Wavelength Selection Method of Algal Fluorescence Spectrum Based on Convex Point Extraction From Feature Region
ZHANG Yong-bin1, ZHU Dan-dan1, CHEN Ying1*, LIU Zhe1, DUAN Wei-liang1, LI Shao-hua2
1. Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
2. Hebei Sailhero Environmental Protection Hi-tech Co., Ltd., Shijiazhuang 050000, China
Abstract：The frequent occurrence of algal bloom seriously affects the Marine environment and human production activities, so it is very important to monitor the phytoplankton in water.3D fluorescence spectroscopy has been widely used in the analysis of algae community composition and the quantitative analysis of algae concentration in water phytoplankton. However, the information redundancy in 3D fluorescence spectrum data has significantly impacted the qualitative and quantitative analysis of algae.In order to solve the problem of spectral information redundancy, a new wavelength selection method of 3D fluorescence spectrum based on the combination of feature region and convex point extraction is proposed.Taking Aureococcus anophagefferens, Chlorella Vulgaris, and Synechococcus elongatus as the research object, the Savitzky-Golay convolution smoothing method was used to preprocess the 3D fluorescence spectrum to solve the problem of spectral noise caused by external factors. The Mahalanobis distance method was used to eliminate the abnormal spectral samples in the 3D fluorescence spectrum data set.The residual concentration method was used to eliminate the abnormal concentration value samples in the 3D fluorescence spectrum data set.Then the reliability of the convex points under different characteristic regions was measured by the root mean square error of cross-validation (RMSECV) of the PLS regression model, and the wavelength variable was selected. In order to verify the effectiveness of the wavelength selection method, the PLS regression model was established for the three algae species, and the determination coefficient (R2) and root mean square error of cross-validation (RMSECV) were used as the evaluation indexes of the model. Compared with the regression model established with the full spectrum data, the wavelength variables of Aureococcus anophagefferens, Chlorella Vulgaris, and Synechococcus elongatus respectively decreased from 1 071 to 77, 75 and 67, and R2 respectively increased by 0.016 4, 0.002 and 0.032 4. RMSECV was respectively reduced by 1.8×105, 2.0×105 and 2.6×105. Compared with the UVE method, the wavelength variables of Aureococcus anophagefferens, Chlorella Vulgaris, and Synechococcus elongatus were respectively reduced by 599, 357 and 317, and R2 was respectively increased by 0.014 5, 0.000 4 and 0.012 3, RMSECV was respectively decreased by 1.6×105, 7.0×104 and 1.6×105. After the selection of wavelength variables by the method of feature region combined with convex point extraction, the redundant information is reduced, and the model’s prediction ability is improved.
Key words：Phytoplankton; 3D fluorescence spectroscopy; Feature region; Convex point extraction; Wavelength selection
