2026-04-27T12:40:48Zhttps://uvadoc.uva.es/oai/request
oai:uvadoc.uva.es:10324/644572025-02-17T13:21:56Zcom_10324_1157com_10324_931com_10324_894col_10324_1298
UVaDOC author Julve González, Sofia author Manrique Martínez, José Antonio author Veneranda ., Marco author Reyes Rodríguez, Iván author Pascual Sánchez, Elena author Sanz Arranz, José Aurelio author Konstantinidis, Menelaos author Lalla, Emmanuel Alexis author Charro Huerga, María Elena author Rodríguez Gutiez, Eduardo author López Rodríguez, José M. author Sanz Requena, José Francisco author Delgado Iglesias, Jaime author González Delgado, Manuel Ángel author Rull Pérez, Fernando author López Reyes, Guillermo Eduardo 2024-01-11T13:41:56Z 2024-01-11T13:41:56Z 2023 Journal of Raman Spectroscopy, 2023, vol. 54, n. 11, p. 1353-1366 0377-0486 https://uvadoc.uva.es/handle/10324/64457 10.1002/jrs.6611 1353 11 1366 Journal of Raman Spectroscopy 54 1097-4555 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. eng Attribution-NonCommercial-NoDerivatives 4.0 Internacional Machine learning methods applied to combined Raman and LIBS spectra: Implications for mineral discrimination in planetary missions info:eu-repo/semantics/article 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URL https://uvadoc.uva.es/bitstream/10324/64457/1/Machine-learning-methods-applied.pdf File MD5 4b1a8c96b63dc098f59bb8d059a5ba2c 1830803 application/pdf Machine-learning-methods-applied.pdf