CN103364359A - Application of SIMCA pattern recognition method to near infrared spectrum recognition of medicinal material, rhubarb - Google Patents
Application of SIMCA pattern recognition method to near infrared spectrum recognition of medicinal material, rhubarb Download PDFInfo
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Abstract
The invention discloses application of an SIMCA pattern recognition method to near infrared spectrum recognition of a medicinal material, rhubarb, which relates to a method for identifying traditional Chinese medicines and in particular relates to a method for identifying the authenticity and producing area of the medicinal material, rhubarb. The identification method is characterized by comprising the following steps of: 1, treating an unknown sample; 2, scanning the sample by a near infrared transmission spectrometer; and 3, performing computational analysis on the scanning result, and judging the authenticity of the unknown sample, wherein the spectrum conditions of the near infrared transmission spectrometer are that a Spectrum 400 type FT-NIR spectrometer of Perkin-Elmer is used, that an integrating sphere diffuse reflection sampling system, a near-infrared light source and a Pbs detector are equipped, that the scanning range is 8000-4000 cm<-1>, that the resolution ratio is 4cm<-1>, that the temperature is 20 DEG C, and that the air humidity is 50 percent.
Description
Technical field:
The present invention relates to a kind of discrimination method of Chinese medicine, special standby relate to a kind of true and false of rhubarb medicinal material and the discrimination method in the place of production.
Background technology:
Rhubarb medicinal material derives from dry rhizome and the root of polygonaceae Rheum palm leaf group plant, the former plant of genuine rhubarb has three kinds, is respectively: sorrel Rheum palmatum L., the ancient especially big yellow Rheum Tanguticum Maxim.ex Balf. of Tang and Rheum officinale Rheum officinal Baill..Other has some adulterant rheum officinales to be taken as the rheum officinale use in certain areas, mainly contains four kinds: North China rheum officinale R.franzenbachii Munt., Rheum hotaoense C. Y. Cheng et C. T. Kao R.hataoense Cheng.et Kao., Radix et Rhizoma Rhei Wittrockii R.wittrochii Lundstr. and Radix Rhei emodi R.emodi Wall. etc.Genuine rhubarb has stronger catharsis effect and liver-protecting and blood fat-reducing effect, and the adulterant rheum officinale has the effect of astringing to arrest bleeding, heat clearing and inflammation relieving, discharge function a little less than.Therefore, the medicinal material evaluation is that the evaluation of the true and false and genuineness is the important step of the authenticity that guarantees rheum officinale, definite curative effect and drug safety.The discrimination method of rheum officinale and adulterant thereof comprises appearance character, microscopic features, thin-layered chromatography and infra-red sepectrometry etc.
[1]Near-infrared diffuse reflection spectrum (Near Infrared Diffuse Reflectance pectroscopy, NIRDRS) have fast, the characteristics such as harmless, original position and no consumption, be widely used for the industry fields such as petrochemical industry, agricultural, food and biological chemistry, also begun in recent years to have the research report aspect complicated Natural Medicine Analysis.
Adopt the NIRDRs method to set up the discrimination method of main producing region rhubarb medicinal material and adulterant thereof, the model for a plurality of places of production carries out the SIMCA discriminant classification simultaneously, has obtained preferably result.The SIMCA method claims again the similarity analysis method, the basic ideas that the SIMCA classification is used for quality assessment namely are that sample is sorted out by feature, make each classification represent a kind of feature, and set up respectively all kinds of principal component regression models, with each model unknown sample is carried out discriminatory analysis, can estimate its feature according to the category attribute of unknown sample.
Summary of the invention:
The invention provides a kind of discrimination method of the rhubarb medicinal material true and false, it is characterized in that, may further comprise the steps:
Wherein the spectral conditions of the described near-infrared transmission spectrometer of step 2 is as follows:
Adopt the Spectrum 400 type FT-NIR spectrometers of Perkin-Elmer company, be furnished with integrating sphere diffuse reflection sampling system, near-infrared light source, Pbs detecting device, sweep limit 8000-4000cm
-1, resolution 4cm
-1, 20 ℃ of temperature, air humidity 50%.
Wherein described in the step 1 unknown sample is processed, step is as follows:
Sample is dried to constant weight under 60 ℃, pulverize, and crosses 100 mesh sieves, and the sample powder of getting after sieving is in right amount put into the quartz specimen cup, mixes, and scans.
Wherein described in the step 3 scanning result is carried out computational analysis, method is as follows:
At first, the original spectrum of medicinal material is carried out band selection, the intercepting wavelength coverage is 7200-4000cm
-1Spectrum, again this spectrum is carried out noise and eliminate (Noise Weighting) and eliminating atmosphere (Atmospheric Weighting), selecting the SIMCA pattern to carry out Components analysis differentiates, calculate each sample class distance, as correct criterion, differentiate conclusion with distance value＜1.00.
The present invention also provides the discrimination method in a kind of rhubarb medicinal material place of production, it is characterized in that, may further comprise the steps:
Wherein the spectral conditions of the described near-infrared transmission spectrometer of step 2 is as follows:
Adopt the Spectrum 400 type FT-NIR spectrometers of Perkin-Elmer company, be furnished with integrating sphere diffuse reflection sampling system, near-infrared light source, Pbs detecting device, sweep limit 8000-4000cm
-1, resolution 4cm
-1, 20 ℃ of temperature, air humidity 50%.
Wherein, described in the step 1 unknown sample is processed, step is as follows:
Sample is dried to constant weight under 60 ℃, pulverize, and crosses 100 mesh sieves, and the sample powder of getting after sieving is in right amount put into the quartz specimen cup, mixes, and scans.
Wherein, described in the step 3 scanning result is carried out computational analysis, method is as follows:
At first, the original spectrum of medicinal material is carried out band selection, the intercepting wavelength coverage is 7200-4000cm
-1Spectrum, again this spectrum is carried out noise and eliminate (Noise Weighting), eliminating atmosphere (Atmospheric Weighting) and polynary scatter correction (MSC), selecting the SIMCA pattern to carry out Components analysis differentiates, calculate each sample class distance, as correct criterion, differentiate conclusion with distance value＜1.00.
The present invention also comprises a kind of computation model method for building up based on NIRDRs technology and SIMCA pattern, it is characterized in that, may further comprise the steps:
Rhubarb medicinal material and adulterant thereof are collected in the ground such as Sichuan, Qinghai, Gansu, and all samples is identified the true and false through professor Li Tianxiang of Tianjin University Of Traditional Chinese Medicine, and wherein the certified products sample is 52,25 in adulterant sample,
Wherein, spectral conditions is as follows: adopt the Spectrum 400 type FT-NIR spectrometers of Perkin-Elmer company, be furnished with integrating sphere diffuse reflection sampling system, near-infrared light source, Pbs detecting device, sweep limit 8000-4000cm
-1, resolution 4cm
-1, 20 ℃ of temperature, air humidity 50%,
The present invention also comprises a kind of computation model method for building up based on NIRDRs technology and SIMCA pattern, it is characterized in that, may further comprise the steps:
Method of the present invention obtains through following experiment screening, and experimentation is as follows
1 materials and methods
1.1 sample source and method
Rhubarb medicinal material and adulterant thereof are collected in the ground such as Sichuan, Qinghai, Gansu, and all samples is identified the true and false through professor Li Tianxiang of Tianjin University Of Traditional Chinese Medicine, and wherein the certified products sample is 52,25 in adulterant sample, and sample message sees Table 1-1.All samples is dried to constant weight under 60 ℃, pulverize, and crosses 100 mesh sieves, and the sample powder of getting after sieving is in right amount put into the quartz specimen cup, mixes, and scans by 1.2 lower instrument conditions.
Collection and the composition of table 1-1 test sample
1.2 instrument and equipment
Adopt the Spectrum 400 type FT-NIR spectrometers of Perkin-Elmer company, be furnished with integrating sphere diffuse reflection sampling system, near-infrared light source, Pbs detecting device, sweep limit 8000-4000cm
-1, resolution 4cm
-1, 20 ℃ of temperature, air humidity 50%, each sample repeats 32 times, is averaging spectrum (seeing Fig. 1).Adopt the SIMCA pattern recognition analysis assembly of the AssureID Methed Explorer software of Perkin-Elmer company to carry out spectral manipulation and analysis.
1.3 analytical approach
Pattern-recognition generally is that the principle according to " things of a kind come together, people of a mind fall into the same group " is carried out the classification of sample, and currently used method mainly contains mahalanobis distance method, linear learning machine method, K-averaging method, and the method such as SIMCA.SIMCA (Soft Independent Modeling of Class Analogy, independent soft type method) be a kind of statistical method based on constituent analysis (PCA), by types of objects is carried out respectively principal component analysis (PCA), then sound out on this basis the fitting degree of unknown sample and known sample model, solve the attaching problem of unknown sample, the method is widely used in the qualitative analysis of spectrum, chromatogram.Because rheum officinale near infrared spectrum characteristic variable number is more, this test intended selects the SIMCA mode identification method that rhubarb medicinal material is carried out qualitative investigation.
In this research, the SIMCA pattern-recongnition method at first carries out principal component analysis (PCA) for the spectroscopic data matrix of each class sample, under the major component space, calculates in all kinds of classes, mahalanobis distance between class, set up discrimination model, and come the quality of evaluation model with discrimination and reject rate; Then according to this model unknown sample is classified, the class model of namely souning out this unknown sample and each sample respectively carries out match, to determine the classification of unknown sample.
2 results and analysis
2.1 the pre-service of positive pseudo-model and foundation
2.1.1 the selection of bands of a spectrum
Although all spectral coverage data of recording of nir instrument may be used to modeling theoretically, but the characteristic information that different spectral coverage embodies has larger difference, in actual modeling process, often need the spectrum spectral coverage is screened, to avoid introducing too much redundant information, improve model performance.This experiment is thought and is chosen 7200-4000cm through the test of many times evaluation
-1The time near infrared spectrum show obvious fluctuation, the model of foundation can comprehensively react the feature of collection of illustrative plates, and reaches preferably separating effect.
2.1.2 spectrum pre-service
In the process of NIRDRS spectra collection, because the impact of the factors such as sample particle size, homogeneity, instrument state tends to cause the spectrum baseline to produce skew or drift.For from the spectrum measuring data, fully extracting effective information, reduce various errors to the impact of model, therefore original spectrum need to pass through pre-service before the modeling, comprise that the S-G convolution is level and smooth, single order and second order differentiate (1st or 2nd derivative), polynary scatter correction (MSC, Multiplicative Scatter Correction), standardization (SNV, Standard Normal Variate) etc. method is processed, to eliminate the impact of the factors such as high frequency noise, baseline wander, sample concentration and light scattering.This test is adopted respectively former spectrum and is set up model through pretreated data of method such as Noise Weighting, Atmospheric Weighting, MSC, SNV, first order derivative, second derivatives, be that index is estimated model with model to the prediction accuracy of unknown sample technique, find by contrast, original spectrum is eliminated (Noise Weighting) and eliminating atmosphere (Atmospheric Weighting) through noise, and the SIMCA model performance is optimum.Pretreated spectrum is seen Fig. 2.
2.1.3 the foundation of positive pseudo-model
23 of 49 of random choose genuine rhubarbs, adulterant rheum officinale are used for setting up model as training set from 77 rheum officinale samples, and remaining 3 certified products and 2 adulterant rheum officinales are used as forecast set and carry out modelling verification.Use the SIMCA classification that above-mentioned sample is set up the positive pseudo-model of differentiating, discrimination and the reject rate of modeling result after with closs validation represents, sees Table 1-2.And calculate in the class of each sample, mahalanobis distance between class, classify with the form of scatter diagram, see Fig. 3.
Discrimination and the False Rate result (PLSCONFM translation) of table 1-2 rhubarb medicinal material true and false model
The class mahalanobis distance scatter diagram of the positive pseudo-model of Fig. 1-3 rhubarb medicinal material
Result's demonstration, the discrimination of positive pseudo-modeling collection all reaches 100%, and reject rate is respectively 82% and 98%, has good recognition capability, can substantially determine the positive puppet of medicinal material, can be used for differentiating the positive puppet of unknown medicinal material.In addition, from scatter diagram, can find out very intuitively the sample characteristics difference of certified products model and adulterant model.
2.1.4 known sample aligns the checking of pseudo-model
In order further to investigate the difference between the unknown sample, and to the proved that predicts the outcome of this model, this paper has carried out the SIMCA pattern recognition analysis to 5 forecast set samples, estimates as inter-object distance with horse formula distance, and acquired results table 1-3 as seen.Each unknown sample presents different category features, with distance value＜1.00 as correct criterion, 80% forecast set sample can be by belonging to the identification of model originally, and can not be by the pattern-recognition of other models, illustrate that the SIMCA disaggregated model can carry out Real-Time Evaluation to the near infrared smooth bark information of arbitrary unknown sample substantially, and provide directly perceived, the index that quantizes in order to differentiate whether medicinal material certified products of this sample, being applicable to obviously development and application becomes a kind of effectively quick discriminating means.
The positive pseudo-model forecast set the result of table 1-3 rhubarb medicinal material
2.2 pre-service and the foundation of model are differentiated in the place of production
2.2.1 the selection of bands of a spectrum
This experiment is thought and is chosen 7800-4000cm through the test of many times evaluation
-1The model of Shi Jianli can comprehensively react the feature of collection of illustrative plates, and reaches preferably separating effect.
2.2.2 spectrum pre-service
This experiment is adopted respectively former spectrum and is set up model through pretreated metric data of method such as MSC, SNV, single order differential, second-order differentials, be that index is estimated model with model to the prediction accuracy of unknown sample technique, find by contrast, original spectrum after noise is eliminated (Noise Weighting) and eliminating atmosphere (Atmospheric Weighting) and polynary scatter correction (MSC), SIMCA disaggregated model best performance.Pretreated spectrum is seen Fig. 4.
2.2.3 the foundation of place of production model
24 of 9 of random choose Qinghai rheum officinales, 14 of Gansu rheum officinales, Sichuan rheum officinale are used for setting up model as training set from 52 rheum officinale certified products, and all the other 3 Gansu rheum officinales, 1 Qinghai rheum officinale and 1 Sichuan rheum officinale are used as forecast set.Use the SIMCA classification that above-mentioned sample is set up the place of production and differentiate model, discrimination and the reject rate of modeling result after with closs validation represents, sees Table 1-4.Make graph discovery with front 3 principal component scores of all sample spectroscopic datas again, 3 principal component scores figure can reflect the separately situation of each place of production sample intuitively.Simultaneously, calculate in the class of each sample, mahalanobis distance between class, classify with the form of scatter diagram, see Fig. 5 to 8.
Discrimination and the reject rate result of table 1-4 rhubarb medicinal material place of production model
The result shows, the discrimination of three place of production modeling collection all reaches 100%, reject rate is then lower slightly, from Fig. 1-5, can find out, the rhubarb medicinal material in three kinds of places of production can separate arbitrarily basically, distance between the rheum officinale in Qinghai and Sichuan is relatively near, relatively far away with the rheum officinale distance in Gansu, this phenomenon can be explained with following reason: the reason that at first is processing technology, the tradition of Sichuan rheum officinale and the Qinghai rheum officinale region of gathering is more approaching, cutting to medicinal material, air-dry processing custom is comparatively similar, and mostly the Gansu rheum officinale is through section, dry in the shade, so with other medicinal material apart from each others.Can find out very intuitively the sample characteristics difference between any two models, three-dimensional plot can carry out the prediction of provenance to unknown sample especially from naked eyes, analyze easy, quick.Next is the reason of the external conditions such as geographic position, weather, soil, collecting time, the collecting season of Gansu rheum officinale is between March to April, and Sichuan, Qinghai rheum officinale need to could begin the work of gathering owing to reasons such as being located in highlands and weather after mid-April, and these are also comparatively obvious on the medical material quanlity impact.
2.2.4 known sample is to the checking of the pseudo-model in the place of production
In order further to investigate the difference between the unknown sample, and to the proved that predicts the outcome of this model, this paper has carried out the SIMCA pattern recognition analysis to 5 forecast set samples, estimates as inter-object distance with horse formula distance, and acquired results table 1-5 as seen.Forecast sample is judged as exactly, and to belong to the target rheum officinale sample height of a class together similar to him, and can open respectively with the target rheum officinale of other types, distance value is less, similarity is higher, with distance value＜1.00 as correct criterion, the rate of accuracy reached to 80% of pattern-recognition illustrates that this method of discrimination can carry out to the state of the art of arbitrary unknown sample the differentiation of real-time provenance substantially, and being applicable to development and application becomes a kind of effectively quick place of production discriminating means.
Table 1-5 rhubarb medicinal material place of production model prediction collection the result
List of references
[1] Zhou Qun, Li Jing, Liu Jun, etc. the Two-Dimensional Correlation IR Spectroscopy of authenticity of Chinese rhubarb [J] analytical chemistry, 2003,31 (9): 1058.
[2] Chen Quansheng, Zhao Jiewen, Zhang Haidong waits application [J] Food Science of .SIMCA mode identification method near infrared light spectrum discrimination tealeaves, 2006,27 (4): 186-189.
[3] Zhang Ning, Zhang Dequan, Li Shurong, etc. near infrared spectrum in conjunction with trace to the source Primary Study [J] Transactions of the Chinese Society of Agricultural Engineering in the mutton place of production of SIMCA method, 2008,24 (12): 309-312.
[4] Zhao Longlian, Zhang Luda, Li Junhui, etc. Wavelet Packet Entropy and Fisher differentiate application [J] spectroscopy and the spectral analysis in the near infrared spectroscopy discriminating Chinese herb rhubarb true and false, and 2008,28 (4): 817-820.
[5] Wang Jiajun, Wang Fan, Ma Ling waits the .SIMCA classification to be combined near infrared spectrum with the PLS algorithm and is applied to quality control [J] spectroscopy and the spectral analysis of cigarette paper, 2006,20 (10): 1858-1862.
Description of drawings:
The near-infrared diffuse reflectance original spectrum stacking diagram of all samples of Fig. 1
The positive pseudo-model modeling collection sample spectrum pretreating effect figure of Fig. 2
The class mahalanobis distance scatter diagram of the positive pseudo-model of Fig. 3 rhubarb medicinal material
Model modeling collection sample spectrum pretreating effect figure is differentiated in Fig. 4 place of production
The two-dimensional map scatter diagram of Fig. 6 rhubarb medicinal material Qinghai and Gansu class model
The two-dimensional map scatter diagram of Fig. 7 rhubarb medicinal material Qinghai and Sichuan class model
The two-dimensional map scatter diagram of Fig. 8 rhubarb medicinal material Sichuan and Gansu class model
Embodiment:
Further specify by the following examples the present invention, but not as limitation of the present invention.
Authenticity of Chinese rhubarb medicinal material discrimination method,
Scanning result carries out computational analysis, and method is as follows:
At first, the original spectrum of medicinal material is carried out band selection, the intercepting wavelength coverage is 7200-4000cm
-1Spectrum, again this spectrum is carried out noise and eliminate (Noise Weighting) and eliminating atmosphere (Atmospheric Weighting), selecting the SIMCA pattern to carry out Components analysis differentiates, calculate each sample class distance, as correct criterion, differentiate conclusion with distance value＜1.00.
The result is as follows:
Rhubarb medicinal material place of production discrimination method,
Scanning result carries out computational analysis, and method is as follows:
At first, the original spectrum of medicinal material is carried out band selection, the intercepting wavelength coverage is 7200-4000cm
-1Spectrum, again this spectrum is carried out noise and eliminate (Noise Weighting), eliminating atmosphere (Atmospheric Weighting) and polynary scatter correction (MSC), selecting the SIMCA pattern to carry out Components analysis differentiates, calculate each sample class distance, as correct criterion, differentiate conclusion with distance value＜1.00.The result is as follows:
Claims (10)
1. the discrimination method of a rhubarb medicinal material true and false is characterized in that, may further comprise the steps:
Step 1 is processed unknown sample;
Step 2 scans with the near-infrared transmission spectrometer;
Step 3 is carried out computational analysis with scanning result, judges the true and false of unknown sample;
The spectral conditions of wherein said near-infrared transmission spectrometer is as follows:
Adopt the Spectrum 400 type FT-NIR spectrometers of Perkin-Elmer company, be furnished with integrating sphere diffuse reflection sampling system, near-infrared light source, Pbs detecting device, sweep limit 8000-4000cm
-1, resolution 4cm
-1, 20 ℃ of temperature, air humidity 50%.
2. according to claim 1 discrimination method is characterized in that, described in the step 1 unknown sample is processed, step is as follows:
Sample is dried to constant weight under 60 ℃, pulverize, and crosses 100 mesh sieves, and the sample powder of getting after sieving is in right amount put into the quartz specimen cup, mixes, and scans.
3. according to claim 1 discrimination method is characterized in that, described in the step 3 scanning result is carried out computational analysis, method is as follows:
At first, the original spectrum of medicinal material is carried out band selection, the intercepting wavelength coverage is 7200-4000cm
-1Spectrum, again this spectrum is carried out that noise is eliminated and eliminating atmosphere, select the SIMCA pattern to carry out Components analysis and differentiate, calculate each sample class distance, as correct criterion, differentiate conclusion with distance value＜1.00.
4. the discrimination method in a rhubarb medicinal material place of production is characterized in that, may further comprise the steps:
Step 1 is processed unknown sample;
Step 2 scans with the near-infrared transmission spectrometer;
Step 3 is carried out computational analysis with scanning result, judges the source of unknown sample;
Wherein the spectral conditions of the described near-infrared transmission spectrometer of step 2 is as follows:
Adopt the Spectrum 400 type FT-NIR spectrometers of Perkin-Elmer company, be furnished with integrating sphere diffuse reflection sampling system, near-infrared light source, Pbs detecting device, sweep limit 8000-4000cm
-1, resolution 4cm
-1, 20 ℃ of temperature, air humidity 50%.
5. according to claim 4 discrimination method is characterized in that, described in the step 1 unknown sample is processed, step is as follows:
Sample is dried to constant weight under 60 ℃, pulverize, and crosses 100 mesh sieves, and the sample powder of getting after sieving is in right amount put into the quartz specimen cup, mixes, and scans.
6. according to claim 4 discrimination method is characterized in that, described in the step 3 scanning result is carried out computational analysis, method is as follows:
At first, the original spectrum of medicinal material is carried out band selection, the intercepting wavelength coverage is 7200-4000cm
-1Spectrum, again this spectrum is carried out noise elimination, eliminating atmosphere and polynary scatter correction, select the SIMCA pattern to carry out Components analysis and differentiate, calculate each sample class distance, as correct criterion, differentiate conclusion with distance value＜1.00.
7. the computation model method for building up based on NIRDRs technology and SIMCA pattern that is used for the method for claim 1 is characterized in that, may further comprise the steps:
Step 1, sample collection
Rhubarb medicinal material and adulterant thereof are collected in the ground such as Sichuan, Qinghai, Gansu, and wherein the certified products sample is 52,25 in adulterant sample;
Step 2, all samples is dried to constant weight under 60 ℃, pulverize, and crosses 100 mesh sieves, and the sample powder of getting after sieving is in right amount put into the quartz specimen cup, mixes, and scans, and each sample repeats 32 times, is averaging spectrum.Wherein, spectral conditions is as follows: adopt the Spectrum 400 type FT-NIR spectrometers of Perkin-Elmer company, be furnished with integrating sphere diffuse reflection sampling system, near-infrared light source, Pbs detecting device, sweep limit 8000-4000cm
-1, resolution 4cm
-1, 20 ℃ of temperature, air humidity 50%,
Step 3 adopts the SIMCA pattern recognition analysis assembly of the AssurelD Methed Explorer software of Perkin-Elmer company to carry out spectral manipulation and analysis; 23 of 49 of random choose genuine rhubarbs, adulterant rheum officinale are used for setting up model as training set from 77 rheum officinale samples, remaining 3 certified products and 2 adulterant rheum officinales are used as forecast set and carry out modelling verification, use the SIMCA classification that above-mentioned sample is set up the positive pseudo-model of differentiating, discrimination and the reject rate of modeling result after with closs validation represents, and calculate in the class of each sample, mahalanobis distance between class, classify with the form of scatter diagram.
8. the computation model method for building up based on NIRDRs technology and SIMCA pattern that is used for the method for claim 4 is characterized in that, may further comprise the steps:
Step 1, sample collection
24 of 9 of random choose Qinghai rheum officinales, 14 of Gansu rheum officinales, Sichuan rheum officinale are used for setting up model as training set from 52 rheum officinale certified products, and all the other 3 Gansu rheum officinales, 1 Qinghai rheum officinale and 1 Sichuan rheum officinale are used as forecast set;
Step 2, all samples is dried to constant weight under 60 ℃, pulverize, and crosses 100 mesh sieves, and the sample powder of getting after sieving is in right amount put into the quartz specimen cup, mixes, and scans, and each sample repeats 32 times, is averaging spectrum;
Step 3, use the SIMCA classification that above-mentioned sample is set up the place of production and differentiate model, discrimination and the reject rate of modeling result after with closs validation represents, map with front 3 principal component scores of all sample spectroscopic datas again, 3 principal component scores figure reflect the separately situation of each place of production sample, simultaneously, calculate in the class of each sample, mahalanobis distance between class, classify with the form of scatter diagram.
9. according to claim 7 or 8 any one methods, it is characterized in that, comprise original spectrum is carried out pre-service, reduce various errors to the impact of model.
10. according to claim 7 or 8 any one methods, it is characterized in that, comprise and choose 7200-4000cm
-1The time near infrared spectrum, the model of foundation can comprehensively react the feature of collection of illustrative plates, and reaches preferably separating effect.
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