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...

Teljes leírás

Elmentve itt :
Bibliográfiai részletek
Szerzők: Janovszky Patrick
Kéri Albert
Palásti Dávid J.
Brunnbauer Lukas
Domoki Ferenc
Limbeck Andreas
Galbács Gábor
Dokumentumtípus: Cikk
Megjelent: 2023
Sorozat:SCIENTIFIC REPORTS 13 No. 1
Tárgyszavak:
doi:10.1038/s41598-023-37258-y

mtmt:34028055
Online Access:http://publicatio.bibl.u-szeged.hu/27562
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520 3 |a 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. 
650 4 |a Kémiai tudományok 
650 4 |a Általános orvostudomány 
700 0 1 |a Kéri Albert  |e aut 
700 0 1 |a Palásti Dávid J.  |e aut 
700 0 1 |a Brunnbauer Lukas  |e aut 
700 0 1 |a Domoki Ferenc  |e aut 
700 0 1 |a Limbeck Andreas  |e aut 
700 0 1 |a Galbács Gábor  |e aut 
856 4 0 |u http://publicatio.bibl.u-szeged.hu/27562/1/Janovszky_et_al-2023-Scientific_Reports.pdf  |z Dokumentum-elérés