Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra
Although Raman spectroscopy has been widely used as a noninvasive analytical tool in various applications, backgrounds in Raman spectra impair its performance in quantitative analysis. Many algorithms have been proposed to separately correct the background spectrum by spectrum. However, in real appl...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Analytical Methods in Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2018/9031356 |
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| _version_ | 1832565725498179584 |
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| author | Long Chen Yingwen Wu Tianjun Li Zhuo Chen |
| author_facet | Long Chen Yingwen Wu Tianjun Li Zhuo Chen |
| author_sort | Long Chen |
| collection | DOAJ |
| description | Although Raman spectroscopy has been widely used as a noninvasive analytical tool in various applications, backgrounds in Raman spectra impair its performance in quantitative analysis. Many algorithms have been proposed to separately correct the background spectrum by spectrum. However, in real applications, there are commonly multiple spectra collected from the close locations of a sample or from the same analyte with different concentrations. These spectra are strongly correlated and provide valuable information for more robust background correction. Herein, we propose two new strategies to remove background for a set of related spectra collaboratively. Based on weighted penalized least squares, the new approaches will use the fused weights from multiple spectra or the weights from the average spectrum to estimate the background of each spectrum in the set. Background correction results from both simulated and real experimental data demonstrate that the proposed collaborative approaches outperform traditional algorithms which process spectra individually. |
| format | Article |
| id | doaj-art-85d9cf1e5358420195501a63cb2888c9 |
| institution | Kabale University |
| issn | 2090-8865 2090-8873 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Analytical Methods in Chemistry |
| spelling | doaj-art-85d9cf1e5358420195501a63cb2888c92025-02-03T01:06:45ZengWileyJournal of Analytical Methods in Chemistry2090-88652090-88732018-01-01201810.1155/2018/90313569031356Collaborative Penalized Least Squares for Background Correction of Multiple Raman SpectraLong Chen0Yingwen Wu1Tianjun Li2Zhuo Chen3Faculty of Science and Technology, University of Macau, E11 Avenida da Universidade, Taipa, MacauFaculty of Science and Technology, University of Macau, E11 Avenida da Universidade, Taipa, MacauFaculty of Science and Technology, University of Macau, E11 Avenida da Universidade, Taipa, MacauChemistry and Chemical Engineering, College of Biology, Hunan University, Changsha 410082, ChinaAlthough Raman spectroscopy has been widely used as a noninvasive analytical tool in various applications, backgrounds in Raman spectra impair its performance in quantitative analysis. Many algorithms have been proposed to separately correct the background spectrum by spectrum. However, in real applications, there are commonly multiple spectra collected from the close locations of a sample or from the same analyte with different concentrations. These spectra are strongly correlated and provide valuable information for more robust background correction. Herein, we propose two new strategies to remove background for a set of related spectra collaboratively. Based on weighted penalized least squares, the new approaches will use the fused weights from multiple spectra or the weights from the average spectrum to estimate the background of each spectrum in the set. Background correction results from both simulated and real experimental data demonstrate that the proposed collaborative approaches outperform traditional algorithms which process spectra individually.http://dx.doi.org/10.1155/2018/9031356 |
| spellingShingle | Long Chen Yingwen Wu Tianjun Li Zhuo Chen Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra Journal of Analytical Methods in Chemistry |
| title | Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra |
| title_full | Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra |
| title_fullStr | Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra |
| title_full_unstemmed | Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra |
| title_short | Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra |
| title_sort | collaborative penalized least squares for background correction of multiple raman spectra |
| url | http://dx.doi.org/10.1155/2018/9031356 |
| work_keys_str_mv | AT longchen collaborativepenalizedleastsquaresforbackgroundcorrectionofmultipleramanspectra AT yingwenwu collaborativepenalizedleastsquaresforbackgroundcorrectionofmultipleramanspectra AT tianjunli collaborativepenalizedleastsquaresforbackgroundcorrectionofmultipleramanspectra AT zhuochen collaborativepenalizedleastsquaresforbackgroundcorrectionofmultipleramanspectra |