Modeling with Multiple Correlated Spectral Data Based on Approximating the Nonlinear Spectrum Induced by Scattering

In the spectral quantitative analysis of scattering solution, the improvement of accuracy is seriously restricted by the nonlinearity caused by scattering, and even the measurement will fail due to the influence of scattering. The important reasons are that the modeling variables are greatly affected by nonlinearity, and the information contained in the modeling data cannot represent the scattering characteristics. In this paper, a method is proposed, in which the spectral data of several optical pathlengths with equal space are combined as the modeling data set of a sample. These highly correlated spectral data contain relatively nonlinear information. The addition of the spectral data provides more options for the selection of principal components in modeling with PLS method. By giving lower weight to the corresponding wavelength which is greatly affected by scattering, the model is insensitive to scattering and the prediction accuracy is improved. Through the spectral quantitative analysis experiment on strong scattering material, the prediction accuracy of the model was 61.7% higher than that of the traditional method and was 58.5% higher than that of the variable sorting for normalization method. The feasibility of the method is verified.

Automated summary

A method is proposed in this paper to improve accuracy in spectral quantitative analysis by combining spectral data from different optical path lengths and giving lower weight to wavelengths greatly affected by scattering, resulting in increased prediction accuracy and insensitivity to scattering. Through experiments on strong scattering materials, the feasibility of the method was verified and showed significantly higher prediction accuracy compared to traditional and normalization methods.