[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122834-en":3,"doc-seo-122834-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122834,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning methods applied to combined Raman and LIBS spectra - Implications for mineral discrimination in planetary missions","The combined analysis of geological targets using complementary spectroscopic techniques can improve the characterization of mineral phases on Mars, as implemented by the SuperCam instrument aboard the Perseverance rover. This study evaluates and compares multiple machine learning models for carbonate mineral characterization from Raman-LIBS data. A Ca–Mg prediction curve is built from hydromagnesite and calcite mixtures at controlled concentration ratios, then models are trained on Raman-only, LIBS-only, and fused datasets. Performance comparisons show that Gaussian process and ensemble approaches using the combined dataset outperform single-technique analysis.","Received: 14 November 2022 Revised: 17 September 2023 Accepted: 2 October 2023  \nDOI: 10.1002/jrs.6611  \nSPECIAL I SSUE - RESEARC H ARTICLE  \nMachine learning methods applied to combined Raman and LIBS spectra: Implications for mineral discrimination in planetary missions  \nSofía Julve-Gonzalez 1  | Jose A. Manrique 1,2  | Marco Veneranda 1  | Ivn Reyes-Rodríguez 1 | Elena Pascual-Sanchez 1 | Aurelio Sanz-Arranz 1 | Menelaos Konstantinidis 3 | Emmanuel A. Lalla 3,4 | María E. Charro 1 | Eduardo Rodriguez-Gutiez 1 | José M. Lopez-Rodríguez 1 |  \nJosé F. Sanz-Requena 1 | Jaime Delgado-Iglesias 1 | Manuel A. Gonzalez 1 | Fernando Rull 1 | Guillermo Lopez-Reyes 1   \n1ERICA Research Group, Universidad de Valladolid (UVa), Valladolid, Spain 2Université de Toulouse 3 Paul Sabatier, CNRS, CNES, Toulouse, France 3Centre for Research in Earth and Space Science, York University, Toronto, Ontario, Canada  \n4Canandensys Aerospace Corporation, Bolton, Ontario, Canada  \nCorrespondence  \nSofía Julve-Gonzalez, ERICA research group, Universidad de Valladolid (UVa), Valladolid, Spain.  \nEmail: sofia.julve@estudiantes.uva.es  \nFunding information Agencia Estatal de Investigacin, Grant/Award Number:  \nPID2022-142490OB-C32; Ministry of Economy and Competitiveness, Grant/Award Number:  \nRDE2018-102600-T; European Union  \nAbstract  \nThe 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.  \nKEYWOR DS  \ndata combination, LIBS, machine learning, PCA, Raman  \nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.  \n© 2023 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons Ltd.  \n1354  \nJULVE-GONZALEZ ET AL.  \n1 | INTRODUCTION  \nIn recent years, several spectroscopic methods have been used to characterize the geology of Mars, including Raman spectroscopy and Laser-Induced Breakdown Spectroscopy (LIBS) .1–5 On the one hand, Raman spectroscopy provides molecular information about the crystallographic structure of the target, thus allowing the discrimination of the mineral phases that compose it. On the other hand, LIBS provides information about its elemental composition thanks to its characteristic emission spectrum. Taking advantage of their complementarity, the SuperCam instrument onboard the NASA/Mars 2020 Perseverance rover is the first instrument operating in space that is capable of performing simultaneous Ramanand LIBS analysis on the same target.6 SuperCamemerges as an evolution of","cbCaitZnN17AseFN","https://ap.wps.com/l/cbCaitZnN17AseFN","pdf",1830803,1,14,"English","en",105,"# Introduction\n## Complementarity of Raman spectroscopy and LIBS in Mars geology\n## SuperCam and its relevance to Mars 2020 objectives\n## Carbonates at Jezero Crater and implications for biosignatures","[{\"question\":\"Why combine Raman spectroscopy and LIBS for Mars mineral analysis?\",\"answer\":\"Raman spectroscopy provides molecular/crystallographic information for discriminating mineral phases, while LIBS provides elemental composition via emission spectra. Their complementarity enables more robust characterization on the same target.\"},{\"question\":\"How is the dataset for carbonate mineral prediction constructed in this study?\",\"answer\":\"A Ca–Mg prediction curve is created by mixing hydromagnesite and calcite at different concentration ratios, then samples are characterized by both Raman and LIBS to generate corresponding datasets.\"},{\"question\":\"Which machine learning approaches perform best when using fused Raman-LIBS data?\",\"answer\":\"Gaussian process regression and ensemble methods optimized on the combined Raman-LIBS dataset outperform the models based on Raman-only or LIBS-only data.\"}]","Machine learning methods applied to combined Raman and LIBS spectra - Implications for mineral discrimination in planetary missions | PDF",1785813155,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-methods-applied-to-combined-raman-and-libs-spectra-implications-for-mineral-discrimination-in-planetary-missions","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-methods-applied-to-combined-raman-and-libs-spectra-implications-for-mineral-discrimination-in-planetary-missions/122834/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why combine Raman spectroscopy and LIBS for Mars mineral analysis?","Question",{"text":75,"@type":76},"Raman spectroscopy provides molecular/crystallographic information for discriminating mineral phases, while LIBS provides elemental composition via emission spectra. Their complementarity enables more robust characterization on the same target.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the dataset for carbonate mineral prediction constructed in this study?",{"text":80,"@type":76},"A Ca–Mg prediction curve is created by mixing hydromagnesite and calcite at different concentration ratios, then samples are characterized by both Raman and LIBS to generate corresponding datasets.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approaches perform best when using fused Raman-LIBS data?",{"text":84,"@type":76},"Gaussian process regression and ensemble methods optimized on the combined Raman-LIBS dataset outperform the models based on Raman-only or LIBS-only data.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]