Laser-Induced Breakdown Spectroscopy coupled with chemometrics
for the analysis of steel: The issue of spectral outliers
filtering

J 2016

Laser-Induced Breakdown Spectroscopy coupled with chemometrics for the analysis of steel: The issue of spectral outliers filtering

POŘÍZKA, Pavel, Jakub KLUS, David PROCHAZKA, Erik KÉPEŠ, Aleš HRDLIČKA et. al.

Základní údaje

Originální název

Laser-Induced Breakdown Spectroscopy coupled with chemometrics for the analysis of steel: The issue of spectral outliers filtering

Autoři

POŘÍZKA, Pavel (203 Česká republika), Jakub KLUS (203 Česká republika), David PROCHAZKA (203 Česká republika), Erik KÉPEŠ (203 Česká republika), Aleš HRDLIČKA (203 Česká republika, domácí), Jan NOVOTNÝ (203 Česká republika), Karel NOVOTNÝ (203 Česká republika, garant, domácí) a Jozef KAISER (203 Česká republika)

Vydání

Spectrochimica Acta B, Elsevier, 2016, 0584-8547

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10406 Analytical chemistry

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.241

Kód RIV

RIV/00216224:14310/16:00093775

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.1016/j.sab.2016.08.008

UT WoS

000389497400012

Klíčová slova anglicky

Laser-Induced Breakdown Spectroscopy; LIBS; Outlier filtering; Principal Component Analysis; PCA; Linear correlation; Total spectral intensity; Soft Independent Modelling of Class Analogies; SIMCA

Štítky

AKR, rivok

Změněno: 6. 4. 2017 21:48, Ing. Andrea Mikešková

Anotace

V originále

In this manuscript we highlight the necessity of outlier filtering prior the multivariate classification in Laser-Induced Breakdown Spectroscopy (LIBS) analyses. For the purpose of classification we chose to analyse BAM steel standards that possess similar composition of major and trace elements. To assess the improvement in figures of merit we compared the performance of three outlier filtering approaches (based on Principal Component Analysis, linear correlation and total spectral intensity) already separately discussed in the LIBS literature. The truncated data set was classified using Soft Independent Modelling of Class Analogies (SIMCA). Yielded results showed significant improvement in the performance of multivariate classification coupled to filtered data. The best performance was observed for the total spectral intensity filtering approach gaining the analytical figures of merit (overall accuracy, sensitivity, and specificity) over 98%. It is noteworthy that the results showed relatively low sensitivity and high specificity of the SIMCA algorithm regardless of the presence of outliers in the data sets. Moreover, it was shown that the variance in the data topology of training and testing data sets has a great impact on the consequent data classification.

Návaznosti

LQ1601, projekt VaV

Název: CEITEC 2020 (Akronym: CEITEC2020)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, CEITEC 2020

Citovat

POŘÍZKA, Pavel, Jakub KLUS, David PROCHAZKA, Erik KÉPEŠ, Aleš HRDLIČKA, Jan NOVOTNÝ, Karel NOVOTNÝ a Jozef KAISER. Laser-Induced Breakdown Spectroscopy coupled with chemometrics for the analysis of steel: The issue of spectral outliers filtering. Spectrochimica Acta B. Elsevier, 2016, roč. 123, September, s. 114-120. ISSN 0584-8547. Dostupné z: https://dx.doi.org/10.1016/j.sab.2016.08.008.

@article{1374285,
   author = {Pořízka, Pavel and Klus, Jakub and Prochazka, David and Képeš, Erik and Hrdlička, Aleš and Novotný, Jan and Novotný, Karel and Kaiser, Jozef},
   article_number = {September},
   doi = {http://dx.doi.org/10.1016/j.sab.2016.08.008},
   keywords = {Laser-Induced Breakdown Spectroscopy; LIBS; Outlier filtering; Principal Component Analysis; PCA; Linear correlation; Total spectral intensity; Soft Independent Modelling of Class Analogies; SIMCA},
   language = {eng},
   issn = {0584-8547},
   journal = {Spectrochimica Acta B},
   title = {Laser-Induced Breakdown Spectroscopy coupled with chemometrics for the analysis of steel: The issue of spectral outliers filtering},
   url = {http://www.sciencedirect.com/science/article/pii/S0584854716301367},
   volume = {123},
   year = {2016}
}

TY  - JOUR
ID  - 1374285
AU  - Pořízka, Pavel - Klus, Jakub - Prochazka, David - Képeš, Erik - Hrdlička, Aleš - Novotný, Jan - Novotný, Karel - Kaiser, Jozef
PY  - 2016
TI  - Laser-Induced Breakdown Spectroscopy coupled with chemometrics for the analysis of steel: The issue of spectral outliers filtering
JF  - Spectrochimica Acta B
VL  - 123
IS  - September
SP  - 114-120
EP  - 114-120
PB  - Elsevier
SN  - 05848547
KW  - Laser-Induced Breakdown Spectroscopy
KW  - LIBS
KW  - Outlier filtering
KW  - Principal Component Analysis
KW  - PCA
KW  - Linear correlation
KW  - Total spectral intensity
KW  - Soft Independent Modelling of Class Analogies
KW  - SIMCA
UR  - http://www.sciencedirect.com/science/article/pii/S0584854716301367
N2  - In this manuscript we highlight the necessity of outlier filtering prior the multivariate classification in Laser-Induced Breakdown Spectroscopy (LIBS) analyses. For the purpose of classification we chose to analyse BAM steel standards that possess similar composition of major and trace elements. To assess the improvement in figures of merit we compared the performance of three outlier filtering approaches (based on Principal Component Analysis, linear correlation and total spectral intensity) already separately discussed in the LIBS literature. The truncated data set was classified using Soft Independent Modelling of Class Analogies (SIMCA). Yielded results showed significant improvement in the performance of multivariate classification coupled to filtered data. The best performance was observed for the total spectral intensity filtering approach gaining the analytical figures of merit (overall accuracy, sensitivity, and specificity) over 98%. It is noteworthy that the results showed relatively low sensitivity and high specificity of the SIMCA algorithm regardless of the presence of outliers in the data sets. Moreover, it was shown that the variance in the data topology of training and testing data sets has a great impact on the consequent data classification.
ER  -

POŘÍZKA, Pavel, Jakub KLUS, David PROCHAZKA, Erik KÉPEŠ, Aleš HRDLIČKA, Jan NOVOTNÝ, Karel NOVOTNÝ a Jozef KAISER. Laser-Induced Breakdown Spectroscopy coupled with chemometrics for the analysis of steel: The issue of spectral outliers filtering. \textit{Spectrochimica Acta B}. Elsevier, 2016, roč.~123, September, s.~114-120. ISSN~0584-8547. Dostupné z: https://dx.doi.org/10.1016/j.sab.2016.08.008.

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