On the utilization of principal component analysis in laser-induced breakdown spectroscopy data analysis, a review

• Published in: Spectrochimica acta. Part B: Atomic spectroscopy 2018-10, Vol.148, p.65-82
• Main Authors: Pořízka, Pavel, Klus, Jakub, Képeš, Erik, Prochazka, David, Hahn, David W., Kaiser, Jozef
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Analytical methods; Communities; Data; Data analysis; Data processing; Information processing; Instrumentation; Laser induced breakdown spectroscopy; Lasers; Multivariate analysis; Principal components analysis; Scientific papers; Scientific visualization; Signal processing; Spectroscopic analysis; Spectroscopy; Spectrum analysis; Studies; Visualization
• ISSN: 0584-8547; 1873-3565
• DOI: 10.1016/j.sab.2018.05.030

An implementation of a fast, robust, and effective algorithm is inevitable in modern multivariate data analysis (MVDA). The principal component analysis (PCA) algorithm is becoming popular not only in the spectroscopic community because it complies with the qualities mentioned above. PCA is, therefore, often used for the processing of detected multivariate signal (characteristic spectra). Over the past decade, PCA has been adopted by the Laser-Induced Breakdown Spectroscopy (LIBS) community and the number of scientific articles referring to PCA steadily increases. The interest in PCA is not caused only by the basic need to obtain a fast data visualization on a lower dimensional scale and to inspect the most prominent variables. Most recently, PCA has also been applied to yield unconventional data analyses, i.e. processing of large scale LIBS maps. However, a rapid development of LIBS-related instrumentation and applications has led to some non-uniform methodologies in the implementation and utilization of MVDA, including PCA. Thus, in this work, we critically assess and elaborate on the approaches to utilize PCA in LIBS data processing. The aim of this article is also to derive some implications and to suggest advice in data preprocessing, visualization, dimensionality reduction, model building, classification, quantification and non-conventional multivariate mapping. This review reflects also other MVDA algorithms than PCA and consequently, presented conclusions and recommendations can be generalized. [Display omitted] •Implementation of principal component analysis to processing of laser-induced breakdown spectroscopy data•Thorough review of articles on pattern recognition, classification and regression•Advices on data preprocessing and understanding of PCA outputs