Quantitative analysis of composition in molten steel by LIBS based on improved partial least squares

MA Cuihong; CUI Jinlong

doi:10.7510/jgjs.issn.1001-3806.2016.06.021

In order to study the effect of laser induced breakdown spectroscopy (LIBS) matrix effect on measurement accuracy, the improved partial least squares (PLS)was used to forecast the data and reduce the influence of matrix effect on the to-be-detected parameters. The comparative study with univariate calibration and partial least squares model calibration shows that the fitting degree of calibration curves of Mn and Si improve from 0.991 and 0.993 to 0.996 and 0.997, and the relative error of prediction for validation samples were decreased from 6.231%, 6.912% to 5.510%, 6.125%.The above results show that the improved PSL can reduce matrix effects and improve the calibration accuracy. The analysis of performance has been improved significantly.

Quantitative analysis of composition in molten steel by LIBS based on improved partial least squares

1. Science and Technology, North China University, Tangshan 063000, China

Received Date: 2015-10-09

Accepted Date: 2015-10-28

Available Online: 2016-11-25

Abstract: In order to study the effect of laser induced breakdown spectroscopy (LIBS) matrix effect on measurement accuracy, the improved partial least squares (PLS)was used to forecast the data and reduce the influence of matrix effect on the to-be-detected parameters. The comparative study with univariate calibration and partial least squares model calibration shows that the fitting degree of calibration curves of Mn and Si improve from 0.991 and 0.993 to 0.996 and 0.997, and the relative error of prediction for validation samples were decreased from 6.231%, 6.912% to 5.510%, 6.125%.The above results show that the improved PSL can reduce matrix effects and improve the calibration accuracy. The analysis of performance has been improved significantly.

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Reference (17)

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Quantitative analysis of composition in molten steel by LIBS based on improved partial least squares

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