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Infrared spectroscopy wavelength selection method based on partitioned sparse Bayesian optimization

A sparse Bayesian, infrared spectroscopy technology, applied in the measurement of color/spectral characteristics, etc., can solve the problems of strong robustness, small amount of calculation, and few adjustable parameters.

Inactive Publication Date: 2015-11-18
ZHONGBEI UNIV
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Problems solved by technology

[0006] In order to overcome the shortcomings of large amount of calculation and weak robustness existing in the infrared spectrum wavelength selection method in the prior art, the present invention proposes a block-based sparse Bayonet algorithm with small amount of calculation, few adjustable parameters and strong robustness. A Yassian-optimized method for wavelength selection in infrared spectroscopy

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  • Infrared spectroscopy wavelength selection method based on partitioned sparse Bayesian optimization
  • Infrared spectroscopy wavelength selection method based on partitioned sparse Bayesian optimization
  • Infrared spectroscopy wavelength selection method based on partitioned sparse Bayesian optimization

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Embodiment Construction

[0036] The following embodiments will further describe the present invention in conjunction with the accompanying drawings.

[0037] The following combination figure 1 and figure 2 The present invention is described in detail.

[0038] like figure 1 As shown, assuming that there are N samples, the infrared spectrum signal scanned by the spectrometer is The corresponding content of the components to be analyzed is Among them, P is the number of wavelength points in the infrared spectrum, and generally N<<P.

[0039] According to the principle of chemometrics, the content prediction model of the components to be analyzed can be expressed as

[0040] Y=Φx+v(1)

[0041] in, is the regression coefficient to be fitted; is the noise error.

[0042] To achieve wavelength selection, using convex optimization theory, the problem can be transformed into the following l 1 Norm sparse optimization problem:

[0043] x ^ η ...

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Abstract

The invention relates to the technical field of infrared spectroscopy wavelength, and more specifically, relates to a novel infrared spectroscopy wavelength selection method based on sparse Bayesian study. The sparse optimized wavelength selection method utilizes the spectral structure priori knowledge and the related priori knowledge of co-linearity between spectrums. The provided method has the advantages of little calculation quantity, few adjustable parameters, and strong robustness. For the first time, the spectral structure priori knowledge and the related priori knowledge of co-linearity between spectrums are utilized, and the spectral partitioned sparse structure can be determined in a self-adapting mode. Then sparse Bayesian study algorithm is adopted to calculate out the optimal solution of the sparse optimization problem so as to screen out the best wavelength point combination. The provided method can be widely applied to the field of solid-phase / liquid-phase / gas-phase infrared spectrum wavelength selection.

Description

technical field [0001] The present invention relates to the technical field of infrared spectrum wavelength, more specifically, relates to a novel infrared spectrum wavelength selection method based on sparse Bayesian learning, which is a method that utilizes prior knowledge of spectral structure and prior knowledge of collinearity between spectra Sparse optimized wavelength selection method. Background technique [0002] Infrared spectroscopic analysis is a new analytical technique, which has been widely used in the fields of agriculture, chemical industry and environmental monitoring due to its advantages of fast, non-destructive and non-polluting. However, infrared spectroscopy usually has the characteristics of multiple wavelength points, overlapping absorption peaks, and serious collinear relationship between wavelength points, which makes subsequent qualitative and quantitative analysis difficult. Therefore, the study of wavelength selection methods has important prac...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/35
Inventor 吴其洲赵耀霞陈媛媛张艳双刘泉水武彦涛聂江稳霍志华
Owner ZHONGBEI UNIV
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