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However, many traditional multivariate analysis algorithms must reduce the spectral dimension or extract the characteristic spectral lines in advance, which may result in information loss and reduced accuracy. Indeed, improving the precision and interpretability of LIBS quantitative analysis is a critical challenge in Mars exploration. To solve this problem, this paper proposes an end-to-end lightweight quantitative modeling framework based on ensemble convolutional neural networks (ECNNs). This method eliminates the need for dimensionality reduction of the raw spectrum along with other pre-processing operations. We used the ChemCam calibration dataset as an example to verify the effectiveness of the proposed approach. Compared with partial least squares regression (a linear method) and extreme learning machine (a nonlinear method), our proposed method resulted in a lower root-mean-square error for major element prediction (54% and 73% lower, respectively) and was more stable. We also delved into the internal learning mechanism of the deep CNN model to understand how it hierarchically extracts spectral information features. The experimental results demonstrate that the easy-to-use ECNN-based regression model achieves excellent prediction performance while maintaining interpretability.<\/jats:p>","DOI":"10.3390\/rs15133422","type":"journal-article","created":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T02:28:46Z","timestamp":1688696926000},"page":"3422","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4662-1198","authenticated-orcid":false,"given":"Yan","family":"Yu","sequence":"first","affiliation":[{"name":"Intelligent Robotics Lab, School of Artificial Intelligence, Jilin University, Changchun 130012, China"},{"name":"Engineering Research Center of Knowledge-Driven Human\u2013Machine Intelligence, Ministry of Education, Changchun 130012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7069-6782","authenticated-orcid":false,"given":"Meibao","family":"Yao","sequence":"additional","affiliation":[{"name":"Intelligent Robotics Lab, School of Artificial Intelligence, Jilin University, Changchun 130012, China"},{"name":"Engineering Research Center of Knowledge-Driven Human\u2013Machine Intelligence, Ministry of Education, Changchun 130012, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"122780","DOI":"10.1016\/j.talanta.2021.122780","article-title":"A real-world approach to identifying animal bones and Lower Pleistocene fossils by laser induced breakdown spectroscopy","volume":"235","author":"Caceres","year":"2021","journal-title":"Talanta"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106183","DOI":"10.1016\/j.sab.2021.106183","article-title":"A review of artificial neural network based chemometrics applied in laser-induced breakdown spectroscopy analysis","volume":"180","author":"Li","year":"2021","journal-title":"Spectrochim. 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