1. A method for tracing and identifying soybean origin based on combination of MALDI-TOF/TOF and IRMS technology is characterized by comprising the following steps:

s1, preparing a standard sample: selecting soybean samples with different producing areas of a definite region, respectively squeezing the soybean samples of each producing area to obtain a standard soybean oil sample and grinding the soybean samples to obtain a standard soybean powder sample, and extracting water-soluble protein of the standard soybean powder sample as a target object, wherein the soybean samples are derived from at least two different producing areas;

s2, acquiring mass spectrum data and stable isotope ratio data of the standard sample: respectively acquiring mass spectrum data information of the standard soybean oil samples in different producing areas by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer to obtain high-resolution mass spectrum data of the standard soybean oil samples; acquiring mass spectrum data information of the water-soluble protein of the standard soybean powder samples in different producing areas by using an isotope mass spectrometer to obtain stable isotope ratio data of the water-soluble protein;

s3, determining a standard soybean oil sample targeting compound: matching the triglyceride compound mass spectrum data in the high resolution mass spectrum data obtained in the step S2 with standard triglyceride compound data in a lipid compound database to determine a triglyceride targeting compound of the standard sample;

s4, establishing a soybean producing area traceability identification model: after the triglyceride targeting compound in the standard soybean oil sample is determined, the high-resolution mass spectrum data of the triglyceride compound corresponding to the triglyceride targeting compound is normalized and averaged through analysis software, the normalized and averaged data of the standard soybean oil sample of the same producing area and the stable isotope ratio data of the water-soluble protein of the standard soybean powder sample are simultaneously introduced into modeling software to be processed in one or more modes of a principal component analysis method, a partial least square method-discriminant analysis method and an orthogonal partial least square method regression analysis method, and then the normalized and averaged data of the standard soybean oil sample of different producing areas and the stable isotope ratio data of the water-soluble protein of the standard soybean powder sample are sequentially introduced into modeling software to obtain the characteristic distribution rule of the soybean oil in the standard soybean oil sample of different producing areas and the stable isotope distribution rule of the water-soluble protein of the standard soybean powder sample Establishing a soybean origin tracing identification model based on combination of MALDI-TOF/TOF and stable isotope ratio analysis;

s5, result prediction: squeezing a soybean sample to be detected to obtain a soybean oil sample to be detected, grinding and extracting to obtain soybean water-soluble protein to be detected, collecting triglyceride compound high-resolution mass spectrum data of the soybean oil sample to be detected by a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and collecting stable isotope ratio data of the soybean water-soluble protein to be detected by an isotope mass spectrometer; and simultaneously introducing the triglyceride compound high-resolution mass spectrum data and the stable isotope ratio data of the soybean sample to be detected into the soybean origin tracing and identifying model in the step S4, and performing origin tracing prediction on the soybean to be detected.

2. The method for tracing to source and identifying origin of soybean according to claim 1, wherein in step S2, the method for acquiring high resolution mass spectrum data of standard soybean oil sample comprises: the method comprises the steps of dipping standard soybean oil samples by using a cotton swab, printing the standard soybean oil samples on a target plate containing a matrix, wherein the matrix is 2, 5-dihydroxybenzoic acid, calibrating each 6 standard soybean oil samples once by using polyethylene glycol, parallelly measuring each standard soybean oil sample for three times, and placing the target plate in a matrix-assisted laser desorption ionization time-of-flight mass spectrometer for mass spectrum data acquisition in a positive ion mode.

3. The method for tracing to source of soybean origin according to claim 1, wherein the extraction of water-soluble protein from the standard soybean meal sample in step S1 is as follows: grinding and crushing a soybean sample to prepare a 30g standard soybean powder sample, weighing 1g standard soybean powder sample, adding 10mL of ultrapure water, performing ultrasonic treatment for 30min under the rotation condition of 10000 revolutions, centrifuging for 5min, extracting 1mL of supernatant protein extract, adding the supernatant protein extract into a 3K ultrafiltration centrifugal tube, centrifuging to obtain proteins with molecular weights of more than 3K Da respectively, transferring the centrifuged proteins into a sample injection bottle, and freeze-drying for more than 8 hours until all the proteins are dried to obtain solid protein powder of the water-soluble proteins.

4. The method for identifying the soybean origin tracing according to claim 3, wherein the isotope mass spectrometer is one of an on-line gas analysis-stable isotope mass spectrometer GasBench-IRMS, an elemental analysis-isotope mass spectrometer EA-IRMS, a gas chromatography-isotope mass spectrometer GC-IRMS and a liquid chromatography-isotope mass spectrometer LC-IRMS.

5. The method for identifying soybean origin source tracing according to claim 4, wherein the method for collecting stable isotope ratio data of water-soluble protein of standard soybean flour sample in step S2 comprises:

a1. selecting an element analysis-isotope mass spectrometer EA-IRMS, placing solid protein powder of water-soluble protein in a tin cup and a silver cup, and respectively detecting the isotope ratio of stable carbon and nitrogen and the isotope ratio of stable hydrogen and oxygen in the water-soluble protein;

a2. tuning a correction element analysis-isotope mass spectrometer EA-IRMS;

a3. introducing water-soluble protein wrapped by tin cups into a 980 ℃ high-temperature combustion furnace to obtain CO₂And N₂Gas of CO₂And N₂Introduction into isotope mass spectrometer host for delta in water-soluble protein¹⁵N and delta¹³C, detecting; introducing water soluble protein wrapped by silver cup into 1380 deg.C pyrolysis furnace to obtain CO and H₂Gas, CO and H₂Introduction into isotope mass spectrometer host for water-soluble protein delta²H and delta¹⁸And (4) detecting.

6. The method for identifying the origin of soybean according to claim 5, wherein in step a2, the method for tuning the EA-IRMS is as follows: introducing high-purity CO, H2, N2 and CO2 into the elemental analysis-isotope mass spectrometer EA-IRMS as reference gases, and then using the known delta²H、δ¹⁸O、δ¹⁵N and delta¹³And C, calibrating the delta value of the stable isotope of the reference gas by using the standard substance with the C value, and using the delta value of the stable isotope calibrated by the reference gas as a detection standard to correct the delta value of the stable isotope in the water-soluble protein of the standard soybean powder sample detected in the step a3.

7. The method for identifying the origin of soybean according to claim 1, wherein said step S4 further comprises a blind sample verification step, said blind sample verification step comprising: selecting a plurality of soybean verification samples in the same region, respectively squeezing the soybean verification samples in a definite region to obtain a soybean oil sample to be verified and water-soluble protein of the soybean powder sample to be verified after grinding and extraction, collecting triglyceride compound high-resolution mass spectrum data of the soybean oil sample to be verified by a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and collecting water-soluble protein stable isotope ratio data of the soybean powder sample to be verified by an isotope mass spectrometer; and simultaneously introducing triglyceride compound high-resolution mass spectrum data and water-soluble protein stable isotope ratio data of the soybean verification sample into the soybean origin tracing and identifying model in the step S4, and verifying the origin tracing accuracy of the soybean verification sample.

8. The method for identifying the origin of soybean according to claim 1, wherein the method for determining the target compound of step S3 comprises: dividing the high-resolution mass spectrum data in the soybean oil sample into 2 regions according to the molecular weight range of the target object in the high-resolution mass spectrum data: the molecular weight is in a first region of 800-1000, the molecular weight is in a second region of 700-800, the first region and the second region are characteristic regions of soybean oil lipid metabolism, animal and plant triglyceride compounds with the molecular weight range of 700-950 in a lipid compound database and soybean oil triglyceride compounds in the first region and the second region are matched and screened, and the triglyceride compounds in 64 soybean oils with the molecular weight range of 850-930Da are determined to be the triglyceride targeting compounds.

9. The method for identifying soybean origin tracing according to claim 1, wherein in step S4, the normalized and averaged data of the standard soybean oil sample and the water-soluble protein stable isotope ratio data of the standard soybean flour sample of the same soybean origin are simultaneously introduced into modeling software for the orthogonal partial least squares regression analysis to construct the OPLS-DA soybean origin tracing identification model based on the combination of MALDI-TOF/TOF and stable isotope ratio analysis.

10. The method for identifying soybean origin source tracing according to claim 1, wherein in the process of preparing the standard samples in step S1, the soybean samples are from n different regions, the soybean samples are divided into n x (n-1)/2 groups, each group of soybean samples consists of two soybean samples from different origins, and a two-country soybean origin source tracing identification model is established for identifying the corresponding soybean country or region in the two-country soybean origin source tracing identification model according to steps S2, S3 and S4.

Background

The field of food quality safety detection relates to two major quality safety problems, namely the risk problem of toxic and harmful substances and the authenticity problem of products. Regarding the detection problem of harmful substances in food, a large number of standards and detection methods reported in literatures at home and abroad detect toxic and harmful substances in food, and regarding the authenticity problem of the quality of food, the detection method has attracted attention and attention of consumers at home and abroad in recent ten years, and gradually becomes a hot spot and a difficult problem in the field of food quality detection. Currently, the technology for detecting the authenticity of food mainly comprises fingerprint technology such as ultraviolet spectrum and infrared spectrum, atomic spectrum technology such as atomic absorption, emission and fluorescence, isotope mass spectrum technology, high-resolution mass spectrum technology, nuclear magnetic resonance technology, raman spectrum technology, and omics technology which is started in the nineteenth 20 th century, and the food omics in the omics technology comprise omics technology based on omics and epigenomics, transcriptomics, proteomics, metabonomics and lipidomics, and the like, and proteomics, metabonomics and lipidomics are commonly used in the field of food inspection, so that the problems of true and false functional components of food, food nutrient content and food origin tracing can be judged through the omics technology. In the field of origin tracing authenticity identification of food, isotope mass spectrometry technology and omics technology are two relatively reliable identification technologies, but the methods and patents for origin tracing of soybeans by using omics technology are few.

The patent application with publication number CN111272861A discloses a MALDI-TOF detection method of polypeptide in food. The method comprises extracting polypeptide from food matrix, eliminating interference substances, detecting by MALDITOF MS to obtain polypeptide distribution in food, detecting mass spectrum in positive ion mode, irradiating with 337nmN2 laser, and setting energy at 50% -70%. Compared with the traditional polypeptide detection method, the method has the advantages of simple sample pretreatment method, high tolerance to sample impurities, good sensitivity, high precision, wide detection range, extremely short detection time, large information amount and simple analysis method. In addition, the method can detect a plurality of samples at one time and has high-throughput characteristic. The method provided by the invention is favorable for providing guarantee for the rapid detection of the nutrition evaluation, the origin tracing identification, the ingredient adulteration and the quality process control of the food. The method is used for identifying the polypeptide in the food to identify the producing area of the video, but is limited by the detection difficulty of the polypeptide in part of the food, the detection difficulty of the polypeptide in different producing areas of the same product is higher, and the method is difficult to apply to the source tracing identification of the soybean producing area.

Patent application No. CN111474275A discloses a method for tracing potato origin based on mineral elements and stable isotopes, which comprises: collecting potato samples of potato production bases in inner Mongolia, Heilongjiang, Xinjiang and Sichuan provinces respectively, and preparing analysis samples; determining the contents of aluminum, manganese, copper and zinc in the obtained analysis sample, and determining the ratio of the carbon stable isotope to the carbon stable isotope delta 13C and the ratio of the nitrogen stable isotope to the nitrogen stable isotope delta 15N; and (5) bringing the measurement result into a discrimination model to judge the production area of the potato sample. The method for tracing the potato origin of the invention is based on the mineral elements and stable isotope content of different origins, can use objective detection technical means to indicate the sources of different potato products, has the accuracy rate of not less than 95.3 percent, fills the gap of the lack of potato origin tracing technology, can provide data support for protecting potato regional brands with the advantages of origins, avoids the phenomenon of insufficient production, and has wide application prospect. The method cannot establish a soybean origin tracing model based on lipidomics.

The patent application with the publication number of CN106560698A discloses a plant production place identification method based on multiple detection technologies, and the invention discloses a method for identifying the production place of Wuyi rock tea by combining near infrared spectrum, stable isotope and trace element data, belongs to the technical field of authenticity identification of geographic marking products, and aims to solve the problems that single detection data cannot represent all key information of production place traceability and different types of detection data are matched in data used in a metrological method in a combined mode. The method is based on a least square support vector machine (LS-SVM) model, near infrared, stable isotopes and trace elements are fused together for modeling analysis, the recognition rate is the highest and reaches 100.0 percent, the recognition rate is far higher than that of an LS-SVM judgment result established by single data, the recognition rate of blind samples reaches 100 percent, and the method has a good application prospect. The invention is also suitable for identifying the production places of Chinese torreya, lotus root starch and other plant samples, and the invention is not suitable for plants except tea, but for plants rich in oil, a near infrared method is not suitable.

Disclosure of Invention

The invention aims to provide a soybean origin tracing and identifying method based on combination of MALDI-TOF/TOF and IRMS technologies, which is a novel soft ionization biological mass spectrometry technology based on MALDI-TOF/TOF and is combined with a stable isotope ratio analysis technology to establish a soybean origin tracing and identifying method based on the fusion of MALDI-TOF/TOF and stable isotope content analysis.

In order to achieve the purpose, the technical scheme adopted by the invention is as follows:

a soybean origin tracing identification method based on combination of MALDI-TOF/TOF and IRMS (stable isotope labeling) technology comprises the following steps:

s1, preparing a standard sample: selecting soybean samples with different producing areas of a definite region, respectively squeezing the soybean samples of each producing area to obtain a standard soybean oil sample and grinding the soybean samples to obtain a standard soybean powder sample, and extracting water-soluble protein of the standard soybean powder sample as a target object, wherein the soybean samples are derived from at least two different producing areas;

s2, acquiring mass spectrum data and stable isotope ratio data of the standard sample: respectively acquiring mass spectrum data information of the standard soybean oil samples in different producing areas by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer to obtain high-resolution mass spectrum data of the standard soybean oil samples; acquiring mass spectrum data information of the water-soluble protein of the standard soybean powder samples in different producing areas by using an isotope mass spectrometer to obtain stable isotope ratio data of the water-soluble protein;

s3, determining a standard soybean oil sample targeting compound: matching the triglyceride compound mass spectrum data in the high resolution mass spectrum data obtained in the step S2 with standard triglyceride compound data in a lipid compound database to determine a triglyceride targeting compound of the standard sample;

s4, establishing a soybean producing area traceability identification model: after the triglyceride targeting compound in the standard soybean oil sample is determined, the high-resolution mass spectrum data of the triglyceride compound corresponding to the triglyceride targeting compound is normalized and averaged through analysis software, the normalized and averaged data of the standard soybean oil sample of the same producing area and the stable isotope ratio data of the water-soluble protein of the standard soybean powder sample are simultaneously introduced into modeling software to be processed in one or more modes of a principal component analysis method, a partial least square method-discriminant analysis method and an orthogonal partial least square method regression analysis method, and then the normalized and averaged data of the standard soybean oil sample of different producing areas and the stable isotope ratio data of the water-soluble protein of the standard soybean powder sample are sequentially introduced into modeling software to obtain the characteristic distribution rule of the soybean oil in the standard soybean oil sample of different producing areas and the stable isotope distribution rule of the water-soluble protein of the standard soybean powder sample Establishing a soybean origin tracing identification model based on combination of MALDI-TOF/TOF and stable isotope ratio analysis;

s5, result prediction: squeezing a soybean sample to be detected to obtain a soybean oil sample to be detected, grinding and extracting to obtain soybean water-soluble protein to be detected, collecting triglyceride compound high-resolution mass spectrum data of the soybean oil sample to be detected by a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and collecting stable isotope ratio data of the soybean water-soluble protein to be detected by an isotope mass spectrometer; and simultaneously introducing the triglyceride compound high-resolution mass spectrum data and the stable isotope ratio data of the soybean sample to be detected into the soybean origin tracing and identifying model in the step S4, and performing origin tracing prediction on the soybean to be detected.

Preferably, in step S2, the method for acquiring high resolution mass spectrometry data of the standard soybean oil sample includes: standard soybean oil samples were printed using cotton swabs dipped in one or more of 2, 5-dihydroxybenzoic acid, sinapic acid and 2-cyano-4-hydroxycinnamic acid on target plates containing a matrix calibrated once per 6 standard soybean oil samples with polyethylene glycol (PEG) and assayed in triplicate for each standard soybean oil sample, and the target plates were placed in the matrix assisted laser desorption ionization time-of-flight mass spectrometer for mass spectrometric data acquisition in positive ion mode.

More preferably, the substrate is 2, 5-dihydroxybenzoic acid.

Preferably, the water-soluble protein extraction process of the standard soybean meal sample in step S1 is as follows: grinding and crushing a soybean sample to prepare a 30g standard soybean powder sample, weighing 1g standard soybean powder sample, adding 10mL of ultrapure water, performing ultrasonic treatment for 30min under the rotation condition of 10000 revolutions, centrifuging for 5min, extracting 1mL of supernatant protein extract, adding the supernatant protein extract into a 3K ultrafiltration centrifugal tube, centrifuging to obtain proteins with molecular weights of more than 3K Da respectively, transferring the centrifuged proteins into a sample injection bottle, and freeze-drying for more than 8 hours until all the proteins are dried to obtain solid protein powder of the water-soluble proteins.

Preferably, the isotope mass spectrometer is one of an online gas analysis-stable isotope mass spectrometer GasBench-IRMS, an element analysis-isotope mass spectrometer EA-IRMS, a gas chromatography-isotope mass spectrometer GC-IRMS and a liquid chromatography-isotope mass spectrometer LC-IRMS.

Preferably, the method for collecting stable isotope ratio data of water-soluble protein of the standard soybean flour sample in step S2 includes:

a1. selecting an element analysis-isotope mass spectrometer EA-IRMS, placing solid protein powder of water-soluble protein in a tin cup and a silver cup, and respectively detecting the isotope ratio of stable carbon and nitrogen and the isotope ratio of stable hydrogen and oxygen in the water-soluble protein;

a2. tuning a correction element analysis-isotope mass spectrometer EA-IRMS;

a3. introducing water-soluble protein wrapped by tin cups into a 980 ℃ high-temperature combustion furnace to obtain CO₂And N₂Gas of CO₂And N₂Introduction into isotope mass spectrometer host for delta in water-soluble protein¹⁵N and delta¹³C, detecting; introducing water soluble protein wrapped by silver cup into 1380 deg.C pyrolysis furnace to obtain CO and H₂Gas, CO and H₂Introduction into isotope mass spectrometer host for water-soluble protein delta²H and delta¹⁸And (4) detecting.

Preferably, in the step a2, the method for tuning the calibration elemental analysis-isotope mass spectrometer EA-IRMS is as follows: introducing high-purity CO, H2, N2 and CO2 into the elemental analysis-isotope mass spectrometer EA-IRMS as reference gases, and then using the known delta²H、δ¹⁸O、δ¹⁵N and delta¹³And C, calibrating the delta value of the stable isotope of the reference gas by using the standard substance with the C value, and using the delta value of the stable isotope calibrated by the reference gas as a detection standard to correct the delta value of the stable isotope in the water-soluble protein of the standard soybean powder sample detected in the step a3.

Preferably, the processing and analysis of the data during calibration is performed by Isodat3.0 software of Thermo Scientific, USA.

Preferably, the matrix-assisted laser desorption ionization time-of-flight mass spectrometer collects high-resolution mass spectrum data of the standard sample on a Flex Control software, and the triglyceride compound high-resolution mass spectrum data is qualitatively and quantitatively subjected to normalization and average processing Analysis on the Flex Analysis software.

Preferably, the operating conditions of the matrix-assisted laser desorption ionization time-of-flight mass spectrometer are as follows: the mass spectrometer adopts a positive ion mode to acquire data, the ion source voltage is 20kV, the laser frequency is 1000Hz, the laser energy is 50 percent, 1500 laser points are acquired on each high-resolution mass spectrogram acquired by the matrix-assisted laser desorption ionization time-of-flight mass spectrometer, the calibration relative error is less than 5ppm, and the scanning range is 500-2000 Da.

Preferably, the inductively coupled plasma emission spectrometry ICP-AES has the following working conditions: the detection wavelength is 396.152 nm; the power is 1500W; plasma gas flow rate: 10.0L/min; the auxiliary gas flow is 0.55L/min; flow rate of atomizing gas: 0.55L/min; the rotating speed of the peristaltic pump is 50r/min, and the observation mode is as follows: axial direction; carrier gas: 99.996% high purity argon;

the operating conditions of inductively coupled plasma mass spectrometry ICP-MS are as follows: the mode adopted is Standard (STD); argon (more than or equal to 99.998 percent, high purity); 20, scanning/reading; reading/repetition number is 1; the repetition times are 3; the flow rate of atomizing gas is 0.84L/min; the auxiliary air flow is 1.2L/min; the plasma gas flow rate is 1500L/min; ICP radio frequency power is 1500W; peristaltic pump 20 rpm. Sampling cone and intercepting cone: a platinum cone.

Preferably, the step S4 further includes a blind sample verification step, where the blind sample verification step includes: selecting a plurality of soybean verification samples in the same region, respectively squeezing the soybean verification samples in a definite region to obtain a soybean oil sample to be verified and water-soluble protein of the soybean powder sample to be verified after grinding and extraction, collecting triglyceride compound high-resolution mass spectrum data of the soybean oil sample to be verified by a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and collecting water-soluble protein stable isotope ratio data of the soybean powder sample to be verified by an isotope mass spectrometer; and simultaneously introducing triglyceride compound high-resolution mass spectrum data and water-soluble protein stable isotope ratio data of the soybean verification sample into the soybean origin tracing and identifying model in the step S4, and verifying the origin tracing accuracy of the soybean verification sample.

Further preferably, in step S3, the lipid compound database is the lipid compound database LIPID MAPS Lipidomics Gateway published in the united states, the standard triglyceride compound data is 6899 triglyceride compounds of the tiranylglycerols in the lipid compound database LIPID MAPS Lipidomics Gateway, and the 64 triglyceride compounds common to the soybean oil sample are determined as the triglyceride targeting compounds of the standard sample by Flex Analysis software qualitative Analysis.

Preferably, the method for determining the target compound of step S3 includes: dividing the high-resolution mass spectrum data in the soybean oil sample into 2 regions according to the molecular weight range of the target object in the high-resolution mass spectrum data: the molecular weight is in a first region of 800-1000, the molecular weight is in a second region of 700-800, the first region and the second region are characteristic regions of soybean oil lipid metabolism, animal and plant triglyceride compounds with the molecular weight range of 700-950 in a lipid compound database and soybean oil triglyceride compounds in the first region and the second region are matched and screened, and the triglyceride compounds in 64 soybean oils with the molecular weight range of 850-930Da are determined to be the triglyceride targeting compounds.

Preferably, in step S4, the data obtained by normalizing and averaging the standard soybean oil sample of the same place of production soybean and the data of the stable isotope ratio of water-soluble protein in the standard soybean flour sample are simultaneously introduced into modeling software to perform an orthogonal partial least squares regression analysis, so as to construct an OPLS-DA soybean origin tracing identification model based on the combination of MALDI-TOF/TOF and stable isotope ratio analysis.

Preferably, the step S4 further includes an optimization step of the soybean origin tracing and identifying model, where the optimization step includes: and determining the partial triglyceride compounds with large contribution in the triglyceride targeting compound through the VIP value of a soybean origin tracing identification model, and selecting 38 triglyceride compounds as the preferred triglyceride targeting compounds. Selecting the triglyceride compounds with 38 VIP values which have large contribution degree from 64 triglyceride compounds with the molecular weight range of 850-930Da as the preferred triglyceride targeting compounds, wherein the VIP value is a variable projection importance analysis value and comprises the triglyceride compounds with the following molecular weights: 853.7, 854.7, 875.7, 876.7, 877.7, 878.7, 879.7, 880.7, 881.7, 882.8, 883.7, 893.7, 894.7, 895.7, 896.7, 897.7, 898.7, 899.7, 900.7, 901.7, 902.7, 903.7, 904.7, 905.7, 906.7, 907.7, 909.7, 915.7, 916.7, 917.7, 918.7, 919.7, 920.7, 921.7, 922.7, 923.7, 908.7.

Preferably, during the selection process of the triglyceride targeting compound, according to hotelling's and DModx indexes, outlier samples exceeding 99% confidence intervals and all soybean oil samples in the area with a small number of occurrences in the identification model are deleted, so as to optimize the soybean origin tracing identification model.

Preferably, in the process of preparing the standard sample in step S1, the soybean samples are from n different regions, the soybean samples are divided into n × (n-1)/2 groups, each group of soybean samples consists of two soybean samples from different origins, and each group of soybean samples is used for establishing a two-country soybean origin tracing identification model according to steps S2, S3 and S4, so as to identify the corresponding soybean country or region in the two-country soybean origin tracing identification model.

Preferably, in the preparation of the standard sample in step S1, the soybean samples originate from at least three different countries or regions, so that the step S4 establishes a multi-country soybean origin tracing model.

More preferably, in the standard sample preparation process in step S1, the soybean sample is obtained from five countries including bacui, russia, usa, canada, yerba mate, china and argentina.

Preferably, in the preparation process of the standard sample in step S1, the soybean sample is from usa, brazil, argentina, canada, yerba mate, russia, and china, and a traceability model of soybean origin in usa-brazil, usa-argentina, usa-canada, usa-yerba mate, usa-russia, and usa-china is established according to steps S2, S3, and S4 in sequence, and is used for identifying the soybean origin of soybean origin in usa and other countries.

Has the advantages that:

according to the soybean origin tracing and identifying method based on the analysis and fusion of stable isotope ratio values of MALDI-TOF/TOF and IRMS, triglyceride compound high-resolution mass spectrum data of a soybean oil sample to be detected and the stable isotope ratio value of water-soluble protein in the soybean powder sample are referenced, the two data are fused and then introduced into a soybean origin tracing and identifying model, origin tracing prediction of the soybean to be detected is carried out, and the origin tracing accuracy of the soybean is improved; and the mass spectrum data information of the standard samples of different producing areas is respectively collected by adopting a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and the mass spectrum data collection can be carried out without diluting the standard samples, so that the high-resolution mass spectrum data of the standard samples are obtained, MALDI-TOF/TOF is taken as a novel soft ionization biological mass spectrum technology, and the method has obvious advancement and advantages in biological species identification, food freshness judgment and food type identification, the MALDI-TOF/TOF technology can accurately obtain the high-resolution mass spectrum data of the triglyceride targeting compounds in the soybean and soybean oil samples, and a multivariate statistical analysis discrimination prediction model can be established by utilizing less high-resolution mass spectrum data of the triglyceride targeting compounds, and the accuracy of the soybean producing area traceability identification is further improved by combining with multi-country and two-country soybean producing area traceability identification models, the method can also improve the traceability accuracy of identifying the soybean origin of two specific countries by independently establishing two-country soybean origin traceability identification models. Compared with a method for singly identifying the American soybean by using a stable isotope ratio tracing method, the method has the advantages that the accuracy is high, is improved by more than 10 percent, and is also improved compared with the accuracy of singly identifying the American soybean by using a MALDI-TOF/TOF tracing method, so that the method can effectively identify the source between the American soybean and other main soybean producing countries, and avoids the situation that the multi-country soybean enters China after being mixed.

Drawings

FIG. 1 shows the water-soluble protein delta of a soy flour sample measured in accordance with the present invention¹⁵Isotopic mass spectra of N values;

FIG. 2 shows a sample of soy flour measured in accordance with the present inventionWater-soluble protein delta¹³Isotopic mass spectra of C values;

FIG. 3 shows the water-soluble protein delta of a soy flour sample measured in accordance with the present invention²Isotopic mass spectra of H values;

FIG. 4 shows the water-soluble protein delta of a soy flour sample measured in accordance with the present invention¹⁸Isotopic mass spectra of O values;

FIG. 5 shows an isotope ratio-based multi-national OPLS-DA soybean origin tracing identification model established with water-soluble protein of soybean flour samples as a target object;

FIG. 6 is a mass spectrum of a sample of soybean oil measured in accordance with the present invention;

FIG. 7 shows a MALDI-TOF/TOF-based multi-national OPLS-DA soybean origin tracing identification model;

FIG. 8 shows a traceability model of the soybean origin of Brazil-American two countries OPLS-DA based on MALDI-TOF/TOF and stable isotope ratio analysis

FIG. 9 is a graph showing an Argentina-American two-country OPLS-DA soybean provenance identification model based on MALDI-TOF/TOF and stable isotope ratio analysis;

FIG. 10 is a graph showing a Canada-United states two-country OPLS-DA soybean origin tracing identification model based on MALDI-TOF/TOF and stable isotope ratio analysis;

FIG. 11 shows a traceability model of the origin of soybean of both OPLS-DA of Uyghur-USA based on MALDI-TOF/TOF and stable isotope ratio analysis;

FIG. 12 is a graph showing a Russian-American two-country OPLS-DA soybean provenance identification model based on MALDI-TOF/TOF and stable isotope ratio analysis;

FIG. 13 shows a traceability system model of China-United states two countries OPLS-DA soybean origin based on MALDI-TOF/TOF and stable isotope ratio analysis;

FIG. 14 shows a stable isotope ratio-based multi-national OPLS-DA soybean origin tracing identification model established with soybean meal samples as target targets;

fig. 15 shows a stable isotope ratio-based multinational OPLS-DA soybean origin tracing identification model established by using a soybean oil sample as a target object.

Detailed Description

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.

The technical solution of the present invention is described in detail with specific examples below.

The experimental device in the soybean oil sample data acquisition process includes: selecting an Ultraflex (matrix assisted laser Desorption) -TOF/TOF mass spectrometer of Bruker, Germany; HPLC 20AD high performance liquid chromatograph of Shimadzu corporation, Japan; an Xbridge BEH C18 column (100 mm. times.2.1 mm,3 μm) from Waters, USA; and an oil press;

selecting a reagent: acetonitrile, acetone (chromatographically pure, semer feishel, usa); PEG calibrator; 2, 5-dihydroxybenzoic acid substrate (CNW corporation, ampere, china);

instruments used in data acquisition of soy water-soluble protein: flash EA 2000 elemental analyzer, DELTA V Advantage isotope mass spectrometer and ConFlo IV interface (Thermo Scientific, usa); Milli-Q ultra pure water instruments (Millipore, USA); model SW 30H ultrasound (SWISS SONO SWISS); SIGMA 3-30KS high speed refrigerated centrifuge (Sigma centrifuge, Germany); sartorius BT 223S thousandth balance (Sartorius, germany); an oil press; a vacuum freeze dryer (Alpha 1-2LD PLUS, Christ, Germany); a pulverizer (Shanghai Jiading grain and oil Co., Ltd., JL 064); silver cups (IVA analytechnik, germany); tin cups (Sammer. USA); he gas (purity more than 99%) as carrier gas, and CO₂Standard reference gas (purity greater than 99.9995%), CO standard reference gas (purity greater than 99.9995%), and H₂Standard reference gas (purity greater than 99.9995%); n is a radical of₂Standard reference gas (purity greater than 99.9995%); ultrafiltration centrifuge tubes (Centrifugal Filter Units, Millipore, USA, 3K/10K))。

EA-IRMS uses carbon and nitrogen isotope standard substances: IAEA-600 caffeine: delta¹³C＝-27.771‰，δ¹⁵N ═ 1.00 ‰, based on V-PDB, purchased from international atomic energy agency; EA-IRMS with hydrogen-oxygen isotope standard substance: EMA-P1 polyamide: delta²H＝-25.3‰，δ¹⁸20.99% O based on V-SMOW, available from elementary Microanalysis, england; the experimental water was ultrapure water, and the impedance value was 18.2 M.OMEGA.cm.

Sources of soybean and soybean oil samples: the soybean standard sample is obtained from 807 soybean samples collected by the related directly-affiliated customs and foreign purchase of the whole country, wherein the quantity of the soybean samples of each production place is as follows: wherein 152 of the us samples, 423 of the brazil samples, 96 of the canada samples, 68 of the argentina samples, 14 of the yerba mate samples, 25 of the russian samples, and 29 of the chinese samples; soybean oil samples 334: of these, 36 us samples, 226 brazil samples, 7 ukraine samples, 46 argentina samples, 1 mexico sample, and 16 russian samples.

A method for tracing and identifying the origin of soybean based on the combination of MALDI-TOF/TOF and IRMS technologies comprises the following steps:

s1, preparing a standard sample: selecting soybean samples with different producing areas of a definite region, respectively squeezing the soybean samples of each producing area to obtain a standard soybean oil sample and grinding the soybean samples to obtain a standard soybean powder sample, and extracting water-soluble protein of the standard soybean powder sample as a target object, wherein the soybean samples are derived from at least two different producing areas;

s2, acquiring mass spectrum data and stable isotope ratio data of the standard sample: respectively acquiring mass spectrum data information of the standard soybean oil samples in different producing areas by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer to obtain high-resolution mass spectrum data of the standard soybean oil samples; acquiring mass spectrum data information of the water-soluble protein of the standard soybean powder samples in different producing areas by using an isotope mass spectrometer to obtain stable isotope ratio data of the water-soluble protein, wherein the IRMS technology is a stable isotope technology;

s3, determining a standard soybean oil sample targeting compound: matching the triglyceride compound mass spectrum data in the high resolution mass spectrum data obtained in the step S2 with standard triglyceride compound data in a lipid compound database to determine a triglyceride targeting compound of the standard sample;

s4, establishing a soybean producing area traceability identification model: after the triglyceride targeting compound in the standard soybean oil sample is determined, the high-resolution mass spectrum data of the triglyceride compound corresponding to the triglyceride targeting compound is normalized and averaged through analysis software, the normalized and averaged data of the standard soybean oil sample of the same producing area and the stable isotope ratio data of the water-soluble protein of the standard soybean powder sample are simultaneously introduced into modeling software to be processed in one or more modes of a principal component analysis method, a partial least square method-discriminant analysis method and an orthogonal partial least square method regression analysis method, and then the normalized and averaged data of the standard soybean oil sample of different producing areas and the stable isotope ratio data of the water-soluble protein of the standard soybean powder sample are sequentially introduced into modeling software to obtain the characteristic distribution rule of the soybean oil in the standard soybean oil sample of different producing areas and the stable isotope distribution rule of the water-soluble protein of the standard soybean powder sample Establishing a soybean origin tracing identification model based on combination of MALDI-TOF/TOF and stable isotope ratio analysis;

s5, result prediction: squeezing a soybean sample to be detected to obtain a soybean oil sample to be detected, grinding and extracting to obtain soybean water-soluble protein to be detected, collecting triglyceride compound high-resolution mass spectrum data of the soybean oil sample to be detected by a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and collecting stable isotope ratio data of the soybean water-soluble protein to be detected by an isotope mass spectrometer; and simultaneously introducing the triglyceride compound high-resolution mass spectrum data and the stable isotope ratio data of the soybean sample to be detected into the soybean origin tracing and identifying model in the step S4, and performing origin tracing prediction on the soybean to be detected.

The step S4 further includes a blind sample verification step, which includes: selecting a plurality of soybean verification samples in the same region, respectively squeezing the soybean verification samples in a definite region to obtain a soybean oil sample to be verified and water-soluble protein of the soybean powder sample to be verified after grinding and extraction, collecting triglyceride compound high-resolution mass spectrum data of the soybean oil sample to be verified by a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and collecting water-soluble protein stable isotope ratio data of the soybean powder sample to be verified by an isotope mass spectrometer; and simultaneously introducing triglyceride compound high-resolution mass spectrum data and water-soluble protein stable isotope ratio data of the soybean verification sample into the soybean origin tracing and identifying model in the step S4, and verifying the origin tracing accuracy of the soybean verification sample.

Multi-country OPLS-DA soybean origin tracing and identifying model example 1

Selecting soybean samples with different producing areas of a definite region, respectively physically squeezing the soybean samples of each producing area to obtain a standard soybean oil sample and grinding the soybean samples to obtain a standard soybean powder sample, and extracting water-soluble protein of the standard soybean powder sample to be used as a target object.

The process of collecting the quality spectrum data of the standard soybean oil sample is as follows: standard soybean oil samples from different origins with well-defined regions were printed on a target plate containing a substrate using 2, 5-dihydroxybenzoic acid as a substrate using a cotton swab dipped with the standard soybean oil samples, calibrated once with PEG for each 6 standard soybean oil samples, and assayed in triplicate for each standard soybean oil sample. Placing a target plate in the matrix-assisted laser desorption ionization time-of-flight mass spectrometer, wherein the working conditions of the matrix-assisted laser desorption ionization time-of-flight mass spectrometer are as follows: the mass spectrometer adopts a positive ion mode to acquire data, the voltage of an ion source is 20kV, the laser frequency is 1000Hz, the laser energy is 50 percent, the high-resolution mass spectrogram of the soybean oil sample is shown in figure 6, 1500 laser points are acquired on each high-resolution mass spectrogram acquired by the matrix-assisted laser desorption ionization time-of-flight mass spectrometer, the calibration relative error is less than 5ppm, and the scanning range is 500 plus 2000 Da. Respectively acquiring mass spectrum data information of the standard soybean oil samples in different producing areas by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer to obtain high-resolution mass spectrum data of the standard soybean oil samples; the matrix-assisted laser desorption ionization time-of-flight mass spectrometer acquires the high-resolution mass spectrum data of the standard soybean oil sample on Flex Control software, and the high-resolution mass spectrum data of the triglyceride compound is qualitatively and quantitatively normalized and averagely processed and analyzed on the Flex Analysis software.

Matching the triglyceride compound mass spectrum data in the obtained high-resolution mass spectrum data with standard triglyceride compound data in a lipid compound database, wherein the lipid compound database is LIPID MAPS Lipidomics Gateway published in the United states, the standard triglyceride compound data is 6899 triglyceride compounds of Triradylgylcols in LIPID MAPS Lipidomics Gateway, determining the molecular weight range of a target object in the high-resolution mass spectrum data through Flex Analysis software qualitative Analysis, and dividing the high-resolution mass spectrum data in a soybean oil sample into 2 regions: the molecular weight is in a first region of 800-1000, the molecular weight is in a second region of 700-800, the first region and the second region are characteristic regions of soybean oil lipid metabolism, animal and plant triglyceride compounds with the molecular weight range of 700-950 in a lipid compound database and soybean oil triglyceride compounds in the first region and the second region are matched and screened, and the triglyceride compounds in 64 soybean oils with the molecular weight range of 850-930Da are determined to be the triglyceride targeting compounds. After the triglyceride targeting compound is determined, carrying out normalization and average processing on triglyceride compound high-resolution mass spectrum data corresponding to the triglyceride targeting compound through analysis software, and carrying out orthogonal partial least squares regression analysis method processing on the processed data to obtain the characteristic distribution rules of the soybean oil in different producing areas in a standard soybean oil sample, thereby constructing a soybean producing area traceability identification model based on lipidomics.

The water soluble protein extraction procedure for the standard soy flour sample was as follows: grinding and crushing a soybean sample to prepare a 30g standard soybean powder sample, weighing 1g standard soybean powder sample, adding 10mL of ultrapure water, performing ultrasonic treatment for 30min under the rotation condition of 10000 revolutions, centrifuging for 5min, extracting 1mL of supernatant protein extract, adding the supernatant protein extract into a 3K ultrafiltration centrifugal tube, centrifuging to obtain proteins with molecular weights of more than 3K Da respectively, transferring the centrifuged proteins into a sample injection bottle, and freeze-drying for more than 8 hours until all the proteins are dried to obtain solid protein powder of the water-soluble proteins.

The isotope mass spectrometer is one of an on-line gas analysis-stable isotope mass spectrometer GasBench-IRMS, an element analysis-isotope mass spectrometer EA-IRMS, a gas chromatography-isotope mass spectrometer GC-IRMS and a liquid chromatography-isotope mass spectrometer LC-IRMS. In the present example, an elemental analysis-isotope mass spectrometer EA-IRMS was selected to analyze the stable isotope ratio of the soybean water-soluble protein. The stable isotope ratio analysis of the soybean water-soluble protein is realized by an element analyzer and a tandem isotope mass spectrometer. EA-IRMS allows the high-precision determination of the average composition of stable isotopes of hydrogen, carbon, oxygen, nitrogen, sulfur, etc. in organic or inorganic samples. The EA-IRMS method comprises wrapping sample in tinfoil, weighing, directly placing into EA automatic sample injector, and converting into NO by high temperature combustion in combustion tube_x，CO₂，SO₂Or H₂O, and the like. According to the detection requirement, the mixed gas is separated by a gas chromatographic column and the like to remove interfering gas, and the target gas finally enters an isotope mass spectrometer for detection. In the carbon isotope mass spectrometry, the mixed gas generated by the combustion of the sample enters a reduction tube along with the helium flow, and NO is contained in the reduction tube_xConversion to N₂Excess of O₂Is removed, and then the mixed gas is passed through a drying tube to remove H₂O, then realizing CO through a gas chromatographic column₂And N₂To obtain single CO₂A gas.

The method for acquiring the stable isotope ratio data of the water-soluble protein of the standard soybean powder sample comprises the following steps:

a1. selecting an elemental analysis-isotope mass spectrometer EA-IRMS, placing weighed solid protein powder of the water-soluble protein in a tin cup and a silver cup, and respectively detecting the isotope ratio of stable carbon and nitrogen and the isotope ratio of stable hydrogen and oxygen in the water-soluble protein;

a2. tuning a correction element analysis-isotope mass spectrometer EA-IRMS;

a3. putting the water-soluble protein wrapped by the tin cup into an EA automatic sample injector, and introducing the water-soluble protein into a 980 ℃ high-temperature combustion furnace to obtain CO₂And N₂Gas of CO₂And N₂Introducing helium mixture into isotope mass spectrometer host to perform delta in water-soluble protein¹⁵N and delta¹³C, detecting; introducing water soluble protein wrapped by silver cup into 1380 deg.C pyrolysis furnace to obtain CO and H₂Gas, CO and H₂Introducing mixed helium into isotope mass spectrometer host to perform delta of water-soluble protein²H and delta¹⁸And (4) detecting.

In the step a2, the method for tuning the correction element analysis-isotope mass spectrometer EA-IRMS is as follows: introducing high-purity CO, H2, N2 and CO2 as reference gases and introducing high-purity He gas as a drainage gas into the elemental analysis-isotope mass spectrometer EA-IRMS, and then using the known delta²H、δ¹⁸O、δ¹⁵N and delta¹³And C, calibrating the delta value of the stable isotope of the reference gas by using the standard substance with the C value, and using the delta value of the stable isotope calibrated by the reference gas as a detection standard to correct the delta value of the stable isotope in the water-soluble protein of the standard soybean powder sample detected in the step a3. The processing and analysis of the data during calibration was performed by Isodat3.0 software from Thermo Scientific, USA. Known as delta²H、δ¹⁸O、δ¹⁵N and delta¹³The standard substance of the C value is: carbon and nitrogen isotope standard substance IAEA-600 caffeine: delta¹³C＝-27.771‰，δ¹⁵N ═ 1.00 ‰, based on V-PDB, purchased from international atomic energy agency; hydroxide isotope standard EMA-P1 polyamide: delta²H＝-25.3‰，δ¹⁸O20.99 per mill based on V-SMOW.

Element analysis-isotope mass spectrometer EA-IRMS isotope mass spectrum is different from organic mass spectrum for conventionally determining molecular weight of target object in sample by CO₂、H₂、CO、N₂And indirectly detecting the small molecular compound to determine the isotopic abundance ratio of carbon, hydrogen, oxygen and nitrogen. By CO₂Detection of water-soluble protein delta of standard soybean meal sample by target gas¹³C value, H₂Detection of water-soluble protein delta of standard soybean meal sample by target gas²Detection of water-soluble protein delta of standard soybean powder sample by H and CO target gas¹⁸O，N₂Determination of the Delta of Water-soluble proteins of Standard Soybean flour samples with target gas¹⁵And N is added. The isotope chromatogram of the sample is shown in FIGS. 1-4.

Collecting delta of water-soluble protein of standard soybean powder sample by element analysis-isotope mass spectrometer EA-IRMS²H、δ¹⁸O、δ¹⁵N and delta¹³And C value is obtained by describing a statistical analysis method, summarizing and sorting an excel list, introducing the excel list into SIMCA 14.1 software, and performing treatment in an orthogonal partial least square regression analysis method mode to obtain the distribution rules of the soybean isotopes in different production areas, thereby constructing an isotope identification model for tracing the soybean production areas. As shown in FIG. 5, the delta of water soluble protein for the standard soy flour sample was selected²H、δ¹⁸O、δ¹⁵N and delta¹³The result of the multi-country identification model of OPLS-DA constructed by C shows that the discrimination of soybean sample producing areas in the countries such as Brazil, America, Argentina, Uraguay and Canada is relatively good. By 4 variables delta to water soluble protein in soybean²H、δ¹⁸O、δ¹⁵N and delta¹³C analysis of the degree of contribution to the differentiation of the producing area, delta¹⁵N contributes most to the source tracing of the place of origin, followed by delta²H and delta¹⁸O，δ¹³C contributes the least.

In this example, a brazil soybean sample, a american soybean sample, a chinese soybean sample, an argentine soybean sample, a canadian soybean sample, a yerba mate soybean sample, and a russian soybean sample with specific regions are selected to prepare a standard soybean oil sample and a standard soybean powder sample, soybeans in different regions in the standard soybean oil sample are respectively subjected to matrix-assisted laser desorption ionization time-of-flight mass spectrometer to acquire high-resolution mass spectrum data, the triglyceride compound high-resolution mass spectrum data are subjected to Flex Control software, Flex Analysis software normalization and average processing Analysis, and elemental Analysis-isotope mass spectrometer EA-IRMS is used to acquire the dataDelta of water soluble protein of standard soybean flour sample²H、δ¹⁸O、δ¹⁵N and delta¹³And C value, summarizing and sorting the processed triglyceride compound high-resolution mass spectrum data and the stable isotope ratio data of the water-soluble protein into an excel list, introducing SIMCA 14.1 software to perform the regression analysis method by the orthogonal partial least squares method, and thus constructing a multi-country OPLS-DA soybean origin tracing identification model based on MALDI-TOF/TOF and stable isotope ratio analysis.

In order to avoid that the samples of part of countries or regions are distributed together in the identification model in a crossed way and influence the accuracy of the prediction identification of the model, the soybean origin tracing identification model needs to be further optimized, and the optimization steps comprise: and determining partial triglyceride compounds with large contribution degree in the target compounds through the VIP value of the soybean origin tracing identification model, and deleting abnormal value samples exceeding 99% confidence intervals and all soybean oil samples in the area with small number in the identification model according to hotelling's and DModx indexes so as to optimize the soybean origin tracing identification model. Therefore, the triglyceride compounds with the large VIP value contribution degree of 38 triglyceride compounds are selected from 64 triglyceride compounds with the molecular weight range of 850-930Da as the preferred triglyceride targeting compounds, and comprise the triglyceride compounds with the following molecular weights: 853.7, 854.7, 875.7, 876.7, 877.7, 878.7, 879.7, 880.7, 881.7, 882.8, 883.7, 893.7, 894.7, 895.7, 896.7, 897.7, 898.7, 899.7, 900.7, 901.7, 902.7, 903.7, 904.7, 905.7, 906.7, 907.7, 909.7, 915.7, 916.7, 917.7, 918.7, 919.7, 920.7, 921.7, 922.7, 923.7, 908.7. As shown in fig. 7, the optimized MALDI-TOF/TOF based multi-country OPLS-DA soybean origin tracing and identifying model can significantly distinguish samples of different countries, especially brazil and non-brazil soybean samples; and Brazil, Russia, the United states and the like, but the samples of the United states and Canada producing areas in the multi-country identification model are distributed together in a cross mode, so that the accuracy of the model for the source-tracing prediction identification of the soybean producing areas in the United states and Canada is influenced.

Therefore, the final base isThe soybean origin tracing identification model combining MALDI-TOF/TOF and stable isotope ratio analysis is characterized in that the optimized triglyceride targeting compound and characteristic contribution degree element data delta¹⁵N、δ²H、δ¹⁸O and delta¹³And C is constructed by fusing an isotope identification model for tracing the soybean producing area and a soybean producing area tracing identification model based on lipidomics. It is capable of distinguishing significantly between samples of soybean from brazil, russia, usa, argentina and canadian origins.

After the multi-country OPLS-DA soybean origin tracing and identifying model is established, blind sample verification needs to be carried out on the model, and the blind sample verification steps comprise: selecting a plurality of soybean verification samples in the same region, respectively squeezing the soybean verification samples in a definite region to obtain a soybean oil sample to be verified and water-soluble protein of the soybean powder sample to be verified after grinding and extraction, collecting triglyceride compound high-resolution mass spectrum data of the soybean oil sample to be verified by a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and collecting water-soluble protein stable isotope ratio data of the soybean powder sample to be verified by an isotope mass spectrometer; and simultaneously introducing triglyceride compound high-resolution mass spectrum data and water-soluble protein stable isotope ratio data of the soybean verification sample into a multi-country OPLS-DA soybean origin tracing identification model based on MALDI-TOF/TOF and stable isotope ratio analysis, and verifying origin tracing accuracy of the soybean verification sample.

After the blind sample verification is completed, respectively squeezing a soybean oil sample to be detected to obtain a soybean oil sample to be detected and grinding and extracting to obtain soybean water-soluble protein to be detected, collecting triglyceride compound high-resolution mass spectrum data of the soybean oil sample to be detected by a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and collecting stable isotope ratio data of the soybean water-soluble protein to be detected by an isotope mass spectrometer; and simultaneously introducing triglyceride compound high-resolution mass spectrum data and stable isotope ratio data of the soybean sample to be detected into a soybean origin tracing identification model based on MALDI-TOF/TOF and stable isotope ratio analysis, and performing origin tracing prediction on the soybean to be detected.

Two-country soybean producing area tracing and identifying model example 1

In this embodiment, in the process of preparing the standard sample in step S1, assuming that the soybean samples originate from n different regions, the soybean samples are divided into n × (n-1)/2 groups, each group of soybean samples consists of two soybean samples with different origins, and a two-country soybean origin tracing and identifying model is established for identifying the corresponding soybean country or region in the two-country soybean origin tracing and identifying model according to steps S2, S3 and S4.

In this example, a American soybean sample having a definite area of Brazil soybean was selected to prepare water-soluble proteins of a standard soybean oil sample and a standard soybean meal sample, and the soybean oil samples in the two different areas are respectively subjected to matrix-assisted laser desorption ionization time-of-flight mass spectrometer to acquire triglyceride compound high-resolution mass spectrum data, Flex Control software, Flex Analysis software normalization and average processing Analysis, collecting stable isotope ratio data of the water-soluble proteins of the soybean meal samples in the two different areas by EA-IRMS respectively, introducing the processed high-resolution mass spectrum data and the processed stable isotope ratio data into SIMCA 14.1 software for processing by an orthogonal partial least squares regression analysis method, thereby establishing a Brazil-American two-country OPLS-DA soybean origin tracing identification model based on MALDI-TOF/TOF and stable isotope ratio analysis. As can be seen from fig. 8, in the brazilian-U.S. two-country OPLS-DA soybean origin tracing and identifying model, the U.S. and brazilian soybean samples can be significantly distinguished, in order to further verify the determination accuracy of the two-country pressed soybean oil origin tracing and identifying model, the U.S. soybean sample and the brazilian soybean sample are selected to be pressed and ground respectively to extract water-soluble protein, and then model blind sample verification is performed, and the verification result shows that the determination accuracy of the soybean oil sample from the bas source is 100%, and the determination accuracy of the sample from the U.S. source is 90.0%.

Two-country soybean producing area tracing and identifying model example 2

In this example, Argentina sample US soybean samples with well-defined regions were selected to make the water soluble protein of the standard soybean oil sample and the standard soybean flour sample, and the soybean oil samples in the two different areas are respectively subjected to matrix-assisted laser desorption ionization time-of-flight mass spectrometer to acquire triglyceride compound high-resolution mass spectrum data, Flex Control software, Flex Analysis software normalization and average processing Analysis, collecting stable isotope ratio data of the water-soluble proteins of the soybean meal samples in the two different areas by EA-IRMS respectively, introducing the processed high-resolution mass spectrum data and the processed stable isotope ratio data into SIMCA 14.1 software for processing by an orthogonal partial least squares regression analysis method, thereby establishing an Argentina-American two-country OPLS-DA soybean origin tracing identification model based on MALDI-TOF/TOF and stable isotope ratio analysis. It can be seen from fig. 9 that american and brazilian soybean samples can be significantly distinguished in the argttin-american two-country OPLS-DA soybean origin tracing identification model, in order to further verify the determination accuracy of the two-country pressed soybean oil origin tracing identification model, the american soybean sample and the argttin soybean sample are selected to be pressed and ground respectively to extract water-soluble protein, and then model blind sample verification is performed, and the verification result shows that the determination accuracy of the argttin-derived soybean oil sample is 85%, and the determination accuracy of the american-derived sample is 90.0%.

Two-country soybean producing area tracing and identifying model example 3

In this example, a Canadian soybean sample, a American soybean sample, with a well-defined region was selected to make the water-soluble protein of a standard soybean oil sample and a standard soybean flour sample, and the soybean oil samples in the two different areas are respectively subjected to matrix-assisted laser desorption ionization time-of-flight mass spectrometer to acquire triglyceride compound high-resolution mass spectrum data, Flex Control software, Flex Analysis software normalization and average processing Analysis, collecting stable isotope ratio data of the water-soluble proteins of the soybean meal samples in the two different areas by EA-IRMS respectively, introducing the processed high-resolution mass spectrum data and the processed stable isotope ratio data into SIMCA 14.1 software for processing by an orthogonal partial least squares regression analysis method, thereby establishing a Canada-United states two-country OPLS-DA soybean origin tracing identification model based on MALDI-TOF/TOF and stable isotope ratio analysis. As can be seen from fig. 10, in the canadian-U.S. two-country OPLS-DA soybean origin tracing and identifying model, the U.S. and brazil soybean samples can be significantly distinguished, and in order to further verify the determination accuracy of the two-country pressed soybean oil origin tracing and identifying model, the U.S. soybean sample and the canadian soybean sample are selected to be pressed and ground respectively to extract water-soluble protein, and then the model blind sample verification is performed, and the verification result shows that the determination accuracy of the canadian soybean oil sample is 85%, and the determination accuracy of the U.S. soybean oil sample is 80.0%.

Two-country soybean producing area tracing and identifying model example 4

This example describes only the differences from the above examples, in this example, russian, chinese, and yerba mate soybean samples and american soybean samples with specific regions were selected to prepare water-soluble proteins of the standard soybean oil sample and the standard soybean meal sample, and the water-soluble proteins of the soybean oil samples in the two different regions were collected by the matrix-assisted laser desorption ionization time-of-flight mass spectrometer respectively to obtain the triglyceride compound high-resolution mass spectrometry data, the Flex Control software, the Flex Analysis software for normalization and average processing Analysis, and the water-soluble proteins of the soybean meal samples in the two different regions were collected by the EA-IRMS respectively to obtain the stable isotope ratio data, and the processed high-resolution mass spectrometry data and stable isotope ratio data were introduced into the SIMCA 14.1 software for the orthometric least squares regression Analysis, thereby sequentially establishing the yerba mate-american soybean protein based on MALDI-TOF/TOF and stable isotope ratio Analysis, The method is a traceability identification model of OPLS-DA soybean production places of three countries, namely Russia-America and China-America. It can be seen from fig. 11, 12, and 13 that soybean samples of the united states and the rest three countries can be significantly distinguished in the three two-country OPLS-DA soybean origin tracing identification models, in order to further verify the determination accuracy of the two-country pressed soybean origin tracing identification model, the american soybean samples and the russian, chinese, and yerba mate soybean samples are selected, pressed and ground respectively to extract water-soluble proteins, and then model blind sample verification is performed, and the verification result shows that the determination accuracy of the russian, chinese, and yerba mate soybean oil samples is 100%, and the determination accuracy of the american soybean samples is more than 95.0%.

As can be seen from the four embodiments of the two-country soybean origin traceability identification model and the multi-country identification model, the two-country soybean origin traceability identification model has higher traceability accuracy.

Comparative example 1

In this comparative example, in order to verify the influence of different types of target targets of soybeans on the establishment of an OPLS-DA identification model based on stable isotope ratio analysis, soybean samples with definite production places are selected and respectively ground to obtain soybean powder samples as the target targets, and EA-IRMSS is used for collecting delta in the soybean powder samples²H、δ¹⁸O、δ¹⁵N and delta¹³And C, establishing a multi-country OPLS-DA soybean origin tracing identification model by using an orthogonal partial least squares regression analysis method, wherein the established model is shown in FIG. 14.

Comparative example 2

In the comparative example, in order to verify the influence of different types of target objects of soybeans on the establishment of an OPLS-DA identification model based on stable isotope ratio analysis, soybean oil samples with definite production places are selected to be used as the target objects after being squeezed respectively, and EA-IRMSS is used for collecting delta in the soybean oil samples²H、δ¹⁸O、δ¹⁵N and delta¹³And C, establishing a multi-country OPLS-DA soybean origin tracing identification model by using an orthogonal partial least square regression analysis method, wherein the established model is shown in FIG. 15.

As shown in FIGS. 5, 14 and 15, the comparative analysis of the delta of water-soluble protein of the multi-national OPLS-DA soybean origin tracing model based on the standard soybean flour sample in example 1²H、δ¹⁸O、δ¹⁵N and delta¹³C, comparing the OPLS-DA multinational identification model constructed by C with that of comparative example 1 and comparative example 2 to distinguish the influence of different target objects on the stable isotope traceability identification model. The comparison results show that the same sample, the soybean flour sample, the soybean oil sample and the isotope of the water-soluble proteinThe identification model can basically distinguish soybean samples from soybean oil samples in the United states and Brazil, but in the aspect of distinguishing the samples from the American samples in other countries, the soybean water-soluble protein is selected as a target object and is obviously superior to the soybean powder samples and the soybean oil samples. For this purpose, the delta of the water-soluble protein of the soybean flour sample was selected separately²H、δ¹⁸O、δ¹⁵N and delta¹³C, establishing a soybean origin tracing based on the combination of MALDI-TOF/TOF and IRMS technologies, and being capable of obviously distinguishing the soybean sample origins of Brazil, the United states, Argentina, Uyery, Canada and other countries.

The embodiment of the method for tracing and identifying the origin of the soybean based on the combination of MALDI-TOF/TOF and IRMS technology provided by the invention is explained in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the core concepts of the present invention. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.