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Fluorescence Excitation–Emission Matrix Spectroscopy and Boosting Regression Tree Model to Detect Dissolved Organic Carbon in Water
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.
Fluorescence Excitation–Emission Matrix Spectroscopy and Boosting Regression Tree Model to Detect Dissolved Organic Carbon in Water
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.
Fluorescence Excitation–Emission Matrix Spectroscopy and Boosting Regression Tree Model to Detect Dissolved Organic Carbon in Water
2021
Article (Journal)
Electronic Resource
Unknown
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