Laser Induced Breakdown Spectroscopy (LIBS): Application to Geological Materials-=SUP=-*-=/SUP=-

Laser-induced breakdown spectroscopy (LIBS) is emerging as an analytical tool for investigating geological materials. The unique abilities of this technique proven its potential in the area of geology. Detection of light elements, portability for in-field analysis, spot detection, and no sample preparation are some features that make this technique appropriate for the study of geological materials. The application of the LIBS technique has been tremendously developed in recent years. In this report, results obtained from previous and most recent studies regarding the investigation of geological materials LIBS technique are reviewed. Firstly, we introduce investigations that report the advancement in LIBS instrumentation, its applications, especially in the area of gemology and the extraterrestrial/planetary exploration have been reviewed. Investigation of gemstones by LIBS technique is not widely reviewed in the past as compared to LIBS application in planetary exploration or other geological applications. It is anticipated that for the classification of gemstones samples, huge data set is appropriate and to analyze this data set, multivariate/chemometric methods will be useful. Recent advancement of LIBS instrumentation for the study of meteorites, depth penetration in Martian rocks and its regolith proved the feasibility of LIBS used as robotic vehicles in the Martian environment. Keywords: LIBS, Gemstone, geological samples, Extra-terrestrial

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Classification of Greek Olive Oils from Different Regions by Machine Learning-Aided Laser-Induced Breakdown Spectroscopy and Absorption Spectroscopy

Molecules ◽

10.3390/molecules26051241 ◽

2021 ◽

Vol 26 (5) ◽

pp. 1241

Author(s):

Nikolaos Gyftokostas ◽

Eleni Nanou ◽

Dimitrios Stefas ◽

Vasileios Kokkinos ◽

Christos Bouras ◽

...

Keyword(s):

Machine Learning ◽

Olive Oil ◽

Absorption Spectra ◽

Absorption Spectroscopy ◽

Laser Induced Breakdown Spectroscopy ◽

Spectroscopic Techniques ◽

Breakdown Spectroscopy ◽

Olive Oils ◽

Laser Induced Breakdown

In the present work, the emission and the absorption spectra of numerous Greek olive oil samples and mixtures of them, obtained by two spectroscopic techniques, namely Laser-Induced Breakdown Spectroscopy (LIBS) and Absorption Spectroscopy, and aided by machine learning algorithms, were employed for the discrimination/classification of olive oils regarding their geographical origin. Both emission and absorption spectra were initially preprocessed by means of Principal Component Analysis (PCA) and were subsequently used for the construction of predictive models, employing Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All data analysis methodologies were validated by both “k-fold” cross-validation and external validation methods. In all cases, very high classification accuracies were found, up to 100%. The present results demonstrate the advantages of machine learning implementation for improving the capabilities of these spectroscopic techniques as tools for efficient olive oil quality monitoring and control.

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Effect of data set size on geochemical quantification accuracy with laser-induced breakdown spectroscopy

Spectrochimica Acta Part B Atomic Spectroscopy ◽

10.1016/j.sab.2021.106073 ◽

2021 ◽

Vol 177 ◽

pp. 106073

Author(s):

M. Darby Dyar ◽

Cai R. Ytsma

Keyword(s):

Laser Induced Breakdown Spectroscopy ◽

Data Set ◽

Breakdown Spectroscopy ◽

Set Size ◽

Laser Induced Breakdown ◽

Quantification Accuracy

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Classification of Chinese green tea grade using laser-induced breakdown spectroscopy

10.13031/aim.201700453 ◽

2017 ◽

Author(s):

Hongyang Zhang ◽

Qibing Zhu ◽

Min Huang ◽

Ya Guo

Keyword(s):

Green Tea ◽

Laser Induced Breakdown Spectroscopy ◽

Breakdown Spectroscopy ◽

Laser Induced Breakdown

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Measurement of Plasma Parameters of Fe-Lines in Lipstick Using Laser-Induced Breakdown Spectroscopy

NeuroQuantology ◽

10.14704/nq.2021.19.10.nq21149 ◽

2021 ◽

Vol 19 (10) ◽

pp. 01-07

Author(s):

M.H. Asmaa ◽

Sami A. Habana

Keyword(s):

Electron Temperature ◽

Debye Length ◽

Nuclear Data ◽

Laser Induced Breakdown Spectroscopy ◽

Current Investigation ◽

Plasma Parameters ◽

Data Set ◽

Breakdown Spectroscopy ◽

Laser Induced Breakdown ◽

Business Sectors

Electron thickness and temperature of laser prompted Iron plasma boundaries, among different boundaries, were estimated. Plasma was delivered through the connection of high pinnacle power Nd: YAG laser at the key frequency of 1064 nm with a pellet target contains a limited quantity of lipstick from nearby business sectors. Lines from Fe II at 238.502 nm, Fe II at 254.904 nm, Fe II at 262.370 nm, Fe II at 286.545 nm and Fe I at 349.779 nm were utilized to assess the plasma boundaries. The current investigation was completed to assess electron temperature (Te), electron thickness (ne), plasma recurrence, Debye length and Debye number (ND). Laser-incited breakdown spectroscopy LIBS method was used for examining and deciding ghastly discharge lines. ID of change lines from all spectra was completed by contrasting ghostly lines and NIST nuclear data set.

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Towards in-situ chemical classification of seafloor deposits: Application of neural networks to underwater laser-induced breakdown spectroscopy

OCEANS 2017 - Aberdeen ◽

10.1109/oceanse.2017.8084734 ◽

2017 ◽

Cited By ~ 1

Author(s):

Soichi Yoshino ◽

Tomoko Takahashi ◽

Blair Thornton

Keyword(s):

Neural Networks ◽

Laser Induced Breakdown Spectroscopy ◽

Chemical Classification ◽

Breakdown Spectroscopy ◽

Laser Induced Breakdown ◽

Underwater Laser

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In situ classification of rocks using stand-off laser-induced breakdown spectroscopy with a compact spectrometer

Journal of Analytical Atomic Spectrometry ◽

10.1039/c8ja00001h ◽

2018 ◽

Vol 33 (3) ◽

pp. 461-467 ◽

Cited By ~ 16

Author(s):

W. T. Li ◽

Y. N. Zhu ◽

X. Li ◽

Z. Q. Hao ◽

L. B. Guo ◽

...

Keyword(s):

High Accuracy ◽

Remote Detection ◽

Laser Induced Breakdown Spectroscopy ◽

Breakdown Spectroscopy ◽

Laser Induced Breakdown

The ASPI-LDA algorithm combined with a compact spectrometer to achieve high accuracy classification, which has a great potential for field in situ remote detection.

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