Enhancing Laser-Induced Breakdown Spectroscopy Quantification Through Minimum Redundancy and Maximum Relevance-Based Feature Selection
Laser-induced breakdown spectroscopy (LIBS) is a rapid, non-contact analytical technique that is widely applied in various fields. However, the high dimensionality and information redundancy of LIBS spectral data present challenges for effective model development. This study aims to assess the effec...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2072-4292/17/3/416 |
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| author | Manping Wang Yang Lu Man Liu Fuhui Cui Rongke Gao Feifei Wang Xiaozhe Chen Liandong Yu |
| author_facet | Manping Wang Yang Lu Man Liu Fuhui Cui Rongke Gao Feifei Wang Xiaozhe Chen Liandong Yu |
| author_sort | Manping Wang |
| collection | DOAJ |
| description | Laser-induced breakdown spectroscopy (LIBS) is a rapid, non-contact analytical technique that is widely applied in various fields. However, the high dimensionality and information redundancy of LIBS spectral data present challenges for effective model development. This study aims to assess the effectiveness of the minimum redundancy and maximum relevance (mRMR) method for feature selection in LIBS spectral data and to explore its adaptability across different predictive modeling approaches. Using the ChemCam LIBS dataset, we constructed predictive models with four quantitative methods: random forest (RF), support vector regression (SVR), back propagation neural network (BPNN), and partial least squares regression (PLSR). We compared the performance of mRMR-based feature selection with that of full-spectrum data and three other feature selection methods: competitive adaptive re-weighted sampling (CARS), Regressional ReliefF (RReliefF), and neighborhood component analysis (NCA). Our results demonstrate that the mRMR method significantly reduces the number of selected features while improving model performance. This study validates the effectiveness of the mRMR algorithm for LIBS feature extraction and highlights the potential of feature selection techniques to enhance predictive accuracy. The findings provide a valuable strategy for feature selection in LIBS data analysis and offer significant implications for the practical application of LIBS in predicting elemental content in geological samples. |
| format | Article |
| id | doaj-art-99c4d0446350492daa5887129e9cb1bf |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-99c4d0446350492daa5887129e9cb1bf2025-08-20T03:12:35ZengMDPI AGRemote Sensing2072-42922025-01-0117341610.3390/rs17030416Enhancing Laser-Induced Breakdown Spectroscopy Quantification Through Minimum Redundancy and Maximum Relevance-Based Feature SelectionManping Wang0Yang Lu1Man Liu2Fuhui Cui3Rongke Gao4Feifei Wang5Xiaozhe Chen6Liandong Yu7College of Control Science and Engineering, China University of Petroleum (East China), No. 66, West Changjiang Road, Huangdao District, Qingdao 266580, ChinaCollege of Control Science and Engineering, China University of Petroleum (East China), No. 66, West Changjiang Road, Huangdao District, Qingdao 266580, ChinaCollege of Control Science and Engineering, China University of Petroleum (East China), No. 66, West Changjiang Road, Huangdao District, Qingdao 266580, ChinaCollege of Control Science and Engineering, China University of Petroleum (East China), No. 66, West Changjiang Road, Huangdao District, Qingdao 266580, ChinaCollege of Control Science and Engineering, China University of Petroleum (East China), No. 66, West Changjiang Road, Huangdao District, Qingdao 266580, ChinaCollege of Control Science and Engineering, China University of Petroleum (East China), No. 66, West Changjiang Road, Huangdao District, Qingdao 266580, ChinaCollege of Control Science and Engineering, China University of Petroleum (East China), No. 66, West Changjiang Road, Huangdao District, Qingdao 266580, ChinaCollege of Control Science and Engineering, China University of Petroleum (East China), No. 66, West Changjiang Road, Huangdao District, Qingdao 266580, ChinaLaser-induced breakdown spectroscopy (LIBS) is a rapid, non-contact analytical technique that is widely applied in various fields. However, the high dimensionality and information redundancy of LIBS spectral data present challenges for effective model development. This study aims to assess the effectiveness of the minimum redundancy and maximum relevance (mRMR) method for feature selection in LIBS spectral data and to explore its adaptability across different predictive modeling approaches. Using the ChemCam LIBS dataset, we constructed predictive models with four quantitative methods: random forest (RF), support vector regression (SVR), back propagation neural network (BPNN), and partial least squares regression (PLSR). We compared the performance of mRMR-based feature selection with that of full-spectrum data and three other feature selection methods: competitive adaptive re-weighted sampling (CARS), Regressional ReliefF (RReliefF), and neighborhood component analysis (NCA). Our results demonstrate that the mRMR method significantly reduces the number of selected features while improving model performance. This study validates the effectiveness of the mRMR algorithm for LIBS feature extraction and highlights the potential of feature selection techniques to enhance predictive accuracy. The findings provide a valuable strategy for feature selection in LIBS data analysis and offer significant implications for the practical application of LIBS in predicting elemental content in geological samples.https://www.mdpi.com/2072-4292/17/3/416laser-induced breakdown spectroscopy (LIBS)feature selectionmRMRChemCamplanetary exploration |
| spellingShingle | Manping Wang Yang Lu Man Liu Fuhui Cui Rongke Gao Feifei Wang Xiaozhe Chen Liandong Yu Enhancing Laser-Induced Breakdown Spectroscopy Quantification Through Minimum Redundancy and Maximum Relevance-Based Feature Selection Remote Sensing laser-induced breakdown spectroscopy (LIBS) feature selection mRMR ChemCam planetary exploration |
| title | Enhancing Laser-Induced Breakdown Spectroscopy Quantification Through Minimum Redundancy and Maximum Relevance-Based Feature Selection |
| title_full | Enhancing Laser-Induced Breakdown Spectroscopy Quantification Through Minimum Redundancy and Maximum Relevance-Based Feature Selection |
| title_fullStr | Enhancing Laser-Induced Breakdown Spectroscopy Quantification Through Minimum Redundancy and Maximum Relevance-Based Feature Selection |
| title_full_unstemmed | Enhancing Laser-Induced Breakdown Spectroscopy Quantification Through Minimum Redundancy and Maximum Relevance-Based Feature Selection |
| title_short | Enhancing Laser-Induced Breakdown Spectroscopy Quantification Through Minimum Redundancy and Maximum Relevance-Based Feature Selection |
| title_sort | enhancing laser induced breakdown spectroscopy quantification through minimum redundancy and maximum relevance based feature selection |
| topic | laser-induced breakdown spectroscopy (LIBS) feature selection mRMR ChemCam planetary exploration |
| url | https://www.mdpi.com/2072-4292/17/3/416 |
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