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Ion Mobility Spectrometry Spectrum Reconstruction and Characteristic Peaks Extraction Algorithm Research

文献类型：期刊论文

作者Zhang Gen-wei2; Peng Si-long1,3; Guo Teng-xiao2; Yang Jie2; Yang Jun-chao2; Zhang Xu2; Cao Shu-ya2; Huang Qi-bin2
刊名SPECTROSCOPY AND SPECTRAL ANALYSIS
出版日期2020-09-01
卷号40期号:9页码:2681-2685
关键词Ion mobility spectrometry Spectrum reconstruction Characteristic peaks extraction Sparse representation Surrogate function algorithm
ISSN号1000-0593
DOI10.3964/j.issn.1000-0593(2020)09-2681-05
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 a low detection limit. It is widely used to detect chemical warfare agents, illegal drugs and explosives. The original spectrum contains not only sample information, but also noise. Especially when the concentration of the analyte is low, the accuracy of qualitative and quantitative analysis based on IMS technology is seriously influenced. It is necessary to reconstruct the spectrum before qualitative and quantitative analysis. In our article, a new method simultaneously achieved the spectrum reconstruction, and characteristic peaks extraction was proposed. In the optimization function, we chose l(1), norm as the linear penalty. The regularization parameter A was used to adjust the scale of the penalty in the optimization. Solve the optimization function, a Gaussian dictionary was constructed to represent the shape of peak firstly, and the surrogate function algorithm was adopted to solve it. When the root mean squared error between the reconstructed and original spectrum achieved the set threshold, the algorithm was stopped. To evaluate the performance of our method proposed, the simulated data set and DMMP sample data set were used. The simulated data set was composed of Gaussian functions and Gaussian noise. Meanwhile, we compared our method with wavelet using a soft threshold, wavelet using hard threshold and S-G smoothing methods. 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 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
WOS记录号WOS:000576358300005
出版者OFFICE SPECTROSCOPY & SPECTRAL ANALYSIS
源URL[http://ir.ia.ac.cn/handle/173211/42101]  
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.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.State Key Lab NBC Protect Civilian, Beijing 102205, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhang Gen-wei,Peng Si-long,Guo Teng-xiao,et al. Ion Mobility Spectrometry Spectrum Reconstruction and Characteristic Peaks Extraction Algorithm Research[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2020,40(9):2681-2685.
APA Zhang Gen-wei.,Peng Si-long.,Guo Teng-xiao.,Yang Jie.,Yang Jun-chao.,...&Huang Qi-bin.(2020).Ion Mobility Spectrometry Spectrum Reconstruction and Characteristic Peaks Extraction Algorithm Research.SPECTROSCOPY AND SPECTRAL ANALYSIS,40(9),2681-2685.
MLA Zhang Gen-wei,et al."Ion Mobility Spectrometry Spectrum Reconstruction and Characteristic Peaks Extraction Algorithm Research".SPECTROSCOPY AND SPECTRAL ANALYSIS 40.9(2020):2681-2685.

入库方式： OAI收割

来源：自动化研究所

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