CN112461809B - Rapid identification method for rosewood varieties - Google Patents
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- 235000009984 Pterocarpus indicus Nutrition 0.000 title claims abstract description 48
- 238000000034 method Methods 0.000 title claims abstract description 18
- 241000533793 Tipuana tipu Species 0.000 title 1
- 238000001228 spectrum Methods 0.000 claims abstract description 164
- 238000001237 Raman spectrum Methods 0.000 claims abstract description 52
- 241000750718 Pterocarpus santalinus Species 0.000 claims abstract description 50
- 244000086363 Pterocarpus indicus Species 0.000 claims abstract description 42
- 238000002189 fluorescence spectrum Methods 0.000 claims abstract description 42
- 238000001069 Raman spectroscopy Methods 0.000 claims abstract description 36
- 235000003385 Diospyros ebenum Nutrition 0.000 claims abstract description 15
- 241000792913 Ebenaceae Species 0.000 claims abstract description 15
- 238000012417 linear regression Methods 0.000 claims abstract description 4
- 238000000926 separation method Methods 0.000 claims abstract description 4
- 230000003595 spectral effect Effects 0.000 claims description 46
- 230000005284 excitation Effects 0.000 claims description 12
- 238000001506 fluorescence spectroscopy Methods 0.000 claims description 9
- 238000010606 normalization Methods 0.000 claims description 6
- 240000005020 Acaciella glauca Species 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 3
- 239000013307 optical fiber Substances 0.000 claims description 3
- 235000003499 redwood Nutrition 0.000 claims description 3
- 230000003287 optical effect Effects 0.000 claims description 2
- 241001446503 Pterocarpus Species 0.000 claims 5
- 235000008632 Santalum album Nutrition 0.000 abstract description 6
- 239000002023 wood Substances 0.000 abstract description 6
- 238000005516 engineering process Methods 0.000 abstract description 3
- 235000011470 Adenanthera pavonina Nutrition 0.000 abstract description 2
- 238000004458 analytical method Methods 0.000 abstract description 2
- 238000001499 laser induced fluorescence spectroscopy Methods 0.000 abstract description 2
- 241000221035 Santalaceae Species 0.000 abstract 2
- 238000009659 non-destructive testing Methods 0.000 abstract 1
- 239000000126 substance Substances 0.000 description 5
- 240000000513 Santalum album Species 0.000 description 4
- 244000285940 beete Species 0.000 description 2
- 235000011222 chang cao shi Nutrition 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000000717 retained effect Effects 0.000 description 2
- 240000001606 Adenanthera pavonina Species 0.000 description 1
- 244000037364 Cinnamomum aromaticum Species 0.000 description 1
- 235000014489 Cinnamomum aromaticum Nutrition 0.000 description 1
- 244000168525 Croton tiglium Species 0.000 description 1
- 235000011511 Diospyros Nutrition 0.000 description 1
- 244000236655 Diospyros kaki Species 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
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- 230000001678 irradiating effect Effects 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
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- 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
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- 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
- 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
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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
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
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 signalsAnd
step 1.4, in order to eliminate the spectral intensity change caused by factors such as laser energy, etc., theAndnormalization 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 asAnd
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 spectrumMinusThe portion of the subtracted spectrum with a negative value was set to 0 and the processed spectrum was recorded asWill spectrumMinusThe portion of the subtracted spectrum with a negative value was set to 0 and the processed spectrum was recorded as
Step 1.6, as the excitation wavelength increases, the peak wavelength of the generated Raman spectrum also increases, possibly resulting in spectrum inA certain Raman spectrum peak in (1) and spectrumThe other raman spectrum peak in (a) coincides or partially coincides, resulting in a processed spectrumThe 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 analyzedAndoverlapping and comparing, taking the spectrum with larger value of each wavelength to form the processed spectrum and recording the spectrumNamely forOf which the spectral value is taken as a spectrumAndthe larger of (a); will be provided withAs a sample ST1(ii) a Raman spectrum of;
step 1.7, spectraMinusThe subtracted spectra are recorded asThe 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 ofAndsample SL1,SL2,SL3,…,SLnRespectively, the Raman spectrum and the fluorescence spectrum ofAnd
step 2: extracting a characteristic spectrum and establishing a discrimination model;
Step 2.2, for Raman spectroscopyThe average is determined in accordance with step 2.1, and the average spectrum is recorded
Step 2.4, for spectraTaking 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 spectraAndseparately obtain lambdaZ1Spectral intensities at wavelengths, respectivelyAndwill be provided withIs divided byNamely, it is
Step 2.6, for spectraAndseparately obtain lambdaZ2,λZ3,λZ4,…,λZ10Spectral intensity at wavelength and corresponding k is calculated with reference to step 2.5λValues, respectively, are
Step 2.7, for spectraAndseparately obtain lambdaE1,λE2,λE3,…,λE10Spectral intensity at wavelength and corresponding k is calculated with reference to step 2.5λValues, respectively, are
Step 2.8, forThe 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, forTaking 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 ofObtaining lambda1,λ2,λ3,λ4,λ5,λ6Characteristic spectrum of wavelength, noted
Step 2.11, for Raman spectroscopySeparately obtaining lambda according to step 2.101,λ2,λ3,λ4,λ5,λ6Characteristic spectra of wavelengths, respectively
Step 2.12, for Raman spectroscopySeparately obtaining lambda according to step 2.101,λ2,λ3,λ4,λ5,λ6Characteristic spectra of wavelengths, respectively
Step 2.13, for sample ST1,ST2,ST3,…,STnFluorescence spectrum ofThe average is determined in accordance with step 2.1, and the average spectrum is recorded
Step 2.14, for fluorescence spectroscopyFinding 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 2.16, for fluorescence spectroscopyAndobtaining lambda according to steps 2.13-2.15F1The spectra at the wavelengths, respectivelyAnd
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 ofAnd fluorescence characteristic spectrumConstruction of 6 feature quantities6 feature quantities are respectively recorded as
Step 2.18, for sample ST2,ST3,ST4,…,STnRaman characteristic spectrum of And fluorescence characteristicsOptical spectrum6 corresponding characteristic quantities are respectively constructed according to the step 2.17 and are respectively marked as
Step 2.19, for sample SL1,SL2,SL3,…,SLnRaman characteristic spectrum of And fluorescence characteristic spectrum6 corresponding characteristic quantities are respectively constructed according to the step 2.17 and are respectively marked as
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 asAnd
step 3.3, for spectraAndperforming 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 asAnd
step 3.4, for Raman spectroscopyReferring to step 2.10, λ is obtained1,λ2,λ3,λ4,λ5,λ6Characteristic spectrum of wavelength, noted
Step 3.5, for fluorescence SpectroscopyWith reference to step 2.15, λ is obtainedF1Characteristic spectrum at wavelength, noted
Step 3.6, for the characteristic spectrumAnd6 characteristic quantities are constructed according to the step 2.17 and are respectively marked asStep 3.7, 6 characteristic quantitiesSubstituting 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
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 signalsAnd
step 1.4, in order to eliminate the spectral intensity change caused by factors such as laser energy, etc., theAndnormalization 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 asAnd
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 spectrumMinusThe portion of the subtracted spectrum with a negative value was set to 0 and the processed spectrum was recorded asWill spectrumMinusThe portion of the subtracted spectrum with a negative value was set to 0 and the processed spectrum was recorded as
Step 1.6, as the excitation wavelength increases, the peak wavelength of the generated Raman spectrum also increases, possibly resulting in spectrum inA certain Raman spectrum peak inAnd spectrum of lightThe other raman spectrum peak in (a) coincides or partially coincides, resulting in a processed spectrumThe 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 analyzedAndoverlapping and comparing, taking the spectrum with larger value of each wavelength to form the processed spectrum and recording the spectrumNamely forOf which the spectral value is taken as a spectrumAndthe larger of (a); will be provided withAs a sample ST1(ii) a Raman spectrum of;
step 1.7, spectraMinusThe subtracted spectra are recorded asThe 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 ofAndsample SL1,SL2,SL3,…,SLnRespectively, the Raman spectrum and the fluorescence spectrum ofAnd
step 2: extracting a characteristic spectrum and establishing a discrimination model;
Step 2.2, for Raman spectroscopyThe average is determined in accordance with step 2.1, and the average spectrum is recorded
Step 2.4, for spectraTaking 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 spectraAndseparately obtain lambdaZ1Spectral intensities at wavelengths, respectivelyAndwill be provided withIs divided byNamely, it is
Step 2.6, for spectraAndseparately obtain lambdaZ2,λZ3,λZ4,…,λZ10Spectral intensity at wavelength and corresponding k is calculated with reference to step 2.5λValues, respectively, are
Step 2.7, for spectraAndseparately obtain lambdaE1,λE2,λE3,…,λE10Spectral intensity at wavelength and corresponding k is calculated with reference to step 2.5λValues, respectively, are
Step 2.8, forThe 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, forTaking 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 ofObtaining lambda1,λ2,λ3,λ4,λ5,λ6Characteristic spectrum of wavelength, noted
Step 2.11, for Raman spectroscopySeparately obtaining lambda according to step 2.101,λ2,λ3,λ4,λ5,λ6Characteristic spectra of wavelengths, respectively
Step 2.12, for Raman spectroscopySeparately obtaining lambda according to step 2.101,λ2,λ3,λ4,λ5,λ6Characteristic spectra of wavelengths, respectively
Step 2.13, for sample ST1,ST2,ST3,…,STnFluorescence spectrum ofThe average is determined in accordance with step 2.1, and the average spectrum is recorded
Step 2.14, for fluorescence spectroscopyFinding 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 2.16, for fluorescence spectroscopyAndobtaining lambda according to steps 2.13-2.15F1The spectra at the wavelengths, respectivelyAnd
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 ofAnd fluorescence characteristic spectrumConstruction of 6 feature quantities6 feature quantities are respectively recorded as
Step 2.18, for sample ST2,ST3,ST4,…,STnRaman characteristic spectrum of And fluorescence characteristic spectrum6 corresponding characteristic quantities are respectively constructed according to the step 2.17 and are respectively marked as
Step 2.19, for sample SL1,SL2,SL3,…,SLnRaman characteristic spectrum of And fluorescence characteristic spectrum6 corresponding characteristic quantities are respectively constructed according to the step 2.17 and are respectively marked as
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 asAnd
step 3.3, for spectraAndperforming 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 asAnd
step 3.4, for Raman spectroscopyReferring to step 2.10, λ is obtained1,λ2,λ3,λ4,λ5,λ6Characteristic spectrum of wavelength, noted
Step 3.5, for fluorescence SpectroscopyWith reference to step 2.15, λ is obtainedF1Characteristic spectrum at wavelength, noted
Step 3.6, for the characteristic spectrumAnd6 characteristic quantities are constructed according to the step 2.17 and are respectively marked as
Step 3.7, 6 characteristic quantitiesSubstituting 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
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 signalsAnd
step 1.4, in order to eliminate the spectral intensity change caused by factors such as laser energy, etc., theAndnormalization 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 asAnd
1.5, subtracting the spectral signals acquired under different excitation wavelengths, and removing the fluorescence signals and reserving the Raman signals; will spectrumMinusThe portion of the subtracted spectrum with a negative value was set to 0 and the processed spectrum was recorded asWill spectrumMinusThe portion of the subtracted spectrum with a negative value was set to 0 and the processed spectrum was recorded as
Step 1.6, to reduce the intensity of Raman spectral peaksLoss of signal, spectralAndoverlapping and comparing, taking the spectrum with larger value of each wavelength to form the processed spectrum and recording the spectrumNamely forOf which the spectral value is taken as a spectrumAndthe larger of (a); will be provided withAs a sample ST1(ii) a Raman spectrum of;
step 1.7, spectraMinusThe subtracted spectra are recorded asThe 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 ofAndsample SL1,SL2,SL3,…,SLnRespectively, the Raman spectrum and the fluorescence spectrum ofAnd
step 2: extracting a characteristic spectrum and establishing a discrimination model;
Step 2.2, for Raman spectroscopyThe average is determined in accordance with step 2.1, and the average spectrum is recorded
Step 2.4, for spectraTaking 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 spectraAndseparately obtain lambdaZ1Spectral intensities at wavelengths, respectivelyAndwill be provided withIs divided byNamely, it is
Step 2.6, for spectraAndseparately obtain lambdaZ2,λZ3,λZ4,…,λZ10Spectral intensity at wavelength and corresponding k is calculated with reference to step 2.5λValues, respectively, are
Step 2.7, for spectraAndseparately obtain lambdaE1,λE2,λE3,…,λE10Spectral intensity at wavelength and corresponding k is calculated with reference to step 2.5λValues, respectively, are
Step 2.8, forThe 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, forTaking 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 ofObtaining lambda1,λ2,λ3,λ4,λ5,λ6Characteristic spectrum of wavelength, noted
Step 2.11, for Raman spectroscopySeparately obtaining lambda according to step 2.101,λ2,λ3,λ4,λ5,λ6Characteristic spectra of wavelengths, respectively
Step 2.12, for Raman spectroscopySeparately obtaining lambda according to step 2.101,λ2,λ3,λ4,λ5,λ6Characteristic spectra of wavelengths, respectively
Step 2.13, for sample ST1,ST2,ST3,…,STnFluorescence spectrum ofThe average is determined in accordance with step 2.1, and the average spectrum is recorded
Step 2.14, for fluorescence spectroscopyFinding 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 2.16, for fluorescence spectroscopyAndobtaining lambda according to steps 2.13-2.15F1The spectra at the wavelengths, respectivelyAnd
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 ofAnd fluorescence characteristic spectrumConstruction of 6 feature quantities6 feature quantities are respectively recorded as
Step 2.18, for sample ST2,ST3,ST4,…,STnRaman characteristic spectrum of And fluorescence characteristic spectrum6 corresponding characteristic quantities are respectively constructed according to the step 2.17 and are respectively marked as
Step 2.19, for sample SL1,SL2,SL3,…,SLnRaman characteristic spectrum of And fluorescence characteristic spectrum6 corresponding characteristic quantities are respectively constructed according to the step 2.17 and are respectively marked as
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 asAnd
step 3.3, for spectraAndperforming 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 asAnd
step 3.4, for Raman spectroscopyReferring to step 2.10, λ is obtained1,λ2,λ3,λ4,λ5,λ6Characteristic spectrum of wavelength, noted
Step 3.5, for fluorescence SpectroscopyWith reference to step 2.15, λ is obtainedF1Characteristic spectrum at wavelength, noted
Step 3.6, for the characteristic spectrumAnd6 characteristic quantities are constructed according to the step 2.17 and are respectively marked as
Step 3.7, 6 characteristic quantitiesSubstituting 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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CN102012371A (en) * | 2009-09-07 | 2011-04-13 | 上海镭立激光科技有限公司 | Counterfeiting method and system utilizing combined ultraviolet laser Raman and fluorescence spectrum |
CN103820569A (en) * | 2014-03-20 | 2014-05-28 | 中国科学院上海有机化学研究所 | Special probe, primer, gene chip and method for detecting pterocarpus dalbergioides Benth. |
CN111157507A (en) * | 2020-03-05 | 2020-05-15 | 南京市产品质量监督检验院 | Method for identifying pterocarpus santalinus wood species by fluorescence spectrum |
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Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102012371A (en) * | 2009-09-07 | 2011-04-13 | 上海镭立激光科技有限公司 | Counterfeiting method and system utilizing combined ultraviolet laser Raman and fluorescence spectrum |
CN103820569A (en) * | 2014-03-20 | 2014-05-28 | 中国科学院上海有机化学研究所 | Special probe, primer, gene chip and method for detecting pterocarpus dalbergioides Benth. |
CN111157507A (en) * | 2020-03-05 | 2020-05-15 | 南京市产品质量监督检验院 | Method for identifying pterocarpus santalinus wood species by fluorescence spectrum |
Non-Patent Citations (1)
Title |
---|
五种俗称"紫檀"木材的鉴别;周旭等;《森林工程》;20130131;第29卷(第1期);第26-31、35页 * |
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