Analysis of antique bronze coins by Laser Induced Breakdown Spectroscopy and multivariate analysis

Bachler, M. Orlić; Bišćan, Marijan; Kregar, Zlatko; Badovinac, Ivana Jelovica; Dobrinić, Julijan; Milošević, Slobodan

doi:10.1016/j.sab.2016.08.010

Cited by 15 publications

(5 citation statements)

References 28 publications

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“…To improve the quantification performance of LIBS for coal analysis, there has been an ongoing trend to use data processing methods such as standardization [6,7], variable selection [8,9] and multivariate regression [10,11]. Among these methods, variable selection is of great importance to ensure the accuracy and reliability of LIBS quantification- [12].…”

Section: Introductionmentioning

confidence: 99%

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

LYU 吕,

SONG 宋,

HOU 侯

et al. 2024

Plasma Sci. Technol.

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Nowadays, laser-induced breakdown spectroscopy (LIBS) has become a widely used atomic spectroscopic technique for rapid coal analysis. While the vast spectral information in LIBS contains signal uncertainty, which can impact its quantification performance. In this work, we proposed a hybrid variable selection method to improve the performance of LIBS quantification. Important variables are first identified using Pearson’s correlation coefficient (PCC), mutual information (MI), least absolute shrinkage and selection operator (LASSO) and random forest (RF), and then filtered and combined with empirical variables related to fingerprint elements of coal ash content. Subsequently, these variables are fed into partial least squares regression (PLSR). Additionally, in some models, certain variables unrelated to ash content were removed manually to study the variable deselection’s impact on model performance. The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method. It is significantly better than the variable selection only based on empirical knowledge and in most cases outperforms the baseline method. The results showed that on all three datasets, hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest RMSEP values of 1.605, 3.478 and 1.647, respectively, which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables, which are 1.959, 3.718 and 2.181, respectively. The EMP-LASSO-PLSR model with 20 selected variables exhibited a significant improving performance after variable deselection, with RMSEP values dropping from 1.635, 3.962, 1.647 to 1.483, 3.086, 1.567, respectively. Such results demonstrate that using empirical knowledge as a support to data-driven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

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Section: Introductionmentioning

confidence: 99%

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

LYU 吕,

SONG 宋,

HOU 侯

et al. 2024

Plasma Sci. Technol.

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“…Based on this information, assumptions about the improvement in production technology over the years could be made. There is a variety of analytical studies which are combined with PCA [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Laser-Induced Breakdown Spectroscopy and X-ray Fluorescence Analysis of Bronze Objects from the Late Bronze Age Baley Settlement, Bulgaria

Penkova

Malcheva

Grozeva

et al. 2023

QuBS

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In the presented work, a total of 60 bronze artefacts from the prehistoric settlement of Baley, Bulgaria were analyzed by means of laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence spectroscopy (XRF). The archaeological finds were excavated from three levels, with a time span from the 15th century BC to the first half of the 11th century BC. The obtained analytical information was used for quantitative estimation of the amount of tin, lead and arsenic, which determine the mechanical properties of the alloy and the manufacturing technology. Based on the estimated quantities of these elements, a chemometric statistical analysis (principal component analysis—PCA) was performed to classify and divide the samples into separate groups according to the production dating. The data obtained in this study can be used for comparison with the elemental content in deposits from other settlements of this period.

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“…[24][25][26] However, the previous LIBS study of coins is to analyze and compare ancient coins from different periods. 22,27 The object of comparison are coins from different periods from the same area, and the differences in the elements found reflect their history, which is of great archaeological significance.…”

Section: Introductionmentioning

confidence: 99%

Rapid Elemental Determination And Discrimination Of Coins Using Laser-Induced Breakdown Spectroscopy

Liu¹

2021

At.Spectrosc.

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Damaged coins can be identified effectively via spectral analysis based on LIBS, which is of great significance for coin recycling. This paper takes the Renminbi (RMB), the Chinese currency, as the example, including the denominations of YI FEN, ER FEN, WU FEN, YI JIAO, WU JIAO and YI YUAN. Some characteristic lines of Mg, Al, Fe, Cr, Cu, Sn, Ni, Na and Ca were observed in the spectra, as well as the molecular bands of AlO. Principal component analysis (PCA) was used to reduce the dimension of the spectra of the different RMB coins. The samples after dimension reduction are classified by k-Nearest Neighbors (KNN), and 4 categories were obtained with a classification accuracy of 100%. Further, new spectra of different denomination RMB coins were added to the original data for the same analysis. The results are in good agreement which shows the potential of the combination of LIBS, PCA and KNN for the analysis and identification of different coins.

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Analysis of antique bronze coins by Laser Induced Breakdown Spectroscopy and multivariate analysis

Cited by 15 publications

References 28 publications

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-Induced Breakdown Spectroscopy and X-ray Fluorescence Analysis of Bronze Objects from the Late Bronze Age Baley Settlement, Bulgaria

Rapid Elemental Determination And Discrimination Of Coins Using Laser-Induced Breakdown Spectroscopy

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