On the utilization of principal component analysis in laser-induced breakdown spectroscopy data analysis, a review

Pořízka, Pavel; Klus, Jakub; Képeš, Erik; Procházka, David; Hahn, David W.; Kaiser, Jozef

doi:10.1016/j.sab.2018.05.030

Cited by 201 publications

(98 citation statements)

References 170 publications

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“…That said, for all of these methods to work well individually, they require very accurate and stable algorithms for effective quantification, identification, and overall inference. Indeed, Pořízka et al propose that the “future of LIBS lies in the implementation of Multivariate Data Analysis Algorithms.” This requirement is significantly increased when they are to work in tandem. In the spirit of such a unified approach and such a conjecture, the submodel LMM is a step in this direction, particularly as space travel and analysis of spatial bodies becomes more commonplace.…”

Section: Resultsmentioning

confidence: 99%

“…[10][11][12][13][14][15][16] However, it is uncertain which analytical procedure should be accepted as a standard methodology. Principal component analysis (PCA), for example, has been a particularly popular method given its success in data visualization and pattern recognition on a lower-dimensional scale 17 ; however, its efficacy in LIBS data will vary from application to application and between instruments. To that end, a chemometric framework for the quantitative analysis of the preflight calibration LIBS data from ChemCam and the Planetary Instrumentation Laboratory (PIL) at York University is proposed; the result of which is an improvement in the prediction of the aforementioned datasets.…”

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confidence: 99%

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On the application of a novel linear mixture model on laser‐induced breakdown spectroscopy: Implications for Mars

Konstantinidis

Cote

Lalla

et al. 2019

Journal of Chemometrics

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As the exploration of Mars and other solar system bodies becomes more prevalent, the importance of accurate methods in chemical analyses has increased. The use of laser‐induced breakdown spectroscopy (LIBS) in such analyses requires that well understood and accurate statistical methods exist for appropriate interpretation of resulting spectra. Many multivariate techniques have been developed for the elemental quantification of LIBS; however, each still has its limitations. In an endeavor to improve upon existing methodologies, a new algorithm is proposed using the ChemCam preflight calibration dataset and a dataset from the characterization of a LIBS/Raman sensor prototype developed at York University. The algorithm which was developed in this work is a linear mixture model within a submodel clustering framework. The cross validation and test results of the model on both datasets were reported using various metrics for each element under consideration (root mean square error, relative standard deviation, and R2 value). The algorithm was subsequently compared with other well established chemometric models on both datasets, such as principal component regression, partial least square regression, and ordinary least squares regression. Further validation of the algorithm was achieved by comparing the results presented herein to previously published results on the ChemCam data. The samples in each dataset are highly representative of Martian geology, which, given the overwhelming success of the algorithm on both datasets, suggests that subsequent implementation of the proposed algorithm on larger databases may have significant implications for Martian geochemical analyses and for planetary exploration as a whole.

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Section: Resultsmentioning

confidence: 99%

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On the application of a novel linear mixture model on laser‐induced breakdown spectroscopy: Implications for Mars

Konstantinidis

Cote

Lalla

et al. 2019

Journal of Chemometrics

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“…The main limitation of this method is that we do not list particular elements of the tested material, but it may be considered as future work. Additional feature extraction and classification methods, such as principal component analysis [ 57 ], can be applied in the future. Hence, the sensitivity of the described method can also be further improved.…”

Section: Resultsmentioning

confidence: 99%

Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS

Rzecki

Sośnicki

Baran

et al. 2018

Sensors

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Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.

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“…Ambos são muito usados na análise exploratória de dados, proposição de modelos de classificação e de calibração. 73,74 A quimiometria pode ser dividida em quatro grandes vertentes: (i) planejamento fatorial e metodologia de superfície de respostas; (ii) análise exploratória de dados químicos; (iii) modelos de classificação; e (iv) calibração multivariada. [75][76][77][78][79] Todas as vertentes referidas são amplamente empregadas no desenvolvimento de métodos analíticos por LIBS.…”

Section: Libs E Quimiometriaunclassified

Laser Induced-Breakdown Spectroscopy (Libs): Histórico, Fundamentos, Aplicações E Potencialidades

Costa¹,

Augusto²,

Castro³

et al. 2019

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This review attempts to present a broad view of the technique laser-induced breakdown spectroscopy (LIBS), since its origin, fundamentals, sample preparation strategies, use of chemometric tools for data processing, the advantages and limitations with regard to qualitative and quantitative analysis, aplications and future trends as an analytical technique that is in consolidation in analytical chemistry. LIBS is a rapid and versatile, which is able of providing qualitative and quantitative analytical information for practically any sample, in a virtually non-destructive way, without any substantial sample preparation. The instrumentation is simple, robust, compact, and enables remote analysis. The advancement of the LIBS applications in the areas of agriculture, environmental, food, biological/pharmaceutical, forensic, metallurgy and electronic waste were discussed in detail in this review. We consider that this review will be an important tool concerning dissemination of the LIBS, mainly for audience of undergraduate courses, and disciplines related to instrumental analysis.

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On the utilization of principal component analysis in laser-induced breakdown spectroscopy data analysis, a review

Cited by 201 publications

References 170 publications

On the application of a novel linear mixture model on laser‐induced breakdown spectroscopy: Implications for Mars

On the application of a novel linear mixture model on laser‐induced breakdown spectroscopy: Implications for Mars

Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS

Laser Induced-Breakdown Spectroscopy (Libs): Histórico, Fundamentos, Aplicações E Potencialidades

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