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快速稳健偏最小二乘回归及其在近红外光谱分析中的应用
引用本文:成忠,陈德钊.快速稳健偏最小二乘回归及其在近红外光谱分析中的应用[J].光谱学与光谱分析,2006,26(6):1046-1050.
作者姓名:成忠  陈德钊
作者单位:1. 浙江大学化学工程与生物工程学系,浙江,杭州,310027;浙江科技学院生物与化学工程系,浙江,杭州,310012
2. 浙江大学化学工程与生物工程学系,浙江,杭州,310027
基金项目:国家自然科学基金 , 浙江省科技计划
摘    要:现代近红外光谱,作为一种间接分析技术,将建立校正模型,实现对未知样本的定量分析.针对近红外光谱分析灵敏度低、抗干扰性差的弱点,构建一种快速稳健的偏最小二乘回归(RRPLSR)算法.它运用峭度法快速识别离群点,排除它们后,再实施偏最小二乘回归,消除复共线性,建立稳健可靠的定量校正模型.将RRPLSR方法实际应用于鱼类物质的近红外光谱数据分析,实现脂肪含量的定量检测,效果良好.与已有的其他方法相比,它能准确识别离群点,所建模型预测性能良好,且计算省时,效率高,适用于快速检测.

关 键 词:偏最小二乘  离群点识别  峭度法  稳健回归  近红外光谱  定量检测
文章编号:1000-0593(2006)06-1046-05
收稿时间:2005-01-18
修稿时间:2005-06-06

Rapid and Robust Partial Least Squares Regression and Its Application to NIR Spectroscopy Analysis
CHENG Zhong,CHEN De-zhao.Rapid and Robust Partial Least Squares Regression and Its Application to NIR Spectroscopy Analysis[J].Spectroscopy and Spectral Analysis,2006,26(6):1046-1050.
Authors:CHENG Zhong  CHEN De-zhao
Institution:1. Department of Chemical and Biochemical Engineering, Zhejiang University, Hangzhou 310027, China;2. Department of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310012, China
Abstract:Modern near infrared spectroscopy(NIRS),as an indirect analytical technique,is used to carriy out quantitative analysis of unknown samples by establishing a model with calibration samples.Taking into account the low sensitivity and poor disturbance rejection of NIRS,a new robust version of the SIMPLS algorithm was constructed from a robust covariance matrix for high-dimensional data and robust linear regression in the present paper.Because SIMPLS was based on the empirical cross-covariance matrix between the response variables and the regressors and on linear least squares regression,the results were affected by abnormal observations in the data set.In order to eliminate their negative impact on the accuracy and reliability of the model,a simple multivariate outlier-detection procedure and a robust estimator for the covariance matrix were embedded in the SIMPLS regression framework,based on the use of information obtained from projections onto the directions that maximize and minimize the kurtosis coefficient of the projected data.Finally,application of the proposed kurtosis-SIMPLS method to the NIR analysis was presented with a comparison to the SIMPLS.The results show that kurtosis-SIMPLS method not only finds out the very outliers from the data set with less computational cost,but also holds better prediction performance and steady capability for the normal samples.
Keywords:Partial least squares  Outliers detection  Kurtosis  Robust regression  Near infrared spectroscopy  Quantitative analysis
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