|
|
|
|
|
|
| Ion Mobility Spectrometry Spectrum Reconstruction and Characteristic Peaks Extraction Algorithm Research |
| ZHANG Gen-wei1, PENG Si-long2, 3, GUO Teng-xiao1，YANG Jie1, YANG Jun-chao1, ZHANG Xu1, CAO Shu-ya1*, HUANG Qi-bin1* |
1. State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China
2. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3. University of Chinese Academy of Sciences, Beijing 100190, China |
|
|
|
|
Abstract 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 l1 norm as the linear penalty. The regularization parameter λ 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.
|
|
Received: 2019-07-31
Accepted: 2019-11-12
|
|
|
|
Corresponding Authors:
CAO Shu-ya, HUANG Qi-bin
E-mail: caoshuya@163.com；fhxw108@sohu.com
|
|
[1] Costanzo M T, Boock J J. Int. J. Mass Spectrom., 2017, 422: 188.
[2] Cumeras R, Figueras E, Davis C, et al. Analyst, 2015, 140(5): 1376.
[3] Hernández-Mesa M, Escourrou A, Monteau F, et al. Trends in Analytical Chemistry, 2017, 94: 520.
[4] Szymanska E, Davies A, Buydens L. Analyst, 2016, 141: 5689.
[5] WANG Xin, HE Hao, FAN Xian-guang, et al(王 昕，何 浩，范贤光, 等). Spectroscopy and Spectral Analysis(光谱学与光谱学分析), 2018, 38(1): 93.
[6] Arteagafalconi J, Osman H A, Saddik A E. R-Peak Detection Algorithm Based on Differention. IEEE International Symposium on Intelligent Signal Processing., IEEE, 2015.
[7] Lu J, Trnka M J, Roh S H, et al. Journal of the American Society for Mass Spectrometry, 2015, 26: 2141.
[8] Kazimierczuk K, Kasprzak P. Sensors, 2015, 15: 234.
[9] Alfons, Andreas. Computational Statistics & Data Analysis, 2016, 93: 421.
[10] Zhang G, Peng S, Cao S, et al. International Journal of Mass Spectrometry, 2019, 436: 147.
[11] Zong Jingjing, Qiu Tianshuang. Biomedical Signal Processing and Control, 2017, 34: 195.
[12] Kopczynski D, Rahmann S. Algorithms for Molecular Biology, 2015, 10(1): 17. |
| [1] |
ZHOU Bing, LI Bing-xuan*, HE Xuan, LIU He-xiong，WANG Fa-zhen. Classification of Camouflages Using Hyperspectral Images Combined With Fusing Adaptive Sparse Representation and Correlation Coefficient[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3851-3856. |
| [2] |
KONG Hui-hua1, 2, LIAN Xiang-yuan1, CHEN Ping2, PAN Jin-xiao1, 2. Research on Color Characterization of Material Components Based on Spectral CT[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3612-3617. |
| [3] |
ZHU Hai-jiang1, TANG Hao1, SUN Jing-xian1, DU Zhen-xia2. Classification Method of Liquor Quality Based on Time and Frequency Spectrum Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2962-2968. |
| [4] |
LIANG Kun1, 2, ZHANG Xia-xia1, 2, DING Jing1, 2, XU Jian-hong3, HAN Dong-shen1, 2, SHEN Ming-xia1, 2*. Discrimination of Wheat Scab Infection Level by Fourier Mid-Infrared Technology Combined with Sparse Representation Based Classification Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(10): 3251-3255. |
| [5] |
SHI Hai-xia1, XU Qing1, 2, WANG Han2, 3, LIU You-jiang2, LI Shan2, HU Jun2, 3, LI Yue1, 2*, CHEN Chi-lai2*. Study on the Effect of He-N2 Ratio in Mixed Carrier Gas on Separation of VOC Aliasing Peaks in FAIMS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(09): 2713-2718. |
| [6] |
WU Zhi-yuan1，YU Jian-wen1，2，LIU You-jiang1，CHEN Chi-lai1*. The Solution of Non-Linear Function of Ion Mobility Based on FAIMS Spectrum Peak Position[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(10): 3170-3174. |
| [7] |
WANG Cai-ling1, 2, WANG Hong-wei3, HU Bing-liang1, WEN Jia4, XU Jun5, LI Xiang-juan2 . A Novel Spatial-Spectral Sparse Representation for Hyperspectral Image Classification Based on Neighborhood Segmentation [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(09): 2919-2924. |
| [8] |
WEN Jia1, 2, ZHAO Jun-suo2, WANG Cai-ling3, XIA Yu-li2 . Decomposition of Interference Hyperspectral Images Using Improved Morphological Component Analysis [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(01): 254-258. |
| [9] |
HAO Dong-mei1, ZHOU Ya-nan1, WANG Yu1 , ZHANG Song1, YANG Yi-min1, LIN Ling2, LI Gang2, WANG Xiu-li3 . Recognition of Water-Injected Meat Based on Visible/Near-Infrared Spectrum and Sparse Representation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(01): 93-98. |
| [10] |
JIANG Lu-lu1, LUO Mei-fu1, ZHANG Yu1，YU Xin-jie2, KONG Wen-wen3, LIU Fei3* . Identification of Transmission Fluid Based on NIR Spectroscopy by Combining Sparse Representation Method with Manifold Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(01): 64-68. |
| [11] |
LIU Sheng1, 2, HUANG Chao-qun1, 3, SHEN Cheng-yin1, 3, CHU Yan-nan1, 3* . Asymmetric Control Method for Ion Shutter and the Resolution Improvement of Ion Mobility Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(11): 2881-2885. |
| [12] |
LI Hu1,3, NIU Wen-qi2, WANG Hong-mei1, HUANG Chao-qun1, JIANG Hai-he1, CHU Yan-nan1* . Photoionization Ion Mobility Spectrometry (UV-IMS) for the Isomeric Volatile Organic Compounds [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(01): 29-32. |
| [13] |
LI Hu1,3, HAN Hai-yan1, NIU Wen-qi2, WANG Hong-mei1, HUANG Chao-qun1, JIANG Hai-he1, CHU Yan-nan1* . Rapid Detection of Residual Cyclohexanone in Disposable Medical Devices by Ultraviolet Photoionization Ion Mobility Spectrometry (UV-IMS) [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(01): 46-49. |
| [14] |
LI Zhuang1, 2，LIN Bing-tao1, 2，KONG De-yi1*，CHEN Chi-lai1，CHENG Yu-peng1，2，WANG Huan-qin1，MEI Tao1 . FAIMS of Trace Volatile Organic Compounds [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(01): 12-15. |
| [15] |
ZHAO Rui-zhen1, WANG Fei1, LUO A-li2, ZHANG Yan-xia2 . A Method for Auto-Extraction of Spectral Lines Based on Sparse Representation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2009, 29(07): 2010-2013. |
|
|
|
|
