CN113406246B - Method for tracing origin of soybean and soybean oil by characterizing triglyceride based on LC-Q-TOF-MS - Google Patents
Method for tracing origin of soybean and soybean oil by characterizing triglyceride based on LC-Q-TOF-MS Download PDFInfo
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- G—PHYSICS
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Abstract
The invention discloses a soybean and soybean oil origin tracing method based on LC-Q-TOF-MS characterization of triglyceride, which adopts a liquid chromatography-four-rod time-of-flight mass spectrometer to collect IDA-MS high-resolution mass spectrum data of soybean, and performs multi-element statistical analysis methods such as principal component analysis, partial least square method-discriminant analysis, orthogonal partial least square method regression analysis and the like on triglyceride compound marker Peaks data of soybean oil sample China to obtain characteristic distribution rules of soybean oil in different origins, constructs a multi-element statistical analysis, discrimination and prediction model, further improves the accuracy of soybean and soybean oil origin tracing identification by combining multi-country and two-country soybean and soybean oil origin tracing identification models.
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 LC-Q-TOF-MS 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.
The patent application with publication number CN104360004A discloses a method for identifying the authenticity of nidus Collocaliae by utilizing LC-Q-TOF combined statistical analysis. Adding a formic acid solution into a sample to be measured, then carrying out boiling water bath, cooling, and filtering by a filtering membrane to obtain a treated sample to be measured; collecting mass spectrum information of the sample to be detected by using a liquid chromatograph-four-pole time-of-flight mass spectrometer after the treatment, and extracting a characteristic compound; and (3) transferring the obtained characteristic compound information of the sample to be detected into a bird's nest true and false identification model for prediction. And judging the bird's nest as a genuine product when the accuracy is 80% or above, and judging the bird's nest as a counterfeit product when the accuracy is not above. The invention also establishes a bird's nest authenticity identification model, and only one authenticity identification model is needed during identification, so that the detection is simpler and easier to operate. The method is used for identifying the bird's nest products, but due to the different components of bird's nest and soybean, the method can not be used for extracting the target compound of soybean and further completing the tracking of the soybean production place.
Disclosure of Invention
The invention aims to provide a method for tracing the origin of soybeans and soybean oil by characterizing triglycerides based on LC-Q-TOF-MS, so as to solve the technical problems.
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 LC-Q-TOF-MS 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 soybean oil samples, and gradually diluting the soybean oil samples by 100-200 times by using a diluting solvent to obtain a standard sample, wherein the standard sample comprises at least two soybean samples with different producing areas;
s2, mass spectrum data acquisition of a standard sample: respectively acquiring mass spectrum data information of the standard samples in different areas by using a liquid chromatograph-four-level rod time-of-flight mass spectrometer to obtain IDA-MS high-resolution mass spectrum data of triglyceride compounds of the standard samples;
s3, determining a targeting compound: matching triglyceride compound spectrum data in the IDA-MS high-resolution mass spectrum data obtained in the step S2 with standard triglyceride compound data in a lipid compound database, and determining a triglyceride compound targeting compound of the standard sample;
s4, establishing a soybean and soybean oil production area traceability identification model: analyzing the IDA-MS high-resolution mass spectrum data through analysis software to obtain triglyceride compound marker observation peak value data of soybean oil in the standard sample, processing the triglyceride compound marker observation peak value data in the standard sample in different areas 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 to obtain characteristic distribution rules of soybean oil in different production areas in the standard sample, and constructing a soybean and soybean oil production area tracing identification model based on lipidomics; the marker observation peak value data is marker View peaks data, the marker View peaks data of the triglyceride of the standard sample is obtained through analysis of MasterView analysis software, and the soybean origin tracing identification model judges and predicts the soybean and soybean oil origins in different areas through multiple statistical analysis;
S5, predicting a result: squeezing a soybean sample to be detected to obtain a soybean oil sample, gradually diluting the soybean oil sample by 100-200 times by using a diluting solvent to obtain a sample to be detected, acquiring IDA-MS high-resolution mass spectrum data of the sample to be detected by using a liquid chromatograph-four-rod flight time mass spectrometer, and introducing the data into a soybean and soybean oil origin tracing identification model to predict an origin tracing result.
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 triradyl glycerides in the lipid compound database LIPID MAPS Lipidomics Gateway, and the standard triglyceride compound targeting compound of 116 triglyceride compounds which are common to soybean oil samples is determined by qualitative analysis by PeakView software.
Preferably, the dilution solvent is a mixed solution of methanol and ethyl acetate, and the ratio of methanol to ethyl acetate in the dilution solvent is 1:1, the soybean oil sample is diluted by 200 times step by using a methanol-ethyl acetate mixed solution.
Preferably, the step of collecting mass spectrum data of the standard sample in the step S2 is as follows:
And placing the diluted standard sample in a sample injector of the liquid chromatograph-four-level rod time-of-flight mass spectrometer, separating and analyzing the standard sample through the liquid chromatograph in the liquid chromatograph-four-level rod time-of-flight mass spectrometer, collecting mass spectrum data of the standard sample through a mass spectrometer in the liquid chromatograph-four-level rod time-of-flight mass spectrometer, respectively obtaining primary mass spectrum information and secondary mass spectrum information of the standard sample through primary TOF-MS scanning and secondary IDA-MS scanning of the mass spectrometer, wherein the secondary mass spectrum information is IDA-MS high-resolution mass spectrum data, determining triglyceride compound targeting compounds of the standard sample through the IDA-MS high-resolution mass spectrum data, and carrying out directional quantitative processing analysis on the IDA-MS high-resolution mass spectrum data through analysis software so as to construct a soybean and soybean oil production area tracing identification model.
Preferably, the liquid chromatography conditions of the liquid chromatograph in the liquid chromatography-four-pole time-of-flight mass spectrometer are: the flow rate is 0.5 mu L/min, the column temperature is 40 ℃, the Xbridge BEH C18 chromatographic column is eluted in a gradient way, and the sample injection amount is 2 mu L; the mobile phase A is isopropanol, the mobile phase B is acetonitrile, and the content of the mobile phase B in different time periods is as follows: 0min,70% b;0-5min,70-65% B;5-8min,65% B;10-10.5min,65-70% B;10.5-15min,70% B.
The four-stage rod time-of-flight mass spectrometry conditions of a mass spectrometer in the liquid chromatograph-four-stage rod time-of-flight mass spectrometer are: the mass spectrometer adopts a positive ion mode to collect data, and an ion source is as follows: ESI and APCI composite sources; the positive ion scanning mode is as follows: APCI source is connected with an automatic correction system, and one-stage TOF-MS scans the accurate mass range: 100-2000 Da, wherein the data acquisition time is 100ms, the DP is 100V, and the CE is 10V, wherein the DP is the declustering voltage, and the CE is the collision energy; secondary IDA-MS scans accurate mass range: 50-2000 Da, DP:100V, CE: 35+ -15V; the mass spectrometer adopts a high sensitivity mode, the data acquisition time is 50ms, the signal threshold is 100cps, 6 times of data are acquired each time of circulation, and dynamic background subtraction is adopted.
Preferably, the liquid chromatograph-four-stage rod time-of-flight mass spectrometer is a Shimadzu LC20AD liquid chromatograph, the mass spectrometer is a Triple TOF 5600+ mass spectrometer, the automatic correction system is a CDS system, and the four-stage rod time-of-flight mass spectrometry conditions further include: every 10 samples are automatically corrected for 1 time, the flow rate of the APCI positive ion correction liquid is 0.3mL/min, and the air curtain air pressure is as follows: the ion source atomizing gas pressure was 40 psi: the ion source assisted heating gas pressure was 50 psi: 50psi, ion source temperature: all IDA-MS high-resolution mass spectrum data acquired by a mass spectrometer at 500 ℃ under 5500V of ion source voltage are acquired by analysis TF 1.6 software of ABSciex company, and after qualitative and quantitative processing analysis of the IDA-MS high-resolution mass spectrum data on PeakView, masterView software, the data are imported into SIMCA 14.0 software (Umetrics company of Switzerland) to perform principal component analysis, partial least square method discriminant analysis and orthogonal partial least square method discriminant analysis to obtain distribution rules of triglyceride and metabolite of soybeans in different producing areas, so that a soybean and soybean oil producing area tracing identification model based on lipidomics is constructed.
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 the soybean verification samples with definite areas into the soybean and soybean oil origin tracing identification model in the step (S4) through acquiring IDA-MS high-resolution mass spectrum data by a liquid chromatograph-four-rod time-of-flight mass spectrometer to verify the origin tracing accuracy of the soybean verification samples.
Preferably, the method for determining a targeting compound in step S3 includes: according to the molecular weight range of the target object in the IDA-MS high-resolution mass spectrum data, dividing the IDA-MS high-resolution mass spectrum data in the soybean oil sample into 3 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 triglyceride compounds in 114 soybean oil with molecular weight ranging from 766 Da to 920Da as target compounds, wherein the first region and the second region have molecular weights ranging from 800 to 1000, the second region has molecular weights ranging from 550 to 800, and the third region has molecular weights below 550.
Preferably, in the step S4, the observed peak data of the triglyceride compound marker in the standard sample is processed by an orthorhombic least square regression analysis method, and an OPLS-DA soybean and soybean oil origin tracing identification model based on lipidomics is constructed.
Preferably, in the step S4, the observed peak data of the triglyceride compound markers in the standard sample is processed by a partial least square method-discriminant analysis method, and a source tracing identification model of PLS-DA soybean and soybean oil based on lipidomics is constructed.
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 target compounds through the VIP values of the soybean and soybean oil origin tracing identification models, 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.
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 and argentina during the standard sample preparation in step S1.
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.
The beneficial effects are that:
according to the soybean and soybean oil origin tracing method based on LC-Q-TOF-MS characterization of triglyceride, liquid chromatography-four-rod time-of-flight mass spectrometer is adopted to collect IDA-MS high-resolution mass spectrum data of soybean, marker View Peaks data of triglyceride compounds are obtained through analysis software, main component analysis, partial least square method-discriminant analysis, orthogonal partial least square method regression analysis and other multivariate statistical analysis methods are carried out on the marker View Peaks data of the triglyceride compounds in a soybean oil sample, soybean oil characteristic distribution rules of different origins are obtained, a multivariate statistical analysis discriminant prediction model is constructed, and the accuracy of soybean and soybean oil origin tracing identification is further improved by combining multiple countries 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 before optimization of the invention;
FIG. 3 shows a PLS-DA soybean and soybean oil origin tracing identification model of the present invention;
FIG. 4 is a graph showing the VIP value distribution of triglyceride compounds in the OPLS-DA soybean and soybean oil origin traceability identification model of the invention;
FIG. 5 shows the optimized multi-country OPLS-DA soybean and soybean oil origin tracing identification model of the invention;
FIG. 6 shows the model for the traceability identification of the source of Brazil-American two-country OPLS-DA soybean and soybean oil of the invention;
FIG. 7 shows the russia-American multi-national OPLS-DA soybean and soybean oil origin tracing identification model of the invention;
FIG. 8 shows a Canada-American multi-national OPLS-DA soybean and soybean oil origin traceability identification model of the invention;
FIG. 9 shows the Argentina-American multi-national OPLS-DA soybean and soybean oil origin tracing identification model of the invention;
FIG. 10 shows a soybean oil sample triglyceride compound PCA identification model and an OPLS-DA identification model constructed based on three solvents;
FIG. 11 shows the identification model of the lipidomic PCA-class origin of soybean oil triglycerides by three solvent dilution methods.
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: triple TOF5600+ high resolution mass spectrometer from ABsciex, USA was selected; HPLC 20AD high performance liquid chromatograph from shimadzu corporation; xbridge BEH C18 column (100 mm. Times.2.1 mm,3 μm) from Waters, USA; VORTEX 4 VORTEX mixer from IKA, germany; oil presses;
reagent selection: methanol, acetonitrile, acetone, ethyl acetate (chromatographic purity, sameimers, usa);
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 LC-Q-TOF-MS 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 soybean oil samples, and gradually diluting the soybean oil samples by 100-200 times by using a diluting solvent to obtain a standard sample, wherein the standard sample comprises at least two soybean samples with different producing areas;
s2, mass spectrum data acquisition of a standard sample: respectively acquiring mass spectrum data information of the standard samples in different areas by using a liquid chromatograph-four-level rod time-of-flight mass spectrometer to obtain IDA-MS high-resolution mass spectrum data of triglyceride compounds of the standard samples;
s3, determining a targeting compound: matching triglyceride compound spectrum data in the IDA-MS high-resolution mass spectrum data obtained in the step S2 with standard triglyceride compound data in a lipid compound database, and determining a triglyceride compound targeting compound of the standard sample;
s4, establishing a soybean and soybean oil production area traceability identification model: analyzing the IDA-MS high-resolution mass spectrum data through analysis software to obtain triglyceride compound marker observation peak value data of soybean oil in the standard sample, processing the triglyceride compound marker observation peak value data in the standard sample in different areas 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 to obtain characteristic distribution rules of soybean oil in different production areas in the standard sample, and constructing a soybean and soybean oil production area tracing identification model based on lipidomics; the marker observation peak value data is marker View peaks data, the marker View peaks data of the triglyceride of the standard sample is obtained through analysis of MasterView analysis software, and the soybean origin tracing identification model judges and predicts the soybean and soybean oil origins in different areas through multiple statistical analysis;
S5, predicting a result: squeezing a soybean sample to be detected to obtain a soybean oil sample, gradually diluting the soybean oil sample by 100-200 times by using a diluting solvent to obtain a sample to be detected, acquiring IDA-MS high-resolution mass spectrum data of the sample to be detected by using a liquid chromatograph-four-rod flight time mass spectrometer, and introducing the data into a soybean and soybean oil origin tracing identification model to predict an origin tracing result.
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 the soybean verification samples with definite areas into the soybean and soybean oil origin tracing identification model in the step (S4) through acquiring IDA-MS high-resolution mass spectrum data by a liquid chromatograph-four-rod time-of-flight mass spectrometer 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
The soybean oil sample is obtained by physically squeezing soybean samples with different producing areas in a definite region through an oil press, and a methanol-ethyl acetate mixed solution is selected as a diluting solvent, wherein the ratio of methanol to ethyl acetate in the diluting solvent is 1:1, the soybean oil sample is diluted by 200 times step by using a methanol-ethyl acetate mixed solution.
The standard sample obtained after dilution is placed in a sample injector of the liquid chromatograph-four-pole time-of-flight mass spectrometer, the standard sample is separated and analyzed through the liquid chromatograph in the liquid chromatograph-four-pole time-of-flight mass spectrometer, then mass spectrum data acquisition of the standard sample is carried out through a mass spectrometer in the liquid chromatograph-four-pole time-of-flight mass spectrometer, primary mass spectrum information and secondary mass spectrum information of the standard sample are respectively obtained through primary TOF-MS scanning and secondary IDA-MS scanning of the mass spectrometer, the secondary mass spectrum information is IDA-MS high-resolution mass spectrum data, an IDA-MS high-resolution mass spectrum of a soybean oil sample is shown in figure 1, a triglyceride compound target compound of the standard sample is determined through the IDA-MS high-resolution mass spectrum data, and the method for determining the target compound comprises the following steps: according to the molecular weight range of the target object in the IDA-MS high-resolution mass spectrum data, dividing the IDA-MS high-resolution mass spectrum data in the soybean oil sample into 3 areas: the method comprises the steps of carrying out matching screening on animal and plant source triglyceride compounds with the 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, determining triglyceride compounds in 114 soybean oil with the molecular weight ranging from 766 Da to 920Da as lipidomic analysis target compounds, and applying the target compounds in identification model research of tracing the lipid tissue of the triglyceride in the soybean oil, wherein the first region with the molecular weight ranging from 800 to 1000, the second region with the molecular weight ranging from 550 to 800 and the third region with the molecular weight below 550 are soybean oil lipid metabolism characteristic regions.
The lipid compound database is a published lipid compound database LIPID MAPS Lipidomics Gateway in the United states, the standard triglyceride compound data are 6899 triglyceride compounds of Triradyl glycerides in the lipid compound database LIPID MAPS Lipidomics Gateway, and the common 116 triglyceride compounds of the soybean oil sample are determined to be triglyceride compound targeting compounds of the standard sample through qualitative analysis of PeakView software.
The liquid chromatography conditions of the liquid chromatograph in the liquid chromatography-four-level-rod time-of-flight mass spectrometer are as follows: the flow rate is 0.5 mu L/min, the column temperature is 40 ℃, the Xbridge BEH C18 chromatographic column is eluted in a gradient way, and the sample injection amount is 2 mu L; the mobile phase A is isopropanol, the mobile phase B is acetonitrile, and the content of the mobile phase B in different time periods is as follows: 0min,70% b;0-5min,70-65% B;5-8min,65% B;10-10.5min,65-70% B;10.5-15min,70% B.
The four-stage rod time-of-flight mass spectrometry conditions of a mass spectrometer in the liquid chromatograph-four-stage rod time-of-flight mass spectrometer are: the mass spectrometer adopts a positive ion mode to collect data, and an ion source is as follows: ESI and APCI composite sources; the positive ion scanning mode is as follows: APCI source is connected with an automatic correction system, and one-stage TOF-MS scans the accurate mass range: 100-2000 Da, wherein the data acquisition time is 100ms, the DP is 100V, and the CE is 10V, wherein the DP is the declustering voltage, and the CE is the collision energy; secondary IDA-MS scans accurate mass range: 50-2000 Da, DP:100V, CE: 35+ -15V; the mass spectrometer adopts a high sensitivity mode, the data acquisition time is 50ms, the signal threshold is 100cps, 6 times of data are acquired each time of circulation, and dynamic background subtraction is adopted.
The liquid chromatograph-four-stage rod time-of-flight mass spectrometer is an Shimadzu LC20AD liquid chromatograph, the mass spectrometer is a Triple TOF 5600+ mass spectrometer, the automatic correction system is a CDS system, and the four-stage rod time-of-flight mass spectrometer conditions further comprise: every 10 samples are automatically corrected for 1 time, the flow rate of the APCI positive ion correction liquid is 0.3mL/min, and the air curtain air pressure is as follows: the ion source atomizing gas pressure was 40 psi: the ion source assisted heating gas pressure was 50 psi: 50psi, ion source temperature: and (3) at 500 ℃, carrying out quantitative analysis on all IDA-MS high-resolution mass spectrum data acquired by a mass spectrometer at 5500V of ion source voltage in the analysis TF 1.6 software of ABSciex company, introducing the quantitative analysis on the IDA-MS high-resolution mass spectrum data into SIMCA 14.0 software (Umetrics Inc. Switzerland) after qualitative treatment, and carrying out orthogonal partial least square regression analysis on the observed peak data of triglyceride compound markers in the standard sample to obtain distribution rules of triglyceride and metabolites of soybeans in different places, thereby constructing OPLS-DA soybeans and soybean oil place-of-origin tracing identification models based on lipidomics.
In the embodiment, standard samples are prepared by selecting 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, respectively acquiring IDA-MS high-resolution mass spectrum data of soybeans in different areas in the standard samples through a liquid chromatography-four-rod time-of-flight mass spectrometer, qualitatively and quantitatively processing and analyzing the data by PeakView, masterView software, then acquiring triglyceride compound marker peaks data of the soybean samples in different areas in the standard samples, and performing orthogonal bias least square regression analysis on observed peak data of triglyceride compound markers in the standard samples in different areas, thereby constructing a multi-country OPLS-DA soybean and soybean oil origin identification model.
As can be seen from fig. 2, the multi-country OPLS-DA soybean and soybean oil origin traceability identification model can significantly distinguish between the soybean samples of brazil, russia, argentine and america origins, and since the american, canadian and argentine origins samples are distributed together in a crossing manner, the prediction identification accuracy of the model is affected, and therefore, the soybean and soybean oil origin traceability identification model needs to be further optimized, and the optimization steps include: 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, 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. As shown in fig. 4, the VIP values of the identification models are used for determining which variables have large contribution degrees, and finally determining triglyceride compounds with molecular weights of 873.6967, 875.7123, 851.7123, 877.7280, 853.7280, 865.7280, 913.7280, 829.7280, 915.7436, 767.6184, 835.6810, 879.7436, 917.7593, 859.7749, 855.7436, 895.779, 825.6967, 921.7906, 887.8062 and 869.7593 and the like, so that the contribution degrees of the triglyceride compounds to the multi-country OPLS-DA soybean and soybean oil origin tracing identification models are large, and the research optimizes the OPLS-DA soybean and soybean oil origin tracing identification models according to the hall's and dmedx indexes and deleting relatively small number of chinese and yerba samples. As shown in fig. 5, samples in different countries, especially samples in brazil, russia, united states and other places can be distinguished remarkably by using the optimized multi-country OPLS-DA soybean and soybean oil origin traceability identification model;
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, acquiring IDA-MS high-resolution mass spectrum data of the soybean verification samples in the specific area through a liquid chromatograph-four-rod time-of-flight mass spectrometer, and introducing the data into a multi-country soybean and soybean oil origin tracing identification model to verify the origin tracing accuracy of the soybean verification samples.
After blind sample verification is completed, squeezing a soybean sample to be detected to obtain a soybean oil sample to be detected, gradually diluting the soybean oil sample to be detected by a diluting solvent for 100-200 times to obtain a sample to be detected, and then acquiring IDA-MS high-resolution mass spectrum data of the sample to be detected through a liquid chromatograph-four-rod time-of-flight mass spectrometer, and introducing the data into a soybean and soybean oil origin tracing identification model to predict origin tracing results.
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, observed peak data of triglyceride compound markers in the standard samples were subjected to partial least squares-discriminant analysis to construct a lipid histology-based PLS-DA soybean and soybean oil origin tracing 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.
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, soybean oil samples in the two different areas are respectively subjected to liquid chromatography-four-rod time-of-flight mass spectrometry to acquire IDA-MS high-resolution mass spectrometry data, qualitative and quantitative analysis is performed on PeakView, masterView software to obtain triglyceride compound marker views peak data of soybean samples in different producing areas in the standard samples, and observed peak data of triglyceride compound markers in the standard samples in the two areas are imported into SIMCA 14.1 software to be processed by an orthogonal bias least square regression analysis method, so that a Brazil-American two-country OPLS-DA soybean and soybean oil producing area traceability identification model is established. From fig. 6, 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, and in order to further verify the determination accuracy of the two-country pressed soybean oil origin traceability identification model, 24 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 83.7% and the determination accuracy of the us-derived sample is 82.9%.
Two-country soybean and soybean oil origin traceability identification model example 2
In this embodiment, a standard sample is prepared by selecting a russian soybean sample and a american soybean sample with definite regions, respectively acquiring IDA-MS high-resolution mass spectrum data of the soybean samples in the two different regions by a liquid chromatograph-four-rod time-of-flight mass spectrometer, qualitatively and quantitatively processing and analyzing the data by PeakView, masterView software to obtain triglyceride compound marker views peak data of the soybean samples in different producing regions in the standard sample, and introducing observed peak data of triglyceride compound markers in the standard samples in the two regions into SIMCA 14.1 software to perform orthogonal partial least square regression analysis, thereby establishing a russian-american multi-OPLS-DA soybean and soybean oil producing region identification model. From fig. 7, it can be seen that the russia-american two-country OPLS-DA soybean and the soybean oil origin tracing identification model can significantly distinguish the american and russia soybean oil samples, and in order to further verify the determination accuracy of the two-country pressed soybean oil origin tracing identification model, 24 american pressed soybean oil samples and 5 russia pressed soybean oil samples were selected for model blind sample verification, and the verification results indicate that the determination accuracy of the brazil-derived soybean oil sample and the russia-derived sample is 100%.
Two-country soybean and soybean oil origin traceability identification model example 3
In this embodiment, a canadian soybean sample and a metacarpal soybean sample with definite regions are selected to prepare a standard sample, the soybean oil samples in the two different regions are respectively subjected to liquid chromatography-four-rod time-of-flight mass spectrometry to acquire IDA-MS high-resolution mass spectrometry data, qualitative and quantitative analysis is performed on PeakView, masterView software to obtain triglyceride compound marker views peak data of soybean samples in different production regions in the standard sample, and observed peak data of triglyceride compound markers in the standard samples in the two regions are imported into SIMCA 14.1 software to be processed by an orthogonal partial least square regression analysis method, so that a canadian-American multi-national OPLS-DA soybean and soybean oil production region traceability identification model is established. From fig. 8, it can be seen that the canadian-american two-country OPLS-DA soybean and soybean oil origin traceability identification model can significantly distinguish between the american and canadian soybean oil samples, and in order to further verify the accuracy of the determination of the two-country pressed soybean oil origin traceability identification model, 24 american pressed soybean oil samples and 15 canadian pressed soybean oil samples were selected for model blind sample verification, and the verification result shows that the 24 american pressed soybean oil samples accurately determine 17 and the 15 canadian pressed soybean oil samples accurately determine 8.
Two-country soybean and soybean oil origin traceability identification model example 4
This example only describes the differences from the above example in that in this example, a standard sample was prepared by selecting a soybean sample with a definite region of Argentina and a soybean sample of America, and the soybean oil samples of these two different regions were collected by liquid chromatography-four-rod time-of-flight mass spectrometry respectively to obtain IDA-MS high resolution mass spectrometry data, and after qualitative and quantitative analysis by PeakView, masterView software, triglyceride compound marker views peak data of soybean samples of different production regions in the standard sample were obtained, and the observed peak data of triglyceride compound markers in the standard samples of the two regions were imported into SIMCA 14.1 software for orthogonal partial least squares regression analysis, thereby creating a model for identifying the origin of Argentina-American multi-national OPLS-DA soybean and soybean oil. As can be seen in fig. 9, the sample of argentine-pressed soybean oil is more concentrated in the score map of the argentine-us two-country OPLS-DA soybean and soybean oil origin traceability identification model, while the sample of us-pressed soybean oil is more dispersed in the distribution area of the model score map, and some us-pressed soybean oil is distributed to the range of the argentine distribution area. To further verify the accuracy of the determination of the united states and argentine-pressed soybean oil origin traceability identification model, 15 argentine-pressed soybean oil samples and 24 united states-pressed soybean oil samples were selected for model blind sample verification. Research results show that 15 Argentina pressed soybean oil samples accurately judge 8, and 24 American pressed soybean oil samples accurately judge 16.
Comparative example 1
In the comparative example, in order to verify the influence of different dilution solvents on the clustering effect of an identification model based on the tracing of the triglyceride lipidomic production area of a soybean oil sample, the comparative example selects the soybean oil sample with a definite production area, respectively presses the soybean oil sample to obtain the soybean oil sample, dilutes the soybean oil sample by using acetone as the dilution solvent, then acquires IDA-MS high-resolution mass spectrum data of triglyceride compounds in the soybean oil sample by using a liquid chromatography-four-level rod time-of-flight mass spectrometer LC-Q-TOF-MS, and utilizes two multivariate variable statistical analysis models of principal component analysis PCA and orthogonal partial least square regression analysis method to process the triglyceride high-resolution mass spectrum data.
Comparative example 2
In the comparative example, soybean samples with definite producing areas are respectively selected and pressed to obtain soybean oil samples, the soybean oil samples are diluted by using isopropyl alcohol triester as a diluting solvent, then IDA-MS high-resolution mass spectrum data of triglyceride compounds in the soybean oil samples are collected by using a liquid chromatography-four-level flight time mass spectrometer LC-Q-TOF-MS, and the high-resolution mass spectrum data of the triglyceride compounds are processed by using two multivariate variable statistical analysis models, namely principal component analysis PCA and orthogonal partial least squares regression analysis (OPLS-DA).
Comparative analysis of the effect of different dilution solvents of multinational OPLS-DA soybean and soybean oil origin traceability identification model example 1, comparative example 1 and comparative example 2 on origin traceability identification of soybean oil triglycerides. As shown in fig. 10, the PCA model and the OPLS-DA model after the soybean oil sample was diluted by the acetone (acetone), the mixed solvent of methanol and ethyl acetate (EA-MEOH), and the Isopropanol (isopanol) solvent, it is known that the aggregation effect of the acetone, methanol and ethyl acetate solvents was somewhat synchronous, and the aggregation effect of the Isopropanol diluted soybean oil sample was significantly different from that of the other two solvent dilution methods. In particular, the analysis result of the OPLS-DA model shows that the aggregation area of the soybean oil sample diluted by isopropanol is obviously different from that of the soybean oil sample diluted by isopropanol by the mixed solution of methanol and ethyl acetate. In order to further examine the influence of different solvents on the lipidomic analysis of the triglyceride in the soybean oil, a PCA-class model is constructed as shown in FIG. 11, and the problems of good and bad source tracing identification results of the soybean oil samples diluted by the three solvents are judged.
As can be seen from the PCA-class origin tracing identification model in FIG. 11, the isopropyl alcohol diluted soybean oil tracing identification model can only distinguish the difference between the pressed soybean oil and the finished soybean oil, and cannot identify the soybean oil samples from different countries, the mixed solution of methanol and ethyl acetate diluted soybean oil sample can identify the sample sources of the pressed soybean oil and the finished soybean oil indiscriminately, the acetone diluted soybean oil sample can also identify the origin of the soybean oil sample, but the dispersity of sample aggregation is higher, and in consideration of the larger influence of acetone on the liquid chromatographic column, the isopropyl alcohol diluted soybean oil tracing identification model is not suitable for being used as a dilution solvent for the soybean oil sample, and the mixed solution of methanol and ethyl acetate is selected as the dilution solvent for the soybean oil sample to be obviously superior to other two dilution solvents.
The above description is provided for an embodiment of a method for tracing the origin of soybean and soybean oil based on LC-Q-TOF-MS characterization of triglycerides. 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 LC-Q-TOF-MS 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 soybean oil samples, and gradually diluting the soybean oil samples by 100-200 times by using a diluting solvent to obtain a standard sample, wherein the standard sample comprises at least two soybean samples with different producing areas;
s2, mass spectrum data acquisition of a standard sample: respectively acquiring mass spectrum data information of the standard samples in different areas by using a liquid chromatograph-four-level rod time-of-flight mass spectrometer to obtain IDA-MS high-resolution mass spectrum data of triglyceride compounds of the standard samples;
S3, determining a targeting compound: matching triglyceride compound spectrum data in the IDA-MS high-resolution mass spectrum data obtained in the step S2 with standard triglyceride compound data in a lipid compound database, and determining a triglyceride compound targeting compound of the standard sample;
s4, establishing a soybean and soybean oil production area traceability identification model: analyzing the IDA-MS high-resolution mass spectrum data through analysis software to obtain triglyceride compound marker observation peak value data of soybean oil in the standard sample, processing the triglyceride compound marker observation peak value data in the standard sample in different areas 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 to obtain characteristic distribution rules of soybean oil in different production areas in the standard sample, and constructing a soybean and soybean oil production area tracing identification model based on lipidomics;
s5, predicting a result: squeezing a soybean sample to be detected to obtain a soybean oil sample, gradually diluting the soybean oil sample by 100-200 times by using a diluting solvent to obtain a sample to be detected, acquiring IDA-MS high-resolution mass spectrum data of the sample to be detected by using a liquid chromatograph-four-rod flight time mass spectrometer, and introducing the data into a soybean and soybean oil origin tracing identification model to predict an origin tracing result;
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 the soybean verification samples with definite areas into the soybean and soybean oil origin tracing identification model in the step S4 through acquiring IDA-MS high-resolution mass spectrum data by a liquid chromatograph-four-rod time-of-flight mass spectrometer 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 object in the IDA-MS high-resolution mass spectrum data, dividing the IDA-MS high-resolution mass spectrum data in the soybean oil sample into 3 areas: a first region with molecular weight of 800-1000, a second region with molecular weight of 550-800 and a third region with molecular weight below 550, wherein the first region and the second region are metabolic characteristic regions of soybean oil and fat, and triglyceride compounds of animal and plant sources with molecular weight ranging from 700-950 in a lipid compound database are matched and screened with soybean oil triglyceride compounds in the first region and the second region, and triglyceride compounds in 114 soybean oil with molecular weight ranging from 766-920Da are determined as targeting compounds;
The dilution solvent is a methanol-ethyl acetate mixed solution, and the ratio of methanol to ethyl acetate in the dilution solvent is 1:1, gradually diluting the soybean oil sample by 200 times by using a methanol-ethyl acetate mixed solution;
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 target compounds through VIP values of soybean and soybean oil origin tracing identification models, 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; and determining triglyceride compounds with large contribution degree through the VIP value of the identification model, and finally determining the triglyceride compounds with molecular weights of 873.6967, 875.7123, 851.7123, 877.7280, 853.7280, 865.7280, 913.7280, 829.7280, 915.7436, 767.6184, 835.6810, 879.7436, 917.7593, 859.7749, 855.7436, 895.779, 825.6967, 921.7906, 887.8062 and 869.7593 to have large contribution degree to the multi-country OPLS-DA soybean and soybean oil origin tracing identification model.
2. The method for tracing the origin of soybean and soybean oil according to claim 1, wherein the step of collecting the mass spectrum data of the standard sample in step S2 is as follows:
and placing the diluted standard sample in a sample injector of the liquid chromatograph-four-level rod time-of-flight mass spectrometer, separating and analyzing the standard sample through the liquid chromatograph in the liquid chromatograph-four-level rod time-of-flight mass spectrometer, collecting mass spectrum data of the standard sample through a mass spectrometer in the liquid chromatograph-four-level rod time-of-flight mass spectrometer, respectively obtaining primary mass spectrum information and secondary mass spectrum information of the standard sample through primary TOF-MS scanning and secondary IDA-MS scanning of the mass spectrometer, wherein the secondary mass spectrum information is IDA-MS high-resolution mass spectrum data, determining triglyceride compound targeting compounds of the standard sample through the IDA-MS high-resolution mass spectrum data, and carrying out directional quantitative processing analysis on the IDA-MS high-resolution mass spectrum data through analysis software so as to construct a soybean and soybean oil production area tracing identification model.
3. The method for tracing the origin of soybean and soybean oil according to claim 2, wherein the liquid chromatography conditions of the liquid chromatograph in the liquid chromatography-four-pole time-of-flight mass spectrometer are: the flow rate is 0.5 mu L/min, the column temperature is 40 ℃, the Xbridge BEH C18 chromatographic column is eluted in a gradient way, and the sample injection amount is 2 mu L; the mobile phase A is isopropanol, the mobile phase B is acetonitrile, and the content of the mobile phase B in different time periods is as follows: 0min,70% b;0-5min,70-65% B;5-8min,65% B;10-10.5min,65-70% B;10.5-15min,70% B;
The four-stage rod time-of-flight mass spectrometry conditions of a mass spectrometer in the liquid chromatograph-four-stage rod time-of-flight mass spectrometer are: the mass spectrometer adopts a positive ion mode to collect data, and an ion source is as follows: ESI and APCI composite sources; the positive ion scanning mode is as follows: APCI source is connected with an automatic correction system, and one-stage TOF-MS scans the accurate mass range: 100-2000 Da, wherein the data acquisition time is 100ms, the DP is 100V, and the CE is 10V, wherein the DP is the declustering voltage, and the CE is the collision energy; secondary IDA-MS scans accurate mass range: 50-2000 Da, DP:100V, CE: 35+ -15V; the mass spectrometer adopts a high sensitivity mode, the data acquisition time is 50ms, the signal threshold is 100cps, 6 times of data are acquired each time of circulation, and dynamic background subtraction is adopted.
4. The method for tracing the origin of soybean and soybean oil according to claim 1, wherein in the step S4, the observed peak data of the triglyceride compound markers in the standard sample is processed by an orthogonal partial least squares regression analysis method, and an OPLS-DA soybean and soybean oil origin tracing identification model based on lipidomics is constructed.
5. The method for tracing the origin of soybean and soybean oil according to claim 1, wherein in the step S4, the observed peak data of the triglyceride compound markers in the standard sample is processed by a partial least square method-discriminant analysis method, and a PLS-DA soybean and soybean oil origin tracing identification model based on lipidomics is constructed.
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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