Issue 9, 2022

Improving laser-induced breakdown spectroscopy regression models via transfer learning

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.

Graphical abstract: Improving laser-induced breakdown spectroscopy regression models via transfer learning

Supplementary files

Article information

Article type
Paper
Submitted
24 máj 2022
Accepted
11 aug 2022
First published
12 aug 2022

J. Anal. At. Spectrom., 2022,37, 1883-1893

Improving laser-induced breakdown spectroscopy regression models via transfer learning

E. Képeš, J. Vrábel, P. Pořízka and J. Kaiser, J. Anal. At. Spectrom., 2022, 37, 1883 DOI: 10.1039/D2JA00180B

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