CN107941939B - A method to differentiate organic and non-organic rice using metabolomic technology - Google Patents

Abstract

A method for distinguishing organic rice and non-organic rice by using metabolomics technology belongs to the technical field of grain quality detection. The method is as follows: after pre-processing the organic rice sample and the non-organic rice sample respectively, the separation of chemical components in the pre-treated sample is realized by applying an ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high-resolution mass spectrometry method Then, the original data of the two samples obtained were preprocessed, and finally the multivariate statistical analysis method orthogonal partial least squares-discriminant analysis model was used to distinguish organic rice and non-organic rice, and the S-curve was used to obtain the influence on the discrimination. The larger factors are identified through the open source online database massbank. The analysis method of the present invention can effectively distinguish organic rice from non-organic rice, and can intuitively discriminate the classification situation, without the need for relevant verification and analysis, the conclusion can be obtained, and the detection result is accurate and reliable.

Description

A method to differentiate organic and non-organic rice using metabolomic technology

technical field

The invention belongs to the technical field of grain quality detection, in particular to a method for distinguishing organic rice and non-organic rice by utilizing metabolomics technology.

Background technique

Rice is one of the three major food crops in the world, and about 1/3 of the world's population is the staple food of rice. Rice has long been the largest food crop in production and consumption, and basically the majority of rice growers and consumers are in Asia. The improvement of rice quality is inseparable from the living standards and health of people in these areas. Rice is the main staple food for the vast majority of people in my country, and it is also my country's main export product. With the continuous improvement of people's living standards, the requirements for the quality of rice are getting higher and higher, and organic rice has gradually entered the table of consumers.

Organic rice refers to rice products that come from the organic agricultural production system, are produced and processed in accordance with international organic agricultural production requirements and corresponding standards, and are certified by an independent organic food certification agency. Since organic rice is not allowed to use any chemically synthesized pesticides, fertilizers, growth regulators, etc. during the entire process of planting and growth, it is more susceptible to factors such as pests and weeds. Therefore, organic rice products have the advantages of low yield, low rice milling rate, High cost features. At present, the consumption of organic rice in my country is increasing at an annual rate of 30% to 50%. It is predicted that in the next 10 years, the average annual growth rate of organic rice in my country will reach 20% to 30%. The rapid development of the organic rice industry is also accompanied by many problems. Many manufacturers' behavior of "substituting shoddy products for genuine products" has greatly disrupted the normal development of the organic rice product market. Therefore, it is of great practical significance to establish a method for identifying organic rice.

Metabolomics is an important branch of systems biology, a science that mainly conducts qualitative and quantitative detection of endogenous and exogenous small molecule metabolites. Metabolomics can comprehensively study the complex metabolic processes and products of plants, so it has received extensive attention in the field of plant research in recent years. The development of plant metabolomics makes it possible to analyze the structure of plant secondary metabolism network, rate-limiting steps, to analyze the process of cell activity, and to find the relationship between plants.

According to different research purposes, metabolomics can be divided into non-targeted metabolomics and targeted metabolomics. Untargeted metabolomics is a kind of unbiased metabolomics analysis, mainly for the systematic and comprehensive analysis of endogenous metabolites in organisms; targeted metabolomics is a kind of directional analysis, mainly for a specific type of metabolites. Analysis of metabolites. At present, non-targeted metabolomics analysis is widely used in the differential analysis of plant active substances, metabolic mechanisms and related metabolic networks, especially the differential identification of plant varieties and origins due to its differential research on secondary metabolites. .

The existing testing items for organic rice are only limited to a few common indicators, and cannot make a comprehensive and effective judgment on the quality of organic rice, and the artificial judgment method is greatly affected by various factors and is too convincing.

SUMMARY OF THE INVENTION

The purpose of the present invention is to solve the problem that the quality of rice cannot be comprehensively and effectively judged by the existing detection means, and to provide a method for distinguishing organic rice and non-organic rice by using metabolomics technology.

To achieve the above object, the technical scheme adopted by the present invention is as follows:

A method for distinguishing organic rice from non-organic rice using metabolomics technology, the method is as follows:

After the organic rice samples and non-organic rice samples were pre-treated with organic solvents, the ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high-resolution mass spectrometry method was used to realize the pretreatment of organic rice samples and non-organic rice samples. separation and determination of the chemical components, then preprocess the raw data of the obtained organic rice samples and non-organic rice samples by ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high-resolution mass spectrometry, and finally apply the multivariate statistical analysis method orthogonal Partial least squares-discriminant analysis model distinguishes organic rice and non-organic rice, and uses the S-curve diagram to obtain the factors that have a greater impact on the discrimination, and uses the open source online database massbank to identify these substances.

The beneficial effects of the present invention relative to the prior art are:

(1) The metabolomics technology combined with the ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high-resolution mass spectrometry technology was applied to the analysis of secondary metabolites in rice samples, which was highly convincing. Non-targeted metabolomics technology can study the overall situation of metabolites, which can better reflect the overall situation of the sample. At the same time, statistical knowledge can be applied to find differential metabolites to provide technical support for further research.

(2) The analysis method of the present invention can effectively distinguish organic rice from non-organic rice, the results are displayed in the form of OPLS-DA score map, and the differential substances (potential biomarkers) are identified by using an open-source online database. The classification situation can be intuitively judged without the need for relevant verification and analysis, and conclusions can be drawn, and the detection results are accurate and reliable.

(3) The pretreatment method of rice samples is simple and fast, and the technical requirements for the testing personnel are relatively low after the method is established. In the whole pretreatment process, in order to obtain as much metabolite information of the rice sample as possible, the extraction reagent is a mixed solvent of methanol and water to dissolve the test sample. The pretreatment steps are simple and easy to operate. After experimental verification, the unsuitable pretreatment method cannot extract the endogenous metabolites of the rice sample to the maximum extent, which will cause the detection results that are not easy to distinguish and make the detection results inaccurate.

(4) Simplify the operation process: The traditional detection needs to configure standard samples, draw a standard curve, and at the same time need to measure a variety of chemical substances, the process is cumbersome, time-consuming and labor-intensive. The non-targeted metabolomics method is used for the determination, the pretreatment process is simple, the operation is convenient, and the on-machine operation is relatively convenient, and batch processing can be performed, which saves time and effort, and has high reliability.

(5) Aiming at the analysis method of using ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high-resolution mass spectrometry to distinguish organic rice and non-organic rice by metabolomics, the present invention optimizes and selects a set of samples for analysis according to the characteristics of rice samples. The ultra-high performance liquid chromatography conditions and the quadrupole-time-of-flight high-resolution mass spectrometry conditions for obtaining the best separation effect and detection effect have been verified by experiments. Non-organic rice is effectively differentiated.

(6) On the basis of the OPLS-DA model, use the S curve to screen out the factors that have a greater impact on the separation, and use the database to identify these substances. The identified substances can be used as target substances to lay the foundation for the identification and analysis of organic rice in the future.

(7) The present invention uses quadrupole-time-of-flight high-resolution mass spectrometry, which has higher resolution and can obtain more accurate and more substances compared with other detection methods, which has significant advantages in data analysis.

Description of drawings

Fig. 1 is the OPLS-DA scatter plot of organic rice and non-organic rice samples;

Figure 2 is an S-curve diagram of the OPLS-DA model.

Detailed ways

The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments, but are not limited thereto. Any modification or equivalent replacement of the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention shall cover within the scope of the present invention.

Embodiment 1: This embodiment records a method for distinguishing organic rice and non-organic rice using metabolomics technology, and the method is as follows:

After the organic rice samples and non-organic rice samples were pre-treated with organic solvents, the ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high-resolution mass spectrometry method was used to realize the pretreatment of organic rice samples and non-organic rice samples. separation and determination of the chemical constituents, and then preprocess the raw data of the obtained organic rice samples and non-organic rice samples by ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high-resolution mass spectrometry (UHPLC-Q-TOF MS), Finally, the orthogonal partial least squares-discriminant analysis (OPLS-DA) model of multivariate statistical analysis method was used to distinguish organic rice from non-organic rice, and the S-plot was used to obtain the factors (potential biological Markers), which are identified through the open-source online database massbank (http://www.massbank.jp/).

Specific embodiment 2: a method for distinguishing organic rice and non-organic rice using metabolomics technology described in specific embodiment 1, the organic rice samples and non-organic rice samples are all varieties of Daohuaxiang, Longjing, Songjing one or more of.

Embodiment 3: A method for distinguishing organic rice and non-organic rice by using metabolomics technology described in Embodiment 1, the pretreatment is specifically: pulverizing the organic rice sample and the non-organic rice sample, and passing 1 mm Aperture sieve, then sieved organic rice samples and non-organic rice samples are mixed with organic solvent, ultrasonicated, centrifuged, and filtered through an organic filter membrane, that is, the pretreatment is completed, and the samples that can be tested on the machine are obtained.

Embodiment 4: A method for distinguishing organic rice and non-organic rice using metabolomics technology described in Embodiment 3, the organic rice samples and non-organic rice samples and the organic solvent are added in a ratio of 1 g: (3-10) mL, preferably 1 g: 5 mL, can ensure good extraction effect of metabolites in the sample; the organic solvent is methanol aqueous solution, and the volume fraction is 60-85%, most preferably 70%.

Embodiment 5: In the method for distinguishing organic rice and non-organic rice by metabolomics technology described in Embodiment 3, the ultrasonic time is 10-40 min, preferably 30 min; the specific conditions of centrifugation are: under the condition of 4 ℃ Centrifuge at 8000～12000rpm for 15～30min, preferably at 10000rpm for 20min.

Embodiment 6: In the method for distinguishing organic rice and non-organic rice by metabolomics technology described in Embodiment 3, the pore size of the organic filter membrane is 0.20-0.25 μm, preferably 0.22 μm.

Embodiment 7: A method for distinguishing organic rice and non-organic rice using metabolomics technology described in Embodiment 1, the ultra-high performance liquid chromatography conditions are: adopt octadecyl-bonded silica gel column ( C18 column); A phase: formic acid aqueous solution, B phase: formic acid acetonitrile solution, gradient elution process: 0～1.5min, 15%B; 1.5～5.0min, 15-55%B; 5.0～17.0min, 55～70 %B; 17.0-20.0min, 70-90%B; 20.0-21.0min, 90-15%B.

Specific embodiment 8: a method for distinguishing organic rice and non-organic rice using metabolomics technology described in specific embodiment 7, the volume fraction of formic acid in the formic acid aqueous solution is 0.1%; the formic acid acetonitrile solution The volume fraction of formic acid was 0.1%; the flow rate was 0.3 mL/min, the column temperature was 36°C; the injection volume was 5 μL.

Embodiment 9: A method for distinguishing organic rice and non-organic rice using metabolomics technology described in Embodiment 1, the quadrupole-time-of-flight high-resolution mass spectrometer selects Aglient 6540UHD accurate-mass QTOF spectrometer, The mass spectrometry conditions were: dual ESI sources, positive ion mode, dry gas temperature 325 °C, flow rate 9 L/min; nebulizer pressure 45 psi; capillary voltage 4000 V; sampling cone voltage, 140 V; extraction cone voltage, 65 V; scanning range, m /z: 50 to 2000; scan mode: Full Scan; reference ions are m/z: 301.998139 and 1033.988109.

Embodiment 10: A method for distinguishing organic rice and non-organic rice by metabolomics technology described in Embodiment 1, the UHPLC-Q-TOF MS raw data of the obtained organic rice sample and non-organic rice sample is adopted. The MS Convert software is used to convert the format, and then the XCMS software is used for preprocessing. The preprocessing refers to the extraction, peak alignment, and noise removal of the chromatographic peaks in the original data of the total ion chromatogram to obtain the retention time and peak height of each peak. , peak area and mass-to-charge ratio data, and then display the results in the form of a multivariate analysis method OPLS-DA score map, and screen out the substances (potential markers) that have a greater impact on the identification results through the S-curve chart, and use the online database. Massbank identifies potential markers.

Explanation of terms: Multivariate statistical analysis method is a general term for a class of methods for processing multivariate statistical data based on multivariate statistical distribution. It is an important branch of statistics with rich theoretical achievements and many applied methods. Commonly used multivariate statistical analysis methods mainly include: multiple regression analysis, cluster analysis, discriminant analysis, principal component analysis, factor analysis, correspondence analysis, canonical correlation analysis, etc. The present invention mainly adopts the orthogonal partial least squares-discriminant analysis method (Orthogonal partial least squares-discriminant analysis, OPLS-DA).

Instruments and Equipment:

Aglient 1290UHPLC system, Agilent, Germany;

Acquity BEH C18column (2.1id×150mm, particle size 1.7μm), American Waters Company;

Aglient 6540UHD accurate-mass QTOF spectrometer, Germany Aglient company;

3K15 laboratory high-speed desktop refrigerated centrifuge, Sigma, Germany;

Milli-Q water purifier, Millipore, USA;

MX-S vortex instrument, Dalong Xingchuang Experimental Instrument (Beijing) Co., Ltd.;

KQ-700DE CNC ultrasonic instrument, Kunshan Ultrasonic Instrument Co., Ltd.;

Materials and Reagents:

Organic rice (certified by China Green China Organic Food Certification Center), obtained by various rice production companies;

Non-organic rice, obtained by farmers in various places;

Ultrapure water (18.2MΩ·cm), obtained from Milli-Q water purifier;

Acetonitrile (chromatographically pure), Merck, Germany;

Methanol (chromatographically pure), Sigma Company, USA;

Anhydrous formic acid (chromatographically pure), Merck, Germany.

The principle of the analysis method of the present invention is as follows: different planting methods will lead to differences in endogenous metabolites in rice, and these differences directly affect the quality of rice products. UHPLC can be used to separate chemical substances in rice, and mass spectrometry technology can be used to detect, The liquid phase and mass spectrometry data of rice can be obtained, and then metabolomics analysis technology is applied to analyze the obtained data by OPLS-DA technology, so as to visually distinguish organic and non-organic rice samples.

High performance liquid chromatography is an emerging liquid chromatography technology using small particle packing column (particle size less than 2μm) and ultra-high pressure system (pressure greater than 105kPa), which can significantly improve the resolution and detection sensitivity of chromatographic peaks, while greatly shortening the time. Analytical cycle for separations and high-throughput studies of complex mixtures in small quantities. At the same time, the Aglient 6540UHDaccurate-mass QTOF spectrometer has the characteristics of ultra-high resolution, ultra-high speed, ultra-high sensitivity, etc., which can more accurately detect the chemical substances in rice, making the results more reliable and convincing.

Specifically include the following steps:

After the organic rice samples and non-organic rice samples were pre-treated with organic solvents respectively, the separation and determination of the chemical components of the pre-treated samples were realized by using the ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high-resolution mass spectrometry method. Then, the UHPLC-Q-TOF MS raw data of the obtained rice samples were transformed and preprocessed, and finally the multivariate statistical analysis method orthogonal partial least squares-discriminant analysis (OPLS-DA) model was used to distinguish organic rice samples from non-organic rice samples. Organic rice samples.

For rice samples from different origins, the planting environment is also affected by altitude, latitude and longitude, light, temperature, humidity, etc., so even if they are both organic rice, the metabolite composition and content in rice samples from different origins will not be exactly the same, so There will be certain differences between them, but through the experimental verification of the present invention, this does not affect the final detection result, and can be effectively distinguished. In certain embodiments of the present invention, the rice sample is selected from one or more of Daohuaxiang, Songjing, and Longjing.

In the whole pretreatment process, in order to obtain as much metabolite information of the sample as possible, a mixed solvent of methanol and water was used as the extraction reagent to dissolve the test sample, and after ultrasonication, it was centrifuged through an organic filter membrane for on-machine detection. The pretreatment steps are simple and easy to operate. In a preferred embodiment of the present invention, the volume fraction of methanol in the methanol aqueous solution is 70%. The present invention also conducts research on other extraction reagents, such as ethanol aqueous solution, acetone solution and isopropanol solution, but the obtained effect is not ideal, and the metabolites in the rice sample cannot be extracted comprehensively.

In some preferred embodiments of the present invention, the sample pretreatment process includes: pulverizing the rice sample, sieving (1mm pore size), mixing with an organic solvent, ultrasonicating, centrifuging, and passing through an organic filter membrane to obtain a sample that can be tested on the machine . Among them, the ultrasonic time is 10 to 40 minutes, preferably 30 minutes; the centrifugation conditions: 8000 to 12000 rpm for 15 to 30 minutes at 4°C, preferably 10000 rpm for 20 minutes; the pore size of the organic filter membrane is 0.20 to 0.25 μm, preferably 0.22 μm; The extraction effect of metabolites is good, and the addition ratio of sample to organic solvent is 1g:(3-10)mL, preferably 1g:5mL. Among them, the addition ratio of the rice sample to the organic solvent is more critical, which can fully dissolve the test sample, thereby obtaining more sample metabolite information and making the final detection result more accurate.

Chromatographic separation and mass spectrometry data collection are carried out simultaneously. In order to separate and identify each component, appropriate chromatographic and mass spectrometry conditions must be selected.

Aiming at the characteristics of rice sample components, the present invention investigates the influence of conditions such as mobile phase, gradient elution process, column temperature and sample injection volume in ultra-high performance liquid chromatography on separation efficiency and analysis speed, and finally optimizes the screening to obtain a set of Ultra-high performance liquid chromatography conditions for the best separation of samples.

In a preferred embodiment of the present invention, the ultra-high performance liquid chromatography conditions are: use an octadecyl-bonded silica gel column (C18 column); phase A: formic acid aqueous solution, phase B: formic acid acetonitrile, gradient elution process: 0- 1.5min, 15%B; 1.5-5.0min, 15-55%B; 5.0-17.0min, 55-70%B; 17.0-20.0min, 70-90%B; 20.0-21.0min, 90-15%B . The volume fraction of formic acid in the formic acid aqueous solution was 0.1%, and the volume fraction of formic acid in the formic acid acetonitrile solution was 0.1%; the flow rate was 0.3 mL/min, the column temperature was 36 °C; the injection volume was 5 μL.

The rice sample is a sample with complex components, and the gradient elution procedure obtained by the screening of the present invention can better separate the components of the complex substances in the rice sample, and lay a solid foundation for the subsequent identification of organic rice and non-organic rice. . The present invention also adopts other gradient elution procedures. It is found that an inappropriate gradient elution procedure cannot effectively separate each component, and thus cannot effectively distinguish organic and non-organic rice.

According to the component characteristics of rice samples, in order to improve the atomization and ionization conditions of the compounds and improve the sensitivity, the present invention investigates the resolution, gas flow rate, spray voltage and other conditions, and finally optimizes the screening to obtain a set of four compounds that make the detection effect accurate. Stage rod-time-of-flight high-resolution mass spectrometry conditions.

In a preferred embodiment of the present invention, Aglient 6540UHDaccurate-mass Q-TOF spectrometer is selected for quadrupole-time-of-flight high-resolution mass spectrometer, and the mass spectrometry conditions are: dual ESI sources, positive ion mode, drying gas temperature 325°C, flow rate 9L/min ; Nebulizer pressure 45psi; Capillary voltage 4000V; Sampling cone voltage, 140V; Extraction cone voltage, 65V; Scanning range, m/z: 50～2000; Scanning mode: Full Scan; reference ion is m/ z: 301.998139 and 1033.988109.

It has been verified by experiments that through the process conditions of ultra-high performance liquid chromatography and mass spectrometry, organic rice samples and non-organic rice samples can be effectively distinguished on the OPLS-DA score map.

Qualitative and quantitative information on many endogenous compounds can be determined using metabolomic techniques. This information appears as many signal peaks on the output spectrum and as chromatographic peaks at different retention times on the chromatogram.

In terms of processing effect and convenience, in a preferred embodiment of the present invention, the UHPLC-Q-TOF MS raw data of the obtained rice samples and non-organic rice samples are converted using MS Convert software, and then using XCMS software. Preprocessing, which refers to the extraction, peak alignment, and noise removal of chromatographic peaks in the original data of the total ion current chromatogram to obtain the retention time, peak height, peak area and mass-to-charge ratio data of each peak; The multivariate statistical analysis method OPLS-DA score map is used to display the discrimination results.

Among them, MS Convert software is a data conversion format software developed by Proteo Wizard, and XCMS is a general-purpose software for processing LC-MS raw data developed by Scripps Center.

The method of distinguishing is: in the OPLS-DA score chart, compare the area to which the organic rice sample point and the non-organic rice sample point belong. If the two samples belong to two clearly separated areas, it means that the difference between the two is obvious. .

Example 1:

A method for distinguishing organic rice from non-organic rice using metabolomic technology, including the following steps:

(1) Sample pretreatment

Pulverize the rice sample, sieve (1 mm aperture), weigh 600 mg of the sample into a 5 mL centrifuge tube, add 3 mL of extraction solvent (70% methanol solution (v/v)), vortex and mix to dissolve, sonicate for 30 min at 4°C Centrifuge at 10,000 rpm for 20 min, and then pass the supernatant through a 0.22 μm organic filter membrane, put it on the machine, and load a sample volume of 5 μL.

(2) Application of Aglient 1290UHPLC system in series with Aglient 6540UHD accurate-mass Q-TOFspectrometer instrument to achieve separation and determination of chemical components in samples.

(1) Liquid chromatography parameters

Chromatographic column: Acquity BEH C18 column (2.1 id x 150 mm, particle size 1.7 μm) (Acquity, Waters, Milford, MA, USA).

Liquid phase: A phase, 0.1% formic acid aqueous solution, B phase: 0.1% formic acid acetonitrile; flow rate: 0.3 mL/min; column temperature 36°C.

time/min A/% B/% 0 85 15 1.5 85 15 5.0 45 55 17.0 30 70 20.0 10 90 21.0 85 15

(2) Mass spectrometry parameters

Positive ion mode

Ionization mode Dual ESI source scan mode FullScan drying gas temperature 325℃ flow rate 9L/min Nebulizer pressure 45psi Capillary voltage 4000V Sampling cone voltage 140V Extract the cone voltage 65V Scanning range m/z 50～2000 Reference ion m/z 301.998139 and 1033.988109

(3) Data processing and multivariate statistical analysis

The UHPLC-Q-TOF MS raw data of the obtained rice samples were formatted by MS Convert and preprocessed by the XCMS software package. , remove noise and other processing, obtain the retention time, peak height, noise removal and other processing of each peak, obtain the retention time, peak height, peak area and mass-to-charge ratio data of each peak, and then use the multivariate statistical analysis method of SIMCA-P software The discrimination results are displayed in the form of an OPLS-DA score map.

OPLS-DA is a supervised test method and a regression modeling method of multiple dependent variables to multiple independent variables. OPLS-DA is an extension of PLS-DA, that is, it first uses the orthogonal signal correction technology to decompose the X matrix information into two types of information related to Y and not related to Y, and then filters out the information irrelevant to the classification, so that the relevant information is mainly Focusing on the first prediction component, compared with the PLS-DA model, OPLS-DA can better distinguish the differences between sample groups, improve the validity and analytical ability of the model, and make the analysis results simple and easy to interpret. The visualization of the discriminant effect is more obvious.

(4) Achievement display of application objects:

The rice samples were 20 organic rice samples and 20 non-organic rice samples, and the operations were performed according to the procedures (1) to (3) to obtain the OPLS-DA scatter score chart of organic rice and non-organic rice.

The degree of aggregation and dispersion of each sample can be seen from the OPLS-DA scatter diagram. Each point represents a sample, in which O1-O20 represents 20 organic rice samples (dots), and C1-C20 represents 20 non-organic rice samples. Samples (squares). As shown in Figure 1, organic rice samples are distributed in the left half of the scatterplot (negative half-axis of X-axis), and non-organic rice is mainly concentrated in the right half of the scattergram (positive half-axis of X-axis). This shows that organic rice and non-organic rice have certain differences in the composition and content of secondary metabolites, and also shows that the OPLS-DA model can well distinguish organic and non-organic rice samples.

According to the high reliability (correlation) and high magnitude (covariance) of the data, some factors (points farther from the subject) that have a greater impact on the classification of the OPLS-DA model can be manually selected, and we set them as It is called a potential biomarker. As can be seen from Figure 2, a total of 30 potential biomarkers were screened, including 15 overexpression factors (upper-right corner of S-curve) and 15 low-expression factors (lower-left corner of S-curve).

To date, there are no authoritative guidelines or reference standards that can be applied to the identification of non-targeted metabolomic compounds. In this example, we used the online open source database Massbank (http://www.massbank.jp/) to identify the 30 most statistically significant potential biomarkers above, and the results are shown in Table 1. For 8 of the compounds, we gave the names of the compounds with higher probability, and for 12 of them, we annotated the molecular formula. Histidinol, Malvin, Pinoresinol, Lagochiline, 4-Methylumbelliferyl glucuronide, Coumarin 106 , Nα-Benzoyl-L-arginine (Nα-Benzoyl-L-arginine) and hydrocinchonine (Hydrocinchonine) eight substances can be regarded as the identification of organic rice and non-organic rice markers.

Table 1 Identification of potential biomarkers

in FIG. 1,

1. Determined by Metlin and MassBank databases

2. Based on peak ions

3. Identification level: 1. Identified compound, 2. Putative annotated compound, 3. Putative characteristic compound, 4. Unknown compound

4. NM - no match

Claims (7)

1. a method utilizing metabolomics technology to distinguish organic rice and non-organic rice, is characterized in that: described method is as follows: After the organic rice samples and non-organic rice samples were pre-treated with organic solvents, the ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high-resolution mass spectrometry method was used to realize the pretreatment of organic rice samples and non-organic rice samples. separation and determination of the chemical components, then preprocess the raw data of the obtained organic rice samples and non-organic rice samples by ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high-resolution mass spectrometry, and finally apply the multivariate statistical analysis method orthogonal Partial least squares-discriminant analysis model distinguishes organic rice and non-organic rice, and uses the S-curve diagram to obtain the factors that have a greater impact on the discrimination, and identifies these substances through the open source online database Massbank; The pretreatment is specifically: pulverizing the organic rice sample and the non-organic rice sample, passing through a 1mm aperture sieve, then mixing the sieved organic rice sample and non-organic rice sample with an organic solvent, ultrasonication, centrifugation, and organic filtration. Membrane, that is, the pretreatment is completed, and the samples that can be tested on the machine are obtained; the addition ratio of the organic rice samples and non-organic rice samples to the organic solvent is 1g: 3-10mL; the organic solvent is methanol aqueous solution, and the volume The score is 60% to 85%; The ultra-high performance liquid chromatography conditions are: adopt octadecyl-bonded silica gel column; A phase: formic acid aqueous solution, B phase: formic acid acetonitrile solution, gradient elution process: 0～1.5min, 15% B; 1.5～ 5.0min, 15%~55%B; 5.0~17.0min, 55%~70%B; 17.0~20.0min, 70%~90%B; 20.0~21.0min, 90%~15%B. 2. a kind of method that utilizes metabolomics technology to distinguish organic rice and non-organic rice according to claim 1, it is characterized in that: described organic rice sample and non-organic rice sample varieties are Daohuaxiang, Longjing, pine One or more of japonica. 3. a kind of method that utilizes metabolomics technology to distinguish organic rice and non-organic rice according to claim 1, is characterized in that: ultrasonic time is 10～40min; The concrete condition of centrifugation is: 8000～12000rpm under 4 ℃ of conditions Centrifuge for 15-30 min. 4 . The method for distinguishing organic rice from non-organic rice by metabolomics technology according to claim 1 , wherein the organic filter membrane has a pore diameter of 0.20-0.25 μm. 5 . 5. a kind of method utilizing metabolomics technology to distinguish organic rice and non-organic rice according to claim 1 is characterized in that: the volume fraction of formic acid in the described formic acid aqueous solution is 0.1%; the described formic acid acetonitrile solution The volume fraction of formic acid was 0.1%; the flow rate was 0.3 mL/min, the column temperature was 36°C; the injection volume was 5 μL. 6. a kind of method that utilizes metabolomics technology to distinguish organic rice and non-organic rice according to claim 1, it is characterized in that: described quadrupole-time-of-flight high-resolution mass spectrometer selects Aglient 6540 UHD accurate-massQTOF spectrometer , the mass spectrometry conditions are: dual ESI sources, positive ion mode, dry gas temperature 325°C, flow rate 9L/min; nebulizer pressure 45psi; capillary voltage 4000V; sampling cone voltage, 140V; extraction cone voltage, 65V; scanning range, m/z: 50～2000; scan mode: Full Scan; reference ions are m/z: 301.998139 and 1033.988109. 7. a kind of method utilizing metabolomics technology to distinguish organic rice and non-organic rice according to claim 1, is characterized in that: to the UHPLC-Q-TOF MS raw data of the organic rice sample obtained and the non-organic rice sample The MSConvert software is used to convert the grid, and then the XCMS software is used for preprocessing. The preprocessing refers to the extraction, peak alignment, and noise removal of the chromatographic peaks in the original data of the total ion chromatogram to obtain the retention time and peak height of each peak. , peak area and mass-to-charge ratio data, and then differentiated and displayed the results in the form of a multivariate analysis method OPLS-DA score chart, and screened out substances that have a greater impact on the identification results through the S-curve chart, and used the online database Massbank to identify potential markers. identify.

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