基于交互式自模型混合物分析的近红外光谱波长变量优选方法  

A Wavelength Selection Method Based on Simple-to-Use Interactive Self-modeling Mixture Analysis for Near Infrared Spectroscopy

作　　者：李丽娜[1] 李庆波[1] 张广军[1] 

机构地区：[1]北京航空航天大学,精密光机电一体化技术教育部重点实验室,北京100191

出　　处：《分析化学》2009年第6期823-827,共5页Chinese Journal of Analytical Chemistry

基　　金：国家自然科学基金资助项目(No.60708026);北京市优秀人才培养资助项目(No.20081D1600600348);教育部长江学者和创新团队发展计划资助项目(No.IRT0705);北京航空航天大学博士研究生创新基金资助

摘　　要：为了提高近红外光谱定量分析的预测精度和建模效率,提出了一种基于交互式自模型的混合物分析的波长优选方法,根据光谱各波长变量的纯度值和标准差值,选择含有用信息的波长变量,并引入相关权函数解决变量间共线性问题。通过依次迭代选择的变量建立定量校正模型,由交互验证均方根预测误差(RM-SECV)确定最佳波长变量个数。应用该波长变量优选方法对具有不同葡萄糖含量的两组(四成分葡萄糖水溶液实验和人体血浆实验)近红外光谱数据进行分析,两组数据中分别只选择了全部变量的0.3%建立定量校正模型,其验证集葡萄糖浓度的均方根预测误差(RMSEP)分别减少为669和15 mg/L。与全谱范围及优选波段建立的定量校正模型比较,本方法能够通过波长变量优选最小化冗余信息、提高预测精度及建模效率。In order to improve prediction accuracy and modeling efficiency for quantitative calibration in near infrared spectroscopy, a novel wavelength variable selection method based on SIMPLISMA （simple-to-use interactive self-modeling mixture analysis） was proposed. According to the value of purity and standard devia- tion, the wavelength with maximum information was selected. And then the correlation weight function was introduced to solve the colinearity between variables. By constructing quantitative calibration model with itera- tively selected wavelength variables, the root mean square error of cross validation （RMSECV） was utilized to determinate the optimal number of selected variables. Two experimental NIR spectral data, four components mixture solution and plasma, for glucose concentration analysis were utilized to evaluate the proposed variable selection method. Only 0.3% variables of all spectra data for these two experimental data were used to quanti- tative calibration, and the root mean square error of prediction （RMSEP） of validation set of glucose concen- tration was decreased to 66.9 mg/dL and 1.5 mg/dL respectively. Comparing with the quantitative calibration model constructed with full spectral region and informative spectral band, the proposed variable selection method can minimize redundant information and is helpful to yield a more efficient calibration with higher prediction accuracy.

关 键 词：交互式自模型混合物分析 变量选择 偏最小二乘 近红外光谱 定量校正 

分 类 号：O657.33[理学—分析化学]