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Effective Selection and Application of Ethanol Characteristic Spectrum in Gasoline
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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。Abstract: Near-infrared spectroscopy rapid analysis is one of the main methods to detect the ethanol content in gasoline, and the selection of the spectrum is an important factor affecting the prediction accuracy of the rapid detection model. In this study, a rapid analysis method of near-infrared spectroscopy based on an effective characteristic spectrum was established to improve the accuracy of detection of ethanol content in gasoline. By comparing the near-infrared spectra of gasoline with different concentrations of ethanol content, the effective characteristic spectrum of ethanol molecules in gasoline was determined to be 4524.183~5044.869 cm−1 and 5985.961~7108.329 cm−1. The optimal pre-processing method for near-infrared spectroscopy was chosen, and modeling analysis was carried out using the full-spectrum and effective characteristic spectrum. The relevant parameters of the model established using the effective characteristic spectrum are as follows: the root-mean-square error of cross-validation (RMSECV) is 0.5849, the internal cross-validation correlation coefficient (${{R}}_{\text{CV}}^{\text{2}}$ ) is 0.9991, the root-mean-square error of prediction (RMSEP) is 0.6090, and the external verification correlation coefficient (${{R}}_{\text{P}}^{\text{2}}$ ) of the prediction set is 0.9989. Compared with the full-spectrum modeling analysis, the RMSECV was reduced by 30.27 %, and the RMSEP was reduced by 18.58%. In conclusion, the quantitative analysis model established by using the characteristic spectrum has higher accuracy and can meet the need for rapid analysis of ethanol content in 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 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 1.8817 0.9980 1.4163 0.9942 一阶导数 0.8388 0.9984 0.7480 0.9982 标准正态变换 1.1763 0.9909 1.6889 0.9792 矢量归一化 1.4365 0.9944 1.6948 0.9918 多元散射校正 1.3668 0.9952 2.1895 0.9862 Savitzky-Golay卷积平滑 0.8606 0.9980 1.3472 0.9948 
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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 0.9681 0.9984 0.9848 0.9986 一阶导数 0.5849 0.9991 0.6090 0.9989 标准正态变换 0.9524 0.9932 1.6504 0.9922 矢量归一化 0.9765 0.9985 1.4466 0.9934 多元散射校正 0.9583 0.9950 1.2998 0.9882 Savitzky-Golay卷积平滑 0.7011 0.9989 0.8940 0.9977
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