A method for identifying sorghum varieties based on differences in metabolite composition

Abstract

The application discloses a kind of based on metabolite composition difference's sorghum variety identification method, belong to white liquor brewing raw grain analysis field.Identification method specifically includes: one, different sorghum variety sample pretreatment;Two, after being removed by pretreatment, supernatant is separated by ultra-high performance liquid chromatography-mass spectrometry Metabolite, qualitative, relative quantitative analysis;Three, the total metabolite information of the obtained sorghum is analyzed to realize preliminary identification by principal component, and then the difference metabolite between varieties is screened by OPLS-DA, and the difference metabolite set is further constructed OPLS-DA discriminant model to achieve the purpose of distinguishing conventional sorghum and hybrid sorghum.The application provides a new method for accurately distinguishing between conventional sorghum varieties and hybrid varieties for brewing, and ensuring the brewing quality of raw grains.

Description

Technical Field

[0001] This invention belongs to the field of raw material analysis for Baijiu brewing, and specifically relates to a method for identifying conventional and hybrid sorghum varieties based on differences in metabolic composition and combined with chemometrics. Background Technology

[0002] Sorghum is the main raw material for traditional baijiu (Chinese white liquor) brewing. Sorghum is widely cultivated in my country, with numerous varieties. As the baijiu industry continues to develop, the demand for sorghum is constantly increasing, leading to the development of various hybrid sorghum varieties based on local conventional varieties. Due to differences in the aroma and brewing techniques of large-scale baijiu producers across different regions, the types and quality of sorghum varieties required also vary. The brewing techniques of renowned baijiu-producing areas in Southwest China have been developed through long-term practice based on local raw materials and climate. Their brewing grains mainly consist of local conventional glutinous sorghum varieties, such as Guojiao Hong No. 1, Hongyingzi, Langnuohong, Luzhou Hong No. 1, and Qingkeyang. Therefore, accurately distinguishing between conventional and hybrid sorghum varieties is crucial for ensuring the quality of the baijiu.

[0003] Traditional methods for identifying sorghum varieties primarily rely on sensory evaluation and physicochemical analysis to distinguish between glutinous and japonica sorghum. However, some hybrid glutinous sorghum varieties are extremely similar to conventional varieties in appearance and physicochemical properties, making them difficult to differentiate using these traditional methods. While DNA and spectral methods for variety identification are gaining traction, research on differentiating conventional and hybrid sorghum based on metabolomics is scarce. In recent years, non-targeted metabolomics technology has been widely applied in plant quality certification and evaluation, resource identification, and variety identification. However, LC-MS-based non-targeted metabolomics methods have not yet been used to explore the chemical characteristic metabolites of different types of Chinese brewing sorghum. Therefore, establishing a metabolomics-based method to analyze the metabolic differences between conventional and hybrid sorghum varieties, and to accurately distinguish variety types, is of great significance to the development of the wine industry in southern Sichuan and northern Guizhou. Summary of the Invention

[0004] The purpose of this invention is to establish a method for analyzing the differences between brewing sorghum varieties based on ultra-high performance liquid chromatography-tandem Fourier transform mass spectrometry, and to accurately identify conventional and hybrid varieties, providing a new reference for the identification and screening of conventional brewing sorghum varieties.

[0005] The technical solution adopted by this invention to solve its technical problem is: a method for identifying sorghum varieties based on differences in metabolic composition, specifically including the following steps:

[0006] S1. Place the sorghum seed sample in a centrifuge tube, add grinding beads and methanol aqueous solution containing L-2-chlorophenylalanine, grind on a cryo-tissue homogenizer, extract by low-temperature ultrasonication, and then centrifuge the sample after allowing it to stand at ultra-low temperature and take the supernatant.

[0007] S2. The supernatant obtained in step S1 is subjected to ultra-high performance liquid chromatography-mass spectrometry for the separation, qualitative and relative quantitative analysis of metabolites to obtain information on the total metabolites of sorghum.

[0008] S3. After performing a preliminary analysis of the overall sorghum metabolite information obtained in step S2 using PCA, further screening of differential metabolites is conducted using OPLS-DA analysis. An OPLS-DA discriminant model is then constructed using these differential metabolites to distinguish between conventional sorghum and hybrid sorghum.

[0009] In step S1, the sorghum seed sample is mature and dried grain, the centrifuge tube has a capacity of 2 ml, and the grinding beads have a diameter of 6 mm.

[0010] In step S1, the ratio of the methanol aqueous solution containing L-2-chlorophenylalanine to the sorghum seed sample is 8 mL / g, the volume concentration of methanol is 80%, and the concentration of L-2-chlorophenylalanine in the methanol solution is 0.02 mg / mL.

[0011] In step S1, the parameters set for the cryo-tissue homogenizer are: temperature -10℃, frequency 50Hz, and homogenization time 6min; low-temperature ultrasonic extraction conditions are: 5℃, 40KHz, and 30min; sample settling conditions are: -20℃ and 30min; and centrifugation parameters are: 13000g, 4℃, and time 15min.

[0012] In step S2, while the supernatant is transferred for separation, qualitative and relative quantitative analysis of metabolites using ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS), 20 μL of supernatant is transferred from each sample and mixed to serve as a quality control sample. During the sample analysis, a quality control sample (QC) is inserted at regular intervals to assess the stability of the detection process.

[0013] In step S2, the chromatographic conditions for liquid chromatography are as follows:

[0014] Chromatographic column: ACQUITYUPLCHSST3 (100mm×2.1mm, 1.8μm); Mobile phase A: 95% water + 5% acetonitrile, containing 0.1% formic acid; Mobile phase B: 47.5% acetonitrile + 47.5% isopropanol + 5% water, containing 0.1% formic acid; Column temperature 40℃, injection volume 2uL;

[0015] Elution gradient: 0-3.5 min, 0-24.5% B; 3.5-5 min, 24.5-65% B; 5-5.5 min, 65-100% B; 5.5-7.4 min, 100% B; 7.4-7.6 min, 100-51.5% B; 7.6-7.8 min, 51.5-0% B; 7.8-9 min, 0% B; 9-10 min, 0% B;

[0016] Flow rate settings: 0-5.5min, 0.4mL / min; 5.5-7.4min, 0.4-0.6mL / min; 7.4-7.6min, 0.6mL / min; 7.6-7.8min, 0.6-0.5mL / min; 7.8-9min, 0.5-0.4mL / min; 9-10min, 0.4mL / min.

[0017] In step S2, the mass spectrometry conditions are as follows: positive and negative ion scanning modes are used respectively, with a scanning range of 70-1050 m / z; heating temperature is 425℃; capillary temperature is 325℃; spray voltage is ±3500V; S-Lens voltage is 50V; collision energy is 20-40-60ev; sheath flow rate is 50arb; auxiliary flow rate is 13arb; resolution is Full MS 60000; resolution is MS 27500; metabolic databases include mainstream public databases such as HMDB and METIN.

[0018] In step S3, the method for constructing an OPLS-DA discriminant model based on differential metabolites includes the following steps:

[0019] (1) PCA analysis was performed on the information of total sorghum metabolites obtained in step S2 to achieve preliminary differentiation of varieties, and the reproducibility of the metabolomics dataset was verified by QC sample distribution.

[0020] (2) Sorghum samples were completely separated and differentially expressed by variety using OPLS-DA. The screening criteria were VIP>2 and T test p<0.05.

[0021] (3) The differential metabolite information and its relative amount of the sorghum variety samples selected in step (2) are used as independent variables, and the variety information corresponding to the sorghum samples is used as dependent variables. A discriminant model is constructed for the differential metabolites of different sorghum varieties through OPLS-DA, and the sorghum varieties are divided into "conventional varieties" and "hybrid varieties".

[0022] Furthermore, in step (3), the differential metabolites of different sorghum varieties used to construct the OPLS-DA discriminant model include 46 metabolites: 5-(3',4',5'-trihydroxyphenyl)-γ-valerol-O-methyl-4'-O-glucoside, syringin-3-glucoside, 6-[(2-{4-[(3-{[4-(hydroxy)-3-hydroxy-4-(hydroxymethyl)oxy-2-yl]oxy}-4,5-dihydroxy-6-(hydroxymethyl)oxy-2-yl)oxy]phenyl}-4-oxy-3,4-dihydroxy-2h-1-benzopyran-7-yl)oxy]-3, 4,5-Trihydroxyoxy-2-carboxylic acid, 2-feruloyl-1,2'-disinophilic gentiobiose, epicatechin-3-O-β-D-isopropylpyranoside, 6-[4-(6-carboxyl-5-{2,6-dihydroxy-4-[6-hydroxy-7-(3-methylbutyl-2-o-1-yl)-1-benzofuran-2-yl]phenyl}-3-methylcyclohexyl-3-o-1-yl)-3-hydroxyphenoxy]-3,4,5-trihydroxyoxane-2-carboxylic acid, 6-(3-vinylphenoxy)-3,4,5-trihydroxyoxane-2-carboxylic acid, 8-hydroxy-2-methoxy-6-methyl- 1,4-Naphthoquinone, 3,4,5-trihydroxy-6-{4-[(1E)-3-oxy-3-[(3,4,5,6-tetrahydroxyoxy-2-yl)methoxy]propyl-1-root-1-yl]phenoxy}oxy-2-carboxylic acid, chitosan, rutin, trans-6-octen-2,4-diyneic acid, N1,N5,N10-tricaffeoylsemidine, 6”-O-acetyl daidzein, 3-Op-coumarylquinic acid, 5-O-methylvesamidol, 6-{[2,2-dimethyl-6-(3-oxy-3-phenylpropyl)-2H-chromene-5-yl]oxy}-3,4,5-trihydroxy Oxyalkane-2-carboxylic acid, gallic acid catechin-4β-ol, 6-{4-[7-({6-[(acetoxy)methyl]-3-{[3,4-dihydroxy-4-(hydroxymethyl)-2-tetrahydrofuranyl]oxy}-4,5-dihydroxy-2-yl}oxy)-5-hydroxy-4-oxo-4H-chromen-2-yl]phenoxy}-3,4,5-trihydroxyoxyalkane-2-carboxylic acid, Corinone aldehyde benzoate, 3,9-dihydroxy-2-(2-hydroxypropyl-2-yl)-2h,3H,7h-furan[3,2-g]chromen-7-one, furanone 4-(6-malonyl glycoside), 3,4,5-Trihydroxy-6-[(4-O-2-phenyl-4H-chromen-3-yl)oxo]oxoalkane-2-carboxylic acid, γ-l-glutamine-γ-l-glutamine-l-methionine, lorazepam glucoside, α-hydrojuglone-4-ObD-glucose, garcinone, putrescine, glycerophosphate ethanolamine, vaccinia glycoside, 5-fluorodeoxyuridine monophosphate, N-acetylputrescine, D-gluconic acid, 2-methylbutylcarnitine, oleoylglycine, sesquiterpenes Terpene lactones, 4'-O-methyl-(-)-epicatechin-3'-O-glucuronide, N-acetylaspartic acid, 12,13-epoxy-11-hydroxy-9,15-octadecadienoic acid, 4'-O-methyl-(-)-epicatechin-3'-O-β-glucuronide, 1-hydroxyacetylacetone, 5-[(2,4,5-trihydroxyphenyl)methyl]tetrahydrofuran-2-one, L-glutamine, carvedin A, phosphatidylglycerol.

[0023] Furthermore, among the 46 differentially expressed metabolites, conventional sorghum upregulated 26 metabolites compared to hybrid sorghum, including: 5-(3',4',5'-trihydroxyphenyl)-γ-valerol-O-methyl-4'-O-glucoside, syringin-3-glucoside, -[(2-{4-[(3-{[4-(hydroxy)-3-hydroxy-4-(hydroxymethyl)oxy-2-yl]oxy}-4,5-dihydroxy-6-(hydroxymethyl)oxy-2-yl)oxy]phenyl}-4-oxy-3,4-dihydroxy-2h-1-benzopyran-7-yl)oxy]-3,4,5-trihydroxyoxy-2-carboxylic acid, and 2-feruloyl-1,2'-dimustine. Gentianoyl gentiobiose, epicatechin-3-O-β-D-isopropylpyranoside, 6-[4-(6-carboxy-5-{2,6-dihydroxy-4-[6-hydroxy-7-(3-methylbutyl-2-o-1-yl)-1-benzofuran-2-yl]phenyl}-3-methylcyclohexyl-3-o-1-yl)-3-hydroxyphenoxy]-3,4,5-trihydroxyoxane-2-carboxylic acid, 6-(3-vinylphenoxy)-3,4,5-trihydroxyoxane-2-carboxylic acid, 8-hydroxy-2-methoxy-6-methyl-1,4-naphthoquinone, 3,4,5-trihydroxy-6-{4-[(1E)-3-oxy-3-[(3,4,5,6-tetrahydroxy [Oxy-2-yl)methoxy]propyl-1-root-1-yl]phenoxy}oxy-2-carboxylic acid, chitosan, rutin, trans-6-octen-2,4-diyneic acid, N1,N5,N10-tricaffeoylsemidine, 6”-O-acetyl daidzein, 3-Op-coumaryl quinic acid, 5-O-methyl vesamiol, 6-{[2,2-dimethyl-6-(3-oxy-3-phenylpropyl)-2H-chromen-5-yl]oxy}-3,4,5-trihydroxyoxane-2-carboxylic acid, gallic acid catechin-4β-ol, 6-{4-[7-({6-[(acetoxy)methyl]-3-{[3,4-dihydroxy-4-(hydroxymethyl)-2-tetrahydro] [Furfural]oxy]-4,5-dihydroxy-2-yl]oxy)-5-hydroxy-4-oxy-4H-chromen-2-yl]phenoxy]-3,4,5-trihydroxyoxane-2-carboxylic acid, Corinone aldehyde benzoate, 3,9-dihydroxy-2-(2-hydroxypropyl-2-yl)-2h,3H,7h-furan[3,2-g]chromen-7-one, furanone 4-(6-malonyl glycoside), 3,4,5-trihydroxy-6-[(4-oxy-2-phenyl-4H-chromen-3-yl)oxy]oxane-2-carboxylic acid, γ-l-glutamine-γ-l-glutamine-l-methionine, lorazepam glucoside, α-hydrojuglone 4-ObD-glucose.

[0024] Furthermore, among the 46 differentially expressed metabolites, conventional sorghum had 20 metabolites downregulated compared to hybrid sorghum, including: garcinolone, putrescine, glycerophosphate ethanolamine, vaccinia glycoside, 5-fluorodeoxyuridine monophosphate, N-acetylputrescine, D-gluconic acid, 2-methylbutylcarnitine, oleoylglycine, sesquiterpene lactone, 4'-O-methyl-(-)-epicatechin-3'-O-glucuronide, N-acetylaspartic acid, 12,13-epoxy-11-hydroxy-9,15-octadecadienoic acid, 4'-O-methyl-(-)-epicatechin-3'-O-β-glucuronide, 1-hydroxyacetylacetone, 5-[(2,4,5-trihydroxyphenyl)methyl]tetrahydrofuran-2-one, L-glutamine, carvedin A, and phosphatidylglycerol.

[0025] Furthermore, in step (3), the OPLS-DA discriminant model established based on 46 differential markers of sorghum varieties includes one predictive component and four orthogonal components, R 2 X, R 2 Y and Q 2 The values ​​are 0.725, 0.971, and 0.95, respectively.

[0026] The beneficial effects of this invention are as follows: Based on non-targeted metabolomics, this invention measures the total metabolites of sorghum, uses PCA and OPLS-DA to screen out varietal differential metabolites as varietal markers, and constructs an OPLS-DA discriminant model based on the characteristics of varietal markers, which can accurately distinguish between conventional and hybrid sorghum varieties for brewing, and provides a new method for identifying sorghum varieties. Attached Figure Description

[0027] Figure 1 This is a schematic diagram showing the preliminary differentiation results of PCA analysis on 1048 metabolites in sorghum samples according to an embodiment of the present invention;

[0028] Figure 2 The results of OPLS-DA analysis of 1048 metabolites from all sorghum samples are shown in the diagram (a is a scatter plot, b is a permutation test plot).

[0029] Figure 3 Cluster heatmaps were generated for 46 differentially expressed metabolites selected based on VIP value > 2 and p < 0.05.

[0030] Figure 4 This is a schematic diagram illustrating the varietal differentiation of sorghum samples using the OPLS-DA discriminant model constructed based on 46 varietal differential marker metabolites.

[0031] Figure 5 The prediction table is an evaluation table for the OPLS-DA discriminant model constructed based on 46 differential marker metabolites of different varieties. Detailed Implementation

[0032] The technical solution of the present invention can be implemented in the following manner.

[0033] A method for identifying sorghum varieties based on differences in metabolic composition includes the following steps:

[0034] S1. Place the sorghum seed sample in a centrifuge tube, add grinding beads and methanol aqueous solution containing L-2-chlorophenylalanine, grind on a cryo-tissue homogenizer, extract by low-temperature ultrasonication, and then centrifuge the sample after allowing it to stand at ultra-low temperature and take the supernatant.

[0035] S2. The supernatant obtained in step S1 is subjected to ultra-high performance liquid chromatography-mass spectrometry for the separation, qualitative and relative quantitative analysis of metabolites to obtain information on the total metabolites of sorghum.

[0036] S3. After performing a preliminary analysis of the overall sorghum metabolite information obtained in step S2 using PCA, differential metabolites are further screened out using OPLS-DA. An OPLS-DA discriminant model is then constructed using these differential metabolites to distinguish between conventional sorghum and hybrid sorghum.

[0037] In step S3, the method for constructing an OPLS-DA discriminant model based on differential metabolites includes the following steps:

[0038] (1) PCA analysis was performed on the information of total sorghum metabolites obtained in step S2 to achieve preliminary differentiation of varieties, and the reproducibility of the metabolomics dataset was verified by QC sample distribution.

[0039] (2) Sorghum samples were completely separated and differentially expressed by variety using OPLS-DA. The screening criteria were VIP>2 and T test p<0.05.

[0040] (3) The differential metabolite information and its relative amount of the sorghum variety samples selected in step (2) are used as independent variables, and the variety information corresponding to the sorghum samples is used as dependent variables. A discriminant model is constructed for the differential metabolites of different sorghum varieties through OPLS-DA, and the sorghum varieties are divided into "conventional varieties" and "hybrid varieties".

[0041] The technical solution and effects of the present invention will be further explained below through practical examples.

[0042] The example demonstrates the construction of a sorghum variety identification model based on differences in metabolic composition.

[0043] (1) Separation, qualitative and relative quantitative analysis of metabolites in sorghum samples

[0044] Nine samples of conventional sorghum for brewing and 20 samples of hybrid sorghum were collected from the Southwest region, with three replicates for each sample. The conventional sorghum samples were all from Sichuan, while the hybrid sorghum samples were from both Sichuan and Shanxi. Seeds of each variety that were free from mechanical damage, mold, and of uniform size were selected and stored at -80℃ before analysis.

[0045] The metabolic composition of sorghum samples was analyzed using ultra-high performance liquid chromatography-tandem Fourier transform mass spectrometry (UHPLC-QExactiveHF-X). The specific steps were as follows: 50 mg of sorghum grains were weighed into a 2 mL centrifuge tube, and a 6 mm diameter grinding bead was added; 0.4 mL of 80% (v / v) methanol aqueous solution containing 0.02 mg / mL L-2-chlorophenylalanine (internal standard) was added; the sample was homogenized for 6 min using a cryo-tissue homogenizer (-10℃, 50 Hz); low-temperature ultrasonic extraction was performed for 30 min (5℃, 40 kHz); the sample was allowed to stand at -20℃ for 30 min; centrifuged at 13000 g, 4℃ for 15 min, and the supernatant was transferred for analysis; additionally, 20 μL of supernatant from each sample was transferred and mixed as a quality control sample. During the sample analysis, a quality control sample was inserted at regular intervals to assess the stability of the detection process.

[0046] Chromatographic column: ACQUITYUPLCHSST3 (100mm×2.1mm, 1.8μm); mobile phase A: 95% water + 5% acetonitrile (containing 0.1% formic acid), mobile phase B: 47.5% acetonitrile + 47.5% isopropanol + 5% water (containing 0.1% formic acid), column temperature 40℃, injection volume 2uL.

[0047] Elution gradient: 0-3.5 min, 0-24.5% B; 3.5-5 min, 24.5-65% B; 5-5.5 min, 65-100% B; 5.5-7.4 min, 100% B; 7.4-7.6 min, 100-51.5% B; 7.6-7.8 min, 51.5-0% B; 7.8-9 min, 0% B; 9-10 min, 0% B.

[0048] Flow rate settings: 0-5.5min, 0.4mL / min; 5.5-7.4min, 0.4-0.6mL / min; 7.4-7.6min, 0.6mL / min; 7.6-7.8min, 0.6-0.5mL / min; 7.8-9min, 0.5-0.4mL / min; 9-10min, 0.4mL / min.

[0049] Positive and negative ion scanning modes were used respectively. Scanning range m / z 70-1050; heating temperature 425℃; capillary temperature 325℃; spray voltage ±3500V; S-Lens voltage 50; collision energy 20-40-60ev; sheath flow rate 50arb; auxiliary flow rate 13arb; resolution (Full MS) 60000; resolution (MS2) 7500.

[0050] The metabolite matrix obtained after peak identification and data preprocessing contains 97 samples (including quality control samples) and 1048 metabolites.

[0051] (2) Construction of OPLS-DA discriminant model based on differential metabolites

[0052] PCA analysis was performed on the total metabolite expression levels of sorghum obtained by ultra-high performance liquid chromatography-mass spectrometry, such as... Figure 1 As shown, sorghum varieties are divided into two categories: "conventional varieties" and "hybrid varieties," denoted by C for "conventional varieties" and NC for "hybrid varieties." The QC control samples are tightly clustered near the center of the graph, indicating high data collection quality. In the score plots of the first two principal components, conventional and hybrid sorghum show some differentiation, but it is not very obvious. This is likely due to significant intra-group differences in brewing sorghum of the same variety caused by variations in origin and intrinsic biological characteristics. The variance contribution rate of PC1 is 19.07%, and that of PC2 is 14.73%. The first two principal components only reflect 33.80% of the characteristics of all metabolites and cannot accurately describe the differences between conventional and hybrid sorghum. Therefore, a supervised multivariate statistical analysis method—orthogonal partial least squares discriminant analysis (OPLS-DA)—can be further employed.

[0053] OPLS-DA can be used to construct a model of the relationship between sample and metabolite expression levels, which can amplify inter-group differences and reduce intra-group differences, thereby reducing the impact of irrelevant differences on the data and enabling better discrimination and separation of samples. Figure 2 The OPLS-DA analysis was performed with the expression levels of 1048 metabolites in sorghum samples as independent variables and the category (conventional variety / hybrid variety) of the sorghum samples as the dependent variable. The scatter plot shows that conventional sorghum and hybrid sorghum are clearly distinguished into two major categories. This OPLS-DA discriminant model consists of one predictive component and two orthogonal components, with R0... 2 X, R 2 Y and Q 2 The values ​​are 0.317, 0.988, and 0.979, respectively, where R... 2 X and R 2 Y represents the explanatory power of the model for matrices X and Y, respectively, and Q represents the explanatory power of the model for matrices X and Y. 2 R represents the predictive power of the model.2 Q 2 A value closer to 1 indicates a better model, while a lower value indicates a worse fit. Furthermore, cross-validation and randomized permutation tests (200 times) were performed on the corresponding OPLS-DA model. In the permutation test plot, as the permutation retention decreases, R0... 2 and Q 2 The regression line shows an upward trend, indicating that the permutation test passed and the model is not overfitting. OPLS-DA analysis results show that there are statistically significant differences between conventional and hybrid sorghum varieties used for brewing.

[0054] The VIP value of the OPLS-DA model can be used as a basis for screening biomarkers for different varieties of brewing sorghum. Based on a VIP value > 2 and p < 0.05 after a t-test, 46 variety-related characteristic metabolites were screened. Cluster analysis was performed on these 46 differentially expressed metabolites. Figure 3 It can be seen that conventional sorghum and hybrid sorghum exhibit distinct grouping. In conventional sorghum, 26 metabolites were upregulated compared to hybrid sorghum, while 20 metabolites were downregulated. Among the 26 upregulated metabolites, 21 metabolites have phenolic structures.

[0055] Using the expression levels of 46 differentially expressed metabolites as independent variables and sorghum variety type (conventional / hybrid) as the dependent variable, an OPLS-DA discriminant model was constructed using OPLS-DA to analyze the differential metabolite expression data of different sorghum varieties. The R-squared value of the constructed OPLS-DA model was [not specified]. 2 X, R 2 Y and Q 2 The values ​​are 0.725, 0.971, and 0.95 respectively, indicating that the discriminant analysis results of this model are highly accurate. Its scatter plot is shown below. Figure 4 As shown, there are significant differences between conventional and hybrid sorghum varieties, which were correctly assigned to their respective categories. The constructed model was validated through prediction and discrimination tests, as follows: Figure 5 As shown, the model's internal prediction accuracy is 100%, indicating that the variety difference markers are reliable and universal. The OPLS-DA discriminant model, based on the characteristics of 46 differential metabolites, has extremely high identification accuracy.

Claims

1. A method for identifying sorghum varieties based on differences in metabolic composition, characterized in that... Includes the following steps: S1. Sorghum seed samples were placed in centrifuge tubes, and grinding beads and methanol-water solution containing L-2-chlorophenylalanine were added. The samples were ground on a cryo-tissue homogenizer, and after ultrasonic extraction at 5°C, they were centrifuged at -20°C and the supernatant was collected. The sorghum seed samples were Sichuan sorghum and Shanxi sorghum. S2. The supernatant obtained in step S1 is subjected to ultra-high performance liquid chromatography-mass spectrometry for the separation, qualitative and relative quantitative analysis of metabolites to obtain information on the total metabolites of sorghum. The chromatographic conditions for the liquid chromatography were as follows: Column: ACQUITY UPLC HSS T3, 100 mm × 2.1 mm, 1.8 µm; Mobile phase A: 95% water + 5% acetonitrile, containing 0.1% formic acid; Mobile phase B: 47.5% acetonitrile + 47.5% isopropanol + 5% water, containing 0.1% formic acid; Column temperature: 40℃; Injection volume: 2 µL; Elution gradient: 0-3.5 min, 0-24.5%B; 3.5-5 min, 24.5-65%B; 5-5.5 min, 65-100%B; 5.5-7.4 min, 100%B; 7.4-7.6 min, 100-51.5%B; 7.6-7.8 min, 51.5-0%B; 7.8-9 min, 0%B; 9-10 min, 0%B; Flow rate setting: 0-5.5 min, 0.4 mL / min; 5.5-7.4 min, 0.4-0.6 mL / min; 7.4-7.6 min, 0.6 mL / min; 7.6-7.8 min, 0.6-0.5 mL / min; 7.8-9 min, 0.5-0.4 mL / min; 9-10 min, 0.4 mL / min; The mass spectrometry conditions were as follows: positive and negative ion scanning modes were used, with a scanning range of 70-1050 m / z; heating temperature was 425℃; capillary temperature was 325℃; spray voltage was ±3500 V; S-Lens voltage was 50 V; collision energy was 20-40-60 eV; sheath gas flow rate was 50 L / hr; auxiliary gas flow rate was 13 L / hr; resolution was Full MS 60000; resolution was MS2 7500; metabolic databases included HMDB and METIN. S3. After performing a preliminary analysis of the overall sorghum metabolite information obtained in step S2 using PCA, further OPLS-DA analysis was used to screen out differential metabolites with VIP>2 and T-test p<0.

05. An OPLS-DA discriminant model was then constructed using the differential metabolites to distinguish between conventional sorghum and hybrid sorghum.

2. The method for identifying sorghum varieties based on differences in metabolic composition according to claim 1, characterized in that: In step S1, the sorghum seed sample is mature and dried grain, the centrifuge tube is 2 ml in size, and the grinding beads are 6 mm in diameter.

3. The method for identifying sorghum varieties based on differences in metabolic composition according to claim 1, characterized in that: In step S1, the ratio of the methanol aqueous solution containing L-2-chlorophenylalanine to the sorghum seed sample was 8 mL / g, the volume concentration of methanol was 80%, and the concentration of L-2-chlorophenylalanine in the methanol solution was 0.02 mg / mL.

4. The method for identifying sorghum varieties based on differences in metabolic composition according to claim 1, characterized in that: In step S1, the following parameter conditions should be met: The parameters set for the cryo-tissue homogenizer were: temperature -10℃, frequency 50 Hz, and homogenization time 6 min. The low-temperature ultrasonic extraction conditions were 5℃, 40KHz, and 30min. The sample was left to stand at -20℃ for 30 minutes. The centrifugation parameters were 13000 g, 4℃, and 15 min.

5. The method for identifying sorghum varieties based on differences in metabolic composition according to claim 1, characterized in that: In step S3, the method for constructing an OPLS-DA discriminant model based on differential metabolites includes the following steps: (1) PCA analysis was performed on the information of total sorghum metabolites obtained in step S2 to achieve preliminary differentiation of varieties, and the reproducibility of the metabolomics dataset was verified by QC sample distribution. (2) The sorghum samples were completely separated and the differential metabolites related to the variety were screened by OPLS-DA. The screening condition was VIP>2 and the T test p<0.

05. (3) The differential metabolite information and its relative amount of the sorghum variety samples selected in step (2) are used as independent variables, and the variety information corresponding to the sorghum samples is used as dependent variables. A discriminant model is constructed for the differential metabolites of different sorghum varieties through OPLS-DA, and the sorghum varieties are divided into "conventional varieties" and "hybrid varieties".

6. The method for identifying sorghum varieties based on differences in metabolic composition according to claim 5, characterized in that: In step (3), the differential metabolites of different sorghum varieties used to construct the OPLS-DA discriminant model include 46 metabolites: 5-(3',4',5'-trihydroxyphenyl)-γ-valerol-O-methyl-4'-O-glucoside, syringin-3-glucoside, 6-[(2-{4-[(3-{[4-(hydroxy)-3-hydroxy-4-(hydroxymethyl)oxy-2-yl]oxy}-4,5-dihydroxy-6-(hydroxymethyl)oxy-2-yl)oxy]phenyl}-4-oxy-3,4-dihydroxy-2h -1-Benzopyran-7-yl)oxy]-3,4,5-trihydroxyoxy-2-carboxylic acid, 2-feruloyl-1,2'-disinophilic gentiobiose, epicatechin-3-O-β-D-isopropylpyranoside, 6-[4-(6-carboxyl-5-{2,6-dihydroxy-4-[6-hydroxy-7-(3-methylbutyl-2-o-1-yl)-1-benzofuran-2-yl]phenyl}-3-methylcyclohexyl-3-o-1-yl)-3-hydroxyphenoxy]-3,4,5-trihydroxyoxane-2-carboxylic acid, 6-(3-vinylphenoxy)-3,4,5-trihydroxyoxane-2 -Carboxylic acid, 8-hydroxy-2-methoxy-6-methyl-1,4-naphthoquinone, 3,4,5-trihydroxy-6-{4-[(1E)-3-oxy-3-[(3,4,5,6-tetrahydroxyoxy-2-yl)methoxy]propyl-1-root-1-yl]phenoxy}oxy-2-carboxylic acid, chitosan, rutin, trans-6-octen-2,4-diyneic acid, N1,N5,N10-tricaffeoyl spermidine, 6''-O-acetyl daidzein, 3-Op-coumaryl quinic acid, 5-O-methyl vesamidol, 6-{[2,2-dimethyl-6-(3-oxy-3-phenylpropyl)- 2H-chromen-5-yl]oxy}-3,4,5-trihydroxyoxane-2-carboxylic acid, gallic acid catechin-4β-ol, 6-{4-[7-({6-[(acetoxy)methyl]-3-{[3,4-dihydroxy-4-(hydroxymethyl)-2-tetrahydrofuranyl]oxy}-4,5-dihydroxy-2-yl}oxy)-5-hydroxy-4-ox-4H-chromen-2-yl]phenoxy}-3,4,5-trihydroxyoxane-2-carboxylic acid, corinolactone aldehyde benzoate, 3,9-dihydroxy-2-(2-hydroxypropyl-2-yl)-2h,3H,7h-furan[3,2-g]chromen-7-one, furanone 4-(6-malonyl glycoside), 3,4,5-Trihydroxy-6-[(4-O-2-phenyl-4H-chromen-3-yl)oxo]oxoalkane-2-carboxylic acid, γ-l-glutamine-γ-l-glutamine-l-methionine, lorazepam glucoside, α-hydrojuglone-4-ObD-glucose, garcinone, putrescine, glycerophosphate ethanolamine, vaccinia glycoside, 5-fluorodeoxyuridine monophosphate, TyrMe-Nap-OH, N-acetylputrescine, D-gluconic acid, 2-methylbutylcarnitine, oleoylglycine, sesquiterpene lactones, 4'-O-methyl-(-)-epicatechin 3'-O-glucuronide, N-acetylaspartic acid, 12,13-epoxy-11-hydroxy-9,15-octadecadienoic acid, 4'-O-methyl-(-)-epicatechin-3'-O-β-glucuronide, 1-hydroxyacetylacetone, 5-[(2,4,5-trihydroxyphenyl)methyl]tetrahydrofuran-2-one, L-glutamine, carvedin A, phosphatidylglycerol.

7. The method for identifying sorghum varieties based on differences in metabolic composition according to claim 6, characterized in that: Of the 46 differentially expressed metabolites, 26 were upregulated in conventional sorghum compared to hybrid sorghum, including: 5-(3',4',5'-trihydroxyphenyl)-γ-valerol-O-methyl-4'-O-glucoside, syringin-3-glucoside, and -[(2-{4-[(3-{[4-(hydroxy)-3-hydroxy-4-(hydroxymethyl)oxy-2-yl]oxy}-4,5-dihydroxy-6-(hydroxymethyl)oxy-2-yl)oxy]phenyl}-4-oxy-3,4-dihydroxy-2h -1-Benzopyran-7-yl)oxy]-3,4,5-trihydroxyoxy-2-carboxylic acid, 2-feruloyl-1,2'-disinophilic gentiobiose, epicatechin-3-O-β-D-isopropylpyranoside, 6-[4-(6-carboxyl-5-{2,6-dihydroxy-4-[6-hydroxy-7-(3-methylbutyl-2-o-1-yl)-1-benzofuran-2-yl]phenyl}-3-methylcyclohexyl-3-o-1-yl)-3-hydroxyphenoxy]-3,4,5-trihydroxyoxane-2-carboxylic acid, 6-(3-vinylphenoxy)-3,4,5-trihydroxyoxane-2 -Carboxylic acid, 8-hydroxy-2-methoxy-6-methyl-1,4-naphthoquinone, 3,4,5-trihydroxy-6-{4-[(1E)-3-oxy-3-[(3,4,5,6-tetrahydroxyoxy-2-yl)methoxy]propyl-1-root-1-yl]phenoxy}oxy-2-carboxylic acid, chitosan, rutin, trans-6-octen-2,4-diyneic acid, N1,N5,N10-tricaffeoyl spermidine, 6''-O-acetyl daidzein, 3-Op-coumaryl quinic acid, 5-O-methyl vesamidol, 6-{[2,2-dimethyl-6-(3-oxy-3-phenylpropyl)- 2H-chromen-5-yl]oxy}-3,4,5-trihydroxyoxane-2-carboxylic acid, gallic acid catechin-4β-ol, 6-{4-[7-({6-[(acetoxy)methyl]-3-{[3,4-dihydroxy-4-(hydroxymethyl)-2-tetrahydrofuranyl]oxy}-4,5-dihydroxy-2-yl}oxy)-5-hydroxy-4-ox-4H-chromen-2-yl]phenoxy}-3,4,5-trihydroxyoxane-2-carboxylic acid, corinolactone aldehyde benzoate, 3,9-dihydroxy-2-(2-hydroxypropyl-2-yl)-2h,3H,7h-furan[3,2-g]chromen-7-one, furanone 4-(6-malonyl glycoside), 3,4,5-trihydroxy-6-[(4-oxo-2-phenyl-4H- -chromene-3-yl)oxo]oxane-2-carboxylic acid, γ-l-glutamine-γ-l-glutamine-l-methionine, lorazepam glucoside, α-hydrojuglone-4-ObD-glucose; Of the 46 differentially expressed metabolites, 20 metabolites were downregulated in conventional sorghum compared to hybrid sorghum, including: garcinolone, putrescine, glycerophosphate ethanolamine, vaccinia glycoside, 5-fluorodeoxyuridine monophosphate, TyrMe-Nap-OH, N-acetylactone, D-gluconic acid, 2-methylbutylcarnitine, oleoylglycine, sesquiterpene lactone, 4'-O-methyl-(-)-epicatechin-3'-O-glucuronide, N-acetylaspartic acid, 12,13-epoxy-11-hydroxy-9,15-octadecadienoic acid, 4'-O-methyl-(-)-epicatechin-3'-O-β-glucuronide, 1-hydroxyacetylacetone, 5-[(2,4,5-trihydroxyphenyl)methyl]tetrahydrofuran-2-one, L-glutamine, carvedin A, and phosphatidylglycerol.

8. The method for identifying sorghum varieties based on differences in metabolic composition according to claim 1, characterized in that: In step (3), the OPLS-DA discriminant model established based on 46 differential markers of sorghum varieties includes one predictive component and four orthogonal components, R 2 X, R 2 Y and Q 2 The values ​​are 0.725, 0.971, and 0.95, respectively.

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