Laser-Induced Breakdown Spectroscopy of Steel: A Comparison of Univariate and Multivariate Calibration Methods

Abstract

Laser-induced breakdown spectroscopy (LIBS) was carried out on twenty-three low to high alloy steel samples to quantify their concentrations of chromium, nickel, and manganese. LIBS spectral data were correlated to known concentrations of the samples and three calibration methods were compared. A standard LIBS calibration technique using peak area integration normalized by an internal standard was compared to peak area integration normalized by total light and the multivariate statistical technique of partial least squares. For the partial least squares analysis, the PLS-1 algorithm was used, where a predictive model is generated for each element separately. Partial least squares regression coefficients show that the algorithm correctly identifies the atomic emission peaks of interest for each of the elements. Predictive capabilities of each calibration approach were quantified by calculating the standard and relative errors of prediction. The performance of partial least squares is on par with using iron as an internal standard but has the key advantage that it can be applied to samples where the concentrations of all elements are unknown.