Improving laser-induced breakdown spectroscopy regression models via transfer learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F22%3APU145574" target="_blank" >RIV/00216305:26620/22:PU145574 - isvavai.cz</a>

Result on the web

<a href="https://pubs.rsc.org/en/content/articlelanding/2022/JA/D2JA00180B" target="_blank" >https://pubs.rsc.org/en/content/articlelanding/2022/JA/D2JA00180B</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1039/d2ja00180b" target="_blank" >10.1039/d2ja00180b</a>

Result language

angličtina

Original language name

Improving laser-induced breakdown spectroscopy regression models via transfer learning

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10406 - Analytical chemistry

Project

<a href="/en/project/GJ20-19526Y" target="_blank" >GJ20-19526Y: Processes of the laser ablation of soft tissues and consequent laser-induced plasma formation</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Analytical Atomic Spectrometry

ISSN

0267-9477

e-ISSN

1364-5544

Volume of the periodical

37

Issue of the periodical within the volume

9

Country of publishing house

GB - UNITED KINGDOM

Number of pages

11

Pages from-to

1883-1893

UT code for WoS article

000842774400001

EID of the result in the Scopus database

2-s2.0-85136452965