CN110470781A - Identify the method for reconstituted milk and ultra-high-temperature sterilized milk - Google Patents
Identify the method for reconstituted milk and ultra-high-temperature sterilized milk Download PDFInfo
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Abstract
The present invention relates to the methods for identifying reconstituted milk and ultra-high-temperature sterilized milk.The described method includes: a) acquiring the non-targeted metabolism group data of the milk sample of reconstituted milk and ultra-high-temperature sterilized milk respectively using high performance liquid chromatography-high resolution mass spectrum, and the characteristic information of the two is extracted respectively；B) analysis model is established, characteristic information is analyzed, determines the otherness metabolin of reconstituted milk and ultra-high-temperature sterilized milk；C) building identifies the quasi- targeting metabolism group detection method of reconstituted milk and ultra-high-temperature sterilized milk with otherness metabolin described in screening step b), obtains the characteristic information of otherness metabolin；D) characteristic information based on the otherness metabolin obtained in step c) constructs PCA-Class discriminant analysis model, and the judgement based on the PCA-Class discriminant analysis model realization to milk sample to be measured.This method is convenient, sensitive, accurate, can be realized the rapid automatized differentiation to ultra-high-temperature sterilized milk and reconstituted milk.
Description
Technical field
The present invention relates to instrument analyses, field of food inspection, in particular to identification reconstituted milk and ultra-high-temperature sterilized milk
Method.
Background technique
Reconstituted milk is also known as " recombined milk " and perhaps " recombined milk " refers to that milk is concentrated, dries as concentrated milk or milk powder,
Suitable water is added again, is made and water, the comparable lotion of non-fat solid ratio in lactogenesis.Since reconstituted milk is blent by milk powder
Form, milk powder after high-temperature process, nutritional ingredient amino acid, protein, vitamin and in terms of with raw material milk phase
Than there is biggish loss, the losses such as especially some vitamins, immunoglobulin, lactoferrin, sulfur-containing amino acid, lysine are tight
Weight, and the nutritional ingredient in plain chocolate saves substantially.
Presently, there are the way for pretending to be fresh milk with reconstituted milk, not only reduce the nutritive value of dairy products, and seriously invade
Consumer legitimate right is violated.Therefore, study suitable identification beacon and improve reconstituted milk detection method it is very necessary, can
The right to know of enough effective protection consumers, and can purify and specification milk product enterprise.
Currently, the method for detection reconstituted milk is concentrated mainly on the context of detection of pyrolytic damage product caused after being heat-treated,
Fluorescent material, heat denatured protein and maillard reaction product (chaff propylhomoser, lactulose) in such as dairy products.Existing national standard side
In method, the content of chaff propylhomoser and lactulose in high performance liquid chromatography and enzymatic isolation method detection liquid milk, this method detection are utilized
It is period length, complex pretreatment, cumbersome, and above two index is easy to be influenced by storage time and environment, holds very much
False positive is easily caused, does not adapt to large batch of sampling Detection.Therefore, it establishes and more accurately and efficiently differentiates reconstituted milk and surpass
The detection method of high-temperature sterilization cream is of great immediate significance.
In view of this, the present invention is specifically proposed.
Summary of the invention
The purpose of the present invention is to provide the method for identifying reconstituted milk and ultra-high-temperature sterilized milk, the method is based on efficient liquid
The combination of phase chromatography-high resolution mass spectrum, it is convenient, sensitive, accurate, it can be realized to the fast automatic of ultra-high-temperature sterilized milk and reconstituted milk
Change and differentiates.
In order to realize above-mentioned purpose of the invention, the following technical scheme is adopted:
Identify the method for reconstituted milk and ultra-high-temperature sterilized milk comprising step:
A) non-target of the milk sample of reconstituted milk and ultra-high-temperature sterilized milk is acquired respectively using high performance liquid chromatography-high resolution mass spectrum
To metabolism group data, and the characteristic information of the two is extracted in the non-targeted metabolism group data of the two respectively；
B) analysis model is established, and the characteristic information is analyzed, determines that the reconstituted milk and the superhigh temperature go out
The otherness metabolin of bacterium cream；
C) building identifies the quasi- targeting metabolism group detection method of the reconstituted milk and the ultra-high-temperature sterilized milk with screening
Otherness metabolin described in step b) obtains the characteristic information of the otherness metabolin；
D) characteristic information based on the otherness metabolin obtained in step c), constructs PCA-Class discriminant analysis
Model, and the judgement based on the PCA-Class discriminant analysis model realization to milk sample to be measured.
Optionally, in step a), the condition using high performance liquid chromatography acquisition data includes:
Gradient elution is carried out with mobile phase A and Mobile phase B；
Under positive ion mode, mobile phase A is formic acid-aqueous solution of 0.05v/v%~0.15v/v%, preferably 0.1v/
Formic acid-aqueous solution of v%；Mobile phase B is formic acid-acetonitrile solution of 0.05v/v%~0.15v/v%, preferably 0.1v/v%
Formic acid-acetonitrile solution；
Under negative ion mode, mobile phase A is 3mM~8mM ammonium acetate-aqueous solution, preferably 5mM ammonium acetate-aqueous solution；Stream
Dynamic phase B is 3mM~8mM ammonium acetate-acetonitrile solution, preferably 5mM ammonium acetate-acetonitrile solution.
Optionally, the program of the gradient elution are as follows:
Under positive ion mode, when 0~5min, Mobile phase B 2v/v%；When 5~8min, Mobile phase B 2v/v%~10v/
V%；When 8~18min, Mobile phase B 10v/v%~95v/v%；After 18.1min, Mobile phase B 100v/v%, and balance 1~
2min；
Under negative ion mode, when 0~5min, Mobile phase B 2v/v%；When 5~8min, Mobile phase B 2v/v%~10v/
V%；When 8~16min, Mobile phase B 10v/v%~95v/v%；After 16.1min, Mobile phase B 100v/v%, and balance 1~
2min。
Optionally, the flow velocity of the gradient elution is 0.2~0.5mL/min, preferably 0.4mL/min.
As an implementation, in step a), the condition using high performance liquid chromatography acquisition data includes:
Under positive ion mode: mobile phase A and B are respectively 0.1% formic acid-water and 0.1% formic acid-acetonitrile solution, Mobile phase B
Gradient is 0min, 2%；5min, 2%；8min, 10%；12min, 95%；18min, 95%；18.1min 100%；Balance
1.9min, flow velocity 0.4mL/min, sample volume be 5 μ L, 40 DEG C of column temperature.Under negative ion mode: mobile phase A and B are respectively 5mM second
Acid amide-water and 5mM ammonium acetate-acetonitrile solution, Mobile phase B gradient be 0min, 2%；5min, 2%；8min, 10%；12min,
95%；16min, 95%；16.1min 100%；Balance 1.9min, flow velocity 0.4mL/min, sample volume be 5 μ L, 40 DEG C of column temperature.
Optionally, in step a), the condition using high resolution mass spectrum acquisition data includes:
Acquisition mode is data correlation acquisition mode (IDA)；
It is 50Da~1000Da using electric spray ion source (ESI), data acquisition range m/z；
Under positive ion mode, spray voltage (Ion Spray Voltage) is 4000V~6000V, preferably 5500V；Remove cluster
Voltage is 20V~120V, preferably 80V；
Under negative ion mode, spray voltage is -6000V~-4000V, preferably -4500V；Go cluster voltage be -20V~-
120V, preferably -80V；
Gas curtain atmospheric pressure (Curtain Gas, CUR) is 15Psi~40Psi, preferably 35Psi；Spray pressure be 15Psi~
70Psi, preferably 50Psi；Heating assist gas pressure power (GS2) is 0Psi~70Psi, preferably 50Psi；Ion source temperature
(Temperature, TEM) is 450 DEG C~550 DEG C, preferably 550 DEG C；Impact energy is 35 ± 15eV.
Optionally, the high resolution mass spectrum is high-resolution flight time mass spectrum.
As an implementation, using high resolution mass spectrum HPLC-Q-TOF, non-targeted metabolism group point is carried out to milk sample
Analysis.
In technical solution of the present invention, using high-resolution quadrupole rod time of-flight mass spectrometer, have high-throughput, highly sensitive
The analytical technology of degree, high precision, can analyze all metabolins in particular organisms sample, and to the generation in extracting solution comprehensively
It thanks to object and carries out qualitative, quantitative analysis, detectability can reach trace level.It, can meanwhile in conjunction with chemometrics method
It identifies and respectively characterizes the factor in ultra-high-temperature sterilized milk and reconstituted milk, and establish that feasibility is strong, the high reconstituted milk of accuracy and superelevation
Warm sterile milk discrimination model can identify for the authenticity of dairy products and provide strong technical support.
It optionally, further include that the non-targeted metabolism group data are carried out in step a) before extracting characteristic information
Pretreated step；
The pretreatment includes: after carrying out noise filtering and baseline correction, to extract, be aligned, standardize to chromatographic peak
And normalization.
Optionally, the extraction scope of the chromatographic peak is 50Da~1000Da；Quality width is 0.01Da~0.03Da, excellent
It is selected as 0.02Da.
Optionally, when intensity < 100 of the chromatographic peak or signal-to-noise ratio < 3, without extracting.
As an implementation, it is described pretreatment include initial data is extracted, filter make an uproar, baseline correction, reservation
Time calibration, peak identification, peak match and obtain two-dimensional data matrix, including mass-to-charge ratio, retention time, peak area etc..
Optionally, the process for extracting the characteristic information includes:
The characteristic information of missing values > 50% in pretreated chromatography peak data is rejected, remaining missing values are with this feature
The 1/2 of information minimum value calculates, and carries out Missing Data Filling；
The characteristic information of relative standard deviation > 20% is rejected, and chromatography peak data is standardized, is extracted special
Reference breath.
Optionally, in step b), the analysis model is selected from non-supervisory analysis, has supervision analysis or chemometrics application
At least one of.
Optionally, the analysis model include principal component analysis, offset minimum binary variance analysis, one-way analysis of variance or
At least one of chemometrics application.
Optionally, in step d), the PCA-Class discriminant analysis model is trained by cross validation.
The method of the cross validation includes:
Using 7 folding cross validations, population sample is equally divided into 7 groups, each subset data is made into one-time authentication collection respectively,
Remaining 6 groups of subset data establishes model as training set；
The residual sum of squares (RSS) for using established model prediction missing group, repetitive operation 7 times, obtains overall residuals squares
With；
If total residual sum of squares (RSS) will be reduced by increasing principal component, retain the principal component；Conversely, then the principal component is picked
It removes, to obtain the predictive ability of model.
As an implementation, identify the method for reconstituted milk and ultra-high-temperature sterilized milk comprising steps of
1) with the milk sample of high resolution mass spectrum detection whole milk powder is prepared respectively reconstituted milk and ultra-high-temperature sterilized milk, to sample
The acquisition of product progress pre-treatment and non-targeted metabolism group data information.
2) non-targeted metabolism group data are pre-processed, the extraction of characteristic information are carried out to the metabolin filtered out,
Then in conjunction with non-supervisory analysis and there is supervision analysis data analysis, chemometrics application is carried out to characteristic value information, using master
The analysis model of constituent analysis (PCA) and orthogonal offset minimum binary variance (OPLS-DA), determination can be by two kinds of newborn Complete Classifications
Significant difference metabolin；
3) the difference metabolin determined based on step 2), building identifies ultra-high-temperature sterilized milk and reconstituted milk intends targeting screening
Method (IDAto MRM), for being directed to the screening of specific metabolite.
4) right based on the quasi- otherness metabolites characteristic information architecture PCA-Class discriminant analysis model for targeting and filtering out
Constructed model is trained, and is realized and is identified to unknown sample.
Optionally, the preparation method of the milk sample includes:
The reconstituted milk or the ultra-high-temperature sterilized milk are centrifuged, are centrifuged again after taking lower liquid to be dissolved in organic solvent,
Upper solution is taken, after micropore filtering film filters, obtains the milk sample.
Optionally, the revolving speed of the centrifugation is 8 × 103Rpm~1 × 104rpm；The time of the centrifugation be 10min~
30min。
Optionally, the organic solvent is selected from least one of acetonitrile, methanol, water or their mixed solvent.
Optionally, the organic solvent is acetonitrile.
Optionally, the aperture of the micropore filtering film is 0.2~0.5 μm, preferably 0.22 μm.
Optionally, the reconstituted milk includes the modulation cream of at least one of concentrated milk and/or milk powder.
Optionally, the reconstituted milk is the modulation cream of whole milk powder.
Optionally, in terms of protein content, the concentration of the reconstituted milk is 1~10g/100mL；Preferably 3g/100mL.
As an implementation, the reconstituted milk is dissolved in water using whole milk powder, and according to protein content 3.0g/
100mL concentration is prepared.
As an implementation, the preparation method of the milk sample includes: to be centrifuged reconstituted milk or ultra-high-temperature sterilized milk, is taken
Lower liquid is dissolved in acetonitrile, then is centrifugated out upper solution, after 0.22 μm of miillpore filter, obtains milk sample, upper machine measurement.
Optionally, the PCA-Class discriminant analysis model is exported with two dimensional image；
When the two dimensional image of sample to be identified falls into fourth quadrant, it is determined as reconstituted milk；
When the two dimensional image of sample to be identified falls into the second quadrant, it is determined as ultra-high-temperature sterilized milk；
When the two dimensional image of sample to be identified falls into first quartile, differentiation is not made in expression；
When the two dimensional image of sample to be identified falls into third quadrant, there is shown now judge by accident.
Compared with prior art, the invention has the benefit that
(1) method provided by the invention for identifying reconstituted milk and ultra-high-temperature sterilized milk, when using high-resolution quadrupole rod flight
Between mass spectrograph carry out, the method is simple and efficient, is easy to operate, and can be analyzed comprehensively metabolin in dairy products, realization
On-line automaticization of dairy products detects, and greatly improves the detection efficiency of sample.
(2) method provided by the invention for identifying reconstituted milk and ultra-high-temperature sterilized milk, the identification model established are feasible
Property it is strong, accuracy is high, can provide convenient and practical technical support for the identification of the authenticity of dairy products.
Detailed description of the invention
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution in the prior art
Embodiment or attached drawing needed to be used in the description of the prior art be briefly described, it should be apparent that, it is described below
Attached drawing is some embodiments of the present invention, for those of ordinary skill in the art, before not making the creative labor
It puts, is also possible to obtain other drawings based on these drawings.
Fig. 1 is the principal component scores figure of ultra-high-temperature sterilized milk (U) and reconstituted milk (R) in one embodiment of the present invention
(PCA)；Wherein, a indicates that principal component scores figure in the positive-ion mode, b indicate principal component scores in the negative ion mode
Figure；
Fig. 2 is to be based on OPLS-DA model in one embodiment of the present invention, ultra-high-temperature sterilized milk and reconstituted milk partially most
Small square difference discriminant analysis result；Wherein, a indicates that OPLS-DA shot chart in the positive-ion mode, b are indicated in anion
OPLS-DA shot chart under mode；
Fig. 3 is the different metabolic object abundance of ultra-high-temperature sterilized milk (U) and reconstituted milk (R) in one embodiment of the present invention
S-plot figure；Wherein, a indicates that S-plot figure in the positive-ion mode, b indicate S-plot figure in the negative ion mode；
Fig. 4 is to pass through the Receiver operating curve (ROC) of cross validation acquisition in one embodiment of the present invention；
Wherein, a indicates that ROC curve in the positive-ion mode, b indicate ROC curve in the negative ion mode；
Fig. 5 is in one embodiment of the present invention, and the Cooman of ultra-high-temperature sterilized milk (U) and reconstituted milk (R) schemes；Wherein, a
Indicate that Cooman figure in the positive-ion mode, b indicate Cooman figure in the negative ion mode.
Specific embodiment
Embodiment of the present invention is described in detail below in conjunction with embodiment, but those skilled in the art will
Understand, the following example is merely to illustrate the present invention, and is not construed as limiting the scope of the invention.It is not specified in embodiment specific
Condition person carries out according to conventional conditions or manufacturer's recommended conditions.Reagents or instruments used without specified manufacturer is
It can be with conventional products that are commercially available.
As an implementation, the reconstituted milk in the present invention is to be dissolved in water with whole milk powder, according to protein content
3.0g/100mL concentration is prepared.
As an implementation, the ultra-high-temperature sterilized milk in the present invention selects commercially available superhigh temperature sterilized milk (UHT).
As an implementation, superhigh temperature sterilized milk is identified based on high-resolution quadrupole rod time of-flight mass spectrometer and restored
The method of cream comprising step:
1) preparation of milk sample
The preparation of milk sample: reconstituted milk is to be dissolved in water with whole milk powder, is prepared according to protein content 3.0g/100mL concentration.
Superhigh temperature sterilized milk (UHT) is commercially available；By dairy products sampling, centrifugation, lower liquid is taken to be dissolved in acetonitrile, then to be centrifugated out upper layer molten
Liquid after crossing 0.22 μm of filter membrane, obtains milk sample, and upper machine is to be measured.
2) acquisition and pretreatment of data
It detects the milk sample of reconstituted milk and ultra-high-temperature sterilized milk respectively using high-resolution quadrupole rod time of-flight mass spectrometer, acquires
Initial data；Initial data is pre-processed and is analyzed, carries out the correction of baseline, the removal of noise, the calibration of retention time,
Identification, the peak match of chromatographic peak obtain two-dimensional data matrix, including mass-to-charge ratio, retention time, peak area etc..In addition, pretreatment
Analysis method further includes removal missing values, rejects the relatively low peak of peak response or is difficult to the peak correctly identified, removal isotope
Peak is extracted peak area as variable, normalized, finally obtains the peak area of all metabolins.
3) data are analyzed
Unsupervised principal component analysis (PCA) is carried out to the peak area result of acquisition and has the orthogonal offset minimum binary of supervision
The analysis (orthPLS-DA) of variance, in conjunction with one-way analysis of variance, finishing screen is selected can be by the characterization factor of two kinds of cream classification.
The characterization factor based on acquisition establishes the detection method of the UPLC-MS/MS of targeting, is measured to sample and obtains the characterization factor
Relative peak area finally identifies ultra-high-temperature sterilized milk based on the characterization factor building filtered out and restores PCA-Class differentiation point
Analyse model.
Embodiment 1
The acquisition and preparation of milk sample
The ultra-high-temperature sterilized milk of 30 brands and the whole milk powder (milk powder) of 30 brands are acquired, Beijing is purchased from, exhales
Human relations Bell two places.Wherein, water is dissolved in using the whole milk powder purchased, according to protein content 3.0g/100mL concentration with obtained
To reconstituted milk.
The preparation method of reconstituted milk are as follows: according to protein content to contain 3.0g in every 100mL cow's milk, milk powder is added to super
In pure water, for constant temperature to 40 DEG C, hand homogeneous obtains the milk sample of reconstituted milk in water-bath.
The preparation of milk sample to be measured: above-mentioned ultra-high-temperature sterilized milk and each 2mL of reconstituted milk are taken respectively, is centrifuged at 8000rpm
30min removes the big lipid material of molecular weight；Lower layer's solution is isolated, 1mL is taken, 2mL acetonitrile is added, vortex 5min precipitates egg
White matter；Then it is centrifuged 10min at 10000rpm, separates upper solution, is filtered with 0.22 μm of micropore filtering film, upper machine waits for
It surveys.
The acquisition of non-targeted metabolism group data
Upper milk sample to be measured is detected using high performance liquid chromatography-high-resolution level four bars aircraft time mass spectrum.
Experiment high performance liquid chromatograph device used, instrument model are HPLC (30A, Shimadzu, Japan), chromatographic column
For C18 (ZORBAX Eclipse C18,3.0mm × 150mm, 1.8 μm, Agilent, USA).Under positive ion mode: mobile phase A
It is respectively 0.1% formic acid-water and 0.1% formic acid-acetonitrile solution with B, Mobile phase B gradient is 0min, 2%；5min, 2%；
8min, 10%；12min, 95%；18min, 95%；18.1min 100%；Balance 1.9min, flow velocity 0.4mL/min, sample volume
For 5 μ L, 40 DEG C of column temperature.Under negative ion mode: mobile phase A and B are respectively 5mM ammonium acetate-water and 5mM ammonium acetate-acetonitrile solution,
Mobile phase B gradient be 0min, 2%；5min, 2%；8min, 10%；12min, 95%；16min, 95%；16.1min 100%；
Balance 1.9min, flow velocity 0.4mL/min, sample volume be 5 μ L, 40 DEG C of column temperature.
Experiment high-resolution level four bars time of-flight mass spectrometer used, instrument model are Q-Triple-TOF-MS (AB
Sciex Inc., Foster city, CA, USA), using electric spray ion source (ESI), data acquisition range m/z:50-
1000Da, using data correlation acquisition mode (IDA) mode, dynamic background is deducted, the spraying electricity of electrospray ionisation positive ion mode
(Ion Spray Voltage): 5500V is pressed, removing cluster voltage is 70V；It is -70V that negative ion mode, which removes cluster voltage,.Spray voltage :-
4500V；Gas curtain atmospheric pressure (Curtain Gas, CUR): 30Psi；Atomization gas pressure (GS1): 45Psi；Heat assist gas pressure power
(GS2): 65Psi；Ion source temperature (Temperature, TEM): 550 DEG C；Impact energy is 35 ± 15eV.
The pretreatment and feature extraction of non-targeted metabolism group data
Software Peakview 2.2 (AB Sciex, USA) is extracted using high resolution mass spectrum data, to the whole colors detected
Spectral peak carries out the filtering of noise and the correction of baseline, extraction, alignment, standardization and the normalization of chromatographic peak is carried out, then to color
The peak feature of spectral peak extracts, and obtains the relative peak area and retention time of whole metabolins, then carries out the visual of data
Change analysis.Extract the parameter at peak: mass number range: 50Da~1000Da, quality width is 0.02Da, peak intensity lower than 100 or
Peak of person's signal-to-noise ratio less than 3 is without extracting.Finally, 1885 and 1077 compounds are obtained under negative ions mode.
All information of all metabolins extracted are imported in data prediction software Excel.Firstly, missing values
Estimation, the characteristic value that will be greater than 50% missing values are rejected, minimum of the remaining missing values with this feature value in all samples
The half of value calculates, and carries out the filling of missing values；Secondly, the filtering of characteristic value, according to the relative standard deviation of quality-control sample
Characteristic value greater than 20% is rejected.All data are standardized again.Finally, data processing software is imported
In SPSS 22.0 and SCIMA-P, visual analyzing is carried out.
Multi-variate statistical analysis
As the important means of multivariate statistical analysis, unsupervised principal component analysis (PCA) and have supervision it is orthogonal partially most
Small two multiply the potential otherness biomarker that analysis (orthPLS-DA) is used in reconstituted milk and ultra-high-temperature sterilized milk
In identification.
As shown in Figure 1, quality-control sample (QC) concentration class in shot chart is very high on PCA shot chart, illustrate pre-treatment and
Instrument state is good, and analysis method is reliable, accurate；Secondly, 96.7% sample is all fallen in 95% confidence interval, first five
A ingredient has been more than 60% to the accumulative explanation degree of difference, and has good separating degree, illustrates that pca model has two kinds of milk
Distinguish distinguishing ability well, there are apparent difference between superhigh temperature sterilized milk (U) and reconstituted milk (R), can by PCA into
Row is distinguished well.
Therefore, further using the analysis method orthPLS-DA model for having supervision, to the conspicuousness of reconstituted milk and UHT milk
Difference metabolin is screened, as a result as shown in Figure 2.R2X(cum)、R2Y (cum) is to indicate model to the solution of X and Y matrix respectively
Release ability, Q2Y (cum) indicates the predictive ability of model, R2Y (cum) and Q2For Y (cum) value closer to 1, illustrating that model is more stable can
It leans on.In OPLS-DA model, the R of the calculating of the first two principal component is calculated in the positive-ion mode2Y (cum) and Q2Y (cum) points
It Wei 0.967 and 0.925.The first two principal component R is calculated in the negative ion mode2Y (cum) and Q2Y (cum) is respectively 0.966 He
0.921, illustrate two models all has good predictive ability (Q2>0.5)。
The biomarker of reconstituted milk is distinguished in S-plot figure screening based on OPLS-DA model, and Fig. 3 indicates the association side of variable
The summation of difference and correlation information, point usually remoter from center, the influence to classification is bigger, in combination with the important of variable
Property (variable importance in the projection, VIP) be greater than 1 principle, filter out a series of otherness
Biomarker, then in conjunction with one-way analysis of variance (T-test, SPSS 22.0) examine after, finishing screen selects cation
71 difference metabolins under lower 94 metabolins of mode and negative ion mode, are listed in Tables 1 and 2 respectively.
The foundation of quasi- targeting metabolism group method
Based on the filtered out difference metabolin of non-targeted metabolism, optimize its parent ion, daughter ion information, go cluster voltage,
Impact energy parameter establishes quasi- targeting metabolism group side (MRM) by level four bars linear ion hydrazine tandem mass spectrum (UPLC-MS/MS)
Method, chromatographic condition and Mass Spectrometry Conditions are consistent with non-targeted detection method parameter, and finishing screen is selected 86 kinds of metabolins and carried out finally
Quasi- targeting detection.Can be seen that two kinds of milk by PCA shot chart (Fig. 1), there are apparent differences.As shown in figure 4, passing through friendship
The Receiver operating curve (ROC) that fork verifying obtains, area under the curve (AUC) are used to estimate the accuracy of each model,
All research models are 1, illustrate that model has good discriminating power.
The foundation of discrimination model
It is used as the foundation and prediction of final mask with PCA-Class model, the critical limit of critical distance (DCrit) is by showing
Property level 0.05 F examine determine, determine data at a distance from disaggregated model.Cooman figure shown in fig. 5, which is shown, to be built
The result of mould collection and forecast set (20 authentic samples) cross division verifying.By in Fig. 5 it can be found that in negative ions mode
Under, can will become apparent from actual sample may be implemented to classify well, and the resolution of 20 samples in the positive-ion mode is more than
90%, resolution has reached 100% under negative ion mode.
The Information in Mass Spectra of targeting metabolism group otherness metabolin under 1 positive ion mode of table based on LC-MS/MS
The Information in Mass Spectra of targeting metabolism group otherness metabolin under 2 negative ion mode of table based on LC-MS/MS
Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Present invention has been described in detail with reference to the aforementioned embodiments for pipe, but those skilled in the art should understand that: its
It is still possible to modify the technical solutions described in the foregoing embodiments, or to some or all of the technical features
It is equivalently replaced；And these are modified or replaceed, various embodiments of the present invention skill that it does not separate the essence of the corresponding technical solution
The range of art scheme.
Claims (10)
1. identifying the method for reconstituted milk and ultra-high-temperature sterilized milk, which is characterized in that the method includes the steps:
A) the non-targeted generation of the milk sample of reconstituted milk and ultra-high-temperature sterilized milk is acquired respectively using high performance liquid chromatography-high resolution mass spectrum
Xie Zuxue data, and the characteristic information of the two is extracted in the non-targeted metabolism group data of the two respectively；
B) analysis model is established, and the characteristic information is analyzed, determines the reconstituted milk and the ultra-high-temperature sterilized milk
Otherness metabolin；
C) building identifies the quasi- targeting metabolism group detection method of the reconstituted milk and the ultra-high-temperature sterilized milk with screening step
B) otherness metabolin described in obtains the characteristic information of the otherness metabolin；
D) characteristic information based on the otherness metabolin obtained in step c), constructs PCA-Class discriminant analysis model,
And the judgement based on the PCA-Class discriminant analysis model realization to milk sample to be measured.
2. the method according to claim 1, wherein acquiring data using high performance liquid chromatography in step a)
Condition includes:
Gradient elution is carried out with mobile phase A and Mobile phase B；
Under positive ion mode, mobile phase A is formic acid-aqueous solution of 0.05v/v%~0.15v/v%, preferably 0.1v/v%'s
Formic acid-aqueous solution；Mobile phase B is formic acid-acetonitrile solution of 0.05v/v%~0.15v/v%, the preferably first of 0.1v/v%
Acid-acetonitrile solution；
Under negative ion mode, mobile phase A is 3mM~8mM ammonium acetate-aqueous solution, preferably 5mM ammonium acetate-aqueous solution；Mobile phase
B is 3mM~8mM ammonium acetate-acetonitrile solution, preferably 5mM ammonium acetate-acetonitrile solution；
Preferably, the program of the gradient elution are as follows:
Under positive ion mode, when 0~5min, Mobile phase B 2v/v%；When 5~8min, Mobile phase B 2v/v%~10v/v%；8
When~18min, Mobile phase B 10v/v%~95v/v%；After 18.1min, Mobile phase B 100v/v%, and balance 1~2min；
Under negative ion mode, when 0~5min, Mobile phase B 2v/v%；When 5~8min, Mobile phase B 2v/v%~10v/v%；8
When~16min, Mobile phase B 10v/v%~95v/v%；After 16.1min, Mobile phase B 100v/v%, and balance 1~2min；
Preferably, the flow velocity of the gradient elution is 0.2~0.5mL/min, preferably 0.4mL/min.
3. the method according to claim 1, wherein acquiring data using the high resolution mass spectrum in step a)
Condition include:
Acquisition mode is data correlation acquisition mode；
Using electric spray ion source, data acquisition range m/z is 50Da~1000Da；
Under positive ion mode, spray voltage is 4000V~6000V, preferably 5500V；Removing cluster voltage is 20V~120V, preferably
80V；
Under negative ion mode, spray voltage is -6000V~-4000V, preferably -4500V；Removing cluster voltage is -20V~-120V, excellent
Choosing -80V；
Gas curtain atmospheric pressure is 15Psi~40Psi, preferably 35Psi；Spray pressure is 15Psi~70Psi, preferably 50Psi；Heating
Assist gas pressure power is 0Psi~70Psi, preferably 50Psi；Ion source temperature is 450 DEG C~550 DEG C, preferably 550 DEG C；Impact energy is
35±15eV；
Preferably, the high resolution mass spectrum is high-resolution flight time mass spectrum.
4. the method according to claim 1, wherein further including pair before extracting characteristic information in step a)
The non-targeted metabolism group data carry out pretreated step；
The pretreatment includes: after carrying out noise filtering and baseline correction, to extract, be aligned, standardize and return to chromatographic peak
One changes；
Preferably, the extraction scope of the chromatographic peak is 50Da~1000Da；Quality width is 0.01Da~0.03Da, preferably
0.02Da；
Preferably, when intensity < 100 of the chromatographic peak or signal-to-noise ratio < 3, without extracting.
5. method according to claim 1 or 4, which is characterized in that the process for extracting the characteristic information includes:
The characteristic information of missing values > 50% in pretreated chromatography peak data is rejected, remaining missing values are with this feature information
The 1/2 of minimum value calculates, and carries out Missing Data Filling；
The characteristic information of relative standard deviation > 20% is rejected, and chromatography peak data is standardized, extracts feature letter
Breath.
6. the method according to claim 1, wherein in step b), the analysis model be selected from non-supervisory analysis,
There are at least one of supervision analysis or chemometrics application；
Preferably, the analysis model includes principal component analysis, offset minimum binary variance analysis, one-way analysis of variance or chemistry
At least one of bibliometric analysis.
7. the method according to claim 1, wherein in step d), by cross validation to the PCA-Class
Discriminant analysis model is trained；
The method of the cross validation includes:
Using 7 folding cross validations, population sample is equally divided into 7 groups, each subset data is made into one-time authentication collection respectively, remaining
6 groups of subset datas as training set, establish model；
The residual sum of squares (RSS) for using established model prediction missing group, repetitive operation 7 times, obtains overall residual sum of squares (RSS)；
If total residual sum of squares (RSS) will be reduced by increasing principal component, retain the principal component；Conversely, then reject the principal component, from
And obtain the predictive ability of model.
8. the method according to claim 1, wherein the preparation method of the milk sample includes:
The reconstituted milk or the ultra-high-temperature sterilized milk are centrifuged, is centrifuged, takes again after taking lower liquid to be dissolved in organic solvent
Layer solution obtains the milk sample after micropore filtering film filters；
Preferably, the revolving speed of the centrifugation is 8 × 103Rpm~1 × 104rpm；The time of the centrifugation is 10min~30min；
Preferably, the organic solvent is selected from least one of acetonitrile, methanol, water or their mixed solvent, more preferably
For acetonitrile；
Preferably, the aperture of the micropore filtering film is 0.2~0.5 μm, more preferably 0.22 μm.
9. the method according to claim 1, wherein the reconstituted milk include in concentrated milk and/or milk powder extremely
A kind of few modulation cream；
Preferably, the reconstituted milk is the modulation cream of whole milk powder；
Preferably, in terms of protein content, the concentration of the reconstituted milk is 1~10g/100mL；Preferably 3g/100mL.
10. the method according to claim 1, wherein the PCA-Class discriminant analysis model is with two dimensional image
Output；
When the two dimensional image of sample to be identified falls into fourth quadrant, it is determined as reconstituted milk；
When the two dimensional image of sample to be identified falls into the second quadrant, it is determined as ultra-high-temperature sterilized milk；
When the two dimensional image of sample to be identified falls into first quartile, differentiation is not made in expression；
When the two dimensional image of sample to be identified falls into third quadrant, there is shown now judge by accident.
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