Method for detecting polyphenol metabolites in tea based on full-targeted metabonomics

Abstract

The invention belongs to the technical field of metabolite analysis, and particularly relates to a method for detecting polyphenol metabolite in tea based on full-targeted metabonomics. According to the method, the methanol aqueous solution is used for extraction, and the ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometer is used for detection, so that the method is simple and convenient to operate and high in extraction efficiency, methanol used in an extracting solution is a common reagent and is easy to obtain, separation is avoided in the extraction process, experimental errors are reduced, and the stability and repeatability of a detection result are ensured. Furthermore, according to the method, qualitative and quantitative analysis is carried out through mass spectrometry and a database, various data models (OPLS-DA, PLS-DA and the like) are established, the stability and repeatability of the whole analysis process are systematically evaluated, the polyphenol metabolites in various tea leaves are accurately analyzed, and a strong technical support is provided for standardization, precision and high-value development of the tea leaf industry.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of metabolite analysis, and particularly relates to a method for detecting polyphenol metabolites in tea based on full-targeted metabolomics. BACKGROUND

[0002] Polyphenols are the most important functional components in tea, mainly including flavones, flavonols, chalcones, aurones, flavonones, flavandiols, phenolic acids, anthocyanidins, flavanols, lignans, tannins, coumarins, isoflavones, proanthocyanidins, stilbenes and other flavonoids. They determine the color and taste of tea and have multiple health benefits (antioxidation, anti-inflammatory, anticancer, etc.). Therefore, the detection and analysis of polyphenols in tea have great market potential and research value.

[0003] Before the maturity of metabolomics technology, the analysis of polyphenols in tea mainly relies on: ① spectrophotometry, such as Folin-Ciocalteu reagent method for determining total phenol content, which is fast and simple, but cannot distinguish specific polyphenols, has little information and poor specificity. ② high performance liquid chromatography (HPLC), which is the mainstream detection method, and can analyze and quantify several major catechins and flavonols. However, this method has certain limitations: limited coverage, usually only 10-20 high-abundance polyphenols can be analyzed, and it is powerless for a large number of low-abundance polyphenol derivatives and their oligomers and polymers; weak qualitative ability, the structure information provided by UV spectrum is limited, and it is difficult to identify unknown peaks or co-elution peaks, which may lead to false identification; low throughput, only limited information of several compounds can be obtained at a time, and it is difficult to cope with large-scale sample analysis. ③ liquid chromatography-mass spectrometry (LC-MS), which has advantages in sensitivity and specificity in the determination of polyphenol metabolites in tea. However, the matrix effect is significant, and the co-extracted substances in tea may inhibit or enhance the ionization efficiency of target polyphenol metabolites at the ion source, resulting in inaccurate quantitative results. For structurally similar polyphenol metabolites, such as isomers, it is difficult to confirm them only by mass spectrum. The limitations of these methods make our understanding of the complex world of tea polyphenols very one-sided, and we cannot fully explain the deep chemical material basis of tea quality differences. Therefore, it is of great significance to establish a systematic qualitative and quantitative method for the detection and analysis of tea polyphenols based on full-targeted metabolomics, high-throughput, high-sensitivity and high-accuracy, which provides strong technical support for the standardization, precision and high-value development of tea industry. SUMMARY

[0004] Therefore, the application provides a method for detecting polyphenol metabolites in tea based on full-targeted metabolomics. The method can realize high-precision and high-throughput analysis of tea polyphenol components and provide a reliable technical means for tea deep processing and development.

[0005] The application provides a method for detecting polyphenol metabolites in tea leaves based on full-targeted metabolomics, comprising the following steps: Different tea leaf samples are extracted by using a methanol aqueous solution to obtain an extract; the tea leaf samples include different types of fresh leaves and finished tea; The extract is subjected to ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry detection, and a database is searched to determine polyphenol metabolite components in different samples; The polyphenol metabolite components are subjected to principal component analysis, partial least squares discriminant analysis and orthogonal partial least squares discriminant analysis, and significant difference metabolites between groups are screened out; The significant difference metabolites are subjected to KEGG network analysis to determine the metabolic processes of different tea leaves.

[0006] Preferably, the separation conditions of the ultra-high performance liquid chromatography include that the chromatographic column is a C 18 column; the mobile phase A is a formic acid aqueous solution, and the mobile phase B is a formic acid acetonitrile solution; and elution is gradient elution.

[0007] Preferably, the chromatographic column is a Waters-C 18 chromatographic column; and the column temperature is 40 DEG C.

[0008] Preferably, the volume concentration of formic acid in the formic acid aqueous solution is 0.1%; the volume concentration of formic acid in the formic acid acetonitrile solution is 0.1%; and the flow rate of the mobile phase A and the mobile phase B is 0.3 mL / min.

[0009] Preferably, the program of the gradient elution is as follows: 0~1.0min, the volume percentage of the mobile phase B is 5%; 1.01~2.0min, the volume percentage of the mobile phase B is linearly increased from 5% to 95%; 2.01~10.0min, the volume percentage of the mobile phase B is 95%; 10.01~11.1min, the volume percentage of the mobile phase B is linearly decreased from 95% to 5%; and 11.11~14min, the volume percentage of the mobile phase B is 5%.

[0010] Preferably, the conditions of the mass spectrometry include that the flow rate of atomizing gas is 1000 L / h, the desolvation gas temperature is 500 DEG C, the ion source temperature is 150 DEG C, the capillary voltage is 3.0 kV; and the cone voltage and the compensation voltage are respectively set to 40 V and 80 V.

[0011] Preferably, the conditions of the mass spectrum include: using MSE scan mode, low energy scan collision energy setting is 6eV, high energy scan collision energy gradient setting is 30~60eV; spray pressure is maintained at 6.5*10 5 Pa, mass scan range is set as m / z 80~1200; in positive and negative ion modes, leucine- enkephalin is used as an external standard, and mass spectrum quality real-time correction is carried out by using Lock SprayTM technology; wherein the negative ion mode uses m / z 554.2615 [M-H] - as a correction reference peak, the positive ion mode uses m / z 556.2771 [M+H] + as a correction reference peak, and the volume flow is set as 5uL / min.

[0012] Preferably, the volume concentration of the methanol aqueous solution is 70%; the mass volume ratio of the tea leaves and the methanol aqueous solution is 10~30mg:500~1500uL; the extraction is vortex oscillation extraction; and the extraction time is 30min.

[0013] Preferably, in the orthogonal partial least squares discriminant analysis, the significant difference metabolic components with VIP value≥1 and P<0.05 are selected as the significant difference metabolites between groups.

[0014] Preferably, before KEGG pathway analysis of the significant difference metabolites, bioinformatics analysis and evaluation are further carried out; the bioinformatics analysis and evaluation include univariate statistical analysis, cluster analysis and violin analysis.

[0015] The present application extracts by using a methanol aqueous solution and detects by using an ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometer, has the advantages of simple operation, high extraction efficiency, common reagent methanol used in the extraction solution, easy availability, no separation in the extraction process, reduced experimental error, and guaranteed stability and repeatability of the detection results.

[0016] Compared with the prior art, the present application has the following advantages: ①Standardized process: a complete and repeatable operation procedure from sample preparation, instrument analysis to data quality control is provided; ②High accuracy: combined with the high separation ability of HPLC and the high selectivity of MRM mode, simultaneous and accurate qualitative and quantitative analysis of a large number of metabolites in a complex matrix is realized.

[0017] ③Coverage: up to 1892 metabolites can be stably detected and quantified from tea samples in one analysis, the "full-targeted" strategy combines the breadth of non-targeted and the accuracy of targeted quantification, and can systematically reveal the whole picture of plant metabolism; ④Quality control system: through multivariate statistical analysis, samples of different groups can be effectively distinguished, and metabolites with significant differences between groups can be accurately screened, to ensure the quality and reliability of experimental data; ⑤Application-oriented: the method directly serves the discovery of differential metabolites and the elucidation of metabolic mechanisms, and has high application value. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows.

[0019] Figure 1 For mass spectrometric analysis of QC sample solution of the embodiment of the present application; Figure 2 For the composition of metabolites in the embodiment of the present application; Figure 3 For PCA analysis of the embodiment of the present application; Figure 4 For PLS-DA score of the embodiment of the present application; Figure 5 For OPLS-DA score of the embodiment of the present application; Figure 6 A is the statistical analysis of significant differential metabolites of Xinyang Kari fresh leaves and Xinyang Kari tea leaves group, and B is the statistical analysis of significant differential metabolites of Xinyang Maojian fresh leaves and Xinyang Maojian tea leaves group; Figure 7 For the differential metabolite clustering analysis in the embodiment of the present application; Figure 8 For the violin plot analysis of 6 differential metabolites in the embodiment of the present application; Figure 9 For the violin plot analysis of 6 differential metabolites in the embodiment of the present application; Figure 10 For the violin plot analysis of 6 differential metabolites in the embodiment of the present application; Figure 11 For the violin plot analysis of 6 differential metabolites in the embodiment of the present application; Figure 12 For the KEEG analysis of significant differential metabolites in the embodiment of the present application. DETAILED DESCRIPTION

[0020] The present application provides an analysis method for polyphenol components in tea based on full-targeted metabolomics, comprising the following steps: The different tea leaf samples are extracted by using methanol aqueous solution to obtain an extract; the tea leaf samples include different types of fresh leaves and finished tea; The extract is detected by using ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry, and polyphenol metabolite components in different samples are determined by searching a database; The polyphenol metabolite components are subjected to principal component analysis, partial least squares discriminant analysis and orthogonal partial least squares discriminant analysis, and significant difference metabolites between groups are screened out; The significant difference metabolites are subjected to KEGG network analysis, and metabolic processes of different teas are determined.

[0021] The different tea leaf samples are extracted by using methanol aqueous solution to obtain an extract.

[0022] As an embodiment of the present application, the volume concentration of the methanol aqueous solution can be 70%; the mass-volume ratio of the tea leaf sample and the methanol aqueous solution can be 10-30 mg: 500-1500 μL; the extraction can be vortex oscillation extraction; and the extraction time can be 30 min.

[0023] The extract is detected by using ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry, and polyphenol metabolite components in different samples are determined by searching a database; and the polyphenol metabolite components are subjected to principal component analysis, partial least squares discriminant analysis and orthogonal partial least squares discriminant analysis, and significant difference metabolites between groups are screened out.

[0024] As an embodiment of the present application, the separation conditions of the ultra-high performance liquid chromatography can include: the chromatographic column is a C 18 column, specifically a Waters-C 18 color column, the column temperature is 40 DEG C; the mobile phase A is formic acid aqueous solution, the volume concentration of formic acid in the formic acid aqueous solution is 0.1%; the mobile phase B is formic acid acetonitrile solution; the volume concentration of formic acid in the formic acid acetonitrile solution is 0.1%; the elution is gradient elution; and the gradient elution program is as follows: 0-1.0 min, the volume percentage content of the mobile phase B is 5%; 1.01-2.0 min, the volume percentage content of the mobile phase B is linearly increased from 5% to 95%; 2.01-10.0 min, the volume percentage content of the mobile phase B is 95%; 10.01-11.1 min, the volume percentage content of the mobile phase B is linearly decreased from 95% to 5%; and 11.11-14 min, the volume percentage content of the mobile phase B is 5%.

[0025] As an embodiment of the present application, the conditions of the mass spectrum include: atomization gas flow 1000 L / h, desolvation gas temperature 500℃, ion source temperature 150℃, capillary voltage 3.0 kV; the cone hole voltage and the compensation voltage are set to 40 V and 80 V respectively; the MSE scan mode is used, the low-energy scan collision energy is 6 eV, and the high-energy scan collision energy gradient is set to 30~60 eV; the spray pressure is maintained at 6.5×10 5 Pa, and the mass scan range is set to m / z 80~1200; in the positive and negative ion modes, leucine-encephalin is used as an external standard, and the Lock Spray™ technology is used for real-time mass spectrum quality correction; wherein the negative ion mode uses m / z 554.2615 [M-H] - as a correction reference peak, the positive ion mode uses m / z 556.2771 [M+H] + as a correction reference peak, and the volumetric flow rate is set to 5 μL / min.

[0026] As an embodiment of the present application, the database can include the HMDB, METLIN or MassBank database.

[0027] As an embodiment of the present application, when the orthogonal partial least squares discriminant analysis is performed, the significant difference metabolic components with VIP value≥1.2 and P<0.01 are selected as the significant difference metabolites between groups.

[0028] As an embodiment of the present application, before the KEGG pathway analysis of the significant difference metabolites, bioinformatics analysis and evaluation are also performed; the bioinformatics analysis and evaluation include univariate statistical analysis, cluster analysis and violin analysis.

[0029] In order to further illustrate the present application, the technical solutions provided by the present application are described in detail below in combination with examples, but they should not be understood as limiting the scope of protection of the present application.

[0030] Example 1 1.1 Sample Xinyang kuding fresh leaves, Xinyang kuding tea leaves, Xinyang Maojian fresh leaves, and Xinyang Maojian tea leaves.

[0031] 1.2 Preparation of tea samples and QC samples: 4 samples were placed in a freeze dryer for 72 hours of vacuum drying, ground into powder, and sieved through a 50-mesh sieve. 20 mg of each sample was dissolved in 1000 μL of 70 vol.% methanol water extract solution, vortexed for 30 minutes, and then centrifuged. The supernatant was filtered through a microporous filter membrane (0.22 μm pore size, PTFE material) to obtain the test sample. 100 μL of each sample solution was accurately taken and mixed to prepare the QC sample solution. Finally, the sample solution was detected by ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry.

[0032] (2) Instrument conditions: Waters-C18 column, 100 mm x 2.1 mm x 1.7 μm, mobile phase: A phase: 0.1% formic acid in ultrapure water, B phase: 0.1% formic acid in acetonitrile, elution gradient: 0-1.0 min, B phase 5%, A phase 95%; 1.01-2.0 min, B phase increased from 5% to 95%, A phase decreased from 95% to 5%; 2.01-10.0 min, B phase maintained at 95%, A phase 5%; 10.01-11.1 min, B phase decreased from 95% to 5%, A phase increased from 5% to 95%; 11.11-14 min, B phase maintained at 5%, A phase 95%; flow rate: 0.3 mL / min; column temperature: 40°C; atomizing gas flow rate: 1000 L / h, desolvation gas temperature: 500°C, ion source temperature: 150°C, capillary voltage: 3.0 kV. The cone voltage and compensation voltage were set to 40 V and 80 V, respectively. MSE scan mode was used, with low-energy scan collision energy of 6 eV and high-energy scan collision energy gradient set to 30-60 eV. The spray pressure was maintained at 6.5 x 10 5 Pa, and the mass scan range was set to m / z 80-1200. Leucine-enkephalin (m / z 554.2615 [M-H] – ) and (m / z 556.2771 [M+H] + ) were used as external standards (Lock SprayTM) for mass real-time correction, and the volume flow rate was set to 5 μL / min.

[0033] Figure 1 (3) Qualitative and quantitative analysis of tea samples: Based on public metabolite databases such as HMDB and METLIN, and combined with the laboratory self-built database, the collected mass spectrometry data was analyzed by comparison. The retention time, mass-to-charge ratio, and signal-to-noise ratio of the metabolites were compared to qualitatively analyze the polyphenol metabolites in the tea samples. Subsequently, the mass spectrometry peaks of the same metabolite in different samples were corrected and integrated using MultiQuant software to ensure the accuracy of the qualitative and quantitative analysis. ​

[0034] Example 2 1. Full-target metabolomics detection and analysis Figure 2 A circular diagram was constructed to classify all detected metabolites according to their categories, identifying 1892 polyphenol metabolites in 16 categories. These included phenolic acids (29.02%), flavonoids (18.02%), flavonols (15.38%), flavanols (5.44%), coumarins (4.92%), lignans (4.65%), isoflavones (3.54%), tannins (3.22%), other flavonoids (2.96%), dihydroflavonoids (2.96%), chalcones (2.22%), anthocyanins (2.17%), proanthocyanidins (2.17%), arbutins (1.11%), dihydroflavonols (0.90%), and aurones (0.79%). Phenolic acids, flavonoids, and flavonols were the three most prevalent polyphenol metabolites.

[0035] 2. Screening of differentially expressed polyphenol metabolites in tea Perform PCA analysis on the sample, such as Figure 3 As shown, the main distinction in PC1 (70.87%) is varietal difference, specifically the difference between the Xinyang Lingmu series (labeled XYLM in the figure) and the Xinyang Maojian series (labeled XYMJ in the figure). The results show that the negative direction of the PC1 axis for Xinyang Lingmu fresh leaves and Xinyang Lingmu tea indicates that they share similar characteristics and are significantly different from Xinyang Maojian fresh leaves and Xinyang Maojian tea. The positive direction of the PC1 axis for Xinyang Maojian fresh leaves and Xinyang Maojian tea indicates that they share similar characteristics and are significantly different from Xinyang Lingmu fresh leaves and Xinyang Lingmu tea. Therefore, varietal variety is the most significant factor influencing the differences in the data.

[0036] The main distinction in PC2 (9.76%) was between fresh leaves and tea leaves. Within the Xinyang Lingmu series, fresh leaves were located at the bottom, and tea leaves at the top. Within the Xinyang Maojian series, fresh leaves were located at the bottom, and tea leaves at the top. This indicates that within the same variety, fresh leaves and tea leaves differ in gene expression or metabolite composition. The tea processing process affected these variables. The sample points within each group were relatively concentrated, indicating good sample repeatability. However, the Xinyang Lingmu tea leaf group showed some dispersion in PC2, possibly due to additional variation introduced by the tea processing process.

[0037] PLS-DA analysis was performed on the samples; the score chart is shown below. Figure 4 ,Depend on Figure 4 It can be seen that the distinction between groups is higher, and each group is circled by an ellipse, indicating the similarity of samples within the group.

[0038] Figure 5Two principal components were obtained by OPLS-DA, and the four groups of samples showed a clear separation trend, R 2 X = 0.843, R 2 Y = 0.999, Q 2 = 0.992, which represented a good fitting and strong predictive ability model. The differential metabolic components could be screened according to the VIP value analysis, and based on the OPLS-DA results, the differential metabolic components between different groups of tea samples were preliminarily screened from the obtained multivariate analysis OPLS-DA model VIP value, and the significantly different metabolic components were further screened by increasing the VIP threshold and P value. The metabolites of the four groups of tea samples were screened according to the above screening conditions, and a total of 1227 differential metabolites were obtained when the VIP value was greater than or equal to 1 and the P value was less than 0.05, and a total of 71 significantly different metabolic components were obtained when the VIP value was greater than or equal to 1.2 and the P value was less than 0.01. These significantly different metabolites were divided into 13 categories (see Table 1). Overall, the differential metabolic components (1227) accounted for 64.85% of the total metabolic components (1892), indicating that the metabolites of different types of tea were quite different.

[0039] Table 1 71 significantly different metabolic components in four groups of different tea samples

[0040] 3、 Figure 6Table 1. The number of significantly changed metabolites in the comparison of fresh leaves and tea leaves of E. fordii and E. sinensis. The number of up-regulated metabolites (red) is 165, the number of down-regulated metabolites (green) is 106, and the number of non-significantly changed metabolites (gray) is 1382.

[0041] Figure 6 Table 2. The number of significantly changed metabolites in the comparison of fresh leaves and tea leaves of E. sinensis. The number of up-regulated metabolites (red) is 120, the number of down-regulated metabolites (green) is 98, and the number of non-significantly changed metabolites (gray) is 1393.

[0042] Compared with E. sinensis, E. fordii has more up-regulated metabolites and fewer down-regulated metabolites. This indicates that the change of polyphenols in E. fordii is more dramatic during the transformation from fresh leaves to tea leaves, and the trend of synthesis and accumulation is more obvious than that of E. sinensis. It is also possible that the processing technology of E. fordii leads to the retention of more substances. Up-regulated metabolites may be components that originally exist in fresh leaves, while down-regulated metabolites may be formed during processing.

[0043] 5. Visualization analysis of metabolites by violin plot. The data was normalized and the clustering heat map was used to visualize the metabolites. Figure 7The relative changes of the differential metabolites (VIP value≥1, P<0.05) were observed for all samples. Figures 8-11 The violin plots of the top 24 differential metabolites (VIP value≥1, P<0.05) are shown in FIG. 6 (the labels from left to right are Xinyang Eurya fresh leaves, Xinyang Eurya tea leaves, Xinyang Maojian fresh leaves, and Xinyang Maojian tea leaves). It can be seen from the figure that the contents of the same metabolite in tea leaves of different groups are significantly different. For example, the content of 7-O-Galloyl-D-sedoheptulose in tea leaves is much lower than that in fresh leaves in Xinyang Eurya, while the content of tea leaves is much higher than that in fresh leaves in Xinyang Maojian, indicating that this substance has different changes in Xinyang Eurya and Xinyang Maojian during tea processing.

[0044] 6. The pathway enrichment analysis of the differential polyphenol metabolites (VIP value≥1, P<0.05) in tea leaves was performed using the KEGG database, as shown in FIG. 6. Figure 12 The vertical axis represents the KEGG metabolic pathway name. The horizontal axis represents the Rich Factor. The higher the Rich Factor, the higher the degree of pathway enrichment. The size of the point represents the number of differential expressed genes in the pathway. The larger the point, the more differential expressed genes in the pathway. The color of the point represents the P-value of the pathway enrichment analysis. The closer the color is to red, the smaller the P-value, and the more significant the enrichment. The closer the color is to purple, the larger the P-value, and the less significant the enrichment. The most significant enrichment pathway is Anthocyanin biosynthesis (anthocyanin biosynthesis) at the top. The point of this pathway is the largest (the most differential genes), and the color is closest to red (the lowest P-value), and the Rich Factor is also high. It indicates that the anthocyanin biosynthesis pathway is significantly different between different samples, and a large number of genes are involved in the regulation.

[0045] Although the above embodiment describes the present application in detail, it is only a part of the embodiments of the present application, but not all the embodiments. Other embodiments can be obtained according to the present embodiment without creativity, and these embodiments all belong to the protection scope of the present application.

Claims

1. A method for detecting polyphenol metabolites in tea based on whole-target metabolomics, comprising the following steps: Different tea samples were extracted using a methanol-water solution to obtain extracts; the tea samples included different types of fresh leaves and finished tea. The extract was analyzed by ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry, and the polyphenol metabolite components in different samples were identified by searching the database. Principal component analysis, partial least squares discriminant analysis, and orthogonal partial least squares discriminant analysis were performed on the polyphenol metabolite components to screen out metabolites with significant differences between groups. KEGG pathway analysis was performed on the significantly different metabolites to determine the metabolic processes of different teas.

2. The method as described in claim 1, characterized in that, The separation conditions for the ultra-high performance liquid chromatography include: a C10 column. 18 The column was used; mobile phase A was an aqueous formic acid solution, and mobile phase B was an aqueous formic acid-acetonitrile solution; elution was gradient elution.

3. The method as described in claim 2, characterized in that, The chromatographic column was a Waters-C10. 18 The chromatographic column was set at a temperature of 40°C.

4. The method as described in claim 2, characterized in that, The formic acid aqueous solution has a formic acid volume concentration of 0.1%; the formic acid acetonitrile solution has a formic acid volume concentration of 0.1%; and the flow rates of mobile phase A and mobile phase B are 0.3 mL / min.

5. The method as described in claim 2, characterized in that, The gradient elution procedure is as follows: From 0 to 1.0 min, the volume percentage of the mobile phase B is 5%; Within 1.01 to 2.0 min, the volume percentage of the mobile phase B increased linearly from 5% to 95%. The mobile phase B has a volume percentage of 95% and a processing time of 2.01~10.0 min. Within 10.01 to 11.1 minutes, the volume percentage of mobile phase B decreased linearly from 95% to 5%. During the 11.11~14 min period, the volume percentage of the mobile phase B was 5%.

6. The method as described in claim 1, characterized in that, The mass spectrometry conditions include: nebulizer gas flow rate of 1000 L / h, desolvation gas temperature of 500℃, ion source temperature of 150℃, capillary voltage of 3.0 kV, and cone voltage and compensation voltage set to 40 V and 80 V, respectively.

7. The method as described in claim 1, characterized in that, The mass spectrometry conditions included: MSE scanning mode, low-energy scan collision energy set to 6 eV, high-energy scan collision energy gradient set to 30–60 eV, and spray pressure maintained at 6.5 × 10⁻⁶ eV. 5 Pa, the mass scan range was set to m / z 80~1200. In both positive and negative ion modes, leucine-enkephalin was used as an external standard, and Lock Spray™ technology was employed for real-time mass spectrometry correction. The negative ion mode used m / z 554.2615 [MH]. - To correct for the reference peak, the positive ion mode was set to m / z 556.2771 [M+H]. + To correct for the reference peak, the volumetric flow rate was set to 5 μL / min.

8. The method as described in claim 1, characterized in that, The volume concentration of the methanol aqueous solution is 70%; the mass-to-volume ratio of the tea leaves to the methanol aqueous solution is 10-30 mg: 500-1500 μL; the extraction is performed by vortex extraction; and the extraction time is 30 min.

9. The method as described in claim 1, characterized in that, In the orthogonal partial least squares discriminant analysis, metabolites with significant differences when the VIP value is ≥1 and P < 0.05 are selected as metabolites with significant differences between groups.

10. The method as described in claim 1, characterized in that, Before performing KEGG pathway analysis on significantly differentially expressed metabolites, bioinformatics analysis and evaluation were also conducted; the bioinformatics analysis and evaluation included univariate statistical analysis, cluster analysis, and violin analysis.

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