Podrobná bibliografie
| Název: |
Ilaps – python software for data reduction and imaging with LA-ICP-MS. |
| Autoři: |
Faltusová, Veronika, Vaculovič, Tomáš, Holá, Markéta, Kanický, Viktor |
| Zdroj: |
JAAS (Journal of Analytical Atomic Spectrometry); Apr2022, Vol. 37 Issue 4, p733-740, 8p |
| Témata: |
PYTHON programming language, LASER ablation inductively coupled plasma mass spectrometry, DATA reduction |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
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| Databáze: |
Complementary Index |
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