ArticleScientific reports2023
Quantitative elemental mapping of biological tissues by laser-induced breakdown spectroscopy using matrix recognition.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 19 citations in OpenAlex.
- Quantitative LA-ICP-MS Imaging of Elemental Distributions with Protein Correlation in 3D Tumor Models.Analytical chemistry · 2026Article
- Influence of Sample Preparation Methods for Porcine Tissue: Calibration-Free LIBS for In Vivo Applications.Journal of biophotonics · 2026Article
- From Fundamentals of Laser-Induced Breakdown Spectroscopy to Recent Advancements in Cancer Detection and Calcified Tissues Analysis: An Overview (2015-2025).Molecules (Basel, Switzerland) · 2025Review
- Line-dropped gelatin multi-element calibration standards in LA-ICP-MS: a statistically verifying comparison with cryosectioned homogenized lung and liver as matrix-matched calibration standards and as corresponding reference materials.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2025Article
- Quantifying Titanium Exposure in Lung Tissues: A Novel Laser-Induced Breakdown Spectroscopy Elemental Imaging-Based Analytical Framework for Biomedical Applications.Small science · 2024Article
- Research progress of the detection and analysis methods of heavy metals in plants.Frontiers in plant science · 2024Review
- Signal enhancement in spark-assisted laser-induced breakdown spectroscopy for discrimination of glioblastoma and oligodendroglioma lesions.Biomedical optics express · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.