CN107941939B - Method for distinguishing organic rice from non-organic rice by utilizing metabonomics technology - Google Patents
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Abstract
A method for distinguishing organic rice from non-organic rice by utilizing a metabonomics technology belongs to the technical field of grain quality detection. The method comprises the following steps: respectively preprocessing an organic rice sample and a non-organic rice sample, then realizing separation and determination of chemical components in the preprocessed samples by using an ultra-high performance liquid chromatography-tandem quadrupole-time-of-flight high-resolution mass spectrometry method, then preprocessing the obtained original data of the two samples, finally distinguishing the organic rice from the non-organic rice by using a multivariate statistical analysis method orthogonal partial least square-discriminant analysis model, obtaining factors with large influence on the discrimination by using an S curve graph, and identifying the substances by using an open-source online database massbank. The analysis method can effectively distinguish the organic rice from the non-organic rice, can visually distinguish the classification condition of the organic rice, does not need to carry out related verification and analysis, can obtain a conclusion, and has accurate and reliable detection results.
Description
Technical Field
The invention belongs to the technical field of grain quality detection, and particularly relates to a method for distinguishing organic rice from non-organic rice by utilizing a metabonomics technology.
Background
Rice is one of three world food crops, and rice is the staple food in 1/3 people all over the world. Production and consumption of rice has long been the largest of food crops, with a substantial majority of rice growers and consumers in asia. The improvement of rice quality and the living standard and health of people in the areas are inseparable. The rice is the main staple food for most people in China and is also the main export product in China. Along with the continuous improvement of living standard of people, the requirement on the quality of rice is higher and higher, and the organic rice gradually enters the dining table of consumers.
The organic rice refers to rice products which come from an organic agricultural production system, are produced and processed according to international organic agricultural production requirements and corresponding standards, and are certified by an independent organic food certification authority. As the organic rice is not allowed to apply any chemically synthesized pesticide, chemical fertilizer, growth regulator and the like in the whole planting and growing process and is more easily influenced by factors such as diseases, insect pests, weeds and the like, the organic rice product has the characteristics of low yield, low polished rice rate, high cost and the like. At present, the consumption of organic rice in China increases at a speed of 30-50% per year, and according to prediction, the annual growth rate of the organic rice in China reaches 20-30% in the next 10 years. The rapid development of the organic rice industry is accompanied by a plurality of problems, and the normal development of the organic rice product market is greatly disturbed by the behavior of a plurality of manufacturers in order to fill up and falsely disturb. Therefore, the establishment of a method for identifying the organic rice has strong practical significance.
Metabonomics is an important branch in system biology, and is a science for qualitatively and quantitatively detecting endogenous and exogenous small molecule metabolites. Metabonomics can comprehensively research plant complex metabolic processes and products thereof, so that the metabonomics have attracted extensive attention in the field of plant research in recent years. The development of plant metabonomics provides possibility for analyzing the structure of a plant secondary metabolic network, limiting speed steps, analyzing the process of cell activities, searching the genetic relationship among plants and the like.
Metabolomics can be divided into non-targeted metabolomics and targeted metabolomics depending on the purpose of the study. The non-targeted metabonomics is unbiased metabonomics analysis and mainly aims at carrying out systematic and comprehensive analysis on endogenous metabolites of organisms; targeted metabolomics is a targeted assay, mainly directed towards the analysis of a specific class of metabolites. At present, due to the difference research of the non-targeted metabonomics analysis on secondary metabolites, the non-targeted metabonomics analysis has wide application in the aspects of plant active substance difference analysis, metabolic mechanism and related metabolic networks, especially the difference identification of plant varieties and production places.
The existing detection items for the organic rice are only limited to a plurality of common indexes, the quality of the organic rice cannot be comprehensively and effectively judged, and the manual judgment method is greatly influenced by various factors and has weak confidence.
Disclosure of Invention
The invention aims to solve the problem that the quality of rice cannot be comprehensively and effectively judged by adopting the conventional detection means, and provides a method for distinguishing organic rice from non-organic rice by utilizing a metabonomics technology.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for distinguishing organic rice from non-organic rice using metabonomic technology, said method comprising:
respectively pretreating an organic rice sample and a non-organic rice sample by adopting an organic solvent, separating and determining chemical components in the pretreated organic rice sample and the non-organic rice sample by using an ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high-resolution mass spectrometry method, preprocessing the ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high-resolution mass spectrometry original data of the obtained organic rice sample and the non-organic rice sample, finally distinguishing the organic rice from the non-organic rice by using a multivariate statistical analysis method orthogonal partial least square-discriminant analysis model, obtaining factors with large influence on the discrimination by using an S-curve diagram, and identifying the substances by using an open source online database massbank.
Compared with the prior art, the invention has the beneficial effects that:
(1) the metabonomics technology is combined with the ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high-resolution mass spectrometry technology to analyze secondary metabolites in the rice sample, and the confidence level is high. The non-targeted metabonomics technology can be used for researching the overall situation of metabolites, can better reflect the overall situation of a sample, and can be used for searching differential metabolites by applying statistical knowledge, thereby providing technical support for further research.
(2) The analysis method can effectively distinguish organic rice from non-organic rice, the result is displayed in the form of an OPLS-DA score map, and differential substances (potential biological markers) are identified by utilizing an open source online database. The classification condition can be visually judged, relevant verification and analysis are not needed, a conclusion can be obtained, and the detection result is accurate and reliable.
(3) The rice sample pretreatment method is simple and quick, and the method has low operation technical requirements on detection personnel after being established. In the whole pretreatment process, in order to ensure that the metabolite information of the rice sample is obtained as much as possible, the mixed solvent of methanol and water is used for extracting the reagent to dissolve the test sample, the test sample can be subjected to machine detection after being subjected to ultrasonic treatment and then is subjected to centrifugal filtration through an organic filter membrane, and the pretreatment step is simple and easy to operate. Experiments prove that an inappropriate pretreatment method cannot extract endogenous metabolites of a rice sample to the maximum extent, and a detection result which is not easy to distinguish is caused, so that the detection result is inaccurate.
(4) The operation flow is simplified: the traditional detection needs to be provided with a standard sample and draw a standard curve, and simultaneously needs to measure various chemical substances, so that the process is complicated, and time and labor are wasted. The method applies a non-targeted metabonomics method for determination, has simple pretreatment process, convenient operation and convenient operation on the computer, can carry out batch processing, saves time and labor and has high reliability.
(5) Aiming at an analysis method for distinguishing organic rice and non-organic rice by applying metabonomics to ultra high performance liquid chromatography-tandem quadrupole-time-of-flight high resolution mass spectrometry, the invention optimally screens a group of ultra high performance liquid chromatography conditions and quadrupole-time-of-flight high resolution mass spectrometry process conditions according to the characteristics of a rice sample, so that the analysis sample obtains the best separation effect and detection effect, and the organic rice and the non-organic rice can be effectively distinguished on an OPLS-DA score chart through the process conditions after experimental verification.
(6) On the basis of an OPLS-DA model, factors which have large influence on separation are screened out by using an S-curve graph, and the substances are identified by using a database. The identified substances can be used as target substances to lay a foundation for the future identification and analysis of the organic rice.
(7) The invention uses the quadrupole-time-of-flight high-resolution mass spectrum, has higher resolution, can obtain more accurate and more substances compared with other detection methods, and has obvious advantages in data analysis.
Drawings
FIG. 1 is a graph of OPLS-DA scatter scores for organic and non-organic rice samples;
FIG. 2 is an S-plot of the OPLS-DA model.
Detailed Description
The technical solutions of the present invention are further described below with reference to the drawings and the embodiments, but the present invention is not limited thereto, and modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
The first embodiment is as follows: the present embodiment describes a method for distinguishing organic rice from non-organic rice using metabonomics technology, which comprises:
respectively pretreating an organic rice sample and a non-organic rice sample by adopting an organic solvent, separating and determining chemical components in the pretreated organic rice sample and the non-organic rice sample by using an ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high resolution mass spectrometry method, then preprocessing the original data of the obtained organic rice sample and the non-organic rice sample by ultra-high performance liquid chromatography tandem quadrupole-time of flight high resolution mass spectrometry (UHPLC-Q-TOF MS), finally distinguishing the organic rice and the non-organic rice by applying an orthogonal partial least squares-discriminant analysis (OPLS-DA) model of a multivariate statistical analysis method, and the S-plot (S-plot) is used to derive factors (potential biomarkers) that have a greater impact on discrimination, these substances are identified by the open source online database, massbank (http:// www.massbank.jp /).
The second embodiment is as follows: in a specific embodiment, the method for distinguishing organic rice from non-organic rice by using metabonomics technology is described, wherein the varieties of the organic rice sample and the non-organic rice sample are one or more of rice flower fragrance, Longjing rice and Songjing rice.
The third concrete implementation mode: in a specific embodiment, the method for distinguishing organic rice from non-organic rice by using a metabonomics technology comprises the following specific pretreatment steps: crushing the organic rice sample and the non-organic rice sample, sieving the crushed organic rice sample and the non-organic rice sample by a sieve with the aperture of 1mm, mixing the sieved organic rice sample and the non-organic rice sample with an organic solvent, performing ultrasonic treatment, centrifuging, and filtering the mixture by an organic filter membrane to finish pretreatment, thereby obtaining a sample which can be detected on a computer.
The fourth concrete implementation mode: in the third embodiment, the method for distinguishing organic rice from non-organic rice by using metabonomics technology comprises the following steps of adding 1g of organic rice sample and 1g of non-organic rice sample to organic solvent: (3-10) mL, preferably 1 g: 5mL, which can ensure better extraction effect of the metabolites in the sample; the organic solvent is a methanol water solution, and the volume fraction is 60-85%, and the most preferable volume fraction is 70%.
The fifth concrete implementation mode: in the method for distinguishing organic rice from non-organic rice by using metabonomics technology, the ultrasound time is 10-40 min, preferably 30 min; the specific conditions of centrifugation are: centrifuging at 8000-12000 rpm for 15-30 min at 4 deg.C, preferably at 10000rpm for 20 min.
The sixth specific implementation mode: in the third embodiment, the organic filter membrane has a pore size of 0.20-0.25 μm, preferably 0.22 μm.
The seventh embodiment: in a specific embodiment, the method for distinguishing organic rice from non-organic rice by using a metabonomics technology comprises the following steps: bonding a silica gel column (C18 column) by using octadecyl; phase A: aqueous formic acid, phase B: formic acid acetonitrile solution, gradient elution flow: 0-1.5min, 15% B; 1.5-5.0min, 15-55% of B; 5.0-17.0min, 55-70% B; 17.0-20.0min, 70-90% B; 20.0-21.0min, 90-15% B.
The specific implementation mode is eight: seventh embodiment of the present invention is a method for distinguishing organic rice from non-organic rice using metabonomics technology, wherein the formic acid in the aqueous formic acid solution has a formic acid volume fraction of 0.1%; the volume fraction of formic acid in the formic acid acetonitrile solution is 0.1 percent; the flow rate is 0.3mL/min, and the column temperature is 36 ℃; the sample size was 5. mu.L.
The specific implementation method nine: in the method for distinguishing organic rice from non-organic rice by utilizing a metabonomics technology, an agent 6540UHD accurate-mass QTOF spectrometer is selected for the quadrupole-time-of-flight high-resolution mass spectrum, and the mass spectrum conditions are as follows: double ESI sources, positive ion mode, drying gas temperature 325 ℃, flow rate 9L/min; atomizer pressure 45 psi; capillary voltage 4000V; sampling cone voltage, 140V; extracting cone voltage, 65V; scanning range, m/z: 50-2000 parts; scanning mode: full Scan; the reference ion is m/z: 301.998139, and 1033.988109.
The detailed implementation mode is ten: in a method for distinguishing organic rice from non-organic rice by utilizing a metabonomics technology, the UHPLC-Q-TOF MS raw data of an obtained organic rice sample and a non-organic rice sample are subjected to lattice conversion by adopting MS Convert software, and then are subjected to pretreatment by XCMS software, wherein the pretreatment refers to extraction, peak alignment and denoising of chromatographic peaks in the raw data of a total ion current chromatogram to obtain retention time, peak height, peak area and mass-to-charge ratio data of each peak, then a distinguishing result is displayed in a form of an OPLS-DA score map by a multivariate analysis method, a substance (potential marker) with a large influence on the distinguishing result is screened out by an S curve map, and the potential marker is distinguished by utilizing an online database massbank.
Interpretation of terms: the multivariate statistical analysis method is a general term of a class of methods for processing multivariate statistical data based on multivariate statistical distribution, and is an important branch with rich theoretical results and numerous application methods in statistics. The commonly used multivariate statistical analysis method mainly comprises the following steps: multivariate regression analysis, cluster analysis, discriminant analysis, principal component analysis, factor analysis, correspondence analysis, canonical correlation analysis, and the like. The invention mainly adopts an orthogonal partial least square-discriminant analysis method (OPLS-DA).
Instruments and equipment:
agent 1290UHPLC system, Agilent;
acuity BEH C18column (2.1 id. times.150 mm, particle size 1.7 μm), Waters corporation, USA;
agent 6540UHD cure-mass QTOF spectrometer, Aglient corporation, Germany;
3K15 laboratory high speed bench refrigerated centrifuge, Sigma, Germany;
Milli-Q water purifier, Millipore, USA;
MX-S vortex apparatus, Darongxing laboratory instruments (Beijing) Inc.;
KQ-700DE model digital control ultrasonic instrument, Kunshan ultrasonic instruments Inc.;
materials and reagents:
organic rice (certified by China Green China organic food certification center) obtained from various rice production companies;
non-organic rice obtained from farmers in various regions;
ultrapure water (18.2M Ω. cm), obtained by Milli-Q water purification;
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 invention is as follows: the method comprises the steps of enabling different planting modes to cause the difference of endogenous metabolites of rice, directly influencing the quality of rice products through the difference, separating chemical substances in the rice by using UHPLC, detecting by using a mass spectrum technology to obtain liquid phase and mass spectrum data of the rice, analyzing the obtained data by using an OPLS-DA technology by using a metabonomic analysis technology, and visually distinguishing organic and non-organic rice samples.
The high performance liquid chromatography is a new liquid chromatography technology which adopts a small-particle filler chromatographic column (the particle size is less than 2 mu m) and an ultrahigh pressure system (the pressure is more than 105kPa), can obviously improve the resolution and the detection sensitivity of chromatographic peaks, greatly shortens the analysis period, and is suitable for the separation and the high-throughput research of trace complex mixtures. Meanwhile, the agent 6540UHDaccurate-mass QTOF spectrometer has the characteristics of ultrahigh separation degree, ultrahigh speed, ultrahigh sensitivity and the like, can more accurately detect chemical substances in rice, and has more reliable and more credible results.
The method specifically comprises the following steps:
respectively pretreating an organic rice sample and a non-organic rice sample by adopting an organic solvent, separating and determining chemical components of the pretreated sample by using an ultra-high performance liquid chromatography-tandem quadrupole-time of flight high-resolution mass spectrometry method, then carrying out lattice transformation and pretreatment on UHPLC-Q-TOF MS (ultra high performance liquid chromatography-time of flight mass spectrometry) original data of the obtained rice sample, and finally distinguishing the organic rice sample from the non-organic rice sample by using an orthogonal partial least squares-discriminant analysis (OPLS-DA) model of a multivariate statistical analysis method.
For rice samples in different producing areas, the planting environment is also influenced by the altitude, the longitude and latitude, the illumination, the temperature, the humidity and the like, so even if the rice samples are organic rice, the metabolite composition and the metabolite content in the rice samples in different producing areas are not completely the same, and therefore, certain difference can be presented among the organic rice samples. In certain embodiments of the invention, the rice sample is selected from one or more of floral rice, pine japonica, long japonica.
In the whole pretreatment process, in order to ensure that the metabolite information of the sample is obtained as much as possible, a mixed solvent of methanol and water is selected as an extraction reagent to dissolve the test sample, and the test sample can be tested on a machine after being subjected to ultrasonic treatment and then is subjected to centrifugal filtration through an organic filter membrane, so that the pretreatment step is simple and easy to operate. In a preferred embodiment of the invention, the volume fraction of methanol in the aqueous methanol solution is 70%. The invention also researches other extraction reagents, such as ethanol water solution, acetone solution and isopropanol solution, but the obtained effect is not ideal, and metabolites in the rice sample cannot be comprehensively extracted.
In certain preferred embodiments of the present invention, the sample pre-treatment process comprises: crushing a rice sample, sieving the crushed rice sample (with the aperture of 1 mm), mixing the crushed rice sample with an organic solvent, performing ultrasonic treatment, centrifuging the mixture, and filtering the mixture through an organic filter membrane to obtain a sample which can be detected on a machine. Wherein the ultrasonic time is 10-40 min, preferably 30 min; centrifugation conditions: centrifuging at 8000-12000 rpm for 15-30 min at 4 ℃, preferably at 10000rpm for 20 min; the aperture of the organic filter membrane is 0.20-0.25 μm, preferably 0.22 μm; in order to ensure that the metabolite extraction effect in the sample is better, the adding proportion of the sample and the organic solvent is 1 g: (3-10) mL, preferably 1 g: 5 mL. The adding proportion of the rice sample and the organic solvent is more critical, and the rice sample can be fully dissolved in the test sample, so that more sample metabolite information can be obtained, and the final detection result is more accurate.
Chromatographic separation and mass spectrometric data acquisition are performed simultaneously, and in order to separate and identify each component, appropriate chromatographic and mass spectrometric analysis conditions must be selected.
Aiming at the characteristics of the components of the rice sample, the invention inspects the influence of conditions such as mobile phase, gradient elution flow, column temperature, sample injection quantity and the like in the ultra-high performance liquid chromatography on the separation efficiency and the analysis speed, and finally optimizes and screens to obtain a group of ultra-high performance liquid chromatography conditions which enable the sample to obtain the best separation effect.
In a preferred embodiment of the invention, the ultra high performance liquid chromatography conditions are: bonding a silica gel column (C18 column) by using octadecyl; phase A: aqueous formic acid, phase B: acetonitrile formate, gradient elution procedure: 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 is 0.1 percent, and the volume fraction of formic acid in the formic acid acetonitrile solution is 0.1 percent; the flow rate is 0.3mL/min, and the column temperature is 36 ℃; the sample size was 5. mu.L.
The rice sample is a sample with complex components, and the gradient elution program obtained by screening can better and powerfully separate the components of complex substances in the rice sample, thereby laying a foundation for the subsequent identification of organic rice and non-organic rice. The invention also adopts other gradient elution procedures, and the improper gradient elution procedure is found, so that each component cannot be effectively separated, and further, organic rice and non-organic rice cannot be effectively distinguished.
Aiming at the component characteristics of a rice sample, in order to improve the atomization and ionization conditions of the compound and improve the sensitivity, the invention finally optimizes and screens the conditions of resolution, gas flow rate, spray voltage and the like to obtain a set of quadrupole-flight time high-resolution mass spectrum conditions which enable the detection effect to be accurate.
In the preferred embodiment of the invention, the quadrupole-time-of-flight high resolution mass spectrometer adopts an active 6540UHDaccurate-mass Q-TOF spectrometer, and the mass spectrum conditions are as follows: double ESI sources, positive ion mode, drying gas temperature 325 ℃, flow rate 9L/min; atomizer pressure 45 psi; capillary voltage 4000V; sampling cone voltage, 140V; extracting cone voltage, 65V; scanning range, m/z: 50-2000 parts; scanning mode: full Scan; the reference ion is m/z: 301.998139, and 1033.988109.
Through experimental verification, the organic rice sample and the non-organic rice sample can be effectively distinguished on the OPLS-DA score map through the technological conditions of the ultra-high performance liquid chromatography and the mass spectrum.
Qualitative and quantitative information can be determined for a number of endogenous compounds using metabolomics techniques. The information is shown as a plurality of signal peaks on the output spectrogram and is shown as chromatographic peaks with different retention times on the chromatographic mass spectrogram.
From the aspect of treatment effect and convenience, in the preferred embodiment of the invention, the UHPLC-Q-TOF MS original data of the obtained rice sample and the non-organic rice sample are subjected to lattice conversion by adopting MS Convert software, and then are subjected to pretreatment by XCMS software, wherein the pretreatment refers to the treatment of extraction, peak alignment, noise removal and the like of chromatographic peaks in the original data of a total ion current chromatogram, so that the retention time, peak height, peak area and mass-to-charge ratio data of each peak are obtained; and then displaying the distinguishing result in the form of an OPLS-DA score map by a multivariate statistical analysis method.
The MS Convert software is data conversion format software developed by Proteo Wizard, and the XCMS is general software developed by Scripps Center for processing LC-MS raw data.
The distinguishing method comprises the following steps: in the OPLS-DA score map, the regions to which the organic rice sample points and the non-organic rice sample points belong are compared, and if the samples of the organic rice sample points and the non-organic rice sample points belong to two obviously separated regions, the difference between the organic rice sample points and the non-organic rice sample points is obvious.
Example 1:
a method for distinguishing organic rice from non-organic rice by using metabonomics technology comprises the following steps:
(1) sample pretreatment
Pulverizing rice sample, sieving (1mm aperture), weighing 600mg sample in 5mL centrifuge tube, adding 3mL extraction solvent (70% methanol solution (v/v)), vortex mixing and dissolving, ultrasonic mixing for 30min, centrifuging at 4 deg.C and 10000rpm for 20min, then passing the supernatant through 0.22 μm organic filter membrane, and loading on machine to obtain 5 μ L sample.
(2) An agent 1290UHPLC system is used for connecting an agent 6540UHD acid-mass Q-TOFspecrometer in series to realize the separation and the determination of chemical components in the sample.
(1) Liquid chromatography parameters
A chromatographic column: acquisty BEH C18column (2.1 id. times.150 mm, particle size 1.7 μm) (Acquisty, Waters, Milford, MA, USA).
Liquid phase: phase a, 0.1% aqueous formic acid, phase B: 0.1% formic acid acetonitrile; flow rate: 0.3 mL/min; the column temperature was 36 ℃.
| 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) Parameters of mass spectrum
Positive ion mode
| Ionization mode | Dual ESI sources |
| Scanning mode | FullScan |
| Temperature of drying gas | 325℃ |
| Flow rate of flow | 9L/min |
| Atomizer pressure | 45psi |
| Capillary voltage | 4000V |
| Sampling cone voltage | 140V |
| Extracting a 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
Performing format conversion on UHPLC-Q-TOF MS original data of the obtained rice sample by adopting MS converter, performing pretreatment by using an XCMS software package, wherein the pretreatment refers to the treatment of extracting chromatographic peaks, aligning the peaks, removing noise and the like in the original data of total ion current chromatography, obtaining the retention time, peak height, noise removal and the like of each peak, obtaining the retention time, peak height, peak area and mass-to-charge ratio data of each peak, and then displaying the identification result in the form of an OPLS-DA score map by using a multivariate statistical analysis method of SIMCA-P software.
OPLS-DA is a supervised inspection method, and is a regression modeling method of multiple dependent variables to multiple independent variables. The OPLS-DA is an extension of PLS-DA, namely, an orthogonal signal correction technology is used firstly, X matrix information is decomposed into two types of information which are related to Y and irrelevant, then information which is irrelevant to classification is filtered, relevant information is mainly concentrated in a first prediction component, and compared with a PLS-DA model, the OPLS-DA can better distinguish differences among sample groups, the effectiveness and the analysis capability of the model are improved, an analysis result is simple and easy to explain, and the judgment effect is more obvious in visualization.
(4) And (3) the result display of the application object:
the rice samples are 20 organic rice samples, 20 non-organic rice samples are operated according to the procedures of (1) to (3), and OPLS-DA scatter plot scores of the organic rice and the non-organic rice are obtained.
The degree of aggregation and dispersion of the individual samples can be seen in the OPLS-DA scattergrams, each dot representing one sample, where O1-O20 represent 20 organic rice samples (circles) and C1-C20 represent 20 non-organic rice samples (squares). As shown in fig. 1, the organic rice samples were distributed in the left half of the scatter diagram (X-axis negative half axis), and the non-organic rice was mainly concentrated in the right half of the scatter diagram (X-axis positive half axis). This indicates that there is some difference in the composition and content of secondary metabolites between organic and non-organic rice, and also that the OPLS-DA model can distinguish the organic and non-organic rice samples well.
According to the high reliability (correlation) and high magnitude (covariance) of the data, some factors (points farther from the subject) that have a large influence on the classification of the OPLS-DA model can be manually selected, and we refer to them as potential biomarkers (potential biomarkers). As can be seen from FIG. 2, a total of 30 potential biomarkers were screened, 15 over-expression factors (top right of S-plot) and 15 under-expression factors (bottom left of S-plot).
Up to now, no authoritative criterion or reference standard may be applied to the identification of non-targeted metabolomic compounds. In this example, we performed substance identification using the online database Massbank (http:// www.massbank.jp /), the 30 most statistically significant biomarkers listed above, and the results are shown in Table 1. Of these 8 compounds we give the names of the compounds with the higher probability, of which 12 compounds we annotate the molecular formula. Eight substances, Histidinol (histidine), malvidin (Malvin), Pinoresinol (Pinoresinol), labyrine (Lagochiline), 4-Methylumbelliferyl glucoside (4-Methylumbelliferyl glucoside), Coumarin 106(Coumarin 106), N alpha-Benzoyl-L-arginine (N alpha-Benzoyl-L-arginine) and hydrogenated cinchonine (hydroxycinine), can be taken as marker substances for identifying organic rice and non-organic rice.
TABLE 1 identification of potential biomarkers
In the context of table 1, the following,
1. determined from Metlin and MassBank databases
2. Based on peak ion
3. And (3) identification level: 1. identified compounds, 2. putative annotated compounds, 3. putative characterized compounds, 4. unknown compounds
NM-no match
Claims (7)
1. A method for distinguishing organic rice from non-organic rice by utilizing a metabonomics technology is characterized in that: the method comprises the following steps:
respectively pretreating an organic rice sample and a non-organic rice sample by adopting an organic solvent, separating and determining chemical components in the pretreated organic rice sample and the non-organic rice sample by using an ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high-resolution mass spectrometry method, preprocessing the ultra-high performance liquid chromatography tandem quadrupole-time-of-flight high-resolution mass spectrometry original data of the obtained organic rice sample and the non-organic rice sample, finally distinguishing the organic rice from the non-organic rice by using a multivariate statistical analysis method orthogonal partial least square-discriminant analysis model, obtaining factors with large influence on the discrimination by using an S curve diagram, and identifying the substances by using an open source online database Massbank;
the pretreatment specifically comprises the following steps: crushing an organic rice sample and a non-organic rice sample, sieving the crushed organic rice sample and the non-organic rice sample by a sieve with the aperture of 1mm, mixing the sieved organic rice sample and the non-organic rice sample with an organic solvent, performing ultrasonic treatment, centrifuging, and filtering the mixture by an organic filter membrane to finish pretreatment so as to obtain a sample which can be detected on a computer; the adding proportion of the organic rice sample and the non-organic rice sample to the organic solvent is 1 g: 3-10 mL; the organic solvent is methanol water solution, and the volume fraction is 60-85%;
the conditions of the ultra-high performance liquid chromatography are as follows: bonding a silica gel column by using octadecyl; phase A: aqueous formic acid, phase B: formic acid acetonitrile solution, gradient elution flow: 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. The method of claim 1 for differentiating between organic rice and non-organic rice using metabolomics technology, wherein the method comprises: the organic rice sample and the non-organic rice sample are one or more of rice flower fragrance, Longjing rice and Songjing rice.
3. The method of claim 1 for differentiating between organic rice and non-organic rice using metabolomics technology, wherein the method comprises: the ultrasonic time is 10-40 min; the specific conditions of centrifugation are: centrifuging at 8000-12000 rpm at 4 deg.C for 15-30 min.
4. The method of claim 1 for differentiating between organic rice and non-organic rice using metabolomics technology, wherein the method comprises: the aperture of the organic filter membrane is 0.20-0.25 μm.
5. The method of claim 1 for differentiating between organic rice and non-organic rice using metabolomics technology, wherein the method comprises: the volume fraction of formic acid in the formic acid aqueous solution is 0.1 percent; the volume fraction of formic acid in the formic acid acetonitrile solution is 0.1 percent; the flow rate is 0.3mL/min, and the column temperature is 36 ℃; the sample size was 5. mu.L.
6. The method of claim 1 for differentiating between organic rice and non-organic rice using metabolomics technology, wherein the method comprises: the quadrupole-time-of-flight high-resolution mass spectrum selects and uses an agent 6540UHD (ultra high intensity) -massQTOF spectrometer, and the mass spectrum conditions are as follows: double ESI sources, positive ion mode, drying gas temperature 325 ℃, flow rate 9L/min; atomizer pressure 45 psi; capillary voltage 4000V; sampling cone voltage, 140V; extracting cone voltage, 65V; scanning range, m/z: 50-2000 parts; scanning mode: full Scan; the reference ion is m/z: 301.998139, and 1033.988109.
7. The method of claim 1 for differentiating between organic rice and non-organic rice using metabolomics technology, wherein the method comprises: the method comprises the steps of performing lattice conversion on UHPLC-Q-TOF MS raw data of an obtained organic rice sample and a non-organic rice sample by adopting MSConvert software, performing pretreatment by using XCMS software, wherein the pretreatment refers to extraction, peak alignment and noise removal treatment on chromatographic peaks in the raw data of a total ion current chromatogram to obtain retention time, peak height, peak area and mass-to-charge ratio data of each peak, displaying a form distinguishing result of an OPLS-DA score map through a multivariate analysis method, screening out substances which have large influence on the distinguishing result through an S-curve map, and identifying potential markers by using an online database Massbank.
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