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

Water

Ke Wang¹  Jie Yu¹  Dibo Hou¹  Hang Yin¹  Pingjie Huang¹  Yu Liu² 

[1] State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China;The National Ocean Technology Center, Tianjin 300112, China;

关键词: water dissolved organic carbon;    excitation emission matrix fluorescence spectroscopy;    in situ monitoring;    boosting regression tree;   

DOI  :  10.3390/w13243612

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown