Provenance classification of nephrite jades using multivariate LIBS: a comparative study

Yu, Jiadong; Hou, Zongyu; Sheta, Sahar; Dong, Jian; Wen, Han; Lu, Taijin; Wang, Zhe

doi:10.1039/c7ay02643a

Cited by 31 publications

(14 citation statements)

References 20 publications

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“…37,38 PCA is a dimensionality-reduction method, which is often combined with other chemometric methods to classify or regress LIBS data. Several reports claim that LIBS coupled with PCA is good enough for classification of samples, 47,48 although for samples with a complex matrix, the analysis results are not good enough; 49,50 other methods are generally used or combined for classification analysis.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…Principle component analysis (PCA) and K-means clustering methods are both effective techniques to reduce the dimensions of the input data and better describe the chemical structure with considerably fewer variables than the raw spectral data. , PCA is a dimensionality-reduction method, which is often combined with other chemometric methods to classify or regress LIBS data. Several reports claim that LIBS coupled with PCA is good enough for classification of samples, , although for samples with a complex matrix, the analysis results are not good enough; , other methods are generally used or combined for classification analysis. Zhang et al used principal component analysis (PCA) to identify and remove abnormal spectra, and then the independent component analysis–wavelet neural network (ICA-WNN) was explored for the classification analysis of coal ash to achieve better results.…”

Section: Resultsmentioning

confidence: 99%

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Coal Discrimination Analysis Using Tandem Laser-Induced Breakdown Spectroscopy and Laser Ablation Inductively Coupled Plasma Time-of-Flight Mass Spectrometry

et al. 2020

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The contribution and impact of combined laser ablation inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOF-MS) and laser-induced breakdown spectroscopy (LIBS) were evaluated for the discrimination analysis of different coal samples. This tandem approach allows simultaneous determination of major and minor elements (C, H, Si, Ca, Al, Mg, etc.) and trace elements (V, Ba, Pb, U, etc.) in the coal. The research focused on coal-classification strategies based on principle component analysis (PCA) combined with K-means clustering, partial least-squares discrimination analysis (PLS-DA), and support vector machine (SVM) for analytical performance. Correlation analyses performed from TOF mass and LIBS emission spectra from the coal samples showed that most major, minor, and trace element emissions had negative correlation with the volatile content. Suitable variables for the classification models were determined from these data. The individual TOF data, LIBS data, and combined data of TOF and LIBS as the inputs for different models were analyzed and compared. In all cases, the results obtained with the combined TOF and LIBS data were found to be superior to those obtained with the individual TOF or LIBS data. The nonlinear SVM model combined with TOF and LIBS data provided the best coal-classification performance, with a classification accuracy of up to 98%.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Coal Discrimination Analysis Using Tandem Laser-Induced Breakdown Spectroscopy and Laser Ablation Inductively Coupled Plasma Time-of-Flight Mass Spectrometry

et al. 2020

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“…Also, the similar approaches that can be called pairwise PLS-DA have been introduced for the classification of nephrite jades and bacterial pathogen species. 17,18 In the pairwise approaches, the classification problem is broken down to n(n − 1)/2 binary problems. Both hierarchical and pairwise approaches showed better classification performances than the corresponding ordinary PLS-DA models.…”

Section: Introductionmentioning

confidence: 99%

Two-Step Partial Least Squares-Discriminant Analysis Modeling for Accurate Classification of Edible Sea Salt Products Using Laser-Induced Breakdown Spectroscopy

et al. 2022

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Laser-induced breakdown spectroscopy (LIBS) has been widely applied to material classification in various fields, and partial least squares-discriminant analysis (PLS-DA) is one of the frequently used classical multivariate statistics to construct classification models based on the LIBS spectra. However, classification accuracy of the PLS-DA model is sensitive to the number of classes and their similarities. Considering this characteristic of PLS-DA, we suggest a two-step PLS-DA modeling approach to improve the classification accuracy. This strategy was demonstrated for a 6-class problem in which six commercial edible sea salts produced in Japan, South Korea, and France are classified using their LIBS spectra. At the first step, test spectra were sorted into four classes and one extended class, composed of the two other most confusing classes, and then the test spectra in the extended class were further classified into each of the two constituent classes which were modeled separately from the other four classes. This two-step classification has been found to remarkably improve the PLS-DA classification accuracy by maximizing the difference between the confusing classes in the second-step modeling.

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“…Wang 13 conducted an analysis and identification of three kinds of Chinese herbal medicines by LIBS with the PCA and ANN models. Yu 14 used PCA, one‐step and pairwise partial least squares discriminant analysis (PLS‐DA), linear discriminant analysis (LDA), and SVM to classify nephrite jades. Oztoprak 15 built the PCA model and partial least‐squares discriminant analysis of LIBS spectra data of kidney stones.…”

Section: Introductionmentioning

confidence: 99%

Identification of aluminum alloy by laser‐induced breakdown spectroscopy combined with machine algorithm

Dai

Zhao

Song

et al. 2021

Micro & Optical Tech Letters

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Discarded aluminum alloys are a form of recyclable metal materials, and their classification and identification are highly important. In this work, laser‐induced breakdown spectroscopy (LIBS) technique combined with principal component analysis (PCA) and least‐squares support‐vector machine (LSSVM) algorithm were used to classify and identify five types of aluminum alloys. Exploratory analysis of five types of aluminum alloys by PCA was performed to achieve better segregation. The identification accuracy of the support‐vector machine (SVM) and LSSVM for aluminum alloy were 98.33% and 100%, respectively. The higher identification success rate was obtained using the LSSVM algorithm. Therefore, the LIBS technique combined with the PCA and LSSVM algorithms represents an efficient approach to identifying aluminum alloys.

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Provenance classification of nephrite jades using multivariate LIBS: a comparative study

Abstract: Provenance classification of nephrite jades is important since the unit price of jade changes drastically with its geological origin.

Cited by 31 publications

References 20 publications

Coal Discrimination Analysis Using Tandem Laser-Induced Breakdown Spectroscopy and Laser Ablation Inductively Coupled Plasma Time-of-Flight Mass Spectrometry

Coal Discrimination Analysis Using Tandem Laser-Induced Breakdown Spectroscopy and Laser Ablation Inductively Coupled Plasma Time-of-Flight Mass Spectrometry

Two-Step Partial Least Squares-Discriminant Analysis Modeling for Accurate Classification of Edible Sea Salt Products Using Laser-Induced Breakdown Spectroscopy

Identification of aluminum alloy by laser‐induced breakdown spectroscopy combined with machine algorithm

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