[Zurück]

A. Balbekova, J. Lohninger, M. Bonta, A. Limbeck, B. Lendl, M. Celikic, G. van Tilborg, R. Dijkhuizen, K. Al-Saad, A. Mohamed, J. Ofner:
"Fourier Transform Infrared (FT-IR) and Laser Ablation Inductively Coupled Plasma-Mass Spectrometry (LA- ICP-MS) Imaging of Cerebral Ischemia: Combined Analysis of Rat Brain Thin Cuts Toward Improved Tissue Classification";
Applied Spectroscopy, 72 (2018), 2; S. 241 - 250.

Microspectroscopic techniques are widely used to complement histological studies. Due to recent developments in the
field of chemical imaging, combined chemical analysis has become attractive. This technique facilitates a deepened analysis
compared to single techniques or side-by-side analysis. In this study, rat brains harvested one week after induction of
photothrombotic stroke were investigated. Adjacent thin cuts from rats´ brains were imaged using Fourier transform
infrared (FT-IR) microspectroscopy and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The
LA-ICP-MS data were normalized using an internal standard (a thin gold layer). The acquired hyperspectral data cubes were
fused and subjected to multivariate analysis. Brain regions affected by stroke as well as unaffected gray and white matter
were identified and classified using a model based on either partial least squares discriminant analysis (PLS-DA) or random
decision forest (RDF) algorithms. The RDF algorithm demonstrated the best results for classification. Improved classification
was observed in the case of fused data in comparison to individual data sets (either FT-IR or LA-ICP-MS). Variable
importance analysis demonstrated that both molecular and elemental content contribute to the improved RDF classification.
Univariate spectral analysis identified biochemical properties of the assigned tissue types. Classification of multisensor
hyperspectral data sets using an RDF algorithm allows access to a novel and in-depth understanding of biochemical
processes and solid chemical allocation of different brain regions.

http://dx.doi.org/10.1177/0003702817734618