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 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.

Automated summary

The study emphasizes the importance of converting signal intensity maps of organic tissues collected by LIBS to elemental concentration maps. A machine learning methodology based on matrix-matched external calibration and linear discriminant analysis is proposed for this task. The results demonstrate the successful recognition of tissue types and conversion of intensity maps to accurate concentration maps for specific elements.