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Issue 10, 2016
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Twelve different types of data normalization for the proposition of classification, univariate and multivariate regression models for the direct analyses of alloys by laser-induced breakdown spectroscopy (LIBS)

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Abstract

This study applies laser-induced breakdown spectroscopy (LIBS) for the direct analysis of 80 metal samples (alloys and steel) for multivariate and univariate regression models, aiming at the determination of 10 analytes (Al, Cr, Cu, Fe, Mn, Mo, Ni, Ti, V and Zn). To optimize the LIBS system, the Doehlert design was used for energy, delay time and spot size adjustment for all samples and analytes. Twelve normalization modes were used to reduce the interference matrix and to improve the calibration models, with error values ranging from 0.27% (Mn) to 14% (Cu and Ni). Models without normalization presented two- to five-fold higher errors. In addition to quantification, classification models (KNN, SIMCA and PLS-DA) were also proposed for sample differentiation. Multivariate and univariate models presented similar performance, and among the classification models, KNN presented the best results, with an accuracy of 100%.

Graphical abstract: Twelve different types of data normalization for the proposition of classification, univariate and multivariate regression models for the direct analyses of alloys by laser-induced breakdown spectroscopy (LIBS)

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Supplementary files

Article information


Submitted
23 Jun 2016
Accepted
06 Jul 2016
First published
06 Jul 2016

J. Anal. At. Spectrom., 2016,31, 2005-2014
Article type
Paper

Twelve different types of data normalization for the proposition of classification, univariate and multivariate regression models for the direct analyses of alloys by laser-induced breakdown spectroscopy (LIBS)

J. P. Castro and E. R. Pereira-Filho, J. Anal. At. Spectrom., 2016, 31, 2005
DOI: 10.1039/C6JA00224B

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