smoothing method. Root mean squared error (RMSE) and signal to noise ratio (SNR) were used to compare the results of different methods. The experiments results show that our method has a significant improvement than other methods. Based on the proposed method, qualitative and quantitative analysis can be carried out.
WOS研究方向Spectroscopy
语种英语
出版者OFFICE SPECTROSCOPY & SPECTRAL ANALYSIS
WOS记录号WOS:000512216000013
源URL[http://ir.ia.ac.cn/handle/173211/28612]  
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 Shu-ya; Huang Qi-bin
作者单位1.State Key Lab NBC Protect Civilian, Beijing 102205, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.Peoples Liberat Army PLA Acad Mil Sci, Res Inst Chem Def, Beijing 102205, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhang Gen-wei,Peng Si-long,Cao Shu-ya,et al. Ion Mobility Spectrometry Spectrum Denoising and Baseline Correction Algorithm by Sparse Representation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2020,40(1):75-79.
APA Zhang Gen-wei.,Peng Si-long.,Cao Shu-ya.,Zhao Jiang.,Yang Liu.,...&Huang Qi-bin.(2020).Ion Mobility Spectrometry Spectrum Denoising and Baseline Correction Algorithm by Sparse Representation.SPECTROSCOPY AND SPECTRAL ANALYSIS,40(1),75-79.
MLA Zhang Gen-wei,et al."Ion Mobility Spectrometry Spectrum Denoising and Baseline Correction Algorithm by Sparse Representation".SPECTROSCOPY AND SPECTRAL ANALYSIS 40.1(2020):75-79.

入库方式： OAI收割

来源：自动化研究所

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