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引用本文:   王拓, 戴连奎, 马万武. 拉曼光谱结合后向间隔偏最小二乘法用于调和汽油辛烷值定量分析. 分析化学, 2018, 46(4): 623-629. doi:  10.11895/j.issn.0253-3820.170278 [复制]

Citation:   WANG Tuo, DAI Lian-Kui, MA Wan-Wu. Quantitative Analysis of Blended Gasoline Octane Number Using Raman Spectroscopy with Backward Interval Partial Least Squares Method. Chinese Journal of Analytical Chemistry, 2018, 46(4): 623-629. doi: 10.11895/j.issn.0253-3820.170278 [复制]

拉曼光谱结合后向间隔偏最小二乘法用于调和汽油辛烷值定量分析

通讯作者:  戴连奎, lkdai@zju.edu.cn

收稿日期: 2017-07-04

接受日期: 2018-02-28

出版日期: 2018-04-01

Quantitative Analysis of Blended Gasoline Octane Number Using Raman Spectroscopy with Backward Interval Partial Least Squares Method

Corresponding author:  DAI Lian-Kui, lkdai@zju.edu.cn

Received Date:  2017-07-04

Accepted Date:  2018-02-28

Published Date:  2018-04-01

采用后向间隔偏最小二乘(Backward interval partial least squares,BiPLS)提取汽油拉曼光谱特征谱段,并用于研究法辛烷值(Research octane number,RON)的定量分析。实验中首先使用SPXY(Sample set partitioning based on joint x-y distances)方法划分训练集、交叉验证集和测试集,并采用稳健回归方法剔除异常的样本数据,再结合BiPLS方法筛选特征谱段,利用特征谱段建立偏最小二乘模型。与全谱段偏最小二乘模型的预测性能对比结果表明,后向间隔偏最小二乘方法可使输入模型的特征数据维数降低50.00%,交叉验证均方根误差(Root mean square error of cross validation,RMSECV)降低18.92%,预测均方根误差(Root mean square error of prediction,RMSEP)降低13.86%。后向间隔偏最小二乘方法可有效提取汽油拉曼光谱的特征谱段,降低模型复杂度,同时提高模型预测精度,在调和汽油研究法辛烷值定量分析方面有较好的应用前景。

关键词:   调和汽油, 拉曼光谱, 辛烷值, 后向间隔偏最小二乘
Key words:   Blended gasoline, Raman spectroscopy, Octane number, Backward interval partial least squares
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拉曼光谱结合后向间隔偏最小二乘法用于调和汽油辛烷值定量分析

王拓, 戴连奎, 马万武

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