A wavelength selection method based on PCA modeling feedback load weighting

A wavelength selection and feedback technology, which is applied in the field of wavelength selection based on principal component analysis modeling feedback load weighting, can solve the problems of not having a single target absorption peak, spectral collinearity affecting the modeling accuracy of long-band spectral data, etc. Achieve the effect of reducing the number of modeling wavelengths, shortening the calculation time, and improving efficiency

Active Publication Date: 2019-03-01
GUILIN UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

[0004] Since the response data of near-infrared and infrared spectra usually do not have obvious single-target absorption peaks, the problem of spectral collinearity has always been a technical obstacle affecting the modeling accuracy of long-wavelength spectral data.

Method used

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  • A wavelength selection method based on PCA modeling feedback load weighting
  • A wavelength selection method based on PCA modeling feedback load weighting
  • A wavelength selection method based on PCA modeling feedback load weighting

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Embodiment 1

[0031] Qualitative identification of caffeine components in coffee samples by NIR spectroscopy. There are a total of 174 powdered coffee solid samples, 116 of which contain caffeine, and 58 samples do not contain caffeine. The continuous long-wave band region of the spectral measurement setting is 1000-2500 (nm), and the interval between adjacent wavelengths is 2nm. Each sample Measure the spectral values ​​of 750 wavelength variables in total; Spectral modeling adopts the mode of leave-one-out cross-checking, utilizes the LWVS method of the present invention to select information wavelength sets with a higher signal-to-noise ratio, and carries out further modeling discrimination in conjunction with the LDA method, Construct a confusion matrix to quantify and measure the accuracy of discrimination.

[0032] The specific steps are: Step 1, calculate the spectral matrix X 174×750 The covariance matrix MX 750×750 , and calculate the eigenvalue LV of the MX matrix 750×1 And the...

Embodiment 2

[0034] Quantitative analysis of total nitrogen content in soil samples by FT-NIR spectroscopy. A total of 135 powdered solid soil samples, the total nitrogen content range is 0.056-0.289 (wt%), and the continuous long-wave band area of ​​the spectral measurement setting is 9800-4200 (cm -1 ), with a spectral resolution of 8cm -1 , each sample measures the spectral value of altogether 1410 wavelength variables; Spectral modeling adopts the mode of leave-one-out cross-check, utilizes the LWVS method of the present invention to select the information wavelength set with higher signal-to-noise ratio, carries out further in conjunction with MLR method For modeling predictions, root mean square deviation (RMSE) is used to quantify and measure the effect of modeling predictions.

[0035] The specific steps are: Step 1, calculate the spectral matrix X 135×1410 The covariance matrix MX 1410×1410 , and calculate the eigenvalue LV of the MX matrix 1410×1 And the feature vector set LO...

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Abstract

The invention discloses a wavelength selection method based on PCA modeling feedback load weighting. Based on PCA algorithm, according to the spectral detection data of different frequencies, Establishing and optimizing the metrology analysis model, weighting and combining the PCA load vector with modeling coefficient feedback, measuring the information contribution for each wavelength variable, and then choosing the information wavelength set with higher signal-to-noise ratio, can effectively reduce the number of wavelengths involved in modeling and reduce the complexity of the model; the wavelength combinations selected can be combined with various simple statistical algorithms such as linear discrimination or multiple linear regression to complete the qualitative or quantitative analysis. This method can improve the work efficiency of spectral information variable screening, and can be applied to the near infrared, infrared, ultraviolet and other frequency bands of spectral dimension reduction and rapid detection. It provides theoretical basis and technical support for the development and application of small special spectral instrument, and is expected to be widely used in thefield of hyperspectral image analysis.

Description

technical field [0001] The invention relates to the field of modeling optimization in near-infrared and infrared spectrum analysis, in particular to a wavelength selection method based on principal component analysis (PCA) modeling feedback load weighting in the modeling process. Background technique [0002] Spectral analysis is a modern rapid detection technology that uses light of different frequencies to measure the spectral response data of substances, and determines the chemical composition and content information in the object to be measured by qualitative or quantitative methods. Infrared light is between visible light and far-infrared light, and can be divided into two common technical spectrum bands: near-infrared (NIR) and mid-infrared (referred to as infrared, MIR). In recent years, with the development of big data science, computer technology and chemometrics, NIR / MIR analysis technology has the characteristics of fast analysis speed, high efficiency, low cost, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/20G06K9/62
CPCG06V10/143G06F18/2135G06F18/214Y02T10/40
Inventor 陈华舟蔡肯乔涵丽
Owner GUILIN UNIVERSITY OF TECHNOLOGY
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