Pollutant concentration fitting method based on ultraviolet-visible spectrum

Abstract

The invention discloses a pollutant concentration fitting method based on an ultraviolet-visible spectrum. The method comprises the following steps: measuring the pollutant concentration, and determining the pollutant concentration corresponding to each spectrum; searching a particular wavelength by using a genetic algorithm, wherein the spectral absorption strengths of different chemical substances on different wavelengths are different, and every substance is required to be fitted through the particular wavelength with an optimal fitting effect; modeling the wavelengths and the concentration sby using partial least squares, wherein an obtained fitting relationship is a relationship among spectroscopic data and the pollutant concentration values and is used for calculating the concentration values of the pollutants in different environments. According to the technical scheme, the interference among the different pollutants is removed by utilizing the absorbance of the different wavelengths in the ultraviolet-visible spectrum, and the fitting precision is effectively improved.

Description

technical field [0001] The invention relates to a pollutant concentration fitting method based on ultraviolet-visible spectra, and belongs to the technical fields of pattern recognition and water quality detection. Background technique [0002] Water quality monitoring refers to the process of monitoring and measuring the types of pollutants in water bodies, the concentration and change trend of various pollutants, and evaluating the water quality status. The monitoring scope includes unpolluted and polluted natural water (rivers, rivers, lakes, sea ​​and groundwater) and various industrial drainage, etc. [0003] With the development of science and technology, the methods and technologies of water quality monitoring have also been rapidly improved. At the same time, more and more modern control methods and methods such as computer control and advanced instrument analysis have achieved extensive results in water quality monitoring and analysis. application. On the basis of...

AI Technical Summary

Problems solved by technology

The disadvantage is that there is a relatively large correlation between the multiple independent variables used for modeling, which will affect the accuracy of the multivariate linear least squares method

[0006] The second scheme is the backpropagation neural network method (see Benjathapanun, N Boyle, W.J.O Grattan, K.T.V.Classification of UV-Vis Spectroscopic data using principal component analysis and neural network techniques[J].Measurement, 1998, (24): 1 -7), the backpropagation neural network of this scheme is a multi-layer feedforward network trained according to the error backpropagation algorithm, and it is one of the most widely used neural network models at present. The disadvantages are slow learning speed and the possibility of training failure Large, it is easy to converge to the local extreme point, and there will be over-fitting phenomenon

[0007] The third scheme is the support vector machine method (Du Shuxin, Wu Xiaoli, Wu Tiejun; the support vector machine method in the ultraviolet spectrum water quality analyzer [J]; Analytical Chemistry; 2004 09 issue), the core support vector machine method of this method is According to the limited sample information, the optimal compromise is sought between the complexity of the model (i.e., the learning accuracy for a specific training sample) and the learning ability (i.e., the ability to identify any sample without error), in order to obtain the best generalization ability, Among its shortcomings, it is difficult to select parameters, and the specific relationship between wavelength and concentration cannot be revealed, so the environmental adaptability is relatively poor

Images