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- 2014 (2) (entfernen)
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- Laser-induced breakdown spectroscopy (2) (entfernen)
A spatial heterodyne spectrometer (SHS) is tested for the first time in combination with laser-induced breakdown spectroscopy (LIBS). The spectrometer is a modified version of the Michelson interferometer in which mirrors are replaced by diffraction gratings. The SHS contains no moving parts and the gratings are fixed at equal distances from the beam splitter. The main advantage is high throughput, about 200 times higher than that of dispersive spectrometers used in LIBS. This makes LIBS-SHS a promising technique for low-light standoff applications. The output signal of the SHS is an interferogram that is Fourier-transformed to retrieve the original plasma spectrum. In this proof-of-principle study, we investigate the potential of LIBS-SHS for material classification and quantitative analysis. Brass standards with broadly varying concentrations of Cu and Zn were tested. Classification via principal component analysis (PCA) shows distinct groupings of materials according to their origin. The quantification via partial least squares regression (PLS) shows good precision (relative standard deviation , 10%) and accuracy (within 6 5% of nominal concentrations). It is possible that LIBS-SHS can be developed into a portable, inexpensive, rugged instrument for field applications.
In this work, the potential of laser-induced breakdown spectroscopy (LIBS) for discrimination and analysis of geological materials was examined. The research was focused on classification of mineral ores using their LIBS spectra prior to quantitative determination of copper. Quantitative analysis is not a trivial task in LIBS measurement because intensities of emission lines in laser-induced plasmas (LIP) are strongly affected by the sample matrix (matrix effect). To circumvent this effect, typically matrix-matched standards are used to obtain matrix-dependent calibration curves. If the sample set consists of a mixture of different matrices, even in this approach, the corresponding matrix has to be known prior to the downstream data analysis. For this categorization, the multielemental character of LIBS spectra can be of help. In this contribution, a principal component analysis (PCA) was employed on the measured data set to discriminate individual rocks as individual matrices against each other according to their overall elemental composition. Twenty-seven igneous rock samples were analyzed in the form of fine dust, classified and subsequently quantitatively analyzed. Two different LIBS setups in two laboratories were used to prove the reproducibility of classification and quantification. A superposition of partial calibration plots constructed from the individual clustered data displayed a large improvement in precision and accuracy compared to the calibration plot constructed from all ore samples. The classification of mineral samples with complex matrices can thus be recommended prior to LIBS system calibration and quantitative analysis.