Asymmetric weighted least squares based Raman spectrum detection baseline correction method

Abstract

The invention belongs to the technical field of Raman scattering, and discloses an asymmetric weighted least squares based Raman spectrum detection baseline correction method. The method includes, fora Raman spectrum to be fitted to a baseline, setting smoothing parameters and an iteration termination condition, initializing weight into unit matrixes during first iteration, and obtaining an initial fitting baseline through minimizing penalized least squares; and performing updating on the weight through a softsign function according to the difference signal between an original spectrum and the fitting baseline, the process being repeated continuously until a termination condition is met. The original Raman spectrum subtracts the final fitting baseline, so that baseline correction of the Raman spectrum can be realized. Correction can be performed on the original Raman spectrum by utilizing the method, so that spectrums after baseline correction can be closer to real Raman signals, theaccuracy of Raman spectrum detection can be enhanced, and the method has important practical significance for the quantitative analysis and applications of the Raman spectrum.

Description

technical field [0001] The invention belongs to the technical field of Raman scattering, and in particular relates to a Raman spectrum detection baseline correction method based on asymmetric weighted least squares. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: Raman spectroscopy is a molecular vibrational spectrum based on the Raman scattering effect. Information such as the position, intensity, and peak width of the characteristic peaks can reflect the structural characteristics of the molecule, so that the detection of material components can be realized. However, during the collection of spectral data, due to the influence of fluorescence from organic molecules, the environment or pollutants in the sample, the measured spectrum often suffers from baseline drift. In some cases, the intensity of the fluorescence signal is much greater than that of the Raman signal, which constitutes a major obstacle to th...

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Problems solved by technology

[0004] (1) The traditional baseline correction needs to define an appropriate polynomial fitting order, and the operation is cumbersome and has great subjectivity

In reality, the baseline shape of the spectrum is complex and changeable. For a large number of high-dimensional spectral data, the method of artificial polynomial baseline fitting is difficult to meet the requirements of accurate and fast batch baseline correction.

[0005] (2) The baseline correction method based on wavelet transform depends on the selection of decomposition function and decomposition scale. The assumption that the signal and background can be well separated in the transform domain is not necessarily satisfied in practice.

In practice, the spectral signal will inevitably have noise. When the frequency components of the spectral signal are not easy to distinguish from the noise characteristics, there will be residual wavelet filtering, resulting in distortion or insufficient baseline correction.

[0006] (3) Penalized least squares method When the spectrum is superimposed with noise, the spectrum corrected by the penalized least squares method may rise, affecting the accuracy of the baseline correction results

However, the spectral data obtained in practice, due to the fluorescence of organic molecules or pollutants in the sample, often causes the phenomenon of "baseline drift" in the Raman spectrum, and sometimes there is strong noise interference, which affects the Raman spectral data further analysis of

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