Modern analytical techniques, including laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy, yield multidimensional data, which are most efficiently used in conjunction with chemometric techniques, including multi-block algorithms. In this study,...

Stark effect is observed in many natural and artificial plasmas and is of great importance for diagnostic purposes. Since this effect alters profiles of spectral lines, it should be taken into account when assessing chemical composition of radiation sources, including stars. Copper is one of the elements which studies of stellar atmospheres deal with. To this end, UV and visible Cu lines are used. However, there is a lack of agreement between existing data on their Stark parameters. It is therefore of interest to obtain new experimental data on these lines and to compare them to previous results. In this work, we have estimated Stark widths and shifts for three blue-green lines at 5105.54, 5153.24, and 5218.20 Å (corresponding transitions are [3d 10 4p] 2 P° → [3d 9 4s 2 ] 2 D and [3d 10 4d] 2 D → [3d 10 4p] 2 P°) observed in a "long-spark" laser-induced plasma. For the first time, we have accurately estimated an impact of hyperfine splitting on the profile shapes of the studied lines taking also into account the isotope shifts. We have shown that both effects considerably influence shift and width of Cu I line at 5105.54 Å, and shifts of Cu I lines at 5153.24 and 5218.20 Å.

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Zooplankton identification has been the subject of many studies. They are mainly based on the analysis of photographs (computer vision). However, spectroscopic techniques can be a good alternative due to the valuable additional information that they provide. We tested the performance of several chemometric techniques (principal component analysis (PCA), non-negative matrix factorisation (NMF), and common dimensions and specific weights analysis (CCSWA of ComDim)) for the unsupervised classification of zooplankton species based on their spectra. The spectra were obtained using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. It was convenient to assess the discriminative power in terms of silhouette metrics (Sil). The LIBS data were substantially more useful for the task than the Raman spectra, although the best results were achieved for the combined LIBS + Raman dataset (best Sil = 0.67). Although NMF (Sil = 0.63) and ComDim (Sil = 0.39) gave interesting information in the loadings, PCA was generally enough for the discrimination based on the score graphs. The distinguishing between Calanoida and Euphausiacea crustaceans and Limacina helicina sea snails has proved possible, probably because of their different mineral compositions. Conversely, arrow worms (Parasagitta elegans) usually fell into the same class with Calanoida despite the differences in their Raman spectra.

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