CN113419014B - MALDI-TOF/TOF-based method for tracing origin of soybean and soybean oil by characterizing triglyceride - Google Patents
MALDI-TOF/TOF-based method for tracing origin of soybean and soybean oil by characterizing triglyceride Download PDFInfo
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Abstract
The invention discloses a soybean and soybean oil origin tracing method based on MALDI-TOF/TOF characterization of triglyceride, which is characterized in that the soybean and soybean oil origin tracing method based on lipidomic is established by using a novel soft ionization biological mass spectrometry technology of MALDI-TOF/TOF, triglyceride compound high-resolution mass spectrometry data of a soybean oil sample to be detected are collected by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and are imported into a soybean and soybean oil origin tracing identification model to perform origin tracing prediction of the soybean to be detected, so that the accuracy of soybean and soybean oil origin tracing is improved.
Description
Technical Field
The invention belongs to the technical field of agricultural product origin tracing, and particularly relates to an origin tracing method for soybean and soybean oil based on MALDI-TOF/TOF characterization of triglyceride.
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, there are a great number of standards and detection methods reported in literature at home and abroad, regarding the authenticity problem of the quality of food, attention and importance of consumers at home and abroad are brought into the last decade, and the detection method gradually becomes a big hot spot and a difficult problem in the field of food quality detection. At present, the authenticity detection technology of foods mainly comprises fingerprint spectrum technologies such as ultraviolet spectrum, infrared spectrum and the like, atomic spectrum technologies such as atomic absorption, emission, fluorescence and the like, isotope mass spectrum technologies, high-resolution mass spectrum technologies, nuclear magnetic resonance technologies, raman spectrum technologies and histology technologies which are emerging in nineties of 20 th century, wherein the food histology in the histology technologies comprises the histology technologies such as histology and apparent genomics, transcriptomics, proteomics, metabonomics and lipidomics, the proteomics, the metabonomics and the lipidomics are common histology technologies in the food inspection field, and the functional components of foods, the content problems of food nutrition components and the source tracing problems of food production places can be judged through the histology technologies. In the field of identification of origin authenticity of foods, isotope mass spectrometry technology and histology technology are two reliable identification technologies, but methods and patents for origin tracing of soybeans by using the histology technology are fewer.
Patent publication No. CN111272861A discloses a MALDI-TOF detection method of polypeptide in food. The method comprises extracting polypeptide from food matrix, removing interference substances, detecting by MALDITOF MS to obtain polypeptide distribution in food, detecting under positive ion mode under mass spectrum working condition, irradiating by 337nmN laser, and setting energy to 50-70%. Compared with the traditional polypeptide detection method, the method has the advantages of simple pretreatment method of the sample, higher Rong Naixing on the impurity of the sample, good sensitivity, high precision, wide detection range, extremely short detection time, larger information quantity and simple analysis method. In addition, the method can detect a plurality of samples at one time and has high flux characteristic. The method provided by the invention is beneficial to provide guarantee for the nutrition judgment, the source tracing identification, the ingredient adulteration and the rapid detection of 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 difficulty of detecting the polypeptide in part of the food, and the difficulty of detecting the polypeptide in different producing areas of the same product is high, so that the method is difficult to apply to the tracing identification of the soybean producing area.
Disclosure of Invention
The invention aims to provide a method for tracing the production area of soybeans and soybean oil based on MALDI-TOF/TOF characterization of triglyceride, which is characterized in that the method is established based on the novel soft ionization biological mass spectrometry technology of MALDI-TOF/TOF, the high-resolution mass spectrometry data of triglyceride compounds of soybean oil samples to be detected are collected by a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and are imported into a soybean and soybean oil production area tracing identification model to perform the production area tracing prediction of the soybeans to be detected, so that the accuracy of the soybean and soybean oil production area tracing is improved.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a method for tracing the origin of soybean and soybean oil based on MALDI-TOF/TOF characterization of triglyceride comprises the following steps:
s1, preparing a standard sample: respectively squeezing soybean samples with different producing areas in a definite region to obtain standard samples with different producing areas, dipping the standard samples with a cotton swab, and printing the standard samples on a target plate containing a matrix, wherein the soybean samples are derived from at least two different producing areas;
s2, mass spectrum data acquisition of a standard sample: respectively acquiring mass spectrum data information of the standard samples of different production places by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer to obtain high-resolution mass spectrum data of the standard samples;
s3, determining a targeting compound: matching triglyceride compound spectrum data in the high-resolution mass spectrum data obtained in the step S2 with standard triglyceride compound data in a lipid compound database, and determining a triglyceride targeting compound of the standard sample;
s4, establishing a soybean and soybean oil production area traceability identification model: after determining the triglyceride targeting compound, carrying out normalization and average treatment on high-resolution mass spectrum data of the triglyceride compound corresponding to the triglyceride targeting compound through analysis software, and then carrying out 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 on the treated data so as to obtain characteristic distribution rules of soybean oil fat in different production places in a standard sample, and constructing a soybean and soybean oil production place tracing identification model based on lipidomics;
s5, predicting a result: squeezing a soybean sample to be detected to obtain a soybean oil sample to be detected, dipping the soybean oil sample to be detected by using a cotton swab, printing the soybean oil sample to be detected on a target plate containing a matrix, collecting high-resolution mass spectrum data of triglyceride compounds of the soybean oil sample to be detected by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and introducing the high-resolution mass spectrum data into a soybean and soybean oil origin tracing identification model to perform origin tracing prediction of the soybean to be detected.
Preferably, in the step S1, PEG is used for calibration once every 6 standard samples, and PEG is polyethylene glycol, and each standard sample is measured in parallel three times.
Preferably, the matrix is one or more of 2, 5-dihydroxybenzoic acid, sinapic acid and 2-cyano-4-hydroxycinnamic acid.
More preferably, the substrate is 2, 5-dihydroxybenzoic acid. The matrix plays a role in dispersing the object to be detected during laser-assisted ionization analysis and increasing ionization efficiency, and the 2, 5-dihydroxybenzoic acid is selected as the matrix according to the soybean as the detection sample, so that the soybean oil sample to be detected can be dispersed more conveniently.
Preferably, the matrix-assisted laser desorption ionization time-of-flight mass spectrometer collects high-resolution mass spectrum data of the standard sample on Flex Control software, and the triglyceride compound high-resolution mass spectrum data is qualitatively and quantitatively subjected to normalization and average treatment Analysis on Flex Analysis software.
Preferably, 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 collect data, the voltage of an ion source is 20kV, the laser frequency is 1000Hz, the laser energy is 50%, 1500 laser points are collected on each high-resolution mass spectrogram collected by the matrix assisted laser desorption ionization time-of-flight mass spectrometer, the relative calibration error is less than 5ppm, and the scanning range is 500-2000Da.
Preferably, the step S4 further includes a blind sample verification step, where the blind sample verification step includes: and (3) selecting a plurality of soybean verification samples in the same area, and introducing high-resolution mass spectrum data of triglyceride compounds acquired by a matrix-assisted laser desorption ionization time-of-flight mass spectrometer of the soybean verification samples in the specific area into the soybean and soybean oil origin tracing identification model in the step S4 to verify the origin tracing accuracy of the soybean verification samples.
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 triradyl glycerides in the lipid compound database LIPID MAPS Lipidomics Gateway, and the common 64 triglyceride compounds of the soybean oil sample are determined to be triglyceride targeting compounds of the standard sample by qualitative Analysis of Flex Analysis software.
Preferably, the method for determining a targeting compound in step S3 includes: according to the molecular weight range of the target in the high-resolution mass spectrum data, the high-resolution mass spectrum data in the soybean oil sample are divided into 2 areas: the method comprises the steps of carrying out matching screening on animal and plant source triglyceride compounds with molecular weight ranging from 700 to 950 in a lipid compound database and soybean oil triglyceride compounds in the first region and the second region, and determining that the triglyceride compounds in 64 soybean oil with molecular weight ranging from 850 to 930Da are triglyceride targeting compounds, wherein the first region and the second region have molecular weights ranging from 800 to 1000 and are soybean oil fat metabolism characteristic regions.
Preferably, in the step S4, high-resolution mass spectrum data of the triglyceride compound in the standard sample is sequentially normalized and averaged, and processed by an orthogonal partial least squares regression analysis method, so as to construct an OPLS-DA soybean and soybean oil origin tracing identification model based on lipidomics;
or in the step S4, carrying out normalization and average treatment and partial least square method-discriminant analysis treatment on the high-resolution mass spectrum data of the triglyceride compounds in the standard sample in sequence, and constructing a PLS-DA soybean and soybean oil origin tracing identification model based on lipidomics.
Preferably, the step S4 further includes an optimization step for tracing the source identification model of soybean and soybean oil, and the optimization step includes: and determining partial triglyceride compounds with large contribution value in the triglyceride targeting compound through VIP values of soybean and soybean oil origin tracing identification models, wherein the VIP values are variable projection importance analysis values, and 38 triglyceride compounds are selected as the triglyceride targeting compounds after optimization.
Preferably, among 64 triglyceride compounds having a molecular weight in the range of 850-930Da, 38 triglyceride compounds having a large VIP value contribution degree are selected as preferable triglyceride targeting compounds, including triglyceride compounds having 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 of the triglyceride targeting compound, it is also necessary to delete outlier samples exceeding the 99% confidence interval and delete all soybean oil samples in the region where the number of occurrences in the identification model is relatively small, according to the hotelling's and the DModx index, to optimize the soybean and soybean oil origin traceability identification model.
Preferably, in the process of preparing the standard samples in the step S1, the soybean samples are derived 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 production regions, and each group of soybean samples is used for establishing a two-country soybean and soybean oil production region tracing identification model according to the steps S2, S3 and S4, so as to identify the country or region of the corresponding soybean and soybean oil in the two-country soybean and soybean oil production region tracing identification model.
Preferably, during the preparation of the standard sample in the step S1, the soybean sample is derived from at least three different countries or regions, so that the step S4 builds a multi-country soybean and soybean oil origin tracing identification model.
More preferably, the soybean sample is derived from five countries of baxi, russia, united states, canada, yerba mate, china and argentina during the standard sample preparation in step S1.
Preferably, during the preparation of the standard sample in the step S1, the soybean sample is derived from us, brazil, argentine, canada, yerba mate, russia, china, and the tracing identification model of the us-brazil, us-argentine, us-canada, us-russia soybean and soybean oil origin is sequentially established according to the steps S2, S3, S4, for identifying the soybean origin in the us and the rest countries.
The beneficial effects are that:
according to the soybean and soybean oil origin tracing method based on MALDI-TOF/TOF characterization of the triglyceride, the matrix assisted laser desorption ionization time-of-flight mass spectrometer is adopted to respectively collect mass spectrum data information of the standard samples of different origins, the operation is simple and convenient, the mass spectrum data collection can be carried out without dilution of the standard samples, so that high-resolution mass spectrum data of the standard samples are obtained, MALDI-TOF/TOF is used as a novel soft ionization biological mass spectrum technology, obvious advancement and advantages are achieved in biological species identification, food freshness judgment and food type identification, MALDI-TOF/TOF technology can accurately obtain the high-resolution mass spectrum data of triglyceride targeting compounds in the soybean and soybean oil samples, a multivariate statistical analysis discrimination prediction model can be established by utilizing the high-resolution mass spectrum data of fewer triglyceride targeting compounds, and the accuracy of soybean and soybean oil origin tracing identification of two countries can be further improved by independently establishing two country and soybean oil origin tracing identification models.
Drawings
FIG. 1 shows the IDA-MS high resolution mass spectrum of soybean oil sample measured by the invention;
FIG. 2 shows a multi-country OPLS-DA soybean and soybean oil origin tracing identification model of the invention;
FIG. 3 shows a PLS-DA soybean and soybean oil origin tracing identification model of the present invention;
FIG. 4 shows the model for the traceability and identification of the production areas of Brazil-American two-country OPLS-DA soybeans and soybean oil according to the invention;
FIG. 5 shows the russia-American multi-national OPLS-DA soybean and soybean oil origin tracing identification model of the invention;
FIG. 6 shows a Canada-American multi-national OPLS-DA soybean and soybean oil origin traceability identification model of the invention;
FIG. 7 shows the Argentina-American multi-national OPLS-DA soybean and soybean oil origin tracing identification model of the invention;
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 explain the specific embodiments of the present invention with reference to the accompanying drawings. It is evident that the drawings in the following description are only examples of the invention, from which other drawings and other embodiments can be obtained by a person skilled in the art without inventive effort.
The technical scheme of the invention is described in detail in the following by specific embodiments.
The experimental equipment comprises: selecting a ultraflextreme MALDI-TOF/TOF mass spectrometer of Bruce, germany; HPLC 20AD high performance liquid chromatograph from shimadzu corporation; xbridge BEH C18 column (100 mm. Times.2.1 mm,3 μm) from Waters, USA; oil presses;
reagent selection: acetonitrile, acetone (chromatographic purity, sameiser, usa); PEG calibration solution; 2, 5-dihydroxybenzoic acid matrix (CNW, annotation, china);
soybean and soybean oil sample sources: the soybean standard samples were obtained from 807 total soybean samples purchased from all relevant customs and abroad, wherein the soybean samples at each production place were as follows: of these, 152 were U.S. samples, 423 were brazil samples, 96 were canadian samples, 68 were argentina samples, 14 were yerba samples, 25 were russia samples, and 29 were chinese samples; soybean oil samples 334: of these, 36 were U.S. samples, 226 were brazil samples, 7 were ukraine samples, 46 were argentine samples, 1 were mexico samples, and 16 were russian samples.
The method for tracing the origin of soybean and soybean oil based on MALDI-TOF/TOF characterization of triglyceride comprises the following steps:
s1, preparing a standard sample: respectively squeezing soybean samples with different producing areas in a definite region to obtain standard samples with different producing areas, dipping the standard samples with a cotton swab, and printing the standard samples on a target plate containing a matrix, wherein the soybean samples are derived from at least two different producing areas;
s2, mass spectrum data acquisition of a standard sample: respectively acquiring mass spectrum data information of the standard samples of different production places by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer to obtain high-resolution mass spectrum data of the standard samples;
s3, determining a targeting compound: matching triglyceride compound spectrum data in the high-resolution mass spectrum data obtained in the step S2 with standard triglyceride compound data in a lipid compound database, and determining a triglyceride targeting compound of the standard sample;
s4, establishing a soybean and soybean oil production area traceability identification model: after determining the triglyceride targeting compound, carrying out normalization and average treatment on high-resolution mass spectrum data of the triglyceride compound corresponding to the triglyceride targeting compound through analysis software, and then carrying out 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 on the treated data so as to obtain characteristic distribution rules of soybean oil fat in different production places in a standard sample, and constructing a soybean and soybean oil production place tracing identification model based on lipidomics;
s5, predicting a result: squeezing a soybean sample to be detected to obtain a soybean oil sample to be detected, dipping the soybean oil sample to be detected by using a cotton swab, printing the soybean oil sample to be detected on a target plate containing a matrix, collecting high-resolution mass spectrum data of triglyceride compounds of the soybean oil sample to be detected by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and introducing the high-resolution mass spectrum data into a soybean and soybean oil origin tracing identification model to perform origin tracing prediction of the soybean to be detected.
The step S4 further includes a blind sample verification step, where the blind sample verification step includes: and (3) selecting a plurality of soybean verification samples in the same area, and introducing high-resolution mass spectrum data of triglyceride compounds acquired by a matrix-assisted laser desorption ionization time-of-flight mass spectrometer of the soybean verification samples in the specific area into the soybean and soybean oil origin tracing identification model in the step S4 to verify the origin tracing accuracy of the soybean verification samples. According to blind sample verification results, selecting and constructing multi-country soybean and soybean oil origin traceability identification models, two-country soybean and soybean oil origin traceability identification models or two jointly identified traceability identification models aiming at soybean samples of different origins, and simultaneously constructing two identification models, so that identification results of the two models can be synthesized, and identification accuracy is guaranteed.
Multi-national OPLS-DA soybean and soybean oil origin traceability identification model example 1
And (3) physically squeezing soybean samples with different producing areas in a definite region by an oil press to obtain standard samples, taking 2, 5-dihydroxybenzoic acid as a matrix, dipping the standard samples by using a cotton swab, printing the standard samples on a target plate containing the matrix, calibrating every 6 standard samples by using PEG, and measuring each standard sample in parallel three times. 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 collect data, the voltage of an ion source is 20kV, the laser frequency is 1000Hz, the laser energy is 50%, the high-resolution mass spectrogram of a soybean oil sample is shown in figure 1, 1500 laser points are collected on each high-resolution mass spectrogram collected by the matrix-assisted laser desorption ionization time-of-flight mass spectrometer, the relative calibration error is less than 5ppm, and the scanning range is 500-2000Da. Respectively acquiring mass spectrum data information of the standard samples of different production places by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer to obtain high-resolution mass spectrum data of the standard samples; and the matrix-assisted laser desorption ionization time-of-flight mass spectrometer acquires high-resolution mass spectrum data of the standard sample on Flex Control software, and the triglyceride compound high-resolution mass spectrum data is qualitatively and quantitatively subjected to normalization and average treatment Analysis on Flex Analysis software.
Matching triglyceride compound spectrum data in the obtained high-resolution mass spectrum data with standard triglyceride compound data in a lipid compound database LIPID MAPS Lipidomics Gateway published in the United states, wherein the standard triglyceride compound data are 6899 triglyceride compounds of Triradyl glycerides in a lipid compound database LIPID MAPS Lipidomics Gateway, determining a target molecular weight range in the high-resolution mass spectrum data through qualitative Analysis of Flex Analysis software, and dividing the high-resolution mass spectrum data in a soybean oil sample into 2 regions: the method comprises the steps of carrying out matching screening on animal and plant source triglyceride compounds with molecular weight ranging from 700 to 950 in a lipid compound database and soybean oil triglyceride compounds in the first region and the second region, and determining that the triglyceride compounds in 64 soybean oil with molecular weight ranging from 850 to 930Da are triglyceride targeting compounds, wherein the first region and the second region with molecular weight ranging from 800 to 1000 are soybean oil fat metabolism characteristic regions. After determining the triglyceride targeting compound, carrying out normalization and average treatment on high-resolution mass spectrum data of the triglyceride compound corresponding to the triglyceride targeting compound through analysis software, and then carrying out one or more modes of principal component analysis, partial least square method-discriminant analysis and orthogonal partial least square method regression analysis on the treated data so as to obtain characteristic distribution rules of soybean oil fat in different production places in a standard sample, and constructing a soybean and soybean oil production place tracing identification model based on lipidomics.
In the embodiment, brazil soybean samples, american soybean samples, chinese soybean samples, argentina soybean samples, canadian soybean samples, uyerba soybean samples and Russian soybean samples with definite areas are selected to manufacture standard samples, soybeans in different areas in the standard samples are respectively subjected to matrix-assisted laser desorption ionization time-of-flight mass spectrometry to acquire high-resolution mass spectrometry data, triglyceride compound high-resolution mass spectrometry data are subjected to Flex Control software, flex Analysis software normalization and average processing Analysis, and the processed data are subjected to orthogonal partial least square regression Analysis, so that a multi-country OPLS-DA soybean and soybean oil production area traceability identification model is constructed.
In order to avoid that samples of some countries or regions are distributed together in the identification model in a crossing way, which affects the prediction identification accuracy of the model, further optimization of the soybean and soybean oil origin traceability identification model is required, the optimization step comprises: and determining partial triglyceride compounds with large contribution value in the target compounds through the VIP values of the soybean and soybean oil origin tracing identification models, wherein the VIP values are variable projection importance analysis values, deleting abnormal value samples exceeding 99% confidence intervals according to the hotelling's and DModx indexes, and deleting all soybean oil samples in the areas with relatively small occurrence numbers in the identification models so as to optimize the soybean and soybean oil origin tracing identification models. Therefore, among 64 triglyceride compounds having a molecular weight in the range of 850-930Da, 38 triglyceride compounds having a large VIP value contribution degree are selected as preferable triglyceride targeting compounds, including triglyceride compounds having 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. 2, samples in different countries can be distinguished more remarkably, particularly samples of brazil and non-brazil soybeans, in an optimized multi-country OPLS-DA soybean and soybean oil origin traceability identification model; and samples of places of origin such as Brazil, russian and the United states, but the samples of places of origin of the United states and Canadian in the multinational identification model are distributed together in a crossing way, so that the accuracy of the model in predicting and identifying the soybean places of origin of the United states and Canadian is affected.
After the multi-country OPLS-DA soybean and soybean oil origin tracing identification model is established, blind sample verification is needed to be carried out on the model, and the blind sample verification step comprises the following steps: and selecting a plurality of soybean verification samples in the same area, collecting high-resolution mass spectrum data of triglyceride compounds by a matrix-assisted laser desorption ionization time-of-flight mass spectrometer from the soybean verification samples in the specific area, introducing the high-resolution mass spectrum data into a multi-country soybean and soybean oil origin tracing identification model, and verifying the origin tracing accuracy of the soybean verification samples. Examples 128 samples were selected for blind sample validation experiments for multinational soybean and soybean oil origin traceability identification models, the 128 sample origin being 40 brazil samples, 30 american samples, 25 argentine samples, 25 canadian samples, and 8 russian samples, respectively. The verification result shows that the identification accuracy of Brazil pressed soybean oil is 95.0%, the identification accuracy of American soybean oil is 50.0%, the identification accuracy of Argentina soybean oil is 72.0%, the identification accuracy of Canadian soybean oil is 80.0% and the identification accuracy of Russian Luo Sida soybean oil is 100%. Therefore, the sample identification accuracy of the United states in the verification result of the multi-country OPLS-DA soybean and soybean oil origin tracing identification model is low, and the model prediction accuracy requirement cannot be met, so that the following embodiment establishes a two-country soybean and soybean oil origin tracing identification model of the United states and other countries to improve the tracing accuracy.
After blind sample verification is completed, squeezing a soybean sample to be detected to obtain a soybean oil sample to be detected, dipping the soybean oil sample to be detected by using a cotton swab, printing the soybean oil sample to be detected on a target plate containing a matrix, collecting high-resolution mass spectrum data of triglyceride compounds of the soybean oil sample to be detected by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and introducing the high-resolution mass spectrum data into a soybean and soybean oil origin tracing identification model to perform origin tracing prediction of the soybean to be detected.
Multinational PLS-DA soybean and soybean oil origin traceability identification model example 2
This example only describes differences from the above examples in that in this example, high-resolution mass spectrometry data of triglyceride compounds in the standard sample were sequentially normalized and averaged, and processed by partial least squares-discriminant analysis, to construct a lipid histology-based PLS-DA soybean and soybean oil origin traceability identification model. As shown in fig. 3, PLS-DA soybean and soybean oil origin traceability identification models can significantly distinguish between bast and non-brazil soybean samples, particularly those of american origin, distributed throughout and significantly distinguished from brazil soybean regions.
Multinational PLS-DA soybean and soybean oil origin traceability identification model example 3
This example only describes differences from the previous examples, in which the substrate is sinapic acid or 2-cyano-4-hydroxycinnamic acid.
Two-country soybean and soybean oil origin traceability identification model example 1
This example only describes the differences from the above example, in this example, in the process of standard sample preparation in step S1, it is assumed that the soybean samples are derived from n different regions, the soybean samples are divided into n× (n-1)/2 groups, each group of soybean samples is composed of two soybean samples of different production sites, and each group of soybean samples is used to establish a two-country soybean and soybean oil production site tracing identification model according to steps S2, S3, S4 for identifying the country or region of soybean and soybean oil corresponding to the two-country soybean and soybean oil production site tracing identification model.
In the embodiment, 104 Brazil soybean samples and 106 American soybean samples with definite areas are selected to manufacture standard samples, and soybean oil samples in the two different areas are respectively subjected to matrix-assisted laser desorption ionization time-of-flight mass spectrometry to acquire triglyceride compound high-resolution mass spectrometry data, flex Control software and Flex Analysis software are used for normalization and average processing Analysis, and the processed data are imported into SIMCA 14.1 software for processing by an orthogonal partial least squares regression Analysis method, so that a Brazil-American two-country OPLS-DA soybean and soybean oil origin tracing identification model is established. From fig. 4, it can be seen that the us and brazil soybean oil samples can be significantly distinguished from the brazil-us two-country OPLS-DA soybean and soybean oil origin traceability identification model, in order to further verify the determination accuracy of the two-country pressed soybean oil origin traceability identification model, 30 us pressed soybean oil samples and 40 brazil pressed soybean oil samples were selected for model blind sample verification, and the verification result shows that the determination accuracy of the brazil-derived soybean oil sample is 95.0% and the determination accuracy of the us-derived soybean oil sample is 90.0%.
Two-country soybean and soybean oil origin traceability identification model example 2
The embodiment only describes the differences from the above embodiment, in this embodiment, a standard sample is prepared by selecting a russian soybean sample and a american soybean sample with definite regions, and the soybean oil samples in the two different regions are respectively subjected to matrix-assisted laser desorption ionization time-of-flight mass spectrometry to collect high-resolution mass spectrum data of triglyceride compounds, flex Control software and Flex Analysis software are used for normalization and average processing Analysis, and the processed data are imported into SIMCA 14.1 software for processing by an orthogonal partial least square regression Analysis method, so that a russian-american multi-country OPLS-DA soybean and soybean oil origin tracing identification model is established. From fig. 5, it can be seen that the russia-american two-country OPLS-DA soybean and the soybean oil origin traceability identification model can be significantly distinguished from the us and russia soybean oil samples, and in order to further verify the determination accuracy of the two-country pressed soybean oil origin traceability identification model, 30 us pressed soybean oil samples and 8 russia pressed soybean oil samples were selected for model blind sample verification, and the verification result shows that the determination accuracy of the russia-derived soybean oil sample is 100% and the determination accuracy of the us-derived soybean oil sample is 96.7%.
Two-country soybean and soybean oil origin traceability identification model example 3
This example only describes the differences from the above example in that in this example, a Canadian soybean sample and a Mei Guo soybean sample were selected for standard sample preparation, and the two different regions of soybean oil samples were respectively subjected to matrix-assisted laser desorption ionization time-of-flight mass spectrometry to collect high resolution mass spectrum data of triglyceride compounds, flex Control software, flex Analysis software normalization and average processing Analysis, and the processed data were imported into SIMCA 14.1 software for processing by orthogonal partial least squares regression Analysis, thereby establishing Canadian-American multi-national OPLS-DA soybean and soybean oil origin traceability identification models. From fig. 6, it can be seen that the canadian-american two-country OPLS-DA soybean and soybean oil origin traceability identification model can significantly distinguish the american and canadian soybean oil samples, and in order to further verify the determination accuracy of the two-country pressed soybean oil origin traceability identification model, 30 american pressed soybean oil samples and 25 canadian pressed soybean oil samples were selected for model blind sample verification, and the verification result shows that the determination accuracy of the russia-derived soybean oil sample is 84% and the determination accuracy of the american-derived soybean oil sample is 80%.
Two-country soybean and soybean oil origin traceability identification model example 4
The embodiment only describes the differences from the above embodiment, in this embodiment, a Canadian soybean sample and a American soybean sample with definite regions are selected to make a standard sample, soybean oil samples in the two different regions are respectively subjected to matrix-assisted laser desorption ionization time-of-flight mass spectrometry to collect high-resolution mass spectrum data of triglyceride compounds, flex Control software and Flex Analysis software are used for normalization and average processing Analysis, and the processed data are imported into SIMCA 14.1 software for processing by an orthogonal partial least square regression Analysis method, so that Argentina-American multi-national OPLS-DA soybean and soybean oil origin tracing identification models are established. As can be seen from fig. 7, the Argentina pressed soybean oil samples were more concentrated in the score plots of Argentina-American two-country OPLS-DA soybean and soybean oil origin traceability identification models, while the American pressed soybean oil samples were more dispersed in the distribution plots of the model score plots. To further verify the accuracy of the determination of the united states and argentine-pressed soybean oil origin traceability identification model, 25 argentine-pressed soybean oil samples and 30 united states-pressed soybean oil samples were selected for model blind sample verification. Research results show that the identification accuracy of Argentina pressed soybean oil samples is 88.0%, and the identification accuracy of American soybean oil samples is 83.3%.
The embodiments of the method for tracing the origin of soybean and soybean oil based on MALDI-TOF/TOF characterization of triglycerides provided by the invention are described above in detail. The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to facilitate an understanding of the core concepts of the invention. It should be noted that it will be apparent to those skilled in the art that the present invention may be modified and adapted without departing from the principles of the present invention, and that such modifications and adaptations are intended to be within the scope of the appended claims.
Claims (6)
1. The method for tracing the origin of soybean and soybean oil based on MALDI-TOF/TOF characterization of triglyceride is characterized by comprising the following steps:
s1, preparing a standard sample: respectively squeezing soybean samples with different producing areas in a definite region to obtain standard samples with different producing areas, dipping the standard samples with a cotton swab, and printing the standard samples on a target plate containing a matrix, wherein the soybean samples are derived from at least two different producing areas;
s2, mass spectrum data acquisition of a standard sample: respectively acquiring mass spectrum data information of the standard samples of different production places by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer to obtain high-resolution mass spectrum data of the standard samples;
s3, determining a targeting compound: matching triglyceride compound spectrum data in the high-resolution mass spectrum data obtained in the step S2 with standard triglyceride compound data in a lipid compound database, and determining a triglyceride targeting compound of the standard sample;
s4, establishing a soybean and soybean oil production area traceability identification model: after determining the triglyceride targeting compound, carrying out normalization and average treatment on high-resolution mass spectrum data of the triglyceride compound corresponding to the triglyceride targeting compound through analysis software, and then carrying out 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 on the treated data so as to obtain characteristic distribution rules of soybean oil fat in different production places in a standard sample, and constructing a soybean and soybean oil production place tracing identification model based on lipidomics;
s5, predicting a result: squeezing a soybean sample to be detected to obtain a soybean oil sample to be detected, dipping the soybean oil sample to be detected by using a cotton swab, printing the soybean oil sample to be detected on a target plate containing a matrix, collecting high-resolution mass spectrum data of triglyceride compounds of the soybean oil sample to be detected by using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer, and introducing the high-resolution mass spectrum data into a soybean and soybean oil origin tracing identification model to carry out origin tracing prediction of the soybean to be detected;
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 area, and introducing high-resolution mass spectrum data of triglyceride compounds acquired by a matrix-assisted laser desorption ionization time-of-flight mass spectrometer of the soybean verification samples in the specific area into a soybean and soybean oil origin tracing identification model in the step S4 to verify the origin tracing accuracy of the soybean verification samples;
the method for determining the targeting compound in the step S3 comprises the following steps: according to the molecular weight range of the target in the high-resolution mass spectrum data, the high-resolution mass spectrum data in the soybean oil sample are divided into 2 areas: a first region with molecular weight of 800-1000 and a second region with molecular weight of 700-800, wherein the first region and the second region are soybean oil metabolism characteristic regions, and triglyceride compounds in 64 soybean oil with molecular weight of 850-930Da are determined as triglyceride targeting compounds by matching and screening animal and plant source triglyceride compounds with molecular weight of 700-950 in a lipid compound database with soybean oil triglyceride compounds in the first region and the second region;
in the step S1, the polyethylene glycol is used for calibration once for every 6 standard samples, and each standard sample is measured in parallel three times;
the step S4 further comprises an optimization step of tracing and identifying the model of the soybean and the soybean oil production places, and the optimization step comprises the following steps: determining partial triglyceride compounds with large contribution value in the triglyceride targeting compound through VIP values of soybean and soybean oil origin tracing identification models, and selecting 38 triglyceride compounds as the preferable triglyceride targeting compounds, wherein the preferable triglyceride targeting compounds 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;
in the step S4, high-resolution mass spectrum data of the triglyceride compounds in the standard sample are subjected to normalization and average treatment and orthogonal partial least square regression analysis in sequence, and an OPLS-DA soybean and soybean oil origin tracing identification model based on lipidomic is constructed.
2. The method of claim 1, wherein the substrate is one or more of 2, 5-dihydroxybenzoic acid, sinapic acid, and 2-cyano-4-hydroxycinnamic acid.
3. The method of claim 1, wherein the matrix assisted laser desorption ionization time-of-flight mass spectrometer collects high resolution mass spectrum data of the standard sample on Flex Control software, and the triglyceride compound high resolution mass spectrum data is qualitatively and quantitatively normalized and averaged for Analysis on Flex Analysis software.
4. The method of claim 2, wherein the working conditions of the matrix-assisted laser desorption ionization time-of-flight mass spectrometer are: the mass spectrometer adopts a positive ion mode to collect data, the voltage of an ion source is 20kV, the laser frequency is 1000Hz, the laser energy is 50%, 1500 laser points are collected on each high-resolution mass spectrogram collected by the matrix assisted laser desorption ionization time-of-flight mass spectrometer, the relative calibration error is less than 5ppm, and the scanning range is 500-2000Da.
5. The method according to claim 1, wherein in step S4, the high-resolution mass spectrum data of the triglyceride compounds in the standard sample are sequentially normalized and averaged, and processed by partial least squares-discriminant analysis method, so as to construct a lipid histology-based PLS-DA soybean and soybean oil origin tracing identification model.
6. The method according to claim 1, wherein in the process of preparing standard samples in step S1, the soybean samples are derived 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 producing regions, and each group of soybean samples is used for establishing a two-country soybean and soybean oil producing region tracing identification model according to steps S2, S3 and S4, so as to identify the country or region of soybean and soybean oil corresponding to the two-country soybean and soybean oil producing region tracing identification model.
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