Selection of characteristic lines is a critical work for both qualitative and quantitative analysis of laser-induced breakdown spectroscopy; it usually needs a lot of time and effort. A novel method combining genetic algorithm, principal component analysis and artificial neural networks (GA-PCA-ANN) is proposed to automatically extract the characteristic spectral segments from the original spectra, with ample feature information and less interference. On the basis of this method, three selection manners: selecting the whole spectral range, optimizing a fixed-length segment and optimizing several non-fixed-length sub-segments were analyzed; and their classification results of steel samples were compared. It is proved that selecting a fixed-length segment with an appropriate segment length achieves better results than selecting the whole spectral range; and selecting several non-fixed-length sub-segments obtains the best result with smallest amount of data. The proposed GA-PCA-ANN method can reduce the workload of analysis, the usage of bandwidth and cost of spectrometers. As a result, it can enhance the classification capability of laser-induced breakdown spectroscopy.
Through a Nd:YAG pulse laser of 1064nm wavelength, a multichannel grating spectrometer, and seven CCD detectors,
the plasma emission spectrum of the 200 - 980nm wavelength rang were simultaneously observed. First, we studied the
influences of some factors like laser energy and measurement time delays on emission intensity of plasma. By
experiments, we found that the unusual phenomenon that the emission intensity of plasma is possibly stronger when laser
energy is smaller for steel samples. However, this stronger intensity obtained under smaller laser energy is not suitable to
quantitative analysis because of poorer repetition. Second, we determined the optimal experimental parameters and
quantitatively analyzed the concentrations of the element Mn, Ni, Cr, V, Ti and Cu in some steel samples under the
optimal experimental parameters. The calibration curves of these elements were built, and good linearity was obtained.
The average relative errors of the quantitative results of these elements are between 13.31% and 4.54%. Consequently,
LIBS can be used for quantitative analysis for steel samples; however, the accuracy of the quantitative results still needs
to be improved.
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