Comparison of brass alloys composition by laser-induced breakdown spectroscopy and self-organizing maps

Pagnotta, Stefano; Grifoni, Emanuela; Legnaioli, Stefano; Lorenzetti, Giulia; Palleschi, Vincenzo

doi:10.1016/j.sab.2014.11.008

Cited by 28 publications

(11 citation statements)

References 51 publications

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“…An automatized data processing procedure was added to the exploration of hyperspectral images. 27 Spectral ranges-ofinterest were cropped around specic lines (Mg, Ca, Na, and K) and later processed using a self-organizing map (SOM) algorithm, which had already been introduced to LIBS data processing, 28 for each sample and range separately. A closer description of SOM in the context of LIBS-data processing can be found in ref.…”

Section: Libsmentioning

confidence: 99%

Imaging margins of skin tumors using laser-induced breakdown spectroscopy and machine learning

Kiss

Šindelářová

Krbal

et al. 2021

J. Anal. At. Spectrom.

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Section: Libsmentioning

confidence: 99%

Imaging margins of skin tumors using laser-induced breakdown spectroscopy and machine learning

Kiss

Šindelářová

Krbal

et al. 2021

J. Anal. At. Spectrom.

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“…The SOM method has been recently applied by the authors to the classification of archaeological bronzes. 16…”

Section: Resultsmentioning

confidence: 99%

“…The SOM method has been recently applied by the authors to the classification of archaeological bronzes. 16 Starting with a grid of eight neurons, we obtained a classification of the coins in six classes. The results of the SOM classification are reported in Tables 2 (groups) and 3 (weights of the corresponding BMU).…”

Section: Classificationmentioning

confidence: 99%

X-Ray Fluorescence Analysis and Self-Organizing Maps Classification of the Etruscan Gold Coin Collection at the Monetiere of Florence

et al. 2016

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The "Monetiere" of Florence hosts the most important collection of Etruscan coins in the world. In the framework of the longstanding collaboration between the Monetiere and the Applied Laser and Spectroscopy Laboratory in Pisa, the Etruscan gold coin collection of the museum was studied. The measurements were performed at the Monetiere, using a portable energy-dispersive X-ray fluorescence (XRF) instrument. The quantitative determination of the gold alloys used for the realization of the coins was obtained applying the fundamental parameters method to the XRF spectra; as a result, using the self-organizing maps method, we were able to classify the coins in four main groups. The main parameter determining the classification is the quantity of silver in the alloy. The results obtained shed some light about the origin of the coins under study.

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“…As a result, the algorithm’s output can be used for various tasks, the most common being clustering and dimensionality reduction (see, e.g., Figure 2 b). Common supervised machine learning algorithms that have been used in LIBS studies comprise Principal Component Analysis (PCA) [ 31 ] and k-Means Clustering [ 32 ], while there have been some works that employ some neural network architectures for unsupervised learning, such as Self-Organizing Maps (SOMs) [ 33 ] and Restricted Boltzmann Machines (RBMs) [ 34 ]. Less commonly, graph theory-based algorithms have been also used for the treatment of LIBS spectra in an unsupervised manner, with impressive results [ 35 ].…”

Section: Chemometrics and Machine/deep Learning For Libsmentioning

confidence: 99%

Laser-Induced Breakdown Spectroscopy: An Efficient Tool for Food Science and Technology (from the Analysis of Martian Rocks to the Analysis of Olive Oil, Honey, Milk, and Other Natural Earth Products)

et al. 2021

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Laser-Induced Breakdown Spectroscopy (LIBS), having reached a level of maturity during the last few years, is generally considered as a very powerful and efficient analytical tool, and it has been proposed for a broad range of applications, extending from space exploration down to terrestrial applications, from cultural heritage to food science and security. Over the last decade, there has been a rapidly growing sub-field concerning the application of LIBS for food analysis, safety, and security, which along with the implementation of machine learning and chemometric algorithms opens new perspectives and possibilities. The present review intends to provide a short overview of the current state-of-the-art research activities concerning the application of LIBS for the analysis of foodstuffs, with the emphasis given to olive oil, honey, and milk.

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Comparison of brass alloys composition by laser-induced breakdown spectroscopy and self-organizing maps

Cited by 28 publications

References 51 publications

Imaging margins of skin tumors using laser-induced breakdown spectroscopy and machine learning

Imaging margins of skin tumors using laser-induced breakdown spectroscopy and machine learning

X-Ray Fluorescence Analysis and Self-Organizing Maps Classification of the Etruscan Gold Coin Collection at the Monetiere of Florence

Laser-Induced Breakdown Spectroscopy: An Efficient Tool for Food Science and Technology (from the Analysis of Martian Rocks to the Analysis of Olive Oil, Honey, Milk, and Other Natural Earth Products)

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