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Wavelength similarity consensus regression-based infrared spectrum quantitative analysis method and device

A technology of infrared spectroscopy and quantitative analysis, which is applied in the field of infrared spectroscopy quantitative analysis and devices based on wavelength similarity consensus, can solve the problem of inaccurate spectral information extraction, achieve extraction, improve prediction accuracy and robustness, and overcome spectral signals. Effects of Difficulty in Information Extraction

Inactive Publication Date: 2011-05-25
EAST CHINA JIAOTONG UNIVERSITY
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Problems solved by technology

[0005] In the process of rapid analysis of the components to be measured using infrared spectral analysis technology, in order to solve the problem of inaccurate extraction of spectral information

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  • Wavelength similarity consensus regression-based infrared spectrum quantitative analysis method and device
  • Wavelength similarity consensus regression-based infrared spectrum quantitative analysis method and device
  • Wavelength similarity consensus regression-based infrared spectrum quantitative analysis method and device

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specific Embodiment approach

[0021] The specific embodiment will be described in conjunction with the following implementation examples. Taking the near-infrared spectrum of pears as an example, the wavelength similarity clustering consensus regression model was constructed for the sugar content index inside pears.

[0022] figure 1 Schematic diagram of infrared spectroscopy quantitative analysis method and device for wavelength similarity clustering consensus regression, figure 2 It is a near-infrared spectrogram with a spectral range of 750-1800 nm, and each spectrum includes 1051 data points. All samples were divided into calibration set and test set according to the ratio of 2:1.

[0023] All sample spectra were clustered, and the intra-class and inter-class distances were calculated using the Euclidean distance method and the average distance method, respectively. image 3 Shown is a schematic diagram of the wavelength similarity clustering consensus regression method. It can be seen from the fig...

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Abstract

The invention relates to a wavelength similarity consensus regression-based infrared spectrum quantitative analysis method and a wavelength similarity consensus regression-based infrared spectrum quantitative analysis device. The device comprises a spectrometer, a preprocessor, a wavelength filter and a partial least-squares regression analyzer which are connected through a data signal line; and the wavelength of the near-infrared spectrum ranges from 780 to 50,000nm. By performing cluster analysis on spectra along the wavelength direction, a spectrum is divided into different information blocks, multiple models are constructed, and the difficulty for a single model method to extract information of a spectrum signal is overcome; and by singly determining factor number for different submodels, effective information is fully extracted, and the prediction accuracy and robustness of an infrared spectrum analysis model are improved.

Description

technical field [0001] The invention relates to an infrared spectrum quantitative analysis method and device, in particular to a wavelength similarity-based consensus regression infrared spectrum quantitative analysis method and device. Background technique [0002] Because the spectral signal obtained by the spectrometer contains both useful information and other random errors (background and noise). Therefore, it is difficult to obtain a model with high prediction accuracy when using the partial least squares regression method for quantitative analysis. To improve the prediction accuracy of the partial least squares regression model, a lot of research work has been carried out, mainly including spectral preprocessing methods and variable screening methods, these methods have been successfully used in spectral background correction, noise removal, Elimination of non-informative variables. However, when using the above methods for information extraction, we often face the ...

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Application Information

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IPC IPC(8): G01J3/30G01N21/35
Inventor 郝勇
Owner EAST CHINA JIAOTONG UNIVERSITY
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