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- Raman (2) (entfernen)
Raman and laser-induced breakdown spectroscopy is integrated into a single system for molecular and elemental microanalyses. Both analyses are performed on the same ~ 0.002 mm² sample spot allowing the assessment of sample heterogeneity on a micrometric scale through mapping and scanning. The core of the spectrometer system is a novel high resolution dual arm Echelle spectrograph utilized for both techniques. In contrast to scanning Raman spectroscopy systems, the Echelle-Raman spectrograph provides a high resolution spectrum in a broad spectral range of 200-6000 cm- 1 without moving the dispersive element. The system displays comparable or better sensitivity and spectral resolution in comparison to a state-of-the-art scanning Raman microscope and allows short analysis times for both Raman and laser induced breakdown spectroscopy. The laser-induced breakdown spectroscopy performance of the system is characterized by ppm detection limits, high spectral resolving power (15,000), and broad spectral range (290-945 nm). The capability of the system is demonstrated with the mapping of heterogeneous mineral samples and layer by layer analysis of pigments revealing the advantages of combining the techniques in a single unified set-up.
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.