RT info:eu-repo/semantics/article
T1 Machine learning methods applied to combined Raman and LIBS spectra: Implications for mineral discrimination in planetary missions
A1 Julve González, Sofia
A1 Manrique, Jose A.
A1 manrique martinez, jose antonio
A1 Veneranda, Marco
A1 Reyes Rodríguez, Iván
A1 Pascual Sánchez, Elena
A1 Sanz Arranz, Aurelio
A1 Konstantinidis, Menelaos
A1 Lalla, Emmanuel Alexis
A1 Charro Huerga, María Elena
A1 Rodríguez Gutiez, Eduardo
A1 López Rodríguez, José M.
A1 Sanz Requena, José Francisco
A1 Delgado Iglesias, Jaime
A1 González, Manuel A .
A1 Rull Pérez, Fernando
A1 López Reyes, Guillermo
K1 Data combination
K1 LIBS
K1 Machine learning
K1 PCA
K1 Raman
K1 25 Ciencias de la Tierra y del Espacio
AB The combined analysis of geological targets by complementary spectroscopic techniques could enhance the characterization of the mineral phases found on Mars. This is indeed the case with the SuperCam instrument onboard the Perseverance rover. In this framework, the present study seeks to evaluate and compare multiple machine learning techniques for the characterization of carbonate minerals based on Raman-LIBS (Laser-Induced Breakdown Spectroscopy) spectroscopic data. To do so, a Ca-Mg prediction curve was created by mixing hydromagnesite and calcite at different concentration ratios. After their characterization by Raman and LIBS spectroscopy, different multivariable machine learning (Gaussian process regression, support vector machines, ensembles of trees, and artificial neural networks) were used to predict the concentration ratio of each sample from their respective datasets. The results obtained by separately analyzing Raman and LIBS data were then compared to those obtained by combining them. By comparing their performance, this work demonstrates that mineral discrimination based on Gaussian and ensemble methods optimized the combine of Raman-LIBS dataset outperformed those ensured by Raman and LIBS data alone. This demonstrated that the fusion of data combination and machine learning is a promising approach to optimize the analysis of spectroscopic data returned from Mars.
PB Wiley
SN 0377-0486
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/64457
UL https://uvadoc.uva.es/handle/10324/64457
LA eng
NO Journal of Raman Spectroscopy, 2023, vol. 54, n. 11, p. 1353-1366
NO Producción Científica
DS UVaDOC
RD 09-may-2024