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Water
Volume 13
Issue 24
10.3390/w13243612
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Article

Fluorescence Excitation–Emission Matrix Spectroscopy and Boosting Regression Tree Model to Detect Dissolved Organic Carbon in Water

by
Hang Yin
1,
Ke Wang
1,2,*,
Yu Liu
3,
Pingjie Huang
1,*,
Jie Yu
1 and
Dibo Hou
1
1
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
2
College of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China
3
The National Ocean Technology Center, Tianjin 300112, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Domenico Cicchella
Water 2021, 13(24), 3612; https://doi.org/10.3390/w13243612
Received: 19 October 2021 / Revised: 16 November 2021 / Accepted: 17 November 2021 / Published: 16 December 2021
(This article belongs to the Section New Sensors, New Technologies and Machine Learning in Water Sciences)
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Abstract

In recent years, optical methods have been proven to be a powerful tool for m onitoring dissolved organic carbon (DOC) in natural waters. However, the effectiveness of this method in marine systems with low DOC concentrations remains to be shown. Herein, a new method based on fluorescence excitation–emission matrix spectroscopy for seawater DOC quantification is proposed. Pre-processing method is investigated to achieve a high signal to noise ratio. Peak-picking operation is then performed to obtain feature peaks. In order to combine the information from sparsely located feature peaks, sparse principal component analysis is applied to identifying important variables used in the following regression procedure. Under these conditions the result of regression analysis can be obtained readily in a given data set coupling with boosting regression tree. The method was tested on samples collected from the East China Sea. Compared to the parallel factor analysis–multivariate linear regression method, experimental results show that the proposed method achieved a more consistent regression output and indicate that the boosting regression tree has potential for DOC quantification even at low concentrations. View Full-Text
Keywords: water dissolved organic carbon; excitation emission matrix fluorescence spectroscopy; in situ monitoring; boosting regression tree water dissolved organic carbon; excitation emission matrix fluorescence spectroscopy; in situ monitoring; boosting regression tree
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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MDPI and ACS Style

Yin, H.; Wang, K.; Liu, Y.; Huang, P.; Yu, J.; Hou, D. Fluorescence Excitation–Emission Matrix Spectroscopy and Boosting Regression Tree Model to Detect Dissolved Organic Carbon in Water. Water 2021, 13, 3612. https://doi.org/10.3390/w13243612

AMA Style

Yin H, Wang K, Liu Y, Huang P, Yu J, Hou D. Fluorescence Excitation–Emission Matrix Spectroscopy and Boosting Regression Tree Model to Detect Dissolved Organic Carbon in Water. Water. 2021; 13(24):3612. https://doi.org/10.3390/w13243612

Chicago/Turabian Style

Yin, Hang, Ke Wang, Yu Liu, Pingjie Huang, Jie Yu, and Dibo Hou. 2021. "Fluorescence Excitation–Emission Matrix Spectroscopy and Boosting Regression Tree Model to Detect Dissolved Organic Carbon in Water" Water 13, no. 24: 3612. https://doi.org/10.3390/w13243612

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