Detail publikace

Effect of experimental parameters and resulting analytical signal statistics in laser-induced breakdown spectroscopy

Originální název

Effect of experimental parameters and resulting analytical signal statistics in laser-induced breakdown spectroscopy

Anglický název

Effect of experimental parameters and resulting analytical signal statistics in laser-induced breakdown spectroscopy

Jazyk

en

Originální abstrakt

The purpose of this work is to provide detailed study of statistical behavior of different types of analytical signals in typical of Laser-Induced Breakdown Spectroscopy (LIBS) measurements. The main goal of this work is to justify usage of arithmetic mean and standard deviation as statistical estimates of expected value of selected analytical signal. In contrary to the general assumption that LIBS data show Gaussian distribution, this paper deals with the hypothesis that the data rather demonstrate Generalized Extreme Value Distribution. The study is realized on 10 selected lines measured on NIST glass standard. In order to cover wide range of possible applications three different spectra internal standardization techniques and their influence on distribution were studied. Finally, assuming that the data comes from a single distribution and the central limit theorem is valid, the influence of accumulations on the line distribution is examined and discussed. Statistical tools used and described in this paper can be utilized by other researchers to confirm their hypotheses and verify utilization of Gaussian distribution or even novel data processing methods.

Anglický abstrakt

The purpose of this work is to provide detailed study of statistical behavior of different types of analytical signals in typical of Laser-Induced Breakdown Spectroscopy (LIBS) measurements. The main goal of this work is to justify usage of arithmetic mean and standard deviation as statistical estimates of expected value of selected analytical signal. In contrary to the general assumption that LIBS data show Gaussian distribution, this paper deals with the hypothesis that the data rather demonstrate Generalized Extreme Value Distribution. The study is realized on 10 selected lines measured on NIST glass standard. In order to cover wide range of possible applications three different spectra internal standardization techniques and their influence on distribution were studied. Finally, assuming that the data comes from a single distribution and the central limit theorem is valid, the influence of accumulations on the line distribution is examined and discussed. Statistical tools used and described in this paper can be utilized by other researchers to confirm their hypotheses and verify utilization of Gaussian distribution or even novel data processing methods.

Dokumenty

BibTex


@article{BUT128951,
  author="Jakub {Klus} and Pavel {Pořízka} and David {Prochazka} and Jan {Novotný} and Karel {Novotný} and Jozef {Kaiser}",
  title="Effect of experimental parameters and resulting analytical signal statistics in laser-induced breakdown spectroscopy",
  annote="The purpose of this work is to provide detailed study of statistical behavior of different types of analytical signals in typical of Laser-Induced Breakdown Spectroscopy (LIBS) measurements. The main goal of this work is to justify usage of arithmetic mean and standard deviation as statistical estimates of expected value of selected analytical signal. In contrary to the general assumption that LIBS data show Gaussian distribution, this paper deals with the hypothesis that the data rather demonstrate Generalized Extreme Value Distribution. The study is realized on 10 selected lines measured on NIST glass standard. In order to cover wide range of possible applications three different spectra internal standardization techniques and their influence on distribution were studied. Finally, assuming that the data comes from a single distribution and the central limit theorem is valid, the influence of accumulations on the line distribution is examined and discussed. Statistical tools used and described in this paper can be utilized by other researchers to confirm their hypotheses and verify utilization of Gaussian distribution or even novel data processing methods.",
  chapter="128951",
  doi="10.1016/j.sab.2016.10.002",
  howpublished="online",
  number="6",
  volume="126",
  year="2016",
  month="december",
  pages="6--10",
  type="journal article in Web of Science"
}