|Generate QR code
Open Access
Subscription Access
Determination of Fluorescent Dissolved Organic Matter Using Three-Dimensional Fluorescence Spectroscopy and Convolutional Neural Networks
Abstract
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 (3DEEMs) 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.
Keywords
fluorescent dissolved organic matter,
convolutional neural networks,
excitation-emission matrix spectra,
self-weighting alternating trilinear decomposition,
quantification
Supporting agencies: This work was supported by the Key R&D Program of Shandong Province, China (No. 2022CXPT019). The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper
About the Authors
J. Yang
CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences; Shandong Key Laboratory of Coastal Environmental Processes; University of Chinese Academy of Sciences
China
China
Jianlian Yang
Yantai Shandong; Beijing
W. Feng
CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences; Shandong Key Laboratory of Coastal Environmental Processes; University of Chinese Academy of Sciences
China
China
Weiwei Feng
Yantai Shandong; Beijing
Z. Cai
CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences; Shandong Key Laboratory of Coastal Environmental Processes
China
China
Zongqi Cai
Yantai Shandong
H. Wang
CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences; Shandong Key Laboratory of Coastal Environmental Processes
China
China
Huanqing Wang
Yantai Shandong
X. Liang
CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences; Shandong Key Laboratory of Coastal Environmental Processes; University of Chinese Academy of Sciences
China
China
Yantai Shandong; Beijing
References
1. D. Vione, C. Minero, L. Carena, Environ. Sci. Process, 23, 1429–1442 (2021), https://doi.org/10.1039/D1EM00273B.
2. G. J. L. Wilson, C. Lu, D. J. Lapworth, A. Kumar, A. Ghosh, V. J. Niasar, S. Krause, D. A. Polya, D. C. Gooddy, L. A. Richards, Sci. Total Environ., 903, 166208 (2023), https://doi.org/10.1016/j.scitotenv.2023.166208.
3. J. Urban-Rich, J. T. McCarty, D. Fernández, J. L. Acuña, J. Exp. Mar. Biol. Ecol., 332, 96–105 (2006), https://doi.org/10.1016/j.jembe.2005.11.023.
4. Y. Zhou, K. Shi, Y. Zhang, E. Jeppesen, X. Liu, Q. Zhou, H. Wu, X. Tang, G. Zhu, Sci. Total Environ., 574, 1588–1598 (2017), https://doi.org/10.1016/j.scitotenv.2016.08.196.
5. H. K. Kwon, G. Kim, T.-H. Kim, S.-E. Park, W. C. Lee, J. Sea Res., 188, 102270 (2022), https://doi.org/10.1016/j.seares.2022.102270.
6. Y. Yamashita, T. Tosaka, R. Bamba, R. Kamezaki, S. Goto, J. Nishioka, I. Yasuda, T. Hirawake, J. Oida, H. Obata, H. Ogawa, Prog. Oceanography, 191, 102510 (2021), https://doi.org/10.1016/j.pocean.2020.102510.
7. P. G. Coble, Mar. Chem., 51, 325–346 (1996), https://doi.org/10.1016/0304-4203(95)00062-3.
8. N. M. Niloy, S. A. Habib, M. I. Islam, Md. M. Haque, M. Shammi, S. M. Tareq, Mar. Pollut. Bull., 195, 115467 (2023), https://doi.org/10.1016/j.marpolbul.2023.115467.
9. X. Wang, H. Zhang, E. Bertone, R. A. Stewart, S. P. Hughes, Environ. Model. Softw., 141, 105053 (2021), https://doi.org/10.1016/j.envsoft.2021.105053.
10. W.-T. Li, J. Jin, Q. Li, C.-F. Wu, H. Lu, Q. Zhou, A.-M. Li, Water Res., 93, 1–9 (2016), https://doi.org/10.1016/j.watres.2016.01.005.
11. Y. Dai, H. Wang, J. Wang, X. Wang, Z. Wang, X. Ge, Spectrochim. Acta. A: Mol. Biomol. Spectrosc., 273, 121059 (2022), https://doi.org/10.1016/j.saa.2022.121059.
12. M. C. Ortiz, S. Sanllorente, A. Herrero, C. Reguera, L. Rubio, M. L. Oca, L. Valverde-Som, M. M. Arce, M. S. Sánchez, L. A. Sarabia, Chemom. Intell. Lab. Syst., 200, 104003 (2020), https://doi.org/10.1016/j.chemolab.2020.104003.
13. A. B. F. Câmara, W. J. O. da Silva, A. C. de O. Neves, H. O. M. A. Moura, K. M. G. de Lima, L. S. de Carvalho, Talanta, 266, 125126 (2024), https://doi.org/10.1016/j.talanta.2023.125126.
14. A. Islam, G. Sun, A. N. Saber, W. Shang, X. Zheng, Y. Zhang, M. Yang, J. Environ. Sci., 122, 174–183 (2022), https://doi.org/10.1016/j.jes.2022.01.046.
15. M. Birenboim, Å. Rinnan, D. Kengisbuch, J. A. Shimshoni, Chemom. Intell. Lab. Syst., 232, 104717 (2023), https://doi.org/10.1016/j.chemolab.2022.104717.
16. S. Catena, S. Sanllorente, L. A. Sarabia, R. Boggia, F. Turrini, M. C. Ortiz, Microchem. J., 154, 104561 (2020), https://doi.org/10.1016/j.microc.2019.104561.
17. Y. Fu, W. Li, T. Liu, Z. Zhang, H. Li, J. Xu, M. Huang, Sci. Total Environ., 865, 160950 (2023), https://doi.org/10.1016/j.scitotenv.2022.160950.
18. H.-B. Wang, Y.-J. Zhang, X. Xiao, S.-H. Yu, W.-Q. Liu, Anal. Methods, 3, 688–695 (2011), https://doi.org/10.1039/C0AY00641F.
19. S. Wang, Q. Cheng, Y. Yuan, C. Wang, S. Ma, Spectrochim. Acta A: Mol. Biomol. Spectrosc., 210, 260–265 (2019), https://doi.org/10.1016/j.saa.2018.11.012.
20. Z. Cheng, N. Zhao, G. Yin, X. Zhang, J. Li, W. Liu, Acta Opt. Sin., 41, 1430001 (2021), https://doi.org/10.3788/AOS202141.1430001.
21. P. Cheng, Y. Zhu, C. Cui, F. Wang, J. Pan, Meas. Control, 55 (2022), https://doi.org/10.1177/00202940221114902.
22. R.-Z. Xu, J.-S. Cao, G. Feng, J.-Y. Luo, Q. Feng, B.-J. Ni, F. Fang, Chem. Eng. J., 430, 132893 (2022), https://doi.org/10.1016/j.cej.2021.132893.
23. X. Wu, Z. Zhao, R. Tian, S. Gao, Y. Niu, H. Liu, Food Chem., 335, 127640 (2021), https://doi.org/10.1016/j.foodchem.2020.127640.
24. R. G. Zepp, W. M. Sheldon, M. A. Moran, Mar. Chem., 89, 15–36 (2004), https://doi.org/10.1016/j.marchem.2004.02.006.
25. A. Lawaetz, C. Stedmon, Appl. Spectrosc., 63, 936–940 (2009), https://doi.org/10.1366/000370209788964548.
26. T. Ohno, Environ. Sci. Technol., 36, 742–746 (2002), https://doi.org/10.1021/es0155276.
27. J. Pan, L. Xia, Q. Wu, Y. Guo, Y. Chen, X. Tian, Ecol. Inform., 70, 101706 (2022), https://doi.org/10.1016/j.ecoinf.2022.101706.
28. Y. Li, D.-G. Cai, Z.-H. Zhu, H. Xu, T.-F. Zheng, J.-L. Chen, S.-J. Liu, H.-R. Wen, Dalton Trans., 52, 4167–4175 (2023), https://doi.org/10.1039/D2DT03230A.
29. Z.-P. Chen, H.-L. Wu, J.-H. Jiang, Y. Li, R.-Q. Yu, Chemom. Intell. Lab. Syst., 52, 75–86 (2000), https://doi.org/10.1016/S0169-7439(00)00081-2.
30. H.-P. Wang, P. Chen, J.-W. Dai, D. Liu, J.-Y. Li, Y.-P. Xu, X.-L. Chu, TrAC Trends Anal. Chem., 153, 116648 (2022), https://doi.org/10.1016/j.trac.2022.116648.
31. A. Krizhevsky, I. Sutskever, G. E. Hinton, Adv. Neural Inf. Process. Syst., Curran Associates, Inc. (2012), https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html (accessed April 16, 2024).
32. D. P. Kingma, J. Ba, Adam: A Method for Stochastic Optimization (2017), arXiv:1412.6980v9, https://doi.org/10.48550/arXiv.1412.6980.
33. A. Yammine, J. Gao, A. H. Kwan, Bio Protoc, 9, e3253 (2019), https://doi.org/10.21769/BioProtoc.3253.
34. F. H. dos Santos Rodrigues, G. G. Delgado, T. Santana da Costa, L. Tasic, BBA Adv., 3, 100091 (2023), https://doi.org/10.1016/j.bbadva.2023.100091.
35. S. Determann, R. Reuter, P. Wagner, R. Willkomm, Part Oceanography Res. Pap., 41, 659–675 (1994), https://doi.org/10.1016/0967-0637(94)90048-5.
36. S. M. Henrichs, J. W. Farrington, Phys. Chem. Earth, 12, 435–443 (1980), https://doi.org/10.1016/00791946(79)90125-3.
37. S. S. Ruhala, J. P. Zarnetske, Sci. Total Environ., 575, 713–723 (2017), https://doi.org/10.1016/j.scitotenv.2016.09.113.
38. Y.-Y. Chang, H.-L. Wu, H. Fang, T. Wang, Z. Liu, Y.-Z. Ouyang, Y.-J. Ding, R.-Q. Yu, Spectrochim. Acta, A: Mol. Biomol. Spectrosc., 204, 141–149 (2018), https://doi.org/10.1016/j.saa.2018.06.031.
39. Yingxin Shang, Kaishan Song, Fengfa Lai, Lili Lyu, Ge Liu, Chong Fang, Junbin Hou, Sining Qiang, Xiangfei Yu, Zhidan Wen, Water Res., 230, 119540 (2023), https://doi.org/10.1016/j.watres.2022.119540.
40. M. H. Jeon, J. Jung, M. O. Park, S. Aoki, T.-W. Kim, S.-K. Kim, Mar. Chem., 235, 104008 (2021), https://doi.org/10.1016/j.marchem.2021.104008.
41. H. Daraei, E. Bertone, J. Awad, R. A. Stewart, C. W. K. Chow, J. Duan, A. Mussared, J. Van Leeuwen, J. Environ. Sci. (2023), https://doi.org/10.1016/j.jes.2023.06.011.
Review
For citations:
Yang J., Feng W., Cai Z., Wang H., Liang X. Determination of Fluorescent Dissolved Organic Matter Using Three-Dimensional Fluorescence Spectroscopy and Convolutional Neural Networks. Zhurnal Prikladnoii Spektroskopii. 2025;92(3):408.
Views:
7
Email this article 