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General Raman spectrum feature extraction method for machine learning substance identification algorithm

A technology of Raman spectroscopy and recognition algorithm, applied in the field of Raman spectroscopy, can solve the problems of missing peak signal strength information and poor feature classification effect, and achieve the effect of improving classification accuracy and strong versatility

Active Publication Date: 2018-03-20
XIAMEN UNIV
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  • Claims
  • Application Information

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Problems solved by technology

This method often loses the intensity information of the peak signal, resulting in poor classification of the extracted features

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  • General Raman spectrum feature extraction method for machine learning substance identification algorithm
  • General Raman spectrum feature extraction method for machine learning substance identification algorithm
  • General Raman spectrum feature extraction method for machine learning substance identification algorithm

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Embodiment Construction

[0038] The following embodiments will further illustrate the present invention in conjunction with the accompanying drawings.

[0039] The present invention comprises the following steps:

[0040] The first step: automatic preprocessing of the spectrum: eliminating noise and subtracting fluorescent background;

[0041] In actual testing, Raman spectrum samples are usually expressed in the form of two-dimensional data, where the abscissa is the wave number, and the ordinate is the spectral signal intensity corresponding to the wave number. Raman spectroscopy sample collection is often affected by many factors, such as the fluorescence background (the main factor) generated by the laser, the burr peaks generated by the radiation, and the inherent noise of the instrument. In order to perform accurate substance identification on Raman spectroscopy, the influence of these factors must be eliminated as much as possible. The present invention uses an automatic spectral preprocessin...

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Abstract

The invention provides a general Raman spectrum feature extraction method for machine learning substance identification algorithm, and relates to Raman spectrum. The general Raman spectrum feature extraction method for machine learning substance identification algorithm includes the steps: performing spectra automatic preprocessing; and acquiring the feature vectors of the spectra. The general Raman spectrum feature extraction method for machine learning substance identification algorithm can perform feature extraction on the Raman spectrum in any specified ranges, wherein the extracted feature vectors are suitable for various matching learning algorithms, thus being high in universality and being not limited by the target substance or test system; the noise interference and the fluorescence background interference can be automatically removed, and at the same time, the information, such as the position and the intensity of peak value signals, can be maintained; the spectrum of each target substance can be effectively identified; the blank spectral feature can be accurately extracted, and the negative and positive samples can be effectively identified and accurately distinguished so as to preferably satisfy the practical demand of substance detection; and the extraction method does not involve complicated calculation and has low demand for storage space, thus being low in the time and space complexity, and being convenient for batch processing and analysis of the spectral data.

Description

technical field [0001] The invention relates to Raman spectroscopy, in particular to a general Raman spectroscopy feature extraction method for machine learning material identification algorithms. Background technique [0002] Raman spectroscopy is based on the Raman scattering effect, a vibration spectrum with molecular fingerprint information, and each substance has unique spectral information that distinguishes it from other substances. Therefore, Raman spectroscopy can detect and analyze substances, and has applications in the fields of materials, chemistry, physics, environmental protection, and life sciences. The current popular surface-enhanced Raman spectroscopy (SERS) technique [1] And the subsequent development of core-shell isolated nanoparticles enhanced Raman spectroscopy (SHINERS) technology [2] , greatly improving the sensitivity of Raman spectroscopy detection, reducing noise and background interference, and greatly improving the universality and applicabil...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G01N21/65
CPCG01N21/65G06F2218/06G06F2218/08G06F2218/12G06F18/2411
Inventor 谢怡游乔贝刘国坤康怀志曾勇明孙锡龙
Owner XIAMEN UNIV