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In this work, we report the results on the application of Laser-Induced Breakdown Spectroscopy (LIBS) to the analysis of coal, for the determination of its Ash Content. This parameter is critical for an efficient use of coal to produce electrical energy in power plants. The results obtained are compared with the ones obtained through conventional chemical laboratory analysis. The study here presented, although performed in the Laboratory, was set up trying to reproduce the real industrial conditions, with the purpose of easing the transfer of the results obtained to the on-line monitoring of coal quality at the power plant. Unlabelled Image • Ash Content of Coal is determined by LIBS. • Real Industrial conditions are reproduced. • Selection of spectra is applied to improve the quality of the results. • An average absolute error of 0.5 w% is achieved. • 4 w% maximum absolute error is obtained. [ABSTRACT FROM AUTHOR]

A 'hybrid' method is proposed for the quantitative analysis of materials by LIBS, combining the precision of the calibration-free LIBS (CF-LIBS) algorithm with the quickness of artificial neural networks. The method allows the precise determination of the samples' composition even in the presence of relatively large laser fluctuations and matrix effects. To show the strength and robustness of this approach, a number of synthetic LIBS spectra of Cu-Ni binary alloys with different composition were computer-simulated, in correspondence of different plasma temperatures, electron number densities and ablated mass. The CF-LIBS/ANN approach here proposed demonstrated to be capable, after appropriate training, of 'learning' the basic physical relations between the experimentally measured line intensities and the plasma parameters. Because of that the composition of the sample can be correctly determined, as in CF-LIBS measurements, but in a much shorter time. [ABSTRACT FROM AUTHOR]

The introduction of "deep learning" algorithms for feature identification in digital imaging has paved the way for artificial intelligence applications that up to a decade ago were considered technologically impossible to achieve, from the development of driverless vehicles to the fully automated diagnostics of cancer and other diseases from histological images. The success of deep learning applications has, in turn, attracted the attention of several researchers for the possible use of these methods in chemometrics, applied to the analysis of complex phenomena as, for example, the optical emission of laser-induced plasmas. In this paper, we will discuss the advantages and disadvantages of convolutional neural networks, one of the most diffused deep learning techniques, in laser-induced breakdown spectroscopy (LIBS) applications (classification and quantitative analysis), to understand the real potential of "deep LIBS" in practical everyday use. In particular, the comparison with the results obtained using "shallow" artificial neural networks will be presented and discussed, taking as a case study the analysis of six bronze samples of known composition. Graphical Abstract [ABSTRACT FROM AUTHOR]

In this paper we face the problem of assessing similarities in the composition of different metallic alloys, using the laser-induced breakdown spectroscopy technique. The possibility of determining the degree of similarity through the use of artificial neural networks and self-organizing maps is discussed. As an example, we present a case study involving the comparison of two historical brass samples, very similar in their composition. The results of the paper can be extended to many other situations, not necessarily associated with cultural heritage and archeological studies, where objects with similar composition have to be compared. [ABSTRACT FROM AUTHOR]

The laser-induced breakdown spectroscopy (LIBS) technique was used for analyzing the composition of an ancient Roman mortar (5th century A.D.), exploiting an experimental setup which allows the determination of the compositions of binder and aggregate in few minutes, without the need for sample treatment. Four thousand LIBS spectra were acquired from an area of 10 mm2, with a 50 mm lateral resolution. The elements of interest in the mortar sample (H, C, O, Na, Mg, Al, Si, K, Ca, Ti, Mn, Fe) were detected and mapped. The collected data graphically shown as compositional images were interpreted using different statistical approaches for the determination of the chemical composition of the binder and aggregate fraction. The methods of false color imaging, blind separation, and self-organizing maps were applied and their results are discussed in this paper. In particular, the method based on the use of self-organizing maps gives well interpretable results in very short times, without any reduction in the dimensionality of the system. [ABSTRACT FROM AUTHOR]

The usual approach to laser-induced breakdown spectroscopy (LIBS) quantitative analysis is based on the use of calibration curves, suitably built using appropriate reference standards. More recently, statistical methods relying on the principles of artificial neural networks (ANN) are increasingly used. However, ANN analysis is often used as a ‘black box’ system and the peculiarities of the LIBS spectra are not exploited fully. An a priori exploration of the raw data contained in the LIBS spectra, carried out by a neural network to learn what are the significant areas of the spectrum to be used for a subsequent neural network delegated to the calibration, is able to throw light upon important information initially unknown, although already contained within the spectrum. This communication will demonstrate that an approach based on neural networks specially taylored for dealing with LIBS spectra would provide a viable, fast and robust method for LIBS quantitative analysis. This would allow the use of a relatively limited number of reference samples for the training of the network, with respect to the current approaches, and provide a fully automatizable approach for the analysis of a large number of samples. [ABSTRACT FROM AUTHOR]

In this paper a new approach to quantitative Laser-Induced Breakdown Spectroscopy (LIBS) analysis of silicate rocks is presented. The method is adapted from the Franzini and Leoni algorithm, a method widely used in X-Ray Fluorescence analysis for correcting the matrix effects in the determination of the composition of geological materials. To illustrate the features of the new method proposed, nine elements were quantified in 19 geological standards by building linear univariate calibration curves, linear multivariate calibration surfaces (PLS) and using Artificial Neural Networks. The results were then compared with the predictions derived from the application of the algorithm here proposed. It was found that the Franzini and Leoni approach gives results much more precise than linear uni- and multivariate approaches, and comparable with the ones derived from the application of Artificial Neural Networks. A definite advantage of the proposed approach is the possibility of building multivariate non-linear calibration surfaces using linear optimization algorithms, a feature which makes the application of the Franzini and Leoni method in LIBS analysis much simpler (and controllable) with respect to the algorithms based on Artificial Neural Networks. Unlabelled Image • The Franzini-Leoni method, used in XRF analysis, is adapted to LIBS. • 19 geological samples are analyzed with this method. • The method is compared with linear/non-linear univariate and multivariate approaches. • The non-linear model can be obtained using linear optimization algorithms. [ABSTRACT FROM AUTHOR]