Study of archaeological coins of different dynasties using libs coupled with multivariate analysis

Awasthi, Shikha; Kumar, Rohit; Rai, G.; Rai, A.K.

doi:10.1016/j.optlaseng.2015.11.005

Cited by 50 publications

(19 citation statements)

References 27 publications

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“…25 The loadings graph is an important tool used in association with the graph of scores that shows the elements or variables responsible for the grouping or separation in the graph of scores. 25 In the case of ICP-OES, PCA was applied to the data sets of the concentrations of 16 elements (except Ni because it was below the LOD) in nine synthetic leathers. The matrix rows are represented by nine samples and the columns correspond to the concentrations of the chemical elements (matrix 9:16).…”

Section: Resultsmentioning

confidence: 99%

Evaluation of the Chemical Composition of Synthetic Leather Using Spectroscopy Techniques

Neiva

Pereira-Filho

2018

Appl Spectrosc

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Synthetic leather samples from Brazil and Paraguay were evaluated in this study using three spectroscopy techniques: inductively coupled plasma optical emission spectrometry (ICP-OES), laser-induced breakdown spectroscopy (LIBS), and wavelength dispersive X-ray fluorescence (WDXRF). The obtained information from each technique was separately inspected with principal component analysis (PCA). The concentrations of the elements determined in the synthetic leathers using ICP-OES decreased in the following order: Ca > Cr > Mg > Ba > Pb > Al > Fe > Zn > Sb > Ni with a concentration range below the limit of quantification (

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Section: Resultsmentioning

confidence: 99%

Evaluation of the Chemical Composition of Synthetic Leather Using Spectroscopy Techniques

Neiva

Pereira-Filho

2018

Appl Spectrosc

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“…21–24 The multivariate analysis combined with LIBS was also utilized in archeology to classify coins with archeological significance through principal component analysis (PCA) and consequently to estimate the age and location of use for the coin. 25,26 Oztoprak et al. transformed medieval ceramics into LIBS laser spectral data to classify archeological ceramic samples, glaze, paint, and clay via PCA.…”

Section: Introductionmentioning

confidence: 99%

“…[21][22][23][24] The multivariate analysis combined with LIBS was also utilized in archeology to classify coins with archeological significance through principal component analysis (PCA) and consequently to estimate the age and location of use for the coin. 25,26 Oztoprak et al transformed medieval ceramics into LIBS laser spectral data to classify archeological ceramic samples, glaze, paint, and clay via PCA. 27 Dingari et al conducted a study on the classification and estimation of chemical components of unknown substances through multivariate analysis for previously specified chemical components, 28 even when LIBS spectroscopy was used for unknown substances.…”

Section: Introductionmentioning

confidence: 99%

Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches

Yang

Yoh

2018

Appl Spectrosc

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A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.

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“…In the last two decades, various methods are applied to solve these problems 20 , such as selecting characteristic peaks or intensive spectral segments 2,21 , using Principal Component Analysis (PCA) [12][13][14][22][23][24][25][26][27] , Partial Least Squares (PLS) [3][4][5]8,21 and other chemometrics techniques 28 . Owing to the ability of extracting effective information, producing stable models and insensitive to noise, chemometrics methods are widely used in LIBS data modeling.…”

Section: Introductionmentioning

confidence: 99%

A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy

Zhang

Sun

Kong

et al. 2017

AOPC 2017: Optical Spectroscopy and Imaging

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Selection of characteristic lines is a critical work for both qualitative and quantitative analysis of laser-induced breakdown spectroscopy; it usually needs a lot of time and effort. A novel method combining genetic algorithm, principal component analysis and artificial neural networks (GA-PCA-ANN) is proposed to automatically extract the characteristic spectral segments from the original spectra, with ample feature information and less interference. On the basis of this method, three selection manners: selecting the whole spectral range, optimizing a fixed-length segment and optimizing several non-fixed-length sub-segments were analyzed; and their classification results of steel samples were compared. It is proved that selecting a fixed-length segment with an appropriate segment length achieves better results than selecting the whole spectral range; and selecting several non-fixed-length sub-segments obtains the best result with smallest amount of data. The proposed GA-PCA-ANN method can reduce the workload of analysis, the usage of bandwidth and cost of spectrometers. As a result, it can enhance the classification capability of laser-induced breakdown spectroscopy.

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Study of archaeological coins of different dynasties using libs coupled with multivariate analysis

Cited by 50 publications

References 27 publications

Evaluation of the Chemical Composition of Synthetic Leather Using Spectroscopy Techniques

Evaluation of the Chemical Composition of Synthetic Leather Using Spectroscopy Techniques

Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches

A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy

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