CN107860739A - A kind of tealeaves mid-infrared light profile classification method of fuzzy K mediations network clustering - Google Patents
A kind of tealeaves mid-infrared light profile classification method of fuzzy K mediations network clustering Download PDFInfo
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Abstract
The invention discloses a kind of middle infrared spectrum of the tealeaves mid-infrared light profile classification method, first collection different cultivars tealeaves of fuzzy K mediations network clustering；Secondly, the middle infrared spectrum of tealeaves sample is pre-processed using multiplicative scatter correction, principal component analysis and linear discriminant analysis；Finally to the test sample comprising authentication information, the local tea variety in a kind of fuzzy K mediations network clustering method differential test sample is used.Fuzzy K mediation network clusterings are incorporated into the learning rate and more new strategy of Kohonen clustering networks by the present invention, are solved the problems, such as sensitive to initial classes center with Fuzzy Kohonen Clustering Network method and are caused cluster result unstable.The present invention has the advantages that detection speed is fast, and Detection accuracy is high, green non-pollution, and testing result is stable.
Description
Technical field
The invention belongs to field of artificial intelligence, especially a kind of tealeaves mid-infrared light of fuzzy K mediations network clustering
Profile classification method.
Background technology
China is the native place of tealeaves, and our people just has the custom drunk tea since ancient times.The polyatomic phenol that contains in tealeaves,
Caffeine, amino acid, vitamin etc. can play health care and pharmacological action, such as：Green tea helps anti-cancer and cancer-preventing, reduces blood fat etc.
Effect.With the continuous improvement of living standards of the people, the requirement more and more higher to tea leaf quality, and some low qualities, with secondary
The tealeaves substituted the bad for the good compromises consumer's interests.Therefore, the important topic that constant value must be studied that discerns between right and wrong of local tea variety, and design
A kind of simple and quick local tea variety discrimination method is very important.
Middle infrared spectrum detection technique as a kind of Fast nondestructive evaluation technology, in recent years applied to food, agricultural product and
In the Nondestructive Detections such as medicine.The wave-number range of middle infrared spectrum is in 4000cm-1~400cm-1Between, it is most inorganic
The fundamental frequency of the chemical bond oscillations of compound and organic compound is in this region.Functional group, the class of compound in different molecules
Other and compound stereochemical structure, its mid infrared absorption spectrum are not quite similar.The tealeaves of different cultivars, its component and content are often
Difference be present, then their middle infrared spectrum has differences.Around this principle, tea can be realized with mid-infrared light spectral technology
The classification of leaf kind.
Fuzzy Kohonen Clustering Network (Tsao E C, Bezdek J C, Pal N R.Fuzzy Kohonen
clustering networks.Pattern Recognition,1994,27(5):757-764.) it is a kind of unsupervised
Learning method.Fuzzy Kohonen Clustering Network is the study that Fuzzy C-Means Clustering (FCM) is incorporated into Kohonen clustering networks
In speed and more new strategy.But due to FCM, there is cause cluster result unstable initial classes center tender subject.
Introduce FCM Fuzzy Kohonen Clustering Network there is also it is identical with FCM the problem of.
The content of the invention
The present invention be directed to existing Fuzzy Kohonen Clustering Network method, in cluster data, there is in initial classes
Heart tender subject and the shortcomings that cause cluster result unstable, propose that a kind of fuzzy K reconciles the tealeaves mid-infrared light of network clustering
Profile classification method.Compared to original Fuzzy Kohonen Clustering Network method, a kind of fuzzy K mediations network clustering of the invention
Tealeaves mid-infrared light profile classification method calculates learning rate using the fuzzy membership of fuzzy K mediations cluster.The present invention has inspection
The advantages that degree of testing the speed is fast, and Detection accuracy is high, green non-pollution, and testing result is stable.
The present invention is achieved through the following technical solutions above-mentioned technical purpose.
A kind of tealeaves mid-infrared light profile classification method of fuzzy K mediations network clustering, comprises the following steps：
S1, gather tealeaves sample middle infrared spectrum：For the tealeaves sample of different cultivars, with infrared spectrometer to tealeaves sample
This is detected, and obtains the infrared spectrum information that diffuses in tealeaves sample, spectral information is stored in computer；By tealeaves sample
Originally it is divided into training sample and test sample；
S2, the successively mid-infrared light using multiplicative scatter correction, principal component analysis and linear discriminant analysis to tealeaves sample
Spectrum is pre-processed；
S3, the test sample to including authentication information in step 2, differentiated using a kind of fuzzy K mediations network clustering method
Local tea variety in test sample.
Further, in the S1 the infrared spectrum information that diffuses refer to the wave-number range of spectrum for 4001.569~
401.1211cm-1, the spectrum for collecting each tealeaves sample is the data of 1868 dimensions.
Further, the classification number for tealeaves sample being set in the S1 is k, number of training nr, test sample number is n.
Further, the S2 is specially：First tealeaves sample middle infrared spectrum is handled with multiplicative scatter correction, then
Spectroscopic data compression is carried out with principal component analysis, the authentication information that spectroscopic data is finally carried out with linear discriminant analysis extracts.
Further, the S3 is specially：
S3.1, initialization：Determine classification number k, test sample number n and weighted index m0Value, n>k>1 ,+∞>m0>1；If
Put iterations initial value r, maximum iteration rmaxAnd worst error parameter ε；It is determined that the initial classes center c of clusterj,0；
S3.2, calculate fuzzy membership angle value u during the r times iterationij,r；
S3.3, calculate learning rate α during the r times iterationij,r；
S3.4, calculate class center c during the r times iterationj,r；
S3.5, when | | cj,r-cj, r-1 | | ＜ ε or r>Rmax then calculates termination, is otherwise recalculated since S3.2,
Wherein cj,rThe class center of -1 jth class when being the r-1 times iteration.
Further, fuzzy membership the angle value uij, r during the r times iteration are calculated：Wherein
Weighted index when mr is the r times iteration, mr=m0- r Δ m, Δ m=(m0-1)/rmax；uij, j-th of sample when r is the r times iteration
Originally it is under the jurisdiction of the fuzzy membership angle value of the i-th class, wherein dij=| | xi-cj, r-1 | |, xiFor i-th of sample data, dit=| | xi-
ct,r-1| |, ct,r-1For the r-1 times iteration when t classes class center.
Further, learning rate α during the r times iteration is calculatedij,r：
Further, class center c during the r times iteration is calculatedj,r：Wherein dil
=| | xi-cl,r-1| |, cl,r-1For the r-1 times iteration when l classes class center；αil,rFor the r times iteration when learning rateuil,rFor the r times iteration when l-th of sample be under the jurisdiction of the fuzzy membership angle value of the i-th class..
Beneficial effects of the present invention are：
1st, a kind of tealeaves mid-infrared light profile classification method of fuzzy K mediations network clustering of the invention is due to calculating the r times
Class center c during iterationj,r, so the stability and cluster accuracy rate in terms of tealeaves mid-infrared light modal data is clustered are better than
Fuzzy Kohonen Clustering Network method, there is the advantages of cluster accuracy rate is high, and cluster speed is fast, and cluster result is stable.
2nd, the present invention is by calculating fuzzy membership angle value and being classified with it, so the present invention includes noise number in cluster
According to mid-infrared light modal data in terms of be better than Kohonen clustering networks, can quickly realize local tea variety with reference to China and foreign countries' spectral technique
Quick and accurate discriminating.
Brief description of the drawings
Fig. 1 is the flow chart of the present invention；
Fig. 2 is the mid-infrared light spectrogram of tealeaves；
Fig. 3 is the tealeaves mid-infrared light spectrogram after MSC processing；
Fig. 4 is the test sample datagram that the middle infrared spectrum of tealeaves obtains after LDA Extraction and discrimination information；
Fig. 5 is initial fuzzy membership angle value figure；
Fig. 6 is fuzzy membership angle value figure caused by a kind of fuzzy K mediations network clustering method；
Fig. 7 is learning rate figure caused by a kind of fuzzy K mediations network clustering method.
Embodiment
Below in conjunction with accompanying drawing, the present invention is described further, but protection scope of the present invention is not limited to this.
Step 1: the collection of tealeaves sample middle infrared spectrum：The tealeaves sample of different cultivars is gathered, with infrared spectrometer pair
Tealeaves sample is detected, and obtains the infrared spectrum information that diffuses in tealeaves sample, spectral information is stored in computer.
Indoor temperature and humidity is kept to be basically unchanged as far as possible in experimentation；In the wave-number range of infrared diffusing reflection spectrum be
4001.569~401.1211cm-1, the spectrum for collecting each tealeaves sample is the data of 1868 dimensions.Tealeaves sample is divided into
Training sample and test sample.Classification number k, number of training n are setrIt is n with test sample number.
Step 2: the middle infrared spectrum of tealeaves sample is pre-processed：First spectrum is entered with multiplicative scatter correction (MSC)
Row processing, then carries out spectroscopic data compression with principal component analysis (PCA), finally carries out spectroscopic data with linear discriminant analysis
Authentication information extracts.
Step 3: to included in step 2 the test sample of authentication information with a kind of fuzzy K mediation network clustering method with
Local tea variety in differential test sample.
It is described in detail below：
1. initialization
(1) classification number k, test sample number n and weighted index m are determined0Value, and meet n>k>1 ,+∞>m0>1；Set
Iterations initial value r, maximum iteration rmaxAnd iteration worst error parameter ε；
(2) the initial classes center c of cluster is determinedj,0。
2. calculate fuzzy membership angle value u during the r times iterationij,r
Wherein：mrFor the r times iteration when weighted index, mr=m0- r Δ m, Δ m=(m0-1)/rmax；uij,rFor the r times
J-th of sample is under the jurisdiction of the fuzzy membership angle value of the i-th class, wherein d during iterationij=| | xi-cj,r-1| |, xiFor i-th of sample number
According to cj,r-1For the r-1 times iteration when jth class class center, dit=| | xi-ct,r-1| |, ct,r-1For the r-1 times iteration when t classes
Class center.
3. calculate learning rate α during the r times iterationij,r：
4. calculate class center c during the r times iterationj,r：
Wherein dil=| | xi-cl,r-1| |, cl,rThe class center of -1 l classes when being the r-1 times iteration；αil,rFor the r times iteration
When learning rate, anduil,L-th of sample is under the jurisdiction of the fuzzy membership of the i-th class when r is the r times iteration
Angle value.
(r is assigned to variable r) to 5.r=r+1 after increasing by 1, r+1
When | | cj,r-cj,r-1| | ＜ ε or r>rmaxThen calculate and terminate, otherwise from " 2. calculate fuzzy person in servitude during the r times iteration
Belong to angle value uij,r" start to recalculate.
A kind of tealeaves mid-infrared light profile classification method of fuzzy K mediations network clustering of the present invention is applied to local tea variety
Discriminating, such as：The discriminating of the local tea varieties such as high-quality green tea, green bamboo snake, Dragon Well tea, Iron Guanyin.Because different cultivars tealeaves, its internal group
Point difference, therefore its middle infrared spectrum is there is also difference, to realize that the discriminating of local tea variety provides condition.The implementation of the present invention
Flow chart is as shown in Figure 1.For convenience of narration, Mount Emei's tealeaves, the high-quality green bamboo snake in Leshan and green bamboo snake inferior are chosen as experiment
Object.
Embodiment：
Step 1：Tealeaves sample middle infrared spectrum gathers：The start of FTIR-7600 type FTIR spectrums analyzer is pre-
Hot 1 hour, scanning times 32, wave number 4001.569cm-1~401.1211cm-1 of spectral scan, sweep spacing are
1.928cm-1, resolution ratio 4cm-1；Tealeaves sample：Mount Emei's tealeaves, the high-quality green bamboo snake in Leshan and green bamboo snake inferior；Tealeaves
Ground crushing, then after being filtered with 40 mesh sieves, respectively take 0.5g respectively with KBr 1:100 uniformly mixing；Each sample takes
Mixture 1g carries out press mold, is then scanned 3 times with spectrometer, takes the average value of 3 times as sample spectrum data；When gathering spectrum
Environment temperature and relative humidity keep relative stability；Every kind of tealeaves gathers 32 samples, obtains 96 samples altogether.Each sample is
The data of one 1868 dimension；It is test set that the tealeaves sample of each kind, which chooses 22, then test sample number n is 66；Residue 10
Individual sample is training set, then number of training nr is 30；Test set is tealeaves sample to be identified, and training set is known kind
Tealeaves sample, setting classification number k=3, the middle infrared spectrum of tealeaves sample are as shown in Figure 2.
Step 2：The middle infrared spectrum of tealeaves sample is pre-processed：First spectrum is entered with multiplicative scatter correction (MSC)
Row processing, then carries out spectroscopic data compression with principal component analysis (PCA), finally carries out spectroscopic data with linear discriminant analysis
Authentication information extracts.
Multiplicative scatter correction (MSC) is carried out to the tealeaves middle infrared spectrum of 96 samples, its result is as shown in Figure 3；Then
Data compression after MSC is handled with principal component analysis (PCA) is as follows to 14 dimensions, PCA 14 characteristic value concrete numerical values：λ1=
293.91, λ2=129.02, λ3=19.00, λ4=14.88, λ5=6.43, λ6=3.82, λ7=2.00, λ8=1.43, λ9=
1.07 λ10=0.63, λ11=0.40, λ12=0.32, λ13=0.27, λ14=0.23.
Tealeaves sample middle infrared spectrum is projected in PCA 14 characteristic vectors and obtains the data of 14 dimensions, i.e., from 1868
Dimension is compressed to 14 dimensions.
It is 2 to set discriminant vectorses number, the mirror of 14 dimension datas obtained using linear discriminant analysis (LDA) extraction PCA processing
Obtain including the training sample and test sample data of authentication information after other information, wherein test sample data are as shown in Figure 4.
Step 3: to included in step 2 the test sample of authentication information with a kind of fuzzy K mediation network clustering method with
Local tea variety in differential test sample.
It is described in detail below：
1. initialization
(1) classification number k, test sample number n and weighted index m are determined0Value, and meet n>k>1 ,+∞>m0>1；Setting changes
Generation number initial value r=0 and maximum iteration are rmax；Iteration worst error parameter ε is set；
The numerical value of initialization is set：From step 1：Classification number k=3 (i.e. three classifications), test sample number n=66,
Weighted index m is set0=2, iterations initial value r=0 and greatest iteration number rmax=100, error higher limit ε=0.005.
(2) the initial classes center c of cluster is determinedj,0；
To the test data operation fuzzy C-means clustering (FCM) in step 2 after LDA is handled, after FCM iteration ends
Cluster centre reconcile the initial cluster center of network clustering method as a kind of fuzzy K, then initial cluster center cj,0For：c1,0
=(- 0.1548,0.0388), c2,0=(0.0034,0.0022), c3,0=(0.1204, -0.0056)；Initial fuzzy membership
Angle value uij,0As shown in Figure 5.
2. fuzzy membership angle value u during the r times iteration is calculated using formula (1)ij,r, experimental result：(this during iteration ends
When r=3) when fuzzy membership angle value uij,3As shown in Figure 6.
3. learning rate during the r times iteration, experimental result are calculated using formula (2)：During iteration ends (now r=3)
When learning rate αij,3As shown in Figure 7.
4. class center c during the r times iteration is calculated using formula (3)j,r, experimental result：R=3 during iteration ends, cj,3
For：
c1,3=[- 0.16240.0344]；c2,3=[0.01010.0037]；c3,3=[0.11760.0011].
5.r=r+1
When | | cj,r-cj,r-1| | ＜ ε or r>rmaxThen calculate and terminate, otherwise from " fuzzy during 2. the r times iteration of calculating
It is subordinate to angle value uij,r" start to recalculate.
According to fuzzy membership angle value uij,3Realize that local tea variety differentiates, differentiate rate of accuracy reached to 93.94%.
It is described above that the present invention is briefly described, not by above-mentioned working range limit value, as long as taking the present invention
Thinking and method of work carry out simple modification and apply to other equipment, or make and changing in the case where not changing central scope principle of the present invention
Enter and retouch etc. behavior, within protection scope of the present invention.
Claims (8)
1. a kind of tealeaves mid-infrared light profile classification method of fuzzy K mediations network clustering, it is characterised in that comprise the following steps：
S1, gather tealeaves sample middle infrared spectrum：For the tealeaves sample of different cultivars, tealeaves sample is entered with infrared spectrometer
Row detection, obtains the infrared spectrum information that diffuses in tealeaves sample, spectral information is stored in computer；By tealeaves sample point
For training sample and test sample；
S2, the middle infrared spectrum of tealeaves sample is entered using multiplicative scatter correction, principal component analysis and linear discriminant analysis successively
Row pretreatment；
S3, the test sample to including authentication information in step 2, use a kind of fuzzy K mediations network clustering method differential test
Local tea variety in sample.
2. a kind of tealeaves mid-infrared light profile classification method of fuzzy K mediations network clustering as claimed in claim 1, its feature exist
In the infrared spectrum information that diffuses refers to that the wave-number range of spectrum is 4001.569~401.1211cm in the S1-1, collect
The spectrum of each tealeaves sample is the data of 1868 dimensions.
3. a kind of tealeaves mid-infrared light profile classification method of fuzzy K mediations network clustering as claimed in claim 1, its feature exist
In the classification number that tealeaves sample is set in the S1 is k, number of training nr, test sample number is n.
4. a kind of tealeaves mid-infrared light profile classification method of fuzzy K mediations network clustering as claimed in claim 1, its feature exist
In the S2 is specially：First tealeaves sample middle infrared spectrum is handled with multiplicative scatter correction, then uses principal component analysis
Spectroscopic data compression is carried out, the authentication information that spectroscopic data is finally carried out with linear discriminant analysis extracts.
5. a kind of tealeaves mid-infrared light profile classification method of fuzzy K mediations network clustering as claimed in claim 1, its feature exist
In the S3 is specially：
S3.1, initialization：Determine classification number k, test sample number n and weighted index m0Value, n>k>1 ,+∞>m0>1；Setting changes
Generation number initial value r, maximum iteration rmaxAnd worst error parameter ε；It is determined that the initial classes center c of clusterj,0；
S3.2, calculate fuzzy membership angle value u during the r times iterationij,r；
S3.3, calculate learning rate α during the r times iterationij,r；
S3.4, calculate class center c during the r times iterationj,r；
S3.5, r=r+1, when | | cj,r-cj,r-1| | ＜ ε or r>rmaxThen calculate and terminate, otherwise recalculated since S3.2,
Wherein cj,r-1For the r-1 times iteration when jth class class center.
6. a kind of tealeaves mid-infrared light profile classification method of fuzzy K mediations network clustering as claimed in claim 5, its feature exist
In, calculate the r times iteration when fuzzy membership angle value uij,r：Wherein mrFor the r times iteration when
Weighted index, mr=m0- r Δ m, Δ m=(m0-1)/rmax；uij,rFor the r times iteration when j-th of sample be under the jurisdiction of the mould of the i-th class
Paste is subordinate to angle value, wherein dij=| | xi-cj,r-1| |, xiFor i-th of sample data, dit=| | xi-ct,r-1| |, ct,r-1For r-1
The class center of t classes during secondary iteration.
7. a kind of tealeaves mid-infrared light profile classification method of fuzzy K mediations network clustering as described in claim 5 or 6, it is special
Sign is, calculates learning rate α during the r times iterationij,r：
8. a kind of tealeaves mid-infrared light profile classification method of fuzzy K mediations network clustering as described in claim 5 or 6, it is special
Sign is, calculates class center c during the r times iterationj,r：Wherein dil=| | xi-
cl,r-1| |, cl,rThe class center of -1 l classes when being the r-1 times iteration；αil,rFor the r times iteration when learning rate, anduil,L-th of sample is under the jurisdiction of the fuzzy membership angle value of the i-th class when r is the r times iteration.
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