Publication detail

Multivariate classification of echellograms: a new perspective in Laser-Induced Breakdown Spectroscopy analysis

POŘÍZKA, P. KLUS, J. MAŠEK, J. RAJNOHA, M. PROCHAZKA, D. MODLITBOVÁ, P. NOVOTNÝ, J. BURGET, R. NOVOTNÝ, K. KAISER, J.

Original Title

Multivariate classification of echellograms: a new perspective in Laser-Induced Breakdown Spectroscopy analysis

English Title

Multivariate classification of echellograms: a new perspective in Laser-Induced Breakdown Spectroscopy analysis

Type

journal article in Web of Science

Language

en

Original Abstract

In this work, we proposed a new data acquisition approach that significantly improves the repetition rates of Laser-Induced Breakdown Spectroscopy (LIBS) experiments, where high-end echelle spectrometers and intensified detectors are commonly used. The moderate repetition rates of recent LIBS systems are caused by the utilization of intensified detectors and their slow full frame (i.e. echellogram) readout speeds with consequent necessity for echellogram-to-1D spectrum conversion (intensity vs. wavelength). Therefore, we investigated a new methodology where only the most effective pixels of the echellogram were selected and directly used in the LIBS experiments. Such data processing resulted in significant variable down-selection (more than four orders of magnitude). Samples of 50 sedimentary ores samples (distributed in 13 ore types) were analyzed by LIBS system and then classified by linear and non-linear Multivariate Data Analysis algorithms. The utilization of selected pixels from an echellogram yielded increased classification accuracy compared to the utilization of common 1D spectra.

English abstract

In this work, we proposed a new data acquisition approach that significantly improves the repetition rates of Laser-Induced Breakdown Spectroscopy (LIBS) experiments, where high-end echelle spectrometers and intensified detectors are commonly used. The moderate repetition rates of recent LIBS systems are caused by the utilization of intensified detectors and their slow full frame (i.e. echellogram) readout speeds with consequent necessity for echellogram-to-1D spectrum conversion (intensity vs. wavelength). Therefore, we investigated a new methodology where only the most effective pixels of the echellogram were selected and directly used in the LIBS experiments. Such data processing resulted in significant variable down-selection (more than four orders of magnitude). Samples of 50 sedimentary ores samples (distributed in 13 ore types) were analyzed by LIBS system and then classified by linear and non-linear Multivariate Data Analysis algorithms. The utilization of selected pixels from an echellogram yielded increased classification accuracy compared to the utilization of common 1D spectra.

Keywords

Analytical chemistry; Geology; Statistics

Released

01.12.2017

Publisher

Springer Nature

Pages from

1

Pages to

12

Pages count

12

URL

Full text in the Digital Library

BibTex


@article{BUT136910,
  author="Pavel {Pořízka} and Jakub {Klus} and Jan {Mašek} and Martin {Rajnoha} and David {Prochazka} and Pavlína {Modlitbová} and Jan {Novotný} and Radim {Burget} and Karel {Novotný} and Jozef {Kaiser}",
  title="Multivariate classification of echellograms: a new perspective in Laser-Induced Breakdown Spectroscopy analysis",
  annote="In this work, we proposed a new data acquisition approach that significantly improves the repetition rates of Laser-Induced Breakdown Spectroscopy (LIBS) experiments, where high-end echelle spectrometers and intensified detectors are commonly used. The moderate repetition rates of recent LIBS systems are caused by the utilization of intensified detectors and their slow full frame (i.e. echellogram) readout speeds with consequent necessity for echellogram-to-1D spectrum conversion (intensity vs. wavelength). Therefore, we investigated a new methodology where only the most effective pixels of the echellogram were selected and directly used in the LIBS experiments. Such data processing resulted in significant variable down-selection (more than four orders of magnitude). Samples of 50 sedimentary ores samples (distributed in 13 ore types) were analyzed by LIBS system and then classified by linear and non-linear Multivariate Data Analysis algorithms. The utilization of selected pixels from an echellogram yielded increased classification accuracy compared to the utilization of common 1D spectra.",
  address="Springer Nature",
  chapter="136910",
  doi="10.1038/s41598-017-03426-0",
  institution="Springer Nature",
  number="3160",
  volume="7",
  year="2017",
  month="december",
  pages="1--12",
  publisher="Springer Nature",
  type="journal article in Web of Science"
}