Sorghum variety identification method based on metabonomics

Abstract

The invention belongs to the technical field of wine brewing and sorghum variety identification and analysis, and relates to a metabonomics-based sorghum variety identification method, which comprises the following steps: (1) acquiring metabolite information in a sorghum sample to be identified; (2) comparing the obtained metabolite information with marker metabolite information corresponding to the sorghum variety, and identifying the sorghum variety category of the sorghum sample to be identified according to a comparison result; wherein in the step (1), the acquisition of the metabolite information comprises the following steps: by taking a methanol aqueous solution as an extraction solvent, extracting the metabolite in the to-be-identified sorghum sample subjected to pretreatment in a vibration extraction manner, so as to obtain an extracting solution; and acquiring metabolite information in the extracting solution through an ultra-high performance liquid chromatography-quadrupole electrostatic field orbitrap high-resolution mass spectrometry technology. Based on the method provided by the invention, identification of sorghum varieties such as glutinous sorghum varieties and red tassel sorghum in Guizhou province can be realized.

Description

Technical Field

[0001] The invention belongs to the technical field of wine making and sorghum variety identification and analysis, and relates to a sorghum variety identification method based on metabolomics. Background Art

[0002] Sorghum, as the raw material for brewing liquor, directly affects the quality of liquor. The main identification methods for different sorghum varieties include agronomic trait observation, physical and chemical index determination and analysis, and DNA sequencing analysis. The above methods are based on a certain characteristic of sorghum to distinguish it from other varieties of sorghum. Its identification ability is mainly determined by a certain characteristic, and there are problems such as limited application scenarios and poor applicability in the identification of closely related sorghum varieties. There are also methods to identify sorghum varieties based on the determination of the content of one or several sorghum components, but China's brewing sorghum has many varieties and a wide range of sources. Identification based only on the results of one or several components may be insufficient.

[0003] At present, there are also many studies on the characterization of specific metabolic compounds such as phenolic acids, flavonoids, and tannins in sorghum grains of different germplasms to reveal the unique nutritional properties and potential health benefits of sorghum. However, there are few studies on the systematic evaluation and comparison of the metabolic compounds of different sorghum varieties to identify sorghum varieties on this basis. Summary of the invention

[0004] The purpose of the present invention is to provide a sorghum variety identification method based on metabolomics. In view of the problems existing in the existing methods for identifying sorghum varieties, the present invention analyzes the component differences of different sorghum varieties based on a non-targeted metabolomics method of high-throughput UHPLC-MS, and proposes a method for identifying sorghum varieties. Through the identification method provided by the present invention, multiple sorghum varieties such as glutinous sorghum varieties and red tassel sorghum in Guizhou Province can be identified efficiently and accurately.

[0005] In one aspect, the present invention provides a method for identifying sorghum varieties based on metabolomics, comprising the following steps:

[0006] (1) Obtaining metabolite information in the sorghum sample to be identified;

[0007] (2) comparing the obtained metabolite information with the marker metabolite information corresponding to the sorghum variety, and identifying the sorghum variety type of the sorghum sample to be identified based on the comparison result;

[0008] Among them, in the step (1), the acquisition of the metabolite information includes: using methanol aqueous solution as the extraction solvent, extracting the metabolites in the sorghum sample to be identified after pre-treatment by shaking extraction to obtain an extract; acquiring the metabolite information in the extract by ultra-high performance liquid chromatography-quadrupole electrostatic field orbital trap high-resolution mass spectrometry technology; the identification types of the sorghum varieties include: identification of glutinous sorghum varieties in Guizhou Province and sorghum varieties outside the province, and identification of red tassel sorghum and other sorghums.

[0009] In some embodiments, the added amount of the extraction solvent is: 1 ml to 30 ml per 1 to 3 g of sorghum powder.

[0010] In some embodiments, the volume ratio of methanol to water in the extraction solvent is 31% to 69%: 69% to 31%; preferably, the volume ratio of methanol to water in the extraction solvent is 40% to 60%: 60% to 40%; preferably, the volume ratio of methanol to water in the extraction solvent is 45% to 55%: 55% to 45%.

[0011] In some embodiments, in step (1), the pre-treatment includes: taking the sorghum sample to be identified, grinding it into powder, and sieving it to obtain powder.

[0012] In some embodiments, the sieve has a pore size of 80-120 mesh.

[0013] In some embodiments, in step (1), the oscillation extraction includes: adding the extraction solvent to the sorghum sample to be identified, performing vortex oscillation extraction, and performing a centrifugation; taking the supernatant after the first centrifugation, performing a second centrifugation, filtering a membrane, and obtaining the extract.

[0014] In some embodiments, the vortex oscillation conditions are: oscillation speed 2000-3000 rpm, extraction time 21 to 39 min; preferably, the oscillation speed is 2200-2600 rpm.

[0015] In some embodiments, the centrifugation is performed at 5000-7000 r / min for 2-5 min.

[0016] In some embodiments, the secondary centrifugation is performed at 10,000 to 14,000 r / min for 5 to 15 min.

[0017] In some embodiments, the liquid chromatography conditions used in the ultra-high performance liquid chromatography-quadrupole electrostatic field orbital trap high-resolution mass spectrometry technology are: Hypersil Gold aQ liquid chromatography column (2.1mm×150mm, 1.9μm); injection volume 3-7μL; mobile phase: phase A is ultrapure water, phase B is methanol; flow rate: 200-400μL / min; gradient elution; Preferably, in the ultra-high performance liquid chromatography-quadrupole electrostatic field orbital trap high-resolution mass spectrometry technology, the liquid chromatography conditions used are: liquid chromatography parameters: Hypersil Gold aQ liquid chromatography column (2.1mm×150mm, 1.9μm); injection volume 5μL; mobile phase: phase A is ultrapure water (positive mode contains 1‰ formic acid, negative mode contains 1‰ ammonia water), phase B is methanol; flow rate: 300μL / min; gradient elution; the entire analysis process takes 35min.

[0018] In some embodiments, the gradient elution procedure includes: elution time 0 to 2 min, the mass fraction of phase A is 85-97%, and the mass fraction of phase B is 3-15%; elution time 22 to 32 min, the mass fraction of phase A is 3-15%, and the mass fraction of phase B is 85-97%; elution time 32.1 to 35 min, the mass fraction of phase A is 85-97%, and the mass fraction of phase B is 3-15%.

[0019] In some embodiments, the mass spectrometry conditions used in the ultra-high performance liquid chromatography-quadrupole electrostatic field orbital trap high-resolution mass spectrometry technology include: in positive ion mode, scanning range: 70-1000m / z, capillary voltage of 3-4kV, sheath gas of 30-40arb, auxiliary gas of 10-20arb, purge gas of 0arb, nebulization temperature of 250-350°C, resolution of 70000, scanning mode of first-level full scan + automatic triggering of second-level, NCE energy of 20kV, 40kV, 60kV, ion transfer tube temperature is 300-400°C; preferably, in the ultra-high performance liquid chromatography-quadrupole electrostatic field orbital trap high-resolution mass spectrometry technology, the mass spectrometry conditions used include: under positive ion mode conditions, scanning range: 70-1000m / z, capillary voltage is 3.6kV, sheath gas is 35arb, auxiliary gas is 15arb, purge gas is 0arb, nebulization temperature is 300°C, resolution is 70000, scanning mode is first-level full scan + automatic triggering of second level, NCE energy is 20kV, 40kV, 60kV, and ion transfer tube temperature is 350°C.

[0020] In some embodiments, the mass spectrometry conditions used include: in negative ion mode, scanning range: 70-1000 m / z, capillary voltage of -2.0-3.0 kV, sheath gas of 35-45 arb, auxiliary gas of 5-15 arb, purge gas of 0 arb, nebulization temperature of 280-320°C, resolution of 70000, scanning mode of full scan at level one + automatic triggering at level two, NCE energy of 20 kV, 40 kV, 60 kV, ion transmission The transfer tube temperature was 300-350°C; the mass spectrometry conditions used included: in negative ion mode, scanning range: 70-1000 m / z, capillary voltage of -2.5 kV, sheath gas of 40 arb, auxiliary gas of 10 arb, purge gas of 0 arb, nebulization temperature of 300°C, resolution of 70000, scanning mode of first level full scan + automatic triggering of second level, NCE energy of 20 kV, 40 kV, 60 kV, and ion transfer tube temperature of 320°C.

[0021] In some embodiments, the method for obtaining marker metabolite information comprises the following steps:

[0022] 1) Extraction: Using methanol aqueous solution as the extraction solvent, the metabolites in the pre-treated sorghum sample were extracted by shaking extraction to obtain an extract;

[0023] 2) Obtaining metabolite information: performing non-targeted metabolomics analysis on the extract by ultra-high performance liquid chromatography-quadrupole electrostatic field orbital trap high-resolution mass spectrometry to obtain metabolite information;

[0024] 3) Screening marker metabolites: analyzing and screening the metabolite information obtained in step 2) to obtain marker metabolites corresponding to the sorghum variety type of the sorghum sample;

[0025] Among them, the screening of the marker metabolites includes: screening the sorghum variety type to which the sorghum sample belongs and other sorghum variety types based on VIP≥1, P<0.05, up-regulated metabolite Fold Change≥2 and down-regulated metabolite FoldChange≤0.5 as standards, so as to obtain the marker metabolite information that distinguishes the sorghum variety type to which the sorghum sample belongs from other sorghum variety types.

[0026] In some embodiments, the analysis of the metabolite information obtained in step 2) includes: performing peak alignment, normalization, peak extraction, deconvolution, compound identification and difference analysis on the data collected in step 2).

[0027] In some embodiments, the difference analysis includes: performing difference analysis by principal component analysis and orthogonal partial least squares discriminant analysis.

[0028] In some embodiments, the method for obtaining marker metabolite information further comprises: performing a hierarchical clustering analysis based on the marker metabolites screened in step 3).

[0029] In some embodiments, the added amount of the extraction solvent is: 1 ml ~ 30 ml / 1 ~ 3 g sorghum powder; in the extraction solvent, the volume ratio of methanol to water is 31% ~ 69%: 69% ~ 31%; preferably, in the extraction solvent, the volume ratio of methanol to water is 40% ~ 60%: 60% ~ 40%; preferably, in the extraction solvent, the volume ratio of methanol to water is 45% ~ 55%: 55% ~ 45%.

[0030] In some embodiments, in step 1), the pretreatment comprises: taking the sorghum sample, grinding it into powder, and sieving it to obtain sorghum sample powder; preferably, the aperture of the sieve is 80-120 meshes.

[0031] In some embodiments, in step 1), the shaking extraction comprises: adding the extraction solvent to the sorghum sample, performing vortex shaking extraction, centrifuging once, centrifuging twice, filtering through a membrane, and obtaining the extract.

[0032] In some embodiments, the vortex oscillation conditions are: oscillation speed 2000-300 rpm, extraction time 21-39 min; preferably, the vortex oscillation conditions are: oscillation speed 2200-2600 rpm.

[0033] In some embodiments, the centrifugation is performed at 5000-7000 r / min for 2-5 min.

[0034] In some embodiments, the secondary centrifugation is performed at 10,000-14,000 r / min for 5-15 min.

[0035] In some embodiments, the liquid chromatography conditions include: Hypersil Gold aQ liquid chromatography column (2.1 mm×150 mm, 1.9 μm); injection volume 3-7 μL; mobile phase: phase A is ultrapure water, phase B is methanol; flow rate: 200-400 μL / min; and gradient elution.

[0036] In some embodiments, the gradient elution procedure includes: elution time 0 to 2 min, the mass fraction of phase A is 85-97%, and the mass fraction of phase B is 3-15%; elution time 22 to 32 min, the mass fraction of phase A is 3-15%, and the mass fraction of phase B is 85-97%; elution time 32.1 to 35 min, the mass fraction of phase A is 85-97%, and the mass fraction of phase B is 3-15%.

[0037] In some embodiments, the mass spectrometry conditions include: under positive ion mode conditions, scanning range: 70-1000 m / z, capillary voltage of 3-4 kV, sheath gas of 30-40 arb, auxiliary gas of 10-20 arb, sweep gas of 0 arb, nebulization temperature of 250-350°C, resolution of 70000, scanning mode of first level full scan + automatic triggering of second level, NCE energy of 20 kV, 40 kV, 60 kV, and ion transfer tube temperature of 300-400°C.

[0038] In some embodiments, the mass spectrometry conditions include: in negative ion mode, scanning range: 70-1000 m / z, capillary voltage of -2.0--3.0 kV, sheath gas of 35-45 arb, auxiliary gas of 5-15 arb, sweep gas of 0 arb, nebulization temperature of 280-320°C, resolution of 70000, scanning mode of first level full scan + automatic triggering of second level, NCE energy of 20 kV, 40 kV, 60 kV, and ion transfer tube temperature of 300-350°C.

[0039] On the other hand, the present invention also provides an application of the method in identifying sorghum varieties.

[0040] In summary, the present application includes at least one of the following beneficial technical effects:

[0041] Based on the problems existing in the existing methods for identifying sorghum varieties, the present invention analyzes the composition differences of different sorghum varieties based on a non-targeted metabolomics method based on high-throughput UHPLC-MS, and provides a method that can efficiently and accurately identify multiple sorghum varieties such as glutinous sorghum varieties and red tassel sorghum in Guizhou Province. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] FIG. 1 is a total ion current diagram of multiple injections of the QC sample in Example 1 of the present invention; wherein FIG. 1(A) is a total ion current diagram of multiple injections of the QC sample in positive ion mode, and FIG. 1(B) is a total ion current diagram of multiple injections of the QC sample in negative ion mode;

[0043] Figure 2 is the quality control diagram in Example 1 of the present invention; wherein, Figure 2 (A) is the quality control diagram in positive ion mode. Figure 2 (B) is the quality control diagram in negative ion mode;

[0044] Figure 3 This is a PCA analysis diagram of glutinous sorghum in Guizhou Province and sorghum outside the province in Example 1 of the present invention; wherein, Figure 3 (A) is the PCA analysis diagram in positive ion mode. Figure 3 (B) is the PCA analysis diagram in negative ion mode;

[0045] Figure 4 This is the OPLS-DA analysis diagram of glutinous sorghum in Guizhou Province and sorghum outside the province in Example 1 of the present invention; wherein, Figure 4 (A) is the OPLS-DA analysis diagram in positive ion mode. Figure 4 (B) is the OPLS-DA analysis diagram in negative ion mode;

[0046] Figure 5 is the OPLS-DA permutation test of glutinous sorghum in Guizhou Province and sorghum outside the province in Example 1 of the present invention (n=200); wherein, Figure 5 (A) is the OPLS-DA permutation test in positive ion mode (n=200), Figure 5 (B) OPLS-DA permutation test in negative ion mode (n=200);

[0047] Figure 6 It is the volcano map of glutinous sorghum in Guizhou Province and sorghum outside the province in Example 1 of the present invention; wherein, Figure 6 (A) is the volcano map in positive ion mode. Figure 6 (B) is the volcano plot in negative ion mode;

[0048] Figure 7 The cluster analysis diagram of 96 different variable contents of waxy sorghum in Guizhou Province relative to sorghum outside the province in Example 1 of the present invention;

[0049] Figure 8 This is a PCA analysis diagram of red tassel sorghum and other sorghums in the province in Example 2 of the present invention; wherein, Figure 8 (A) is the PCA analysis diagram in positive ion mode. Figure 8 (B) is the PCA analysis diagram in negative ion mode;

[0050] Fig. 9 This is an OPLS-DA analysis diagram of red tassel sorghum and other sorghums in the province in Example 2 of the present invention; wherein, Fig. 9 (A) is the OPLS-DA analysis diagram in positive ion mode. Fig. 9 (B) is the OPLS-DA analysis diagram in negative ion mode;

[0051] FIG10 is an OPLS-DA permutation test (n=200) of red tassel sorghum and other sorghums in the province in Example 2 of the present invention; wherein FIG10(A) is an OPLS-DA permutation test in positive ion mode (n=200), and FIG10(B) is an OPLS-DA permutation test in negative ion mode (n=200);

[0052] Fig.11 It is a volcano map of red tassel sorghum and other sorghums in the province in Example 2 of the present invention; wherein, Fig.11 (A) is the volcano map in positive ion mode. Fig.11 (B) is the volcano plot in negative ion mode;

[0053] Fig.12 This is a cluster analysis diagram of 96 different variable contents between red tassel sorghum in Example 2 of the present invention and other sorghums in the province. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is further described below by specific embodiments, which do not limit the protection scope of the present invention. Some non-essential modifications and adjustments made by others based on the concept of the present invention still fall within the protection scope of the present invention.

[0055] The materials and reagents used in the examples of the present invention are as follows:

[0056] Methanol (LC-MS grade) and acetonitrile (LC-MS grade) were purchased from Merck, Germany; anhydrous ethanol (HPLC grade) was purchased from Tiandi Company, USA; acetone (HPLC grade) was purchased from Sinopharm Chemical Reagent Co., Ltd.; 0.22 μm PTFE microporous filter membrane was purchased from Agilent Company, USA; 1 ml disposable syringe was purchased from Jiangxi Hongda Medical Device Group Co., Ltd.

[0057] The information of the instruments and equipment used in the present invention is as follows:

[0058] XP205 analytical balance was purchased from METTLER TOLEDO; IQ7003 water purifier was purchased from MILLIPORE, USA; MIKRO 220R centrifuge was purchased from Hettich, Germany; CT 293Cyclotec TM The pulverizer was purchased from FOSS, Denmark; the 1290 ultra-high performance liquid chromatograph was purchased from Agilent, USA; and the Q-Exactive quadrupole electrostatic field orbital trap high-resolution mass spectrometer was purchased from Thermo, USA.

[0059] Example 1 A method for distinguishing sorghum varieties from those from other provinces based on metabolomics

[0060] 1. Sample preparation

[0061] The sorghum sample information used in this embodiment is shown in Table 1. The categories of sorghum samples include two categories: glutinous sorghum varieties in Guizhou Province and sorghum varieties outside the province. Among them, the variety names of glutinous sorghum varieties in Guizhou Province include: Hongyingzi, Hongzhenzhu, Tainuo No. 9, Hongmaonuo No. 6, Maotaihong No. 2, Liangmaonuo No. 2, Qiangao No. 8, Hongguoliang, and Hongguannuo No. 8; the variety names of sorghum varieties outside the province include: Liaoza 69, Fengza No. 4, Fengza No. 13, Feng Za 25, Jiza 111, Jiza 111, Jiza 166, Jiza 180, Jiza 224, Jiza 229, Jiniao 2, Jiniao 4, Jiniao 12, Jinuoliang 1, Jiliang 1, Jiliang 3, Jiliang 4, Jiliang 5, Jibailiang 1, Jibailiang 5, Jitianza 5, Jitianza 6, Jiniao 5, Chuannuoliang 1, Chuannuoliang 2, Chuannuoliang 15, Jinnuoliang 1, Jinxia 2842. The number of sorghum samples of glutinous sorghum varieties in Guizhou Province is 51, and the number of sorghum samples of sorghum varieties outside the province is 78.

[0062] All sorghum samples were collected separately, ground into powder, passed through a 100-mesh sieve, put into a ziplock bag, sealed and stored in a refrigerator at -18°C for later use and taken out when needed.

[0063] Table 1 Sample information table

[0064]

[0065] 2. Metabolite Extraction

[0066] Accurately weigh 2g of the sorghum powder prepared in step 1, add 20mL of extract (50% methanol aqueous solution) to a 50mL glass test tube, vortex and shake for 30min at a speed of 2400rpm, and centrifuge at 6000r / min for 3min; take 2mL of the supernatant after centrifugation and put it into a 2mL centrifuge tube, centrifuge it at 12000r / min for 10min, pass the sample after the second centrifugation through a 0.22μm PTFE filter membrane, and place it in a glass vial to obtain the sample to be tested. All sorghum samples in step 1 are treated by the above method to obtain the sample to be tested.

[0067] 3. Computer test

[0068] The test samples prepared in step 2 were respectively tested and analyzed by ultra high performance liquid chromatography-quadrupole electrostatic field orbital trap high resolution mass spectrometry (UHPLC-MS) technology; wherein, the supernatants of all sorghum samples obtained by step 2 were mixed in equal parts to obtain quality control (QC) samples, and during the data acquisition process, one QC sample was inserted into every 10 sorghum samples to test the stability and repeatability of the instrument status and data.

[0069] The instrument conditions used are as follows:

[0070] Liquid chromatography parameters: Hypersil Gold aQ liquid chromatography column (2.1mm×150mm, 1.9μm); injection volume 5μL; mobile phase: phase A ultrapure water (positive mode contains 1‰ formic acid, negative mode contains 1‰ ammonia water), phase B is methanol; flow rate: 300μL / min; gradient elution: elution program see Table 2; the entire analysis process is 35min.

[0071] Table 2 Gradient elution program

[0072]

[0073] Mass spectrometry parameters:

[0074] Positive mode: scan range: 70-1000 m / z, capillary voltage is 3.6 kV, sheath gas is 35 arb, auxiliary gas is 15 arb, purge gas is 0 arb, nebulization temperature is 300 °C, resolution is 70000, scan mode is first level full scan + automatic trigger second level, NCE energy is 20 kV, 40 kV, 60 kV, ion transfer tube temperature is 350 °C;

[0075] Negative mode: scanning range: 70-1000 m / z, capillary voltage is -2.5 kV, sheath gas is 40 arb, auxiliary gas is 10 arb, purge gas is 0 arb, nebulization temperature is 300 °C, resolution is 70000, scanning mode is first level full scan + automatic trigger second level, NCE energy is 20 kV, 40 kV, 60 kV, ion transfer tube temperature is 320 °C.

[0076] 4. Data Analysis

[0077] Data preprocessing: First, the QC samples were collected, and the collected data were processed using Compound Discoverer 3.3 software, and the molecular weights identified by the software were imported into the inclusion list in the instrument method. After collecting the data of all sorghum samples, the data were subjected to peak alignment, normalization, peak extraction, deconvolution, and subsequent compound identification and statistical analysis using Compound Discoverer 3.3 software.

[0078] All samples were compared using 50% methanol + 50% water as blank solvent, and the response values ​​of all samples were the response values ​​after removing the blank sample. The values ​​of compound ions that were not detected in some samples were set to zero by default. Metabolites in sorghum samples were identified by searching the internal MS2 database Natural Products Atlas, and compounds with matching scores below 60 were removed by built-in software.

[0079] Difference analysis: Since the amount of data after UPLC-MS / MS analysis is large and complicated, it is a huge challenge to classify the obtained sample data. In this case, the use of chemometric analysis methods such as principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), hierarchical cluster analysis (HCA) to analyze the above data can reduce the dimension of the data and improve the interpretability of the data. The peak area data of metabolites with matching scores greater than 60 were exported from Compound Discover3.3 to Excel. After preprocessing, the peak area matrices of different and quality control (QC) samples were poured into SIMCA14.1 software for principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to understand the overall difference distribution of different types of sorghum, and the differences of different sorghums were analyzed through PCA score graphs and OPLS-DA distribution.

[0080] Screening of signature metabolites: Data matrices consisting of retention time (Rt), main charge ratio (m / z) values, and peak intensity were obtained in positive and negative ion modes, respectively. The fold change (FC) between groups was calculated, and t-tests were performed on the preprocessed data using SPSS19.0. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed using Simca software, and the importance projection (VIP) values ​​of predictors were calculated using Simca software. The model validity was evaluated by permutation test (n=200). VIP≥1, P<0.05, up-regulated metabolite Fold Change≥2, and down-regulated metabolite FoldChange≤0.5 were used as the screening thresholds for differential metabolites, and volcano plots and heat maps were drawn using R language to visualize differential metabolites.

[0081] The results obtained through the above processing and analysis methods are as follows:

[0082] (1) Compound identification

[0083] Based on the metabolite information of 129 sorghum samples of 38 sorghum varieties from different provinces in China collected by UPLC-MS / MS in step 3 above, 441 and 240 metabolites were identified in positive and negative ion modes, respectively, mainly including flavonoids, glycosides, phenolic acids, phenylpropanoids, etc. More than 98% of the metabolites were shared by all sorghum samples, which preliminarily showed that the differences in metabolites of different sorghum samples were mainly reflected in the content rather than the type of metabolites.

[0084] (2) Instrument stability test

[0085] The total ion chromatograms (TIC) of the QC samples in the positive and negative ion detection modes were superimposed and compared (Figure 1 (A), Figure 1 (B)). The results showed that the overlap of the chromatographic peaks was large and the TLC peak shape was complete, which demonstrated the stability and reliability of the data collected by the detection system in the positive and negative ion modes. The quality control results showed that the standard deviation of the QC samples was within ±2STD ( Figure 2 ), and principal component analysis (PCA) was performed on all sorghum samples ( Figure 3 ), the QC samples showed obvious aggregation and the instrument had good stability.

[0086] (3) PCA results

[0087] The results of drawing using PCA are as follows Figure 3 As shown, through R 2 X and Q 2 The value evaluates the quality of the model, R 2 X represents the explanation rate of the model in the X-axis direction, Q 2 Represents the cumulative prediction rate of the model. As can be seen from the figure, sorghum from different regions is well distinguished. Among them, blue represents glutinous sorghum in Guizhou Province, and green represents sorghum outside Guizhou Province. The two are distributed on both sides of the X-axis, indicating that the two are clearly separated on PC1. In the positive mode, the contribution rate of the first two main components is 34.8% (PC1: 19.8%, PC2: 15%, R 2 X: 0.831, Q 2 :0.568), the first two main components contribute 40.1% in the negative mode (PC1: 24.4%, PC2: 15.7%, R 2 X: 0.845, Q 2 :0.608), the model has certain explanatory and predictive capabilities, indicating that there are certain differences in the composition of waxy sorghum in Guizhou Province and sorghum outside Guizhou Province. The PCA model can reduce the computational burden, but it is not the most appropriate method for classifying sorghum samples. In addition, unlike the model analysis of the determined indicators, the visual separation generated by the PCA algorithm cannot explain the exact differences between the samples.

[0088] (4) Orthogonal partial least squares discriminant analysis (OPLS-DA) results

[0089] Orthogonal partial least squares discriminant analysis (OPLS-DA) is a supervised discriminant analysis statistical method from multiple dependent variables to multiple independent variables. The characteristic of this method is that it can remove data changes in the independent variable X that are irrelevant to the classification variable Y, so that the classification information is mainly concentrated on one principal component, and effectively filter out noise irrelevant to the classification information. Compared with PCA, OPLS-DA can better obtain inter-group difference information. The supervised OPLS-DA model is further used to construct a differentiated prediction model to observe the inter-group differences ( Figure 4 ), in positive mode R 2 X, R 2 Y and Q 2 The values ​​are 0.265, 0.981 and 0.977 respectively. In the negative mode, R 2 X, R 2 Y and Q 2 The values ​​are 0.496, 0.984 and 0.981 respectively, indicating that the model has excellent explanatory and predictive capabilities. In order to verify the model, cross validation and random permutation tests (200 times) were performed on the corresponding OPLS-DA model. Figure 5 ), the rightmost R 2 and Q 2 All greater than 0.9, R 2 The intercepts are all less than 0.4, Q 2 The intercepts are all less than 0.05, indicating that the model is not overfitting and the model verification is effective. It is believed that the results can be used for the identification analysis of glutinous sorghum in Guizhou Province.

[0090] (5) Screening results of differential metabolites

[0091] In order to identify the characteristics of differential metabolites of waxy sorghum in Guizhou Province, the significantly differential metabolites were screened according to VIP ≥ 1, P < 0.05, up-regulated metabolite Fold Change ≥ 2 and down-regulated metabolite Fold Change ≤ 0.5, and the differential expression volcano map was drawn ( Figure 6 ). After screening, a total of 96 differential variables were obtained between waxy sorghum in Guizhou Province and sorghum outside the province (as shown in Table 3), 71 up-regulated differential expression variables, and 25 down-regulated differential expression variables. These metabolites can be divided into different categories, mainly including flavonoids, glycosides, phenolic acids, phenylpropanoids, etc. Among them, there are 35 flavonoids, 26 glycosides, 18 phenolic acids, 6 phenylpropanoids, and 11 other compounds.

[0092] Table 3 Summary of differential metabolites

[0093]

[0094]

[0095]

[0096]

[0097] (6) Hierarchical clustering analysis results

[0098] Hierarchical cluster analysis (HCA) is an algorithm that groups variables with similar properties into concentrated clusters. By calculating and comparing the distances between pairs of variables, samples are classified into groups. The dendrogram represents the relationship between samples. Samples with large relative distances represent different samples, while similar samples are close to each other with small distances. A cluster analysis was further performed on the distribution of the 96 different variable contents between glutinous sorghum in Guizhou Province and sorghum outside the province obtained by the above screening. The results are as follows: Figure 7 As shown. The horizontal direction in the figure represents sorghum samples, and the vertical direction represents compounds. Red represents compounds with higher content in sorghum than the average, and blue represents compounds with lower content in sorghum samples than the average. The depth of color represents the degree of content. The results of cluster analysis are similar to those of PCA analysis. Glutinous sorghum in Guizhou Province and sorghum outside the province were divided into two groups. Among them, glutinous sorghum in Guizhou Province is red in most components, which means that the content is higher than that of sorghum outside the province.

[0099] 5. Identification of sorghum varieties

[0100] Based on the fact that 96 compounds such as Apigenin, Calcium pantothenate, Eriodictyol, Neochlorogenic acid, Epicatechin, Cianidanol, Taxifolin, etc. are characteristic markers of glutinous sorghum in Guizhou Province compared with sorghum varieties outside the province, the steps to further determine whether the unknown sorghum sample is glutinous sorghum in Guizhou Province are as follows:

[0101] The peak areas of 96 characteristic marker compounds in the sorghum samples to be identified were obtained, and the results were compared with the peak areas of compounds in glutinous sorghum samples in Guizhou Province and sorghum outside the province for HCA cluster analysis. If the sorghum to be identified and the glutinous sorghum in Guizhou Province were clustered together in the cluster analysis diagram, it was identified as a glutinous sorghum variety in Guizhou Province.

[0102] Example 2 A metabolomics-based method for distinguishing red tassel sorghum from other sorghum varieties in the province

[0103] This example is based on the method in Example 1, and the red tassel sorghum variety is identified from other sorghum varieties in the province, as follows:

[0104] 1. Sample preparation

[0105] In this embodiment, the sample information used is shown in Table 4. The categories of sorghum samples include red tassel sorghum and other sorghum samples. Other sorghum samples include: red pearl, Tainuo No. 9, Hongmaonuo No. 6, Maotaihong No. 2, Liangmaonuo No. 2, Qiangao No. 8, Hongguoliang, and Hongguannuo No. 8. Among them, the number of samples of red tassel sorghum is 15, and the number of samples of other sorghum samples is 36.

[0106] All sorghum samples were collected separately, ground into powder, passed through a 100-mesh sieve, put into a ziplock bag, sealed and stored in a refrigerator at -18°C for later use and taken out when needed.

[0107] Table 4 Sample information table

[0108]

[0109] 2. Metabolite extraction: same as Example 1;

[0110] 3. Computer test: Same as Example 1;

[0111] 4. Data analysis: Same as Example 1;

[0112] The results obtained through the above processing and analysis methods are as follows:

[0113] (1) Identification of metabolites

[0114] Based on the metabolite information of 51 sorghum samples from nine sorghum varieties from different provinces of China collected by UPLC-MS / MS in step three above, 437 and 239 metabolites were identified in positive and negative ion modes, respectively, mainly including flavonoids, glycosides, phenolic acids, phenylpropanoids, etc.

[0115] (2) Instrument stability test results

[0116] The total ion chromatograms (TIC) of the QC samples in the positive and negative ion detection modes were superimposed and compared (Figure 1(A), Figure 1(B)). The results showed that the overlap of the chromatographic peaks was large and the TIC peak shape was complete, which indicated the stability and reliability of the data collected by the detection system in the positive and negative ion modes, and the good stability of the instrument.

[0117] (3) PCA results

[0118] Unsupervised principal component analysis (PCA) was performed on the fingerprint data collected by UHPLC-HRMS in positive and negative modes, and the PCA analysis diagrams of red tassel sorghum and other sorghums in the province were obtained, as shown in Figure 8 As shown in the figure, it can be seen that the samples of red tassel sorghum and other sorghums in the province have little difference and the separation is not obvious. In the positive mode, the contribution rate of the first two main components is 39% (PC1: 22.3%, PC2: 16.7%, R 2 X: 0.677, Q 2 :0.548), the first two main components contribute 42.8% in the negative mode (PC1: 26.6%, PC2: 16.2%, R 2 X: 0.683, Q 2 :0.531).

[0119] (4) Orthogonal partial least squares discriminant analysis (OPLS-DA) results

[0120] In order to improve the distinction between sample groups, a supervised OPLS-DA model was further used to construct a distinguishing prediction model ( Fig. 9 ), it can be seen from the figure that both are well separated in both positive and negative modes. 2 X, R 2 Y and Q 2 The values ​​are 0.493, 0.954 and 0.832 respectively. In the negative mode, R 2 X, R 2 Y and Q 2 The values ​​were 0.526, 0.972 and 0.884, respectively, and the model had excellent explanatory and predictive abilities. In order to verify the model, cross validation and random permutation tests (200 times) were performed on the corresponding OPLS-DA model. In the permutation test graphs (Figure 10 (A), Figure 10 (B)), the intercepts of Q2 were all less than 0.05, indicating that the model was not overfitted and the model validation was effective. It was believed that the results could be used for the identification analysis of red tassel sorghum.

[0121] (5) Screening results of differential metabolites

[0122] In order to identify the characteristics of differential metabolites of red tassel sorghum, significant differential metabolites were screened according to VIP≥1, P<0.05, up-regulated metabolite FoldChange≥2 and down-regulated metabolite FoldChange≤0.5, and the differentially expressed volcano ( Fig.11 After screening, there were 46 differential variables between red tassel sorghum and other sorghums in the province (as shown in Table 5), of which 40 were up-regulated differentially expressed variables and 6 were down-regulated differentially expressed variables.

[0123] Table 5 Summary of the differences between red tassel sorghum and other sorghums in the province

[0124]

[0125]

[0126]

[0127] (6) Cluster analysis

[0128] Cluster analysis was performed on the content distribution of 46 different variables of Hongyingzi relative to other sorghums in the province. The results are as follows Fig.12As shown. The horizontal direction in the figure represents sorghum samples, and the vertical direction represents compounds. Red represents compounds with a content higher than the average in sorghum, and blue represents compounds with a content lower than the average in sorghum samples. The depth of color represents the degree of content. In general, the component content of red tassel sorghum is higher than that of other sorghums in the province. The above cluster analysis results also further show that the 46 differential variables screened by the above method can be used to identify the red tassel sorghum variety from other sorghum varieties in the province.

[0129] 5. Identification of sorghum varieties

[0130] Based on the fact that 46 compounds such as Tectorigenin, Scoparone, Naringenin chalcone, Licochalcone B, Scutellarein and Homobaicalin are characteristic markers of the Hongyingzi sorghum variety relative to other sorghum varieties in the province, the steps to further determine whether the unknown sorghum sample is the Hongyingzi sorghum variety are as follows:

[0131] The peak areas of 46 characteristic marker compounds in the sorghum sample to be identified were obtained, and the results were subjected to HCA cluster analysis together with the peak areas of compounds in red tassel sorghum and other sorghums. If the sorghum to be identified and red tassel sorghum were clustered together in the cluster analysis, it was identified as a red tassel sorghum sample.

[0132] Comparative Example 1

[0133] For the solvent selection of metabolites in the sample extracted based on metabolomics, methanol, acetone, water, etc. are mostly used as extraction solvents. This comparative example is based on Example 1, using QC samples as experimental samples, and trying different solvents such as water, methanol, ethanol, acetonitrile, acetone, etc. as extraction solvents to extract metabolites in sorghum samples. The results show that there are significant differences in the extraction efficiency of metabolites in sorghum by different solvents. The extraction results are processed by Compound Discoverer, and the number of extracted metabolite compounds is used as the basis for comparison. In the positive mode, water>methanol>ethanol>acetonitrile>acetone, and in the negative mode, methanol>water>acetonitrile>ethanol>acetone. Combined with the results of the positive and negative modes, in order to achieve higher extraction efficiency, a solvent system mixed with methanol and water is selected as the extraction solvent to extract metabolites in sorghum samples. The results are shown in Table 8:

[0134] Table 8 Comparison of extraction effects of different extraction solvents

[0135]

[0136] That is, as shown in Table 8, when water, methanol, acetonitrile, ethanol, and acetone were used as extraction solvents to extract sorghum samples, there were significant differences in the extraction efficiency of sorghum metabolites when different extraction solvents were selected. Combined with the results of the positive and negative modes, a mixed solvent system of methanol and water was further selected for extraction. The results showed that the extraction solvents obtained by mixing methanol and water in different proportions also had a significant effect on the extraction efficiency of metabolites in sorghum.

[0137] Comparative Example 2

[0138] This comparative example is based on Example 1, and explores the extraction method of metabolites in sorghum samples based on metabolomics. Different extraction methods such as ultrasonic extraction, vibration extraction, and immersion extraction were tried to extract metabolites in QC sorghum samples. The results are shown in Table 9:

[0139] Table 9 Comparison of extraction effects of different extraction methods

[0140]

[0141] Based on the information disclosed in the existing literature, in theory, ultrasonic extraction can be used to extract sorghum samples to obtain more types of compounds than oscillation extraction. However, as shown in Table 9, combined with the results of the positive and negative modes, in order to achieve higher extraction efficiency (more types of compounds) and better extraction effects, in the extraction method of the sorghum sample of the present invention, the oscillation extraction method can achieve better extraction effects.

[0142] Comparative Example 3

[0143] Based on Example 1, this comparative example further investigated the effects of the oscillation extraction time of 5 min, 10 min, 20 min, 30 min, 40 min, 50 min, and 60 min on the extraction efficiency of metabolites in the QC sorghum sample. The results are shown in Table 9:

[0144] Table 9 Comparison of extraction effects at different extraction times

[0145]

[0146] As shown in the comparison results in Table 9, at different extraction times, the number of extracted metabolites was 426, but within 5 to 30 min, as time increased, the TIC graph response value gradually increased, and after more than 30 min, the response value no longer changed.

[0147] Comparative Example 4

[0148] Based on Example 1, this comparative example further explored the chromatographic conditions and tried different mobile phase combinations to test the effects of different mobile phase combinations on the results of metabolite determination and analysis of the extracted metabolites in the QC sorghum sample. The mobile phase combinations used included: (1) the mobile phase was ultrapure water in phase A and methanol in phase B; (2) the mobile phase was ultrapure water in phase A and acetonitrile in phase B; (3) the mobile phase was ultrapure water in phase A (containing 1‰ formic acid in positive mode and 1‰ ammonia in negative mode) and acetonitrile in phase B; (4) ultrapure water in phase A (containing 1‰ formic acid in positive mode and 1‰ ammonia in negative mode) and methanol in phase B. The results are as follows:

[0149] When the mobile phase was phase A ultrapure water and phase B was methanol, 288 and 105 metabolites were determined in the positive and negative ion modes, respectively; when the mobile phase was phase A ultrapure water and phase B was acetonitrile, 208 and 115 metabolites were determined in the positive and negative ion modes, respectively; when the mobile phase was phase A ultrapure water (containing 1‰ formic acid in the positive mode and 1‰ ammonia in the negative mode) and phase B was acetonitrile, 341 and 166 metabolites were determined in the positive and negative ion modes, respectively; when the mobile phase in the embodiment of the present invention was phase A ultrapure water (containing 1‰ formic acid in the positive mode and 1‰ ammonia in the negative mode) and phase B was methanol, 441 and 240 metabolites were determined in the positive and negative ion modes, respectively.

[0150] It can be seen that the use of different mobile phase chromatographic conditions to measure and analyze the extract has a significant impact on the number of compound types that can be identified, and when the mobile phase system of the embodiment of the present invention is used, more types of metabolites can be identified.

[0151] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A method for identifying sorghum varieties based on metabolomics, characterized in that: The steps include: (1) Obtaining metabolite information in the sorghum sample to be identified; (2) comparing the obtained metabolite information with the marker metabolite information corresponding to the sorghum variety, and identifying the sorghum variety type of the sorghum sample to be identified based on the comparison result; Wherein, in the step (1), obtaining the metabolite information comprises: using methanol aqueous solution as an extraction solvent, extracting the metabolites in the sorghum sample to be identified after pre-treatment by a shaking extraction method to obtain an extract; obtaining the metabolite information in the extract by ultra-high performance liquid chromatography-quadrupole electrostatic field orbital trap high-resolution mass spectrometry technology; The identification types of sorghum varieties include: identification of glutinous sorghum varieties in Guizhou Province and sorghum varieties outside the province, and identification of red tassel sorghum and other sorghums.

2. The identification method according to claim 1, characterized in that: The addition amount of the extraction solvent is: 1ml~30ml / 1~3g sorghum powder; in the extraction solvent, the volume ratio of methanol to water is 31%~69%:69%~31%; preferably, in the extraction solvent, the volume ratio of methanol to water is 40%~60%:60%~40%.

3. The identification method according to claim 1, characterized in that: In the step (1), the pre-treatment comprises: taking the sorghum sample to be identified, grinding it into powder, and sieving it to obtain powder; Preferably, in step (1), the oscillation extraction comprises: adding the extraction solvent to the sorghum sample to be identified, performing vortex oscillation extraction, centrifuging once, centrifuging twice, filtering through a membrane, and obtaining the extract; preferably, the vortex oscillation conditions are: oscillation speed 2000-3000rpm, extraction time 21-39min; preferably, the first centrifugation condition is 5000-7000r / min centrifugation for 2-5min; preferably, the second centrifugation condition is 10000-14000r / min centrifugation for 5-15min.

4. The identification method according to claim 1, characterized in that: Liquid chromatography conditions were as follows: Hypersil Gold aQ liquid chromatography column (2.1 mm × 150 mm, 1.9 μm); injection volume 3-7 μL; mobile phase: phase A was ultrapure water, phase B was methanol; Flow rate: 200-400 μL / min; gradient elution; Preferably, the gradient elution program includes: elution time 0 to 2 min, the mass fraction of phase A is 85-97%, and the mass fraction of phase B is 3-15%; elution time 22 to 32 min, the mass fraction of phase A is 3-15%, and the mass fraction of phase B is 85-97%; elution time 32.1 to 35 min, the mass fraction of phase A is 85-97%, and the mass fraction of phase B is 3-15%.

5. The identification method according to claim 1, characterized in that: The mass spectrometry conditions included: in positive ion mode, scanning range: 70-1000 m / z, capillary voltage of 3-4 kV, sheath gas of 30-40 arb, auxiliary gas of 10-20 arb, purge gas of 0 arb, nebulization temperature of 250-350 °C, resolution of 70000, scanning mode of full scan at level one + automatic triggering of level two, NCE energy of 20 kV, 40 kV, 60 kV, and ion transfer tube temperature of 300-400 °C; Preferably, the mass spectrometry conditions used include: in negative ion mode, scanning range: 70-1000m / z, capillary voltage of -2.0--3.0kV, sheath gas of 35-45arb, auxiliary gas of 5-15arb, purge gas of 0arb, nebulization temperature of 280-320°C, resolution of 70000, scanning mode of first level full scan + automatic triggering of second level, NCE energy of 20kV, 40kV, 60kV, and ion transfer tube temperature of 300-350°C.

6. The identification method according to claim 1, characterized in that: The method for obtaining marker metabolite information comprises the following steps: 1) Extraction: Using methanol aqueous solution as the extraction solvent, the metabolites in the pre-treated sorghum sample were extracted by shaking extraction to obtain an extract; 2) Obtaining metabolite information: performing non-targeted metabolomics analysis on the extract by ultra-high performance liquid chromatography-quadrupole electrostatic field orbital trap high-resolution mass spectrometry to obtain metabolite information; 3) Screening marker metabolites: analyzing and screening the metabolite information obtained in step 2) to obtain marker metabolites corresponding to the sorghum variety type of the sorghum sample; Among them, the screening of the marker metabolites includes: screening the sorghum variety type to which the sorghum sample belongs and other sorghum variety types based on VIP≥1, P<0.05, up-regulated metabolite Fold Change≥2 and down-regulated metabolite FoldChange≤0.5 as standards, so as to obtain the marker metabolite information that distinguishes the sorghum variety type to which the sorghum sample belongs from other sorghum variety types.

7. The identification method according to claim 6, characterized in that: The analysis of the metabolite information obtained in step 2) includes: performing peak comparison, normalization, peak extraction, deconvolution, compound identification and difference analysis on the data collected in step 2); preferably, the difference analysis includes: performing difference analysis by principal component analysis and orthogonal partial least squares discriminant analysis; Preferably, the method for obtaining marker metabolite information further comprises: performing hierarchical clustering analysis based on the marker metabolites screened in step 3).

8. The identification method according to claim 6, characterized in that: The addition amount of the extraction solvent is: 1ml-30ml / 1-3g sorghum powder; in the extraction solvent, the volume ratio of methanol to water is 31%-69%: 69%-31%; preferably, in the extraction solvent, the volume ratio of methanol to water is 40%-60%: 60%-40%; Preferably, in the step 1), the pretreatment comprises: taking the sorghum sample, grinding it into powder, and sieving it to obtain sorghum sample powder; Preferably, in the step 1), the oscillation extraction comprises: adding the extraction solvent to the sorghum sample, performing vortex oscillation extraction, centrifuging once, centrifuging twice, filtering through a membrane, and obtaining the extract; preferably, the conditions for the vortex oscillation are: oscillation speed 2000-3000rpm, extraction time 21-39min; preferably, the conditions for the first centrifugation are centrifugation at 5000-7000r / min for 2-5min; preferably, the conditions for the second centrifugation are centrifugation at 10000-14000r / min for 5-15min.

9. The identification method according to claim 6, characterized in that: Liquid chromatography conditions include: Hypersil Gold aQ liquid chromatography column (2.1 mm×150 mm, 1.9 μm); injection volume 3-7 μL; mobile phase: phase A is ultrapure water, phase B is methanol; flow rate: 200-400 μL / min; gradient elution; Preferably, the gradient elution program includes: elution time 0 to 2 min, the mass fraction of phase A is 85-97%, and the mass fraction of phase B is 3-15%; elution time 22 to 32 min, the mass fraction of phase A is 3-15%, and the mass fraction of phase B is 85 to 97%; elution time 32.1 to 35 min, the mass fraction of phase A is 85 to 97%, and the mass fraction of phase B is 3 to 15%; Preferably, the mass spectrometry conditions include: under positive ion mode conditions, scanning range: 70 to 1000 m / z, capillary voltage is 3 to 4 kV, sheath gas is 30 to 40 arb, auxiliary gas is 10 to 20 arb, purge gas is 0 arb, nebulization temperature is 250 to 350 ° C, resolution is 70000, scanning mode is one-level full scan + automatic triggering of the second level, NCE energy is 20 kV, 40 kV, 60 kV, ion transfer tube temperature is 300 to 400 ° C; Preferably, the mass spectrometry conditions include: in negative ion mode, scanning range: 70 to 1000 m / z, capillary voltage of -2.0 to -3.0 kV, sheath gas of 35 to 45 arb, auxiliary gas of 5 to 15 arb, purge gas of 0 arb, nebulization temperature of 280 to 320°C, resolution of 70000, scanning mode of first-level full scan + automatic triggering of second level, NCE energy of 20 kV, 40 kV, 60 kV, and ion transfer tube temperature of 300 to 350°C.

10. Use of the method according to any one of claims 1 to 9 in identifying sorghum varieties.

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