Quantitative elemental mapping of biological tissues by laser-induced breakdown spectroscopy using matrix recognition

The present study demonstrates the importance of converting signal intensity maps of organic tissues collected by laser-induced breakdown spectroscopy (LIBS) to elemental concentration maps and also proposes a methodology based on machine learning for its execution. The proposed methodology employs...

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Bibliographic Details
Main Authors: Janovszky Patrick
Kéri Albert
Palásti Dávid J.
Brunnbauer Lukas
Domoki Ferenc
Limbeck Andreas
Galbács Gábor
Format: Article
Published: 2023
Series:SCIENTIFIC REPORTS 13 No. 1
Subjects:
doi:10.1038/s41598-023-37258-y

mtmt:34028055
Online Access:http://publicatio.bibl.u-szeged.hu/27562
Description
Summary:The present study demonstrates the importance of converting signal intensity maps of organic tissues collected by laser-induced breakdown spectroscopy (LIBS) to elemental concentration maps and also proposes a methodology based on machine learning for its execution. The proposed methodology employs matrix-matched external calibration supported by a pixel-by-pixel automatic matrix (tissue type) recognition performed by linear discriminant analysis of the spatially resolved LIBS hyperspectral data set. On a swine (porcine) brain sample, we successfully performed this matrix recognition with an accuracy of 98% for the grey and white matter and we converted a LIBS intensity map of a tissue sample to a correct concentration map for the elements Na, K and Mg. Found concentrations in the grey and white matter agreed the element concentrations published in the literature and our reference measurements. Our results revealed that the actual concentration distribution in tissues can be quite different from what is suggested by the LIBS signal intensity map, therefore this conversion is always suggested to be performed if an accurate concentration distribution is to be assessed.
Physical Description:10
ISSN:2045-2322