CN107543838A - A kind of adulterated magnetic resonance detection method for planting butter cream in dilute cream - Google Patents
A kind of adulterated magnetic resonance detection method for planting butter cream in dilute cream Download PDFInfo
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Abstract
The invention provides the adulterated nuclear magnetic resonance discrimination method for planting butter cream in a kind of dilute cream.Step includes：(1) cream samples are collected, and authenticity examination is carried out to sample；(2) adulterated cream samples are prepared using by the cream samples of authenticity examination, gathers sample1H‑NMR；(3) using after data processing1H NMR datas establish PLS DA qualitative models and PCA SVM return Quantitative Analysis Model；(4) unknown whether adulterated cream samples are gathered1H NMR datas, detected using qualitative, the quantitative model of foundation, so as to obtain the qualification result of unknown cream samples.This invention ensures that the accuracy of modeling sample, fast qualitative and the adulterated situation for planting butter cream, technically reliable in dilute cream can be quantitatively detected, it is easy to operate, used model calculating speed is fast, and identification result is accurate, and the quality monitoring for baking goodses such as cream cakes provides technical support.
Description
Technical field
The invention belongs to field of food detection.Specifically, the present invention relates to the adulterated mirror method for distinguishing of food quality and purposes.
More specifically, the present invention relates to the plant butter cream content that plant butter cream and doping whether are adulterated in detection/discriminating dilute cream
Method and purposes.Particularly, it the present invention relates to the use of nuclear magnetic resonance (NMR) and combine offset minimum binary-techniques of discriminant analysis (PLS-
DA) and Principal component analysis-support vector machine (PCA-SVM) Return Law, and then qualitatively and quantitatively detect in dilute cream and plant fat
The method and purposes of cream content.
Background technology
Dilute cream (cream) is the production of processed manufactured fat content 10.0%~80.0% using animal breast as raw material
Product [1].Wherein, the dilute cream of the butterfat containing 30%-40% occupies very big proportion in consumption, and it is after dismissing from flowing shape
It is changed into non-current shape and there is plasticity, therefore also referred to as whipped cream (whip cream) [2], it is to make decorative cakes etc.
The important source material of Western-style bakery.It is with edible hydrogenated oil, sweetener to plant butter cream (non-dairy whip topping)
Deng the margarine [3] for primary raw material, with dilute cream in outward appearance, characteristic and purposes it is all very much like, but price is remote low
In dilute cream.In the application of actual baking goodses, on the one hand businessman pursues health diet to cater to consumer and declares to make at heart
It is 100% animal dilute cream, on the other hand, to pursue more than interests with dilute cream and planting butter cream mixture and substitute
100% whipping cream, the addition for planting butter cream are even more to lean on artificial estimation.In face of bakee market this confusion, in disappear
Association appealing bakery processor cream species used in obligated explicit mark (such as cake room).It is but domestic at present relevant
Cream species and adulterated quantitative examination criteria are not yet put into effect in baking goodses, the missing of related national standard, also to effectively supervision band
Carry out difficulty, and as network bakees the progressively growth of the Western-style bakery consumption such as rise and the cream cake in shop, these
Problem will be protruded more and more.
At present, be to the conventional method of butterfat detection of adulterations sense organ differentiate, gas-chromatography the methods of, wherein sense organ, which differentiates, is
The quality of butterfat is checked by features such as color, taste, characters, and gas-chromatography rule is by cream samples
The aliphatic acid that the fat extracted performs the derivatization out is analyzed, to distinguish the quality of butterfat.However, locate before these methods
Cumbersome, detection time length is managed, while measurement result is affected by human factors big [4-5].
In recent years, also there is scholar using the method for the spectral technique combination Chemical Measurements such as infrared, Raman, fluorescence for oil
Butter of the fat content more than 80% carries out adulterated quantitative differentiate and studies [6-8].Infrared, Raman, fluorescence these spectral techniques are all
It is the method for the Chemical Measurements such as collection of illustrative plates combination PCA, PLS, KNN by gathering whipped fat, adulterated discriminating is carried out to butter.
But by these spectral techniques are limited by its Cleaning Principle, its collection of illustrative plates is to each component accounts energy in the COMPLEX MIXED objects system such as butterfat
Power is limited, it is difficult to realizes and the authenticity of modeling sample is judged that there is the false sample of introducing to cause the true of calibration model
Property reduce and/or exclude special authentic specimen to cause in modeling sample the universality impaired risks of calibration model.
Meanwhile the traditional chemometrics algorithm such as PCA, PLS, KNN is when handling regression modeling problem, is all with classics
Statistics mathematical theory for foundation, be conceived to the basic point of maximum likelihood, it is desirable to which " residual sum of squares (RSS) " is minimum, thus generally need
Want training sample number close to it is infinitely great when its validity could be revealed [21-24] by exact.But because in real work
In to can be obtained sample size often very limited, model overfitting can be made, be that the generalization ability of model is deteriorated.
Nuclear magnetic resonance method has high flux compared with the traditional analysis such as liquid chromatogram, gas chromatography [9-12], weight
The advantages that existing property is good, easy to operate, and structural information is enriched, the global analysis to complex systems such as food has inherent advantage [13-
16], have been used in the analysis of various plants oil and other lipid components [18-20].SVMs (support vector
Machine, SVM) pattern recognition problem to be solved transforms into a quadratic programming optimization problem by method, protects in theory
Globally optimal solution will be obtained by demonstrate,proving it, and technical ability processing nonlinear problem, and can effectively prevents and limited overfitting, particularly suitable
In the data processing [25-28] of small sample set.
But the species of food is various, matrix is complicated, and it is a time and effort consuming to be collected into from the horse's mouth food samples
Thing.Detection method of the prior art may introduce false sample and cause the authenticity of calibration model to reduce and/or incite somebody to action
Special authentic specimen excludes to cause the universality impaired risks of calibration model in modeling sample.
At the same time, these existing spectral method of detection are mixed for butter of the fat content more than 80%
It is assumed that amount differentiates research, and fat-extraction step will be passed through.Therefore, in the prior art not with dilute cream and possible work
Based on the plant butter cream of adulterated additive, using configuring adulterated butter product close to by the way of actual conditions and open
Hair is suitable to the detection method of dilute cream detection.
The content of the invention
Problems to be solved by the invention
Butter cream incorporation dilute cream may will be planted existing in the market effectively to solve the problems, such as to bakee, the invention provides
One kind is based on nuclear magnetic resonance technique, with reference to the adulterated butter for planting butter cream of dilute cream of the PLS-DA and PCA-SVM Returns Law
Qualitative and quantitative detecting method.This method is simple to operate, and a large amount of samples can be handled in the short time, avoids human error, conclusion
Science is reliable, the fast slowdown monitoring suitable for a large amount of actual samples.
The solution used to solve the problem
In order to achieve the above object, the present invention collects the dilute cream for bakeing conventional brand from monitor area and plants butter cream sample
Product, and prepared based on the sample by authenticity examination close to the adulterated butter that uses actually is bakeed, determine it1H-NMR, adulteration qualitative and quantitative model are established using PLS-DA the and PCA-SVM Returns Law, to realize to adulterated plant in dilute cream
The fast and accurately qualitative discrimination and quantitative detection of butter cream.
The present invention provides following method and solved the above problems：
The present invention provides a kind of nuclear magnetic resonance discrimination method for the adulterated plant butter cream of dilute cream, and it includes following step
Suddenly：
(1) standard cream samples and adulterated cream samples are pre-processed；
(2) pretreatment sample that is obtained in acquisition step (1)1H-NMR is composed, and the spectrogram composed to the hydrogen collected enters line number
According to processing；
(3) data obtained in step (2) are used to establish the qualitative discrimination model of the adulterated cream samples and quantitative mirror
Other model；
(4) verify the qualitative discrimination model of the adulterated cream samples or quantitatively differentiate the reliability of model；
(5) using establish it is qualitative, quantitatively differentiate model actual cream samples are differentiated；
Preferably, the standard cream samples are dilute cream and plant butter cream, and the adulterated cream samples are according to certain
The dilute cream and the butter sample of plant butter cream that ratio is prepared.
According to the nuclear magnetic resonance discrimination method of the present invention, wherein, the preparation step of step (1) the adulterated cream samples
Suddenly include：After adding grinding bead in the adulterated cream samples, it is put into oscillator and shakes, make the adulterated cream in bottle
Sample is sufficiently mixed；
Preferably, the preprocess method of step (1) the standard cream samples and/or adulterated cream samples is：Weigh
The 500mg standard cream samples and/or the adulterated cream samples that prepare add 1mL in 2mL EP pipes
CDCl3, it is placed in homogenizer, frequency is put into centrifuge after being 30Hz homogeneous 40s, under the conditions of 4 DEG C, rotating speed 8000r/min
Centrifuge 10min；The clear liquid obtained after 600 μ L centrifugations is pipetted in 5mm nuclear magnetic tubes, it is to be measured；Wherein, the adulterated cream samples
Preparation method is：Two kinds of standard cream are weighed in proportion, and the quality summation that each mix ratio selects the two kinds of cream weighed is
10g, it is put into plastic bottle, adds a diameter of 5mm stainless-steel grinding pearl, be put into after being sealed with lid in multitube turbula shaker
1min is shaken, is sufficiently mixed two in bottle kinds of cream；
Preferably, the specific detection parameters of proton nmr spectra detection are in step (2)：Pulse train noesyig1d, inspection
Testing temperature is 297K,1H 90 ° of pulse width P1 are 10.04 μ s, and spectrum width SWH is 6002.40Hz, and centre frequency O1P is
2400.52Hz, pulse delay time D1 are 10s, and incorporation time D8 is 0.01s,13C 90 ° of pulse PCPD2 of decoupling sequence are 260
μ s, it is 4 that sky, which sweeps number DS, and scanning times NS is 32；
Preferably, the data processing method in step (2) is specially：Measure1H-NMR collection of illustrative plates uses Bruker
Topspin3.2 software processings, conversion points are 64K, LB 1.00Hz, are handled with index window function, baseline and phasing are equal
Carried out using manual mode, TMS is internal standard signal；Collection of illustrative plates MestReNova softwares after processing, with the integration segments pair of δ 0.005
Chemical shift section δ 0.40~8.00 carries out subsection integral, and carrying out area after the signal in the regions of δ 7.21~7.30 during removal is composed returns
One change is handled, and is obtained sample nuclear magnetic spectrum and is changed the exemplary two dimensional matrix to be formed, where each row represents a sample, and each column represents
Intensity integration relative value of the sample in same chemical shift.
According to the nuclear magnetic resonance discrimination method of the present invention, the method that wherein step (3) establishes the qualitative discrimination model is
PLS-DA。
According to the nuclear magnetic resonance discrimination method of the present invention, the method that wherein step (3) establishes the quantitatively discriminating model is
The PCA-SVM Returns Law.
According to the nuclear magnetic resonance discrimination method of the present invention, it is before step (1), in addition to analysis is directed to the standard cream
The step of sample authenticity；
Preferably, if sample has abnormal component, the sample that abnormal component be present is given up.
According to the nuclear magnetic resonance discrimination method of the present invention, it is analyzed the step of being directed to the standard cream samples authenticity and wrapped
Include：Gather the standard cream samples1H-NMR, dimensional matrix data is obtained through data processing, this dimensional matrix data is entered
Row PCA is analyzed, to being considered as doubtful authenticity abnormal sample beyond the sample of 95% confidential interval；The abnormal sample is contrasted with putting
Believe similar cream in section1H-NMR, difference signal peak is found out, with reference to 2D-NMR technologies, structure solution is carried out to difference signal
Analysis, to judge that doubtful sample whether there is true sexual abnormality.
According to the nuclear magnetic resonance discrimination method of the present invention, wherein, acquisition step (2) is described1After H-NMR, the hydrogen is composed
Two-dimensional matrix is converted into, PCA-SVM regression models are established according to following steps：
(a) using the subsection integral value of collection of illustrative plates as independent variable, with the relative amount of contained dilute cream fat in adulterated sample
Dependent variable is exported as fitting, is imported in software；The dependent variable calculates according to formula (1)：
Z=aK/ (aK+bJ) ... (1)
Wherein, Z represents the relative amount of contained dilute cream fat in adulterated sample；A represents dilute cream sample Commercial goods labelses
The fat content of mark；B represents to plant the fat content of butter cream sample Commercial goods labelses mark；K represents dilute cream in adulterated sample
Mass percent；J represents to plant the mass percent of butter cream in adulterated sample；
(b) PCA analyses are carried out to the argument data in step (a), dimension-reduction treatment is carried out to original argument；
(c) RBF is used as kernel function, using the new characteristic variable that dimensionality reduction obtains as input, with step (a)
In obtained Z values be fitting output dependent variable, establish SVM regression models；
(d) the penalty parameter c value and kernel functional parameter g for the SVM regression models established using the method optimization of cross validation
Value, to optimize the obtained c values, g values as model parameter, the data set in training step (c) data, establish PCA-SVM
Model；
Preferably, the c values are 256, and the g values are 0.0625.
According to the nuclear magnetic resonance discrimination method of the present invention, wherein, it is specific to actual cream samples Qualitive test in step (5)
Comprise the following steps：
(1) cream samples to be measured are pre-processed according to step (1)；
(2) operated according to step (2), obtained preprocessing solution1H-NMR；
(3) qualitative discrimination model obtained by claim any one of 1-7 is utilized, identifies whether the sample is that sterling is dilute
Cream.
According to the nuclear magnetic resonance discrimination method of the present invention, it is characterised in that to the qualitative mirror of actual cream samples in step (5)
Do not comprise the following steps also：
(4) if identifying that the sample is adulterated cream, by PCA-SVM regression models as claimed in claim 7,
Identify the adulterated amount of plant butter cream of the sample.
According to the nuclear magnetic resonance discrimination method of the present invention, wherein, identifying the adulterated amount of plant butter cream of the sample is included such as
Lower step：
(a) it is described to gather step (2) in claim 11After H-NMR, hydrogen spectrum is converted into two-dimensional matrix, imported into
In the PCA-SVM models established by claim 7, due to：
Z=aK/ (aK+bJ) ... (1)
K+J=1 ... (2)
Wherein, Z represents the relative amount of contained dilute cream fat in adulterated sample；A represents dilute cream sample Commercial goods labelses
The fat content of mark；B represents to plant the fat content of butter cream sample Commercial goods labelses mark；K represents dilute cream in adulterated sample
Mass percent；J represents to plant the mass percent of butter cream in adulterated sample；
Therefore, K=Zb/ (a+bZ-aZ) ... (3)
A=35%, the b=20% in formula (3) are set, is obtained：
K=4Z/ (7-3Z) ... (4)
The adulterated quantitative analysis results for planting butter cream in dilute cream are calculated by formula (4).
Specifically, exemplary step of the present invention is as follows：
1. the authenticity examination of modeling sample
1.1 sample collection：Collect dilute cream conventional on the market and plant butter cream sample.
1.2 sample pretreatment：About 500mg cream samples are weighed in 2mL EP pipes, add 1mL CDCl3, it is placed in
It is put into matter device after homogeneous 40s (30Hz) in centrifuge, 10min (8000r/min) is centrifuged under the conditions of 4 DEG C.Pipette 600 μ L centrifugations
The clear liquid obtained afterwards is to be measured in 5mm nuclear magnetic tubes.
1.3 testing conditions：By machine testing on the sample handled well in 1.2, the hydrogen nuclear magnetic resonance modal data of sample is obtained.Core
Magnetic Instrument measuring condition is：Pulse train noesyig1d, detection temperature 297K,1H 90 ° of pulse width P1 are 10.04 μ s,
Spectrum width SWH is 6002.40Hz, and centre frequency O1P is 2400.52Hz, and pulse delay time D1 is 10s, and incorporation time D8 is
0.01s,13C 90 ° of pulse PCPD2 of decoupling sequence are 260 μ s, and it is 4 that sky, which sweeps number DS, and scanning times NS is 32.
1.4 data processings and the acquisition of two-dimensional matrix：Measure1H-NMR spectrums are used at Bruker Topspin3.2 softwares
Reason, conversion points are 64K, LB 1.00Hz, are handled with index window function, baseline and phasing are entered using manual mode
OK, TMS is internal standard signal (δ 0.00).Collection of illustrative plates MestReNova (version 6.0.1, Spain) software after processing, with δ
0.005 integration segment carries out subsection integral to chemical shift section δ 0.40~8.00, removes the letter in the regions of δ 7.21~7.30 in spectrum
Area normalization processing is carried out after number, sample nuclear magnetic spectrum is obtained and changes the exemplary two dimensional matrix to be formed, where each row represents one
Individual sample, intensity integration relative value of each column representative sample in same chemical shift.
1.5 sample authenticity examinations：The data obtained in 1.4 are imported into the softwares of SIMCA-p 11.0 and carry out PCA analyses, choosing
Data Standard graduation conversion is carried out with centralization method (Center, Ctr).It is considered as doubtful abnormal sample beyond the sample of 95% confidence level
Product, the hydrogen spectrogram of the sample and sample in credibility interval is contrasted, difference signal peak is found out, with reference to a variety of 2D-NMR technologies, to difference
Xor signal carries out component resolving.According to analysis result, to those because the sample that rotten or species is not inconsistent will be used as abnormal sample
Reject, to those because causing the abnormal sample of analysis will containing the material composition for allowing addition in the national standards such as food additives
Retain.Following qualitative, quantitative model foundation will be used for by the cream samples of abnormality inspection.
2. the foundation and application of adulteration qualitative model
The preparation of 2.1 adulterated samples：
The preparation of the adulterated sample of the dilute cream of gradient containing different quality, respectively by plant butter cream mass fraction 15%, 20%,
50%th, 70%, 100% gradient prepares the adulterated dilute cream sample for planting butter cream, obtains dilute cream content different experiments sample；
6 kinds of laboratory sample is formed with sterling dilute cream again, every kind of 10 samples of gradient, totally 60 samples are standby.
Each quality summation for selecting the two kinds of cream weighed is 10g, is put into 15ml plastic bottle, adds diameter and is about
5mm stainless-steel grinding pearl, is put into multitube turbula shaker after being sealed with lid and shakes 1min, fills two in bottle kinds of cream
Divide mixing.
2.2 data obtain：
60 samples are subjected to pre-treatments according in 1.2, under the conditions of 1.3 on machine testing, carried out according to 1.4 at data
Reason, obtains modeling two-dimensional matrix.
The 2.3 qualitative foundation for distinguishing model：
The two-dimensional matrix obtained in 2.2 is imported into the softwares of SIMCA-p 11.0, the Y of addition representative sample type becomes
Amount, numerical value " 1 " represent the dilute cream sample of adulterated plant butter cream, and numerical value " 2 " represents pure dilute cream sample, from unit variance method
(Unitvariance, UV) method converts to data scale.Using PLS-DA methods by by the pure dilute cream sample of Standard graduation conversion
The intensity integration relative value matrix of product, the slight integration of the adulterated dilute cream sample of butter cream is planted with respect to value matrix and class variable
Y is fitted, and obtains qualitative model.
The certificate authenticity of 2.4 qualitative models：
By arrange experiment random repeatedly (n=200) change classified variable Y put in order to obtain it is corresponding different random
Contribution rate of accumulative total value (R2) and predictive ability value (Q2), model validation is tested.Variable Y order is changed into model and suitable
Sequence does not change the R obtained by model2And Q2Regression fit is done between value, if all Q2In R2Under, and Q2Regression straight line
With the intersection point of y-axis in negative semiaxis, illustrate that qualitative model is reliably effective, can use.
The Qualitive test of 2.5 cream samples：
The dilute cream sample for treating Qualitive test is operated according to 1.2-1.4 steps, obtains testing sample nuclear magnetic spectrum
The matrix data formed is changed, is conducted into 2.3 qualitative discrimination models established.The class variable Y of model prediction is 1
When between ± 0.5, cream to be measured is judged to plant the adulterated dilute cream of butter cream；For Y at 2 ± 0.5, it is pure dilute to judge cream to be measured
Cream.
3. the foundation and application of adulterated quantitative model
The adulterated nuclear magnetic resonance method of discrimination for planting butter cream in above-mentioned dilute cream, after being qualitatively judged, in addition to it is quantitative
Judge, the quantitative judgement includes：
The preparation of 3.1 adulterated samples：
Adulterated ratio in mass fraction from 5%-95% (each point between at intervals of 10%) and 0%, 100% respectively
Weigh and plant butter cream and dilute cream in 15ml plastic bottle, each quality summation for selecting the two kinds of cream weighed is 10g.Put
Enter the stainless-steel grinding pearl that diameter is about 5mm, be put into after being sealed with lid in multitube turbula shaker and shake 1min, made in bottle
Two kinds of cream are sufficiently mixed.
The acquisition of 3.2 training sets and test set sample：
14 dilute creams are divided into two groups at random with randperm functions in Matlab, every group of 7 samples；Equally, will
Plant butter creams for 11 and be divided into two groups, one group of 6 sample, one group of 5 sample.Take first group of 7 sample in dilute cream and plant fat milk
First group of 6 sample in oil, sample is prepared according to the method in 3.1 adulterated sample preparations, totally 213 samples are as training set
(training set) sample；Take second group of 7 sample in dilute cream and plant second group of 5 sample in butter cream, mixed according to 3.1
Method in false sample preparation prepares sample, and totally 112 samples are as test set (testing set) sample.
The foundation of 3.3 quantitative models：
(1) 213 training set samples in 3.2 are obtained what a sample collection of illustrative plates converted according to 1.2~1.4 step operations
Subsection integral relative intensity two-dimensional matrix.
(2) using the subsection integral value of collection of illustrative plates as independent variable, with the relative amount of contained dilute cream fat in adulterated sample
Dependent variable is exported as fitting, is imported in Matlab softwares.Dependent variable calculates according to formula (1)：
Z=aK/ (aK+bJ) (1)
Wherein, Z：The relative amount of contained dilute cream fat in adulterated sample；a：Dilute cream sample Commercial goods labelses mark
Fat content；b：Plant the fat content of butter cream sample Commercial goods labelses mark；K represents the quality percentage of dilute cream in adulterated sample
Number；J represents to plant the mass percent of butter cream in adulterated sample.
(3) PCA analyses are carried out to the argument data in 3.3 (2), obtains explaining original argument 99% master of degree
Composition, i.e., dimension-reduction treatment is carried out to original argument.
(4) RBF is used as kernel function, using the new characteristic variable that dimensionality reduction obtains as input, with 3.3 (2)
Obtained Z values are fitting output dependent variable, establish SVM regression models.
(5) penalty parameter c and kernel functional parameter g for the SVM regression models established using the method optimization of cross validation, with
Optimize obtained c, g values train the data set in 3.3 (4) data, establish quantitative model as model parameter.
3.4 quantitative models are evaluated：
Using the quantitative model established, 112 samples of test set are predicted, obtain proton nmr spectra combination PCA-
The R of SVM methods predicted value and the basically identical result of actual value, 112 sample predicted values and actual value2For 97.43%, RMSEP
For 5.87%, verify that the prediction effect of model is good.
The quantitative discriminating of 3.5 cream samples：
The dilute cream sample for treating quantitative discriminating is operated according to 1.2-1.4 steps, obtains testing sample nuclear magnetic spectrum
The matrix data formed is changed, is conducted into 3.3 quantitative models established, contained dilute milk in sample to be tested can be obtained
The relative amount Z values of oil and fat.Due to
Z=aK/ (aK+bJ) (1)
K+J=1 (2)
Wherein, Z：The relative amount of contained dilute cream fat in sample to be tested；a：The fat content of dilute cream sample；b：Plant
The fat content of butter cream sample；K represents the mass percent of dilute cream in sample to be tested；J represents to plant fat milk in sample to be tested
The mass percent of oil.
Solving equations obtain：
K=Zb/ (a+bZ-aZ) (3)
Because bakee the fat content of conventional dilute cream on the market between 35%~38%, and more using fat content as
35% sample is in the majority；The fat content for planting butter cream is in the majority as 20% sample using fat content between 18%~23%, and more；
So a=35%, b=20% in setting formula 3, are obtained：
K=4Z/ (7-3Z) (4)
K values are the mass fraction shared by dilute cream in sample to be tested, and (1-K) value is to be planted in sample to be tested shared by butter cream
Mass fraction, the adulterated quantitative analysis for planting butter cream in dilute cream can obtain by formula 4.
The effect of invention
The present invention in terms of existing technologies, at least has the following advantages that：
(1) sample pretreatment is simple, only needs deuterated solvent dissolving, centrifugation, easy to operation, one-time detection can
The qualitative and quantitative analysis to sample is realized, high flux sample detection can be achieved.
(2) the PCA-SVM Returns Law are used to plant the adulterated quantitative analysis of butter cream in dilute cream by the present invention, can accelerate model
Calculating speed, improve the prediction accuracy of small sample quantities model.
(3) in preferable scheme, present invention finds the adulterated plant butter cream mixture of dilute cream in qualitative, quantitative model
The necessity of the pretreatment of preparation, and optimize the pretreating scheme of foregoing preparation.It is a discovery of the invention that the pre-treatment step of mixing
After carrying out certain optimization, the degree of accuracy of prediction result significantly improves.Grinding bead is added without in pre-treatment step to be mixed
Sample carry out on the premise of fully vibrating, the RMSECV of quantitative model is more than 7.0%, RMSEP and is more than 15%, model accuracy
Model after substantially less than optimizing.
(4) it is of the invention in the relative amount of contained dilute cream fat in calculating adulterated sample in preferable scheme,
Calculation formula (1) and (4) are introduced in modeling, and then are eliminated fatty in the extraction cream that there must be in other methods
Step.
(5) in preferable scheme, the present invention has carried out the authenticity of modeling sample when establishing quantitative detection model
Check, with reference to a variety of 2D-NMR technologies, doubtful exceptional sample is confirmed.By carrying out specificity to sample before modeling
Check, the false sample of introducing can be reduced and cause the authenticity of calibration model to reduce and/or exclude special authentic specimen
The universality impaired risks of calibration model is caused in modeling sample, and then improves model accuracy in actual applications.
Brief description of the drawings
Fig. 1 is that adulterated cream is qualitative, quantitative model Establishing process figure.
Fig. 2 is adulteration qualitative, the quantitative analysis flow chart of cream samples to be measured.
Fig. 3 is dilute cream, plants butter cream CDCl3Extract1H-NMR collection of illustrative plates.
Fig. 4 is dilute cream, plants butter cream CDCl3Extract1H-NMR collection of illustrative plates PCA analyzes PC1/PC2 shot charts.
Fig. 5 is the arrangement experiment of PLS-DA adulteration qualitatives model.
Fig. 6 is the adulterated quantitative model predicted values of PCA-SVM and actual value correlation.Wherein, (a) is training set data；(b)
For test set data.
Fig. 7 is the adulterated predicted value of 21 cream samples to be measured and true value relationship figure.
Embodiment
Describe various exemplary embodiments, feature and the aspect of the present invention in detail below with reference to accompanying drawing.It is special herein
Word " exemplary " mean " be used as example, embodiment or illustrative ".Any embodiment here as illustrated by " exemplary "
It should not necessarily be construed as preferred or advantageous over other embodiments.
In addition, in order to which the present invention is better described, numerous details is given in embodiment below.
It will be appreciated by those skilled in the art that without some details, the present invention can equally be implemented.In other example,
It is not described in detail for method well known to those skilled in the art, means, equipment and step, in order to highlight the master of the present invention
Purport.
Unless otherwise defined, the technology used in the present invention and scientific terminology have general with the technical field of the invention
The identical meanings that logical technical staff is generally understood that.
Term " dilute cream " (cream) refers to, using animal breast as raw material, processed manufactured fat content 10.0%~
80.0% product.It is changed into non-current shape from flowing shape after dismissing and has plasticity, therefore also referred to as beats
Cream (whip cream).
Term " plant butter cream " (non-dairy whip topping) refers to, based on edible hydrogenated oil, sweetener etc.
Want the margarine of raw material.
Term " principal component analysis " (PCA) refers to, try by primal variable be reassembled into one group it is new mutual unrelated
Several generalized variables, while can therefrom take out several less generalized variables according to being actually needed and reflect as much as possible originally
The statistical method of the information of variable.
Term " offset minimum binary " (PLS) refers to that the quadratic sum by minimizing error finds the optimal letter of one group of data
Number matching.Some absolutely not knowable true value are tried to achieve with most simple method, and make square-error sum as minimum.
Term " SVMs " (SVM) is built upon the VC dimensions theory and Structural risk minization principle of Statistical Learning Theory
On the basis of, according to limited sample information in the complexity (the study precision i.e. to specific training sample) of model and study energy
Seek best compromise between power (ability for identifying arbitrary sample without error), in the hope of obtaining best Generalization Ability.
Embodiment
Embodiment is exemplified below to illustrate the present invention, it will be appreciated by those skilled in the art that the example is only exemplary
Illustrate, and the explanation of non-exclusive.
Test main material
In the market acquires 25 parts of different brands, the wherein cream samples (table 1) in the place of production, dilute cream (fat content
35.0%~38.0%) 14 parts and 11 parts of plant butter cream (fat content 18%~23%).4 DEG C of storages of dilute cream；Plant butter cream-
20 DEG C of storages, are melted using preceding normal temperature, avoid multigelation.All samples use before the shelf-life.
Deuterochloroform (CDCl3, deuterated degree：99.8%, CIL Corp. of the U.S.)；Norell 5mm nuclear magnetic tubes (U.S. Norell
Company).
Table 1 is used for125 cream samples information of H-NMR analyses
| Numbering | The place of production | Species | Numbering | The place of production | Species |
| C-1 | France | Dilute cream | N-1 | TaiWan, China | Plant butter cream |
| C-2 | Italy | Dilute cream | N-2 | Chinese Suzhou | Plant butter cream |
| C-3 | Germany | Dilute cream | N-3 | Chinese Tianjin | Plant butter cream |
| C-4 | Chinese Qingdao | Dilute cream | N-4 | Chinese Foshan | Plant butter cream |
| C-5 | New Zealand | Dilute cream | N-5 | Chinese Yancheng | Plant butter cream |
| C-6 | France | Dilute cream | N-6 | Henan China | Plant butter cream |
| C-7 | Britain | Dilute cream | N-7 | Chinese Suzhou | Plant butter cream |
| C-8 | France | Dilute cream | N-8 | Chinese Yancheng | Plant butter cream |
| C-9 | Denmark | Dilute cream | N-9 | South Korea | Plant butter cream |
| C-10 | Germany | Dilute cream | N-10 | Chinese Foshan | Plant butter cream |
| C-11 | Australia | Dilute cream | N-11 | Chinese Jiangmen | Plant butter cream |
| C-12 | France | Dilute cream | |||
| C-13 | Ireland | Dilute cream | |||
| C-14 | Italy | Dilute cream |
Test key instrument equipment
Bruker AVANCE 600MHZ superconduction fourier transform NMRs instrument is (equipped with BBO probes and topspin3.2
Handle software, Bruker companies of Switzerland)；XS204 electronic balances (Mettler Toledo companies of Switzerland)；Centrifuge
5424R centrifuges (German Eppendorf companies)；TARGIN multitubes turbula shaker (Beijing Ta Jin Science and Technology Ltd.s)；
TissueLyser II homogenizers (German Qiagen companies).
The preparation of 1 adulterated sample of embodiment
Qualitative model：Prepare and mix by the gradient for planting butter cream mass fraction 15%, 20%, 50%, 70%, 100% respectively
The dilute cream sample of butter cream is heeled in, obtains dilute cream content different experiments sample；Again laboratory sample 6 is formed with sterling dilute cream
Kind, every kind of 10 samples of gradient, totally 60 samples are standby.
Quantitative model：Respectively by mass fraction from 5%-95% (each point between at intervals of 10%) and 0%, 100%
Adulterated ratio prepare butter sample.
Qualitative, the quantitative quality summation that the two kinds of cream weighed are each selected with butter is 10g, is put into 15ml modeling
Expect in bottle, add the stainless-steel grinding pearl that diameter is about 5mm, be put into multitube turbula shaker and shake after being sealed with lid
1min, it is sufficiently mixed two in bottle kinds of cream.
The preparation of the sample solution of embodiment 2
The cream samples prepared in embodiment 1 about 500mg is weighed in 2mL EP pipes, adds 1mL CDCl3, it is placed in
It is put into matter device after homogeneous 40s (30Hz) in centrifuge, 10min (8000r/min) is centrifuged under the conditions of 4 DEG C.Pipette 600 μ L centrifugations
The clear liquid obtained afterwards is to be measured in 5mm nuclear magnetic tubes.
The acquisition of the quantitative model training set of embodiment 3 and test set sample
14 dilute creams are divided into two groups at random with randperm functions in Matlab, every group of 7 samples；Equally, will
Plant butter creams for 11 and be divided into two groups, one group of 6 sample, one group of 5 sample.Take first group of 7 sample in dilute cream and plant fat milk
First group of 6 sample in oil, sample is prepared according to the method in 1 adulterated sample preparation of embodiment, totally 213 samples are as training
Collect (training set) sample；Take second group of 7 sample in dilute cream and plant second group of 5 sample in butter cream, according to reality
The method applied in 1 adulterated sample preparation of example prepares sample, and totally 112 samples are as test set (testing set) sample.
Embodiment 41H-NMR composes the establishment of condition determination
NMR 1H carrier frequencies are 600.13MHz, use Bruker standard pulse sequence noesyig1d, detection
Temperature is 297K, and 1H 90 ° of pulse width P1 are 10.04 μ s, and spectrum width SWH is 6002.40Hz, and centre frequency O1P is
2400.52Hz, pulse delay time D1 are 10s, and incorporation time D8 is 0.01s, and 13C 90 ° of pulse PCPD2 of decoupling sequence are
260 μ s, scanning times NS are 32, and sky sweeps number DS as 4.
The data processing of embodiment 5 and the acquisition of two-dimensional matrix
Measure1H-NMR spectrums use Bruker Topspin3.2 software processings, and it is 64K, LB 1.00Hz that conversion, which is counted,
Handled with index window function, baseline and phasing are carried out using manual mode, and TMS is internal standard signal (0.00).After processing
Collection of illustrative plates MestReNova (version 6.0.1, Spain) software, with the integration segments of δ 0.005 to chemical shift section δ 0.40
~8.00 carry out subsection integral, remove and carry out area normalization processing in spectrum after the signal in the regions of δ 7.21~7.30, obtain sample
Nuclear magnetic spectrum changes the exemplary two dimensional matrix to be formed, and where each row represents a sample, and each column representative sample is in same chemical potential
Intensity integration relative value in shifting.
The authenticity examination of the modeling sample of embodiment 6
Be collected into 14 sterling dilute creams and 11 are planted into butter cream to operate according to embodiment 2,4,5, the data waited until
Import the softwares of SIMCA-p 11.0 and carry out PCA analyses, carry out data Standard graduation conversion from centralization method (Center, Ctr), exceed
The sample of 95% confidence level is considered as doubtful abnormal sample.From PCA shot charts (Fig. 4), plant butter cream group and occur one
The abnormal sample of other samples is significantly away from, the chemical composition and other plant butter cream samples for representing the sample have conspicuousness poor
It is different.It is original by consulting1H-NMR collection of illustrative plates simultaneously combines the discovery of a variety of 2D-NMR collection of illustrative plates, compared with other plant butter cream samples, this sample
The trend of abnormal deviation can thus be shown in product on PCA shot charts containing vanillic aldehyde.Vanillic aldehyde is that one kind has milk fragrance
The edible spices of breath, widely used in the food such as cream, candy, therefore, although this sample in PCA shot charts away from its
He plants butter cream sample, but he represents the feature of a kind of plant butter cream sample containing vanillic aldehyde, therefore follow-up quantifies
This sample should be retained in adulteration assay, exceptional sample rejecting should not be done.
The foundation of the adulteration qualitative model of embodiment 7, certificate authenticity will obtain 60 adulteration qualitative aggregate samples in embodiment 1 Product are pressedOperated according to embodiment 2,4,5, obtained data are imported into the softwares of SIMCA-p 11.0, add the Y of representative sample type
Variable, numerical value " 1 " represent the dilute cream sample of adulterated plant butter cream, and numerical value " 2 " represents pure dilute cream sample, from unit variance
Method (Unitvariance, UV) method converts to data scale.Using PLS-DA by by the pure dilute cream sample of Standard graduation conversion
The intensity integration relative value matrix of product, the slight integration of the adulterated dilute cream sample of butter cream is planted with respect to value matrix and class variable
Y is fitted, and obtains qualitative model.The class variable Y of model prediction judges cream to be measured to plant fat milk when between 1 ± 0.5
The adulterated dilute cream of oil；For Y at 2 ± 0.5, it is pure dilute cream to judge cream to be measured.
By arrange experiment random repeatedly (n=200) change classified variable Y put in order to obtain it is corresponding different random
Contribution rate of accumulative total value (R2) and predictive ability value (Q2), model validation is tested.Variable Y order is changed into model and suitable
Sequence does not change the R obtained by model2And Q2Regression fit is done between value, if all Q2In R2Under, and Q2Regression straight line
With the intersection point of y-axis in negative semiaxis, illustrate that qualitative model is reliably effective, can use.Fig. 5 is shown, establishes all of model
Q2In R2Under, and Q2Regression straight line and y-axis intersection point in negative semiaxis, illustrate that established qualitative model is reliably effective.
The foundation of 8 adulterated quantitative model of embodiment
By in embodiment 3 using be related in embodiment 1 method prepared by adulterated quantitative model obtain 213 it is adulterated quantitative mixed
Close sample to operate according to embodiment 2,4,5, obtain the subsection integral relative intensity two-dimensional matrix of a sample collection of illustrative plates conversion, according to
Following steps establish quantitative model：
(1) using the subsection integral value of collection of illustrative plates as independent variable, with the relative amount of contained dilute cream fat in adulterated sample
Dependent variable is exported as fitting, is imported in Matlab softwares.Dependent variable calculates according to formula (1)：
Z=aK/ (aK+bJ) (1)
Wherein, Z：The relative amount of contained dilute cream fat in adulterated sample；a：Dilute cream sample Commercial goods labelses mark
Fat content；b：Plant the fat content of butter cream sample Commercial goods labelses mark；K represents the quality percentage of dilute cream in adulterated sample
Number；J represents to plant the mass percent of butter cream in adulterated sample.
(2) PCA analyses are carried out to the argument data in example 8 (1), will obtained to the explanation degree of original argument 99%
Principal component, i.e., dimension-reduction treatment is carried out to original argument, 13 is reduced to from original 1520 inputs independents variable.
(3) RBF is used as kernel function, using the new characteristic variable that dimensionality reduction obtains as input, with example 8 (1)
In obtained Z values be fitting output dependent variable, establish SVM regression models.
(4) penalty parameter c and kernel functional parameter g for the SVM regression models established using the method optimization of cross validation, it is excellent
Optimal c values after change are 256, and optimal g values are 0.0625.To optimize obtained c, g values are as model parameter, 3.3 (4) of training number
Data set in, establish PCA-SVM and return quantitative model.
The quantitative model of embodiment 9 is evaluated
Model-evaluation index：Using training set validation-cross root-mean-square error (RMSECV) and the coefficient of determination of test set
R2, evaluation index of the predicted root mean square error (RMSEP) as regression model.RMSECV is used for the feasibility for evaluating modeling method
And the predictive ability of gained model, RMSEP are used to evaluate predictive ability of institute's established model to external samples, the two values are smaller,
Show that the degree of accuracy of model is higher, predictive ability is better；R2, be correlation coefficient r square, R2Closer to 1, illustrate desired value
It is better that correlation is surveyed between predicted value.
By in embodiment 8 (1) to data the method that PLS is returned, SVM is returned be respectively adopted be modeled.Adopt respectively
The test set of 112 samples is predicted with the adulterated quantitative model of PLS, SVM and PCA-SVM of foundation, the results are shown in Table 1.Knot
Fruit shows, the RMSECV values of SVM models are less than PLS models, and Training R2Value is more than PLS models, illustrates SVM models
Fitting precision is better than PLS models；Meanwhile the RMSEP values of SVM models are less than PLS models, and Testing R2Value is more than PLS
Model, illustrate that the predictive ability of SVM model to external samples is higher than PLS models.It is both SVM models, by PCA dimensionality reductions
SVM models RMSECV, RMSEP, Testing R2Value is superior to the SVM models of no dimensionality reduction, illustrates that PCA dimensionality reductions can ensure
On the premise of former variable information is constant, by Data Dimensionality Reduction, so that original multidimensional problem greatly simplifies, during effective less operation
Between, improve precision of prediction.
Table 2 PLS, SVM and PCA-SVM Parameters in Mathematical Model
The adulteration qualitative of the testing sample of embodiment 10, quantitative discriminating
By it is known whether be it is adulterated plant butter cream and adulterated ratio to be identified 30 part cream samples according to embodiment 2,
4th, 5 steps are operated, and are obtained testing sample nuclear magnetic spectrum and are changed the matrix data to be formed, and are conducted into embodiment 7 and are built
Judge in vertical qualitative model.Table 3 is the calculated value of 30 parts of cream samples class variables.Sample of the calculated value between 1 ± 0.5
For adulterated plant butter cream sample, predicted value between 2 ± 0.5 for dilute cream sample.According to this decision criteria, 21 adulterated samples
Product, 9 dilute cream samples are obtained for correct classification, and it is 100% to differentiate accuracy.
The cream samples Y value table to be measured of table 3
It will be determined as that matrix data corresponding to adulterated sample is imported into the quantitative model that embodiment 8 is built, due to：
Z=aK/ (aK+bJ) (1)
K+J=1 (2)
Wherein, Z：The relative amount of contained dilute cream fat in sample to be tested；a：The fat content of dilute cream sample；b：Plant
The fat content of butter cream sample；K represents the mass percent of dilute cream in sample to be tested；J represents to plant fat milk in sample to be tested
The mass percent of oil.
So solving equations obtain：
K=Zb/ (a+bZ-aZ) (3)
Because bakee the fat content of conventional dilute cream on the market between 35%~38%, and more using fat content as
35% sample is in the majority；The fat content for planting butter cream is in the majority as 20% sample using fat content between 18%~23%, and more；
So a=35%, b=20% in setting formula 3, are obtained：
K=4Z/ (7-3Z) (4)
K values are the mass fraction shared by dilute cream in sample to be tested, and (1-K) value is to be planted in sample to be tested shared by butter cream
Mass fraction, the adulterated quantitative analysis results for planting butter cream in dilute cream can obtain by formula (4).Calculated according to formula (4)
The adulterated mass fraction of plant butter cream of 21 adulterated cream samples, is as a result shown in Fig. 7.As seen from Figure 7, model predication value and true
Real adulterated value is basically identical, the predicted value of 21 samples and the R of actual value2For 97.50%, RMSEP 5.48, illustrate model
Prediction effect is good, can meet the accuracy of detection requirement of routine monitoring.
Comparative example 1
In the preparation process of 1 adulterated sample of embodiment, it is added without stainless-steel grinding pearl and sample to be mixed is filled
On the premise of dividing vibration, the experimentation of embodiment 9 is repeated, model is established using PCA-SVM homing methods.
By evaluating foregoing quantitative model, it is found that its RMSECV is more than 7.0%, RMSEP and is more than 15%, model is accurate
True property is substantially less than the model after vibrating.
Industrial applicability
The country is not yet put into effect about cream species in baking goodses and adulterated quantitative examination criteria at present, related national standard
Missing, difficulty also is brought to effectively supervision, and as network bakees the Western-style bakeries such as rise and the cream cake in shop
The progressively growth of consumption, these problems will be protruded more and more.The present invention has carried out abnormality inspection to modeling sample first, ties
The chemical component difference of a variety of 2D-NMR technical Analysis sample that peels off is closed, is established using the PCA-SVM Returns Law in dilute cream
The adulterated Quantitative Analysis Model of butter cream is planted, and the performance for the model established with traditional PLS algorithms and simple SVM regression algorithms is entered
Contrast is gone, has as a result shown that the predictive ability of the stability of the quantitative model based on PCA-SVM algorithms, accuracy and model is equal
Better than PLS, SVM algorithm, the quality monitoring for baking goodses such as cream cakes on standard market provides technical support.
The foregoing is only a specific embodiment of the invention, but protection scope of the present invention is not limited thereto, any
Those familiar with the art the invention discloses technical scope in, change or replacement can be readily occurred in, should all be contained
Cover within protection scope of the present invention.Therefore, protection scope of the present invention should be based on the protection scope of the described claims.
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Claims (10)
- It is 1. a kind of for the adulterated nuclear magnetic resonance discrimination method for planting butter cream of dilute cream, it is characterised in that it comprises the following steps：(1) standard cream samples and adulterated cream samples are pre-processed；(2) pretreatment sample that is obtained in acquisition step (1)1H-NMR is composed, and the spectrogram composed to the hydrogen collected is carried out at data Reason；(3) data obtained in step (2) are used to establish the qualitative discrimination model of the adulterated cream samples and quantitatively differentiate mould Type；(4) verify the qualitative discrimination model of the adulterated cream samples or quantitatively differentiate the reliability of model；(5) using establish it is qualitative, quantitatively differentiate model actual cream samples are differentiated；Preferably, the standard cream samples are dilute cream and plant butter cream, and the adulterated cream samples is according to a certain percentage The dilute cream of preparation and the butter sample for planting butter cream.
- 2. nuclear magnetic resonance discrimination method as claimed in claim 1, it is characterised in that step (1) the adulterated cream samples Preparation process includes：After adding grinding bead in the adulterated cream samples, it is put into oscillator and shakes, make in bottle described mixes False cream samples are sufficiently mixed.
- 3. nuclear magnetic resonance discrimination method as claimed in claim 1, the method that wherein step (3) establishes the qualitative discrimination model For PLS-DA.
- 4. nuclear magnetic resonance discrimination method as claimed in claim 1, wherein step (3) establish the method for quantitatively differentiating model For the PCA-SVM Returns Law.
- 5. nuclear magnetic resonance discrimination method as claimed in claim 1, it is before step (1), in addition to analysis is directed to the standard The step of cream samples authenticity；Preferably, if sample has abnormal component, the sample that abnormal component be present is given up Product.
- 6. nuclear magnetic resonance discrimination method as claimed in claim 5, it analyzes the step for the standard cream samples authenticity Suddenly include：Gather the standard cream samples1H-NMR, dimensional matrix data is obtained through data processing, by this two-dimensional matrix number According to PCA analyses are carried out, to being considered as doubtful authenticity abnormal sample beyond the sample of 95% confidential interval；Contrast the abnormal sample With similar cream in confidential interval1H-NMR, difference signal peak is found out, with reference to 2D-NMR technologies, structure is carried out to difference signal Parsing, to judge that doubtful sample whether there is true sexual abnormality.
- 7. nuclear magnetic resonance discrimination method as claimed in claim 5, wherein, acquisition step (2) is described1After H-NMR, by the hydrogen Spectrum is converted into two-dimensional matrix, and PCA-SVM regression models are established according to following steps：(a) using the subsection integral value of collection of illustrative plates as independent variable, using in adulterated sample the relative amount of contained dilute cream fat as Fitting output dependent variable, is imported in software；The dependent variable calculates according to formula (1)：Z=aK/ (aK+bJ) ... (1)Wherein, Z represents the relative amount of contained dilute cream fat in adulterated sample；A represents dilute cream sample Commercial goods labelses mark Fat content；B represents to plant the fat content of butter cream sample Commercial goods labelses mark；K represents the matter of dilute cream in adulterated sample Measure percentage；J represents to plant the mass percent of butter cream in adulterated sample；(b) PCA analyses are carried out to the argument data in step (a), dimension-reduction treatment is carried out to original argument；(c) RBF is used as kernel function, using the new characteristic variable that dimensionality reduction obtains as input, to be obtained in step (a) The Z values arrived are fitting output dependent variable, establish SVM regression models；(d) the penalty parameter c value and kernel functional parameter g values for the SVM regression models established using the method optimization of cross validation, with Optimize the obtained c values, g values as model parameter, the data set in training step (c) data, establish PCA-SVM models.
- 8. nuclear magnetic resonance discrimination method as claimed in claim 1, it is characterised in that step is determined actual cream samples in (5) Property differentiate specifically comprise the following steps：(1) cream samples to be measured are pre-processed according to step (1)；(2) operated according to step (2), obtained preprocessing solution1H-NMR；(3) qualitative discrimination model obtained by claim any one of 1-7 is utilized, identifies whether the sample is the dilute milk of sterling Oil.
- 9. nuclear magnetic resonance discrimination method as claimed in claim 8, it is characterised in that step is determined actual cream samples in (5) Property differentiate also comprise the following steps：(4) if identifying that the sample is adulterated cream, PCA-SVM regression models as claimed in claim 7, identification are passed through The adulterated amount of plant butter cream of the sample.
- 10. nuclear magnetic resonance discrimination method as claimed in claim 9, wherein, identify the adulterated amount bag of plant butter cream of the sample Include following steps：(a) it is described to gather step (2) in claim 11After H-NMR, hydrogen spectrum is converted into two-dimensional matrix, imported into and passes through In the PCA-SVM models that claim 7 is established, due to：Z=aK/ (aK+bJ) ... (1)K+J=1 ... (2)Wherein, Z represents the relative amount of contained dilute cream fat in adulterated sample；A represents dilute cream sample Commercial goods labelses mark Fat content；B represents to plant the fat content of butter cream sample Commercial goods labelses mark；K represents the matter of dilute cream in adulterated sample Measure percentage；J represents to plant the mass percent of butter cream in adulterated sample；Therefore, K=Zb/ (a+bZ-aZ) ... (3)A=35%, the b=20% in formula (3) are set, is obtained：K=4Z/ (7-3Z) ... (4)The adulterated quantitative analysis results for planting butter cream in dilute cream are calculated by formula (4).
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| CN110006938B (en) * | 2019-04-12 | 2022-06-17 | 上海理工大学 | SVM-based method for rapidly screening blended olive oil on site |
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