Traditional Chinese medicine quality marker screening method and angelica dahurica variety quality marker group

Abstract

The invention relates to the technical field of medical biology, and discloses a traditional Chinese medicine quality marker screening method and an angelica dahurica variety quality marker group, the traditional Chinese medicine quality marker screening method and the angelica dahurica variety quality marker group comprise the following steps: respectively crushing, extracting and detecting a newly bred angelica dahurica variety and a farm variety; carrying out pretreatment on mass spectrum data by adopting MZmine 2.5 software; and the coumarin components are screened by adopting a multi-stage mass loss window. According to the traditional Chinese medicine quality marker screening method and the angelica dahurica variety quality marker group, through integration of ultra-high performance liquid chromatography-tandem mass spectrometry and polygon quality defect filtering, extended virtual molecular formula library screening, characteristic molecular network, network annotation transmission and other calculation strategies, a plurality of coumarin components are successfully annotated; and multivariate statistical analysis is adopted, so that the quality marker of the newly cultivated angelica dahurica variety is obtained.

Description

Technical Field

[0001] This invention relates to the field of pharmaceutical biotechnology, specifically to a method for screening quality markers of traditional Chinese medicine and a group of quality markers for Angelica dahurica varieties. Background Technology

[0002] Plant resources are indispensable raw materials in the food, medicine, cosmetics and chemical industries. Their quality and yield directly affect the sustainable development of related industries. Improving the yield and quality of plant raw materials through the breeding of new varieties has become a core strategy for the development of modern agriculture. In the plant quality evaluation system, secondary metabolites are not only key quality markers, but their multi-component synergistic effects also provide a theoretical basis for the research and development of traditional Chinese medicine compound and functional food. Therefore, systematically analyzing the chemical characteristics of plant secondary metabolites can not only provide a scientific basis for their efficacy evaluation, but also point the way for the formulation of new variety breeding strategies.

[0003] Liquid chromatography-mass spectrometry (LC-MS) Mass spectrometry (MS) has become the mainstream technology for compound structure analysis due to its high sensitivity and resolution. However, the insufficient coverage of existing metabolite databases severely restricts the annotation efficiency of plant secondary metabolites. In recent years, the application of intelligent algorithms such as feature-based molecular networks (FBMN) and network annotation transfer (NAP) has provided new ideas for overcoming this bottleneck. Nevertheless, the problem of false positives caused by redundant information in mass spectrometry data still needs to be solved. Therefore, optimizing mass spectrometry data preprocessing methods and establishing accurate feature screening strategies are of great significance for improving the reliability of annotation of secondary metabolites in new plant varieties.

[0004] Angelica dahurica ( Angelicae dahuricae Radix As a traditional Chinese medicinal herb that is both food and medicine, Angelica dahurica has a complex and diverse chemical composition, mainly composed of coumarins, and also contains volatile oils, polysaccharides, alkaloids, and amino acids. Modern pharmacological studies have confirmed that Angelica dahurica possesses multiple biological activities, including analgesia, anti-inflammation, anti-oxidation, anti-tumor, and skin whitening. According to the Chinese Pharmacopoeia (2020 edition), the original plant of Angelica dahurica includes Angelica dahurica (… Angelica dahurica ) and Hangzhou Angelica ( A. dahurica var. formosana It is worth noting that the germplasm resources of Hangzhou Angelica exhibit significant genetic variation, manifested in phenotypic diversity such as stem color and root flavor. While this genetic diversity poses challenges to standardized production, it provides valuable resources for the breeding of new varieties. Traditional Hangzhou Angelica plants are typically 1-1.5 meters tall and are susceptible to wind damage and lodging, which seriously affects yield stability. Currently, through systematic breeding, new varieties of Hangzhou Angelica have been developed with significantly enhanced lodging resistance, more developed root systems, and significantly improved phenotypic uniformity, forming a stark contrast to the purple-green phenotype of traditional varieties. These improved traits give them significant advantages in standardized cultivation, but their secondary metabolite profiles are not yet clear.

[0005] This study fails to effectively utilize new varieties of Angelica dahurica from Hangzhou as research subjects. It lacks a comprehensive approach to integrate strategies such as polygonal quality defect filtering, extended virtual molecular formula libraries, source fragment ion exclusion, and characteristic molecular networks to systematically annotate its coumarin components. Furthermore, by comparing with traditional Angelica dahurica, it employs multidimensional statistical methods such as principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and volcano plots to screen for characteristic coumarins. This aims to lay the foundation for NAD quality evaluation and application development, and to provide methodological references for research on other new medicinal plant varieties.

[0006] In view of this, we propose a method for screening quality markers of traditional Chinese medicine and a group of quality markers for Angelica dahurica varieties. Summary of the Invention

[0007] The purpose of this invention is to provide a method for screening quality markers of traditional Chinese medicine and a group of quality markers for Angelica dahurica varieties, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A method for screening quality markers of traditional Chinese medicine includes the following steps: S1. The newly selected Angelica dahurica varieties and local varieties are respectively crushed, extracted, and... Detection; S2. The mass spectrometry data were preprocessed using MZmine 2.5 software; S3. Multi-level mass defect windows were used to screen coumarin components; S4. Coumarin components were screened using an expanded virtual chemical formula library. S5, Remove source ions; S6. Construct a mass spectrometry molecular network to identify coumarin components; S7. Screening of differential metabolites between newly bred Angelica dahurica varieties and local varieties.

[0009] In a further embodiment, the noise level in S2 is set to... This allows for the acquisition of more potential coumarin-like component nodes.

[0010] In a further embodiment, S5 establishes a triple screening mechanism based on the characteristics of fragment ions within the source: S51. Time correlation: The retention time of fragments within the source must differ from that of their parent ion by no more than ±0.2 min; S52. Mass Correlation: The m / z value of in-source fragments should be smaller than that of the parent ion and should be present in the parent ion. In the spectrum (mass deviation <10 ppm); S53. Spectral Similarity: Intra-source Fragments and Parent Ions The cosine similarity of the spectra must be >0.6. The above multidimensional criteria can effectively distinguish between real metabolites and source interference signals.

[0011] In a further embodiment, the mass spectrometry molecular network construction parameters in S6 are as follows: based on the potential coumarin characteristic peaks obtained by screening according to the GNPS standardization workflow, a characteristic molecular network (FBMN) is constructed, with a parent ion mass tolerance of 0.0075 Da, a fragment ion mass tolerance of 0.0075 Da, and the network edge connection threshold is set to cosine similarity ≥ 0.6, and the number of matched fragments ≥ 4.

[0012] In a further embodiment, the identification in step S6 employs a three-level confidence level evaluation system: Level 1 confidence: retention time of characteristic peaks, m / z of precursor ion, and The spectrum perfectly matched the reference standard. Level 2 confidence: Matches the GNPS spectral library and has been manually verified; Level 3 confidence: Through simulation analysis using NAP tools, combined with differences in FBMN cluster nodes and database retrieval to infer the structure, this grading system strictly adheres to metabolite identification standards to ensure the reliability of the identification results.

[0013] In a further embodiment, the differential metabolite screening criteria in S7 use variable importance projection value (VIP) > 1, fold difference (FC) > 2, and p < 0.01 as significance thresholds.

[0014] In a further scheme, differential metabolites were visualized and screened using volcano plots, and the content distribution of significantly differential coumarin components was displayed using heatmap analysis. All statistical analyses were performed in triplicate, and data preprocessing included standardized steps such as missing value imputation and log2 transformation.

[0015] In a further protocol, a Thermo Fisher Scientific (Germany) Vanquish ultra-high performance liquid chromatography system coupled with an Orbitrap Q Exactive™ HF-X high-resolution mass spectrometer was used for analysis in both positive and negative ion modes.

[0016] In a further embodiment, this embodiment further defines the quality marker group for newly bred Angelica dahurica varieties, wherein the quality markers in S7 are: Cnidicin, 9-Hydroxy-4-[(2S)-2-hydroxy-3-methylbut-3-enoxy] furo [3,2-g]chromen-7-one,8-Geranyloxy-5-methoxypsoralen,Byakangelicol,4-[(3S)-3-(5,5-dimethyl-4-oxofuran-2-yl)-3-hydroxybut oxy]furo[3,2-g]chromen-7-one, 4-(2,3-dihydroxy-3-methylbutoxy)-9-hydroxyfuro[3,2-g]chromen-7-one and tO-Methylbyakangelicin.

[0017] A group of quality markers for Angelica dahurica varieties was obtained by screening using the aforementioned method for screening quality markers of traditional Chinese medicine.

[0018] Compared with the prior art, the present invention provides a method for screening quality markers of traditional Chinese medicine and a group of quality markers for Angelica dahurica varieties, which has the following beneficial effects: 1. This method for screening quality markers of traditional Chinese medicine and the quality marker group of Angelica dahurica varieties, in order to solve the bottleneck problem of insufficient coverage of traditional databases in the annotation of secondary metabolites of traditional Chinese medicine, adopts high resolution... The technology (including low-temperature extraction, gradient elution, and high-precision mass spectrometry parameter control) features high resolution and high sensitivity, with good reproducibility, thus obtaining highly sensitive and reproducible mass spectrometry data, providing a reliable foundation for in-depth analysis of complex components.

[0019] 2. This method for screening quality markers of traditional Chinese medicine and the quality marker group of Angelica dahurica varieties aims to overcome the technical limitation of insufficient annotation depth of secondary metabolites in new plant varieties. It integrates polygonal quality defect filtering (PMDF), extended virtual molecular formula library screening, source fragment ion triple rejection mechanism and characteristic molecular network (FBMN) construction strategy to achieve efficient and accurate annotation of multiple coumarin components in Angelica dahurica.

[0020] 3. This method for screening quality markers of traditional Chinese medicine and the group of quality markers for Angelica dahurica varieties: In order to meet the industry's need for rapid identification of newly bred Angelica dahurica varieties, OPLS-DA and heatmap comparative analysis are used to achieve rapid differentiation of the metabolic phenotypes of newly bred varieties (plant height about 70cm, lodging resistant, and uniform purple basal stems) from traditional farm varieties (plant height 1-1.5m, purple-green stems). The quality markers obtained can be used for rapid screening of newly bred Angelica dahurica varieties. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the AF synthesis results of Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the AF synthesis results of Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the AD synthesis results of Embodiment 3 of the present invention; Figure 4 This is a flowchart of the overall method of the present invention; Figure 5 A block diagram of the triple screening mechanism based on the characteristics of fragment ions within the source in S5 of this invention; Figure 6 The identification process in step S6 of this invention employs a three-level confidence evaluation system.

[0022] in: Figure 1 The results of Example 1 are shown below, based on different noise threshold methods for screening coumarin quality defects: (AC) Distribution of potential coumarin feature peaks screened by MDF1 (polygonal quality defect filtering 1) for methods 1-3; (DF) Comparison of screening results of methods 1-3 by MDF2 (optimized polygonal quality defect filtering 2), with colored scatter dots representing feature peaks that meet the quality defect threshold, and gray background representing the original dataset. Figure 2 As shown in Example 2, the screening efficiency of the extended virtual library is evaluated: (AC) Distribution of potential compounds obtained by screening the extended virtual coumarin library using methods 1-3; (EF) Volcano plots compare the peak detection efficiency of different screening strategies (MDF1, MDF2, virtual library screening, adduct matching, and in-source fragment filtering) in methods 1-3 from multiple dimensions. Red scatter dots indicate significantly upregulated peaks (FC>2, p<0.01), and blue dots indicate significantly downregulated peaks. Figure 3 The results of Example 3 show the differences in coumarin components among different origins of Angelica dahurica: (A) Principal component analysis (PCA) score plots showed significant separation between the NAD and TAD groups (R²X=0.82, Q²=0.75); (B) Orthogonal partial least squares discriminant analysis (OPLS-DA) model validated the differences between groups (R²Y=0.91, Q²=0.83); (C) Volcano plot visualization screened differentially expressed metabolites (VIP>1, FC>2, p<0.01); (D) Heatmap shows the cluster analysis of the relative content of 21 significantly different coumarin components among the groups (red indicates high expression, blue indicates low expression). Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Please see Figures 1-6 This invention takes Angelica dahurica as the research object and specifically provides a technical solution: A method for screening quality markers of traditional Chinese medicine includes the following steps: S1. The newly selected Angelica dahurica varieties and local varieties are respectively crushed, extracted, and... Detection; S2. The mass spectrometry data were preprocessed using MZmine 2.5 software; S3. Multi-level mass defect windows were used to screen coumarin components; S4. Coumarin components were screened using an expanded virtual chemical formula library. S5, Remove source ions; S6. Construct a mass spectrometry molecular network to identify coumarin components; S7. Screening of differential metabolites between newly bred Angelica dahurica varieties and local varieties.

[0025] Example 1 like Figure 1 As shown, this embodiment mainly uses a mass defect screening method to screen for potential coumarin components in Angelica dahurica. The specific steps are as follows: Step 1: After drying the Angelica dahurica root sample, grind it into powder. Accurately weigh 100 mg of the sample powder and add 0.5 mL of pre-cooled 80% methanol solution for vortex extraction. Incubate the sample in an ice bath for 5 min, then centrifuge at 15,000 × g for 20 min at 4°C. Take an appropriate amount of the supernatant and dilute it with HPLC-MS grade water to a final methanol concentration of 53%. Transfer the diluted solution to a new centrifuge tube and centrifuge again at 4°C and 15,000 × g for 20 min. Finally, take the supernatant for HPLC-tandem mass spectrometry (HPLC-MS / MS). )analyze; Step 2: The analysis was performed using a Thermo Fisher Scientific (Germany) Vanquish ultra-high performance liquid chromatography system coupled with an OrbitrapQ Exactive™ HF-X high-resolution mass spectrometer in positive / negative ion mode. Chromatographic separation was performed using a Hypersil Gold column (100 mm × 2.1 mm, 1.9 μm), with a mobile phase of an aqueous solution containing 0.1% formic acid (phase A) and methanol (phase B), at a flow rate of 0.2 mL / min.

[0026] The gradient elution program is as follows: 0-1.5 min: 2% B phase; 1.5-4.5 min: 2% B-85% B phase; 4.5-14.5 min: 85% B-100% B phase; 14.5-14.6 min: 100% B-2% B; 14.6-16 min: 2% B phase.

[0027] Mass spectrometry parameter settings: ion source spray voltage 3.5 kV, capillary temperature 320°C, sheath gas flow rate 35 psi, auxiliary gas flow rate 10 L / min, S-lens RF level 60, auxiliary gas heating temperature 350°C. The raw mass spectrometry data were converted to mzXML format by MSConvert software, and then MZmine 2.5 was used for chromatographic peak detection and peak alignment to study the effect of noise threshold on characteristic peak detection. Step 3: Set three different noise level parameters ( , and The three methods are defined as Method 1, Method 2 and Method 3, respectively. The minimum intensity threshold of the characteristic peak is set to five times the corresponding noise level. The processed csv and mgf format files are uploaded to the Global Natural Product Molecular Network (GNPS) platform after feature screening to construct the Feature Molecular Network (FBMN). Step 4: Establish a Polygonal Mass Defect Filtering (PMDF) model based on a self-built database (containing 153 coumarin components of Angelica dahurica reported in the literature and their 78 unique molecular formulas). Step 5: Calculate the exact mass of the [M+H]+ ions of each compound in the database (exact element mass: C=12.0000 Da, H=1.0078 Da, O=15.9949 Da, S=31.9721 Da), plot a two-dimensional scatter plot of mass defects, and select the outer points to construct a polygonal filter window (mass defect tolerance ±0.001 Da). Step 6: Apply the PMDF model to filter the ion list output by MZmine to finally obtain the characteristic ion set of potential coumarin components.

[0028] Table 1: Examples from Example 1 Key Parameter Table for Testing Example 2 like Figure 2 As shown, this embodiment mainly involves screening for potential coumarin-like components in Angelica dahurica using an expanded virtual compound molecular formula library. The specific steps are as follows: Step 1: After drying the Angelica dahurica root sample, grind it into powder. Accurately weigh 100 mg of sample powder, add 0.5 mL of pre-cooled 80% methanol solution, and vortex extract. After incubating the sample in an ice bath for 5 min, centrifuge at 15,000 × g for 20 min at 4°C. Take an appropriate amount of supernatant and dilute it with liquid chromatography-mass spectrometry grade water to a final methanol concentration of 53%. Transfer the diluted solution to a new centrifuge tube and centrifuge again at 4°C and 15,000 × g for 20 min. Finally, take the supernatant for liquid chromatography-tandem mass spectrometry (LC-MS / MS) analysis. Step 2: The analysis was performed using a Thermo Fisher Scientific (Germany) Vanquish ultra-high performance liquid chromatography system coupled with an OrbitrapQ Exactive™ HF-X high-resolution mass spectrometer in positive / negative ion mode. Chromatographic separation was performed using a Hypersil Gold column (100 mm × 2.1 mm, 1.9 μm), with a mobile phase of an aqueous solution containing 0.1% formic acid (phase A) and methanol (phase B), at a flow rate of 0.2 mL / min.

[0029] The gradient elution program is as follows: 0-1.5 min: 2% B phase; 1.5-4.5 min: 2% B-85% B phase; 4.5-14.5 min: 85% B-100% B phase; 14.5-14.6 min: 100% B-2% B; 14.6-16 min: 2% B phase.

[0030] Mass spectrometry parameter settings: ion source spray voltage 3.5 kV, capillary temperature 320°C, sheath gas flow rate 35 psi, auxiliary gas flow rate 10 L / min, S-lens RF level 60, auxiliary gas heating temperature 350°C. The raw mass spectrometry data were converted to mzXML format by MSConvert software, and then MZmine 2.5 was used for chromatographic peak detection and peak alignment to study the effect of noise threshold on characteristic peak detection. Step 3: Set three different noise level parameters ( , and The three methods are defined as Method 1, Method 2 and Method 3, respectively. The minimum intensity threshold of the characteristic peak is set to five times the corresponding noise level. The processed csv and mgf format files are uploaded to the Global Natural Product Molecular Network (GNPS) platform after feature screening to construct the Feature Molecular Network (FBMN). Step 4: By systematically analyzing the chemical structural characteristics of coumarin components in Angelica dahurica, we summarize their main molecular skeleton types, substituent types and their quantity distribution patterns. Virtual compound molecular formulas are generated through random combination, and after deduplication, an extended coumarin virtual molecular formula library is successfully constructed. Step 5: By precisely matching the theoretical m / z values ​​of the [M+H]+ ions corresponding to the virtual molecular formula (precise elemental mass: C=12.0000 Da, H=1.0078 Da, O=15.9949 Da, S=31.9721 Da) with the experimentally detected precursor ions (mass deviation tolerance <10 ppm), the efficient screening of precursor ions of potential coumarin components is finally achieved.

[0031] Example 3 like Figure 3 , Figure 5 and Figure 6 As shown, this embodiment mainly involves the elimination of in-source examples, structural annotation of coumarin components based on mass spectrometry molecular networks, and screening of mass biomarker groups. The specific steps are as follows: Step 1: Among the potential coumarin components obtained by mass defect screening and virtual compound library screening, there are also a large number of in-source examples. In-source fragment ions refer to secondary ions generated by the unexpected fragmentation of the analyte in the mass spectrometry ion source due to high energy or collision. Step 2: Establish a triple screening mechanism based on the characteristics of fragment ions within the source: (1) Time correlation: The retention time of fragments within the source must differ from that of their parent ion by ±0.2 min; (2) Mass correlation: The m / z value of the source fragment should be smaller than that of the parent ion and exist in the MS² spectrum of the parent ion (mass deviation <10 ppm); (3) Spectral similarity: The cosine similarity of the MS / MS spectrum of the source fragment and the parent ion should be >0.6. The above multidimensional criteria can effectively distinguish between real metabolites and source interference signals. Table 2: Criteria for In-Source Fragment Ion Removal Step 3: Following the GNPS standardized workflow, a feature molecular network (FBMN) is constructed based on the screened potential coumarin characteristic peaks. The key parameters are set as follows: parent ion mass tolerance 0.0075 Da, fragment ion mass tolerance 0.0075 Da, and network edge connection threshold set to cosine similarity ≥ 0.6 and matching fragment number ≥ 4. The network nodes are automatically compared with the GNPS spectral library (retaining matching results with cosine similarity > 0.6), and visualized using Cytoscape software (version 3.9.1) to achieve topological structure resolution of the compound clusters. Step 4: The identification process employs a three-level confidence level evaluation system. Level 1 confidence level means that the retention time of the characteristic peak, the m / z of the precursor ion, and the MS / MS spectrum are completely matched with the reference standard; Level 2 confidence level refers to compounds that match the GNPS spectral library and have been manually verified. 3. The Level 3 confidence level was analyzed using computer simulation with the NAP tool on the GNPS platform. Step 5: By combining the differences between nodes in the same cluster of FBMN and the structure search of the COCONUT database, possible structural formulas are manually inferred. This grading system strictly follows the metabolite identification standards to ensure the reliability of the identification results. Step 6: Perform multivariate statistical analysis using R language (version 4.2.1): Principal component analysis (PCA) was used to analyze the overall metabolic differences between the NAD and TAD groups; orthogonal partial least squares discriminant analysis (OPLS-DA) was used to screen for differential markers between groups, with significance thresholds of variable importance projection value (VIP) > 1, fold change (FC) > 2, and p < 0.01. Differential metabolites were visualized and screened using a volcano plot.

[0032] The distribution of significantly different coumarin components was visualized using heatmap analysis. All statistical analyses were performed in triplicate, and data preprocessing included standardized steps such as missing value imputation and log2 transformation.

[0033] In summary, by integrating ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS / MS) with polygonal quality defect filtering, expanded virtual molecular formula library screening, characteristic molecular networks, and computational strategies involving network annotation transfer, 95 coumarin compounds (43 simple coumarins, 45 furanocoumarins, and 7 dihydrofuranocoumarins) were successfully annotated. Furthermore, multivariate statistical analysis was used to obtain the quality markers in S7 as follows: Cnidicin, 9-Hydroxy-4-[(2S)-2-hydroxy-3-methylbut-3-enoxy] furo [3,2-g]chromen-7-one,8-Geranyloxy-5-methoxypsoralen,Byakangelicol,4-[(3S)-3-(5,5-dimethyl-4-oxofuran-2-yl)-3-hydroxybut oxy]furo[3,2-g]chromen-7-one, 4-(2,3-dihydroxy-3-methylbutoxy)-9-hydroxyfuro[3,2-g]chromen-7-one and tO-Methylbyakangelicin.

[0034] A new group of quality markers for Angelica dahurica varieties was obtained through screening using traditional Chinese medicine quality marker screening methods.

[0035] Table 3: Phenotypic characteristics of the newly selected Angelica dahurica The present invention has been described in detail above. However, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, any modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A method for screening quality markers of traditional Chinese medicine, characterized in that: Includes the following steps: S1. The newly selected Angelica dahurica varieties and local varieties are respectively crushed, extracted, and... Detection; S2. The mass spectrometry data were preprocessed using MZmine 2.5 software; S3. Multi-level mass defect windows were used to screen coumarin components; S4. Coumarin components were screened using an expanded virtual chemical formula library. S5, Remove source ions; S6. Construct a mass spectrometry molecular network to identify coumarin components; S7. Screening of differential metabolites between newly bred Angelica dahurica varieties and local varieties.

2. The method for screening quality markers of traditional Chinese medicine according to claim 1, characterized in that: The noise level in S2 is set as follows: This allows for the acquisition of more potential coumarin-like component nodes.

3. The method for screening quality markers of traditional Chinese medicine according to claim 1, characterized in that: The S5 establishes a triple screening mechanism based on the characteristics of fragment ions within the source: S51. Time correlation: The retention time of fragments within the source must differ from that of their parent ion by no more than ±0.2 min; S52. Mass Correlation: The m / z value of in-source fragments should be smaller than that of the parent ion and should be present in the parent ion. In the spectrum (mass deviation <10 ppm); S53. Spectral Similarity: Intra-source Fragments and Parent Ions The cosine similarity of the spectra must be >0.

6. The above multidimensional criteria can effectively distinguish between real metabolites and source interference signals.

4. The method for screening quality markers of traditional Chinese medicine according to claim 1, characterized in that: The mass spectrometry molecular network construction parameters in S6 are as follows: Based on the potential coumarin characteristic peaks obtained by screening according to the GNPS standardization workflow, a characteristic molecular network (FBMN) is constructed. The mass tolerance of the parent ion is 0.0075 Da, the mass tolerance of the fragment ion is 0.0075 Da, the network edge connection threshold is set to cosine similarity ≥ 0.6, and the number of matched fragments is ≥ 4.

5. The method for screening quality markers of traditional Chinese medicine according to claim 1, characterized in that: The identification in step S6 adopts a three-level confidence evaluation system: Level 1 confidence: retention time of characteristic peaks, m / z of precursor ion, and The spectrum perfectly matched the reference standard. Level 2 confidence: Matches the GNPS spectral library and has been manually verified; Level 3 confidence: Simulation analysis using NAP tool, combined with differences in FBMN cluster nodes and database retrieval to infer the structure.

6. The method for screening quality markers of traditional Chinese medicine according to claim 1, characterized in that: The differential metabolite screening criteria in S7 use significance thresholds such as variable importance projection value (VIP) > 1, fold difference (FC) > 2, and p < 0.

01.

7. The method for screening quality markers of traditional Chinese medicine according to claim 6, characterized in that: Differential metabolites were visualized and screened using volcano plots, and the content distribution of significantly different coumarin components was displayed using heatmap analysis. All statistical analyses were performed in triplicate, and data preprocessing included standardized steps such as missing value imputation and log2 transformation.

8. The method for screening quality markers of traditional Chinese medicine according to claim 1, characterized in that: Analysis was performed using a Thermo Fisher Scientific (Germany) Vanquish ultra-high performance liquid chromatography system coupled with an Orbitrap Q Exactive™ HF-X high-resolution mass spectrometer in both positive and negative ion modes.

9. The method for screening quality markers of traditional Chinese medicine according to claim 1, characterized in that: The quality indicator in S7 is: Cnidicin,9-Hydroxy-4-[(2S)-2-hydroxy-3-methylbut-3-enoxy]furo[3,2-g]chromen-7-one,8-Geranyloxy-5-methoxypsoralen,Byakangelicol,4-[(3S)-3-(5,5-dimethy l-4-oxofuran-2-yl)-3-hydroxybutoxy]furo[3,2-g]chromen-7-one, 4-(2,3-dihydroxy-3-methylbutoxy)-9-hydroxyfuro[3,2-g]chromen-7-on and tO-Methylbyakangelicin.

10. A group of quality markers for Angelica dahurica varieties, characterized in that: The quality marker group was obtained by screening the traditional Chinese medicine quality marker screening method according to any one of claims 1-9.

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