主题:【第六届原创】XRF结合偏最小二乘法定量分析铝土矿中铝、硅、铁含量

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XRF结合偏最小二乘法定量分析铝土矿中铝、硅、铁含量
Analysis of Aluminium,Silicon and Ironin Bauxite with Partial Least Square Regression(PLS ) Method and XRF Analyzer



  在实验室条件下,利用EDX3600BX射线荧光光谱仪获取铝土矿样品的X射线荧光光谱数据,并采用偏最小二乘法(PLS)分别建立铝土矿中铝、硅、铁含量的预测模型。模型所用的光谱范围分别为与铝土矿中铝、硅、铁元素密切相关的波段。alsife最佳主成分数分别为322。模型经交互验证,其预测结果与实测值之间的相关系数R铝、硅、铁分别为0.94070.96850.9990。另外利用一元线性回归分析结果同PLS模型做比较,结果均不及PLS模型的预测结果。 研究表明,通过PLS回归分析方法能明显提高模型预测的准确度。

Abstract In the presentstudy,bauxite samples were scanned by EDX3600B X-ray fluorescence analyzer,andthe relationship between the X-ray fluorescence spectra and the concentrationof aluminium,silicon and iron in bauxite was studied.Forpredicating the aluminium,silicon and iron concentration in bauxite,3PLS model were established respectively with 3,2,2 optimal factors and closelyrelevant electron volt ranges.After cross-calibration,the correlationcoefficient of value predicted by PLS model against that measured by ICP were0.9407,0.9685,0.9990 separately.Meanwhile,the univariate linear regressionmodel was also built.Obviously,the PLS method was better than the linearregression for predication.It showed that the accuracy of prediction wasimproved evidently by PLS model.
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