CN113820428A

CN113820428A - Lipidosome biomarker of milk with different heat processing modes as well as screening method and application thereof

Info

Publication number: CN113820428A
Application number: CN202111180041.XA
Authority: CN
Inventors: 陈刚; 张宏达; 吴华星; 贾曼; 谭冬飞; 王少雷; 陈苏蒙
Original assignee: Institute of Agricultural Quality Standards and Testing Technology for Agro Products of CAAS
Current assignee: Institute of Agricultural Quality Standards and Testing Technology for Agro Products of CAAS
Priority date: 2021-10-11
Filing date: 2021-10-11
Publication date: 2021-12-21
Anticipated expiration: 2041-10-11
Also published as: CN113820428B

Abstract

The application relates to the technical field of food inspection, and relates to a lipide biomarker of milk with different heat processing modes, a screening method and application thereof. The screening method comprises the following steps: obtaining mass spectrograms of lipid samples of at least two different thermal processing modes of milk; processing the mass spectrogram to obtain a lipid qualitative result; carrying out lipid quantification according to the lipid qualitative result, and establishing an analysis model according to the quantitative result; the analytical model was used to determine the differential lipids of milk from different thermal processing regimes as the lipidomic biomarkers. 30 lipid group biomarkers are successfully screened by adopting the method for distinguishing raw milk, ultrahigh temperature sterilized milk, pasteurized milk and prolonged shelf life milk, and 74 lipid group biomarkers are screened for distinguishing pasteurized milk and prolonged shelf life milk. These lipid group biomarkers can effectively distinguish between meristematic milk, pasteurized milk, extended shelf life milk and ultra high temperature sterilized milk.

Description

Lipidosome biomarker of milk with different heat processing modes as well as screening method and application thereof

Technical Field

The application relates to the technical field of food inspection, in particular to a lipide group biomarker of milk with different heat processing modes, a screening method and application thereof.

Background

The milk is rich in nutrient substances and is one of important nutrient sources in life. Besides providing nutrients such as protein, vitamins, fat and calcium, milk also contains many bioactive components which are significant to human health. However, the rich nutrition also makes the milk become an excellent breeding place for various microorganisms, which not only can cause the milk to decay and deteriorate, but also can threaten the human health. To ensure biological safety and consumer safety, sterilization has become an indispensable manufacturing process for commercial milk. To date, heat treatment is the most commonly used Milk sterilization process, and commercially available Milk can be classified into Pasteurized Milk (PM), Extended Shelf Life Milk (EM), and Ultra-high Temperature Sterilized Milk (UM) according to the heat processing method. Although the heat treatment can ensure the safety of the milk, as the heat treatment degree is increased, some physicochemical reactions in the milk, such as Maillard reaction, lactose isomerization, protein denaturation, fat oxidation and the like, are also increased, so that the flavor of the milk is deteriorated and the nutrient content is lost. Therefore, monitoring of the thermal processing of dairy products is of great importance.

The existing milk hot processing identification mode mainly monitors the change of the total amount of one or a class of substances in milk, such as testing the inactivation degree of alkaline phosphatase and peroxidase by using a spectrometry method and a fluorescence method to distinguish the heat load of pasteurization and ultrahigh temperature heat treatment; determining the content of 5-hydroxymethyl furfural, lactulose, furosine and other substances by using an enzyme method or High Performance Liquid Chromatography (HPLC) to distinguish pasteurized milk from UHT milk; the denaturation rate of alpha-lactalbumin or beta-lactoglobulin is monitored by different chromatographic, electrophoretic and immunochemical methods to distinguish milk with different heat processing modes. These methods often distinguish between pasteurized and UHT milk and are prone to false positives.

Disclosure of Invention

The embodiment of the application aims to provide a lipide group biomarker of milk with different heat processing modes, a screening method and application thereof, and the lipide group biomarker can be used for distinguishing raw milk, pasteurized milk, shelf-life-prolonging milk and ultrahigh-temperature sterilized milk.

In a first aspect, the present application provides a method for screening lipid group biomarkers of milk with different heat processing modes, wherein the milk with different heat processing modes comprises raw milk, pasteurized milk, shelf-life-extended milk and ultrahigh-temperature sterilized milk;

the screening method comprises the following steps:

obtaining mass spectrograms of lipid samples of at least two different thermal processing modes of milk;

processing the mass spectrogram to obtain a lipid qualitative result;

carrying out lipid quantification on the lipid qualitative result, and establishing an analysis model according to the quantitative result;

the analytical model was used to determine the differential lipids of milk from different thermal processing regimes as the lipidomic biomarkers.

By adopting the method, the lipid group biomarkers of milk with different heat processing modes are successfully obtained, and the biomarkers can be used for distinguishing raw milk, pasteurized milk, shelf-life-prolonging milk and ultrahigh-temperature sterilized milk.

In other embodiments of the present application, the step of obtaining mass spectra of lipid samples of milk with different thermal processing modes includes:

analyzing milk lipid samples of different thermal processing modes by adopting liquid chromatography-mass spectrometry;

the liquid chromatography-mass spectrometry analysis conditions comprise: mobile phase A and mobile phase B, and the elution procedure is gradient elution;

the mobile phase A comprises acetonitrile aqueous solution and ammonium acetate, the volume fraction of the acetonitrile aqueous solution is 40-60%, and the concentration of the ammonium acetate is 5-20 mmol/L;

mobile phase B included isopropanol, acetonitrile and ammonium acetate; according to the volume ratio, isopropanol: the acetonitrile is 10: 1-5: 1; the concentration of the ammonium acetate is 5 mmol/L-20 mmol/L;

the gradient elution includes:

in other embodiments of the present application, the step of processing the mass spectrum to obtain a qualitative result of the lipid includes:

processing the mass spectrogram by lipidomics qualitative software, wherein the qualitative conditions comprise:

mass number range: 200-2000 Da; peak intensity greater than 100 cps; the MS1 accurate mass deviation is 0.01Da, and the MS2 identifies the accurate mass deviation to be 0.025 Da; the maximum charge number is 2; the smoothing mode is linear weighted moving average, and the smoothing level is 3; selecting lipids having a score greater than 80; setting the peak alignment retention time bias to 0.2 minutes, MS1 mass bias, 0.015 Da; and deleting the characteristics according to the blank information, and manually confirming the annotation and peak value extraction result.

In another embodiment of the present application, the step of performing lipid quantification on the lipid qualitative result includes:

and integrating the peak area of each lipid according to the lipid qualitative result, and quantifying various lipids according to the corresponding lipid internal standard peak area.

In another embodiment of the present application, the step of establishing a differentiation model according to the quantitative result includes:

eliminating lipid species with deletion values larger than 50%, filling the deletion values with 1/5 of the minimum value of the lipid in all positive samples for the lipid with the deletion values smaller than or equal to 50%, and normalizing the data to obtain preprocessed data;

and carrying out data analysis on the preprocessed data to construct a visual chart.

In other embodiments of the present application, the analytical models include sparse partial least squares discriminant analysis (sPLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA), T-test, and fold-of-difference analysis;

a step of using an analytical model to determine differential lipids of milk in different thermal processing regimes as biomarkers of lipidome, comprising:

differential lipids which are distinguished from raw milk, ultrahigh-temperature sterilized milk, pasteurized milk and prolonged shelf milk are screened by sparse partial least square discriminant analysis, 3 main components are selected, and 10 lipids with the greatest importance to the three main component variables are screened respectively; and/or

Differentiating differential lipids of pasteurized milk and shelf-life-prolonging milk by utilizing orthogonal partial least squares discriminant analysis combined with fold difference analysis and T-test screening, wherein the screening conditions comprise: the variable importance is more than 1.7, the change multiple is more than 2 or less than 0.5, the P value is less than 0.01 and the wig appearance rate is less than 0.05.

In a second aspect, the present application provides a lipidome biomarker for differentiating at least two of raw milk, ultra-high temperature sterilized milk, pasteurized milk, extended shelf life milk, comprising lipids as in the following table:

in a third aspect, the present application provides a lipidome biomarker for distinguishing pasteurized milk from extended shelf life milk, comprising lipids as in the following table:

in a fourth aspect, the present application provides a use of the lipid group biomarker provided in the second aspect above for distinguishing between different heat-processed milks, the different heat-processed milks comprising at least two of raw milk, ultra-high temperature sterilized milk, pasteurized milk, extended shelf life milk; or

The use of a lipidic biomarker according to the third aspect above to distinguish between different heat-processed milks, including pasteurized milk and extended shelf life milk.

In a fifth aspect, the present application provides a method for differentiating milk with different heat processing modes, wherein the milk with different heat processing modes comprises at least two of raw milk, ultrahigh-temperature sterilized milk, pasteurized milk and shelf-life-prolonging milk; the method comprises the following steps:

and (3) establishing a distinguishing model by using the lipid group biomarkers of the third aspect or the fourth aspect, and distinguishing unknown different hot-processing mode milk by using the distinguishing model.

Drawings

Fig. 1 is a total ion flow diagram of raw milk in positive ion mode provided in an embodiment of the present application;

FIG. 2 is a total ion flow diagram of pasteurized milk in positive ion mode provided in an embodiment of the present application;

FIG. 3 is a total ion flow diagram of extended shelf life milk in positive ion mode as provided in an example of the present application;

FIG. 4 is a total ion flow diagram of the ultra-high temperature sterilized milk in positive ion mode provided by the embodiment of the present application;

fig. 5 is a total ion flow diagram of raw milk in the negative ion mode provided in the embodiment of the present application;

FIG. 6 is a total ion flow diagram of pasteurized milk in negative ion mode provided in an embodiment of the present application;

FIG. 7 is a total ion flow diagram of extended shelf life milk in negative ion mode as provided in an example of the present application;

FIG. 8 is a total ion flow diagram of milk sterilized at ultra high temperature in negative ion mode according to the present application;

fig. 9 is a sPLS-DA score chart of a milk sample provided in an example of the present application (RM, raw milk n-9; PM, pasteurized milk n-26; EM, extended shelf life milk n-8; U, UM, ultra-high temperature sterilized milk n-17);

FIG. 10 shows the results of the test of the sPLS-DA model in fold 5;

FIG. 11 shows the difference substances before 10 of the difference contribution to principal component 1;

FIG. 12 shows the difference substances before the difference substances contribute to the discrimination of principal component 2 by 10;

FIG. 13 shows the difference substances before contributing 10 to the discrimination of the principal component 3;

FIG. 14 is a heat map of 30 differential lipid profiles in raw milk, ultra-high temperature sterilized milk, pasteurized milk, and extended shelf life milk;

FIG. 15 is a graph of the OPLS-DA scores of pasteurized milk and extended shelf life milk;

figure 16 is a heat map of the 74 differential lipid profiles in pasteurized and extended shelf life milk.

Detailed Description

In order to more clearly illustrate the technical solutions of the present application, the technical solutions of the present application will be clearly and completely described below with reference to the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

In the present application, the instruments and the like are not indicated to manufacturers, and are all conventional products available from regular distributors. The process is conventional unless otherwise specified, and the starting materials are commercially available from the open literature. The examples do not show the specific techniques or conditions, according to the technical or conditions described in the literature in the field, or according to the product specifications.

Embodiments of the present application provide a method for screening lipid group biomarkers of different heat processing types of milk, including raw milk, pasteurized milk, shelf-life extended milk, and ultra-high temperature sterilized milk.

The screening method comprises the following steps:

and step S1, acquiring mass spectrograms of the lipid samples of the milk in different thermal processing modes.

The method comprises the following steps of obtaining mass spectrograms of lipid samples of milk with at least two different thermal processing modes, wherein the mass spectrograms comprise:

and analyzing lipid samples of the milk in different thermal processing modes by adopting liquid chromatography-mass spectrometry.

Further, the milk with different thermal processing modes is obtained by respectively extracting lipid from raw milk, pasteurized milk, shelf-life-extended milk and ultrahigh-temperature sterilized milk.

In some embodiments of the present application, a method of extracting a lipid sample of milk from different thermal processing regimes comprises the steps of:

and diluting each milk sample with water, and adding the extract, the ceramide internal standard and the mixed internal standard to obtain a mixture. The mixture was vortexed, sonicated in an ice-water bath and centrifuged. The supernatant liquid, which contains lipids, was then aspirated into another centrifuge tube. Thereafter, the remaining portion was subjected to two repeated extractions with methyl t-butyl ether. Finally, the three extracts were combined and blown dry under mild nitrogen. The extracted lipids were reconstituted with a reconstitution solution for liquid chromatography-mass spectrometry (LC-MS) analysis.

By repeating the extraction twice on the remaining part, the lipid recovery rate can be improved.

Illustratively, each of the milk samples described above was diluted to 400 μ L with water. After mixing, 960. mu.l of the extract, 6. mu.L of the ceramide internal standard (100. mu.g/mL) and 6. mu.L of the mixed internal standard were added. The mixture was vortexed for 1 minute and then sonicated in an ice water bath at 53Hz for 10 minutes. The mixture was centrifuged at 4000 rpm for 15 minutes and 500. mu.l of the supernatant containing lipids was drawn into another centrifuge tube. Then, the remaining portion was subjected to two repeated extractions using 500. mu.L of methyl t-butyl ether. Finally, the combined three extracts were dried under mild nitrogen. The extracted lipids were reconstituted with 120 μ L of reconstitution liquid for liquid chromatography-mass spectrometry (LC-MS) analysis.

Further, in some embodiments of the present application, the above-mentioned extract comprises one or more of methyl tert-butyl ether and methanol. The above-mentioned double solution includes one or several kinds of dichloromethane and methanol. The above mixed internal standard comprises PC 15: 0-18: 1(d7), PE 15: 0-18: 1(d7), PS 15: 0-18: 1(d7), PG 15: 0-18: 1(d7), PI 15: 0-18: 1(d7), PA 15: 0-18: 1(d7), LPC 18:1(d7), LPE 18:1(d7), CE 18:1(d7), MG 18:1(d7), DG 15: 0-18: 1(d7), TG 15: 0-18: 1(d7) _15:0, SM 18: 1; 2O/18:1(d9) or Cholesterol (d 7).

Further, the milk lipid samples of the raw milk, pasteurized milk, extended shelf life milk and ultra-high temperature sterilized milk obtained above were analyzed by liquid chromatography-mass spectrometry.

In some embodiments of the present application, the liquid chromatography-mass spectrometry conditions comprise: mobile phase A and mobile phase B, and the elution procedure is gradient elution.

The mobile phase A comprises acetonitrile aqueous solution and ammonium acetate, the volume fraction of the acetonitrile aqueous solution is 40-60%, and the concentration of the ammonium acetate is 5-20 mmol/L.

Mobile phase B included isopropanol, acetonitrile and ammonium acetate; according to the volume ratio, isopropanol: the acetonitrile is 10: 1-5: 1; the concentration of the ammonium acetate is 5 mmol/L-20 mmol/L.

The gradient elution includes:

further, in some embodiments of the present application, the column is Phenomen Kinetex C18 column (2.1X 100mm, particle size 1.7 μm); the flow rate was 0.3mL/min, the positive ion mode sample size was 2. mu.L, the negative ion mode sample size was 6. mu.L, and the column temperature was 55 ℃.

Further, in some embodiments of the present application, the liquid chromatography-mass spectrometry described above employs high resolution mass spectrometry.

Further optionally, the conditions of high resolution mass spectrometry include:

mode (2): a data-dependent acquisition mode;

an ion source: an electrospray ion source;

the data acquisition range m/z is 200-2000 Da;

the spraying voltage is as follows: 4500-6000V in a positive ion mode, and-4000-5000V in a negative ion mode; the air pressure of the air curtain is 20-45 psi; the pressure of the spraying gas is 30-80 psi; the pressure of the auxiliary heating gas is 30-80 psi; the temperature of the ion source is 450-600 ℃; the cluster removing voltage is 60-120V; the collision energy was 45. + -.25 eV.

Further optionally, the high resolution mass spectrum is preferably a high resolution time-of-flight mass spectrum.

Further optionally, the instrument used to perform the high resolution mass spectrometry is a UPLC-ESI-TOF mass spectrometer.

After the chromatographic peaks of the lipid samples of the milk in different thermal processing modes are collected by adopting a liquid chromatography-mass spectrum, the characteristic peak information of the chromatographic peaks is extracted.

And step S2, processing the mass spectrogram to obtain a lipid qualitative result.

Processing the mass spectrum to obtain a lipid qualitative result, comprising the following steps:

mass number range: 200-2000 Da; peak intensity greater than 100 cps; the accurate mass deviation of MS1 is 0.01Da, and the accurate mass deviation of MS2 is 0.025 Da; the maximum charge number is 2; the smoothing mode is linear weighted moving average, and the smoothing level is 3; selecting lipids having a score greater than 80; setting the peak alignment retention time bias to 0.2 minutes, MS1 mass bias, 0.015 Da; and deleting the characteristics according to the blank information, and manually confirming the annotation and peak value extraction result.

In some embodiments of the present application, the chromatographic peak obtained in step S1 is processed by lipidomics qualitative software, and the qualitative conditions include: mass number range: 200-2000 Da; peak intensity greater than 100 cps; the accurate mass deviation (MS1) is 0.01Da, and the accurate mass deviation (MS2) is 0.025 Da; the maximum charge number is 2; the smoothing mode is linear weighted moving average, and the smoothing level is 3; selecting lipids having a score greater than 80; setting the peak alignment retention time bias to 0.2 minutes, MS1 mass bias, 0.015 Da; and deleting the features according to the blank information, and manually confirming the annotation and peak extraction result.

Further alternatively, the aforementioned lipidomics characterization software is MS-DIAL 4.70 software (RIKEN Center for stable Resource Science, japan), which pre-processes (for noise setting, baseline correction, peak detection and calibration) the raw data collected by the UPLC-ESI-TOF mass spectrometer to extract characteristic ions therefrom, setting parameters: mass number range: 200-2000 Da; peak intensity greater than 100 cps; the accurate mass deviation (MS1) is 0.01Da, and the accurate mass deviation (MS2) is 0.025 Da; the maximum charge number is 2; the smoothing mode is linear weighted moving average, and the smoothing level is 3; selecting lipids having a score greater than 80; setting the peak alignment retention time deviation to 0.2 minutes, MS1 mass deviation width tolerance, 0.015 Da; and deleting the characteristics according to the blank information. And scanning each lipid chromatographic peak to detect the model ions thereof, and obtaining the identified lipid species and the mass-to-charge ratio, retention time and MS/MS information corresponding to the characteristic peaks thereof.

And step S3, carrying out lipid quantification on the lipid qualitative result, and establishing an analysis model according to the quantitative result.

A step of lipid quantification of lipid qualitative results comprising:

In some embodiments of the present application, a method of lipid quantification comprises the steps of: introducing the retention time, MS information and MS/MS information of the qualitative lipid into high-resolution mass spectrum data processing software, and integrating the qualitative lipid peak; and quantifying various lipids according to the peak areas of the corresponding lipid internal standards.

Further alternatively, the software for processing high-resolution mass spectrometry data is multisant software (AB Sciex, usa) which performs peak extraction on the identified lipids and integrates peak areas of each lipid, setting parameters: retention time offset, 6 s; minimum peak width, 3 points; minimum peak area, 100 cps; the concentration of the lipid internal standard was set to C15 Ceramide-d7, 10. mu.g/mL; PC 15: 0-18: 1(d7), 16.07. mu.g/mL; PE 15: 0-18: 1(d7), 0.57. mu.g/mL; PS 15: 0-18: 1(d7), 0.42. mu.g/mL; PG 15: 0-18: 1(d7), 2.91. mu.g/mL; PI 15: 0-18: 1(d7), 0.91. mu.g/mL; PA 15: 0-18: 1(d7), 0.74. mu.g/mL; LPC 18:1(d7), 2.55. mu.g/mL; LPE 18:1(d7), 0.53. mu.g/mL; CE 18:1(d7), 35.61. mu.g/mL; MG 18:1(d7), 0.2. mu.g/mL; DG 15: 0-18: 1(d7), 0.94. mu.g/mL; TG 15: 0-18: 1(d7) _15:0, 5.73. mu.g/mL; SM 18: 1; 2O/18:1(d9), 3.09. mu.g/mL; cholesterol (d7), 9.84. mu.g/mL; the corresponding relationship between the lipid classes and the internal standard is as follows:

further, the step of establishing a discriminative model based on the quantitative results includes:

In some embodiments of the present application, lipid species with deletion values greater than 50% are excluded, and lipids with deletion values less than or equal to 50% are filled with 1/5 filling the deletion values of the minimum value of the lipid among all positive samples, and the data are normalized to obtain preprocessed data; and importing the preprocessed data into data analysis software, carrying out data analysis, and constructing a visual chart.

And step S4, determining the differential lipid of the milk with different thermal processing modes as the lipidosome biomarker by using an analysis model.

Further, in the present application, the analysis model includes sparse partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis, T-test, and multiple of difference analysis.

In some embodiments of the present application, the data analysis software is selected from the metaboanalyst5.0 software. The differential lipid screening means is preferably selected from one or more of sparse partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis, T-test and fold difference analysis.

Further, the step of using the analytical model to determine differential lipids of milk from different thermal processing regimes as the lipidomic biomarkers comprises:

Specifically, determining differential lipid conditions of milk from different processes includes: analyzing and screening differential lipids distinguishing raw milk, ultrahigh-temperature sterilized milk, pasteurized milk and prolonged shelf milk by using a sparse partial least square method, selecting 3 main components, and respectively screening 10 lipids with the greatest importance to the three main component variables; a model for distinguishing pasteurized milk from milk with a prolonged shelf life is screened by utilizing orthogonal partial least squares discriminant analysis in combination with fold difference analysis and T-test, and the screening conditions comprise: the variable importance is more than 1.7, the change multiple is more than 2 or less than 0.5, the P value is less than 0.01 and the wig appearance rate is less than 0.05.

Some embodiments of the present application provide a lipidome biomarker for differentiating at least two of raw milk, ultra-high temperature sterilized milk, pasteurized milk, extended shelf life milk, comprising lipids as in the following table:

some embodiments of the present application provide a lipidome biomarker for distinguishing pasteurized milk from extended shelf life milk, comprising lipids as in the following table:

some embodiments of the present application provide a use of the lipid group biomarkers provided by the previous embodiments to distinguish between different heat processed milks comprising at least two of raw milk, ultra high temperature sterilized milk, pasteurized milk, extended shelf life milk; or

The foregoing embodiments provide the use of a lipidic biomarker to distinguish between different heat-processed milks, including pasteurized milk and extended shelf life milk.

Some embodiments of the present application provide a method of differentiating between different heat processed milks, the different heat processed milks comprising at least two of raw milk, ultra high temperature sterilized milk, pasteurized milk, extended shelf life milk; the method comprises the following steps:

and (3) establishing a distinguishing model by using the lipid group biomarkers provided by the previous embodiment, and distinguishing unknown different hot processing mode milk by using the distinguishing model.

The following examples demonstrate that the lipidomic biomarkers of the present application distinguish between different heat-processed milks.

Examples

9 Raw Milks (RM), 26 Pasteurized Milks (PM), 8 ESL Milks (EM) and 17 ultra high temperature sterilized milks (UM) from New York Dairy Co.

(1) Collecting and preparing milk sample

60 μ L of each milk sample was diluted to 400 μ L with water and mixed well. Then, 960. mu.l of the extract, 6. mu.l of the ceramide internal standard (100. mu.g/mL), and 6. mu.l of the lipid-mixed internal standard were added to each milk sample, respectively, to obtain a mixture. The extract was methyl tert-butyl ether: methanol (5:1, v/v). The mixture was vortexed for 1 minute and then sonicated in an ice water bath at 53Hz for 10 minutes. The mixture was centrifuged at 4000 rpm for 15 minutes and 500. mu.l of the supernatant containing lipids was drawn into another centrifuge tube. The remaining portion was extracted twice with 500. mu.L of methyl t-butyl ether. Finally, the three extracts were combined and blown dry under mild nitrogen. Extracted lipids were purified with 120 μ L dichloromethane: methanol (1:1, v/v) was reconstituted for liquid chromatography-mass spectrometry (LC-MS) analysis.

The internal lipid mixing standard comprises: PC 15: 0-18: 1(d7), 160.7. mu.g/mL; PE 15: 0-18: 1(d7), 5.7. mu.g/mL; PS 15: 0-18: 1(d7), 4.2. mu.g/mL; PG 15: 0-18: 1(d7), 29.1. mu.g/mL; PI 15: 0-18: 1(d7), 9.1. mu.g/mL; PA 15: 0-18: 1(d7), 7.4. mu.g/mL; LPC 18:1(d7), 25.5. mu.g/mL; LPE 18:1(d7), 5.3. mu.g/mL; CE 18:1(d7), 356.1. mu.g/mL; MG 18:1(d7), 2. mu.g/mL; DG 15: 0-18: 1(d7), 9.4. mu.g/mL; TG 15: 0-18: 1(d7) _15:0, 57.3. mu.g/mL; SM 18: 1; 2O/18:1(d9), 30.9. mu.g/mL.

(2) Data acquisition

And (3) detecting each to-be-detected milk sample obtained in the step (1) by using an ultra-high performance liquid chromatography-high resolution quadrupole time-of-flight mass spectrometer.

The ultra-high performance liquid chromatography apparatus used in the experiment was an ExionLC AC (AB SCIEX, USA) and the column was a Phenomen Kinetex C18 column (2.1X 100mm, particle size 1.7 μm) (Phenomenex, USA). The mobile phase A is acetonitrile aqueous solution with volume fraction of 40-60%, and contains 5-20 mmol/L ammonium acetate; the mobile phase B is isopropanol with the volume ratio of 10: 1-5: 1: acetonitrile, containing 5-20 mmol/L ammonium acetate. The elution gradient was:

a high resolution quadrupole time-of-flight mass spectrometer was used for the experiments, with an instrument model TripleTOF6600 (abciex corporation, usa), using an electrospray ion source (ESI) and electrospray ionization (ESI) parameters applied as follows: air curtain air (CUR), 40 psi; a spray voltage (IS) of 5500V or-4500V in positive and negative ion mode, respectively; nebulizer (GS1), 60 psi; supplemental heating gas (GS2), 60 psi; ion source temperature, 600 ℃; and a declustering voltage (DP), 100 eV; the Collision Energy (CE) was 45. + -.25 eV. Data dependent acquisition, range m/z: 200-. Dynamic exclusion is used to ensure the accuracy of parent and daughter ion mass information. The collected maps are shown in fig. 1-8.

(3) Data preprocessing and feature extraction

Processing the chromatographic peak obtained in the step (2) by using MS-DIAL 4.70 lipidomics qualitative software, wherein the qualitative conditions comprise: mass number range: 200-2000 Da; peak intensity greater than 100 cps; the accurate mass deviation (MS1) is 0.01Da, and the accurate mass deviation (MS2) is 0.025 Da; the maximum charge number is 2; the smoothing mode is linear weighted moving average, and the smoothing level is 3; selecting lipids having a score greater than 80; setting the peak alignment retention time bias to 0.2 minutes, MS1 mass bias, 0.015 Da; and deleting the features according to the blank information, and manually confirming the annotation and peak extraction result. And (3) introducing the lipid qualitative result into MultiQuant software for lipid quantification, integrating the peak area of each lipid, and setting parameters: retention time offset, 6 s; minimum peak width, 3 points; minimum peak area, 100 cps; the concentration of the lipid internal standard was set to C15 Ceramide-d7, 10. mu.g/mL; PC 15: 0-18: 1(d7), 16.07. mu.g/mL; PE 15: 0-18: 1(d7), 0.57. mu.g/mL; PS 15: 0-18: 1(d7), 0.42. mu.g/mL; PG 15: 0-18: 1(d7), 2.91. mu.g/mL; PI 15: 0-18: 1(d7), 0.91. mu.g/mL; PA 15: 0-18: 1(d7), 0.74. mu.g/mL; LPC 18:1(d7), 2.55. mu.g/mL; LPE 18:1(d7), 0.53. mu.g/mL; CE 18:1(d7), 35.61. mu.g/mL; MG 18:1(d7), 0.2. mu.g/mL; DG 15: 0-18: 1(d7), 0.94. mu.g/mL; TG 15: 0-18: 1(d7) _15:0, 5.73. mu.g/mL; SM 18: 1; 2O/18:1(d9), 3.09. mu.g/mL; cholesterol (d7), 9.84. mu.g/mL; the corresponding relationship between lipid classes and internal standards is:

removing the quantitative result according to the deletion value of more than 50 percent, filling the deletion value of the lipid with the deletion value of less than or equal to 50 percent by 1/5 of the minimum value of the lipid in all positive samples, and carrying out normalization processing on the data to obtain preprocessed data; and importing the preprocessed data into Metabioanalyst 5.0 software, carrying out data analysis, and constructing a visual chart.

(4) Data analysis

I, distinguishing raw milk, ultra-high temperature sterilized milk from pasteurized milk and extended shelf life milk

Sparse partial least squares discriminant analysis (sPLS-DA) is a variant of partial least squares discriminant analysis (PLS-DA), which combines the penalty function of Lasso (minimum absolute shrinkage and selection operator) and PLS, and performs variable selection during model calibration, discarding non-information variables. I.e. it makes a sparse assumption that only a few features are responsible for classification, so that it is possible to identify the most diverse substances that can distinguish different samples.

For the sPLS-DA analysis, the error rates of different principal component numbers were calculated by 5-fold cross-checking, and finally 3 principal components were selected, and 10 variables were selected for each principal component to minimize the error rate of the model (FIG. 10). 30 lipid biomarkers were obtained, thirty lipid biomarkers are shown in table i, and the contribution of the thirty lipid biomarkers to the main component discrimination is shown in fig. 11-13. In the sPLS-DA two-dimensional score plot (fig. 9), pasteurized milk was not distinguished from extended shelf life milk, but both were significantly distinguished from raw milk and ultra-high temperature sterilized milk. From the sPLS-DA two-dimensional score chart it can be seen that major component 1 (11.3%) distinguishes UM from three other heat-processed milks, with the main features that lead to UM differentiation from other milks being high amounts of lysophospholipids (LysoGP) and Free Fatty Acids (FFA) (fig. 11). Principal component 2 (12.7%) distinguished raw milk from pasteurized milk and extended shelf life milk (fig. 12). And (3) establishing an sPLS-DA distinguishing model by utilizing the contents of the screened 30 different substances in the milk with different heat processing modes to distinguish the milk with different heat processing modes, wherein the distinguishing model is shown in a figure 14.

TABLE I30 lipid biomarkers to differentiate raw milk, ultra-high temperature sterilized milk from pasteurized milk, extended shelf milk

Application method

And (3) extracting an unknown milk sample by using the same method, detecting the lipid in the table I by using a high-resolution mass spectrum, and quantifying the detected lipid by using mass spectrum data processing software. The quantitative result is discriminated by using the model. Referring to fig. 14, the raw milk falls in the area a, the ultra-high temperature sterilized milk falls in the area B, and the pasteurized milk or the extended shelf life milk falls in the area C (wherein a smaller area is overlapped inside the area C). Therefore, a further distinction is made between pasteurized milk in the C-zone and extended shelf life milk.

II, Distinguishing pasteurized milk from extended shelf life milk

For PM and EM which cannot be distinguished by sPLS-DA, the two are distinguished by orthogonal partial least squares analysis (OPLS-DA). As shown in FIG. 15, R of the OPLS-DA model²X is 11.8%, explaining the response (R) between pasteurized milk and extended shelf life milk²Y) 67.9% of the difference. 1000 replacement tests were performed on the model to verify the resulting R²Y is 0.984, Q²0.804, indicating that the model has better prediction performance and no overfitting. In addition, T-test and Fold Change analysis were performed on the lipid content of EM in PM samples, and the screening condition was set to p<0.05，FC>2，VIP>1.7, 74 lipid group biomarkers were screened between pasteurized milk and extended shelf life milk, the 74 lipid group biomarkers are given in Table II. The content of the screened 74 lipid biomarkers in pasteurized milk and extended shelf life milk is utilized to establish an OPLS-DA distinguishing model, and the visualization result of the model is shown in figure 16.

TABLE II 74 lipidome biomarkers to differentiate pasteurized milk from extended shelf milk

Application method

And extracting an unknown milk sample by using the same method, detecting the lipid in the table II by using a high-resolution mass spectrum, and quantifying the detected lipid by using mass spectrum data processing software. The quantitative result is discriminated by using the model. Referring to fig. 16, the left side area a is the extended shelf life milk and the right side area B is the pasteurized milk.

From the results of the above examples, it can be seen that the present application successfully screened 30 lipid group biomarkers for distinguishing raw milk, ultra-high temperature sterilized milk from pasteurized milk and extended shelf life milk, and screened 74 lipid group biomarkers for distinguishing pasteurized milk from extended shelf life milk. Therefore, the method can effectively distinguish the meristematic milk, the pasteurized milk, the shelf life prolonging milk and the ultrahigh temperature sterilized milk, thereby providing technical support for enterprise product monitoring and industry supervision.

Furthermore, the detection method and the discrimination model established by the method have the advantages of convenience, sensitivity and accuracy, can realize high-flux detection on a large batch of samples, can realize discrimination on processing modes of different hot processing modes of milk, and greatly improve the detection efficiency of the samples.

The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for screening lipid group biomarkers of milk with different heat processing modes is characterized in that the milk with different heat processing modes comprises raw milk, pasteurized milk, shelf-life-prolonging milk and ultrahigh-temperature sterilized milk;

the screening method comprises the following steps:

processing the mass spectrogram to obtain a lipid qualitative result;

using the analytical model to determine differential lipids of the different thermal processing regimes of milk as the lipidomic biomarkers.

2. The method for screening the lipid group biomarkers of milk processed according to different heat treatment modes as claimed in claim 1,

the step of obtaining the mass spectrogram of the lipid sample of the milk with different thermal processing modes comprises the following steps:

the mobile phase B comprises isopropanol, acetonitrile and ammonium acetate; the ratio of isopropanol to isopropanol by volume is: the acetonitrile is 10: 1-5: 1; the concentration of the ammonium acetate is 5 mmol/L-20 mmol/L;

the gradient elution comprises:

3. the method for screening the lipid group biomarkers of milk processed according to different heat treatment modes as claimed in claim 1,

the step of processing the mass spectrogram to obtain a lipid qualitative result comprises:

4. The method for screening the lipid group biomarkers of milk processed according to different heat treatment modes as claimed in claim 1,

the step of lipid quantification of the lipid qualitative result comprises:

5. The method for screening the lipid group biomarkers of milk processed according to different heat treatment modes as claimed in claim 1,

the step of establishing a discriminative model based on the quantitative result includes:

6. The method for screening the lipid group biomarkers of milk processed according to different heat treatment modes as claimed in claim 1,

the analysis model comprises sparse partial least square discriminant analysis, orthogonal partial least square discriminant analysis, T-test and difference multiple analysis;

the step of determining differential lipids of the different thermal processing modes of milk as a lipidomic biomarker using the analytical model comprises:

differential lipids which are distinguished from raw milk, ultrahigh-temperature sterilized milk, pasteurized milk and prolonged shelf milk are screened by using sparse partial least square discriminant analysis, 3 main components are selected, and 10 lipids with the greatest importance to the three main component variables are screened respectively; and/or

Differentiating lipids distinguishing pasteurized milk from extended shelf-life milk using the orthometric partial least squares discriminant analysis in combination with the fold-difference analysis and the T-test screening under screening conditions comprising: the variable importance is more than 1.7, the change multiple is more than 2 or less than 0.5, the P value is less than 0.01 and the wig appearance rate is less than 0.05.

7. A lipidome biomarker for differentiating at least two of raw milk, ultra-high temperature sterilized milk, pasteurized milk, extended shelf life milk, comprising lipids as in the following table:

8. a lipidome biomarker for distinguishing pasteurized milk from extended shelf life milk, comprising lipids as in the following table:

9. use of the lipidic biomarker of claim 7 to distinguish between different heat-processed milks comprising at least two of raw milk, ultra high temperature sterilized milk, pasteurized milk, extended shelf life milk; or

Use of the lipidomic biomarker of claim 8 to distinguish between different heat-processed milks, including pasteurized milk and extended shelf life milk.

10. A method for distinguishing milk with different heat processing modes is characterized in that the milk with different heat processing modes comprises at least two of raw milk, ultrahigh-temperature sterilized milk, pasteurized milk and shelf-life-prolonging milk; the method comprises the following steps:

use of the lipidomic biomarkers of claim 7 or 8 to create a discriminatory model, which is used to discriminate between unknown different heat-processed milks.