Quantitative analysis of water quality parameters COD measurement based on spectral data fusion

Qing Chen; Bin Tang; Zourong Long; Junfeng Miao; Qisen Xiao; Jinfu Zhang; Linfeng Cai; Hang Liu; Ninghui Yang; Mingfu Zhao

doi:10.1117/12.2656746

20 October 2022 Quantitative analysis of water quality parameters COD measurement based on spectral data fusion

Qing Chen, Bin Tang, Zourong Long, Junfeng Miao, Qisen Xiao, Jinfu Zhang, Linfeng Cai, Hang Liu, Ninghui Yang, Mingfu Zhao

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Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124511T (2022) https://doi.org/10.1117/12.2656746
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

UV-Vis absorption spectroscopy is often used to measure COD values. At present, certain points or wavelengths in the spectrum are often obtained for modeling, resulting in deviations in predicting COD values. Aiming at this problem, this study adopts a data fusion method of multi-source spectroscopy (Raman spectroscopy and UV-Vis absorption spectroscopy) to improve the accuracy of predicted COD values. In the experiment, two groups of single spectrum and multi-source spectrum were used for comparison. Potassium hydrogen phthalate was used to simulate the COD value of water quality. The range of COD was 5-100mg/L, and the concentration gradient was 5mg/L. The data fusion of multisource spectra in the experiment uses the normalization method to remove the dimension and then cascades into a new data set matrix. The eigenvalues are screened out through the importance analysis of projection variables, and finally the processed matrix is combined with the partial least squares regression method. The experiment was modeled with 19 training groups, and 1 group was used to verify whether the model was good or bad. The experimental results use the two dimensions of root mean square error and coefficient of determination to evaluate the quality of the model. UV-Vis absorption spectrum R2=0.9993, RMSECV=1.8525; data-level fusion R2=0.9668, RMSECV=1.6113; feature-level fusion R2=0.9766, RMSECV=1.2957. The experimental results show that multi-source spectral fusion has a positive impact on improving the detection of water quality parameters, and the fusion model has stronger predictive ability.

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Qing Chen, Bin Tang, Zourong Long, Junfeng Miao, Qisen Xiao, Jinfu Zhang, Linfeng Cai, Hang Liu, Ninghui Yang, and Mingfu Zhao "Quantitative analysis of water quality parameters COD measurement based on spectral data fusion", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124511T (20 October 2022); https://doi.org/10.1117/12.2656746

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KEYWORDS

Data fusion

Raman spectroscopy

Data modeling

Absorption

Absorbance

Absorption spectroscopy

Quantitative analysis

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