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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

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[J]. 红外技术, 2021, 43(8): 804-808.
引用本文: 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[J]. 红外技术, 2021, 43(8): 804-808.
KANG Ming, HAN Senping, YANG Hongjie, TANG Dedong, LI Yanjun, WANG Zhiqi. Data Preprocessing Method for Infrared Spectra Analysis of Natural Gas Components[J]. Infrared Technology , 2021, 43(8): 804-808.
Citation: KANG Ming, HAN Senping, YANG Hongjie, TANG Dedong, LI Yanjun, WANG Zhiqi. Data Preprocessing Method for Infrared Spectra Analysis of Natural Gas Components[J]. Infrared Technology , 2021, 43(8): 804-808.

基于天然气组分红外光谱图的数据预处理方法研究

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

Data Preprocessing Method for Infrared Spectra Analysis of Natural Gas Components

  • 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。
  • 图  1  标准库建立实验结构图

    Figure  1.  Experiment structure of the standard library

    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

    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

    图  4  经SG平滑后的高斯拟合图

    Figure  4.  Gaussian fit after SG smoothing

    图  5  经滤波后的高斯拟合图

    Figure  5.  Gaussian fit after wave filtering

    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

    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

    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
    R-Square SSE xc w FWHM A Area
    SG smoothing method(N=20) 0.97405 11.79833 2834.49055 39.20784 92.32725 5.59823 550.1827
    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
    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
    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
    2988.78528 34.83569 82..03177 2.8878 252.1598
    下载: 导出CSV
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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

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