CN106442399B - A kind of method that near infrared spectrum differentiates the different same kind fresh tea leaves of planting environment - Google Patents
A kind of method that near infrared spectrum differentiates the different same kind fresh tea leaves of planting environment Download PDFInfo
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Abstract
A kind of method that near infrared spectrum differentiates the different same kind fresh tea leaves of planting environment, the different same kind fresh tea leaves sample near infrared spectrums of planting environment are obtained using near infrared spectrometer scanning, then principal component analysis is carried out to fresh leaf sample spectra, the Artificial Neural Network Prediction Model of much information transfer mode is established using principal component as input value again, specifically includes the following steps: the acquisition of fresh leaf sample and classification, spectra collection, Pretreated spectra, fresh leaf spectrum principal component analysis, Artificial Neural Network Prediction Model is established and model verifying.Realize quick, lossless, the accurate judgement of the different same kind fresh tea leaves of planting environment；Result of study provides a kind of new approaches to inquire into the interaction relationship between tea tree and local cultivation ecological environment.
Description
Technical field
The present invention relates to a kind of method of discrimination of same kind fresh tea leaves, more specifically to a kind of near infrared spectrum is sentenced
The not method of the same kind fresh tea leaves of different planting environments.
Background technique
Tea tree and local environment are entities.Tea tree has adapted to local ecological environmental condition in growth and development process,
And these ecological environments are then related to the existence of tea tree, all have a major impact to the form of tea tree, structure, physiology, biochemical characteristic.
Therefore, the tea tree of same kind is under different planting environments, and there are a little different for the physicochemical character of fresh leaf.But
There is presently no a kind of methods of the same kind fresh tea leaves under accurate, lossless different planting environments of differentiation.
Summary of the invention
It is an object of the invention to for currently without a kind of same kind fresh tea leaves side differentiated under different planting environments
The problem of method, provides a kind of method that near infrared spectrum differentiates the different same kind fresh tea leaves of planting environment.
To achieve the above object, the technical solution of the invention is as follows: a kind of near infrared spectrum differentiates different planting environments
The method of same kind fresh tea leaves, scanning obtains the same kind fresh leaf sample near infrared spectrum of different planting environments, then right
Fresh leaf sample spectra carries out principal component analysis, then the different cultivation rings of much information transfer mode are established using principal component as input value
The Artificial Neural Network Prediction Model of border fresh leaf determines the different planting environments of fresh leaf, specifically includes the following steps:
Step 1: the acquisition of fresh leaf sample and classification
The same kind fresh tea leaves sample of two kinds of different planting environments is acquired respectively, it is different according to planting environment, by sample
Random division is 2 set of calibration set and verifying collection；
Step 2: spectra collection
The near infrared spectrum for obtaining whole fresh leaf samples is scanned using Fourier-type near infrared spectrometer；
Step 3: Pretreated spectra
Applied Chemometrics software carries out derivation and smooth pretreatment to the near infrared spectrum of whole fresh leaf samples, then
Convert fresh leaf sample spectra to pairs of data point；
Step 4: fresh leaf sample spectra principal component analysis
Principal component analysis is carried out using spectroscopic data of the Matlab software to whole fresh leaf samples, acquires whole fresh leaf samples
The score Score1 value and Score2 value, number of principal components and its contribution rate of spectroscopic data；
Step 5: establishing Artificial Neural Network Prediction Model
Using preceding 3 principal components of calibration set sample spectra as input value, with the same kind tea of two kinds of different planting environments
Fresh leaf sample establishes standard nets, jump connection nets and Jordan- by optimizing repeatedly for output valve
Tri- kinds of mode of intelligence transmission fresh leaf difference planting environment Artificial Neural Network Prediction Models of Elman nets, compare three kinds of models
Coefficient R and validation-cross root mean square variance RMSECV value,
Wherein coefficient R formula are as follows:
Validation-cross root mean square variance RMSECV formula are as follows:
In formula, R is related coefficient, and n indicates sample number, yiAnd yi' be respectively i-th of sample in sample sets planting environment
Measured value and planting environment predicted value,For the average value of the measured value of i-th of sample in sample sets, i≤n in formula,
Wherein using coefficient R maximum and the smallest model of validation-cross root mean square variance RMSECV as best model, warp
After obtain best calibration set model；
Step 6: model is verified
To avoid the occurrence of overfitting phenomenon, application verification collection sample to three kinds of obtained calibration set forecast result of model into
Performing check, acquired results coefficient R and verifying collection mean square deviation RMSEP indicate that wherein coefficient R is bigger and verifying collection is equal
Variance RMSEP is smaller, and it is better to indicate test effect, if the planting environment predicted value of the near infrared spectrum obtained at this time and cultivation
Environment measured value is almost the same, then it represents that fine to the prediction effect of verifying collection sample, best calibration set model can be accurate
Predict the different planting environments of fresh leaf sample,
Wherein verifying collection mean square deviation RMSEP formula are as follows:
In formula, n indicates sample number, yiAnd yi' it is respectively the planting environment measured value of i-th sample and cultivation in sample sets
Environmental forecasting value, i≤n in formula.
Fresh leaf sample size is 100 parts in the step one, two kinds of different planting environments each 50, fresh leaf sample, fresh leaf
Sample is that calibration set and verifying collect according to the ratio random division of 7:3.
The fresh leaf sample picked in the step one is bud, the first leaf, the second leaf, third leaf, one leaf of a bud, a bud two
Three leaf of leaf and a bud.
Fourier-type near infrared spectrometer in the step 2 is with silent winged generation that II type Fu of Antaris of U.S.'s match
In leaf near infrared spectrometer, spectral scanning range 4000-10000cm-1, resolution ratio 8cm-1, detector InGaAs, each sample
Product acquire 10 spectrum, every time scanning 64 times, take final spectrum of the average value of 10 acquisition spectrum as the sample.
Chemo metric software in the step 3 is 7.0 software of TQ Analyst 9.4.45 software and OPUS.
Compared with prior art, beneficial effects of the present invention:
Near-infrared spectrum technique is based in the present invention, in conjunction with the artificial neuron of principal component analysis and much information transfer mode
Network model determines different same kind fresh leafs of planting environment, realize the different same kind fresh tea leaves of planting environment it is quick,
Accurately, lossless judgement, the same kind fresh tea leaves of the different planting environments of effective solution determine that difficult problems, result of study are to visit
The interaction relationship begged between tea tree and local cultivation ecological environment provides a kind of new approaches.
Detailed description of the invention
Fig. 1 is whole 100 fresh leaf sample spectrum diagrams in the present invention.
Fig. 2 is Enshi City and 43 fresh leaf sample Scores1 value of Xianfeng County Dragon Well tea and Scores2 value spatial distribution in the present invention
Figure.
Fig. 3 is that standard nets information transmits artificial neural network structure in the present invention.
Fig. 4 is that jump connection nets information transmits artificial neural network structure in the present invention.
Fig. 5 is that Jordan-Elman nets information transmits artificial neural network structure 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.
A kind of method that near infrared spectrum differentiates the different same kind fresh tea leaves of planting environment, scanning obtain different cultivation rings
Then the same kind fresh tea leaves near infrared spectrum in border carries out principal component analysis to sample spectra, then using principal component as input value
The Artificial Neural Network Prediction Model for establishing the different planting environment fresh leafs of much information transfer mode determines that the different of fresh leaf are planted
Train environment.Specifically includes the following steps:
Step 1: the acquisition of fresh leaf sample and classification
The same kind fresh tea leaves sample of two kinds of different planting environments is acquired respectively, it is different according to planting environment, by sample
Random division is 2 set of calibration set and verifying collection；Wherein verifying collection fresh leaf sample is for examining fresh leaf difference planting environment school
The robustness of positive collection prediction model.
Step 2: spectra collection
The near infrared spectrum for obtaining whole fresh leaf samples is scanned using Fourier-type near infrared spectrometer (FT-NIR).
Near infrared spectrum (NIRS) is a kind of electromagnetic wave between visible region and mid-infrared light area, have quickly,
The features such as accurately and without pre-processing, agricultural, petrochemical industry, textile industry, pharmaceuticals industry and tobacco are had been widely used at present
In industry.In tealeaves application field, near-infrared spectrum technique successfully realized to caffeine, tea polyphenols total amount it is pre-
It surveys and traces to the source the judgement etc. on ground to tea.
Step 3: Pretreated spectra
Applied Chemometrics software carries out derivation to the near infrared spectrum of whole fresh leaf samples and smooth wait pre-processes, so
It afterwards converts fresh leaf sample spectra to pairs of data point, establishes fresh leaf place of production calibration set prediction model and verifying collection for subsequent
Model.
Step 4: fresh leaf sample spectra principal component analysis (PCA)
Principal component analysis is carried out using spectroscopic data of the Matlab software to whole fresh leaf samples, acquires whole fresh leaf samples
The score Score1 value and Score2 value, number of principal components and its contribution rate of spectroscopic data.
Step 5: establishing artificial neural network (BP-ANN) prediction model
Using preceding 3 principal components of calibration set sample spectra as input value, with the same kind tea of two kinds of different planting environments
Fresh leaf sample establishes standard nets, jump connection nets and Jordan- by optimizing repeatedly for output valve
Tri- kinds of mode of intelligence transmission fresh leaf difference planting environment Artificial Neural Network Prediction Models of Elman nets, compare three kinds of models
Coefficient R and validation-cross root mean square variance RMSECV value,
Wherein coefficient R formula are as follows:
Validation-cross root mean square variance RMSECV formula are as follows:
In formula, R is related coefficient, and n indicates sample number, yiAnd yi' be respectively i-th of sample in sample sets planting environment
Measured value and planting environment predicted value,For the average value of the measured value of i-th of sample in sample sets, i≤n in formula,
It, should wherein using coefficient R maximum and the smallest model of validation-cross root mean square variance RMSECV as best model
Model accuracy highest obtains best calibration set model after comparison.
Step 6: model is verified
To avoid the occurrence of overfitting phenomenon, application verification collection sample to three kinds of obtained calibration set forecast result of model into
Performing check is the fresh leaf planting environment predicted value that verifying collection sample is predicted with three kinds of obtained calibration set prediction models
It is whether consistent with the measured value known.Acquired results coefficient R and verifying, which collect mean square deviation RMSEP, to be indicated,
Middle coefficient R is bigger and verifying collection mean square deviation RMSEP is smaller, indicates that test effect is better；As a result with the number of verifying collection sample
According to being expressed, if the planting environment predicted value of the near infrared spectrum obtained at this time and planting environment measured value are almost the same,
Indicate fine to the prediction effect of verifying collection sample, best calibration set model can accurately predict the different cultivations of fresh leaf sample
Environment.
Wherein verifying collection mean square deviation RMSEP formula are as follows:
In formula, n indicates sample number, yiAnd yi' it is respectively the planting environment measured value of i-th sample and cultivation in sample sets
Environmental forecasting value, i≤n in formula.
Specifically, fresh leaf sample size is 100 parts in the step one, two kinds of different planting environment fresh leaf samples each 50
A, fresh leaf sample is that calibration set and verifying collect according to the ratio random division of 7:3.
Specifically, the fresh leaf sample picked in the step one is bud, the first leaf, the second leaf, third leaf, a bud one
Three leaf of leaf, two leaves and a bud and a bud.
Specifically, the Fourier-type near infrared spectrometer in the step 2 is with the silent winged generation that Antaris of U.S.'s match
II type Fourier transform near infrared instrument, spectral scanning range 4000-10000cm-1, resolution ratio 8cm-1, detector InGaAs,
Each sample acquires 10 spectrum, every time scanning 64 times, takes final spectrum of the average value of 10 acquisition spectrum as the sample.
Specifically, the chemo metric software in the step 3 is TQ Analyst 9.4.45 software and OPUS 7.0
Software.
Specific embodiment one:
(1) acquisition of fresh leaf sample and classification
Totally 100, the 43 fresh tea leaves sample of Dragon Well tea of Hubei Province's Enshi City and Xianfeng County is acquired respectively, wherein Enshi City and salty
Each 50,43 fresh leaf sample of Feng County Dragon Well tea.Plucking time is 30 days-April 5 March in 2015；The fresh leaf sample of picking is bud, the
One leaf, the second leaf, third leaf, three leaf of one leaf of a bud, two leaves and a bud and a bud.It is different according to cultivation place, sample is drawn at random
It is divided into 2 set of calibration set and verifying collection, wherein 70 samples of calibration set (43 fresh leaf sample each 35 of Enshi City and Xianfeng County Dragon Well tea
It is a)；Verifying collection 30, sample (Enshi City and each 15 of 43 fresh leaf sample of Xianfeng County Dragon Well tea), verifying collection is for examining calibration set mould
The robustness of type.
(2) spectra collection
Referring to Fig. 1, using the silent winged generation that II type Fourier transform near infrared instrument (FT-NIR) of Antaris of U.S.'s match, choosing
With integrating sphere diffusing reflection optical platform；Spectral scanning range 4000-10000cm-1；Resolution ratio 8cm-1, detector InGaAs.
Each sample acquires 10 spectrum, every time scanning 64 times, takes final spectrum of the average value of 10 acquisition spectrum as the sample.
Before spectra collection, which is preheated into 1h, after keeping room temperature and humidity almost the same, fresh leaf sample is packed into and the instrument
Spectrum is acquired in the matched rotating cup of device, whole fresh leaf sample spectras are referring to Fig. 1.
(3) Pretreated spectra
During spectra collection, it will usually which generating high-frequency noise and baseline drift etc. influences the noise of forecast result of model
Therefore information needs to pre-process spectrum before establishing calibration set model.Therefore Applied Chemometrics software TQ
Analyst 9.4.45 software and 7.0 software of OPUS to the near infrared spectrum of whole fresh leaf samples carry out derivation and it is smooth it is equal in advance
Processing；Then 1557 pairs of data points are converted by fresh leaf sample spectra, is used for subsequent data analysis, establishes discrimination model.
(4) fresh leaf spectrum principal component analysis (PCA)
Principal component analysis is carried out to whole fresh leaf spectrum using Matlab software, acquires number of principal components and its contribution rate.Preceding 8
The contribution rate difference of a principal component is as follows:
1 preceding 8 principal component contributor rate of table
As it can be seen from table 1 PC1 contribution rate is maximum, it is 91.53%, is 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 99.76%, completely can be with
Above-mentioned spectral information is represented, subsequent data analysis is used for.
According to the score Score1 value and Score2 value of whole fresh leaf sample spectral datas that above-mentioned principal component analysis acquires
Information obtains the spatial position distribution map of 43 fresh leaf sample of Enshi City and Xianfeng County Dragon Well tea, referring specifically to Fig. 2.
Figure it is seen that the 43 fresh leaf sample overwhelming majority of Enshi City Dragon Well tea is distributed in the first, second quadrant, Xianfeng County
The 43 fresh leaf sample overwhelming majority of Dragon Well tea is distributed in third and fourth quadrant, has small part sample to have with 43 fresh leaf of Enshi City Dragon Well tea and intersects
Distribution, it is mixed in together.Therefore, sample scores1 and scores2 value only are acquired using principal component analysis and then determines its space
The method of position is unable to reach the accurate purpose for differentiating Enshi City and 43 fresh leaf sample of Xianfeng County Dragon Well tea.
(5) artificial neural network (BP-ANN) prediction model is established
When establishing artificial nerve network model, it is desirable that reduction input variable as far as possible, but also want generation as much as possible
Table original spectral data information, therefore, select with above-mentioned Principal Component Analysis screen preceding 3 principal components (contribution rate of accumulative total for
It 99.76%) is input value, using Enshi City and 43 fresh leaf sample type of Xianfeng County Dragon Well tea as output valve (43 sample of Enshi City Dragon Well tea
Value is 1.0000, and Dragon Well tea 43 fresh leaf sample value in Xianfeng County is 2.0000), by optimizing repeatedly, to establish the same of different planting environments
The Artificial Neural Network Prediction Model of one kind fresh tea leaves sample.During establishing model, due to artificial neural network mould
The difference of type internal information transfer mode, and cause the prediction effect for establishing model that can also generate larger difference.In modeling process
In, it has been respectively compared standard nets, tri- kinds of information of jump connection nets and Jordan-Elman nets pass
The prediction effect for passing mode artificial nerve network model, referring specifically to Fig. 3, by the way that preceding 3 principal components are separately input to 3 kinds of people
In artificial neural networks model, compares three kinds of model coefficient Rs and validation-cross root mean square variance RMSECV value, obtained most
Good calibration set prediction model.Best calibration set model is Jordan-Elman nets transfer mode artificial nerve network model, R
For 0.945, RMSECV 0.169.
(6) model is verified
To avoid the occurrence of overfitting phenomenon, 30 parts of samples of application verification collection test to three kinds of calibration set models, institute
Obtaining result coefficient R and verifying collection mean square deviation RMSEP indicates, referring specifically to following table 2:
23 kinds of artificial nerve network model modeling results of table compare
From table 2 it can be seen that 43 fresh leaf sample standard nets structure artificial nerve net of Enshi City and Xianfeng County Dragon Well tea
Network model calibration set coefficient R be 0.710, validation-cross root mean square variance RMSECV be 0.441, when with verifying collection sample into
When performing check, being verified collection model R is 0.584, RMSEP 0.468.43 fresh leaf jump of Enshi City and Xianfeng County Dragon Well tea
Connection nets structure artificial neural network model calibration set R be 0.734, RMSECV 0.358, when with verifying collect sample
When product are tested, being verified collection model R is 0.604, RMSEP 0.452.43 fresh leaf of Enshi City and Xianfeng County Dragon Well tea
Jordan-Elman nets structure artificial neural network model calibration set R is 0.945, RMSECV 0.169, is collected when with verifying
When sample is tested, being verified collection model R is 0.894, RMSEP 0.224.As it can be seen that in 3 kinds of information transmitting sides of foundation
It is optimal with Jordan-Elman nets structural model in formula artificial neural network mode, and standard nets structural model
It is closer to jump connection nets structural model prediction result, prediction effect is less desirable.
30 verifying collection fresh leaf samples are predicted using best Jordan-Elman nets structural model, differentiate knot
Fruit is specifically shown in Table 3.From table 3 it can be seen that best calibration set model can accurately predict the milpa of unknown fresh leaf sample, reach
Ideal prediction effect, determination rate of accuracy 100% are arrived.As it can be seen that Jordan-Elman nets structure artificial nerve net
Quick, the accurate differentiation of different planting environment fresh leaf samples may be implemented in network model.
3 30 verifyings of table collect sample prediction result
Present invention application near-infrared spectrum technique, first scanning obtains the near infrared spectrum of fresh leaf sample, and noise is effectively reduced
After information, principal component analysis is carried out to sample spectra and acquires Score1 value and Score2 value, but 2 classes difference can't be distinguished completely
The fresh leaf sample of planting environment；Then using the Artificial Neural Network with good nonlinear characteristic, former 3 principal components
For input value, tri- kinds of standard nets, jump connection nets and Jordan-Elman nets structures are established
Different planting environments fresh leaf artificial nerve network model, obtain with Jordan-Elman nets structural model prediction effect
Most preferably, verifying collection model R is 0.894, RMSEP 0.224.Realize the different same kind fresh tea leaves of planting environment it is quick,
Accurate to differentiate, the different same kind fresh tea leaves of planting environment of effective solution determine existing problems.Meanwhile result of study
A kind of new approaches are provided to inquire into the interaction relationship between tea tree and local cultivation ecological environment.
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 (3)
1. a kind of method that near infrared spectrum differentiates the different same kind fresh tea leaves of planting environment, which is characterized in that scanning obtains
Then the same kind fresh leaf sample near infrared spectrum of different planting environments carries out principal component analysis to fresh leaf sample spectra, then
The Artificial Neural Network Prediction Model of the different planting environment fresh leafs of much information transfer mode is established using principal component as input value
Determine the different planting environments of fresh leaf, specifically includes the following steps:
Step 1: the acquisition of fresh leaf sample and classification
The same kind fresh tea leaves sample of two kinds of different planting environments is acquired respectively, it is different according to planting environment, sample is random
Be divided into 2 set of calibration set and verifying collection, the fresh leaf sample of picking is bud, the first leaf, the second leaf, third leaf, one leaf of a bud,
Three leaf of two leaves and a bud and a bud；
Step 2: spectra collection
The near infrared spectrum for obtaining whole fresh leaf samples, the Fourier-type near-infrared are scanned using Fourier-type near infrared spectrometer
Spectrometer is with the silent winged generation that II type Fourier transform near infrared instrument of Antaris of U.S.'s match, spectral scanning range 4000-
10000cm-1, resolution ratio 8cm-1, detector InGaAs, each sample acquires 10 spectrum, and it is each to scan 64 times, it takes 10 times
Acquire final spectrum of the average value of spectrum as the sample；
Step 3: Pretreated spectra
Applied Chemometrics software carries out derivation and smooth pretreatment to the near infrared spectrum of whole fresh leaf samples, then will be fresh
Leaf sample spectra is converted into pairs of data point；
Step 4: fresh leaf sample spectra principal component analysis
Principal component analysis is carried out using spectroscopic data of the Matlab software to whole fresh leaf samples, acquires whole fresh leaf sample spectras
The score Score1 value and Score2 value, number of principal components and its contribution rate of data；
Step 5: establishing Artificial Neural Network Prediction Model
Using preceding 3 principal components of calibration set sample spectra as input value, with the same kind fresh tea leaves of two kinds of different planting environments
Sample is output valve, by optimizing repeatedly, establishes standard nets, jump connection nets and Jordan-
Tri- kinds of mode of intelligence transmission fresh leaf difference planting environment Artificial Neural Network Prediction Models of Elman nets, compare three kinds of models
Coefficient R and validation-cross root mean square variance RMSECV value,
Wherein coefficient R formula are as follows:
Validation-cross root mean square variance RMSECV formula are as follows:
In formula, R is related coefficient, and n indicates sample number, yiAnd yi ’The planting environment actual measurement of i-th of sample respectively in sample sets
Value and planting environment predicted value,For the average value of the measured value of i-th of sample in sample sets, i≤n in formula,
Wherein using coefficient R 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 6: model is verified
To avoid the occurrence of overfitting phenomenon, application verification collection sample examines three kinds of obtained calibration set forecast result of model
It tests, acquired results coefficient R and verifying collection mean square deviation RMSEP are indicated, wherein coefficient R is bigger and verifies collection mean square deviation
RMSEP is smaller, and it is better to indicate test effect, if the planting environment predicted value and planting environment of the near infrared spectrum obtained at this time
Measured value is almost the same, then it represents that fine to the prediction effect of verifying collection sample, best calibration set model can be predicted accurately
The different planting environments of fresh leaf sample,
Wherein verifying collection mean square deviation RMSEP formula are as follows:
In formula, n indicates sample number, yiAnd yi' be respectively i-th of sample in sample sets planting environment measured value and planting environment
Predicted value, i≤n in formula.
2. the method that a kind of near infrared spectrum according to claim 1 differentiates the different same kind fresh tea leaves of planting environment,
It is characterized by: fresh leaf sample size is 100 parts in the step one, two kinds of different planting environments each 50, fresh leaf sample,
Fresh leaf sample is that calibration set and verifying collect according to the ratio random division of 7:3.
3. the method that a kind of near infrared spectrum according to claim 1 differentiates the different same kind fresh tea leaves of planting environment,
It is characterized by: the chemo metric software in the step 3 is 7.0 software of TQAnalyst 9.4.45 software and OPUS.
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