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近红外光谱分析中异常值的判别与定量模型优化
引用本文:闵顺耕,李宁,张明祥.近红外光谱分析中异常值的判别与定量模型优化[J].光谱学与光谱分析,2004,24(10):1205-1209.
作者姓名:闵顺耕  李宁  张明祥
作者单位:中国农业大学理学院,北京,100094
摘    要:介绍了利用马氏距离、Cook距离、光谱特征异常值、光谱残差比、化学值绝对误差等指标结合数理统计检验来判断光谱和化学值的异常 ,并利用这些方法进行近红外光谱定量分析中模型优化 ,取得了很好的效果

关 键 词:近红外光谱  异常值  模型适应性  模型优化  马氏距离  多元回归
文章编号:1000-0593(2004)10-1205-05
修稿时间:2002年12月26

Outlier Diagnosis and Calibration Model Optimization for Near Infrared Spectroscopy Analysis
MIN Shun-geng,LI Ning,ZHANG Ming-xiangCollege of Science,China Agricultural University,Beijing ,China.Outlier Diagnosis and Calibration Model Optimization for Near Infrared Spectroscopy Analysis[J].Spectroscopy and Spectral Analysis,2004,24(10):1205-1209.
Authors:MIN Shun-geng  LI Ning  ZHANG Ming-xiangCollege of Science  China Agricultural University  Beijing  China
Institution:College of Science, China Agricultural University, Beijing 100094, China.
Abstract:Outlier diagnosis is a very important step in building near infrared calibration model. Data outlier includes spectral outlier and chemical value outlier. Mahalanobis' distance, ratio of spectral residual and spectral variable leverage test were used to evaluate sample spectral outlier. Cook's distance and the ratio of sample square error of (chemical) value and predict value to the mean square error of (calibration) set were used to test chemical value outlier. Three calibration models of protein content of 50 wheat samples, protein content of 90 corn samples and cyclohexane content of four compounds mixture were investigated. It is demonstrated that outlier test is very helpful for optimizing near infrared calibration model.
Keywords:Near infrared spectroscopy  Multiple outliers  Model suitability  Model optimization  Mahalanobis' distance  Multivariate calibration
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