Issue 7, 2021

Addressing the sparsity of laser-induced breakdown spectroscopy data with randomized sparse principal component analysis

Abstract

Emission spectra yielded by laser-induced breakdown spectroscopy (LIBS) exhibit high dimensionality, redundancy, and sparsity. The high dimensionality is often addressed by principal component analysis (PCA) which creates a low dimensional embedding of the spectra by projecting them into the score space. However, PCA does not effectively deal with the sparsity of the analysed data, including LIBS spectra. Consequently, sparse PCA (SPCA) was proposed for the analysis of high-dimensional sparse data. Nevertheless, SPCA remains underutilized for LIBS applications. Thus, in this work, we show that SPCA combined with genetic algorithms offers marginal improvements in clustering and quantification using multivariate calibration. More importantly, we show that SPCA significantly improves the interpretability of loading spectra. In addition, we show that the loading spectra yielded by SPCA differ from those yielded by sparse partial least squares regression. Finally, by using the randomized SPCA (RSPCA) algorithm for carrying out SPCA, we indirectly demonstrate that the analysis of LIBS data can greatly benefit from the tools developed by randomized linear algebra: RSPCA offers a 20-fold increase in computation speed compared to PCA based on singular value decomposition.

Graphical abstract: Addressing the sparsity of laser-induced breakdown spectroscopy data with randomized sparse principal component analysis

Supplementary files

Article information

Article type
Paper
Submitted
26 Feb 2021
Accepted
22 Apr 2021
First published
22 Apr 2021

J. Anal. At. Spectrom., 2021,36, 1410-1421

Addressing the sparsity of laser-induced breakdown spectroscopy data with randomized sparse principal component analysis

E. Képeš, J. Vrábel, P. Pořízka and J. Kaiser, J. Anal. At. Spectrom., 2021, 36, 1410 DOI: 10.1039/D1JA00067E

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements