Publication detail

Improving laser-induced breakdown spectroscopy regression models via transfer learning

KÉPEŠ, E. VRÁBEL, J. POŘÍZKA, P. KAISER, J.

Original Title

Improving laser-induced breakdown spectroscopy regression models via transfer learning

Type

journal article in Web of Science

Language

English

Original Abstract

Laser-induced breakdown spectroscopy (LIBS) is a well-established analytical tool with relevance in extra-terrestrial exploration. Despite considerable efforts towards the development of calibration-free LIBS approaches, these are currently outperformed by calibration-based approaches to semi-quantitative LIBS analyses. However, the construction of robust calibration models often requires large calibration datasets owing to the extensive matrix effects plaguing the LIBS performance. Moreover, LIBS data are sensitive to changes in the apparatus. Hence, a calibration model constructed for one LIBS system is seldom applicable to a distinct LIBS system. A notable example are the LIBS instruments included in the currently active Mars Rovers' analytical suites, the ChemCam and SuperCam LIBS instruments; while the two instruments exhibit relatively small differences, they required the collection of two separate calibration datasets. Currently, these two datasets are used exclusively for the system they were collected for. In this work, we demonstrate that calibration models constructed for the SuperCam instrument can be improved using data obtained with the ChemCam instrument. Namely, we take advantage of the partial overlap between the targets used to collect the two calibration datasets. Using this overlap, we approximate the function transforming ChemCam spectra into their SuperCam equivalent. Subsequently, the transformed spectra are used to extend the training data available for the regression model constructed for the SuperCam instrument. The proposed approach considerably improves the performance of convolutional neural network regression models.

Keywords

SUPPORT VECTOR MACHINES; CHEMCAM INSTRUMENT; NEURAL-NETWORKS; CCD DETECTORS; ALLOY-STEEL; LIBS; TEMPERATURE; SCIENCE; SYSTEM; SAMPLE

Authors

KÉPEŠ, E.; VRÁBEL, J.; POŘÍZKA, P.; KAISER, J.

Released

31. 8. 2022

Publisher

ROYAL SOC CHEMISTRY

Location

CAMBRIDGE

ISBN

1364-5544

Periodical

Journal of Analytical Atomic Spectrometry

Year of study

37

Number

9

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1883

Pages to

1893

Pages count

11

URL

https://pubs.rsc.org/en/content/articlelanding/2022/JA/D2JA00180B

BibTex

@article{BUT179168,
  author="Erik {Képeš} and Jakub {Vrábel} and Pavel {Pořízka} and Jozef {Kaiser}",
  title="Improving laser-induced breakdown spectroscopy regression models via transfer learning",
  journal="Journal of Analytical Atomic Spectrometry",
  year="2022",
  volume="37",
  number="9",
  pages="1883--1893",
  doi="10.1039/d2ja00180b",
  issn="1364-5544",
  url="https://pubs.rsc.org/en/content/articlelanding/2022/JA/D2JA00180B"
}