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

Varování

Publikace nespadá pod Filozofickou fakultu, ale pod Přírodovědeckou fakultu. Oficiální stránka publikace je na webu muni.cz.
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POŘÍZKA Pavel KLUS Jakub PROCHAZKA David KÉPEŠ Erik HRDLIČKA Aleš NOVOTNÝ Jan NOVOTNÝ Karel KAISER Jozef

Rok publikování 2016
Druh Článek v odborném periodiku
Časopis / Zdroj Spectrochimica Acta B
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Citace
www http://www.sciencedirect.com/science/article/pii/S0584854716301367
Doi http://dx.doi.org/10.1016/j.sab.2016.08.008
Obor Analytická chemie, separace
Klíčová slova Laser-Induced Breakdown Spectroscopy; LIBS; Outlier filtering; Principal Component Analysis; PCA; Linear correlation; Total spectral intensity; Soft Independent Modelling of Class Analogies; SIMCA
Popis 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.
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