Filtern
Dokumenttyp
- Zeitschriftenartikel (2) (entfernen)
Sprache
- Englisch (2)
Referierte Publikation
- ja (2)
Schlagworte
- Chemometrics (2) (entfernen)
The authenticity of objects and artifacts is often
the focus of forensic analytic chemistry. In document fraud
cases, the most important objective is to determine the
origin of a particular ink. Here, we introduce a new
approach which utilizes the combination of two analytical
methods, namely Raman spectroscopy and laser-induced
breakdown spectroscopy (LIBS). The methods provide
complementary information on both molecular and elemental
composition of samples. The potential of this hyphenation
of spectroscopic methods is demonstrated for ten blue
and black ink samples on white paper. LIBS and Raman
spectra from different inks were fused into a single data
matrix, and the number of different groups of inks was
determined through multivariate analysis, i.e., principal
component analysis, soft independent modelling of class
analogy, partial least-squares discriminant analysis, and
support vector machine. In all cases, the results obtained
with the combined LIBS and Raman spectra were found to
be superior to those obtained with the individual Raman or
LIBS data sets.
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.