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A great challenge facing the LIBS community is the difficulty to accurately discriminate rocks with close chemical compositions. A convolutional neural network (CNN) model has been designed in this study to identify twelve types of rock, among which some rocks have similar compositions. Both the training set and the testing set are constructed based on the LIBS spectra acquired by Mars Surface Composition Detector (MarSCoDe) for China\u2019s Tianwen-1 Mars exploration mission. All the spectra were collected from dedicated rock pellet samples, which were placed in a simulated Martian atmospheric environment. The classification performance of the CNN has been compared with that of three alternative machine learning algorithms, i.e., logistic regression (LR), support vector machine (SVM), and linear discriminant analysis (LDA). Among the four methods, it is on the CNN model that the highest classification correct rate has been obtained, as assessed by precision score, recall score, and the harmonic mean of precision and recall. Furthermore, the classification accuracy is inspected more quantitatively via Brier score, and the CNN is still the best performing model. The results demonstrate that the CNN-based chemometrics are an efficient tool for rock identification with LIBS spectra collected in a simulated Martian environment. Despite the relatively small sample set, this study implies that CNN-supported LIBS classification is a promising analytical technique for Tianwen-1 Mars mission and more planetary explorations in the future.<\/jats:p>","DOI":"10.3390\/rs14215343","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T07:17:48Z","timestamp":1666768668000},"page":"5343","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Convolutional Neural Network Chemometrics for Rock Identification Based on Laser-Induced Breakdown Spectroscopy Data in Tianwen-1 Pre-Flight Experiments"],"prefix":"10.3390","volume":"14","author":[{"given":"Fan","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}]},{"given":"Weiming","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"School of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9807-9583","authenticated-orcid":false,"given":"Zhicheng","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6821-449X","authenticated-orcid":false,"given":"Xiangfeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}]},{"given":"Xuesen","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China"}]},{"given":"Liangchen","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0148-3609","authenticated-orcid":false,"given":"Yuwei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, 02430 Kirkkonummi, Finland"}]},{"given":"Rong","family":"Shu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"School of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China"}]},{"given":"Luning","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1080\/10408349708050587","article-title":"Fundamentals and applications of laser-induced breakdown spectroscopy","volume":"27","author":"Rusak","year":"1997","journal-title":"Crit. Rev. Anal. Chem."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1016\/j.apgeochem.2009.02.009","article-title":"LIBS analysis of geomaterials: Geochemical fingerprinting for the rapid analysis and discrimination of minerals","volume":"24","author":"Harmon","year":"2009","journal-title":"Appl. Geochem."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.sab.2018.05.010","article-title":"Using LIBS to diagnose melanoma in biomedical fluids deposited on solid substrates: Limits of direct spectral analysis and capability of machine Learning","volume":"146","author":"Gaudiuso","year":"2018","journal-title":"Spectrochim. Acta Part B At. Spectrosc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2166","DOI":"10.1039\/c1ja10096c","article-title":"Fast single piece identification with a 3D scanning LIBS for aluminium cast and wrought alloys recycling","volume":"26","author":"Werheit","year":"2011","journal-title":"J. Anal. At. Spectrom."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.envres.2009.02.005","article-title":"Heavy metal concentrations in soils as determined by laser-induced breakdown spectroscopy (LIBS), with special emphasis on chromium","volume":"109","author":"Senesi","year":"2009","journal-title":"Environ. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.renene.2019.04.137","article-title":"Rapid discrimination of the categories of the biomass pellets using laser-induced breakdown spectroscopy","volume":"143","author":"Liu","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Burger, M., Skrodzki, P.J., Finney, L.A., Nees, J., and Jovanovic, I. (2020). Remote Detection of Uranium Using Self-Focusing Intense Femtosecond Laser Pulses. Remote Sens., 12.","DOI":"10.3390\/rs12081281"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.1039\/C5JA00255A","article-title":"Quantitative analysis of sedimentary rocks using laser-induced breakdown spectroscopy: Comparison of Support Vector Regression and Partial Least Squares Regression Chemometric Methods","volume":"30","author":"Shi","year":"2015","journal-title":"J. Anal. At. Spectrom."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1007\/s00216-019-01731-3","article-title":"Application of laser-induced breakdown spectroscopy (LIBS) coupled with PCA for rapid classification of soil samples in geothermal areas","volume":"411","author":"Chatterjee","year":"2019","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s11214-012-9902-4","article-title":"The ChemCam Instrument Suite on the Mars Science Laboratory (MSL) Rover: Body Unit and Combined System Tests","volume":"170","author":"Wiens","year":"2012","journal-title":"Space Sci. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.sab.2013.02.003","article-title":"Pre-flight calibration and initial data processing for the ChemCam laser-induced breakdown spectroscopy instrument on the Mars Science Laboratory rover","volume":"82","author":"Wiens","year":"2013","journal-title":"Spectrochim. Acta Part B At. Spectrosc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s11214-012-9912-2","article-title":"The ChemCam Instrument Suite on the Mars Science Laboratory (MSL) Rover: Science Objectives and Mast Unit Description","volume":"170","author":"Maurice","year":"2012","journal-title":"Space Sci. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1002\/2013JE004472","article-title":"Igneous mineralogy at Bradbury Rise: The first ChemCam campaign at Gale crater","volume":"119","author":"Sautter","year":"2014","journal-title":"J. Geophys. Res. Planets"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.sab.2015.02.003","article-title":"A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy","volume":"107","author":"Boucher","year":"2015","journal-title":"Spectrochim. Acta Part B At. Spectrosc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.icarus.2014.08.029","article-title":"Hydrogen detection with ChemCam at Gale crater","volume":"249","author":"Meslin","year":"2015","journal-title":"Icarus"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.icarus.2016.08.026","article-title":"Chemistry of diagenetic features analyzed by ChemCam at Pahrump Hills, Gale crater, Mars","volume":"281","author":"Nachon","year":"2017","journal-title":"Icarus"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.icarus.2018.10.023","article-title":"Using ChemCam LIBS data to constrain grain size in rocks on Mars: Proof of concept and application to rocks at Yellowknife Bay and Pahrump Hills, Gale crater","volume":"321","author":"Sumner","year":"2019","journal-title":"Icarus"},{"key":"ref_18","unstructured":"Wiens, R.C., Maurice, S., McCabe, K., Cais, P., Anderson, R.B., Beyssac, O., Bonal, L., Clegg, S., Deflores, L., and Dromart, G. (2016, January 21\u201325). The SuperCam Remote Sensing Instrument Suite for Mars 2020. Proceedings of the 47th Lunar and Planetary Science Conference, The Woodlands, TX, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1007\/s11214-020-00764-w","article-title":"SuperCam Calibration Targets: Design and Development","volume":"216","author":"Manrique","year":"2020","journal-title":"Space Sci. Rev."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s11214-021-00807-w","article-title":"The SuperCam Instrument Suite on the Mars 2020 Rover: Science Objectives and Mast-Unit Description","volume":"217","author":"Maurice","year":"2021","journal-title":"Space Sci. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s11214-020-00777-5","article-title":"The SuperCam Instrument Suite on the NASA Mars 2020 Rover: Body Unit and Combined System Tests","volume":"217","author":"Wiens","year":"2021","journal-title":"Space Sci. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106347","DOI":"10.1016\/j.sab.2021.106347","article-title":"Post-landing major element quantification using SuperCam laser induced breakdown spectroscopy","volume":"188","author":"Anderson","year":"2022","journal-title":"Spectrochim. Acta Part B At. Spectrosc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1007\/s11214-021-00836-5","article-title":"The MarSCoDe Instrument Suite on the Mars Rover of China\u2019s Tianwen-1 Mission","volume":"217","author":"Xu","year":"2021","journal-title":"Space Sci. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.icarus.2017.01.014","article-title":"Classification of igneous rocks analyzed by ChemCam at Gale crater, Mars","volume":"288","author":"Cousin","year":"2017","journal-title":"Icarus"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1088\/1009-0630\/17\/8\/08","article-title":"Rock and soil classification using PLS-DA and SVM combined with a laser-induced breakdown spectroscopy library","volume":"17","author":"Yang","year":"2015","journal-title":"Plasma Sci. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.sab.2012.05.010","article-title":"Fast identification of biominerals by means of stand-off laser-induced breakdown spectroscopy using linear discriminant analysis and artificial neural networks","volume":"73","author":"Kaiser","year":"2012","journal-title":"Spectrochim. Acta Part B At. Spectrosc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e3092","DOI":"10.1002\/cem.3092","article-title":"Classification and statistical analysis of hydrothermal seafloor rocks measured underwater using laser-induced breakdown spectroscopy","volume":"33","author":"Yelameli","year":"2019","journal-title":"J. Chemometr."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1088\/1009-0630\/17\/11\/06","article-title":"Classification and discrimination of minerals using laser induced breakdown spectroscopy and Raman spectroscopy","volume":"17","author":"Bi","year":"2015","journal-title":"Plasma Sci. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.sab.2014.06.012","article-title":"An artificial neural network approach to laser-induced breakdown spectroscopy quantitative analysis","volume":"99","author":"Pagnotta","year":"2014","journal-title":"Spectrochim. Acta Part B At. Spectrosc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.sab.2017.06.003","article-title":"Classification of wrought aluminum alloys by Artificial Neural Networks evaluation of Laser Induced Breakdown Spectroscopy spectra from aluminum scrap samples","volume":"134","author":"Campanella","year":"2017","journal-title":"Spectrochim. Acta Part B At. Spectrosc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4067","DOI":"10.1039\/C7AN01371J","article-title":"Deep convolutional neural networks for Raman spectrum recognition: A unified solution","volume":"142","author":"Liu","year":"2017","journal-title":"Analyst"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.aca.2016.12.010","article-title":"Convolutional neural networks for vibrational spectroscopic data analysis","volume":"954","author":"Acquarelli","year":"2017","journal-title":"Anal. Chim. Acta"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"034014","DOI":"10.1088\/2058-6272\/aaef6e","article-title":"Detection of K in soil using time-resolved laser-induced breakdown spectroscopy based on convolutional neural networks","volume":"21","author":"Lu","year":"2019","journal-title":"Plasma Sci. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_35","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"105850","DOI":"10.1016\/j.sab.2020.105850","article-title":"A laser-induced breakdown spectroscopy multi-component quantitative analytical method based on a deep convolutional neural network","volume":"169","author":"Li","year":"2020","journal-title":"Spectrochim. Acta Part B At. Spectrosc."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"106417","DOI":"10.1016\/j.sab.2022.106417","article-title":"Laser-induced breakdown spectroscopy combined with a convolutional neural network: A promising methodology for geochemical sample identification in Tianwen-1 Mars mission","volume":"192","author":"Yang","year":"2022","journal-title":"Spectrochim. Acta Part B At. Spectrosc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1038\/s41550-020-1148-6","article-title":"China\u2019s first mission to Mars","volume":"4","author":"Wan","year":"2020","journal-title":"Nat. Astron."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.icarus.2017.08.023","article-title":"Modeling concentric crater fill in Utopia Planitia, Mars, with an ice flow line model","volume":"308","author":"Weitz","year":"2018","journal-title":"Icarus"},{"key":"ref_40","first-page":"5003","article-title":"Northern lowlands of Mars: Evidence for widespread volcanic flooding and tectonic deformation in the Hesperian period","volume":"107","author":"Head","year":"2002","journal-title":"J. Geophys. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.epsl.2008.05.010","article-title":"Thermokarst lakes and ponds on Mars in the very recent (late Amazonian) past","volume":"272","author":"Soare","year":"2008","journal-title":"Earth Planet. Sc. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"E04005","DOI":"10.1029\/2008JE003264","article-title":"Observations of periglacial landforms in Utopia Planitia with the High Resolution Imaging Science Experiment (HiRISE)","volume":"114","author":"Lefort","year":"2009","journal-title":"J. Geophys. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"E06010","DOI":"10.1029\/2006JE002869","article-title":"Deposition and degradation of a volatile-rich layer in Utopia Planitia, and implications for climate history on Mars","volume":"112","author":"Morgenstern","year":"2007","journal-title":"J. Geophys. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"E10009","DOI":"10.1029\/2010JE003640","article-title":"Thermokarst in Siberian ice-rich permafrost: Comparison to asymmetric scalloped depressions on Mars","volume":"115","author":"Ulrich","year":"2010","journal-title":"J. Geophys. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1126\/science.194.4271.1283","article-title":"Inorganic analyses of Martian surface samples at the Viking landing sites","volume":"194","author":"Clark","year":"1976","journal-title":"Science"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1626","DOI":"10.1126\/science.287.5458.1626","article-title":"A global view of Martian surface compositions from MGS-TES","volume":"287","author":"Bandfield","year":"2000","journal-title":"Science"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1366\/000370203322102816","article-title":"Mars Analysis by Laser-Induced Breakdown Spectroscopy (MALIS): Influence of Mars Atmosphere on Plasma Emission and Study of Factors Influencing Plasma Emission with the use of Doehlert Designs","volume":"57","author":"Brennetot","year":"2003","journal-title":"Appl. Spectrosc."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, C., Ling, Z., Zhang, J., Wu, Z., Bai, H., and Liu, Y. (2021). A Stand-Off Laser-Induced Breakdown Spectroscopy (LIBS) System Applicable for Martian Rocks Studies. Remote Sens., 13.","DOI":"10.3390\/rs13234773"},{"key":"ref_49","first-page":"3","article-title":"Rectifier nonlinearities improve neural network acoustic models","volume":"30","author":"Maas","year":"2013","journal-title":"Proc. ICML"},{"key":"ref_50","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_51","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/00220670209598786","article-title":"An Introduction to Logistic Regression Analysis and Reporting","volume":"96","author":"Peng","year":"2002","journal-title":"J. Educ. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"169","DOI":"10.3233\/AIC-170729","article-title":"Linear discriminant analysis: A detailed tutorial","volume":"30","author":"Tharwat","year":"2017","journal-title":"AI Commun."},{"key":"ref_55","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_56","first-page":"37","article-title":"Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation","volume":"2","author":"Powers","year":"2011","journal-title":"J. Mach. Learn. Technol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1002\/bimj.200810443","article-title":"The performance of risk prediction models","volume":"50","author":"Gerds","year":"2008","journal-title":"Biom. J."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.sab.2016.12.003","article-title":"Recalibration of the Mars Science Laboratory ChemCam instrument with an expanded geochemical database","volume":"129","author":"Clegg","year":"2017","journal-title":"Spectrochim. Acta Part B At. Spectrosc."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Cui, Z., Jia, L., Li, L., Liu, X., Xu, W., Shu, R., and Xu, X. (2022). A Laser-Induced Breakdown Spectroscopy Experiment Platform for High-Degree Simulation of MarSCoDe In Situ Detection on Mars. Remote Sens., 14.","DOI":"10.3390\/rs14091954"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5343\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:02:36Z","timestamp":1760144556000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5343"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,25]]},"references-count":59,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215343"],"URL":"https:\/\/doi.org\/10.3390\/rs14215343","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,25]]}}}