Laser-induced breakdown spectroscopy (LIBS) data obtained from the elemental imaging of heterogenous samples were processed with various chemometric algorithms. The intention was to cluster obtained characteristic spectra and to provide additional information about the sample surface composition and distribution of individual matrices. However, there is a gray zone on the boundary of two matrices and the consequent clustering of the spectra obtained on this boundary is ambiguous. This paper focuses on the transition between two well-defined matrices in a simplified case for a better transparency in data visualization. Steel and aluminum samples that are represented by characteristic spectra with significantly distinct structures (e.g., different number of spectral lines). Using a carefully designed experiment, several Fe:Al ratios were ablated and analyzed by principal component analysis (PCA), self-organizing maps (SOM), and standard data metrics. This paper shows the strategy for the discrimination of unrecognized spectra and possibilities in their clustering.
Keywords: LIBS; Laser induced breakdown spectroscopy; PCA; SOM; clustering; data analysis; data metrics; data prediction; mapping; principal component analysis; self-organizing maps.