CN112461809B - Rapid identification method for rosewood varieties - Google Patents

Rapid identification method for rosewood varieties Download PDF

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CN112461809B
CN112461809B CN202011252767.5A CN202011252767A CN112461809B CN 112461809 B CN112461809 B CN 112461809B CN 202011252767 A CN202011252767 A CN 202011252767A CN 112461809 B CN112461809 B CN 112461809B
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raman
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CN112461809A (en
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孙通
胡栋
俞储泽
吴成招
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Zhejiang A&F University ZAFU
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    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/65—Raman scattering
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
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Abstract

The invention relates to a rapid identification method of a rosewood variety, and solves the problems that common consumers are difficult to distinguish Lu's ebony and sandalwood and specialized personnel are required to identify by depending on experience and a standard wood slice microscopic identification method, so that the distinguishing difficulty is high. The method adopts laser Raman spectroscopy and laser-induced fluorescence spectroscopy technologies to quickly and nondestructively identify the pterocarpus santalinus and the pterocarpus lucidus, adopts 3 laser wavelengths to irradiate a rosewood sample and obtain corresponding spectrums, carries out Raman and fluorescence spectrum separation according to fluorescence and Raman spectrum characteristics to obtain Raman and fluorescence characteristic spectrums of the sample, constructs corresponding characteristic quantities to expand the spectrum difference of the pterocarpus santalinus and the pterocarpus lucidus, and then applies multiple linear regression to establish a discrimination model. The Raman spectrometer and the fluorescence spectrometer can realize that a single CCD spectrometer can obtain Raman and fluorescence spectra of a sample, and can distinguish the Lu's ebony and the sandalwood and the red sandalwood through modeling analysis, thereby realizing nondestructive testing and identification.

Description

Rapid identification method for rosewood varieties
Technical Field
The invention belongs to the field of wood variety detection, and relates to a rapid identification method of a rosewood variety.
Background
The rosewood is heartwood of the tree species of pterocarpus, persimmon, croton and cassia, and the structural characteristics, density and wood color of the rosewood meet the requirements of the national standard GB/T18107-2017. The price difference of the rosewood of different varieties is very large. For example, pterocarpus santalinus, commonly known as "lobular sandalwood", has a very expensive price of 65-84 ten thousand per ton because of rare wood. The price of the Lu's black yellow sandalwood produced by the Madagascar is 7-8 ten thousand per ton, which is only one tenth of that of the red sandalwood. Because the Lu's black and yellow sandalwood is very similar to the pterocarpus santalinus in texture and color, the Lu's black and yellow sandalwood cannot be sold as the pterocarpus santalinus by merchants under the drive of huge benefits. For the Lu's ebony and the pterocarpus santalinus, common consumers are difficult to distinguish, and professionals are required to distinguish by relying on experience and a standard wood slice microscopic identification method, so that the distinguishing difficulty is high, and time and labor are wasted.
The laser Raman spectrum technology is a rapid, lossless and green optical detection method. The vibration and rotation information of the substance molecules can be obtained by analyzing the scattering spectra with different incident light frequencies, and the method can be used for identifying the substance structure. The laser-induced fluorescence spectroscopy is a technique of irradiating a sample with laser light to generate fluorescence, and qualitative and quantitative analysis of a substance can be performed by analyzing the position and intensity of a fluorescence spectrum line. In addition, the fluorescence spectrum of a substance is independent of the wavelength of an excitation light source, and is only dependent on the energy level structure of the fluorescent substance itself. The pterocarpus santalinus and the pterocarpus lucidus have similar texture and color, but have certain difference in microstructure, and the two rosewood varieties of the pterocarpus santalinus and the pterocarpus lucidus can be effectively distinguished by combining spectral technology with algorithm analysis modeling.
Disclosure of Invention
The invention solves the problems that common consumers are difficult to distinguish the rosewood pterocarpus santalinus and the rosewood pterocarpus santalinus in rosewood varieties, professionals are required to distinguish the rosewood pterocarpus santalinus and the rosewood pterocarpus santalinus by depending on experience and a standard wood slice microscopic identification method, the distinguishing difficulty is high, time and labor are wasted, and the rapid distinguishing method of the rosewood varieties is provided. The method can separate Raman and fluorescence spectrum signals of sample spectra obtained under the irradiation of different laser wavelengths, and only a single CCD spectrometer is needed to obtain the Raman and fluorescence spectra of the sample, so that the method effectively simplifies the identification device and reduces the cost of the device; according to the spectral characteristics of the pterocarpus santalinus and the pterocarpus lucidus, corresponding characteristic quantities are constructed by Raman and fluorescence characteristic spectrums of the pterocarpus santalinus and the pterocarpus lucidus, the spectrum difference of the pterocarpus santalinus and the pterocarpus lucidus is enlarged, and the accuracy of judgment is improved.
The technical scheme adopted by the invention for solving the technical problems is as follows: a rapid identification method for rosewood varieties is characterized by comprising the following steps:
step 1: separating signals of the Raman spectrum and the fluorescence spectrum;
step 1.1, respectively obtaining n samples of pterocarpus santalinus and pterocarpus lucidus, and respectively recording the n samples as ST1,ST2,ST3,…,STn，SL1,SL2,SL3,…,SLn；
Step 1.2, for pterocarpus santalinus sample ST1Placing an integrating sphere on the sample ST1The output wavelength of the tunable laser is adjusted to 340nm, and then irradiated to the sample S through the window of the integrating sphereT1The generated spectrum signal passes through an integrating sphereCollected and emitted from another window, and then enters a CCD spectrometer through an optical fiber, and the spectral signal is recorded as
Figure BDA0002772135030000021
Step 1.3, adjusting the output wavelengths of the tunable laser to 360nm and 380nm respectively, keeping the output power consistent with the output power of the laser in the step 1.2, and then referring to the step 1.2, carrying out comparison on the sample ST1Collecting spectra, and recording the spectral signals
Figure BDA0002772135030000022
And
Figure BDA0002772135030000023
step 1.4, in order to eliminate the spectral intensity change caused by factors such as laser energy, etc., the
Figure BDA0002772135030000024
And
Figure BDA0002772135030000025
normalization is performed, i.e. for each spectrum, the difference between the spectral intensity of each wavelength in the spectrum minus the minimum spectral intensity in the spectrum is divided by the difference between the maximum spectral intensity and the minimum spectral intensity in the spectrum, and the processed spectra are respectively recorded as
Figure BDA0002772135030000026
And
Figure BDA0002772135030000027
step 1.5, because the fluorescence spectrum is irrelevant to the wavelength of an excitation light source, the fluorescence spectrum peak and the shape of the fluorescence spectrum peak cannot change under different excitation wavelengths, and the Raman spectrum peak can change along with the excitation wavelength; therefore, the spectral signals obtained under different excitation wavelengths are subtracted, so that the fluorescence signal is removed and the raman signal is retained; will spectrum
Figure BDA0002772135030000028
Minus
Figure BDA0002772135030000029
The portion of the subtracted spectrum with a negative value was set to 0 and the processed spectrum was recorded as
Figure BDA00027721350300000210
Will spectrum
Figure BDA00027721350300000211
Minus
Figure BDA00027721350300000212
The portion of the subtracted spectrum with a negative value was set to 0 and the processed spectrum was recorded as
Figure BDA00027721350300000213
Step 1.6, as the excitation wavelength increases, the peak wavelength of the generated Raman spectrum also increases, possibly resulting in spectrum in
Figure BDA00027721350300000214
A certain Raman spectrum peak in (1) and spectrum
Figure BDA00027721350300000215
The other raman spectrum peak in (a) coincides or partially coincides, resulting in a processed spectrum
Figure BDA00027721350300000216
The Raman spectrum peak can be lost or the Raman spectrum peak intensity can be reduced; to obtain all the Raman spectrum peaks of the sample, the spectrum is analyzed
Figure BDA00027721350300000217
And
Figure BDA00027721350300000218
overlapping and comparing, taking the spectrum with larger value of each wavelength to form the processed spectrum and recording the spectrum
Figure BDA00027721350300000219
Namely for
Figure BDA00027721350300000220
Of which the spectral value is taken as a spectrum
Figure BDA00027721350300000221
And
Figure BDA00027721350300000222
the larger of (a); will be provided with
Figure BDA00027721350300000223
As a sample ST1(ii) a Raman spectrum of;
step 1.7, spectra
Figure BDA00027721350300000224
Minus
Figure BDA00027721350300000225
The subtracted spectra are recorded as
Figure BDA00027721350300000226
The spectrum is taken as a sample ST1The fluorescence spectrum of (a);
step 1.8, for Pterocarpus santalinus sample ST2,ST3,ST4,…,STnAnd Lu' S ebony sample SL1,SL2,SL3,…,SLnObtaining spectral signals under the irradiation of three laser wavelengths according to the steps 1.1-1.3, separating Raman spectrum signals and fluorescence spectrum signals of the sample according to the steps 1.4-1.7, and separating the sample ST2,ST3,ST4,…,STnRespectively, the Raman spectrum and the fluorescence spectrum of
Figure BDA00027721350300000227
And
Figure BDA00027721350300000228
sample SL1,SL2,SL3,…,SLnRespectively, the Raman spectrum and the fluorescence spectrum of
Figure BDA00027721350300000229
And
Figure BDA00027721350300000230
step 2: extracting a characteristic spectrum and establishing a discrimination model;
step 2.1, for Raman spectroscopy
Figure BDA0002772135030000031
Average it, i.e.
Figure BDA0002772135030000032
Step 2.2, for Raman spectroscopy
Figure BDA0002772135030000033
The average is determined in accordance with step 2.1, and the average spectrum is recorded
Figure BDA0002772135030000034
Step 2.3, spectrum
Figure BDA0002772135030000035
Minus
Figure BDA0002772135030000036
The subtracted spectra are recorded as
Figure BDA0002772135030000037
Step 2.4, for spectra
Figure BDA0002772135030000038
Taking 10 spectral peaks with the maximum positive value of spectral intensity and 10 spectral peaks with the maximum negative value of spectral intensity, and respectively recording the wavelengths as lambdaZ1,λZ2,λZ3,…,λZ10And λE1,λE2,λE3,…,λE10；
Step 2.5, for spectra
Figure BDA0002772135030000039
And
Figure BDA00027721350300000310
separately obtain lambdaZ1Spectral intensities at wavelengths, respectively
Figure BDA00027721350300000311
And
Figure BDA00027721350300000312
will be provided with
Figure BDA00027721350300000313
Is divided by
Figure BDA00027721350300000314
Namely, it is
Figure BDA00027721350300000315
Step 2.6, for spectra
Figure BDA00027721350300000316
And
Figure BDA00027721350300000317
separately obtain lambdaZ2,λZ3,λZ4,…,λZ10Spectral intensity at wavelength and corresponding k is calculated with reference to step 2.5λValues, respectively, are
Figure BDA00027721350300000318
Step 2.7, for spectra
Figure BDA00027721350300000319
And
Figure BDA00027721350300000320
separately obtain lambdaE1,λE2,λE3,…,λE10Spectral intensity at wavelength and corresponding k is calculated with reference to step 2.5λValues, respectively, are
Figure BDA00027721350300000321
Step 2.8, for
Figure BDA00027721350300000322
The maximum 3 values are taken, the corresponding wavelengths are taken as three characteristic wavelengths of the Raman spectrum and are respectively recorded as lambda1,λ2,λ3；
Step 2.9, for
Figure BDA00027721350300000323
Taking the minimum 3 values, and taking the corresponding wavelengths as the other three characteristic wavelengths of the Raman spectrum, which are respectively marked as lambda4,λ5,λ6；
Step 2.10, for sample ST1Raman spectrum of
Figure BDA00027721350300000324
Obtaining lambda1,λ2,λ3,λ4,λ5,λ6Characteristic spectrum of wavelength, noted
Figure BDA00027721350300000325
Step 2.11, for Raman spectroscopy
Figure BDA00027721350300000326
Separately obtaining lambda according to step 2.101,λ2,λ3,λ4,λ5,λ6Characteristic spectra of wavelengths, respectively
Figure BDA00027721350300000327
Figure BDA00027721350300000328
Figure BDA00027721350300000329
Step 2.12, for Raman spectroscopy
Figure BDA00027721350300000330
Separately obtaining lambda according to step 2.101,λ2,λ3,λ4,λ5,λ6Characteristic spectra of wavelengths, respectively
Figure BDA00027721350300000331
Figure BDA00027721350300000332
Figure BDA0002772135030000041
Step 2.13, for sample ST1,ST2,ST3,…,STnFluorescence spectrum of
Figure BDA0002772135030000042
The average is determined in accordance with step 2.1, and the average spectrum is recorded
Figure BDA0002772135030000043
Step 2.14, for fluorescence spectroscopy
Figure BDA0002772135030000044
Finding the peak with maximum spectral intensity in the wave band range of 400nm to 500nm, and recording the wavelength of the peak as lambdaF1；
Step 2.15, for sample ST1Fluorescence spectrum of
Figure BDA0002772135030000045
Obtaining lambdaF1Spectrum at wavelength, noted
Figure BDA0002772135030000046
Step 2.16, for fluorescence spectroscopy
Figure BDA0002772135030000047
And
Figure BDA0002772135030000048
obtaining lambda according to steps 2.13-2.15F1The spectra at the wavelengths, respectively
Figure BDA0002772135030000049
And
Figure BDA00027721350300000410
step 2.17, in order to effectively fuse the Raman and fluorescence characteristic spectrums of the sample and enlarge the spectrum difference of the pterocarpus santalinus and the pterocarpus lucidus, the Raman and fluorescence characteristic spectrums of the sample are adopted to construct characteristic quantities; for sample ST1Raman characteristic spectrum of
Figure BDA00027721350300000411
And fluorescence characteristic spectrum
Figure BDA00027721350300000412
Construction of 6 feature quantities
Figure BDA00027721350300000413
6 feature quantities are respectively recorded as
Figure BDA00027721350300000414
Step 2.18, for sample ST2,ST3,ST4,…,STnRaman characteristic spectrum of
Figure BDA00027721350300000415
Figure BDA00027721350300000416
And fluorescence characteristicsOptical spectrum
Figure BDA00027721350300000417
6 corresponding characteristic quantities are respectively constructed according to the step 2.17 and are respectively marked as
Figure BDA00027721350300000418
Figure BDA00027721350300000419
Step 2.19, for sample SL1,SL2,SL3,…,SLnRaman characteristic spectrum of
Figure BDA00027721350300000420
Figure BDA00027721350300000421
And fluorescence characteristic spectrum
Figure BDA00027721350300000422
6 corresponding characteristic quantities are respectively constructed according to the step 2.17 and are respectively marked as
Figure BDA00027721350300000423
Figure BDA00027721350300000424
Step 2.20, for Pterocarpus santalinus sample ST1,ST2,ST3,…,STnSetting its class value to 1; for Lu' S ebony sample SL1,SL2,SL3,…,SLnSetting its class value to-1;
step 2.21, correlating 6 characteristic quantities of the pterocarpus santalinus and the pterocarpus lucidus samples with class values thereof by adopting a multiple linear regression method, and establishing a distinguishing model of the pterocarpus santalinus and the pterocarpus lucidus, wherein the specific model is as follows: a is1*G1+a2*G2+a3*G3+a4*G4+a5*G5+a6*G6+ b, where Y is the predicted class value, G1,G2,G3,G4,G5,G6Is 6 characteristic quantities of the samples of pterocarpus santalinus and pterocarpus lucidus, a1,a2,a3,a4,a5,a6B is the intercept, which is the coefficient of the discriminant model;
and step 3: rapidly identifying the rosewood variety;
step 3.1, obtaining one sample to be detected in the two types of redwood varieties and recording the sample as Sp；
Step 3.2, with reference to steps 1.2 and 1.3, a sample S is obtainedpThe spectra under irradiation of 3 laser wavelengths are respectively recorded as
Figure BDA0002772135030000051
And
Figure BDA0002772135030000052
step 3.3, for spectra
Figure BDA0002772135030000053
And
Figure BDA0002772135030000054
performing normalization processing according to the step 1.4, performing separation of Raman spectrum and fluorescence spectrum information according to the steps 1.5-1.8, and recording the separated Raman spectrum and fluorescence spectrum as
Figure BDA0002772135030000055
And
Figure BDA0002772135030000056
step 3.4, for Raman spectroscopy
Figure BDA0002772135030000057
Referring to step 2.10, λ is obtained1,λ2,λ3,λ4,λ5,λ6Characteristic spectrum of wavelength, noted
Figure BDA0002772135030000058
Step 3.5, for fluorescence Spectroscopy
Figure BDA0002772135030000059
With reference to step 2.15, λ is obtainedF1Characteristic spectrum at wavelength, noted
Figure BDA00027721350300000510
Step 3.6, for the characteristic spectrum
Figure BDA00027721350300000511
And
Figure BDA00027721350300000512
6 characteristic quantities are constructed according to the step 2.17 and are respectively marked as
Figure BDA00027721350300000513
Step 3.7, 6 characteristic quantities
Figure BDA00027721350300000514
Substituting the judgment model established in the step 2 to obtain a corresponding Y value; when the Y value is larger than 0, judging that the sample S to be detected ispIs Pterocarpus santalinus, and when the Y value is less than or equal to 0, the sample S to be detected is judgedpIs Lu's ebony, thereby realizing the rapid identification of the pterocarpus santalinus and the Lu's ebony.
According to the method, the pterocarpus santalinus and the pterocarpus lucidus with similar textures and colors can be rapidly identified in a nondestructive mode, Raman and fluorescence spectrum signals of sample spectrums obtained under irradiation of different laser wavelengths are separated, the Raman and fluorescence spectrums of the samples can be obtained only by adopting a single CCD spectrometer, the method effectively simplifies an identification device and reduces the cost of the device; according to the spectral characteristics of the pterocarpus santalinus and the pterocarpus lucidus, corresponding characteristic quantities are constructed by Raman and fluorescence characteristic spectrums of the pterocarpus santalinus and the pterocarpus lucidus, the spectrum difference of the pterocarpus santalinus and the pterocarpus lucidus is enlarged, and the accuracy of judgment is improved.
Detailed Description
The invention is further illustrated by the following specific examples.
Example (b): a rapid identification method for rosewood varieties is characterized by comprising the following steps:
step 1: separating signals of the Raman spectrum and the fluorescence spectrum;
step 1.1, respectively obtaining n samples of pterocarpus santalinus and pterocarpus lucidus, and respectively recording the n samples as ST1,ST2,ST3,…,STn，SL1,SL2,SL3,…,SLn；
Step 1.2, for pterocarpus santalinus sample ST1Placing an integrating sphere on the sample ST1The output wavelength of the tunable laser is adjusted to 340nm, and then irradiated to the sample S through the window of the integrating sphereT1The generated spectrum signal is collected by an integrating sphere and is emitted from another window, and then enters a CCD spectrometer through an optical fiber, and the spectrum signal is recorded as
Figure BDA0002772135030000061
Step 1.3, adjusting the output wavelengths of the tunable laser to 360nm and 380nm respectively, keeping the output power consistent with the output power of the laser in the step 1.2, and then referring to the step 1.2, carrying out comparison on the sample ST1Collecting spectra, and recording the spectral signals
Figure BDA0002772135030000062
And
Figure BDA0002772135030000063
step 1.4, in order to eliminate the spectral intensity change caused by factors such as laser energy, etc., the
Figure BDA0002772135030000064
And
Figure BDA0002772135030000065
normalization is performed, i.e. for each spectrum, it willThe difference of the minimum spectral intensity in the spectrum subtracted by the spectral intensity of each wavelength in the spectrum is divided by the difference of the maximum spectral intensity and the minimum spectral intensity in the spectrum, and the processed spectra are respectively recorded as
Figure BDA0002772135030000066
And
Figure BDA0002772135030000067
step 1.5, because the fluorescence spectrum is irrelevant to the wavelength of an excitation light source, the fluorescence spectrum peak and the shape of the fluorescence spectrum peak cannot change under different excitation wavelengths, and the Raman spectrum peak can change along with the excitation wavelength; therefore, the spectral signals obtained under different excitation wavelengths are subtracted, so that the fluorescence signal is removed and the raman signal is retained; will spectrum
Figure BDA0002772135030000068
Minus
Figure BDA0002772135030000069
The portion of the subtracted spectrum with a negative value was set to 0 and the processed spectrum was recorded as
Figure BDA00027721350300000610
Will spectrum
Figure BDA00027721350300000611
Minus
Figure BDA00027721350300000612
The portion of the subtracted spectrum with a negative value was set to 0 and the processed spectrum was recorded as
Figure BDA00027721350300000613
Step 1.6, as the excitation wavelength increases, the peak wavelength of the generated Raman spectrum also increases, possibly resulting in spectrum in
Figure BDA00027721350300000614
A certain Raman spectrum peak inAnd spectrum of light
Figure BDA00027721350300000615
The other raman spectrum peak in (a) coincides or partially coincides, resulting in a processed spectrum
Figure BDA00027721350300000616
The Raman spectrum peak can be lost or the Raman spectrum peak intensity can be reduced; to obtain all the Raman spectrum peaks of the sample, the spectrum is analyzed
Figure BDA00027721350300000617
And
Figure BDA00027721350300000618
overlapping and comparing, taking the spectrum with larger value of each wavelength to form the processed spectrum and recording the spectrum
Figure BDA00027721350300000619
Namely for
Figure BDA00027721350300000620
Of which the spectral value is taken as a spectrum
Figure BDA00027721350300000621
And
Figure BDA00027721350300000622
the larger of (a); will be provided with
Figure BDA00027721350300000623
As a sample ST1(ii) a Raman spectrum of;
step 1.7, spectra
Figure BDA00027721350300000624
Minus
Figure BDA00027721350300000625
The subtracted spectra are recorded as
Figure BDA00027721350300000626
The spectrum is taken as a sample ST1The fluorescence spectrum of (a);
step 1.8, for Pterocarpus santalinus ST2,ST3,ST4,…,STnAnd Lu' S ebony sample SL1,SL2,SL3,…,SLnObtaining spectral signals under the irradiation of three laser wavelengths according to the steps 1.1-1.3, separating Raman spectrum signals and fluorescence spectrum signals of the sample according to the steps 1.4-1.7, and separating the sample ST2,ST3,ST4,…,STnRespectively, the Raman spectrum and the fluorescence spectrum of
Figure BDA0002772135030000071
And
Figure BDA0002772135030000072
sample SL1,SL2,SL3,…,SLnRespectively, the Raman spectrum and the fluorescence spectrum of
Figure BDA0002772135030000073
And
Figure BDA0002772135030000074
step 2: extracting a characteristic spectrum and establishing a discrimination model;
step 2.1, for Raman spectroscopy
Figure BDA0002772135030000075
Average it, i.e.
Figure BDA0002772135030000076
Step 2.2, for Raman spectroscopy
Figure BDA0002772135030000077
The average is determined in accordance with step 2.1, and the average spectrum is recorded
Figure BDA0002772135030000078
Step 2.3, spectrum
Figure BDA0002772135030000079
Minus
Figure BDA00027721350300000710
The subtracted spectra are recorded as
Figure BDA00027721350300000711
Step 2.4, for spectra
Figure BDA00027721350300000712
Taking 10 spectral peaks with the maximum positive value of spectral intensity and 10 spectral peaks with the maximum negative value of spectral intensity, and respectively recording the wavelengths as lambdaZ1,λZ2,λZ3,…,λZ10And λE1,λE2,λE3,…,λE10；
Step 2.5, for spectra
Figure BDA00027721350300000713
And
Figure BDA00027721350300000714
separately obtain lambdaZ1Spectral intensities at wavelengths, respectively
Figure BDA00027721350300000715
And
Figure BDA00027721350300000716
will be provided with
Figure BDA00027721350300000717
Is divided by
Figure BDA00027721350300000718
Namely, it is
Figure BDA00027721350300000719
Step 2.6, for spectra
Figure BDA00027721350300000720
And
Figure BDA00027721350300000721
separately obtain lambdaZ2,λZ3,λZ4,…,λZ10Spectral intensity at wavelength and corresponding k is calculated with reference to step 2.5λValues, respectively, are
Figure BDA00027721350300000722
Step 2.7, for spectra
Figure BDA00027721350300000723
And
Figure BDA00027721350300000724
separately obtain lambdaE1,λE2,λE3,…,λE10Spectral intensity at wavelength and corresponding k is calculated with reference to step 2.5λValues, respectively, are
Figure BDA00027721350300000725
Step 2.8, for
Figure BDA00027721350300000726
The maximum 3 values are taken, the corresponding wavelengths are taken as three characteristic wavelengths of the Raman spectrum and are respectively recorded as lambda1,λ2,λ3；
Step 2.9, for
Figure BDA00027721350300000727
Taking the minimum 3 values, and taking the corresponding wavelengths as the other three characteristic wavelengths of the Raman spectrum, which are respectively marked as lambda4,λ5,λ6；
Step 2.10, for sample ST1Raman spectrum of
Figure BDA00027721350300000728
Obtaining lambda1,λ2,λ3,λ4,λ5,λ6Characteristic spectrum of wavelength, noted
Figure BDA00027721350300000729
Step 2.11, for Raman spectroscopy
Figure BDA00027721350300000730
Separately obtaining lambda according to step 2.101,λ2,λ3,λ4,λ5,λ6Characteristic spectra of wavelengths, respectively
Figure BDA00027721350300000731
Figure BDA00027721350300000732
Figure BDA00027721350300000733
Step 2.12, for Raman spectroscopy
Figure BDA0002772135030000081
Separately obtaining lambda according to step 2.101,λ2,λ3,λ4,λ5,λ6Characteristic spectra of wavelengths, respectively
Figure BDA0002772135030000082
Figure BDA0002772135030000083
Figure BDA0002772135030000084
Step 2.13, for sample ST1,ST2,ST3,…,STnFluorescence spectrum of
Figure BDA0002772135030000085
The average is determined in accordance with step 2.1, and the average spectrum is recorded
Figure BDA0002772135030000086
Step 2.14, for fluorescence spectroscopy
Figure BDA0002772135030000087
Finding the peak with maximum spectral intensity in the wave band range of 400nm to 500nm, and recording the wavelength of the peak as lambdaF1；
Step 2.15, for sample ST1Fluorescence spectrum of
Figure BDA0002772135030000088
Obtaining lambdaF1Spectrum at wavelength, noted
Figure BDA0002772135030000089
Step 2.16, for fluorescence spectroscopy
Figure BDA00027721350300000810
And
Figure BDA00027721350300000811
obtaining lambda according to steps 2.13-2.15F1The spectra at the wavelengths, respectively
Figure BDA00027721350300000812
And
Figure BDA00027721350300000813
step 2.17, in order to effectively fuse the Raman and fluorescence characteristic spectrums of the sample and enlarge the spectrum difference of the pterocarpus santalinus and the pterocarpus lucidus, the Raman and fluorescence characteristic spectrums of the sample are adopted to construct characteristic quantities; for sample ST1Raman characteristic spectrum of
Figure BDA00027721350300000814
And fluorescence characteristic spectrum
Figure BDA00027721350300000815
Construction of 6 feature quantities
Figure BDA00027721350300000816
6 feature quantities are respectively recorded as
Figure BDA00027721350300000817
Step 2.18, for sample ST2,ST3,ST4,…,STnRaman characteristic spectrum of
Figure BDA00027721350300000818
Figure BDA00027721350300000819
And fluorescence characteristic spectrum
Figure BDA00027721350300000820
6 corresponding characteristic quantities are respectively constructed according to the step 2.17 and are respectively marked as
Figure BDA00027721350300000821
Figure BDA00027721350300000822
Step 2.19, for sample SL1,SL2,SL3,…,SLnRaman characteristic spectrum of
Figure BDA00027721350300000823
Figure BDA00027721350300000824
And fluorescence characteristic spectrum
Figure BDA0002772135030000091
6 corresponding characteristic quantities are respectively constructed according to the step 2.17 and are respectively marked as
Figure BDA0002772135030000092
Figure BDA0002772135030000093
Step 2.20, for Pterocarpus santalinus sample ST1,ST2,ST3,…,STnSetting its class value to 1; for Lu' S ebony sample SL1,SL2,SL3,…,SLnSetting its class value to-1;
step 2.21, correlating 6 characteristic quantities of the pterocarpus santalinus and the pterocarpus lucidus samples with class values thereof by adopting a multiple linear regression method, and establishing a distinguishing model of the pterocarpus santalinus and the pterocarpus lucidus, wherein the specific model is as follows: a is1*G1+a2*G2+a3*G3+a4*G4+a5*G5+a6*G6+ b, where Y is the predicted class value, G1,G2,G3,G4,G5,G6Is 6 characteristic quantities of the samples of pterocarpus santalinus and pterocarpus lucidus, a1,a2,a3,a4,a5,a6B is the intercept, which is the coefficient of the discriminant model;
and step 3: rapidly identifying the rosewood variety;
step 3.1, obtaining one sample to be detected in the two types of redwood varieties and recording the sample as Sp；
Step 3.2, with reference to steps 1.2 and 1.3, a sample S is obtainedpThe spectra under irradiation of 3 laser wavelengths are respectively recorded as
Figure BDA0002772135030000094
And
Figure BDA0002772135030000095
step 3.3, for spectra
Figure BDA0002772135030000096
And
Figure BDA0002772135030000097
performing normalization processing according to the step 1.4, performing separation of Raman spectrum and fluorescence spectrum information according to the steps 1.5-1.8, and recording the separated Raman spectrum and fluorescence spectrum as
Figure BDA0002772135030000098
And
Figure BDA0002772135030000099
step 3.4, for Raman spectroscopy
Figure BDA00027721350300000910
Referring to step 2.10, λ is obtained1,λ2,λ3,λ4,λ5,λ6Characteristic spectrum of wavelength, noted
Figure BDA00027721350300000911
Step 3.5, for fluorescence Spectroscopy
Figure BDA00027721350300000912
With reference to step 2.15, λ is obtainedF1Characteristic spectrum at wavelength, noted
Figure BDA00027721350300000913
Step 3.6, for the characteristic spectrum
Figure BDA00027721350300000914
And
Figure BDA00027721350300000915
6 characteristic quantities are constructed according to the step 2.17 and are respectively marked as
Figure BDA00027721350300000916
Step 3.7, 6 characteristic quantities
Figure BDA00027721350300000917
Substituting the judgment model established in the step 2 to obtain a corresponding Y value; when the Y value is larger than 0, judging that the sample S to be detected ispIs Pterocarpus santalinus, and when the Y value is less than or equal to 0, the sample S to be detected is judgedpIs Lu's ebony, thereby realizing the rapid identification of the pterocarpus santalinus and the Lu's ebony.

Claims (1)

1. A rapid identification method for rosewood varieties is characterized by comprising the following steps:
step 1: separating signals of the Raman spectrum and the fluorescence spectrum;
step 1.1, respectively obtaining n samples of pterocarpus santalinus and pterocarpus lucidus, and respectively recording the n samples as ST1,ST2,ST3,…,STn，SL1,SL2,SL3,…,SLn；
Step 1.2, for pterocarpus santalinus sample ST1Placing an integrating sphere on the sample ST1The output wavelength of the tunable laser is adjusted to 340nm, and then irradiated to the sample S through the window of the integrating sphereT1The generated spectrum signal is collected by an integrating sphere and is emitted from another window, and then enters a CCD spectrometer through an optical fiber, and the spectrum signal is recorded as
Figure FDA0002772135020000011
Step 1.3, adjusting the output wavelengths of the tunable laser to 360nm and 380nm respectively, keeping the output power consistent with the output power of the laser in the step 1.2, and then referring to the step 1.2, carrying out comparison on the sample ST1Collecting spectra, and recording the spectral signals
Figure FDA0002772135020000012
And
Figure FDA0002772135020000013
step 1.4, in order to eliminate the spectral intensity change caused by factors such as laser energy, etc., the
Figure FDA0002772135020000014
And
Figure FDA0002772135020000015
normalization is performed, i.e. for each spectrum, the difference between the spectral intensity of each wavelength in the spectrum minus the minimum spectral intensity in the spectrum is divided by the difference between the maximum spectral intensity and the minimum spectral intensity in the spectrum, and the processed spectra are respectively recorded as
Figure FDA0002772135020000016
And
Figure FDA0002772135020000017
1.5, subtracting the spectral signals acquired under different excitation wavelengths, and removing the fluorescence signals and reserving the Raman signals; will spectrum
Figure FDA0002772135020000018
Minus
Figure FDA0002772135020000019
The portion of the subtracted spectrum with a negative value was set to 0 and the processed spectrum was recorded as
Figure FDA00027721350200000110
Will spectrum
Figure FDA00027721350200000111
Minus
Figure FDA00027721350200000112
The portion of the subtracted spectrum with a negative value was set to 0 and the processed spectrum was recorded as
Figure FDA00027721350200000113
Step 1.6, to reduce the intensity of Raman spectral peaksLoss of signal, spectral
Figure FDA00027721350200000114
And
Figure FDA00027721350200000115
overlapping and comparing, taking the spectrum with larger value of each wavelength to form the processed spectrum and recording the spectrum
Figure FDA00027721350200000116
Namely for
Figure FDA00027721350200000117
Of which the spectral value is taken as a spectrum
Figure FDA00027721350200000118
And
Figure FDA00027721350200000119
the larger of (a); will be provided with
Figure FDA00027721350200000120
As a sample ST1(ii) a Raman spectrum of;
step 1.7, spectra
Figure FDA00027721350200000121
Minus
Figure FDA00027721350200000122
The subtracted spectra are recorded as
Figure FDA00027721350200000123
The spectrum is taken as a sample ST1The fluorescence spectrum of (a);
step 1.8, for Pterocarpus santalinus sample ST2,ST3,ST4,…,STnAnd Lu' S ebony sample SL1,SL2,SL3,…,SLnRoot of Chinese ginsengSpectrum signals under the irradiation of three laser wavelengths are obtained according to the steps 1.1-1.3, the Raman spectrum and fluorescence spectrum signals of the sample are separated according to the steps 1.4-1.7, and the separated sample ST2,ST3,ST4,…,STnRespectively, the Raman spectrum and the fluorescence spectrum of
Figure FDA00027721350200000124
And
Figure FDA00027721350200000125
sample SL1,SL2,SL3,…,SLnRespectively, the Raman spectrum and the fluorescence spectrum of
Figure FDA0002772135020000021
And
Figure FDA0002772135020000022
step 2: extracting a characteristic spectrum and establishing a discrimination model;
step 2.1, for Raman spectroscopy
Figure FDA0002772135020000023
Average it, i.e.
Figure FDA0002772135020000024
Step 2.2, for Raman spectroscopy
Figure FDA0002772135020000025
The average is determined in accordance with step 2.1, and the average spectrum is recorded
Figure FDA0002772135020000026
Step 2.3, spectrum
Figure FDA0002772135020000027
Minus
Figure FDA0002772135020000028
The subtracted spectra are recorded as
Figure FDA0002772135020000029
Step 2.4, for spectra
Figure FDA00027721350200000210
Taking 10 spectral peaks with the maximum positive value of spectral intensity and 10 spectral peaks with the maximum negative value of spectral intensity, and respectively recording the wavelengths as lambdaZ1,λZ2,λZ3,…,λZ10And λE1,λE2,λE3,…,λE10；
Step 2.5, for spectra
Figure FDA00027721350200000211
And
Figure FDA00027721350200000212
separately obtain lambdaZ1Spectral intensities at wavelengths, respectively
Figure FDA00027721350200000213
And
Figure FDA00027721350200000214
will be provided with
Figure FDA00027721350200000215
Is divided by
Figure FDA00027721350200000216
Namely, it is
Figure FDA00027721350200000217
Step 2.6, for spectra
Figure FDA00027721350200000218
And
Figure FDA00027721350200000219
separately obtain lambdaZ2,λZ3,λZ4,…,λZ10Spectral intensity at wavelength and corresponding k is calculated with reference to step 2.5λValues, respectively, are
Figure FDA00027721350200000220
Step 2.7, for spectra
Figure FDA00027721350200000221
And
Figure FDA00027721350200000222
separately obtain lambdaE1,λE2,λE3,…,λE10Spectral intensity at wavelength and corresponding k is calculated with reference to step 2.5λValues, respectively, are
Figure FDA00027721350200000223
Step 2.8, for
Figure FDA00027721350200000224
The maximum 3 values are taken, the corresponding wavelengths are taken as three characteristic wavelengths of the Raman spectrum and are respectively recorded as lambda1,λ2,λ3；
Step 2.9, for
Figure FDA00027721350200000225
Taking the minimum 3 values, and taking the corresponding wavelengths as the other three characteristic wavelengths of the Raman spectrum, which are respectively marked as lambda4,λ5,λ6；
Step 2.10, for sample ST1Raman spectrum of
Figure FDA00027721350200000226
Obtaining lambda1,λ2,λ3,λ4,λ5,λ6Characteristic spectrum of wavelength, noted
Figure FDA00027721350200000227
Step 2.11, for Raman spectroscopy
Figure FDA00027721350200000228
Separately obtaining lambda according to step 2.101,λ2,λ3,λ4,λ5,λ6Characteristic spectra of wavelengths, respectively
Figure FDA00027721350200000229
Figure FDA0002772135020000031
Figure FDA0002772135020000032
Step 2.12, for Raman spectroscopy
Figure FDA0002772135020000033
Separately obtaining lambda according to step 2.101,λ2,λ3,λ4,λ5,λ6Characteristic spectra of wavelengths, respectively
Figure FDA0002772135020000034
Figure FDA0002772135020000035
Figure FDA0002772135020000036
Step 2.13, for sample ST1,ST2,ST3,…,STnFluorescence spectrum of
Figure FDA0002772135020000037
The average is determined in accordance with step 2.1, and the average spectrum is recorded
Figure FDA0002772135020000038
Step 2.14, for fluorescence spectroscopy
Figure FDA0002772135020000039
Finding the peak with maximum spectral intensity in the wave band range of 400nm to 500nm, and recording the wavelength of the peak as lambdaF1；
Step 2.15, for sample ST1Fluorescence spectrum of
Figure FDA00027721350200000310
Obtaining lambdaF1Spectrum at wavelength, noted
Figure FDA00027721350200000311
Step 2.16, for fluorescence spectroscopy
Figure FDA00027721350200000312
And
Figure FDA00027721350200000313
obtaining lambda according to steps 2.13-2.15F1The spectra at the wavelengths, respectively
Figure FDA00027721350200000314
And
Figure FDA00027721350200000315
step 2.17, in order to effectively fuse the Raman and fluorescence characteristic spectrums of the sample and enlarge the spectrum difference of the pterocarpus santalinus and the pterocarpus lucidus, the Raman and fluorescence of the sample are adoptedConstructing characteristic quantity by using the optical characteristic spectrum; for sample ST1Raman characteristic spectrum of
Figure FDA00027721350200000316
And fluorescence characteristic spectrum
Figure FDA00027721350200000317
Construction of 6 feature quantities
Figure FDA00027721350200000318
6 feature quantities are respectively recorded as
Figure FDA00027721350200000319
Step 2.18, for sample ST2,ST3,ST4,…,STnRaman characteristic spectrum of
Figure FDA00027721350200000320
Figure FDA00027721350200000321
And fluorescence characteristic spectrum
Figure FDA0002772135020000041
6 corresponding characteristic quantities are respectively constructed according to the step 2.17 and are respectively marked as
Figure FDA0002772135020000042
Figure FDA0002772135020000043
Step 2.19, for sample SL1,SL2,SL3,…,SLnRaman characteristic spectrum of
Figure FDA0002772135020000044
Figure FDA0002772135020000045
And fluorescence characteristic spectrum
Figure FDA0002772135020000046
6 corresponding characteristic quantities are respectively constructed according to the step 2.17 and are respectively marked as
Figure FDA0002772135020000047
Figure FDA0002772135020000048
Step 2.20, for Pterocarpus santalinus sample ST1,ST2,ST3,…,STnSetting its class value to 1; for Lu' S ebony sample SL1,SL2,SL3,…,SLnSetting its class value to-1;
step 2.21, correlating 6 characteristic quantities of the pterocarpus santalinus and the pterocarpus lucidus samples with class values thereof by adopting a multiple linear regression method, and establishing a distinguishing model of the pterocarpus santalinus and the pterocarpus lucidus, wherein the specific model is as follows: a is1*G1+a2*G2+a3*G3+a4*G4+a5*G5+a6*G6+ b, where Y is the predicted class value, G1,G2,G3,G4,G5,G6Is 6 characteristic quantities of the samples of pterocarpus santalinus and pterocarpus lucidus, a1,a2,a3,a4,a5,a6B is the intercept, which is the coefficient of the discriminant model;
and step 3: rapidly identifying the rosewood variety;
step 3.1, obtaining one sample to be detected in the two types of redwood varieties and recording the sample as Sp；
Step 3.2, with reference to steps 1.2 and 1.3, a sample S is obtainedpThe spectra under irradiation of 3 laser wavelengths are respectively recorded as
Figure FDA0002772135020000049
And
Figure FDA00027721350200000410
step 3.3, for spectra
Figure FDA00027721350200000411
And
Figure FDA00027721350200000412
performing normalization processing according to the step 1.4, performing separation of Raman spectrum and fluorescence spectrum information according to the steps 1.5-1.8, and recording the separated Raman spectrum and fluorescence spectrum as
Figure FDA00027721350200000413
And
Figure FDA00027721350200000414
step 3.4, for Raman spectroscopy
Figure FDA00027721350200000415
Referring to step 2.10, λ is obtained1,λ2,λ3,λ4,λ5,λ6Characteristic spectrum of wavelength, noted
Figure FDA0002772135020000051
Step 3.5, for fluorescence Spectroscopy
Figure FDA0002772135020000052
With reference to step 2.15, λ is obtainedF1Characteristic spectrum at wavelength, noted
Figure FDA0002772135020000053
Step 3.6, for the characteristic spectrum
Figure FDA0002772135020000054
And
Figure FDA0002772135020000055
6 characteristic quantities are constructed according to the step 2.17 and are respectively marked as
Figure FDA0002772135020000056
Step 3.7, 6 characteristic quantities
Figure FDA0002772135020000057
Substituting the judgment model established in the step 2 to obtain a corresponding Y value; when the Y value is larger than 0, judging that the sample S to be detected ispIs Pterocarpus santalinus, and when the Y value is less than or equal to 0, the sample S to be detected is judgedpIs Lu's ebony, thereby realizing the rapid identification of the pterocarpus santalinus and the Lu's ebony.
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