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首页> 外文期刊>Energy & fuels >Data Fusion of Raman and Near-Infrared Spectroscopies for the Rapid Quantitative Analysis of Methanol Content in Methanol-Gasoline
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Data Fusion of Raman and Near-Infrared Spectroscopies for the Rapid Quantitative Analysis of Methanol Content in Methanol-Gasoline

机译：拉曼光谱和近红外光谱数据融合，用于快速定量分析甲醇汽油中的甲醇含量

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摘要

Rapid analysis of methanol content in methanol-gasoline is of great significance to monitor the methanol-gasoline quality. In this work, two different data-fusion strategies based on Raman and near-infrared (NIR) spectroscopies coupled with partial least square (PLS) were constructed and applied for a rapid and accurate analysis of the methanol content in methanol-gasoline. The Raman and NIR spectra of 49 methanol-gasoline samples were recorded, and the characteristic peaks of the methanol-gasoline samples in Raman and NIR spectroscopies were identified. For spectral data fusion, two different data-fusion strategies based on Raman and NIR spectroscopies coupled with PLS were proposed; normalization was used for low-level data fusion, and variable importance in projection (VIP) was used for mid-level data fusion. The different spectra pretreatment methods, latent variables, and variable importance thresholds of VIP were explored and optimized by 5-fold cross-validation (CV) to optimize the PLS calibration model for methanol content analysis. To further prove the predictive performance and stability of the PLS calibration model based on two data-fusion strategies, four PLS calibration models based on Raman, NIR, and two data-fusion strategies were applied to the quantitative analysis of methanol content in methanol-gasoline. The results show that the predictive performance of PLS calibration models based on the two data-fusion strategies is improved, and the PLS calibration model based on mid-level data fusion strategy gave an excellent predictive performance in methanol content analysis, with coefficients of determination of cross-validation (R-cv(2)) and validation set (R-v(2)) of 0.9988 and 0.9905, respectively, and root mean square error of cross-validation (RMSECV) and validation set (RMSEV) of 0.0068 and 0.0288%, respectively. Therefore, data fusion based on Raman and NIR spectroscopies coupled with PLS can give a rapid and accurate quantitative analysis of the methanol content in methanol-gasoline.
机译：快速分析甲醇汽油中甲醇含量对监测甲醇汽油质量具有重要意义。在这项工作中，构建了两种基于拉曼光谱和近红外（NIR）光谱结合偏最小二乘（PLS）的数据融合策略，并将其用于快速准确地分析甲醇汽油中的甲醇含量。记录了49个甲醇汽油样品的拉曼光谱和NIR光谱，并确定了拉曼光谱和NIR光谱中甲醇汽油样品的特征峰。对于光谱数据融合，提出了两种基于拉曼光谱和近红外光谱结合PLS的数据融合策略。归一化用于低级数据融合，变量重要性投影（VIP）用于中级数据融合。通过5倍交叉验证（CV）探索和优化了VIP的不同光谱预处理方法，潜在变量和变量重要性阈值，以优化用于甲醇含量分析的PLS校准模型。为了进一步证明基于两种数据融合策略的PLS校准模型的预测性能和稳定性，将基于拉曼，NIR和两种数据融合策略的四种PLS校准模型用于甲醇汽油中甲醇含量的定量分析。结果表明，基于两种数据融合策略的PLS校准模型的预测性能得到了改善，基于中级数据融合策略的PLS校准模型在甲醇含量分析中具有出色的预测性能，测定系数为交叉验证（R-cv（2））和验证集（Rv（2））分别为0.9988和0.9905，交叉验证的均方根误差（RMSECV）和验证集的均方根误差（RMSEV）为0.0068和0.0288％ ， 分别。因此，基于拉曼光谱和近红外光谱结合PLS的数据融合可以快速准确地定量分析甲醇汽油中的甲醇含量。

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  • 来源
    《Energy & fuels》 |2019年第12期|12286-12294|共9页
  • 作者

    Li Maogang; Xue Jia; Du Yao; Zhang Tianlong; Li Hua;

  • 作者单位

    Xian Shiyou Univ Coll Chem & Chem Engn Xian 710065 Shaanxi Peoples R China;

    Northwest Univ Coll Chem & Mat Sci Minist Educ Key Lab Synthet & Nat Funct Mol Chem Xian 710069 Shaanxi Peoples R China;

    Xian Shiyou Univ Coll Chem & Chem Engn Xian 710065 Shaanxi Peoples R China|Northwest Univ Coll Chem & Mat Sci Minist Educ Key Lab Synthet & Nat Funct Mol Chem Xian 710069 Shaanxi Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
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