Spectral Analysis of Dissolved Organic Carbon in Seawater by Combined Absorption and Fluorescence Technology
Dissolved organic carbon refers to soluble carbon substances in water bodies and can be used as an important indicator for water pollution. Spectroscopic detection is commonly used to detect dissolved organic carbon in seawater. However, independent spectral methods are susceptible to interference,...
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/12/2297 |
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| author | Xuan Cao Feng Xiong Yang Wang Haikuan Ma Yanmin Zhang Yan Liu Xiangfeng Kong Jingru Wang Qian Shi Pingping Fan Yunzhou Li Ning Wu |
| author_facet | Xuan Cao Feng Xiong Yang Wang Haikuan Ma Yanmin Zhang Yan Liu Xiangfeng Kong Jingru Wang Qian Shi Pingping Fan Yunzhou Li Ning Wu |
| author_sort | Xuan Cao |
| collection | DOAJ |
| description | Dissolved organic carbon refers to soluble carbon substances in water bodies and can be used as an important indicator for water pollution. Spectroscopic detection is commonly used to detect dissolved organic carbon in seawater. However, independent spectral methods are susceptible to interference, and insufficient extraction of the data features can occur. Accordingly, this study introduces a multisource spectral fusion method that relies on a combination of principal component analysis and convolutional neural networks to construct the detection model. The Bayesian correction method is used for calibration, and the dissolved organic carbon content of 10 groups of unfiltered seawater samples is analyzed. Correcting the spectral data acquired from samples containing impurities significantly improved the linear correlation coefficient R<sup>2</sup> of dissolved organic carbon from 0.8891 to 0.9838. Similarly, the mean absolute error was significantly reduced from 15.33% to 3.24%, while the individual absolute error was effectively controlled, remaining within 9%. The obtained results show that the developed method effectively integrates the ultraviolet absorption and fluorescence spectral data and overcomes interference from other substances using the Bayesian correction method. Overall, this provides a highly accurate detection system with potential applications in monitoring the marine environment. |
| format | Article |
| id | doaj-art-0ad78d3a48d3449dbbafa9f20fd80f13 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-0ad78d3a48d3449dbbafa9f20fd80f132024-12-27T14:33:31ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011212229710.3390/jmse12122297Spectral Analysis of Dissolved Organic Carbon in Seawater by Combined Absorption and Fluorescence TechnologyXuan Cao0Feng Xiong1Yang Wang2Haikuan Ma3Yanmin Zhang4Yan Liu5Xiangfeng Kong6Jingru Wang7Qian Shi8Pingping Fan9Yunzhou Li10Ning Wu11Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266000, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266000, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266000, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266000, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266000, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266000, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266000, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266000, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266000, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266000, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266000, ChinaInstitute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266000, ChinaDissolved organic carbon refers to soluble carbon substances in water bodies and can be used as an important indicator for water pollution. Spectroscopic detection is commonly used to detect dissolved organic carbon in seawater. However, independent spectral methods are susceptible to interference, and insufficient extraction of the data features can occur. Accordingly, this study introduces a multisource spectral fusion method that relies on a combination of principal component analysis and convolutional neural networks to construct the detection model. The Bayesian correction method is used for calibration, and the dissolved organic carbon content of 10 groups of unfiltered seawater samples is analyzed. Correcting the spectral data acquired from samples containing impurities significantly improved the linear correlation coefficient R<sup>2</sup> of dissolved organic carbon from 0.8891 to 0.9838. Similarly, the mean absolute error was significantly reduced from 15.33% to 3.24%, while the individual absolute error was effectively controlled, remaining within 9%. The obtained results show that the developed method effectively integrates the ultraviolet absorption and fluorescence spectral data and overcomes interference from other substances using the Bayesian correction method. Overall, this provides a highly accurate detection system with potential applications in monitoring the marine environment.https://www.mdpi.com/2077-1312/12/12/2297dissolved organic carbonmultisource spectroscopyprincipal component analysisconvolutional neural networkBayesian correction |
| spellingShingle | Xuan Cao Feng Xiong Yang Wang Haikuan Ma Yanmin Zhang Yan Liu Xiangfeng Kong Jingru Wang Qian Shi Pingping Fan Yunzhou Li Ning Wu Spectral Analysis of Dissolved Organic Carbon in Seawater by Combined Absorption and Fluorescence Technology Journal of Marine Science and Engineering dissolved organic carbon multisource spectroscopy principal component analysis convolutional neural network Bayesian correction |
| title | Spectral Analysis of Dissolved Organic Carbon in Seawater by Combined Absorption and Fluorescence Technology |
| title_full | Spectral Analysis of Dissolved Organic Carbon in Seawater by Combined Absorption and Fluorescence Technology |
| title_fullStr | Spectral Analysis of Dissolved Organic Carbon in Seawater by Combined Absorption and Fluorescence Technology |
| title_full_unstemmed | Spectral Analysis of Dissolved Organic Carbon in Seawater by Combined Absorption and Fluorescence Technology |
| title_short | Spectral Analysis of Dissolved Organic Carbon in Seawater by Combined Absorption and Fluorescence Technology |
| title_sort | spectral analysis of dissolved organic carbon in seawater by combined absorption and fluorescence technology |
| topic | dissolved organic carbon multisource spectroscopy principal component analysis convolutional neural network Bayesian correction |
| url | https://www.mdpi.com/2077-1312/12/12/2297 |
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