相关分析技术消除样品背景对预测模型的影响  

Application of correlation analysis to eliminate the influence of sample background for prediction model

作　　者：金尚忠[1] 吴继明[2] 陈斌[2] 王动民[1] 袁琨[1] 

机构地区：[1]中国计量学院光学与电子科技学院,浙江杭州310018 [2]江苏大学食品与生物工程学院,江苏镇江212013

出　　处：《纺织学报》2008年第6期44-47,共4页Journal of Textile Research

基　　金：浙江省科技计划重点项目(2004C21043)

摘　　要：通过对近红外光谱仪漫反射载样附件的改进,借助漫反射技术测得纯棉分别与涤纶、腈纶以及丝的二组分混合样品光谱。分别建立3个预测模型,用3组含棉量梯度相同而组分不同,每组含7个样品的集合作为预测集,分别用来验证各个预测模型。结果表明,建立预测模型的标样集的样品背景最好与待测样品的背景一致,模型的预测精度才能提高,从而表明,样品的背景对校正模型的影响很大,而借助相关分析算法可以消除样品背景对预测模型的影响,预测均方差从4.267 7提高到2.896 5,相关系数从0.948 8提高到0.977 5,表明相关分析技术可以提高建模预测精度。The near-infrared （NIR） spectrums of pure cotton mixed with polyester, acrylic and silk fibers were measured in virtue of diffuse reflection technology with improved diffuse reflection sample earring accessory. Three prediction models were made respectively for three mixed samples with same commensurate cotton but different contents, and there were 7 samples for each prediction set which was used to verify prediction models. The results have proved that when the sample background of the calibration set was consistent with the prediction set, the predictive precision will be raised. Accordingly, the sample background was strongly influential to the model, and that could be eliminated by using correlation analysis. The root mean square error of prediction （RMSEP） was reduced from 4. 267 7 to 2. 896 5, and the prediction correlation coefficient was improved from 0.948 8 to 0. 977 5, that proves the correlation analysis could enhance the prediction precision.

关 键 词：近红外光谱 纺织纤维 校正模型 相关分析 

分 类 号：O657.33[理学—分析化学]