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汽油中乙醇光谱特征谱段的有效选取及应用

李轲 鲁冰 杜彪 卢小新 刘喆 李庆武 张正东

李轲,鲁冰,杜彪,等. 汽油中乙醇光谱特征谱段的有效选取及应用[J]. 计量科学与技术,2022, 66(5): 19-24 doi: 10.12338/j.issn.2096-9015.2021.0631
引用本文: 李轲,鲁冰,杜彪,等. 汽油中乙醇光谱特征谱段的有效选取及应用[J]. 计量科学与技术,2022, 66(5): 19-24 doi: 10.12338/j.issn.2096-9015.2021.0631
LI Ke, LU Bing, DU Biao, LU Xiaoxin, LIU Zhe, LI Qingwu, ZHANG Zhengdong. Effective Selection and Application of Ethanol Characteristic Spectrum in Gasoline[J]. Metrology Science and Technology, 2022, 66(5): 19-24. doi: 10.12338/j.issn.2096-9015.2021.0631
Citation: LI Ke, LU Bing, DU Biao, LU Xiaoxin, LIU Zhe, LI Qingwu, ZHANG Zhengdong. Effective Selection and Application of Ethanol Characteristic Spectrum in Gasoline[J]. Metrology Science and Technology, 2022, 66(5): 19-24. doi: 10.12338/j.issn.2096-9015.2021.0631

汽油中乙醇光谱特征谱段的有效选取及应用

doi: 10.12338/j.issn.2096-9015.2021.0631
基金项目: 中国计量科学研究院基本科研业务费项目(AKYZZ2131);国家科技基础条件平台项目(APT2101-8);院企横向科研项目(JSFW2102)。
详细信息
    作者简介:

    李轲(1990-),中国计量科学研究院在站博士后,研究方向:化学计量,邮箱:like@nim.ac.cn

    通讯作者:

    张正东(1976- ),中国计量科学研究院副研究员,研究方向:化学计量,邮箱:zhangzhengdong@nim.ac.cn

Effective Selection and Application of Ethanol Characteristic Spectrum in Gasoline

  • 摘要: 近红外光谱快速分析技术是检测汽油中乙醇含量的主要方法之一,光谱谱段的选择是影响快检模型预测准确性的重要因素。本研究建立了一种基于有效特征谱段的近红外光谱快速分析方法,提高了汽油中乙醇含量检测的准确度。通过对比不同浓度乙醇含量的汽油近红外光谱图,确定了汽油中乙醇分子的有效特征谱段是4524.183~5044.869 cm−1和5985.961~7108.329 cm−1。选择最优的近红外光谱预处理方法,分别使用近红外光谱全谱段和有效特征谱段进行建模分析。使用特征谱段建立的数据模型相关参数如下:交叉验证均方根误差(RMSECV)是0.5849,内部交叉验证相关系数(${{R}}_{\rm{CV}}^{{2}}$)是0.9991,预测均方根误差(RMSEP)是0.6090,预测集外部验证相关系数(${{R}}_{\rm{P}}^{{2}}$)是0.9989。相较于全波长建模分析,使用特征谱段建立模型的RMSECV降低了30.27%, RMSEP降低了18.58%。综上,使用特征谱段建立的模型准确度较高,能够满足汽油中乙醇含量快速分析的需求。
  • 图  1  乙醇汽油的近红外光谱图

    Figure  1.  Near-infrared spectra of ethanol gasoline

    图  2  使用一阶导数处理后的近红外光谱图

    Figure  2.  Near-infrared spectra processed with first order derivative

    图  3  X变量解释方差和主成分相关关系图

    Figure  3.  Correlation between the X-variable explained variance and the principal component

    图  4  Y变量解释方差和主成分相关关系图

    Figure  4.  Correlation between explained variance and principal component of the Y -variable

    图  5  乙醇含量校正模型的回归曲线

    Figure  5.  Regression curve of ethanol content correction model

    图  6  预测集乙醇含量的预测结果

    Figure  6.  The prediction results of the ethanol content in the prediction set

    表  1  不同预处理方法的全波长建模模型性能比较

    Table  1.   Performance comparison of full-spectrum modeling models with different pre-processing methods

    预处理方法校正集预测集
    RMSECV$ {{R}}_{\text{CV}}^{\text{2}} $RMSEP$ {{R}}_{\text{P}}^{\text{2}} $
    原始光谱1.88170.99801.41630.9942
    一阶导数0.83880.99840.74800.9982
    标准正态变换1.17630.99091.68890.9792
    矢量归一化1.43650.99441.69480.9918
    多元散射校正1.36680.99522.18950.9862
    Savitzky-Golay卷积平滑0.86060.99801.34720.9948
    下载: 导出CSV

    表  2  不同预处理方法的特征谱段建模模型性能比较

    Table  2.   Performance comparison of characteristic spectrum modeling models with different pre-processing methods

    预处理方法校正集预测集
    RMSECV$ {{R}}_{\text{CV}}^{\text{2}} $RMSEP$ {{R}}_{\text{P}}^{\text{2}} $
    原始光谱0.96810.99840.98480.9986
    一阶导数0.58490.99910.60900.9989
    标准正态变换0.95240.99321.65040.9922
    矢量归一化0.97650.99851.44660.9934
    多元散射校正0.95830.99501.29980.9882
    Savitzky-Golay卷积平滑0.70110.99890.89400.9977
    下载: 导出CSV
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  • 网络出版日期:  2022-04-14

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