Determination of Fluorescent Dissolved Organic Matter Using Three-Dimensional Fluorescence Spectroscopy and Convolutional Neural Networks
文献类型：期刊论文
| 作者 | Yang, Jianlian1,2,3; Feng, Weiwei1,2,3; Cai, Zongqi1,2; Wang, Huanqing1,2; Liang, Xinghui1,2,3 |
| 刊名 | JOURNAL OF APPLIED SPECTROSCOPY
 |
| 出版日期 | 2025-07-09 |
| 页码 | 11 |
| 关键词 | fluorescent dissolved organic matter convolutional neural networks excitation-emission matrix spectra self-weighting alternating trilinear decomposition quantification |
| ISSN号 | 0021-9037 |
| DOI | 10.1007/s10812-025-01950-w |
| 通讯作者 | Feng, Weiwei(wwfeng@yic.ac.cn) |
| 英文摘要 | Fluorescent dissolved organic matter (FDOM) - particularly tryptophan (Trp), tyrosine (Tyr), and humic acid (HA) - serves as a crucial indicator in environmental monitoring. This study introduced a novel quantitative analysis approach for analyzing three-dimensional excitation-emission matrix spectra (3D-EEMs) of FDOM using convolutional neural networks (CNNs). The performance of the CNN model was evaluated and compared with the self-weighting alternating trilinear decomposition (SWATLD) algorithm. Results revealed that the proposed model significantly outperforms the SWATLD algorithm. Specifically, the CNN model achieved R2, RMSE, and MAPE values of 0.964, 0.047, and 14.950%, respectively, while for the SWATLD algorithm, these values were 0.944, 0.062, and 17.439%. Augmentation of the original spectral dataset did not yield a substantial improvement in the performance of the SWATLD algorithm, but it significantly enhanced the prediction ability of the CNN model. This enhancement was evident in the improved R2, RMSE, and MAPE values of 0.989, 0.030, and 12.837%, highlighting the critical role of data augmentation in boosting the performance of the CNN model, especially when dealing with a limited dataset. Application of the CNN model to water samples from Laizhou Bay yielded satisfactory results, enabling a simple and rapid analysis of FDOM in seawater. Therefore, an accurate and convenient analytical model was developed based on EEMs and CNNs, which can swiftly determine the concentration of FDOM in the environment and provide valuable references for environmental monitoring and early warning. |
| WOS关键词 | SENSORS ; PEAK ; DOM |
| WOS研究方向 | Spectroscopy |
| 语种 | 英语 |
| WOS记录号 | WOS:001527686600001 |
| 资助机构 | Key R&D Program of Shandong Province, China |
| 源URL | [http://ir.yic.ac.cn/handle/133337/41209]  |
| 专题 | 烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室 |
| 通讯作者 | Feng, Weiwei |
| 作者单位 | 1.Shandong Key Lab Coastal Environm Proc, Yantai, Shandong, Peoples R China 2.Chinese Acad Sci, Yantai Inst Coastal Zone Res, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai, Shandong, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Jianlian,Feng, Weiwei,Cai, Zongqi,et al. Determination of Fluorescent Dissolved Organic Matter Using Three-Dimensional Fluorescence Spectroscopy and Convolutional Neural Networks[J]. JOURNAL OF APPLIED SPECTROSCOPY,2025:11. |
| APA | Yang, Jianlian,Feng, Weiwei,Cai, Zongqi,Wang, Huanqing,&Liang, Xinghui.(2025).Determination of Fluorescent Dissolved Organic Matter Using Three-Dimensional Fluorescence Spectroscopy and Convolutional Neural Networks.JOURNAL OF APPLIED SPECTROSCOPY,11. |
| MLA | Yang, Jianlian,et al."Determination of Fluorescent Dissolved Organic Matter Using Three-Dimensional Fluorescence Spectroscopy and Convolutional Neural Networks".JOURNAL OF APPLIED SPECTROSCOPY (2025):11. |
入库方式： OAI收割
来源：烟台海岸带研究所
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