Method for extracting raman characteristic peaks employing improved principal component analysis

Patent metadata

Field	Value
Publication number	US-12050179-B2
Application number	US-202017765846-A
Country	US
Kind code	B2
Filing date	Jun 12, 2020
Priority date	Oct 8, 2019
Publication date	Jul 30, 2024
Grant date	Jul 30, 2024

Abstract

A method for extracting Raman characteristic peaks employing improved principal component analysis comprising: using a confocal microscopic Raman-spectroscopic instrument to collect Raman spectroscopic data from surfaces of pork and beef samples; and performing preprocessing on the Raman spectroscopic data, performing principal component analysis, establishing a principal component loading scatter plot, extracting dot characteristics from the principal component loading scatter plot, analyzing same, and performing filtering on the dot characteristics to obtain Raman characteristic peaks. The method for extracting Raman characteristic peaks employing improved principal component analysis is used to extract Raman characteristic peaks from pork and beef samples, and then the Raman characteristic peaks are inputted into a classifier to undergo classification, thereby achieving high accuracy and quick classification.

First claim

What is claimed is:

1. A method for extracting Raman characteristic peaks employing improved principal component analysis, comprising:
1) Using a confocal microscopic Raman-spectroscopic instrument to collect Raman spectroscopic data from surfaces of pork and beef samples;
2) Performing preprocessing on the Raman spectroscopic data, then performing principal component analysis, then establishing a principal component loading scatter plot, then analyzing and extracting scatter characteristics of the principal component loading scatter plot, and filtering the Raman characteristic peaks according to the scatter characteristics, the step 2) comprising:
2.1) for a Raman spectrum dataset B(B 1 , B 2 , . . . , B n ) of n samples and m wave bands obtained after the preprocessing, that is, each spectrum B i containing m wave bands, adopting a random sampling method to extract ⅔ spectrum from the dataset B to form a training set C(C 1 , C 2 , . . . , C 2n/3 ), then performing the principal component analysis on the training set C to extract first two principal components_PC 1 _and_PC 2 expressed as:

P
⁢

C
1

=

∑

k
=
1

m

α

1
⁢
k

⁢

β
k

P
⁢

C
2

=

∑

k
=
1

m

α

2
⁢
k

⁢

β
k

where β k is a k-th wave band, α 1k represents a load factor corresponding to the k-th wave band under the first principal component, and_α 2k represents a load factor corresponding to the k-th wave band under the second principal component;
2.2) drawing a load distribution diagram in a form of polar coordinates establishing a two-dimensional coordinate graph with a load coefficient α 1k as a horizontal axis and a load coefficient α 2k as a vertical axis, taking the load coefficient α 1k and the load coefficient α 2k corresponding to a Raman shift point β k of a same wave band in the two principal components as scatter coordinates (α 1k , α 2k ) drawn in the two-dimensional coordinate graph to form the principal component loading scatter; then converting scatters from Cartesian coordinates (α 1k , α 2k ) into polar coordinates (d k , θ k ), and dividing, according to angles of the polar coordinates, a wavelength range occupied by all scatters into eight regions D i (i=1, 2, . . . , 8) respectively being

[

0
,

π
4

)

,

[

π
4

,

π
2

)

,

[

π
2

,

3
⁢
π

4

)

,

[

3
⁢
π

4

,
π

)

,

[

π
,

5
⁢
π

Assignees

• Univ Zhejiang

Inventors

• Rao Xiuqin
• Zhang Yanning
• Zhang Xiaomin
• Wang Yitian
• Lin Yangyang
• Ying Yibin

Classifications

• G01N33/12 Primary

  Meat; Fish · CPC title

• G01N2201/1293

  resolving multicomponent spectra · CPC title

• G01N21/65 Primary

  Raman scattering · CPC title