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

Area of Science:

• Analytical Chemistry
• Biomedical Engineering
• Spectroscopy

Background:

• Laser-induced breakdown spectroscopy (LIBS) generates signal intensity maps of organic tissues.
• Direct interpretation of LIBS intensity maps can misrepresent elemental distribution.
• Accurate elemental concentration mapping is vital for biological and medical research.

Purpose of the Study:

• To demonstrate the necessity of converting LIBS signal intensity maps to elemental concentration maps.
• To propose and validate a machine learning-based methodology for this conversion.
• To improve the accuracy of elemental distribution assessment in biological tissues.

Main Methods:

• Development of a machine learning approach using linear discriminant analysis for pixel-by-pixel matrix recognition.
• Application of matrix-matched external calibration for quantitative analysis.
• Validation using a swine (porcine) brain tissue sample.

Main Results:

• Achieved 98% accuracy in distinguishing grey and white matter using automated matrix recognition.
• Successfully converted LIBS intensity maps to elemental concentration maps for Na, K, and Mg.
• Concentration values obtained were consistent with literature and reference measurements.

Conclusions:

• The conversion of LIBS intensity maps to elemental concentration maps is essential for accurate tissue analysis.
• The proposed machine learning methodology provides a reliable approach for quantitative elemental mapping.
• LIBS-based elemental concentration mapping offers valuable insights into tissue composition.