CN106560692A - Wuyi rock tea production place identification method through combination of four detection technologies - Google Patents
Wuyi rock tea production place identification method through combination of four detection technologies Download PDFInfo
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- 238000005516 engineering process Methods 0.000 title claims abstract description 11
- 241001122767 Theaceae Species 0.000 title claims abstract 26
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- 239000011573 trace mineral Substances 0.000 claims abstract description 43
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- 238000004458 analytical method Methods 0.000 claims abstract description 13
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 48
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- 235000001014 amino acid Nutrition 0.000 claims description 48
- 239000001257 hydrogen Substances 0.000 claims description 26
- 229910052739 hydrogen Inorganic materials 0.000 claims description 26
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Classifications
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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
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- 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
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/3103—Atomic absorption analysis
-
- 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
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/62—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
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- Chemical & Material Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Biochemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Chemical Kinetics & Catalysis (AREA)
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- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention relates to a Wuyi rock tea production place identification method through combination of four detection technologies, and belongs to the technical field of geographical indication product authenticity recognition. In the prior art, the single detection data cannot represent all production place traceability key information, the data matching problem exists when different types of the detection data are subjected to combined use in the metrology method, and other problems exist. A purpose of the present invention is to solve the problems in the prior art. According to the present invention, based on the partial least square discrimination model, the near infrared characteristic spectrum data, the stable isotope data, the trace element data and the amino acid data of the rock teas (produced inside and outside the geographical indication production place) from different production places are integrally fused, the analysis model is established, the sample is extracted, and the rock tea production place is objectively and accurately determined by using the model, wherein the recognition rate is high, achieves 100.0%, and is higher than the PLSDA determining result of the single data, and the recognition rate of the blind sample achieves 100%; and the method has the good application prospect, and can be used as the Wuyi rock tea production place traceability recognition technical method.
Description
(1) technical field
The present invention relates to be based on the Wuyi cliff tea place of production Intelligent detecting method of various inspection technologies, that is, combine near infrared light
The method that spectrum, stable isotope, trace element, amino acid data differentiate the Wuyi cliff tea place of production, belongs to geography symbol product true
Property technology of identification field.
(2) background technology
According to the definition of GB/T 17924-2008, geography symbol product is referred to using the raw material for originating from specific region, is pressed
Produced in specific region according to traditional handicraft, it is geographical that quality, characteristic or reputation depend in itself its Local Geographical Indication
Feature, and by the examination ＆ verification approval of legal procedure Jing with the product of Local Geographical Indication name nominating.Tealeaves is typical geographical sign protection
Product, have Wuyi cliff tea, Anxi Tieguanyin Tea, clovershrub, Yongchun Buddha's hand, Xihu Longjing Tea, Anji white tea, Keemun black tea, Pu'er tea,
Nearly 50 kinds of geography symbol product tealeaves such as Biluochun tea.
At present, sample tea Production area recognition identification research has been carried out both at home and abroad, instrument detection combines chemometrics application side
Method is main Production area recognition method, and instrument detection method mainly has near infrared spectrum, isotope mass spectrometry, liquid chromatogram, sensing
Device etc.；Conventional metrology method includes offset minimum binary, principal component analysis, artificial neural network, SVMs etc..
Extensively using in the detection of tealeaves original producton location, Zhou etc. utilizes near infrared spectrometer to 25 to Near Infrared Spectroscopy Detection Technology
Individual Xihu Longjing Tea and 70 Zhejiang Dragon Well tea samples are detected and are set up Fei Shi discriminant function models, training set, cross validation
Set and the recognition accuracy difference 96.7%, 95.3% and 96.7% of test set.Zhou Jian etc. is to 4 Longjing tea kinds (dragon
Well 43, colony's kind, meet frost and black ox morning) near infrared detection is carried out, and model is set up using PLS, its 4 kind tealeaves accuracys rate
Respectively 89.8%, 90.9%, 96.1% and 99.5%.Account for jasmine etc. using near infrared spectrometer scan 10 parts of Xihu Longjing Teas and
18 parts of Zhejiang Longjing Tea samples, cluster analysis shows that West Lake Dragon Well tea has specific characteristic and constitutes a class by itself, Jinyun and Xinchang
Show similar spectral signature and there is intersection, what was produced from Fuyang also constitutes a class by itself.Zhao Jiewen etc. using near infrared spectrometer to Dragon Well tea,
Pilochun (a green tea), hair peak and Iron Guanyin (each 20 parts) are detected, and set up forecast model, calibration set and forecast set using principal component
Differentiate that accuracy rate is respectively 98.75% and 95.0%, but Dragon Well tea sample is easily mistaken for Pilochun (a green tea).Chen Quansheng etc. adopts near infrared light
Spectrum is detected to Dragon Well tea, Pilochun (a green tea), Iron Guanyin and Keemun black tea, and sets up a kind of SIMCA (classification based on principal component analysis
Method) discrimination model, the recognition accuracy of Dragon Well tea, Pilochun (a green tea), Keemun black tea and Iron Guanyin is respectively 90%, 80%, 100% and
100%；Li Xiaoli etc. hooks green grass or young crops to Xihu Longjing Tea, Zhejiang Dragon Well tea, Yang Yan, snow-broth cloud is green and LUSHAN YUNWU CHA (each 30 parts) is carried out closely
Infrared detection, and model is set up using PCA, in addition to Xihu Longjing Tea and Zhejiang Dragon Well tea exist and partly overlap, remaining is equal
Can distinguish well.
Isotope is the zoic natural label of institute, closely related with biological growing environment, therefore isotope matter
Spectrum (IRMS) provides science, reliable discrimination method for the identification of tealeaves original producton location.IRMS has been widely used at present various
In the original producton location detection of agricultural product,Deng using isotope mass spectrometry and NMR spectrum, with reference to principal component analysis, can
Ideally differentiate the red wine of three different regions of Slovenia.Brescia etc. determines the δ in milk using IRMS13C、δ15N
With Ba constituent contents, the milk in different original producton locations is distinguished, it was demonstrated that IRMS is applied to dairy products.Martinelli etc. to from
The bubble grape wine of the U.S., South America, Europe and Australia carries out isotope detection, finds there is significant difference.Tamara etc.
Stable isotope in 43 parts of India, 23 parts of Sri Lanka and 12 parts of Chinese teas is determined, nonlinear analysis shows that tealeaves is originated in
The judgement on ground is easily affected by discriminant function, and the tealeaves in country variant producing region is distinguished well.
Wang Rui etc. adopts ICP-AES, 36 pomegranates to the 6 main places of production in Xinjiang
The content of 12 kinds of metallic elements is measured in the edible part (pulp) of sample and seed, using principal component analysis PCA and linearly
Discriminant analysis LDA carries out overall merit to metallic element in pomegranate edible part and seed.As a result show：PCA draws 2 three factors
Model, respectively illustrates 84.29% and 60.33% of metallic element data in pomegranate edible part and seed；By can to pomegranate
Metallic element composition carries out PCA in food part, and 36 pomegranate samples can be divided into 6 classes by PCA, coincide with the actual place of production.Chen Hui
Beijing Shunyi, Hebei Fuping and 65, the area of Pingshan, Hebei Province three chaste honey are determined Deng using inductivity coupled plasma mass spectrometry
38 kinds of constituent contents in sample, and using PCA and reverse transfer artificial neural network chaste honey is carried out according to different sources
Analysis, the overall accuracy rate of crosscheck is 95.4%.
Different agricultural product are because of the difference of residing geography, weather, environment etc., species, the content of conventional and special chemical composition
It is all different, the difference of its species and content can be determined by from the same agricultural product area of different sources by chromatographic technique
Separate, reach the purpose that the place of production is traced to the source.In the virgin oil using high-performance liquid chromatogram determination Greece such as Longobardi
15 kinds of amino acid equal sizes, finally obtain the presence of pole significant difference (p ＜ 0.01), the party between 26 samples using variance analysis
Method effectively can make a distinction in the olive oil of separate sources.K ü c ü k etc. have studied three kinds of Turkey honeybees from different regions
12 kinds of amino acid contents of honey, contrast finds that due to the difference in the place of production content of various different sources amino acid has larger difference
(p ＜ 0.05), can carry out different sources source and judge.Collomb etc. carries out color to the milk of Switzerland's Different Altitude Regions production
Analysis of spectrum, determines its amino acid composition and content, and the amino acid composition and content for finding three Different Altitude Regions milk is present
Notable difference.
The domestic and international discrimination method to geography symbol product is can be seen that from above-mentioned example a lot, but much grind
Study carefully the part that still has some deficits, insufficient sample size of such as sampling is few, it is impossible to ensure the accuracy and representativeness of sample；Sample space is selected
Select span big, often selected from country variant, different regions, inherently tool makes a big difference；Even have selected difference in addition
Kind sample is compared, and differs greatly in itself between different cultivars, therefore this kind of discrimination method is produced to the geographical sign of small range
The product place of production differentiates that reference is little；Modeling method is carried out using single detection data with reference to metrology method, single detection number
According to the full detail that the place of production is traced to the source cannot be represented, cause Production area recognition rate relatively low, above-mentioned these have had a strong impact on geographical sign product
The innovation and breakthrough of product resist technology.For as above situation, it is necessary to set up the Wuyi cliff tea place of production of four kinds of detection techniques of joint
Discrimination method, i.e., a kind of joint near-infrared, stable isotope, trace element, the Wuyi cliff tea place of production discriminating side of amino acid data
Method.
(3) content of the invention
Present invention aim at solving single detection data cannot represent whole key messages and the difference that the place of production is traced to the source
Type detection data in metrology method the problems such as existing Data Matching is used in combination, there is provided based on various inspection skills
The Wuyi cliff tea place of production Intelligent detecting method of art, joint near infrared spectrum, stable isotope, trace element and amino acid data are built
Vertical Wuyi cliff tea Production area recognition modelling technique method, the method is based on offset minimum binary discrimination model, by different sources rock tea
(including rock tea outside in the geographical sign place of production and place of production) near-infrared characteristic spectrum data, stable isotope data, micro- number
According to, amino acid data fusion together, analysis model is set up, is extracted after sample using model is objective, accurate judgement rock tea is produced
Ground.
The technical solution used in the present invention is：
The Wuyi cliff tea place of production discrimination method of four kinds of detection techniques of joint, that is, merge near infrared spectrum, stable isotope, micro-
The method that secondary element and amino acid data differentiate the Wuyi cliff tea place of production, methods described includes：
(A) different sources rock tea sample is gathered：
Sample accounting ＞ 50% in 100 parts of sample number ＞ outside Wuyi cliff tea producing region, and the kilometer range of producing region periphery 50；Wuyi
Sample number is 2～3 times of sample outside producing region in rock tea producing region, and sample range covers each manufacturing enterprise in major production areas, and per enterprise
Industry should be no less than 3 samples；
(B) the near-infrared characteristic spectrum data of different sources rock tea sample are determined：
64 scanning, characteristic spectrum band takes its mean value, and sweep limits is 12000-4000cm-1, the interval of data point
For 1.928cm-1, 25 DEG C of room temperature, humidity keeps stable, Non-Destructive Testing, without the need for using the pre-treatment such as crushing, using identical charging side
Method, feeding quantity, charging is detected by finishing.
(C) hydrogen, oxygen, nitrogen, four kinds of stable isotope mass spectrometric datas of carbon of different sources rock tea sample are determined：
δ13C、δ15N、δ18O、δ2H、δ86The stable isotope assay such as Sr, each sample at least replicate analysis 3 times with
On, average as final result.
Wuyi cliff tea stable isotope data are trained by SVM-RFE (Support vector regression feature elimination approach)
And prediction, random repetition 100 times, and the aspect of model to each variable are ranked up, the isotope for filtering out rock tea original producton location is special
Variable is levied, its clooating sequence is hydrogen, oxygen, nitrogen, carbon, strontium.And the sensitivity using forecast set computation model increases dimension precision, resolution ratio
Increase dimension precision, discrimination and increase dimension precision, by computing repeatedly 100 average results, hydrogen, oxygen, nitrogen, the mould of four kinds of data compositions of carbon
Type, its discrimination highest, up to 93.93%, therefore modeling only needs to select hydrogen, oxygen, nitrogen, four kinds of data of carbon, without the need for strontium
Detected Deng other stable isotope contents.
(D) caesium, copper, calcium, the rubidium trace element data of different sources rock tea sample are determined
With atomic absorption spectrometry Ca, Mg, Mn constituent content, with inductivity coupled plasma mass spectrometry survey Ti, Cr,
Co, Ni, Cu, Zn, Rb, Cd, Cs, Ba, Sr constituent content.Dry tea sample micro-wave digestion, clears up and finishes, and whether observation digestion solution is clear
Clearly, if muddy, repeatedly pressure dispelling step, if clarifying completely, is measured after constant volume with above-mentioned instrument.
Trace element data is trained and is predicted by SVM-RFE methods, it is random to repeat 100 times, and to each variable
The aspect of model is ranked up, and filters out the Trace Elements Features variable in rock tea original producton location, and is calculated per one-dimensional change by forecast set
Model after amount is cumulative increases dimension precision, obtains caesium, copper, calcium, rubidium, strontium, barium feature ordering order.Then to characteristic variable by certainly
So sequence is combined step by step, and is increased dimension precision, discrimination and increased using the sensitivity increasing dimension precision of forecast set computation model, resolution ratio
Dimension precision, the model being made up of caesium, copper, calcium, rubidium trace element, its discrimination increases dimension precision highest, illustrate this four kinds it is micro-
Information between secondary element has complementarity, it is only necessary to select the caesium for modeling, copper, calcium, four kinds of trace elements of rubidium to be detected,
Without the need for detecting to other trace elements.
(E) the amino acid data of different sources rock tea sample are determined：
Parallel determination twice, is made even to be detected to 27 kinds of amino acid in different sources rock tea sample using HPLC methods
Average.
By SVM-RFE methods Wuyi cliff tea amino acid composition data are trained and are predicted, it is random to repeat 100 times, and
The aspect of model of each variable is ranked up, the characteristic variable in tealeaves original producton location is filtered out, and is calculated per one-dimensional by forecast set
Model after variable is cumulative increases dimension precision, determines its clooating sequence for asparagine, proline, tryptophan, phosphorus monoethanolamine, urine
Element and valine.Then characteristic variable is combined step by step by natural order, and the sensitivity using forecast set computation model increases
Dimension precision, resolution ratio increase dimension precision, discrimination and increase dimension precision, by asparagine, proline, tryptophan, four kinds of ammonia of phosphorus monoethanolamine
The model of base acid composition, its discrimination highest illustrates that the information between this four kinds of amino acid has complementarity, it is only necessary to select modeling
Asparagine, proline, tryptophan, four kinds of amino acid of phosphorus monoethanolamine are detected.
(F) combine near-infrared, stable isotope, trace element, amino acid, catechin and electronic tongues data and set up different
Place of production rock tea authentication data storehouse
(1) every near-infrared data (Y-axis data) are spliced in Excel data forms, all column datas of often going are constituted
Every near-infrared data；
(2) the stable isotope data of each sample are pressed into hydrogen, oxygen, nitrogen, carbon sequential concatenation after near-infrared data, will be micro-
Secondary element data press caesium, copper, calcium, rubidium splicing after stable isotope, and amino acid data are pressed into asparagine, proline, color ammonia
After trace element, the Excel tables of data of sample composition, is ordered with data1 in Wuyi cliff tea producing region for acid, phosphorus monoethanolamine sequential concatenation
Name；The Excel tables of data of sample composition outside Wuyi cliff tea producing region, with data2 names；
(3) the edit functions in MATLAB softwares are run, data1.xls, data2.xls is opened, with Mat file formats guarantor
Deposit, filename corresponds to data1.mat, data2.mat；
(4) data segmentation：With reference to the Duplex segmentation procedures that R.D.Snee and Michal Daszykowski set up, by number
According to being divided into two subsets so as to cover approximate same region and possess similar statistical property；By sample data normalization and
Orthogonalization, calculates Euclidean distance two-by-two between sample；Two samples for selecting Euclidean distance maximum enter training set, remaining sample
In, two maximum samples of Euclidean distance enter checking collection；In remaining sample after first polling, with training set Euclidean distance most
Big sample enters training set, and the sample maximum with checking collection Euclidean distance enters checking collection；Repeat step, until selected sample
Product are divided into two subsets.The sample number of specified forecast set, it is intended that the 65-70% of sum is used as in original producton location in Wuyi cliff tea producing region
Pattern number A1, takes at random 65-70% outside Wuyi cliff tea producing region and, as original producton location external model number A2, sets up Duplex segmentation procedures.
(5) K- foldings cross verification：It is K subset (usually dividing equally) by sample data set random division, by a son
, used as checking collection, remaining K-1 group subset is used as training set for collection；It is overlapping K time in turn using K subset as checking collection, obtain
To the result of K time, and with the mean value of K result as grader or the performance indications of model.Under K- folding methods, each sample
Data are all used as training data, used also as checking data, it is to avoid overlearning and the generation of deficient learning state.
(6) PLS differentiates the foundation of model：To the fusion near-infrared after step (4) and the segmentation of (5) data, surely
Determine isotope, trace element, amino acid data, using Partial Least Squares Method and set up PLSDA models；
(G) unknown place of production sample to be measured is taken according to above-mentioned steps B, C, D and E, determine near-infrared characteristic spectrum data, stablize
Isotope mass spectrometry data, trace element data, amino acid data, substitute into above-mentioned PLSDA models, if predicting the outcome by data measured
Less than 0, then testing sample is judged for sample outside the Wuyi cliff tea place of production；If predicting the outcome more than 0, judge that testing sample is Wuyi
Sample in the rock tea place of production.
Specifically, segmentation procedure is respectively in the step (E)：[model1, test1]=Duplex (data1, A1) and
[model2, test2]=Duplex (data2, A2), obtains model1, test1, model2, test2.
The modeling approach of PLS：Can be returned under conditions of independent variable has serious multiple correlation
Modeling, in Partial Least-Squares Regression Model, tries to remove hardly important correlation variable, remaining independent variable regression coefficient
To easily explain, and be easier to identification system information and noise, it is to avoid give up the system information that should retain.Specifically,
In the step (E) PLS differentiate model to set up process as follows：
A () merges training set：Xxxc=[data1 (model1,：)；Data2 (model2,：)]；
B () merges forecast set：Xxxp=[data1 (test1,：)；Data2 (test2,：)]；
C () seeks training set averaged spectrum：Mx=mean (xxxc)；
D () training set deducts averaged spectrum：Xxxc=xxxc-ones (A, 1) * mx；
A is：A1+A2；
E () forecast set deducts averaged spectrum：Xxxp=xxxp-ones (B, 1) * mx；
B is：Original producton location build-in test collection number B1 and test set number B2 sums outside original producton location；
(f) response variable：Yyc=-ones (A, 2)；yyc(1：A1,1)=1；yyc(A1+1：A, 2)=1；
A1 is total number of samples C1 in original producton location with B1 sums；
A2 is total number of samples C2 outside original producton location with B2 sums；
G () rolls over validation-cross with K-：
Indices=crossvalidation (' Kfold ', x, k)；
(h) modeling process：
[betattt, www, BETAPLS1]=plsbasetotal (xxxc, yyc (：, 1), lvp1)；
[betattt, www, BETAPLS2]=plsbasetotal (xxxc, yyc (：, 2), lvp2)；
Cy=[xxxc*BETAPLS1 (：, lvp1), xxxc*BETAPLS2 (：, lvp2)]；
Py=[xxxp*BETAPLS1 (：, lvp1), xxxp*BETAPLS2 (：, lvp2)]；
[rrt, cyy]=max (cy ')；
[rwwrt, pyy]=max (py ')；
I () calculates the sensitivity of model and resolution ratio in training process：
Err01=length (find (cyy (1：A1)==1))/A1；
Err02=length (find (cyy (A1+1：A1+A2)==2))/110；
J () calculates the sensitivity of model and resolution ratio during prediction unknown sample：
Err1a=length (find (pyy (1：B1)==1))/B1；
Err1b=1-length (find (pyy (B1+1：B1+B2)==1))/B2；
K () preserves and predicts the outcome：save cyy cyy；save pyy pyy；
L the first row of () py is and predicts the outcome.
Can draw and provide detailed results：
bar(cy(：, 1))；
figure
bar(py(：, 1))
Wherein Duplex programs are as follows：
Wherein Kfoldcv programs are as follows：
Wherein plsbasetotal programs are as follows：
The beneficial effects are mainly as follows：The present invention is based on offset minimum binary discrimination model, by different sources rock
Tea (including rock tea outside in the geographical sign place of production and place of production) near-infrared characteristic spectrum data, stable isotope data, trace element
Data and amino acid data fusion together, set up analysis model, extract after sample using model is objective, accurately judge rock
The tea place of production, discrimination highest, up to 100.0%, higher than the differentiation result of single data PLSDA.
(4) specific embodiment
With reference to specific embodiment, the present invention is described further, but protection scope of the present invention is not limited in
This：
Embodiment 1：
A, collection different sources rock tea sample
The geographical protection domain of Wuyi cliff tea, i.e. Fujian Province Wuyishan City are defined in GB (GB/T 18745-2006)
In administrative division, the present invention Wuyi cliff tea geographical sign protection area Wuyi street, Chong An streets, on plum, Xing Cun, five husbands, haze
Paddy, Xinfeng street, Yang Zhuan, Xing Tian, lower plum, Wu village carry out sample collection in 11 administrative regions, random in each administrative region
3 sample points (respectively with A, B, C sign) are selected, totally 33 sample points, sampling scope covers major production areas substantially, each sampling
Point 15 parts (being indicated with A-1, A-2......A-15 respectively) of sampling, obtains 495 parts of geographical sign protection area Wuyi cliff tea samples
Product, it is separately in Fujian Province in addition to Wuyishan City other counties and cities (Jianyang, Jian'ou, ZhangZhou, Quanzhou, Songxi, have stable political situation) and Guangxi, expensive
Rock tea sample outside 11 site collection protection zones such as state, Jiangxi (Wuyuan, Ganzhou), each place sample 15 parts (respectively with 1,
2......15 indicated), obtain 165 non-geographic sign protection area rock tea samples.Sample number and ground in the geographical sign place of production
The ratio of sample number is 3: 1 outside the reason mark place of production.
B, different sources rock tea near-infrared characteristic spectrum data
Non-Destructive Testing, Brooker TENSOR37, using identical charging process, feeding quantity (range estimation), charging is finished and carried out
Detection.Table 1 is 15 emerging field B sample parts near-infrared tables of data, and wherein X-axis is wave-length coverage, and Y-axis is absorbance.
Table 1：15 emerging field A sample parts near-infrared tables of data
C, different sources rock tea hydrogen, oxygen, nitrogen, four kinds of stable isotope mass spectrometric datas of carbon
δ13C、δ15N、δ18O、δ2H、δ86Sr is determined by Thermo Fisher MAT253 stable isotopes mass spectrograph.It is geographical
The inside and outside rock tea sample isotope ratio Jing said methods detection in mark producing region, the selected parts part rock tea sample isotope ratio number of table 2
According to table.
Table 2：Part rock tea sample isotope ratio statistical form
By SVM Wuyi cliff tea stable isotope data are trained and are predicted, it is random to repeat 100 times, and to each change
The aspect of model of amount is ranked up, and the isotopic characteristic variable order for filtering out rock tea original producton location is hydrogen, oxygen, nitrogen, carbon, strontium；And
Using the sensitivity of forecast set computation model, resolution ratio, discrimination, by 100 average results are computed repeatedly, 3 are shown in Table.
Table 3：Isotopic characteristic variable combined result situation
| Aspect of model variable is combined | Sensitivity increases dimension precision | Resolution ratio increases dimension precision | Discrimination increases dimension precision |
| Hydrogen | 0.8964 | 0.8821 | 0.8925 |
| Hydrogen+oxygen | 0.9047 | 0.8141 | 0.8800 |
| Hydrogen+oxygen+nitrogen | 0.9429 | 0.8056 | 0.9050 |
| Hydrogen+oxygen+nitrogen+carbon | 0.9592 | 0.8836 | 0.9393 |
| Hydrogen+oxygen+nitrogen+carbon+strontium | 0.9132 | 0.8223 | 0.9066 |
As shown in Table 3, after the isotopic data of hydrogen and oxygen is combined, Model Identification rate declines, and illustrates oxygen and hydrogen pair
The contribution of original producton location feature has stronger correlation；And add after carbon and nitrogen isotope data, Model Identification rate rises, and reaches
93.93%, illustrate that nitrogen and carbon have preferably complementary, therefore modeling only needs to select hydrogen, oxygen, nitrogen, four kinds of data of carbon, builds
The data of strontium need not be increased in mould, in actually detected, the content of isotope strontium is without the need for detection.
D, caesium, copper, calcium, four kinds of trace element datas of rubidium for determining different sources rock tea sample
After tea microwave to be measured is cleared up, whether observation digestion solution is clarified, if muddy, repeatedly pressure dispelling step, if complete
Full clarification, using Ca, Mg, Mn constituent content in Hitachi 180-50 atomic absorption spectrometry sample liquids, using Thermo
Fisher XSeries II inductivity coupled plasma mass spectrometries determine micro-wave digestion liquid in Ti, Cr, Co, Ni, Cu, Zn, Rb, Cd,
Cs, Ba, Sr constituent content.Table 4 is the inside and outside rock tea sample trace element data table in selected parts part geographical sign producing region.
Table 4：Part rock tea sample trace element statistical form
Trace element data is trained by SVM-RFE and is predicted, it is random to repeat 100 times, and to the mould of each variable
Type feature is ranked up, and filters out the Trace Elements Features variable in rock tea original producton location, and is calculated per one-dimensional variable by forecast set
Model after cumulative increases dimension precision, obtains caesium, copper, calcium, rubidium, strontium, barium feature ordering order.Then nature is pressed to characteristic variable
Sequence is combined step by step, and the sensitivity using forecast set computation model increases dimension precision, resolution ratio increasing dimension precision, discrimination increasing dimension
Precision, the model being made up of caesium, copper, calcium, rubidium trace element, its discrimination increases dimension precision and is up to 0.8121, illustrates this
Information between four kinds of trace elements has complementarity, it is only necessary to select caesium, copper, calcium, four kinds of trace elements of rubidium of modeling.
E, the amino acid data for determining different sources rock tea sample
27 kinds of amino acid in different sources rock tea sample are detected using efficient liquid phase derivatization method, parallel determination
Twice, average, part rock tea 7 kinds of amino acid content data of sample are shown in Table 5.
The different sources part rock 7 kinds of amino acid content (units of tea of table 5：Percentage)
By SVM Wuyi cliff tea amino acid composition data are trained and are predicted, it is random to repeat 100 times, and to each change
The aspect of model of amount is ranked up, and filters out the characteristic variable in tealeaves original producton location, and calculates tired per one-dimensional variable by forecast set
Plus model afterwards increases dimension precision, determine that its clooating sequence is asparagine, proline, tryptophan, phosphorus monoethanolamine, urea and figured silk fabrics
Propylhomoserin.Then characteristic variable is combined step by step by natural order, and using forecast set computation model sensitivity increase dimension precision,
Resolution ratio increases dimension precision, discrimination and increases dimension precision, is made up of asparagine, proline, tryptophan, phosphorus monoethanolamine amino acid
Model, its discrimination increases dimension precision and is up to 0.78, illustrates that the message complementary sense between 27 kinds of amino acid is weaker, it is only necessary to select
Four kinds of amino acid of modeling.
F, fusion near-infrared, stable isotope, trace element, amino acid, catechin product different with the foundation of electronic tongues data
Ground rock tea authentication data storehouse
(1) every near-infrared data are spliced in Excel data forms, all column datas of often going constitute every near-infrared
Data.
(2) the stable isotope data of each sample are pressed into hydrogen, oxygen, nitrogen, carbon order, trace element presses caesium, copper, calcium, rubidium
Sequentially, amino acid presses asparagine, proline, tryptophan, phosphorus monoethanolamine order, splices successively after near-infrared data, geographical
Mark producing region in sample constitute 495 rows, 4160 row (near-infrared Y-axis data totally 4148 row, successively increase hydrogen, oxygen, nitrogen, carbon, caesium,
Copper, calcium, rubidium, asparagine, proline, tryptophan, phosphorus monoethanolamine, are changed into 4160 row) Excel tables of data, with data1 life
Name；Sample constitutes 165 rows, the Excel tables of data of 4160 row outside geographical sign producing region, with data2 names.
(3) the edit functions in MATLAB softwares are run, data1.xls, data2.xls is opened, with Mat file formats guarantor
Deposit, filename corresponds to data1.mat, data2.mat；
(4) data segmentation：With reference to R.W.Kennard and L.A.Stone in Computer aided design of
The upper methods describeds of experiments, take at random the individual samples of 330 (A1) as model in original producton location, take 110 outside original producton location at random
(A2) individual sample sets up kenstone segmentation procedures as model, [model1, test1]=kenstone (data1,330) and
(110) data2, obtains model1, test1, model2, test2 to [model2, test2]=kenstone.
(5) to above-mentioned data modeling PLSDA：
1. training set is merged：Xxxc=[data1 (model1,：)；Data2 (model2,：)]；
2. forecast set is merged：Xxxp=[data1 (test1,：)；Data2 (test2,：)]；
3. training set averaged spectrum is sought：Mx=mean (xxxc)；
4. training set deducts averaged spectrum：Xxxc=xxxc-ones (440,1) * mx；
5. forecast set deducts averaged spectrum：Xxxp=xxxp-ones (220,1) * mx；
6. response variable：Yyc=-ones (440,2)；yyc(1：330,1)=1；yyc(331：440,2)=1；
7. validation-cross is rolled over K-：
Indices=crossvalidation (' Kfold ', x, k)；
8. modeling process：[betattt, www, BETAPLS1]=plsbasetotal (xxxc, yyc (：, 1), lvp1)；
[betattt, www, BETAPLS2]=plsbasetotal (xxxc, yyc (：, 2), lvp2)；
Cy=[xxxc*BETAPLS1 (：, lvp1), xxxc*BETAPLS2 (：, lvp2)]；
Py=[xxxp*BETAPLS1 (：, lvp1), xxxp*BETAPLS2 (：, lvp2)]；
[rrt, cyy]=max (cy ')；
[rwwrt, pyy]=max (py ')；
9. the sensitivity of model and resolution ratio in training process is calculated：
Err01=length (find (cyy (1：330)==1))/330；
Err02=length (find (cyy (331：440)==2))/110；
10. the sensitivity of model and resolution ratio during prediction unknown sample are calculated：
Err1a=length (find (pyy (1：165)==1))/165；
Err1b=1-length (find (pyy (166：220)==1))/55；
Preservation predicts the outcome：save cyy cyy；save pyy pyy；
The first of py is classified as and predicts the outcome, and is the outer sample in original producton location less than 0, is sample in original producton location more than 0, draw to
Go out detailed results：
bar(cy(：, 1))；
figure
bar(py(：, 1))；
G, PLSDA Model Identification rate
By above-mentioned modeling method respectively near infrared spectrum, stable isotope, trace element, amino acid and four fusions
Data are modeled analysis, and it the results are shown in Table 6.
Table 6：PLSDA disaggregated models differentiate that result collects
As known from Table 6, there is complementarity between the characteristic index representated by each detection technique, is built using PLSDA of the present invention
Mould method is analyzed respectively near infrared spectrum, stable isotope, trace element, amino acid and four fused datas, with
When four fused datas are model data, discrimination highest, up to 100.0%, far above the PLSDA of single data result is differentiated.
H, the detection of blind sample
Mang Yang monitoring groups buy rock tea sample from Wuyi cliff tea peasant household, the step such as monitoring is shone green grass or young crops, make conventional green grass or young crops, completes, really
The Local Geographical Indication attribute of rock tea sample is protected, above-mentioned sample is used as sample in the geographical sign producing region in blind sample；From Jianyang, Jian'ou,
Rock tea is bought on the ground such as Wuyuan, and used as sample outside the geographical sign producing region in blind sample, above-mentioned blind sample comes from not with modeling rock tea sample
Same producer.Analysis testing staff does not learn in advance the place of production attribute of blind sample to be measured, randomly selects several pieces, detects, then by this
Bright method is judged blind sample place of production attribute, and is checked with Mang Yang monitoring groups, determines blind sample discrimination.By 20,60,
100 blind samples substitute into above-mentioned PLSDA models according to the blind sample data that step B, C, D, E are obtained, and judge its geographical sign attribute, its
Differentiate that accuracy rate reaches 100.0%.
Embodiment 2：
Using modeling data same as Example 1, Duplex segmentation procedures are used in data segmentation, and with K- validation-cross is rolled over,
PLSDA, neutral net ELM and least square method supporting vector machine LS-SVM models are set up respectively, and near-infrared data are constant, it is stable same
Position element, trace element, amino acid respectively according to hydrogen, oxygen, nitrogen, carbon, strontium, Cs, Cu, Ca, Rb, Sr, Ba, asparagine, proline,
Tryptophan, phosphorus monoethanolamine, urea, valine splice after near-infrared data, its Model Identification rate is respectively 91.7%,
85.8%th, 82.1%.
Embodiment 3：
Using modeling data same as Example 1, Duplex segmentation procedures are used in data segmentation, and with K- validation-cross is rolled over,
PLSDA, neutral net ELM and least square method supporting vector machine LS-SVM models are set up respectively, and near-infrared data are constant, it is stable same
Position element, trace element, amino acid respectively according to hydrogen, oxygen, nitrogen, carbon, strontium, Cs, Cu, Ca, Rb, Sr, Ba, asparagine, proline,
Tryptophan, phosphorus monoethanolamine splice after near-infrared data, and its Model Identification rate is respectively 95.9%, 86.3%, 90.1%.
Embodiment 4：
Using modeling method same as Example 1, Duplex segmentation procedures are used in data segmentation, and with K- validation-cross is rolled over,
PLSDA, neutral net ELM and least square method supporting vector machine LS-SVM models are set up respectively, and near-infrared data are constant, it is stable same
Position element, trace element, amino acid are respectively according to hydrogen, oxygen, nitrogen, carbon, strontium, Cs, Cu, Ca, Rb, asparagine, proline, color ammonia
Acid, phosphorus monoethanolamine splice after near-infrared data, and its Model Identification rate is respectively 98.3%, 88.5%, 90.7%.
Embodiment 5：
Using modeling method same as Example 1, Duplex segmentation procedures are used in data segmentation, and with K- validation-cross is rolled over,
PLSDA, neutral net ELM and least square method supporting vector machine LS-SVM models are set up respectively, and near-infrared data are constant, it is stable same
Position element, trace element, amino acid are respectively according to hydrogen, oxygen, nitrogen, carbon, Cs, Cu, Ca, Rb, asparagine, proline, tryptophan, phosphorus
Monoethanolamine splices after near-infrared data, and its Model Identification rate is respectively 100.0%, 90.3%, 92.4%.
Embodiment 6～10：
Rock tea sample, near-infrared data, isotopic data, trace element data, amino acid data, catechin data and
Electronic tongues data etc. are same as Example 1, distinct methods are respectively adopted and are differentiated.Embodiment 8-10 is respectively adopted existing invention
Patent CN103630528A (application number 201210307144.2), CN102455320A (application number 201010526790.9),
CN103245713A (application number 201310095950.2) methods described differentiated, embodiment 6～10 and the Index areas of embodiment 1
It is not shown in Table 7.
Table 7：Embodiment 1 and the comparable situation table of embodiment 6～10
Can draw from above-mentioned comparative result, using the inventive method, its blind sample detects discrimination up to more than 100.0%,
Can trace to the source technology of identification method as the Wuyi cliff tea place of production.
Claims (4)
1. the Wuyi cliff tea place of production discrimination method of four kinds of inspection technologies is combined, and methods described includes：
(A) different sources rock tea sample is gathered：
Sample accounting ＞ 50% in 100 parts of sample number ＞ outside Wuyi cliff tea producing region, and the kilometer range of producing region periphery 50；Wuyi cliff tea
Sample number is 2～3 times of sample outside producing region in producing region, and sample range covers each manufacturing enterprise in major production areas, and every enterprise should
No less than 3 samples；
(B) the near-infrared characteristic spectrum data of different sources rock tea sample are determined：
Near infrared detection parameter：64 scanning, characteristic spectrum band is the mean value of 64 scanning, and sweep limits is 12000-
4000cm-1, data point at intervals of 1.928cm-1, at 25 DEG C, humidity keeps stable, each sample for room temperature control during collection
Spectra collection 1 time；
(C) hydrogen, oxygen, nitrogen, four kinds of stable isotope mass spectrometric datas of carbon of different sources rock tea sample are determined：
δ13C、δ15N、δ18O、δ2H、δ86The stable isotope assay such as Sr, each sample at least replicate analysis more than 3 times, takes
Mean value is used as final result；
Wuyi cliff tea stable isotope data are trained and in advance by SVM-RFE (Support vector regression feature elimination approach)
Survey, random repetition 100 times, and the aspect of model to each variable is ranked up, the isotopic characteristic for filtering out rock tea original producton location becomes
Amount, its clooating sequence is hydrogen, oxygen, nitrogen, carbon, strontium；And using the sensitivity of forecast set computation model, resolution ratio and discrimination,
By computing repeatedly 100 average results, hydrogen, oxygen, nitrogen, the model of four kinds of data compositions of carbon, discrimination highest, up to 93.93%, because
This modeling only need to select hydrogen, oxygen, nitrogen, four kinds of data of carbon, detect without the need for other stable isotope contents to strontium etc.；
(D) caesium, copper, calcium, the rubidium trace element data of different sources rock tea sample are determined
With atomic absorption spectrometry Ca, Mg, Mn constituent content, with inductivity coupled plasma mass spectrometry survey Ti, Cr, Co, Ni,
Cu, Zn, Rb, Cd, Cs, Ba, Sr constituent content.Dry tea sample micro-wave digestion, clears up and finishes, and whether observation digestion solution is clarified, if
Muddiness, then repeatedly pressure dispelling step, if clarifying completely, is measured after constant volume with above-mentioned instrument；
Trace element data is trained and is predicted by SVM-RFE methods, it is random to repeat 100 times, and to the model of each variable
Feature is ranked up, and filters out the Trace Elements Features variable in rock tea original producton location, and calculates tired per one-dimensional variable by forecast set
Plus model afterwards increases dimension precision, caesium, copper, calcium, rubidium, strontium, barium feature ordering order are obtained；Then natural order is pressed to characteristic variable
It is combined step by step, and using the sensitivity of forecast set computation model, resolution ratio, discrimination, it is micro by caesium, copper, calcium, rubidium
The model of element composition, discrimination highest illustrates that the information between this four kinds of trace elements has complementarity, need to only select what is modeled
Caesium, copper, calcium, four kinds of trace elements of rubidium are detected, without the need for being measured to other trace elements；
(E) the amino acid data of different sources rock tea sample are determined：
Parallel determination twice, is averaged to be detected to 27 kinds of amino acid in different sources rock tea sample using HPLC methods；
By SVM-RFE methods Wuyi cliff tea amino acid composition data are trained and are predicted, it is random to repeat 100 times, and to each
The aspect of model of variable is ranked up, and filters out the characteristic variable in tealeaves original producton location, and is calculated per one-dimensional variable by forecast set
Model after cumulative increases dimension precision, determine its clooating sequence for asparagine, proline, tryptophan, phosphorus monoethanolamine, urea and
Valine；Then characteristic variable is combined step by step by natural order, and the sensitivity using forecast set computation model increases dimension essence
Degree, resolution ratio increase dimension precision, discrimination and increase dimension precision, by asparagine, proline, tryptophan, four kinds of amino acid of phosphorus monoethanolamine
The model of composition, its discrimination highest illustrates that the information between this four kinds of amino acid has complementarity, it is only necessary to select the day of modeling
Winter acid amides, proline, tryptophan, four kinds of amino acid of phosphorus monoethanolamine are detected；
(F) combine near-infrared, stable isotope, trace element, amino acid data and set up different sources rock tea authentication data storehouse
(1) every near-infrared data (Y-axis data) are spliced in Excel data forms, all column datas of often going are constituted per bar
Near-infrared data；
(2) the stable isotope data of each sample are pressed into hydrogen, oxygen, nitrogen, carbon sequential concatenation after near-infrared data, by micro unit
Prime number splices after stable isotope according to caesium, copper, calcium, rubidium is pressed, and amino acid data are pressed into asparagine, proline, tryptophan, phosphorus
After trace element, the Excel tables of data of sample composition, is named monoethanolamine sequential concatenation with data1 in Wuyi cliff tea producing region；It is military
The Excel tables of data of sample composition outside smooth rock tea producing region, with data2 names；
(3) the edit functions in MATLAB softwares are run, data1.xls, data2.xls is opened, with the preservation of Mat file formats,
Filename corresponds to data1.mat, data2.mat；
(4) data segmentation：The 65～70% of sum are randomly selected in Wuyi cliff tea producing region as pattern number A1 in original producton location, at random
65～70% are taken outside Wuyi cliff tea producing region as original producton location external model number A2, Duplex segmentation procedures are set up；
(5) K- foldings cross verification：It is K subset (usually dividing equally) by sample data set random division, a subset is made
For checking collection, remaining K-1 group subset is used as training set；It is overlapping K time in turn using K subset as checking collection, obtain K
Secondary result, and with the mean value of K result as grader or the performance indications of model；
(6) PLS differentiates the foundation of model：To the fusion near-infrared after step (4) and the segmentation of (5) data, stablize same
Position element, trace element, amino acid data, using Partial Least Squares Method and set up PLSDA models；
(G) unknown place of production sample to be measured is taken according to above-mentioned steps B, C, D and E, determine near-infrared characteristic spectrum data, stablize same position
Quality modal data, trace element data and amino acid data, by data measured above-mentioned PLSDA models are substituted into, if it is little to predict the outcome
In 0, then testing sample is judged for sample outside the Wuyi cliff tea place of production；If predicting the outcome more than 0, testing sample is judged for Wuyi rock
Sample in the tea place of production.
2. the method for claim 1, it is characterised in that segmentation procedure is respectively in the step (E)：[model1,
Test1]=Duplex (data1, A1) and [model2, test2]=Duplex (data2, A2), obtain model1, test1,
model2、test2。
3. the method for claim 1, it is characterised in that PLS differentiates the foundation of model in the step (E)
Process is as follows：
A () merges training set：Xxxc=[data1 (model1,：)；Data2 (model2,：)]；
B () merges forecast set：Xxxp=[data1 (test1,：)；Data2 (test2,：)]；
C () seeks training set averaged spectrum：Mx=mean (xxxc)；
D () training set deducts averaged spectrum：Xxxc=xxxc-ones (A, 1) * mx；
A is：A1+A2；
E () forecast set deducts averaged spectrum：Xxxp=xxxp-ones (B, 1) * mx；
B is：Original producton location build-in test collection number B1 and test set number B2 sums outside original producton location；
(f) response variable：Yyc=-ones (A, 2)；yyc(1：A1,1)=1；yyc(A1+1：A, 2)=1；
A1 is total number of samples C1 in original producton location with B1 sums；
A2 is total number of samples C2 outside original producton location with B2 sums；
G () rolls over validation-cross with K-：
Indices=crossvalidation (' Kfold ', x, k)；
(h) modeling process：
[betattt, www, BETAPLS1]=plsbasetotal (xxxc, yyc (：, 1), lvp1)；
[betattt, www, BETAPLS2]=plsbasetotal (xxxc, yyc (：, 2), lvp2)；
Cy=[xxxc*BETAPLS1 (：, lvp1), xxxc*BETAPLS2 (：, lvp2)]；
Py=[xxxp*BETAPLS1 (：, lvp1), xxxp*BETAPLS2 (：, lvp2)]；
[rrt, cyy]=max (cy ')；
[rwwrt, pyy]=max (py ')；
I () calculates the sensitivity of model and resolution ratio in training process：
Err01=length (find (cyy (1：A1)==1))/A1；
Err02=length (find (cyy (A1+1：A1+A2)==2))/110；
J () calculates the sensitivity of model and resolution ratio during prediction unknown sample：
Err1a=length (find (pyy (1：B1)==1))/B1；
Err1b=1-length (find (pyy (B1+1：B1+B2)==1))/B2；
K () preserves and predicts the outcome：save cyy cyy；save pyy pyy；
L the first row of () py is and predicts the outcome.
Can draw and provide detailed results：
bar(cy(：, 1))；
figure
bar(py(：, 1)).
4. the method for claim 1, it is characterised in that offset minimum binary (PLSDA) modeling method of the present invention is near
The fused data of infrared, stable isotope, trace element and amino acid is modeled analysis, and Model Identification rate highest reaches
100.0%, differentiate result far above single data PLSDA；For 20,60,100 blind samples, detection discrimination reaches
100.0%, the inventive method can trace to the source technology of identification method as the Wuyi cliff tea place of production.
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