CN105181650B - A method of quickly differentiating local tea variety using near-infrared spectrum technique - Google Patents
A method of quickly differentiating local tea variety using near-infrared spectrum technique Download PDFInfo
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Abstract
The present invention is a kind of method quickly differentiating local tea variety using near-infrared spectrum technique, the near-infrared diffusing reflection spectrum of tealeaves is acquired near infrared spectrometer first, dimension-reduction treatment is carried out to the higher-dimension near infrared spectrum of tealeaves with principal component analysis (PCA) again, the extraction of the variety classification information of tealeaves spectroscopic data is carried out with linear discriminant analysis (LDA), and the discriminatory analysis of local tea variety is finally carried out using a kind of new broad sense noise clustering method.The present invention has detection speed fast, differentiates that accuracy rate is high, it is environmentally protective, it can be achieved that local tea variety accurate discriminating.
Description
Technical field
The present invention relates to a kind of technical fields of local tea variety discrimination method, and in particular to a kind of to use near infrared spectrum skill
The method that art quickly differentiates local tea variety.
Background technology
Tealeaves is one of three big beverage of the world, it contains the organic substances such as tea polyphenols, protein and amino acid, also contains
The inorganic substances such as potassium, calcium and magnesium have and calm the nerves, and improving eyesight and heat-clearing and other effects, often drinking tea is beneficial to the health of people.Leshan bamboo
Ye Qing is the distinctive tealeaves brand in Leshan, but exists in tea market and adulterate phenomenon, and ordinary consumer without
Method recognizes high-quality well-known tea and tealeaves inferior, is often deceived.In addition, shoddy poor quality tealeaves compromises Famous High-quality Tea
Brand prestige, has encroached on consumers' rights and interests, and puzzlement is brought to the marketing of Famous High-quality Tea.So it is simple, easy to study a kind of method
It is very important in the discrimination method of operation, the fast local tea variety of detection speed.
Near Infrared Spectroscopy Detection Technology is applied to the detection of tea leaf quality in recent years as a kind of Fast nondestructive evaluation technology
In analysis.The near-infrared spectrum technique such as dragon is opened, principal component analysis and Dian Ze discriminant analyses are to non-fermented tea, semi-fermented tea and hair
Ferment tea carries out sort research.The near-infrared spectrum technique such as Ning Jingming and neural network distinguish Pu'er of three kinds of Various Fermenting Degrees
Tea.Total anthocyanidin content of near-infrared spectrum technique and ant group optimization model inspection jasmine tea such as Huang.Ren etc. uses near-infrared
The chemical composition of spectral technique detection black tea traces to the source ground with identification tealeaves.The near-infrared spectrum technique such as He, it is partially minimum
Two multiply discriminant analysis and euclidean distance method detection tealeaves traces to the source ground.Near-infrared spectrum technique and the multispectral image such as Xiong
The Determination of Polyphenols of system detectio extra-strong tea.
Fuzzy C-Means Clustering (FCM) is famous fuzzy clustering algorithm, is widely used general, but FCM is to noise
Data sensitive.Noise cluster is a kind of fuzzy clustering algorithm, it is suitable for handling the clustering of Noise Data, noise cluster
Regard noise data as a classification to handle, but noise cluster has dependence to parameter, meanwhile, the mesh of noise cluster
Scalar functions be built upon sample to class center vector Euclidean distance square on the basis of, they cluster topological structure compare
Accuracy rate is frequently not highly desirable when the data of complexity.
It is a kind of high dimensional data with the collected tealeaves near-infrared of the near infrared spectrometer modal data that diffuses, by dimension
The cluster topological structure of data is more complicated after compression and feature extraction, when clustering progress data clusters according to noise, due to making an uproar
The Euclidean distance that sound cluster uses carrys out metric data, then Clustering Effect is undesirable.
Invention content
The present invention is for the defect of noise clustering method in the prior art and insufficient problem, it is proposed that a kind of detection speed
Soon, differentiate that accuracy rate is high, it is environmentally protective, it can be achieved that accurately differentiating for local tea variety is a kind of quick using near-infrared spectrum technique
Differentiate the method for local tea variety；The simple data problem of topological structure can only be clustered to solve noise clustering method, is improved
The accuracy rate of noise cluster.
The purpose of the present invention is what is realized by following technological means：It is a kind of quickly to differentiate tea using near-infrared spectrum technique
The method of leaf kind, it is characterised in that include the following steps：
Step 1: the acquisition of tealeaves sample near infrared spectrum：The tealeaves sample of different cultivars is acquired near infrared spectrometer,
Obtain the near-infrared diffusing reflection spectrum of tealeaves sample；
Step 2: carrying out dimension-reduction treatment to tealeaves sample near infrared spectrum：Using principal component analytical method (PCA) by tealeaves
Sample near infrared spectrum is transformed to low-dimensional data from high dimensional data；
Step 3: the authentication information of extraction tealeaves sample near infrared spectrum：Tealeaves is extracted using linear discriminant analysis (LDA)
The authentication information of sample near infrared spectrum；
Step 4: operation Fuzzy C-Means Clustering is to obtain initial cluster center；
Step 5: carrying out the discriminating of local tea variety with a kind of broad sense noise clustering method：According to the initial clustering of step 4
Center operation broad sense noise clustering method obtains fuzzy membership, and the discriminating of local tea variety can be realized according to fuzzy membership.
Step 1: the near-infrared diffusing reflection spectrum described in two, three, because the near-infrared of different tealeaves samples diffuses
Spectrum contains the different inside quality information of tealeaves, different, the corresponding near-infrared of its inside quality of the different tealeaves of kind
Diffusing reflection spectrum also differs, this is the principle of the present invention.
Broad sense noise clustering method in the step 5 using the broad sense noise of the p powers based on Euclidean distance cluster into
The classification of row local tea variety, it is specific as follows：
(1) is initialized
Tealeaves near infrared spectrum number of samples n (+∞ ＞ n ＞ 1) is set, and sample class number c (n ＞ c ＞ 1), weight refers to
Number m (+∞ ＞ m ＞ 1) and p (+∞ ＞ p ＞ 1), primary iteration number r=1, greatest iteration number rmax, error upper limit value ε, initially
Change class center vi,0；
(2) calculating parameters αik：
Here σ2It is the variance of sample；αikFor kth (k=1,2 ... ..., n) a sample of i-th (i=1,2 ... ..., c) classification
This parameter；Dik,r=| | xk-vi,r-1| | it is xk-vi,r-1Euclidean distance, xkFor k-th of sample, vi,r-1Repeatedly for the r-1 times
For when the i-th class class center vector；Djk,r=| | xk-vj,r-1| | it is xk-vj,r-1Euclidean distance, νj,r-1For the r-1 times iteration
When jth class class center vector；For population sample mean value, xjFor j-th of sample；
(3) fuzzy membership angle value u when the r times iteration of calculatingik,r；
Here it is subordinate to angle value uik,rK-th of sample is under the jurisdiction of the fuzzy membership of the i-th class when indicating to iterate to calculate for the r times
Value；Dik,r=| | xk-vi,r-1| |, vi,r-1For the r-1 times iteration when the i-th class class center vector；
(4) class center v when the r times iteration of calculatingi,r；
Work as maxi||vi,r-vi,r-1| | ＜ ε or r=rmaxWhen, iteration ends；Otherwise, r=r+1, return to step (2) after
Continuous iterative calculation.
The present invention has following clear advantage compared with prior art：
1, the present invention carries out the classification of local tea variety using the broad sense noise cluster of the p powers based on Euclidean distance；To
The simple data problem of topological structure can only be clustered by solving noise clustering method, improve the accuracy rate of noise cluster.2, originally
Inventive method acquires the near-infrared diffusing reflection spectrum of tealeaves near infrared spectrometer, then with principal component analysis (PCA) to tealeaves
Higher-dimension near infrared spectrum carries out dimension-reduction treatment, and the variety classification information of tealeaves spectroscopic data is carried out with linear discriminant analysis (LDA)
Extraction, finally utilize a kind of new broad sense noise clustering method to carry out the discriminatory analysis of local tea variety.3, the present invention has inspection
Degree of testing the speed is fast, differentiates that accuracy rate is high, it is environmentally protective, it can be achieved that local tea variety accurate discriminating.
Description of the drawings
Fig. 1 is the flow diagram of the present invention；
Fig. 2 is the diffusing reflection atlas of near infrared spectra of tealeaves sample in the present invention；
Fig. 3 is the 2-D data figure obtained after linear discriminant analysis feature extraction in the present invention；
Fig. 4 is the fuzzy membership figure of the method for the present invention；
Fig. 5 is the cluster accuracy rate figure that the method for the present invention realizes that local tea variety differentiates.
Specific implementation mode
Below in conjunction with description of the drawings, the present invention is described in further detail with specific implementation mode：One kind of the present invention
The near infrared spectrum local tea variety discrimination method of broad sense noise cluster is suitable for the discriminatory analysis of local tea variety, implementation of the invention
Flow is as shown in Figure 1.
Embodiment
Step 1: the acquisition of tealeaves sample near infrared spectrum：The tealeaves sample of different cultivars is acquired near infrared spectrometer,
Obtain the near-infrared diffusing reflection spectrum of tealeaves sample.
Acquire high-quality Leshan green bamboo snake, Leshan green bamboo snake inferior and Mount Emei Mao Fengsan kind tealeaves, the sample of each tealeaves
Number is 32, adds up to 96 samples.All tealeaves samples be ground smashing after through 40 mesh screens, each sample take 0.5g respectively with
Potassium bromide presses 1:100 take mixture 1g to carry out press mold processing after evenly mixing.It is being acquired near infrared light time spectrum Laboratory Temperature
About 25 DEG C of degree, relative humidity was in 50% or so, FTIR-7600 type Fourier transform near infrared analyzers booting preheating 1 hour.Light
Spectrum analysis instrument scans each tealeaves sample 32 times, and the wave-number range of spectral scan is 4001.569~401.1211cm-1, scanning room
It is divided into 1.9285cm-1, the near infrared spectrum of each tealeaves sample is the high dimensional data of 1868 dimensions.Each specimen sample 3 times, takes it
The experimental data that average value is established as following model.The atlas of near infrared spectra of tealeaves sample is as shown in Figure 2.
Step 2: carrying out dimension-reduction treatment to tealeaves sample near infrared spectrum：Using principal component analytical method (PCA) by tealeaves
Sample near infrared spectrum is transformed to low-dimensional data from high dimensional data.
The data for being tieed up the near infrared spectrum data boil down to 20 of 96 samples using principal component analysis.
Step 3: the authentication information of extraction tealeaves sample near infrared spectrum：Tealeaves is extracted using linear discriminant analysis (LDA)
The authentication information of sample near infrared spectrum.
13 samples are chosen from each tealeaves sample and form tealeaves sample training collection, then training set total sample number is 39
A, remaining sample forms tealeaves test sample collection, then test set total sample number is 57.20 dimensions are calculated by running LDA
The discriminant vectors of training set sample, and preceding 2 discriminant vectors are taken, the test set sample of 20 dimensions is projected into this 2 discriminant vectors
On, the LDA shot charts of test sample are as shown in Figure 3.
Step 4: operation Fuzzy C-Means Clustering is to obtain initial cluster center.
The weighted index m=2.0 of Fuzzy C-Means Clustering (FCM), greatest iteration number r are setmax=100, error upper limit value
The initial classes center vector of ε=0.00001, FCM are preceding 3 data of the test data of Fig. 3.In the class for calculating the FCM of gained
Heart vector is：
v1,0=[- 0.097 0.0026]
v2,0=[0.0198-0.0910]
v3,0=[0.0660 0.0472]
Step 5: carrying out the discriminating of local tea variety with broad sense noise clustering method：According to the initial cluster center of step 4
Operation broad sense noise clustering method obtains fuzzy membership, and the discriminating of local tea variety can be realized according to fuzzy membership.
Broad sense noise clustering method in the step 5 is as follows：
(1) is initialized
Tealeaves near infrared spectrum number of samples n=57, sample class number c=3, weighted index m=2 and p (+∞ are set
＞ p ＞ 1), primary iteration number r=1, greatest iteration number rmax=100, error upper limit value ε=0.00001, initialization class center
vi,0(i=1,2,3)；
(2) calculating parameters αik：
Here σ2It is the variance of sample；αikFor kth (k=1,2 ... ..., n) a sample of i-th (i=1,2 ... ..., c) classification
This parameter；Dik,r=| | xk-vi,r-1| | it is xk-vi,r-1Euclidean distance, xkFor k-th of sample, vi,r-1Repeatedly for the r-1 times
For when the i-th class class center vector；Djk,r=| | xk-vj,r-1| | it is xk-vj,r-1Euclidean distance, νj,r-1For the r-1 times iteration
When jth class class center vector；For population sample mean value, xjFor j-th of sample.
(3) fuzzy membership angle value u when the r times iteration of calculatingik,r；
Here it is subordinate to angle value uik,rK-th of sample is under the jurisdiction of the fuzzy membership of the i-th class when indicating to iterate to calculate for the r times
Value；Dik,r=| | xk-vi,r-1| |, vi,r-1For the r-1 times iteration when the i-th class class center vector.
Experimental result：The iteration ends as iteration 7 times (r=7), fuzzy membership angle value u at this timeik,7Numerical value such as Fig. 4
It is shown, take u in k-th of sampleik,7Maximum value corresponding to i values, that is, judge that k-th of sample belongs to the i-th class.Work as weighted index
When p is respectively 2,3,4,5,6,7,8,9,10,11,12,13, cluster accuracy rate such as Fig. 5 can be obtained according to the value of fuzzy membership
It is shown.
(4) class center v when the r times iteration of calculatingi,r；
Work as maxi||vi,r-vi,r-1| | ＜ ε or r=rmaxWhen, iteration ends；Otherwise, r=r+1, return to step (2) after
Continuous iterative calculation.
Experimental result：R=7 when iteration ends, vi,7For：
A judges with v0,7Which kind of tealeaves for class center belongs to：
SoValue it is minimum, then judgement is with v0,7Belong to high-quality Leshan green bamboo snake for the tealeaves at class center.
B judges with v1,7Which kind of tealeaves for class center belongs to：
SoValue it is minimum, then judgement is with v1,7Belong to Mount Emei Mao Feng for the tealeaves at class center.
C judges with v2,7Which kind of tealeaves for class center belongs to：
SoValue it is minimum, then judgement is with v2,7Belong to Leshan green bamboo snake inferior for the tealeaves at class center.
The above, only a part of specific implementation mode of the invention, scope of protection of the present invention is not limited thereto,
Any one skilled in the art in the technical scope disclosed by the present invention, the change or replacement that can be readily occurred in,
It should be covered by the protection scope of the present invention.
Claims (1)
1. a kind of method quickly differentiating local tea variety using near-infrared spectrum technique, it is characterised in that include the following steps：
Step 1: the acquisition of tealeaves sample near infrared spectrum：The tealeaves sample of different cultivars is acquired near infrared spectrometer, is obtained
The near-infrared diffusing reflection spectrum of tealeaves sample；
Step 2: carrying out dimension-reduction treatment to tealeaves sample near infrared spectrum：It is using principal component analytical method that tealeaves sample is closely red
External spectrum is transformed to low-dimensional data from high dimensional data；
Step 3: the authentication information of extraction tealeaves sample near infrared spectrum：It is closely red using linear discriminant analysis extraction tealeaves sample
The authentication information of external spectrum；
Step 4: operation Fuzzy C-Means Clustering is to obtain initial cluster center；
Step 5: carrying out the discriminating of local tea variety with a kind of broad sense noise clustering method：According to the initial cluster center of step 4
Operation broad sense noise clustering method obtains fuzzy membership, and the discriminating of local tea variety can be realized according to fuzzy membership；
Broad sense noise clustering method in the step 5 carries out tea using the broad sense noise cluster of the p powers based on Euclidean distance
The classification of leaf kind, it is specific as follows：
(1) is initialized
Setting tealeaves near infrared spectrum number of samples n (+∞ ＞ n ＞ 1), sample class number c (n ＞ c ＞ 1), weighted index m (+
∞ ＞ m ＞ 1) and p (+∞ ＞ p ＞ 1), primary iteration number r=1, greatest iteration number rmax, error upper limit value ε, initialize class in
Heart vi,0；
(2) calculating parameters αik：
Here σ2It is the variance of sample；αikFor kth (k=1,2 ... ..., n) a sample of i-th (i=1,2 ... ..., c) classification
Parameter；Dik,r=| | xk-vi,r-1| | it is xk-vi,r-1Euclidean distance, xkFor k-th of sample, vi,r-1For the r-1 times iteration when
The class center vector of i-th class；Djk,r=| | xk-vj,r-1| | it is xk-vj,r-1Euclidean distance, νj,r-1For the r-1 times iteration when
The class center vector of j classes；For population sample mean value, xjFor j-th of sample；
(3) fuzzy membership angle value u when the r times iteration of calculatingik,r；
Here it is subordinate to angle value uik,rK-th of sample is under the jurisdiction of the fuzzy membership angle value of the i-th class when indicating to iterate to calculate for the r times；Dik,r
=| | xk-vi,r-1| |, vi,r-1For the r-1 times iteration when the i-th class class center vector；
(4) class center v when the r times iteration of calculatingi,r；
Work as maxi||vi,r-vi,r-1| | ＜ ε or r=rmaxWhen, iteration ends；Otherwise, r=r+1, return to step (2) continue to change
In generation, calculates.
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| CN106408012A (en) * | 2016-09-09 | 2017-02-15 | 江苏大学 | Tea infrared spectrum classification method of fuzzy discrimination clustering |
| CN106645021B (en) * | 2016-12-30 | 2020-01-03 | 中南民族大学 | Method for distinguishing origin of famous green tea by porphyrin near-infrared holographic probe |
| CN106934416B (en) * | 2017-02-23 | 2021-03-30 | 广州讯动网络科技有限公司 | Big data-based model matching method |
| CN107271394A (en) * | 2017-05-16 | 2017-10-20 | 江苏大学 | A kind of fuzzy Kohonen differentiates the tealeaves infrared spectrum sorting technique of clustering network |
| CN109187424A (en) * | 2018-09-30 | 2019-01-11 | 杭州国辰迈联机器人科技有限公司 | A kind of tea composition analysis device and analysis method |
| CN110243805B (en) * | 2019-07-30 | 2020-05-22 | 江南大学 | Fish bone detection method based on Raman hyperspectral imaging technology |
| CN110987866A (en) * | 2019-12-19 | 2020-04-10 | 汉谷云智(武汉)科技有限公司 | Gasoline property evaluation method and device |
| CN112014346B (en) * | 2020-09-03 | 2021-09-07 | 中国地质大学（武汉） | Coal producing area tracing method based on infrared spectrum |
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| CN103389281A (en) * | 2012-05-09 | 2013-11-13 | 云南天士力帝泊洱生物茶集团有限公司 | Pu'er tea clustering analysis method based on near-infrared spectroscopy |
| CN103048273B (en) * | 2012-11-09 | 2014-12-03 | 江苏大学 | Fruit near infrared spectrum sorting method based on fuzzy clustering |
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