Near-infrared( NIR) spectroscopy has been widely employed as a process analytical tool( PAT) in various fields; the most important reason for the use of this method is its ability to record spectra in real time to cap...Near-infrared( NIR) spectroscopy has been widely employed as a process analytical tool( PAT) in various fields; the most important reason for the use of this method is its ability to record spectra in real time to capture process properties. In quantitative online applications,the robustness of the established NIR model is often deteriorated by process condition variations,nonlinear of the properties or the high-dimensional of the NIR data set. To cope with such situation,a novel method based on principal component analysis( PCA) and artificial neural network( ANN) is proposed and a new sample-selection method is mentioned. The advantage of the presented approach is that it can select proper calibration samples and establish robust model effectively. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional partial leastsquares( PLS),principal component regression( PCR) and several other modeling methods, the proposed approach was found to achieve good accuracy in the prediction of gasoline properties. An application of the proposed method is also reported.展开更多
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 dis...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。展开更多
基金National Natural Science Foundations of China(Nos.U1162202,61222303)National High-Tech Research and Development Program of China(No.2013AA040701)the Fundamental Research Funds for the Central Universities and Shanghai Leading Academic Discipline Project,China(No.B504)
文摘Near-infrared( NIR) spectroscopy has been widely employed as a process analytical tool( PAT) in various fields; the most important reason for the use of this method is its ability to record spectra in real time to capture process properties. In quantitative online applications,the robustness of the established NIR model is often deteriorated by process condition variations,nonlinear of the properties or the high-dimensional of the NIR data set. To cope with such situation,a novel method based on principal component analysis( PCA) and artificial neural network( ANN) is proposed and a new sample-selection method is mentioned. The advantage of the presented approach is that it can select proper calibration samples and establish robust model effectively. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional partial leastsquares( PLS),principal component regression( PCR) and several other modeling methods, the proposed approach was found to achieve good accuracy in the prediction of gasoline properties. An application of the proposed method is also reported.
文摘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。
