CN104713848A - Method for distinguishing radix-paeoniae-alba production places on basis of near-infrared analysis technology - Google Patents
Method for distinguishing radix-paeoniae-alba production places on basis of near-infrared analysis technology Download PDFInfo
- Publication number
- CN104713848A CN104713848A CN201510141849.5A CN201510141849A CN104713848A CN 104713848 A CN104713848 A CN 104713848A CN 201510141849 A CN201510141849 A CN 201510141849A CN 104713848 A CN104713848 A CN 104713848A
- Authority
- CN
- China
- Prior art keywords
- sample
- production
- place
- spectrum
- service
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a method for distinguishing radix-paeoniae-alba production places on the basis of a near-infrared analysis technology. The method is fast and lossless, the operation is simple and the defects of large volume and inconvenience in carrying and the like of the conventional near-infrared spectrum analysis instrument are avoided. The method comprises the following specific steps of: (1) pretreating collected radix-paeoniae-alba medicinal material samples, wherein the pretreatment method comprises the steps of drying for 24 hours under the temperature of 60 DEG C, crushing and sieving by 40 meshes; (2) at the temperature of 25 DEG C, adopting a miniature near-infrared spectrum analyzer and acquiring near-infrared spectrums of samples by a diffuse-reflection mode; (3) preprocessing the original spectrums, wherein the optimum preprocessing method is combination of SG-1st derivative and MSC; the optimum modeling spectrum band is 286-1459nm; the optimum potential-variable number is 4; (4) adopting a KS algorithm to divide samples into a calibration set and a validation set, utilizing the calibration set to establish a quantitative judgment and analysis model, and adopting the validation set to validate the model; and (5) evaluating the predicative capability of the established quantitative judgment and analysis model of the radix-paeoniae-alba production places.
Description
Technical field
The invention belongs to Chinese crude drug detection technique field, be specifically related to a kind of root of herbaceous peony place of production based on NIR technology and sentence method for distinguishing.
Background technology
The root of herbaceous peony (Paeoniae Radix Alba) is ranunculaceae plant Chinese herbaceous peony (Paeonia lactiflora Pall.) dry root, the micro-hardship of property, bitter acid, there is nourishing blood and liver, slow swollen pain relieving, astringe yin receives the effects such as sweat, is the main Chinese medicinal materials simply in national new Chinese medicine ginseng branch tuckahoe oral liquid prescription.China root of herbaceous peony is mainly distributed in the ground such as Anhui, Zhejiang, Sichuan, Shandong, and the root of herbaceous peony wherein originating in Hui nationality is the most famous with the paenoiae alba originating in Zhejiang.
Because Chinese crude drug source is complicated, white Peony Root drug effect, the difference in quality of Different sources are very large, and the fake and forged phenomenon of Chinese crude drug remains incessant after repeated prohibition in addition, adds the difficulty that medicinal material is differentiated.Therefore, need the effective ways setting up a kind of white Peony Root qualitative discrimination badly, realize the discriminatory analysis accurately of white Peony Root place of production quick nondestructive.Traditional Chinese crude drug quality control discrimination method depends on empirical method, and this method is very high to personnel qualifications, and does not meet the requirement of modern production.The discrimination methods such as microscopical characters method, physics and chemistry differential method, chromatography are also the common methods of Chinese medical herb, and the microscopical characters method scope of application is narrow, and physics and chemistry differential method, chromatography etc. have to waste time and energy, consume reagent, destroy the weak points such as sample.
Near-infrared spectral analysis technology (NIRS) is as green analytical technology a kind of in analysis field, because it is harmless, efficient and be adapted at the outstanding features such as line analysis, become one of analytical technology important in agriculture field, petrochemical industry, pharmaceutical field, Field of Tobacco.Qualitative analysis is the importance that NIRS applies at the field of Chinese medicines, has been widely used in the place of production of Chinese crude drug, True-false distinguish.
Traditional Near-Infrared Spectroscopy Instruments volume is large, price is high, is unfavorable for on-the-spot express-analysis.Miniature near infrared spectroscopy instrument has more easy, portability feature, is the development trend of near-infrared spectral analysis technology.Adopt miniature near infrared spectrometer to carry out qualitative discrimination analysis to white Peony Root, the qualitative discrimination of the quick nondestructive of white Peony Root can be realized.
Summary of the invention
The object of the invention is to set up a kind of root of herbaceous peony place of production based on NIR technology and sentence method for distinguishing.
Method of discrimination of the present invention is quick, harmless, accurate, convenient, is a kind of green analytical technology.
For solving the problem, the present invention adopts following technical scheme:
A method for distinguishing is sentenced in the root of herbaceous peony place of production based on NIR technology, comprises the steps:
(1) near infrared spectrum of the white Peony Root sample in each modeling place of production is gathered;
(2) K-S algorithm is adopted sample to be divided into calibration set and checking collection;
(3) SG5-1stderivative is carried out in conjunction with MSC pre-service to the near infrared spectrum of the white Peony Root sample of calibration set; When latent variable number is 4, build the PLSDA qualitative analysis model that the white Peony Root place of production differentiates in 1286-1459nm interval;
(4) near infrared spectrum treating unknown sample is gathered; And carry out SG5-1stderivative and MSC pre-service, obtain pre-processed spectrum;
(5) will treat that the pre-processed spectrum of unknown sample inputs the PLSDA qualitative analysis model of described white Peony Root place of production differentiation, calculate the distance of this unknown sample to each center, place of production respectively, and center, the place of production and the nearest place of production of unknown sample are judged to be that the place of production of unknown sample belongs to.
Preferably, before step (1), to the white Peony Root sample in the modeling place of production with treat that unknown sample carries out pre-service.
Preferably, described pretreated concrete operation step is: by RADIX PAEONIAE ALBA sample drying 24h under 60 DEG C of conditions, after pulverizing, crosses 40 mesh sieves.
Preferably, in step (2), calibration set and checking concentrate the ratio of sample number to be 4-5:1.
Preferably, in step (2), utilize calibration set Sample Establishing discriminatory analysis model, in this, as criterion, checking collection sample is predicted, utilizes Model Identification rate and reject rate to evaluate model result.
Preferably, instrument parameter during spectra collection is specific as follows: wavelength coverage is 908-1676nm; 100%Spectralon
tMstandard white plate is reference; Integral time: 5000 μ s; Scanning times: 100.
Preferably, in step (3), described SG5-1stderivative substitutes in conjunction with MSC pre-service SG5-1st derivative or MSC pre-service.
Preferably, in step (3), after carrying out pre-service to spectrum, iPLS method is adopted to be optimized spectrum range.
Preferably, in step (3), pre-service is carried out to spectrum and after being optimized spectrum range, adopts validation-cross method to be optimized latent variable number.
Discrimination and the reject rate of modelling verification collection and calibration set all reach 100%, show model robustness and accuracy good, meet analyze requirement.
The inventive method quick nondestructive, simple and easy to do, fit accurately and reliably, in carrying out the free of contamination qualitative discrimination analysis of quick nondestructive to white Peony Root, be conducive to carrying out quality control to the crude drug of Chinese medicine from source.
Beneficial effect: the present invention establishes a kind of harmless, more portable, Chinese crude drug qualitative discrimination method fast.By miniature near infrared spectrometer, qualitative discrimination is carried out to the white Peony Root place of production, place of production differentiation can be carried out to white Peony Root on market, achieve the quality control to crude drug in Chinese Traditional Medicine, farthest can save production cost, enhance productivity, ensure the quality of production.In addition, the present invention is compared with conventional method, there is the advantages such as method is simple, the running time is short, easy to operate, solve that traditional Near-Infrared Spectroscopy Instruments volume is large, price is high simultaneously, be unfavorable for the shortcomings such as on-the-spot express-analysis, facilitate the application of near-infrared spectrum technique in Chinese crude drug qualitative discrimination.The present invention establishes the near infrared qualitative discrimination analytical model in the white Peony Root place of production first.
Accompanying drawing explanation
Fig. 1 be the present invention under the room temperature of 25 DEG C, the near-infrared diffuse reflectance primary light spectrogram of three places of production (amount to 79 parts) the white Peony Root powdered sample adopting MicroNIR1700 type near infrared spectrometer (JDSU company of the U.S.) to gather.
Fig. 2 is the principal component scores figure of the white Peony Root sample near infrared spectrum that the present invention obtains.(note: black round dot represents calibration set sample, red spots representative checking collection sample).
Fig. 3 is the SG5-1 that the present invention obtains
stspectrogram after derivative and MSC process.
Fig. 4 is 3 Different sources sample P LSDA qualitative discrimination analytical model result figure that the present invention obtains.
Embodiment
Below in conjunction with example, the present invention is further illustrated.
The place of production of embodiment 1 white Peony Root differentiates
First choose 79 parts of root of herbaceous peony samples, wherein Heze City, Shandong Province (hz) 30 parts, Hui nationality (bz) 24 parts, Pan'an, Zhejiang (pa) 25 parts, through expert appraisal.By sample dry 24h at 60 DEG C, pulverize, cross 60 mesh sieves, be loaded in valve bag, be placed in silica gel drier for subsequent use.
Adopt the miniature near infrared spectrometer of U.S. JDSU company MicroNIR1700 to gather the near infrared spectrum of 79 parts of white Peony Root powder, each sample repeated acquisition 3 spectrum, get its averaged spectrum.Instrument parameter arranges as follows: acquisition mode is chosen as diffuse reflectance; Wavelength coverage: 908-1676nm; With 100%Spectralon
tMstandard white plate is reference; Integral time: 5000 μ s; Scanning times: 100.The near-infrared diffuse reflection spectrum adopting above-mentioned parameter collection 79 parts of White Peony Root samples to obtain is shown in Fig. 1.
K-S method is adopted the sample of three Different sources to be divided calibration set and checking collection respectively.In 79 increment product, 64 increment product are as calibration set, and 15 increment product are as checking collection sample.What division calibration set and checking collected the results are shown in Table 1.
Table 1 sample sets division information
Fig. 2 is 3 place of production sample principal component scores figure, and its orbicular spot representative checking collection sample, cross point represents calibration set sample.As can be seen from the figure checking collection sample is evenly distributed in calibration set sample, shows that sample sets divides more reasonable.
Adopt SG5-1 respectively
stderivative, MSC, SG5-1
stthe method of derivative and MSC combination carries out pre-service to original spectrum, modeling in full spectral range 908-1676nm, more each model reject rate and discrimination, selects reject rate and all large the most best preprocess method of preprocess method of discrimination.Concrete outcome is in table 2.
Table 2 different preprocessing procedures discriminatory analysis result
As shown in table 2, as employing SG5-1
stafter derivative and MSC combined treatment spectrum, institute's established model result discrimination and reject rate all reach 100%, so select the method process spectrum.SG5-1
stderivative and MSC combined treatment spectrum as shown in Figure 3.
SG5-1 is carried out to spectrum
stafter derivative and MSC combined treatment, iPLS band selection method is adopted to be optimized modeling spectrum range, investigate selected wave band modeling effect when range of variables number is 50,40,30 respectively, and with full spectrum range modeling comparison, consider discrimination, reject rate, latent variable number and wave band variable number, select best modeled wave band.Concrete outcome is as shown in table 3:
Table 3 adopts wave band modeling result selected by iPLS-30,40,50
As shown in table 3, when discontinuous variable number is 50,40,30, to build ripple effect identical for selected wave band, and it is identical with original spectrum modeling, but adopt wave band interval selected by iPLS-30 minimum, to the compression of variable and the extraction of effective information better, and its latent variable number and other are also very nearly the same, therefore select the interval modeling of wave band 1286-1459nm selected by iPLS-30 method.
Spectrum is carried out SG5-1
stderivative and MSC combined treatment, in the interval modeling of 1286-1459nm, according to validation-cross prediction residual (RMSECV)-latent variable number (LVs) graph of a relation, compare model result when latent factor number is 4,5,6 respectively, Optimization Modeling latent variable number.
Set up qualitative discrimination analytical model result according to different latent variable number, final selection latent variable number is 4 is best modeled latent variable number.
Spectrum is through SG5-1
stderivative and MSC combined treatment, when latent variable number is 4, build PLSDA the best qualitative analysis model that the white Peony Root place of production differentiates as shown in Figure 4 in the interval institute of 1286-1459nm, from model result can find out every class sample all with other class samples to good division.Model Identification rate and reject rate all reach 100%.
The PLSDA qualitative analysis model that the pre-processed spectrum of unknown sample inputs the described white Peony Root place of production and differentiates will be treated, treat described in obtaining whether unknown sample carrys out the Qualitive test result in the self-modeling place of production as shown in table 4.
In order to investigate the outer recognition capability of model further, this research adopt 10 of the place of production, Sichuan to treat the sample in unknown sample and three modeling places of production is each separately gets 10 pre-processed spectrum joining model to be identified and input the DA qualitative analysis model that the described white Peony Root place of production differentiates, treat described in obtaining whether unknown sample carrys out the Qualitive test result in the self-modeling place of production as shown in table 4:
Table 4 treats that unknown sample differentiates result
Known by table 4, this discriminatory analysis model can accurately differentiate the place of production of the sample to be discriminated in the modeling place of production, can accurately belong to the sample to be discriminated in the non-modeling place of production.This result further illustrates, and the method has very strong directive significance to setting up white Peony Root place of production model bank.
By reference to the accompanying drawings the specific embodiment of the present invention is described although above-mentioned; but not limiting the scope of the invention; on the basis of technical scheme of the present invention, those skilled in the art do not need to pay various amendment or distortion that creative work can make still within protection scope of the present invention.
Claims (9)
1. a method for distinguishing is sentenced in the root of herbaceous peony place of production based on NIR technology, it is characterized in that, comprises the steps:
(1) near infrared spectrum of the white Peony Root sample in each modeling place of production is gathered;
(2) K-S algorithm is adopted sample to be divided into calibration set and checking collection;
(3) SG5-1stderivative is carried out in conjunction with MSC pre-service to the near infrared spectrum of the white Peony Root sample of calibration set; When latent variable number is 4, build the PLSDA qualitative analysis model that the white Peony Root place of production differentiates in 1286-1459nm interval;
(4) near infrared spectrum treating unknown sample is gathered; And carry out SG5-1stderivative and MSC pre-service, obtain pre-processed spectrum;
(5) will treat that the pre-processed spectrum of unknown sample inputs the PLSDA qualitative analysis model of described white Peony Root place of production differentiation, calculate the distance of this unknown sample to each center, place of production respectively, and center, the place of production and the nearest place of production of unknown sample are judged to be that the place of production of unknown sample belongs to.
2. method according to claim 1, is characterized in that, before step (1), to the white Peony Root sample in the modeling place of production with treat that unknown sample carries out pre-service.
3. method according to claim 1, is characterized in that, described pretreated concrete operation step is: by RADIX PAEONIAE ALBA sample drying 24h under 60 DEG C of conditions, after pulverizing, crosses 40 mesh sieves.
4. method according to claim 1, is characterized in that, in step (2), calibration set and checking concentrate the ratio of sample number to be 4-5:1.
5. method according to claim 1, it is characterized in that: in step (2), utilize calibration set Sample Establishing discriminatory analysis model, in this, as criterion, checking collection sample is predicted, utilizes Model Identification rate and reject rate to evaluate model result.
6. method according to claim 1, is characterized in that: instrument parameter during spectra collection is specific as follows: wavelength coverage is 908-1676nm; 100%Spectralon
tMstandard white plate is reference; Integral time: 5000 μ s; Scanning times: 100.
7. method according to claim 1, is characterized in that: in step (3), and described SG5-1stderivative substitutes in conjunction with MSC pre-service SG5-1st derivative or MSC pre-service.
8. method according to claim 1, is characterized in that: in step (3), after carrying out pre-service, adopts iPLS method to be optimized spectrum range to spectrum.
9. method according to claim 1, is characterized in that: in step (3), carries out pre-service and after being optimized spectrum range, adopt validation-cross method to be optimized latent variable number to spectrum.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510141849.5A CN104713848A (en) | 2015-03-27 | 2015-03-27 | Method for distinguishing radix-paeoniae-alba production places on basis of near-infrared analysis technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510141849.5A CN104713848A (en) | 2015-03-27 | 2015-03-27 | Method for distinguishing radix-paeoniae-alba production places on basis of near-infrared analysis technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN104713848A true CN104713848A (en) | 2015-06-17 |
Family
ID=53413361
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510141849.5A Pending CN104713848A (en) | 2015-03-27 | 2015-03-27 | Method for distinguishing radix-paeoniae-alba production places on basis of near-infrared analysis technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104713848A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107024450A (en) * | 2017-03-27 | 2017-08-08 | 云南小宝科技有限公司 | A kind of method for differentiating different brands and hop count milk powder based on near-infrared spectrum technique |
| CN109975236A (en) * | 2019-04-04 | 2019-07-05 | 山东省农业科学院农业质量标准与检测技术研究所 | A method of identifying the honeysuckle place of production using near-infrared spectrum technique |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101961379A (en) * | 2009-07-24 | 2011-02-02 | 天津天士力现代中药资源有限公司 | Near infrared spectrum identification method for red sage roots |
| CN101961360A (en) * | 2009-07-24 | 2011-02-02 | 天津天士力现代中药资源有限公司 | Near infrared spectrum identification method for pseudo-ginseng |
| CN103776777A (en) * | 2014-01-09 | 2014-05-07 | 北京中医药大学 | Method for identifying ginsengs with different growth patterns by using near infrared spectrum technology and determining content of components in ginsengs |
-
2015
- 2015-03-27 CN CN201510141849.5A patent/CN104713848A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101961379A (en) * | 2009-07-24 | 2011-02-02 | 天津天士力现代中药资源有限公司 | Near infrared spectrum identification method for red sage roots |
| CN101961360A (en) * | 2009-07-24 | 2011-02-02 | 天津天士力现代中药资源有限公司 | Near infrared spectrum identification method for pseudo-ginseng |
| CN103776777A (en) * | 2014-01-09 | 2014-05-07 | 北京中医药大学 | Method for identifying ginsengs with different growth patterns by using near infrared spectrum technology and determining content of components in ginsengs |
Non-Patent Citations (1)
| Title |
|---|
| 杨海龙: "近红外光谱分析技术应用于山楂和党参药材质量控制的研究", 《山东大学2014年度硕士学位论文》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107024450A (en) * | 2017-03-27 | 2017-08-08 | 云南小宝科技有限公司 | A kind of method for differentiating different brands and hop count milk powder based on near-infrared spectrum technique |
| CN109975236A (en) * | 2019-04-04 | 2019-07-05 | 山东省农业科学院农业质量标准与检测技术研究所 | A method of identifying the honeysuckle place of production using near-infrared spectrum technique |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yin et al. | A review of the application of near-infrared spectroscopy to rare traditional Chinese medicine | |
| CN107677647B (en) | Method for identifying origin of traditional Chinese medicinal materials based on principal component analysis and BP neural network | |
| Mok et al. | Chemical information of Chinese medicines: A challenge to chemist | |
| CN110514611B (en) | Chemical pattern recognition method for establishing and evaluating quality of traditional Chinese medicine based on pharmacodynamic information | |
| CN102539566B (en) | Method for fast detecting content of dioscin in dioscorea zingiberensis by utilizing near infrared spectrum technology | |
| CN104730030A (en) | Method for true and false identification and place of origin judgment of codonopsis pilosula based on near infrared analysis technology | |
| CN104237060B (en) | Multi-index quick detection method of honeysuckle | |
| CN101231274B (en) | Method for rapid measuring allantoin content in yam using near infrared spectrum | |
| Liang et al. | Data fusion of laser induced breakdown spectroscopy (LIBS) and infrared spectroscopy (IR) coupled with random forest (RF) for the classification and discrimination of compound salvia miltiorrhiza | |
| CN109297929B (en) | Method for establishing quality grading of salvia miltiorrhiza decoction pieces by utilizing near infrared technology | |
| CN108663337B (en) | Method for measuring tanshinone component and application thereof | |
| CN103411906A (en) | Near infrared spectrum qualitative identification method of pearl powder and shell powder | |
| CN103033486A (en) | Method for near infrared spectrum monitoring of quality of pericarpium citri reticulatae and citrus chachiensis hortorum medicinal materials | |
| Liu et al. | Quality control strategies of medicine food homology materials based on fingerprint profiling and chemometrics: Citri Reticulata Pericarpium as an example | |
| CN101780141A (en) | Method for measuring content of tannin in red sage root extracting solution by near infrared spectrum | |
| CN108760677A (en) | A kind of rhizoma pinellinae praeparata based on near-infrared spectrum technique mixes pseudo- discrimination method | |
| CN104713848A (en) | Method for distinguishing radix-paeoniae-alba production places on basis of near-infrared analysis technology | |
| Wulandari et al. | Determination of total flavonoid content in medicinal plant leaves powder using infrared spectroscopy and chemometrics | |
| CN103411895A (en) | Near infrared spectrum identification method of adulteration of pearl powder | |
| Li et al. | Discrimination of raw and sulfur-fumigated ginseng based on Fourier transform infrared spectroscopy coupled with chemometrics | |
| CN111323407A (en) | Raman spectrum detection method for rapidly determining content of traditional Chinese medicine polysaccharide | |
| Ming-Liang et al. | A gradient-based discriminant analysis method for process quality control of carbonized TCM via Fourier transform near infrared spectroscopy: A case study on carbonized Typhae Pollen | |
| Fan et al. | Quality assessment of Fritillariae cirrhosae using portable NIR spectrometer | |
| Chen et al. | Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy | |
| CN107703095A (en) | A kind of binary based near infrared spectrum and Chemical Measurement mixes pseudo- pseudo-ginseng quantitative analysis method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20150617 |
|
| RJ01 | Rejection of invention patent application after publication |