Abstract
This work presents a new method for forward variable selection and calibration and its evaluation for manganese determination in steel by laser-induced breakdown spectroscopy (LIBS). A compact and low-cost LIBS instrument was used, based on a microchip laser and a grating mini-spectrometer containing a non-intensified, non-gated, and non-cooled linear sensor array. Sixty steel samples were analyzed, with known manganese concentrations from 0.106 to 1.696wt%. The spectra (1757 variables between 200 and 850nm) were acquired under the continuous application of laser pulses at 100Hz and using 80, 400, and 1000ms integration times. The new method generated a mathematic combination of the selected variables and the results were calibrated against the manganese content by linear or quadratic regression. The best results were obtained using the spectra from all integration times together, with 31 selected variables and root mean square errors of cross-validation and prediction of 0.015 and 0.033, respectively. Compared to Jack-knife partial least squares regression, the new method presented lower prediction errors and numbers of selected variables, with the advantages of no data pretreatment and a simpler mathematic calculation. Graphical abstract New method for forward variable selection and calibration applied to manganese determination in steel by laser induced breakdown spectroscopy.
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