CN110308111A - A method of using the near-infrared spectrum technique quick predict Yuanan yellow tea bored yellow time - Google Patents
A method of using the near-infrared spectrum technique quick predict Yuanan yellow tea bored yellow time Download PDFInfo
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Abstract
A method of using the near-infrared spectrum technique quick predict Yuanan yellow tea bored yellow time, comprising: the acquisition of fresh leaf sample and classification；Scanning obtains the near infrared spectrum of different bored yellow time fresh leaf samples；After carrying out pretreatment cancelling noise information to sample spectra, it converts sample spectra to pairs of data point；Whole spectroscopic datas are divided into 20 subintervals again, establish the least square method supporting vector machine method model of each subinterval data respectively, filter out the best subinterval data of modeling；Using Principal Component Analysis extracting, compression optimal spectrum subinterval information；Using principal component scores as input value, neuron number and transmission function are constantly adjusted, unsupervised Kohonen structure artificial neural network prediction model is established；Model robustness is examined.The purpose for improving prediction bored yellow time accuracy and enhancing Model Practical is played in quick, accurate, the objective prediction for realizing the yellow tea sample bored yellow time.
Description
Technical field
It is more specifically to a kind of to apply near infrared spectrum the present invention relates to the method for prediction yellow tea bored yellow time a kind of
The method of technology quick predict Yuanan yellow tea bored yellow time.
Background technique
Yellow tea is one of big teas in China six, and Hubei Province is then the longest with Yuanan yellow tea history.Hubei Yuanan weather temperature
Fertile with abundant rainfall, loosing soil, good ecological environment is highly beneficial to growth of tea plant, ensure that tea leaf quality is excellent,
Therefore Yuanan yellow tea is known as good merchantable brand in the tea of Hubei, is always tea product more salable, annual product in tea market
All supply falls short of demand, preferred by everyone.
In processing, the standard of plucking of Yuanan yellow tea fresh leaf is generally simple bud, one leaf of a bud and two leaves and a bud, it is desirable that fresh leaf
Delicate, fresh, fresh leaf cleanliness is good, and the yellow tea golden yellow color processed in this way, pekoe appear, and fragrance faint scent is lasting, and flavour mellow time
Sweet, soup look is apricot yellow bright, and tea residue is light yellow neat and well spaced.The basic processing technology of Yuanan yellow tea are as follows: water-removing-is bored yellow-dry；Wherein bored Huang work
Sequence is the manufacturing procedure of most critical, is the basis to form yellow tea exclusive " dry tea is golden yellow, soup look is yellow bright and tea residue is light yellow ".Yellow tea
There is close corresponding relationship between bored Huang time and component content, the researchs such as Chen Ling are pointed out: with the growth of bored yellow time, being done
The decline of tea color green, yellow appears, soup look by it is green it is bright become pale yellow bright, the fresh alcohol of flavour is tasty and refreshing, slightly tender perfume (or spice)；In bored yellow mistake
Cheng Zhong, the decline of Polyphenols content, amino acid content rise, and soluble protein slowly declines, and soluble sugar content slightly increases,
Water extraction content is slightly promoted as the bored yellow time extends, and chlorophyll a, chlorophyll b and Chlorophyll content gradually decrease.Cause
This, under the same conditions, the bored Huang time is the key factor for influencing yellow tea millet paste green tea.As it can be seen that holding accurately and in time
The yellow tea bored yellow time helps to improve and improves the quality of yellow tea, improves commodity value, promotes the economic value of yellow tea；Also it helps
Enhance itself economic strength in local tea grower, improve itself living condition, realizes the target shaken off poverty early as early as possible.Therefore, how
The real-time judgment yellow tea bored yellow time, it is very important.
Currently, Yuanan yellow tea, which generallys use artificial method, voluntarily records bored yellow time, but the busy season processed in yellow tea, by
Very big in labor intensity, tea processing personnel are easy to fatigue conditions occurred, thus will cause manual time-keeping method subjectivity
It is very strong and can not precisely grasp the bored yellow time, it is easy to cause and the excessive situation of the bored yellow insufficient or bored Huang of yellow tea occurs, it might therefore
Cause to reduce yellow tea quality, brings biggish economic loss to processing factory and dealer.Therefore, need it is a kind of in time, it is accurate,
The objectively method of prediction Yuanan yellow tea bored yellow time.
Summary of the invention
It is an object of the invention to can not the real-time and precise palm using the manual record bored yellow time for existing Yuanan yellow tea
It holds the bored yellow time, easily lead to the defects of tea leaf quality reduces, a kind of application near-infrared spectrum technique quick predict Yuanan Huang is provided
The method of tea bored yellow time.
To achieve the above object, the technical solution of the invention is as follows: a kind of application near-infrared spectrum technique quick predict
The method of Yuanan yellow tea bored yellow time, scanning obtain the near infrared spectrum of different bored yellow time fresh leaf samples, to sample spectra into
After row pretreatment cancelling noise information, sample spectra is converted into pairs of data point；Whole spectroscopic datas are divided into 20 again
A subinterval establishes the least square method supporting vector machine method model of each subinterval data respectively, filters out the best of modeling
Subinterval data；It reapplies Principal Component Analysis and compression extracting is carried out to best subinterval information data point, with principal component scores
To input numerical value, by adjusting hidden layer neuron number and information transmission function, the artificial of different bored yellow time samples is established
Neural network prediction model, for predicting the bored yellow time of yellow tea, specifically includes the following steps:
Step 1: the acquisition of fresh leaf sample and classification
Acquire three Hubei Province, Anji, Yuanan County white tea kind simple bud, one leaf of a bud and two leaves and a bud different parts fresh leaf samples
Product carry out bored Huang to sample after finishing, while precisely recording the bored yellow time；It is different according to the bored yellow time, sample is divided into school
2 set of positive collection and verifying collection are respectively used to establish calibration set near infrared prediction model and steady to calibration set prediction model
Strong property is tested；
Wherein, one leaf of a bud by simple bud, the first leaf and it is longer stalk constitute, two leaves and a bud by simple bud, the first leaf, the second leaf and
Long stalk is constituted；
Step 2: spectral scan
The near infrared spectrum for obtaining all bored yellow samples, spectral scanning range 4000- are scanned using near infrared spectrometer
10000cm-1, resolution ratio 8cm-1, detector InGaAs, each sample acquires 3 spectrum, every time scanning 64 times, then to 3
The spectrum of secondary acquisition is averaged, and the final spectrum using averaged spectrum as the sample establishes model for subsequent；
Step 3: spectral noise information pre-processing
Using a plurality of chemo metric softwares near infrared spectrum obtained in step 2 using vector method for normalizing into
Row denoising sound preconditioning, improves the signal-to-noise ratio of spectrum, is conducive to establish steady prediction model；After spectrum denoising, then by sample
Product spectral translation is pairs of data point；
Step 4: spectrum subinterval divides
Whole spectral information data are divided into 20 spectral information subintervals, precisely the screening reflection sample bored yellow time
Spectral information subinterval, establish least square method supporting vector machine model for subsequent；
Step 5: least square method supporting vector machine (LS-SVM) model foundation
Present invention application LS-SVM method establishes the prediction model in each spectral information subinterval respectively, and comparison model is related
Coefficient (correlation coefficient of calibration, Rc) and validation-cross root mean square variance (root mean
Square error of calibration, RMSECV) size, the optimal spectrum information subinterval number that preliminary screening models out
According to achieving the purpose that the spectral information for precisely screening the reflection bored yellow time, wherein Rc is maximum, RMSECV is minimum, indicates to establish
Least square method supporting vector machine model result is best,
Wherein, RMSECV calculation formula are as follows:
Rc calculation formula are as follows:
In formula, n indicates sample number, yi and yi' be respectively i-th of sample in sample sets measured value and predicted value,For sample
Product concentrate the average value of the measured value of i-th of sample, i≤n in formula；
Step 6: unsupervised Kohonen structure artificial neural network prediction model is established
Using the bored yellow time of the further precisely pre- sample of nonlinear Artificial Neural Network, comprising:
1) optimal spectrum information subinterval principal component analysis
Principal component analysis is carried out using optimal spectrum information subinterval data of the principal component analytical method (PCA) to screening,
Obtain independent contribution rate value, contribution rate of accumulative total value and the principal component scores of each principal component；Principal component contribution rate of accumulative total >=
85% just effectively can model spectrum subinterval information by representative sample；
2) Artificial Neural Network Prediction Model is established
Using the principal component scores in optimal spectrum information subinterval as input value, using the sample different bored yellow time as output valve,
The Artificial Neural Network Prediction Model of unsupervised Kohonen structure is established using 2 software of Neuro Shell,
Unsupervised Kohonen structure artificial neural network contain 1 hidden layer, 3,4,5 neuron numbers and logistic,
Tri- kinds of information transmission functions of linear [0,1] and tanh, comparison model related coefficient (correlation coefficient of
Calibration, Rc) and validation-cross root mean square variance (root mean square error of calibration,
RMSECV) size obtains best near infrared prediction model, wherein Rc is bigger, RMSECV is smaller, indicates model prediction effect
Fruit is better；
Wherein RMSECV calculation formula are as follows:
Rc calculation formula are as follows:
In formula, n indicates sample number, yi and yi' be respectively i-th of sample in sample sets measured value and predicted value,For sample
Product concentrate the average value of the measured value of i-th of sample, i≤n in formula；
Wherein using coefficient R c maximum and the smallest model of validation-cross root mean square variance RMSECV as best model, warp
After obtain best calibration set model；
Step 7: model robustness is examined
It tests using collection sample is all verified to the Artificial Neural Network Prediction Model effect of different bored yellow times, institute
Obtain result related coefficient (correlation coefficient of prediction, Rp) and verifying mean square deviation (root
Mean square error of prediction, RMSEP) it indicates, wherein Rp is bigger, RMSEP is smaller, indicates that model is steady
Property it is better, can accurate pre- sample the bored yellow time；
Wherein RMSEP calculation formula are as follows:
Rp calculation formula are as follows:
In formula, n indicates sample number, yi and yi' be respectively i-th of sample in sample sets measured value and predicted value, i in formula
≤n。
In the step one fresh leaf sample size be 120 parts, fresh leaf sample according to 3:1 ratio cut partition be calibration set and
Verifying collection, wherein 90, calibration set sample, verifying collect 30, sample.
The optimal spectrum information subinterval data that modeling is filtered out in the step 5 are 6406.4-6703.4cm-1。
In the step 6 using principal component analytical method (PCA) to the optimal spectrum information subinterval data of screening into
When row principal component analysis, the spectral information in optimal spectrum subinterval is represented using preceding 3 principal components.
4 neurons and logistic information transmission function is selected to establish different bored Huang time sample in the step 6
Unsupervised Kohonen structure artificial neural network model.
Compared with prior art, beneficial effects of the present invention:
1, after the present invention first rejects bored yellow sample noise information, pairs of data point is converted by sample spectra and is saved, so
Whole spectroscopic datas are divided into 20 subintervals afterwards, establish the least square method supporting vector machine of each subinterval data respectively
Method model filters out the best subinterval data of modeling；Best subinterval data point is carried out using principal component analytical method
Principal component analysis is compressed spectral information and is extracted, and the operand of model is advantageously reduced, and increases the robustness of model；
The Artificial Neural Network Prediction Model for establishing the different bored yellow times using principal component scores as input value again, realize sample it is bored yellow when
Between quick, accurate, objective prediction, play the purpose for improving prediction bored yellow time accuracy and enhancing Model Practical.
2, the present invention precisely screens the spectral information of reflection sample bored yellow time using least square method supporting vector machine method,
The principal component scores of optimal spectrum information are obtained by constantly practicing comparison prediction effect, as input data, by not
The disconnected neuron number and transmission function optimized inside unsupervised Kohonen structure artificial neural network method repeatedly,
The accurate pre- sample bored yellow time is achieved the purpose that.
Detailed description of the invention
Fig. 1 is whole bored yellow time atlas of near infrared spectra of 120 fresh leafs samples difference in the present invention.
Fig. 2 is unsupervised Kohonen Artificial Neural Network Structures in the present invention.
Specific embodiment
Below in conjunction with Detailed description of the invention and specific embodiment, the present invention is described in further detail.
Referring to Fig. 1 to Fig. 2, a method of using the near-infrared spectrum technique quick predict Yuanan yellow tea bored yellow time, sweep
The near infrared spectrum for obtaining different bored yellow time fresh leaf samples is retouched, pretreatment cancelling noise information then is carried out to sample spectra
Afterwards, then pairs of data point is converted in excel table for sample spectra to save；Then whole spectroscopic datas are divided into 20
A subinterval establishes the least square method supporting vector machine method model of each subinterval data respectively, filters out the best of modeling
Subinterval data；It reapplies Principal Component Analysis and compression extracting is carried out to sub- block information data point, be defeated with principal component scores
Enter numerical value, by adjusting hidden layer neuron number and information transmission function, establishes the artificial neuron of different bored yellow time samples
Network Prediction Model, for predicting the bored yellow time of yellow tea.Specifically includes the following steps:
Step 1: the acquisition of fresh leaf sample and classification
Acquire three Hubei Province, Anji, Yuanan County white tea kind simple bud, one leaf of a bud and two leaves and a bud different parts fresh leaf samples
Product carry out bored Huang to sample after finishing, while precisely recording the bored yellow time；It is different according to the bored yellow time, sample is divided into school
2 set of positive collection and verifying collection are respectively used to establish calibration set near infrared prediction model and steady to calibration set prediction model
Strong property is tested.
Wherein, one leaf of a bud above is made of simple bud, the first leaf and longer stalk, and two leaves and a bud is by simple bud, the first leaf, the
Two leaves and long stalk are constituted；
Step 2: spectral scan
Using the silent winged generation of U.S.'s match, your II type Fourier Transform Near Infrared instrument of Antaris scanning obtains all bored Huangs
The near infrared spectrum of sample, spectral scanning range 4000-10000cm-1, resolution ratio 8cm-1, detector InGaAs, each sample
Product acquire 3 spectrum, every time scanning 64 times；Then the spectrum acquired to 3 times is averaged, using averaged spectrum as the sample
Final spectrum establishes model for subsequent.
Step 3: spectral noise information pre-processing
Since there is background informations existing for high-frequency noise and baseline upset during spectral scan, if not to light
Spectral noise pre-processed, be directly used in establish prediction model will cause model prediction effect it is poor, and model is also unstable
It is fixed, therefore Denoising disposal is carried out to former spectral information before modeling.Using a plurality of chemo metric softwares to step in this step
Near infrared spectrum obtained in rapid two is using smooth, first derivative, second dervative, multiplicative scatter correction and vector method for normalizing
Denoising sound preconditioning is carried out, the signal-to-noise ratio of spectrum is improved, to be conducive to establish steady prediction model；Vector normalizing therein
The influence of the linear translation in sample spectra can be deducted by changing preprocess method, individually be corrected to every spectrum, with compared with
Strong information processing capability is optimal spectrum preprocess method.
After spectrum denoising, then by sample spectra pairs of data point (X-Y one-to-one correspondence) is converted, is stored in excel
In table.
Step 4: spectrum subinterval divides
Near infrared spectrum contains all information of sample, such as the place of production, plucking time, bored yellow time and component content information
Deng, therefore, in order to improve the prediction effect of model, need to screen the spectral information wave band of reflection sample bored yellow time, removal with
Model useless spectral information；Model prediction accuracy not only can be improved, the operand of model can also be substantially reduced, reduce
The operation time of modeling reduces modeling cost.Therefore, whole spectral information data are divided into 20 spectral informations by the present invention
Subinterval, precisely the spectral information subinterval of screening reflection sample bored yellow time, establishes least square supporting vector for subsequent
Machine model.
Step 5: least square method supporting vector machine (LS-SVM) model foundation
Least square method supporting vector machine (LS-SVM) model is a kind of classification method based on statistical theory, is mainly passed through
The prediction that a separating hyperplane carrys out implementation model is constructed, is widely used with good generalization ability and robustness；Meanwhile
LS-SVM method realizes final decision function by solving system of linear equations, reduces to a certain extent and solves difficulty, mentions
High solving speed, to be allowed to be suitable in general practical application.Therefore, in order to Yuanan yellow tea sample is better anticipated
The bored yellow time, present invention application LS-SVM method establishes the prediction model in each spectral information subinterval, comparison model respectively
Related coefficient (correlation coefficient of calibration, Rc) and validation-cross root mean square variance (root
Mean square error of calibration, RMSECV) size, the optimal spectrum information sub-district that preliminary screening models out
Between data, achieve the purpose that the spectral information for precisely screening the reflection bored yellow time.Wherein, Rc is maximum, RMSECV is minimum, indicates to build
Vertical least square method supporting vector machine model result is best；
Wherein, RMSECV calculation formula are as follows:
Rc calculation formula are as follows:
In formula, n indicates sample number, yi and yi' be respectively i-th of sample in sample sets measured value and predicted value,For sample
Product concentrate the average value of the measured value of i-th of sample, i≤n in formula.
Meanwhile the LS-SVM method also selects to be which kind of optimal spectrum preprocess method in verification step three in turn.
Step 6: unsupervised Kohonen structure artificial neural network prediction model is established
On the basis of above-mentioned steps five, although tentatively having obtained the spectral information of reflection bored yellow time, input model
Spectroscopic data point it is also more, and be likely to that there is also non-linear relations between each data point, therefore, in order to more accurate
Pre- sample the bored yellow time, the present invention is bored using the further accurate pre- sample of nonlinear Artificial Neural Network
The yellow time.Include:
1) optimal spectrum information subinterval principal component analysis
When establishing Artificial Neural Network, it is desirable that the data of input are less, therefore, it is necessary to further compress,
The spectral information of sample is extracted, and principal component analytical method (PCA) is exactly a kind of method of effective extracting spectral information.Cause
This, using the principal component analysis program in Matlab 2012a software using principal component analytical method (PCA) to the best of screening
Spectral information subinterval data carry out principal component analysis, obtain the independent contribution rate value of each principal component, contribution rate of accumulative total value and
Principal component scores, and just effectively spectrum subinterval information can be modeled by representative sample in principal component contribution rate of accumulative total >=85%.
2) Artificial Neural Network Prediction Model is established
Using the principal component scores in optimal spectrum information subinterval as input value, using the sample different bored yellow time as output valve,
The Artificial Neural Network Prediction Model of unsupervised Kohonen structure is established using 2 software of Neuro Shell；
Unsupervised Kohonen structure artificial neural network method contain 1 hidden layer, 3,4,5 neuron numbers and
Logistic, linear [0,1] and tri- kinds of information transmission functions of tanh.In order to reach optimal prediction effect, a large amount of realities are needed
It tests data and optimal neuron is further screened to 9 kinds of obtained unsupervised Kohonen structure artificial neural networks
Several and transmission function combination, can reach optimal prediction effect；Therefore comparison model related coefficient (correlation
Coefficient of calibration, Rc) and validation-cross root mean square variance (root mean square error of
Calibration, RMSECV) size, obtain best near infrared prediction model, wherein Rc is bigger, RMSECV is smaller, table
Representation model prediction effect is better.
Wherein, RMSECV calculation formula are as follows:
Rc calculation formula are as follows:
In formula, n indicates sample number, yi and yi' be respectively i-th of sample in sample sets measured value and predicted value,For sample
Product concentrate the average value of the measured value of i-th of sample, i≤n in formula.
Wherein using coefficient R c maximum and the smallest model of validation-cross root mean square variance RMSECV as best model, warp
After obtain best calibration set model.
Step 7: model robustness is examined
To avoid the occurrence of over-fitting, a steady bored yellow time prediction model is established, the mesh of practical application is reached
, therefore, test using collection sample is all verified to the Artificial Neural Network Prediction Model effect of different bored yellow times, institute
Obtain result related coefficient (correlation coefficient of prediction, Rp) and verifying mean square deviation (root
Mean square error of prediction, RMSEP) it indicates, wherein Rp is bigger, RMSEP is smaller, indicates that model is steady
Property it is better, can accurate pre- sample the bored yellow time.
Wherein RMSEP calculation formula are as follows:
Rp calculation formula are as follows:
In formula, n indicates sample number, yi and yi' be respectively i-th of sample in sample sets measured value and predicted value, i in formula
≤n。
Specifically, fresh leaf sample size is 120 parts in the step one, fresh leaf sample is drawn at random according to the ratio of 3:1
It is divided into calibration set and verifying collection, wherein 90, calibration set sample, verifying collection 30, sample.
Specifically, the optimal spectrum information subinterval data for filtering out modeling in the step 5 are 6406.4-
6703.4cm-1。
Specifically, in the optimal spectrum information sub-district using principal component analytical method (PCA) to screening in the step 6
Between data carry out principal component analysis when, the spectral information in optimal spectrum subinterval is represented using preceding 3 principal components.
Specifically, when 4 neurons and logistic information transmission function being selected to establish Bu Tong bored yellow in the step 6
Between sample unsupervised Kohonen structure artificial neural network model.
In conclusion the present invention provides a kind of application near-infrared spectrum technique combination unsupervised Kohonen structure
For Artificial Neural Network for accurately predicting the bored yellow time of yellow tea sample, the present invention normalizes pretreatment side by vector
Method obtains optimal spectrum, by least square method supporting vector machine method and unsupervised Kohonen structure artificial neural network
Method combines, and realizes the bored Huang of Yuanan yellow tea sample to simple bud, the processing of three standard of plucking of one leaf of a bud and two leaves and a bud
The accurate prediction of time.It greatly reduces model calculation amount, simplify model structure, while improving the prediction accuracy of model
With the practicability of enhancing model.
Specific embodiment one:
(1) acquisition of fresh leaf sample and classification
Hubei Province, Anji, Yuanan County white tea kind simple bud (no stalk), one leaf of a bud are acquired (by simple bud, the first leaf and longer stalk
Constitute) and two leaves and a bud (being made of simple bud, the first leaf, the second leaf and long stalk) three totally 120, different parts fresh leaf sample.Sample
Bored Huang is carried out after product water-removing, while precisely recording the bored yellow time.Different according to the bored yellow time, sample is according to 3:1 ratio cut partition
2 set of calibration set and verifying collection, wherein 90 samples of calibration set, verify collection 30, sample, for examining calibration set model
Robustness.
(2) spectral scan
Using II type Fourier Transform Near Infrared instrument (FT-NIR) of the silent winged generation that Antaris of U.S.'s match, select integral
Ball diffusing reflection optics platform scanner obtains the near infrared spectrum of all bored yellow samples, spectral scanning range 4000-10000cm-1, point
Resolution 8cm-1, detector InGaAs.Each sample acquires 3 spectrum, every time scanning 64 times, the spectrum then acquired to 3 times
It is averaged, the final spectrum using averaged spectrum as the sample establishes model for subsequent.
Before scanning sample spectra, which is preheated 30 minutes, keeps room temperature and humidity basic one
After cause, then sample being fitted into rotating cup matched with instrument and carries out spectral scan, the dress sample thickness of each sample is consistent,
Guarantee that near infrared light can not penetrate sample, all bored yellow sample spectra is referring to Fig. 1.
(3) spectral noise information pre-processing
During spectra collection, it will usually which generating background information existing for high-frequency noise and baseline upset etc. influences model
Therefore the noise information of prediction effect needs to pre-process spectrum before establishing calibration set model.Applied Chemometrics
Software TQ Analyst 9.4.45 software and 7.0 software of OPUS carry out the near infrared spectrum of all bored yellow samples flat respectively
Sliding, first derivative, second dervative, multiplicative scatter correction and vector normalization pretreatment, then converts each sample spectra to
1560 pairs of data points are used for subsequent data analysis in excel table, establish prediction model.
By comparing, it is known that optimal spectrum preprocess method is vector normalization.
(4) spectrum subinterval divides
Whole spectroscopic data points are divided into 20 spectral information subintervals, the data point that each subinterval is contained is 78
It is a.
(5) least square method supporting vector machine (LS-SVM) model foundation
The least square method supporting vector machine model of each spectrum subinterval data, the table that acquired results are seen below are established respectively
1:
1 least square method supporting vector machine Method Modeling result of table
| Model number | Spectrum range/cm-1 | Related coefficient | RMSECV |
| 1 | 3999.6-4296.6 | 0.4677 | 8.96 |
| 2 | 4300.5-4597.5 | 0.5383 | 8.02 |
| 3 | 4601.3-4898.3 | 0.5724 | 7.82 |
| 4 | 4902.2-5199.1 | 0.494 | 8.31 |
| 5 | 5203-5500 | 0.4701 | 8.78 |
| 6 | 5503.8-5800.8 | 0.4422 | 9.01 |
| 7 | 5804.7-6101.7 | 0.4552 | 8.94 |
| 8 | 6105.5-6402.5 | 0.8119 | 7.58 |
| 9 | 6406.4-6703.4 | 0.8908 | 6.55 |
| 10 | 6707.2-7004.2 | 0.8077 | 7.94 |
| 11 | 7008-7305 | 0.7587 | 7.01 |
| 12 | 7308.9-7605.9 | 0.7712 | 6.95 |
| 13 | 7609.7-7906.7 | 0.7445 | 7.11 |
| 14 | 7910.6-8207.6 | 0.6048 | 7.53 |
| 15 | 8211.4-8508.4 | 0.6405 | 6.93 |
| 16 | 8512.3-8809.2 | 0.6673 | 6.63 |
| 17 | 8813.1-9110.1 | 0.5973 | 7.94 |
| 18 | 9113.9-9407.1 | 0.6332 | 7.04 |
| 19 | 9410.9-9704 | 0.6531 | 7.25 |
| 20 | 9707.9-10000 | 0.6572 | 7.05 |
The close of 20 subintervals is established respectively using least square method supporting vector machine method it can be seen from table 1 above
Infrared model, when RMSECV is minimum and when coefficient R c maximum, the spectrum range modeled at this time is optimal modeling sub-district
Between.Therefore, work as 6406.4-6703.4cm-1When, model related coefficient 0.8908, RMSECV 6.55, the minimum two established at this time
It is best to multiply supporting vector machine model result, it can thus be appreciated that best modeled spectrum subinterval are as follows: 6406.4-6703.4cm-1。
(6) Unsupervised Kohonen structure artificial neural network prediction model is established, comprising:
1) optimal spectrum information subinterval principal component analysis
Principal component is carried out to pretreated bored yellow sample optimal spectrum subinterval data using Matlab 2012a software
Analysis, acquires number of principal components, contribution rate and principal component score value.The contribution rate difference of preceding 8 principal components is as shown in Table 2 below:
2 preceding 8 principal component contributor rates of table
From table 2 it can be seen that PC1 contribution rate is maximum, is 90.78%, drastically reduced from PC1-PC8 principal component contributor rate,
PC8 contribution rate is only 0.01%.Wherein, the contribution rate of accumulative total of tri- principal components of PC1, PC2 and PC3 is 99.64%, can be complete
The spectral information in optimal spectrum subinterval is represented, subsequent data analysis is used for, 3 principal component scores are for example following before calibration set sample
Shown in table 3:
3 principal component scores before 3 calibration set sample of table
2) Unsupervised Kohonen structure artificial neural network prediction model is established
For the robustness for effectively improving model, adverse effect of the input to model of noise information is reduced, it is desirable that when modeling
Input variable is few as far as possible, but also effectively to represent original spectral data information, therefore, with above-mentioned principal component analysis screening
Preceding 3 principal component scores be input value, using the sample different bored yellow time as output valve, through repeatedly optimizing neuron number and biography
Delivery function establishes the Artificial Neural Network Prediction Model of 9 kinds of differences bored yellow time.
When establishing model, since the difference of model intrinsic nerve member number and output inter-layer information transmission function can be to mould
Type prediction effect generates large effect；Therefore, in the artificial neural network mould for establishing unsupervised Kohonen structure
When type, it is respectively compared the influence of different neuron numbers and different internal information transmission functions to model prediction result, specifically
Referring to following table 4.By the way that preceding 3 principal component scores are input in the artificial nerve network model, compare model correlation
Coefficients R c and validation-cross root mean square variance RMSECV value, obtain optimum prediction model.Best calibration set model are as follows: slab1 tool
There are 4 neurons, transmission function logistic, at this point, model Rc is 0.974, RMSECV 2.5.
49 kinds of artificial nerve network model results of table
(7) model robustness is examined
To prevent over-fitting, 30 parts of samples of application verification collection test to calibration set model, acquired results
Indicate that concrete outcome is referring to table 4 above with coefficient R p and verifying collection mean square deviation RMSEP.
From table 4, it can be seen that the artificial neural network of different bored yellow time sample unsupervised Kohonen structures
In model, when neuron is 3, transmission function is [0,1] linear, best calibration set model Rc is 0.948, RMSECV is
4.2, when with all 30 verifying collection samples test to calibration set model robustness, being verified collection model Rp is
0.934,4.8 RMSEP.When neuron is 4, transmission function is logistic, best calibration set model Rc be 0.974,
RMSECV is 2.5, when with all 30 verifying collection samples test to calibration set model robustness, is verified collection model
Rp is 0.965, RMSEP 2.7.When neuron is 5, transmission function is logistic, best calibration set model Rc is
0.965,2.6 RMSECV are verified when with all 30 verifying collection samples test to calibration set model robustness
Collect model Rp be 0.941, RMSEP 3.9.As it can be seen that the same unsupervised Kohonen structure of application but it is internal not
In the artificial nerve network model established in the case where with neuron number and different information transmission functions, to have 4 nerves
Member and transmission function are best for the Bu Tong bored yellow time Sample intraocular's Neural Network model predictive result established when logistic, mould
Type prediction effect is best；Secondly the Bu Tong bored Huang time sample to be established when with transmission function being logistic with 5 neurons
Product artificial nerve network model；Worst be with 3 neurons and transmission function is the Bu Tong bored of linear [0,1] Shi Jianli
Yellow time Sample intraocular neural network model.It follows that same Artificial Neural Network Modeling method, but intrinsic nerve member
Several differences with information transmission function can generate large effect to the prediction result for establishing model, therefore, when establishing model,
Reasonably select neuron number and information transmission function.
30 are tested for the best artificial nerve network model established when logistic using 4 neurons and transmission function
The bored yellow time of card collection sample predicted, the table 5 that prediction result is seen below.As can be seen from Table 5, the sample bored yellow time is true
The difference (deviation) of value and predicted value all in ± 1.0 ranges, shows that model predicts that correctly differentiation rate is to all samples
100%.As it can be seen that the Bu Tong bored yellow time sample established when 4 neurons of application are logistic with transmission function
Unsupervised Kohonen structure artificial neural network model realizes quick, the Accurate Prediction to the bored yellow time.
5 30 bored yellow time prediction results (minute) of verifying collection sample of table
In conclusion the present invention provides a kind of application near-infrared spectrum technique combination unsupervised Kohonen structure
Artificial Neural Network is first rejected sample noise information, is obtained best for accurately predicting the bored yellow time of yellow tea sample
Preprocessing procedures are vector normalization, convert pairs of data point in excel table for sample spectra and save；Then will
Whole spectroscopic datas are divided into 20 subintervals, establish the least square method supporting vector machine method of each subinterval data respectively
Model filters out the best subinterval data (6406.4-6703.4cm of modeling-1, account for the 5.00% of whole spectroscopic datas)；Using
Principal component analysis filters out preceding 3 principal components, and the contribution rate of accumulative total of preceding 3 principal components is 99.64%, and former 3 principal components obtain
It is divided into the unsupervised Kohonen structure artificial nerve that input value establishes different neuron numbers and information transmission function
Network Prediction Model, with the unsupervised Kohonen established when being logistic with 4 neurons and transmission function
Structure artificial Neural Network model predictive result is best (Rp=0.965, RMSEP=2.7)；Secondly for 5 neurons and
The unsupervised Kohonen structure artificial neural network model that transmission function is established when being logistic；It is worst for
The unsupervised Kohonen structure artificial neural network that 3 neurons and transmission function are linear [0,1] Shi Jianli
Model.Therefore, the present invention is by least square method supporting vector machine method and unsupervised Kohonen structure artificial nerve net
Network method combines, and perfection realizes the Yuanan yellow tea sample to simple bud, the processing of three standard of plucking of one leaf of a bud and two leaves and a bud
The accurate of product bored yellow time predicts (prediction deviation all in ± 1.0 ranges, predictablity rate 100%), the prediction of foundation
Model does not only reach the mesh of the model calculation amount that substantially reduces (modeling spectroscopic data accounts for whole spectroscopic datas 5.00%), simplified model
, while also acting as the purpose of the prediction accuracy for improving model and enhancing Model Practical.
The above content is a further detailed description of the present invention in conjunction with specific preferred embodiments, and it cannot be said that
Specific implementation of the invention is only limited to these instructions.For those of ordinary skill in the art to which the present invention belongs, exist
Under the premise of not departing from present inventive concept, a number of simple deductions or replacements can also be made, and above structure all shall be regarded as belonging to
Protection scope of the present invention.
Claims (5)
1. a kind of method using the near-infrared spectrum technique quick predict Yuanan yellow tea bored yellow time, which is characterized in that scanning obtains
The near infrared spectrum for obtaining different bored yellow time fresh leaf samples, after carrying out pretreatment cancelling noise information to sample spectra, sample light
Spectrum is converted into pairs of data point；Whole spectroscopic datas are divided into 20 subintervals again, establish each subinterval number respectively
According to least square method supporting vector machine method model, filter out the best subinterval data of modeling；Reapply Principal Component Analysis
Compression extracting is carried out to best subinterval information data point, is input numerical value with principal component scores, by adjusting hidden layer nerve
First number and information transmission function establish the Artificial Neural Network Prediction Model of different bored yellow time samples, for predicting yellow tea
The bored yellow time, specifically includes the following steps:
Step 1: the acquisition of fresh leaf sample and classification
Three Hubei Province, Anji, Yuanan County white tea kind simple bud, one leaf of a bud and two leaves and a bud different parts fresh leaf samples are acquired,
Bored Huang is carried out to sample after finishing, while precisely recording the bored yellow time；It is different according to the bored yellow time, sample is divided into calibration set
With 2 set of verifying collection, it is respectively used to establish calibration set near infrared prediction model and to calibration set prediction model robustness
It tests；
Wherein, one leaf of a bud is made of simple bud, the first leaf and longer stalk, and two leaves and a bud is by simple bud, the first leaf, the second leaf and long stalk
It constitutes；
Step 2: spectral scan
The near infrared spectrum for obtaining all bored yellow samples, spectral scanning range 4000-10000cm are scanned using near infrared spectrometer-1, resolution ratio 8cm-1, detector InGaAs, each sample acquires 3 spectrum, every time scanning 64 times, then acquires to 3 times
Spectrum is averaged, and the final spectrum using averaged spectrum as the sample establishes model for subsequent；
Step 3: spectral noise information pre-processing
Near infrared spectrum obtained in step 2 is gone using vector method for normalizing using a plurality of chemo metric softwares
Noise pretreatment, improves the signal-to-noise ratio of spectrum, is conducive to establish steady prediction model；After spectrum denoising, then by sample light
Spectrum is converted into pairs of data point；
Step 4: spectrum subinterval divides
Whole spectral information data are divided into 20 spectral information subintervals, precisely the light of screening reflection sample bored yellow time
Least square method supporting vector machine model is established for subsequent in spectrum information subinterval；
Step 5: least square method supporting vector machine (LS-SVM) model foundation
Present invention application LS-SVM method establishes the prediction model in each spectral information subinterval, comparison model related coefficient respectively
(correlation coefficient of calibration, Rc) and validation-cross root mean square variance (root mean
Square error of calibration, RMSECV) size, the optimal spectrum information subinterval number that preliminary screening models out
According to achieving the purpose that the spectral information for precisely screening the reflection bored yellow time, wherein Rc is maximum, RMSECV is minimum, indicates to establish
Least square method supporting vector machine model result is best,
Wherein, RMSECV calculation formula are as follows:
Rc calculation formula are as follows:
In formula, n indicates sample number, yi and yi' be respectively i-th of sample in sample sets measured value and predicted value,For sample sets
In i-th of sample measured value average value, i≤n in formula；
Step 6: unsupervised Kohonen structure artificial neural network prediction model is established
Using the bored yellow time of the further precisely pre- sample of nonlinear Artificial Neural Network, comprising:
1) optimal spectrum information subinterval principal component analysis
Principal component analysis is carried out using optimal spectrum information subinterval data of the principal component analytical method (PCA) to screening, is obtained
Independent contribution rate value, contribution rate of accumulative total value and the principal component scores of each principal component；In principal component contribution rate of accumulative total >=85%
Effectively spectrum subinterval information can be modeled by representative sample；
2) Artificial Neural Network Prediction Model is established
Using the principal component scores in optimal spectrum information subinterval as input value, using the sample different bored yellow time as output valve, application
2 software of Neuro Shell establishes the Artificial Neural Network Prediction Model of unsupervised Kohonen structure,
Unsupervised Kohonen structure artificial neural network contain 1 hidden layer, 3,4,5 neuron numbers and logistic,
Tri- kinds of information transmission functions of linear [0,1] and tanh, comparison model related coefficient (correlation coefficient of
Calibration, Rc) and validation-cross root mean square variance (root mean square error of calibration,
RMSECV) size obtains best near infrared prediction model, wherein Rc is bigger, RMSECV is smaller, indicates model prediction effect
Fruit is better；
Wherein RMSECV calculation formula are as follows:
Rc calculation formula are as follows:
In formula, n indicates sample number, yi and yi' be respectively i-th of sample in sample sets measured value and predicted value,For sample sets
In i-th of sample measured value average value, i≤n in formula；
Wherein using coefficient R c maximum and the smallest model of validation-cross root mean square variance RMSECV as best model, through comparing
After obtain best calibration set model；
Step 7: model robustness is examined
It tests using collection sample is all verified to the Artificial Neural Network Prediction Model effect of different bored yellow times, gained knot
Fruit related coefficient (correlation coefficient of prediction, Rp) and verifying mean square deviation (root mean
Square error of prediction, RMSEP) it indicates, wherein Rp is bigger, RMSEP is smaller, indicates model robustness more
It is good, can accurate pre- sample the bored yellow time；
Wherein RMSEP calculation formula are as follows:
Rp calculation formula are as follows:
In formula, n indicates sample number, yi and yi' be respectively i-th of sample in sample sets measured value and predicted value, i≤n in formula.
2. a kind of side using the near-infrared spectrum technique quick predict Yuanan yellow tea bored yellow time according to claim 1
Method, it is characterised in that: fresh leaf sample size is 120 parts in the step one, and fresh leaf sample is school according to the ratio cut partition of 3:1
Positive collection and verifying collect, wherein 90, calibration set sample, verifying collection 30, sample.
3. a kind of side using the near-infrared spectrum technique quick predict Yuanan yellow tea bored yellow time according to claim 1
Method, it is characterised in that: the optimal spectrum information subinterval data that modeling is filtered out in the step 5 are 6406.4-
6703.4cm-1。
4. a kind of side using the near-infrared spectrum technique quick predict Yuanan yellow tea bored yellow time according to claim 1
Method, it is characterised in that: in the optimal spectrum information subinterval using principal component analytical method (PCA) to screening in the step 6
When data carry out principal component analysis, the spectral information in optimal spectrum subinterval is represented using preceding 3 principal components.
5. a kind of side using the near-infrared spectrum technique quick predict Yuanan yellow tea bored yellow time according to claim 1
Method, it is characterised in that: 4 neurons and logistic information transmission function is selected to establish the Bu Tong bored yellow time in the step 6
The unsupervised Kohonen structure artificial neural network model of sample.
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