Author
Erik Képeš
Other affiliations: Brno University of Technology
Bio: Erik Képeš is an academic researcher from Central European Institute of Technology. The author has contributed to research in topics: Laser-induced breakdown spectroscopy & Principal component analysis. The author has an hindex of 5, co-authored 9 publications receiving 135 citations. Previous affiliations of Erik Képeš include Brno University of Technology.
Papers
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TL;DR: This work critically assess and elaborate on the approaches to utilize PCA in LIBS data processing, and derives some implications and suggests advice in data preprocessing, visualization, dimensionality reduction, model building, classification, quantification and non-conventional multivariate mapping.
143 citations
TL;DR: The central idea was to simulate the so-called “out-of-sample” classification, implying various real-world applications, in a very large dataset, containing high-dimensional elemental spectra.
40 citations
TL;DR: In this article, the authors highlight the necessity of outlier filtering prior the multivariate classification in Laser-Induced Breakdown Spectroscopy (LIBS) analyses and demonstrate that the variance in the data topology of training and testing data sets has a great impact on the consequent data classification.
33 citations
TL;DR: In this paper, the effects of numerical background estimation and subtraction on LIBS data in terms of limits of detection estimated by the signal-to-noise ratio method, considering several fundamentally different background estimation algorithms: polynomial fitting, heuristic estimation, wavelet smoothing, and nonparametric modelling.
Abstract: Spectra acquired by laser-induced breakdown spectroscopy comprise both a valuable analyte signal and an undesired background signal originating from various sources. The latter is commonly suppressed, at least partially, by gating of the signal acquisition, or treated numerically during the data processing. The numerical treatment might lead to loss of information or introduction of spectral artefacts, depending on the applied methodology. Consequently, background subtraction might significantly influence both univariate and multivariate analysis of the LIBS data. While various baseline correction methods have recently been studied and compared for multivariate LIBS analysis, their influence on LIBS data remains unexplored. Therefore, the present work aims to elucidate the effects of numerical background estimation and subtraction on LIBS data in terms of limits of detection estimated by the signal-to-noise ratio method, considering several fundamentally different background estimation algorithms: polynomial fitting, heuristic estimation, wavelet smoothing, and non-parametric modelling. A threshold gate delay value was observed above which the numerical treatment of spectral background has to be done cautiously. In addition, it was found that the optimal measurement parameters and selection of the emission line yielding the best results depend on the planned spectral processing.
18 citations
TL;DR: An extensive dataset of laser-induced breakdown spectroscopy (LIBS) spectra is presented for the pre-training and evaluation of LIBS classification models, aimed at helping with the development and testing of classification and clustering methodologies.
Abstract: In this work, we present an extensive dataset of laser-induced breakdown spectroscopy (LIBS) spectra for the pre-training and evaluation of LIBS classification models. LIBS is a well-established spectroscopic method for in-situ and industrial applications, where LIBS is primarily applied for clustering and classification tasks. As such, our dataset is aimed at helping with the development and testing of classification and clustering methodologies. Moreover, the dataset could be used to pre-train classification models for applications where the amount of available data is limited. The dataset consists of LIBS spectra of 138 soil samples belonging to 12 distinct classes. The spectra were acquired with a state-of-the-art LIBS system. Lastly, the composition of each sample is also provided, including estimated uncertainties.
17 citations
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TL;DR: This work critically assess and elaborate on the approaches to utilize PCA in LIBS data processing, and derives some implications and suggests advice in data preprocessing, visualization, dimensionality reduction, model building, classification, quantification and non-conventional multivariate mapping.
143 citations
TL;DR: In this paper, the authors report recent instrumental configurations, data processing methodologies and applications related to laser induced breakdown spectroscopy (LIBS-based imaging) for elemental imaging and report the wide variety of laboratory applications that have benefited from LIBS mapping techniques, such as those in the biomedical, geological and industrial fields.
127 citations
TL;DR: Methods for raw signal improvement including sample preparation, system optimization, and especially plasma modulation, which modulates the laser-induced plasma evolution process for higher signal repeatability and signal-to-noise ratio, were reviewed and discussed.
Abstract: Laser-induced breakdown spectroscopy (LIBS) is regarded as the future superstar for chemical analysis, but the relatively high measurement uncertainty and error remain the persistent challenges for its technological development as well as wide applications. In the present work, mechanisms of measurement uncertainty generation and basic principle of signal uncertainty and matrix effects impacting quantification performance were explained. Furthermore, methods for raw signal improvement including sample preparation, system optimization, and especially plasma modulation, which modulates the laser-induced plasma evolution process for higher signal repeatability and signal-to-noise ratio, were reviewed and discussed. Different LIBS mathematical quantification methods including calibration-free methods and calibration methods, which were classified into physical-principle based calibration model, data-driven based calibration model, and hybrid model, were discussed and compared. Overall, a framework of quantification improvement strategy including key steps and main way-out was summarized and recommended for LIBS future development.
91 citations
TL;DR: In this paper, a review of the most relevant advances in the field of laser-induced breakdown spectroscopy (LIBS) analysis is presented, with a focus on the applications of LIBS in geosciences/geology.
78 citations
TL;DR: In the last few years, LIBS has become an established technique for the assessment of elemental concentrations in various sample types as discussed by the authors, since this technique allows to associate the obtained elemental composition information with the spatial coordinates of the investigated sample.
69 citations