Ilaps - python software for data reduction and imaging with LA-ICP-MS

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Publikace nespadá pod Ekonomicko-správní fakultu, ale pod Přírodovědeckou fakultu. Oficiální stránka publikace je na webu muni.cz.
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FALTUSOVÁ Veronika VACULOVIČ Tomáš HOLÁ Markéta KANICKÝ Viktor

Rok publikování 2022
Druh Článek v odborném periodiku
Časopis / Zdroj Journal of Analytical Atomic Spectrometry
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Citace
www https://pubs.rsc.org/en/content/articlelanding/2022/ja/d1ja00383f
Doi http://dx.doi.org/10.1039/d1ja00383f
Klíčová slova LA-ICP-MS; data; python; imgMS; graphical software
Popis In the past couple of years, laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been widely used for trace element analysis in solid samples and the study of their distribution. The data acquired by this technique are exceedingly complex and call for a wealth of processing steps, such as data segmentation, background correction, calibration and normalisation. Imaging of the distribution additionally requires a reconstruction of the data into a two-dimensional matrix. In this work, we present a new standalone software written in python - Ilaps, that allows easy and straightforward processing of the LA-ICP-MS data for bulk analysis and imaging. Ilaps is built using a free, open-source python package imgMS, which simplifies the customisation of the used functions. The combination of imgMS as a python package with a standalone graphical software makes it possible to be used by researchers with or without programming knowledge at the same level. However, at the same time, it encourages open collaboration for the software to evolve. Ilaps does not require any data preparation, and can process a bulk analysis or convert the data into an image in a matter of minutes. The software offers an automatic selection of the signals that are representative of the composition of the sample, but at the same time allows the user to exclude contaminants manually. We discuss the key advantages of Ilaps and show its potential in two separate cases, one focused on bulk analysis of archaeological glass samples and the other on imaging of a mouse brain.
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