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

Laser-induced breakdown spectroscopy signal enhancement effect for argon caused by the presence of gold nanoparticles

The effect of the presence of nanoparticles (NPs) on the laser induced breakdown spectroscopy (LIBS) signal of argon gas was studied experimentally. 10–20 nm diameter gold NPs, produced by a spark discharge nanoparticle generator, were suspended in argon gas. The effect of particle size, number concentration and mass concen- tration, as well as laser pulse energy on the LIBS argon signal was systematically investigated. It was found that the breakdown threshold of the gas decreases considerably, facilitating the detection of Ar emission at such laser fluences, which do not allow plasma formation without the presence of the NPs. Our observations persist even at aerosol mass concentrations that are too low to allow the direct detection of nanoparticles. The effect, which is attributed to electron thermo- and field emission induced by the high intensity laser pulse, shows an asymp- totically increasing magnitude with the aerosol mass concentration. The signal enhancement was found to be 102–104 and the effect is suggested to be useful in trace gas analysis or for the indirect detection of NPs. The achievable indirect aerosol mass concentration detection limit was estimated to be in the parts per trillion regime (as low as 50 ng⋅m 3) which is comparable to the best literature values reported for direct analysis

Qualitative Analysis of Glass Microfragments Using the Combination of Laser-Induced Breakdown Spectroscopy and Refractive Index Data

We have successfully demonstrated that although there are significant analytical challenges involved in the qualitative discrimination analysis of sub-mm sized (microfragment) glass samples, the task can be solved with very good accuracy and reliability with the multivariate chemometric evaluation of laser-induced breakdown spectroscopy (LIBS) data or in combination with pre-screening based on refractive index (RI) data. In total, 127 glass samples of four types (fused silica, flint, borosilicate and soda–lime) were involved in the tests. Four multivariate chemometric data evaluation methods (linear discrimination analysis, quadratic discrimination analysis, classification tree and random forest) for LIBS data were evaluated with and without data compression (principal component analysis). Classification tree and random forest methods were found to give the most consistent and most accurate results, with classifications/identifications correct in 92 to 99% of the cases for soda–lime glasses. The developed methods can be used in forensic analysis

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

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