基于天然气组分红外光谱图的数据预处理方法研究

康明; 韩森坪; 杨洪杰; 唐德东; 李妍君; 汪智琦

基于天然气组分红外光谱图的数据预处理方法研究

1.
重庆科技学院电气工程学院重庆 401331
2.
中国石油化工股份有限公司荆门分公司湖北荆门 448000

基金项目:

重庆市技术创新与应用发展专项 cstc2019jscx-msxmX0054

重庆市重庆科技学院研究生科技创新计划项目 YKJCX1920405

详细信息

作者简介:
康明（1997-），女，硕士研究生，主要从事天然气组分图谱解析研究。E-mail：1551043341@qq.com

中图分类号: TE81
计量
- 文章访问数: 261
- HTML全文浏览量: 92
- PDF下载量: 38
- 被引次数: 0
出版历程
- 收稿日期: 2021-01-05
- 修回日期: 2021-01-19
- 刊出日期: 2021-08-20

Data Preprocessing Method for Infrared Spectra Analysis of Natural Gas Components

1.
College of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
2.
China Petroleum & Chemical Corporation Jingmen Branch, Jingmen 448000, China

摘要

摘要: 利用红外光谱分析仪对天然气组分进行组分分析时所获得光谱信号往往会受杂散光、噪声、基线漂移等因素的干扰，从而影响最终定量分析结果，故需要在建模前对原始光谱进行预处理。为解决仪器测量光谱图的噪声干扰问题，本文提出一种Savitzky-Golay平滑法结合sym6小波函数软阈值去噪法对光谱图进行预处理。将传统的预处理方法与SG平滑法结合小波函数法进行对比分析。结果表明，以SG平滑法结合sym6小波函数软阈值去噪法对光谱图进行预处理，其拟合优度数值最高为0.98652，残差平方和数值最低为5.50694，证明使用该方法后的函数分峰拟合效果最佳，处理效果优于传统方法。
- 天然气 /
- 预处理 /
- 红外光谱图 /
- 去噪
Abstract: When using infrared spectroscopy to analyze the components of natural gas, the obtained spectral signals often contain interference from stray light, noise, baseline drift, and other factors, which affects the resulting quantitative analysis. Therefore, it is necessary to preprocess the original spectrum before modeling. As a potential solution, an SG smoothing method combined with the soft threshold denoising method of the sym6 wavelet function was proposed to preprocess the spectrogram. The traditional preprocessing method and the proposed method are compared and analyzed. The results show that when the proposed method is used to preprocess the spectrogram, the highest goodness of fit value is 0.98652, and the lowest residual sum of squares value is 5.50694, which proves that the function peak fitting effect is the best after using this method, and the processing effect is better than that of the traditional method.
- natural gas /
- pretreatment /
- infrared spectra /
- denoising

HTML全文

图 1 标准库建立实验结构图

Figure 1. Experiment structure of the standard library

下载: 全尺寸图片幻灯片

图 2 混合光谱经SG+db3小波去噪后的光谱图

Figure 2. The mixed spectrum is denoised by SG+db3 wavelet

下载: 全尺寸图片幻灯片

图 3 经SG+sym小波去噪后混合气体光谱图

Figure 3. Spectrum of mixed gas after denoising by SG+ sym wavelet

下载: 全尺寸图片幻灯片

图 4 经SG平滑后的高斯拟合图

Figure 4. Gaussian fit after SG smoothing

下载: 全尺寸图片幻灯片

图 5 经滤波后的高斯拟合图

Figure 5. Gaussian fit after wave filtering

下载: 全尺寸图片幻灯片

图 6 经SG+db3小波去噪后的高斯拟合图

Figure 6. Gaussian fit after SG+db3 wavelet denoising

下载: 全尺寸图片幻灯片

图 7 经SG+sym6小波去噪后的高斯拟合图

Figure 7. Gaussian fit after SG+sym6 wavelet denoising

下载: 全尺寸图片幻灯片

表 1 SG平滑+sym6小波变换与传统方法性能指标对比

Table 1. Performance index comparison of SG smoothing + sym6 wavelet transform and traditional methods

Pretreatment method	Fitting performance evaluation index		Various fitting parameters
Pretreatment method	R-Square	SSE	x_c	w	FWHM	A	Area
SG smoothing method（N=20）	0.97405	11.79833	2834.49055	39.20784	92.32725	5.59823	550.1827
SG smoothing method（N=20）	0.97405	11.79833	2981.79713	43.97818	103.56054	2.81837	310.68118
Median filter	0.97146	14.0104	2837.10274	45.2766707	106.618	5.27621	598.804
Median filter	0.97146	14.0104	2987.60154	37.49498	88.29445	2.94599	296.87252
SG(N=20)+db3 3-layer decomposition wavelet	0.9732	11.82987	2833.61284	38.94472	91.7078	5.60191	546.85842
SG(N=20)+db3 3-layer decomposition wavelet	0.9732	11.82987	2982.83007	47.12747	110.97672	2.74588	324.37366
SG(N=20)+sym6 4-layer decomposition wavelet	0.98652	5.50694	2836.78074	47.98166	112.9881	5.03243	605.2586
SG(N=20)+sym6 4-layer decomposition wavelet	0.98652	5.50694	2988.78528	34.83569	82..03177	2.8878	252.1598

下载: 导出CSV

参考文献(15)

[1]	JIAO Y, LI Z, CHEN X, et al. Preprocessing methods for near-infrared spectrum calibration[J]. Journal of Chemometrics, 2020, 34(11): e3306.
[2]	YANG J, DU L, GONG W, et al. Analyzing the performance of the first-derivative fluorescence spectrum for estimating leaf nitrogen concentration[J]. Optics Express, 2019, 27(4): 3978. doi: 10.1364/OE.27.003978
[3]	LI Y, HUANG Y, XIA J, et al. Quantitative analysis of honey adulteration by spectrum analysis combined with several high-level data fusion strategies[J]. Vibrational Spectroscopy, 2020, 108: 103060. doi: 10.1016/j.vibspec.2020.103060
[4]	第五鹏瑶, 卞希慧, 王姿方, 等. 光谱预处理方法选择研究[J]. 光谱学与光谱分析, 2019, 39(9): 2800-2806. https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201909027.htm DIWU Pengyao, BIAN Xi Hui, WANG Zifang, et al. Selection of spectral pretreatment methods[J]. Spectroscopy and Spectral Analysis, 2019, 39 (9): 2800-2806 https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201909027.htm
[5]	Bastiaansen W, Klein S, Steegers㏕heunissen R P, et al. VP24.02: towards a novel human embryonic brain atlas: fully automated preprocessing of prenatal ultrasound using artificial intelligence[J]. Ultrasound in Obstetrics and Gynecology, 2020, 56(S1): 159-159.
[6]	HUANG X, HUANG C, ZHAI G, et al. Data processing method of multibeam bathymetry based on sparse weighted LS-SVM machine algorithm[J]. IEEE Journal of Oceanic Engineering, 2019, 45(4): 1538-1551.
[7]	DING Y, ZHANG W, ZHAO X, et al. A hybrid random forest method fusing wavelet transform and variable importance for the quantitative analysis of K in potassic salt or using laser-induced breakdown spectroscopy[J]. Journal of Analytical Atomic Spectrometry, 2020, 35(6): 1131-1138. doi: 10.1039/D0JA00010H
[8]	ZHANG J, WEN H, TANG L. Improved smoothing frequency shifting and filtering algorithm for harmonic analysis with systematic error compensation[J]. IEEE Transactions on Industrial Electronics, 2019, 66(12): 9500-9509. doi: 10.1109/TIE.2019.2892664
[9]	杨帆, 王鹏, 张宁超, 等. 一种基于小波变换的改进滤波算法及其在光谱去噪方面的应用[J]. 国外电子测量技术, 2020, 39(8): 98-104. https://www.cnki.com.cn/Article/CJFDTOTAL-GWCL202008021.htm YANG Fan, WANG Peng, ZHANG Ningchao, et al. An improved filtering algorithm based on wavelet transform and its application in spectral denoising[J]. Foreign Electronic Measurement Technology, 2020, 39 (8): 98-104. https://www.cnki.com.cn/Article/CJFDTOTAL-GWCL202008021.htm
[10]	Mahajan G R, Das B, Gaikwad B, et al. Monitoring properties of the salt-affected soils by multivariate analysis of the visible and near- infrared hyperspectral data[J]. Catena, 2021, 198: 105041. doi: 10.1016/j.catena.2020.105041
[11]	侯培国, 李宁, 常江, 等. SG平滑和IBPLS联合优化水中油分析方法的研究[J]. 光谱学与光谱分析, 2015, 35(6): 1529-1533. doi: 10.3964/j.issn.1000-0593(2015)06-1529-05 HOU Peiguo, LI Ning, CHANG Jiang, et al. Research on SG smoothing and IBPLS joint optimization of oil-in-water analysis method[J]. Spectroscopy and Spectral Analysis, 2015, 35(6): 1529-1533. doi: 10.3964/j.issn.1000-0593(2015)06-1529-05
[12]	Zikiou N, Lahdir M, Helbert D. Hyperspectral image classification using graph-based wavelet transform[J]. International Journal of Remote Sensing, 2020, 41(7): 2624-2643. doi: 10.1080/01431161.2019.1694194
[13]	Bunaciu A A, Aboul-Enein H Y. Adulterated drug analysis using FTIR spectroscopy[J]. Applied Spectroscopy Reviews, 2021, 56(5): 423-437. doi: 10.1080/05704928.2020.1811717
[14]	LIU F, ZHANG Y, Yildirim T, et al. Signal denoising optimization based on a Hilbert-Huang transform-triple adaptable wavelet packet transform algorithm[J]. EPL (Europhysics Letters), 2019, 124(5): 54002. doi: 10.1209/0295-5075/124/54002
[15]	ZHANG M, LU C, LIU C. Improved double-threshold denoising method based on the wavelet transform[J]. OSA Continuum, 2019, 2(8): 2328-2342. doi: 10.1364/OSAC.2.002328