A method and system for analyzing components of a kidney-tonifying and essence-nourishing compound

Abstract

The application relates to the technical field of traditional Chinese medicine component analysis, and discloses a component analysis method and system of a kidney-tonifying and essence-nourishing compound. The method comprises the following steps: physically crushing a to-be-measured sample and performing multi-solvent gradient extraction to generate a multi-dimensional digital fingerprint; a dynamic component network model is constructed based on the fingerprint, wherein the model takes characteristic peaks as nodes and takes correlation strength and active synergistic relationship between peaks as edges; pharmacological priori knowledge rules are injected into the model; core efficacy component clusters, potential auxiliary component clusters and inter-cluster regulation pathways are identified by calculating the correlation between structure-activity relationship and metabolic paths; a targeted experiment is designed according to the identification result to verify and optimize the model, and a structured report is generated. The method converts discrete component lists into a systematic component correlation network, automatically realizes function cluster identification and pathway analysis, and upgrades the component identification to system efficacy analysis capability.

Description

Technical Field

[0001] This invention relates to the field of traditional Chinese medicine component analysis technology, specifically to a method and system for component analysis of a kidney-tonifying and essence-boosting compound. Background Technology

[0002] In the field of modern research on traditional Chinese medicine, the component analysis of kidney-tonifying and essence-boosting compounds typically employs analytical techniques such as chromatography and mass spectrometry to obtain the chemical fingerprint information of the samples. Existing technologies, by comparing the obtained characteristic peak data with standards or compound databases, can achieve qualitative identification and quantitative determination of one or more chemical components. The final output of this conventional method is often a discrete list of chemical component names and contents, or a spectrum labeled with a few indicator components.

[0003] This existing technical approach has limitations. Its analytical process ends with the individual identification of each component, lacking in-depth exploration and characterization of the intrinsic relationships between the numerous identified chemical components. Different characteristic peaks and the compounds they represent appear independent in the analytical results, and potential physical co-occurrence patterns and correlations in content changes between them are not effectively extracted and utilized. Furthermore, after obtaining the component list, the interpretation of their functions heavily relies on researchers manually searching literature and making subjective summaries—a reactive, fragmented, and inefficient approach. Existing technologies lack a computational framework that can automatically and systematically integrate the physical correlation information of chemical components with their known biological activities during the analytical process, and perform holistic reasoning based on pharmacological knowledge.

[0004] The technical problem this invention aims to solve is to overcome the shortcomings of existing methods that simply break down complex systems into isolated components, achieve a cognitive leap from "component list" to "component system," and automatically analyze the component combinations that directly contribute to the core efficacy, the component groups with auxiliary regulatory effects, and the potential synergistic pathways between them. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for analyzing the components of a kidney-tonifying and essence-enhancing compound, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a method for component analysis of a kidney-tonifying and essence-boosting compound, the method comprising: Physical disruption and multi-solvent gradient extraction were performed on the original sample of the kidney-tonifying and essence-benefiting compound to be tested to generate a multidimensional digital fingerprint of the compound. The multidimensional digital fingerprint is input into a preset compound basic feature library for matching and expansion. Each feature peak is associated with the candidate compound name, chemical formula and known biological activity description. Based on the association results, a dynamic component network model is constructed with feature peaks as nodes and inter-peak association strength and activity synergy as edges. Pharmacological prior knowledge rules are injected into the dynamic component network model. The nodes and edges in the model are traversed, the structure-activity relationship strength and metabolic pathway correlation between nodes are calculated, the core functional component clusters and potential auxiliary component clusters are identified, and the regulatory pathways between component clusters are marked. Based on the identified core functional component clusters and regulatory pathways, targeted validation experiments were designed to obtain experimental feedback data. The experimental feedback data was then used to perform parameter correction and confidence assessment on the dynamic component network model, ultimately generating a structured analysis report.

[0007] Preferably, the original sample of the kidney-tonifying and essence-benefiting compound to be tested undergoes physical disruption and multi-solvent gradient extraction to generate a multidimensional digital fingerprint of the kidney-tonifying and essence-benefiting compound to be tested, including: Obtain an extract containing multiple components, and simultaneously acquire morphological images and spectral scanning data of the original sample; Supercritical chromatography is performed on the extract of the multi-stage components to capture all characteristic peaks that appear during the separation process. The retention time, peak area and mass-to-charge ratio of each characteristic peak are spatiotemporally aligned and fused with the morphological image and spectral scanning data to generate a multidimensional digital fingerprint of the kidney-tonifying and essence-nourishing compound to be tested. The specific steps of the multi-solvent gradient extraction include: The physically broken original sample was first extracted using a non-polar solvent to separate the lipid-soluble components into an extract and residue. The residue was subjected to a second extraction using a polar solvent to separate the water-soluble component extract and the secondary residue. The secondary residue was subjected to a third extraction using a mixed solvent to separate the intermediate polar component extract. The lipid-soluble component extract, water-soluble component extract, and intermediate polar component extract were concentrated and preliminarily purified to obtain a three-stage extract for analysis. The morphological images capture the surface texture and color distribution of the sample using macro imaging technology, while the spectral scanning data obtains the overall chemical bond vibration information of the sample using a near-infrared spectrometer.

[0008] Preferably, the specific process of performing supercritical chromatographic separation is as follows: The three-stage extract to be analyzed was sequentially injected into a supercritical fluid chromatography system, and separation was performed using a combination of programmed pressure increase and gradient temperature change. The separated effluents are monitored online by an ultraviolet detector and a mass spectrometer. The ultraviolet detector records the characteristic ultraviolet absorption curve of each effluent component, and the mass spectrometer records the corresponding molecular ion peak and fragment ion information. The retention time, UV absorption curve, and mass-to-charge ratio information corresponding to the same elution component are bound together to form a characteristic peak data package; All feature peak data packets are arranged in order of retention time, and pixel region mapping is performed with the morphological images acquired in the initial identification stage. Band feature association is performed with the spectral scanning data to complete the assembly of the multidimensional digital fingerprint.

[0009] Preferably, the specific steps for constructing the dynamic component network model include: Each feature peak data packet in the multidimensional digital fingerprint is compared with the standard spectrum in the compound basic feature library; When the similarity exceeds a preset threshold, the feature peak corresponding to the feature peak data package is marked as an identified peak, and the compound name, chemical formula and biological activity description of the corresponding entry in the compound basic feature library are inherited. When the similarity is lower than a preset threshold, the feature peaks corresponding to the feature peak data packets are marked as unidentified peaks. Based on their mass-to-charge ratio information and fragmentation patterns, the fragmentation pattern database in the library is called to infer structural fragments and generate inferred compound categories and activity descriptions. Using all characteristic peaks as nodes, and the proximity of each characteristic peak on the chromatogram, the correlation of mass spectrometry fragments, and the inferred metabolic transformation relationship as edges, a component relationship network is initialized. The determined attributes of the identified peaks and the inferred attributes of the unidentified peaks are used as node attributes. The proximity, correlation and transformation relationship strength are quantified as edge weights to complete the construction of the dynamic component network model.

[0010] Preferably, the process of injecting pharmacological prior knowledge rules into the dynamic component network model, traversing the nodes and edges in the model, calculating the structure-activity relationship strength and metabolic pathway correlation between nodes, and identifying the core functional component clusters and potential auxiliary component clusters includes: The pharmacological prior knowledge rules define the set of target proteins, classical pathways of action, and known combination patterns of active ingredients related to the kidney-tonifying and essence-replenishing effects. The bioactivity descriptions of nodes in the dynamic component network model are matched with the target protein set and classical pathways to calculate the efficacy weight of each node in the context of tonifying the kidney and replenishing essence. Traverse the dynamic component network model to find the node clusters with high efficacy weights, and the subnetworks formed by connecting these nodes through high-weight edges. Mark such subnetworks as core efficacy component clusters. Around the core functional component cluster, identify the set of nodes that are connected to it through regulatory pathway edges and that enhance the stability or activity of the core cluster nodes, and mark them as potential auxiliary component clusters. The regulatory pathways specifically refer to the relationships between components that promote absorption, reduce metabolism, synergistically enhance effects, or antagonize transformation.

[0011] Preferably, the design and execution of the targeted verification experiment includes: Based on the identified core functional component clusters, the corresponding components are selectively enriched from the three-stage extract to be analyzed to prepare a verification sample; Design bioactivity tests at the cellular or in vitro enzyme levels, apply the validation samples to specific cell models or enzyme targets related to kidney tonification and essence replenishment, and detect changes in key biomarkers. The experimental feedback data obtained includes dose-response curves, half-maximal effective concentrations, and percentages of activation or inhibition of specific pathways. The activity intensity data from the experimental feedback data is fed back to the efficacy weight attribute of the corresponding node in the dynamic component network model, and the confidence scores of the corresponding node and associated edges are adjusted according to the consistency between the activity data and the predicted activity.

[0012] Preferably, the structured analysis report includes at least the following parts: The structured analysis report includes qualitative and quantitative results of the components, structure-activity relationship diagrams, and confidence ratings; The qualitative and quantitative results section lists detailed information on all identified and unidentified peaks, including retention time, presumed or confirmed compound name, quantitative or semi-quantitative results, and associated bioactivity descriptions. The structure-activity relationship diagram visually displays the dynamic component network model, highlighting the core functional component clusters, potential auxiliary component clusters, and regulatory pathways. The confidence rating section provides a comprehensive confidence level for each qualitative or quantitative conclusion in the report, based on the degree of matching, validation experimental data, and model consistency. The quantitative or semi-quantitative results are derived from the peak area in the characteristic peak data package, which is calculated by comparing it with the standard curve or by normalization.

[0013] Preferably, the step of matching the bioactivity descriptions of nodes in the dynamic component network model with the target protein set and classical pathways, and calculating the efficacy weight of each node in the context of tonifying the kidney and replenishing essence, specifically includes: A standardized mapping dictionary is established between the set of kidney-tonifying and essence-nourishing target proteins, classical pathways of action, and bioactivity descriptions of known effective components as defined in the pharmacological prior knowledge rules. Each mapping relationship in the mapping dictionary is associated with an initial matching score. Traverse each node in the dynamic component network model to obtain the bioactivity description text of the node; Natural language processing is performed on the bioactivity description text to extract keywords or phrases related to the target, pathway and pharmacological effects. The extracted keywords or phrases are matched with the standardized mapping dictionary to identify kidney-tonifying and essence-boosting target proteins and / or classical pathways related to the bioactivity description of the node. Based on the number of successfully matched target protein set elements, the number of action pathways, and the corresponding initial matching scores, and taking into account the connectivity of the node in the model, the preliminary efficacy weight of the node is calculated. Based on the efficacy component combination patterns defined in the pharmacological prior knowledge rules, the preliminary efficacy weights of multiple nodes existing in the same combination pattern are synergistically enhanced and corrected to obtain the final efficacy weight of each node in the context of tonifying the kidney and replenishing essence. The update mechanism of the compound basic feature library includes: When the analysis, verification and report generation phase confirms the accurate structure or new activity of the unidentified peak, a new standard entry is generated. The new standard entry includes at least the final determined multidimensional digital fingerprint features of the unidentified peak, compound structure information, and verified bioactivity data; The new standard entries will be added to the basic feature library of compounds for matching and expansion in subsequent analysis of other kidney-tonifying and essence-boosting compounds.

[0014] Preferably, the kidney-tonifying and essence-boosting compound to be tested is selected from one or more of the following components: At least one of the following: deer antler peptide, maca powder, cistanche, ginseng powder, eucommia male flower pollen, polygonatum powder, deer blood peptide, oyster peptide powder, wolfberry powder, burdock root powder, L-arginine, taurine, zinc lactate, vitamin B1, and vitamin B2.

[0015] Preferably, the present invention also includes a component analysis system for kidney-tonifying and essence-enhancing compounds, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the steps of the component analysis method for kidney-tonifying and essence-enhancing compounds as described above.

[0016] Compared with the prior art, the beneficial effects of the present invention are: After matching multidimensional digital fingerprints with a basic compound feature library, candidate information is associated with each feature peak, and a dynamic component network model is constructed with feature peaks as nodes and inter-peak correlation strength and activity synergy as edges. The inter-peak correlation strength edges quantify the interdependence of physical coexistence and signal changes of different chemical components in the sample, while the activity synergy edges define possible pharmacodynamic interaction patterns between them based on the known bioactivity descriptions of each component. This model structure changes the organization of component data, transforming originally discrete and independent feature peaks into interconnected nodes in a network. The physical coexistence patterns and potential functional connections between chemical components are simultaneously integrated into a computable relational graph, providing a data foundation with multiple correlation dimensions for subsequent analysis based on network topology and connectivity attributes.

[0017] In the dynamic network model, formalized pharmacological prior knowledge rules are injected, and the strength of structure-activity relationships and metabolic pathway correlations between nodes are calculated by traversing nodes and edges. Based on the correlation strength and the pharmacodynamic logic defined by the rules, the system automatically identifies sets of nodes that are closely connected in the network and collectively point to specific core biological activities, i.e., core functional component clusters, and distinguishes sets of nodes with supportive and regulatory functions as potential auxiliary component clusters. The model can automatically label known biological metabolic or signal regulation pathways that may exist between these component clusters based on the knowledge rule base. This achieves automated mapping and structured analysis from the physical correlation network of chemical components to the network for explaining specific biological functions. By procedurally integrating and reasoning about pharmaceutical knowledge, this method replaces the high dependence on manual experience interpretation, revealing the potential material basis and action pathways of multi-component synergistic effects in complex compounds through an objective and repeatable computational process. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the working principle of the component analysis method for the kidney-tonifying and essence-replenishing compounds described in this invention. Figure 2 This is a flowchart of supercritical chromatography separation and fingerprint assembly; Figure 3 A comparative chart showing the importance of kidney-tonifying and essence-boosting compounds under different analytical strategies; Figure 4 A flowchart for the design and execution of targeted validation experiments; Figure 5 The dose-response curves of different enrichments on testosterone secretion from testicular interstitial cells are shown. Detailed Implementation

[0019] 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.

[0020] Please see Figure 1 This invention provides a method for component analysis of a kidney-tonifying and essence-benefiting compound. The method includes: starting with physical disruption and multi-solvent gradient extraction of the original sample of the kidney-tonifying and essence-benefiting compound to be tested, obtaining an extract containing components of different polarities through this process, and simultaneously acquiring morphological images and spectral scanning data of the sample; then performing supercritical chromatography separation on these extracts to capture all characteristic peaks during the separation process, and spatiotemporally aligning and fusing the chromatographic and mass spectrometric information of each characteristic peak with the aforementioned images and spectral data to generate a multidimensional digital fingerprint of the kidney-tonifying and essence-benefiting compound to be tested. The generated multidimensional digital fingerprint is input into a preset compound basic feature library for matching and expansion, associating each characteristic peak with candidate compound names, chemical formulas, and known bioactivity descriptions, and based on the matching and inference results, constructing a dynamic component network model with characteristic peaks as nodes and inter-peak correlation strength and activity synergy as edges. Pharmacological prior knowledge rules are injected into the constructed dynamic component network model. These rules define the targets, pathways, and component combinations related to kidney tonification and essence replenishment. By traversing the nodes and edges in the model, the strength of structure-activity relationships and metabolic pathway correlations between nodes are calculated, thereby identifying core functional component clusters and potential auxiliary component clusters, and labeling the regulatory pathways between component clusters. Based on the identified core functional component clusters and regulatory pathway information, targeted validation experiments are designed and executed to obtain experimental feedback data. This feedback data is used to perform parameter calibration and confidence assessment on the dynamic component network model, ultimately generating a structured analysis report containing qualitative and quantitative results of components, structure-activity relationship diagrams, and confidence ratings.

[0021] In one embodiment of the present invention, an extract containing multiple components is obtained, and morphological images and spectral scanning data of the original sample are simultaneously acquired. The morphological images capture the surface texture and color distribution of the sample using macro imaging technology, and the spectral scanning data acquires the overall chemical bond vibration information of the sample using a near-infrared spectrometer. Supercritical fluid chromatography is performed on the extract containing the multiple components to capture all characteristic peaks appearing during the separation process. The retention time, peak area, and mass-to-charge ratio information of each characteristic peak are spatiotemporally aligned and fused with the morphological images and spectral scanning data to generate a multidimensional digital fingerprint of the kidney-tonifying and essence-benefiting compound to be tested. The specific steps of the multi-solvent gradient extraction include: first extraction of the physically fragmented original sample using a non-polar solvent to separate the lipid-soluble component extract and residue; second extraction of the residue using a polar solvent to separate the water-soluble component extract and secondary residue; third extraction of the secondary residue using a mixed solvent to separate the intermediate polar component extract; and concentration and preliminary purification of the lipid-soluble component extract, water-soluble component extract, and intermediate polar component extract to obtain a tertiary extract for analysis.

[0022] In this practical implementation, wolfberry (Glycyrrhiza uralensis) was used as the original sample for the kidney-tonifying and essence-nourishing compounds to be tested. The original wolfberry sample, after physical crushing, was subjected to a three-stage analyte extraction process using a multi-solvent gradient extraction method. Simultaneously, morphological images and spectral scanning data of the original wolfberry sample were acquired before extraction. Morphological images were captured using a macro imaging system equipped with a high-resolution lens, which recorded the unique wrinkled texture and uneven orange-red to dark red color distribution on the surface of the wolfberry sample. Spectral scanning data were acquired using a Fourier transform near-infrared spectrometer. The spectrometer scanned the entire sample, recording the absorption spectrum in the wavenumber range of 4000 cm⁻¹ to 10000 cm⁻¹. This spectrum carries the fundamental and overtone vibrational information of chemical bonds such as OH, CH, and NH in the sample.

[0023] In some embodiments, the specific steps of multi-solvent gradient extraction are performed in a temperature-controlled extraction apparatus. The physically crushed raw wolfberry sample is first extracted using n-hexane as a non-polar solvent at 50°C for 2 hours, followed by solid-liquid separation to obtain a lipid-soluble extract and a first-extraction residue. The first-extraction residue is then secondly extracted using pure water as a polar solvent at 90°C for 1.5 hours, followed by another solid-liquid separation to obtain a water-soluble extract and a second-extraction residue. A third extraction is performed using a 7:3 volume ratio ethanol-water mixture as an intermediate polar solvent at 70°C for 2 hours, followed by filtration to obtain an intermediate polar extract. The obtained lipid-soluble component extract, water-soluble component extract, and intermediate polar component extract were concentrated using a rotary evaporator at a suitable temperature. The concentrated extracts were then preliminarily purified using a solid-phase extraction column to finally obtain three clear analytes: a hexane extract, a water extract, and an ethanol-water extract.

[0024] In practice, supercritical fluid chromatography was used to separate the three-stage extracts. Hexane, water, and ethanol-water extracts were sequentially injected into the supercritical fluid chromatography system, using carbon dioxide as the mobile phase and adding a modifier. The separation process employed a programmed pressurization and gradient temperature control mode; for example, the column pressure was linearly increased from 10 MPa to 30 MPa, and the column temperature was programmed from 35°C to 60°C. The separated elutions were monitored online using a tandem UV detector and a high-resolution mass spectrometer. The UV detector recorded the characteristic UV absorption curves of each elution component at wavelengths of 254 nm and 330 nm, while the high-resolution mass spectrometer recorded the corresponding precise molecular ion peaks and fragment ion information in positive and negative ion modes using an electrospray ionization source. All characteristic peaks appearing during the separation process were captured, and the retention time, peak area, and mass-to-charge ratio information of each characteristic peak were spatiotemporally aligned and fused with previously acquired morphological images and near-infrared spectral data of Lycium barbarum. The fusion process involves establishing a mapping relationship between the retention time axis of the characteristic peaks and the wavenumber axis of the near-infrared spectrum and the pixel coordinate axis of the morphological image, generating a multidimensional digital fingerprint of wolfberry. Specifically, the fusion process refers to constructing a multidimensional data structure, namely the multidimensional digital fingerprint of wolfberry, by unifying the spatiotemporal reference and feature correlation of the characteristic peak information obtained from supercritical fluid chromatography, the overall chemical bond vibration information of wolfberry collected by near-infrared spectrometer, and the sample surface texture and color distribution information captured by macro imaging system. This fusion process includes the following steps: First, the characteristic peak information comes from the separation process of the three-stage extracts to be analyzed (including n-hexane extract, water extract, and ethanol-water extract) in a supercritical fluid chromatography system. The system adopts a programmed pressure increase and gradient temperature change mode, and records the retention time, peak area, mass-to-charge ratio, characteristic ultraviolet absorption curve, molecular ion peak, and fragment ion information of each eluting component through online monitoring by ultraviolet detector and mass spectrometer. Each eluting component corresponds to a characteristic peak data package, where the retention time serves as the basis for temporal positioning of the characteristic peak in the chromatogram. Secondly, near-infrared spectral information was acquired using a Fourier transform near-infrared spectrometer, scanning from 4000 cm⁻¹ to 10000 cm⁻¹. The fundamental and overtone vibrations of chemical bonds such as OH, CH, and NH in the wolfberry sample were recorded, forming a continuous spectral curve with wavenumber as the abscissa. This spectral data reflects the overall chemical composition and functional group distribution of the sample. Thirdly, morphological image information was captured using macro imaging technology, recording the texture and color distribution of the wolfberry sample surface, particularly the pixel intensity statistical characteristics in specific color channels (such as the red channel). The image is based on pixel coordinates, with each pixel corresponding to a specific location on the sample surface and its color and texture information.The key to the fusion process lies in associating the retention time axis of the characteristic peaks with the wavenumber axis of the near-infrared spectrum and the pixel coordinate axis of the morphological image. Specifically, by synchronously recording the retention time of the characteristic peaks with the acquisition time of the near-infrared spectrum, the characteristic peaks are associated with the absorption characteristics of specific wavenumber intervals in the near-infrared spectrum (such as wavenumber intervals related to sugars or glycosides), thereby establishing a connection between the characteristic peaks and the spectral bands reflecting specific chemical functional groups. At the same time, based on the principle of multi-solvent gradient extraction, the components extracted by solvents of different polarities may correspond to specific physical regions on the sample surface (such as areas rich in lipid glands or areas with darker colors). By analyzing the statistical characteristics of the pixel intensity of these regions in the morphological image, they are associated with the characteristic peak data package, thereby associating the pixel coordinates of the morphological image with the retention time of the characteristic peaks. Finally, each characteristic peak data packet is integrated with its corresponding near-infrared spectral band features and morphological image local feature codes. All characteristic peak data packets are arranged in order of retention time, forming a structured data file containing retention time, peak area, mass-to-charge ratio, ultraviolet absorption curve, near-infrared absorbance array, and image texture feature codes—that is, the multidimensional digital fingerprint of wolfberry. This fingerprint comprehensively characterizes the chemical and physical information of the sample under different analytical dimensions. This fusion process can be understood as being achieved using a multidimensional data matrix, where rows represent different characteristic peaks, and columns contain retention time, peak area, mass-to-charge ratio sequence, corresponding near-infrared characteristic band absorbance array, and associated image texture feature codes.

[0025] Optionally, during the generation of multidimensional digital fingerprints, a color-component correlation factor is introduced to quantify the potential relationship between the color distribution of morphological images and the concentration of chemical components. The color-component correlation factor is calculated by analyzing the statistical characteristics of pixel intensity in specific color channels of the image and the normalized values ​​of the corresponding feature peak areas. The formula is expressed as: Where: symbol Represents the color-component correlation factor, symbol Represents the total number of regions with significant color differences segmented from a morphological image, denoted by the symbol. Representing the An image region in a specified color channel (e.g., the average pixel intensity value of the red channel), symbol Representing all The mean of the average pixel intensity values ​​of each region in this color channel, denoted by [symbol]. The representation is associated with the first through spatial mapping. The normalized total peak area of ​​the feature peak set of each image region, denoted by [symbol]. Representing all The mean of the normalized total peak area corresponding to each region.

[0026] In some embodiments, data comparison is reflected in the number of characteristic peaks contributed by extracts with different polarity solvents. Analysis of wolfberry samples showed that the hexane extract captured 15 characteristic peaks after supercritical chromatography, the water extract captured 28 characteristic peaks, and the ethanol-water extract captured 35 characteristic peaks. The characteristic peak sets of these three extraction stages together constitute a complete multidimensional digital fingerprint of the wolfberry sample, with the ethanol-water extract contributing the most characteristic peaks, indicating that the intermediate polar solvent has a wide extraction coverage of relevant compounds in wolfberry. It can be understood that each characteristic peak data package not only contains chromatographic and mass spectrometric information, but also associates the visual texture features and near-infrared spectral fragments of specific regions of the sample through mapping relationships. Near-infrared spectral data, such as the spectral absorption band near wavenumber 5200 cm⁻¹, was correlated with multiple characteristic peaks separated from the ethanol-water extract with retention times in the range of 8.5 to 9.5 minutes. These characteristic peaks all showed fragment patterns related to sugars or glycosides in the mass spectrometry. Optionally, the assembly of the multidimensional digital fingerprint ultimately forms a structured data file, which indexes all feature peak data packets in order of retention time. Each data packet stores the retention time, peak area, mass-to-charge ratio list, ultraviolet absorption spectrum array, associated near-infrared spectral band identifier, and associated image region feature vector.

[0027] In some other embodiments, the kidney-tonifying and essence-boosting compound to be tested may be a compound preparation or a raw material for health food, containing one or more of the following active ingredients: deer antler peptide, maca powder, cistanche, ginseng powder, eucommia male flower pollen, polygonatum powder, deer blood peptide, oyster peptide powder, wolfberry powder, burdock root powder, L-arginine, taurine, zinc lactate, vitamin B1, vitamin B2, etc. These raw materials are usually present in the form of tablets, capsules, powders, etc., and may contain pharmaceutical or food-grade excipients such as microcrystalline cellulose, isomaltitol, and maltodextrin.

[0028] When analyzing the above-mentioned compound kidney-tonifying and essence-nourishing compounds using the method of this invention, attention should be paid to the consumption limits of some of the raw materials. For example, the recommended daily intake of artificially cultivated ginseng (cultivation period ≤ 5 years) should not exceed 3 grams, Eucommia ulmoides male pollen should not exceed 6 grams per day, and maca should not exceed 25 grams per day. During the analysis process of this invention, these limits can be used to conduct a compliance assessment of the quantitative results of the components, and this information should be noted in the structured analysis report.

[0029] Taking a certain kidney-tonifying and essence-boosting tablet as an example, its formula includes deer antler peptide, maca powder, cistanche, ginseng powder, eucommia male flower pollen, polygonatum powder, deer blood peptide, etc. Through the method of this invention, the active ingredients can be systematically identified, network-modeled, and their efficacy clusters analyzed. Particular attention can be paid to peptides, polysaccharides, glycosides, and other component groups that may be related to kidney tonification and essence boosting, and their synergistic effects can be evaluated through verification experiments.

[0030] This method can be used to analyze such compound products, enabling simultaneous identification and quantification of multiple components, and revealing potential synergistic or auxiliary relationships between different raw material components, providing a scientific basis for the analysis of the material basis of product efficacy, quality control, and formula optimization.

[0031] In one embodiment of the present invention, see [reference] Figure 2 The three-stage extract to be analyzed were sequentially injected into a supercritical fluid chromatography system, and separation was performed using a programmed pressurization and gradient temperature control mode. The separated elutions were monitored online using a UV detector and a mass spectrometer. The UV detector recorded the characteristic UV absorption curve of each elution component, and the mass spectrometer recorded the corresponding molecular ion peak and fragment ion information. The retention time, UV absorption curve, and mass-to-charge ratio information corresponding to the same elution component were bound together to form a characteristic peak data package. All characteristic peak data packages were arranged in order of retention time, and pixel-region mapping was performed with the morphological images acquired in the initial identification stage. Band feature correlation was also performed with the spectral scanning data to complete the assembly of the multidimensional digital fingerprint.

[0032] In practice, the concentrated and purified hexane extract, water extract, and ethanol-water extract are sequentially injected into the autosampler of the supercritical fluid chromatography (SCLC) system in the order of lipid solubility, water solubility, and intermediate polarity. The SCLC system uses a packed column with supercritical carbon dioxide as the main mobile phase, and methanol is pumped in proportionally as a modifier according to the properties of the extract. The separation process employs a combined programmed pressure increase and gradient temperature control. For example, for the ethanol-water extract, the column pressure is linearly increased from 12.0 MPa to 28.5 MPa over 15 minutes, while the column temperature is gradient-increased from 40°C to 55°C at a rate of 0.8°C per minute and held for 3 minutes. The system pressure is maintained stable by precisely controlling the back pressure valve.

[0033] In some embodiments, the separated elutions are monitored online using a UV detector and a mass spectrometer. The UV detector is equipped with a diode array and performs a full-wavelength scan in the wavelength range of 200 nm to 400 nm. For each elution chromatographic peak, the characteristic UV absorption curve at its maximum absorption wavelength is recorded, and the complete UV spectrum is saved. The mass spectrometer employs a high-resolution time-of-flight mass spectrometer, with an electrospray ionization source scanning alternately in positive and negative ion modes. It records the precise molecular ion peak and secondary fragment ion information corresponding to each elution component in real time, and the mass spectrometry data acquisition frequency is synchronized with the UV detection signal. The retention time, complete UV absorption spectrum data, and mass spectrum containing precise mass-to-charge ratio and fragment information for the same elution component are bundled together and packaged into a characteristic peak data package.

[0034] To evaluate the separation performance of the combined pressurization and gradient temperature control mode, a comprehensive separation performance index is introduced. This index combines two parameters: separation degree and peak capacity, and is calculated using a specific algorithm. The formula is expressed as: Where: symbol Represents the overall effectiveness index of the separation process, symbol Represents the total number of adjacent peak pairs that are separated from the baseline in a single separation run, with the symbol […]. Representing the Resolution of adjacent chromatographic peaks, symbol It is a weighting coefficient, symbol This represents the number of chromatographic peaks detected per unit time during the separation run, i.e., peak capacity.

[0035] In practice, all characteristic peak data packets are arranged in ascending order of their corresponding retention times, forming an ordered list of chromatographic peaks. This list is then mapped pixel-wise to the morphological images of the original wolfberry samples collected in the initial stage. The mapping relationship is established based on the theoretical correlation between the types of compounds extracted by different polarity extraction solutions and the different tissue structures of the samples. For example, characteristic peaks of lipid-soluble components are mapped to pixels in areas rich in lipid glands in the image. Simultaneously, the characteristic peak data packets are correlated with the near-infrared spectral data of the original wolfberry samples. For instance, characteristic peaks indicating glycosides in mass spectrometry are correlated with the characteristic absorption bands of hydroxyl groups in the near-infrared spectrum. In essence, the data assembly process is completed through an integrated software platform that integrates the ordered list of characteristic peak data packets, the image pixel mapping table, and the spectral band correlation table to generate a unified, multi-dimensional digital fingerprint file. In some embodiments, the data comparison is reflected in the completeness of the characteristic peak data packets under different separation conditions. The number of characteristic peak data packets obtained by using the combined mode of programmed pressure increase and gradient temperature change is 78, while the number of data packets obtained by using only isothermal pressure and isothermal conditions to separate the same sample is 61. The combined mode shows better separation capability and captures more potential chemical composition information.

[0036] In one embodiment of the present invention, each feature peak data packet in the multidimensional digital fingerprint is compared with a standard spectrum in a basic compound feature library for similarity. When the similarity exceeds a preset threshold, the feature peak corresponding to the feature peak data packet is marked as an identified peak, and inherits the compound name, chemical formula, and biological activity description of the corresponding entry in the basic compound feature library. When the similarity is below the preset threshold, the feature peak corresponding to the feature peak data packet is marked as an unidentified peak, and based on its mass-to-charge ratio information and fragmentation pattern, the fragmentation pattern database in the library is called to infer structural fragments, generating inferred compound categories and activity descriptions. Using all feature peaks as nodes, and the proximity of each feature peak on the chromatogram, the correlation of mass spectrometry fragments, and the inferred metabolic transformation relationship as edges, a component relationship network is initialized. The determined attributes of the identified peaks and the inferred attributes of the unidentified peaks are used as node attributes, and the proximity, correlation, and transformation relationship strength are quantified as edge weights to complete the construction of the dynamic component network model. The method also includes an update mechanism for a basic compound feature library. When the analysis process confirms the accurate structure or new activity of an unidentified peak, a new standard entry is generated. The new standard entry includes at least the final determined multidimensional digital fingerprint features of the unidentified peak, compound structure information, and verified bioactivity data. The new standard entry is then added to the basic compound feature library.

[0037] In practice, each characteristic peak data packet in the multidimensional digital fingerprint is compared with the standard spectra in the preset compound basic feature library for similarity. The similarity comparison is performed by calculating a comprehensive score based on multiple indicators, including mass spectrometry fragment ion matching degree, consistency of maximum absorption wavelength in the ultraviolet spectrum, and relative deviation of retention time. The preset similarity threshold is set at 0.85. When the calculated comprehensive similarity score exceeds the preset threshold of 0.85, the characteristic peak corresponding to the characteristic peak data packet is marked as an identified peak, and the detailed information of the corresponding entry in the compound basic feature library is automatically inherited. When the comprehensive similarity score is below the preset threshold of 0.85, the characteristic peak corresponding to the characteristic peak data packet is marked as an unidentified peak. For unidentified peaks, the system automatically calls the fragmentation pattern database built into the compound basic feature library to infer structural fragments based on their accurate mass-to-charge ratio information and fragment ion patterns. The fragmentation pattern database contains characteristic fragmentation pathways of common natural products. By comparison, the system generates the inferred compound category "flavonoid C-glycoside" and the inferred bioactivity description "may have anti-inflammatory and germ cell protective effects."

[0038] In some embodiments, a structural similarity score is calculated. The structural similarity score is obtained by weighted calculation of mass spectrometry similarity, ultraviolet spectral similarity, and retention time index similarity. The formula is expressed as: Where: symbol Represents structural similarity score, symbol , , These represent the weights for mass spectrometry similarity, ultraviolet spectral similarity, and retention time index similarity, respectively. ,symbol The symbol represents the mass spectrometry similarity calculated by comparing the intensity and distribution of fragment ions in the characteristic peak data package with those in the standard spectrum. The symbol represents the UV spectral similarity calculated by comparing the UV absorption spectral profiles of the characteristic peak data package with those of the standard spectrum. The retention time index is calculated by comparing the measured retention time of the characteristic peak data package with the predicted retention time index of the standard spectrum under the same chromatographic conditions.

[0039] In the specific implementation, a component relationship network is initialized using all labeled characteristic peaks as nodes. Edges between nodes are established based on three relationships: the proximity of retention times of each characteristic peak in the supercritical chromatogram, the correlation of whether shared characteristic fragments or complementary ion pairs exist in mass spectrometry fragment ions, and the metabolic transformation relationship inferred based on known biological metabolic transformation laws. The determined attributes of identified peaks and the inferred attributes of unidentified peaks are used as attribute labels for nodes. The retention time proximity, mass spectrometry fragment correlation, and the strength of metabolic transformation relationships are quantified as edge weights, completing the construction of the dynamic component network model. This dynamic component network model can be understood as being stored in a graph data structure in the computer. Node objects contain fields such as compound identifier, type, and activity description, while edge objects contain fields for relationship type and weight value. Data comparison reflects the degree of structured information before and after model construction. Before construction, there was only a list of 78 isolated characteristic peak data packets; after construction, a network model containing 78 nodes and 215 edges is formed, revealing the potential connections between peaks.

[0040] Optionally, the implementation also includes a mechanism for updating a basic compound feature library. When subsequent analysis and verification stages confirm the accurate structure of an unidentified peak as "kaempferol-3-O-rutinoside" through NMR spectroscopy or reference standard comparison, and obtain verified bioactivity data on its ability to promote testosterone secretion from testicular interstitial cells, the system generates a new standard entry. The new standard entry includes at least the final determined multidimensional digital fingerprint of the unidentified peak, the compound structure information "kaempferol-3-O-rutinoside," and the verified bioactivity data. The new standard entry is added to the basic compound feature library. In some embodiments, data comparison is reflected in the increase of entries in the basic compound feature library. Before analyzing the wolfberry sample, the basic compound feature library contained 12,000 standard entries; after completing this analysis and updating, the number of entries in the basic compound feature library increases to 12,001. The newly added entries can be used for matching and expansion in subsequent analyses of other kidney-tonifying and sperm-benefiting compounds.

[0041] In one embodiment of the present invention, pharmacological prior knowledge rules are injected into the dynamic component network model. These rules define a set of target proteins, classical pathways, and known combinations of functional components related to the kidney-tonifying and essence-boosting effects. The bioactivity descriptions of nodes in the dynamic component network model are matched with the set of target proteins and classical pathways, and the efficacy weight of each node in the context of kidney-tonifying and essence-boosting is calculated. This process specifically includes: establishing a standardized mapping dictionary between the set of target proteins for kidney-tonifying and essence-boosting, classical pathways, and bioactivity descriptions of known functional components as defined in the pharmacological prior knowledge rules; each mapping relationship in the mapping dictionary is associated with an initial matching score; traversing each node in the dynamic component network model to obtain the bioactivity description text of the node; and performing natural language processing on the bioactivity description text to extract the relevant target proteins and pathways. Keywords or phrases related to pharmacological effects are extracted and matched with the standardized mapping dictionary to identify kidney-tonifying and essence-boosting target proteins and / or classical pathways related to the bioactivity description of the node. Based on the number of successfully matched target protein set elements, the number of pathways, and the corresponding initial matching scores, and considering the connectivity of the node in the model, the preliminary efficacy weight of the node is calculated. According to the efficacy component combination patterns defined in the pharmacological prior knowledge rules, the preliminary efficacy weights of multiple nodes existing in the same combination pattern are synergistically enhanced and corrected to obtain the final efficacy weight of each node in the context of kidney-tonifying and essence-boosting. The dynamic component network model is traversed to find node clusters with high efficacy weights and subnetworks formed by high-weight edges connecting these nodes, and these subnetworks are marked as core efficacy component clusters. Around the core efficacy component clusters, a set of nodes connected to them through regulatory pathway edges and enhancing the stability or activity of the core cluster nodes is identified and marked as potential auxiliary component clusters. The regulatory pathways refer to the relationships between components that promote absorption, reduce metabolism, synergistically enhance, or antagonize transformation.

[0042] In the specific implementation, the constructed dynamic component network model of wolfberry is used as the operation object. Pharmacological prior knowledge rules are injected and applied. These rules exist in the form of a computer-readable rule base, defining the target protein set, classical action pathways, and known functional component combination patterns related to the kidney-tonifying and essence-boosting effects. The target protein set includes protein entries such as "androgen receptor," "5α-reductase," and "antioxidant response element." The classical action pathways include pathway entries such as "testosterone synthesis signaling pathway" and "Nrf2-ARE antioxidant pathway." The known functional component combination patterns are described in the form of rules.

[0043] In some embodiments, a standardized mapping dictionary is established between the set of kidney-tonifying and sperm-benefiting target proteins, classical pathways of action, and bioactivity descriptions of known active ingredients, as defined in the pharmacological prior knowledge rules. Each mapping relationship in the mapping dictionary is associated with an initial matching score. Each node in the dynamic component network model is traversed to obtain the bioactivity description text of the node. For example, for a node labeled "betaine," its bioactivity description text is "promotes sperm motility and has antioxidant effects." Natural language processing is performed on the bioactivity description text to extract keywords or phrases related to the target proteins, pathways, and pharmacological effects. For example, the keywords "sperm motility" and "antioxidant" are extracted from the above text. The extracted keywords or phrases are matched with the standardized mapping dictionary to identify the kidney-tonifying and sperm-benefiting target proteins and / or classical pathways of action related to the node's bioactivity description.

[0044] In practice, the initial efficacy weight of a node is calculated based on the number of successfully matched target protein set elements, the number of action pathways, and the corresponding initial matching scores, while also considering the node's connectivity in the dynamic component network model. According to the efficacy component combination patterns defined in the pharmacological prior knowledge rules, the initial efficacy weights of multiple nodes existing within the same combination pattern are synergistically enhanced and corrected to obtain the final efficacy weight of each node in the context of tonifying the kidney and replenishing essence. This synergistic enhancement correction is achieved through an additive factor, the magnitude of which depends on the ratio of the number of nodes simultaneously identified in the combination pattern to the total number of expected nodes defined by the combination pattern. The formula is expressed as: Where: symbol The final effectiveness weight of the node, symbol The initial effectiveness weight of the node, symbol Represents the synergistic enhancement coefficient, symbol This represents the number of nodes in the current dynamic component network model that are within the same combinatorial pattern defined by the pharmacological prior knowledge rule and have been identified, denoted by the symbol. This represents the total number of nodes included in the combined pattern definition.

[0045] Optionally, the dynamic component network model is traversed to identify clusters of nodes with high final efficacy weights, as well as subnetworks formed by these nodes connected by high-weight edges. These subnetworks are labeled as core efficacy component clusters. Around the core efficacy component clusters, sets of nodes connected to them via regulatory pathway edges and enhancing the stability or activity of the core cluster nodes are identified and labeled as potential auxiliary component clusters. Regulatory pathways specifically refer to the relationships between components that promote absorption, reduce metabolism, synergistically enhance effects, or antagonize transformation. Data comparison reflects the changes in the importance ranking of network nodes before and after applying pharmacological prior knowledge rules. See Table 1, which shows a fragment of the results of weight calculation and cluster identification.

[0046] Table 1: Node Efficiency Weight Calculation and Component Cluster Identification Table It is understandable that in Table 1, "betaine" and "kaempferol-3-O-rutin" are identified as belonging to the same known combination pattern of active ingredients, "Pattern A". Therefore, their initial efficacy weights have been corrected for synergistic enhancement, resulting in higher final efficacy weights. "Betaine" and "Lycium barbarum polysaccharide" are identified as members of the core active ingredient cluster due to their high final efficacy weights and high edge weights between them. "Kaempferol-3-O-rutin," connected to the core cluster through a "synergistic enhancement" edge and capable of enhancing the activity of core nodes, is marked as a member of the potential auxiliary ingredient cluster. In some embodiments, data comparison is also reflected in the outputs of different analytical strategies. Without injecting pharmacological prior knowledge rules, there is a difference between the main components identified solely based on peak area size and the core active ingredient cluster members identified after injecting rules; the latter focuses more on the set of components with clear biological activity associations.

[0047] See Figure 3 This study demonstrates the differences in the proportion / weight of various compounds under two strategies: peak area analysis alone and the incorporation of pharmacological rules. Specifically, compounds such as betaine and wolfberry polysaccharides had proportions of 0.18 and 0.25 respectively in peak area analysis alone (orange column), while their weight proportions increased to 0.28 and 0.25 after incorporating pharmacological prior knowledge rules (blue column). Similarly, components such as kaempferol-3-O-rutin and ethyl linoleate had proportions of 0.08 and 0.30 in peak area analysis alone, but their weights were adjusted to 0.12 and 0.08 after incorporating the rules. This difference reflects the intervention effect of pharmacological rules: peak area-based analysis only reflects the content ratio of components, while after rule injection, the weight calculation combines the kidney-tonifying and essence-boosting bioactivity of compounds, target matching degree and synergistic relationship between components, so that the weight of components with clear efficacy association (such as betaine) is increased, while the weight of components with only high content but weak efficacy association (such as ethyl linoleate) is reduced, ultimately achieving the optimization of component importance ranking from "content-driven" to "efficacy-driven".

[0048] In one embodiment of the present invention, see [reference] Figure 4 Based on the identified core functional component clusters, corresponding components are directionally enriched from the tertiary extract to be analyzed, and validation samples are prepared. Cellular or in vitro enzyme-level bioactivity tests are designed, and the validation samples are applied to specific cell models or enzyme targets related to kidney tonification and essence replenishment to detect changes in key biomarkers. Experimental feedback data is obtained, including dose-response curves, half-maximal effective concentrations, and percentage activation or inhibition of specific pathways. The activity intensity data from the experimental feedback data is fed back to the efficacy weight attribute of the corresponding node in the dynamic component network model, and the confidence scores of the corresponding node and associated edges are adjusted according to the consistency between the activity data and the predicted activity. The structured analysis report includes at least the following parts: qualitative and quantitative results of components, structure-activity relationship diagram, and confidence rating. The qualitative and quantitative results section lists detailed information on all identified and unidentified peaks, including retention time, speculative or confirmed compound name, quantitative or semi-quantitative results, and associated bioactivity descriptions. The quantitative or semi-quantitative results are derived from the peak area in the characteristic peak data package, calculated by comparison with a standard curve or by normalization. The structure-activity relationship diagram visualizes the dynamic component network model, highlighting the core active ingredient clusters, potential auxiliary ingredient clusters, and regulatory pathways. The confidence rating section provides a comprehensive confidence level for each qualitative or quantitative conclusion in the report, based on matching degree, validation experimental data, and model consistency.

[0049] In specific implementation, preparative liquid chromatography (HPLC) is used to directionally enrich chromatographic peak components corresponding to "betaine" and "Lycium barbarum polysaccharide" from the three-stage analyte extracts, particularly the water extract and ethanol-water extract. The elution fractions are collected separately and freeze-dried to prepare validation samples, "betaine enrichment" and "Lycium barbarum polysaccharide enrichment." In some embodiments, cellular-level bioactivity tests are designed, and the validation samples are applied to specific cell models related to kidney tonification and sperm replenishment. Mouse testicular interstitial cell lines are selected as cell models, and the cells are divided into a control group, a "betaine enrichment" treatment group, a "Lycium barbarum polysaccharide enrichment" treatment group, and a mixed treatment group. The cell culture systems in the treatment groups are treated with validation samples at different concentration gradients. After culture for a specific time, cell supernatants and lysates are collected. Changes in key biomarkers are detected, including testosterone concentration in the cell culture supernatant, the activity of the antioxidant enzyme superoxide dismutase in the cell lysate, and indicators reflecting cell viability. The experimental feedback data included dose-response curves of testosterone secretion under different concentration treatments, the calculated half-maximal effective concentration for promoting testosterone secretion, and the percentage of activation of Nrf2-ARE antioxidant pathway reporter gene expression.

[0050] In practice, the activity intensity data from the experimental feedback is fed back to the efficacy weight attribute of the corresponding node in the dynamic component network model of Lycium barbarum. For example, the "betaine enrichment" experimental group showed a significant testosterone-promoting effect, and the measured activity intensity values ​​were compared with the activity description predicted by the "betaine" node in the model. Based on the consistency between the activity data and the predicted activity, the confidence scores of the corresponding nodes and associated edges were adjusted. This adjustment process follows a model parameter correction rule, which defines the mapping relationship between the deviation range between the experimental verification results and the model predictions and the adjustment amount of the confidence score. To quantify the adjustment magnitude, a confidence correction factor is introduced. The confidence correction factor is calculated based on the ratio of the logarithm of the experimentally measured half-maximum effective concentration to the expected value of the activity intensity predicted by the model.

[0051] Optionally, the structured analysis report shall include at least the following sections: qualitative and quantitative results of components, structure-activity relationship (SAR) diagram, and confidence level rating. The qualitative and quantitative results section lists detailed information on all identified and unidentified peaks, including retention times, speculative or confirmed compound names, quantitative or semi-quantitative results, and associated bioactivity descriptions. Quantitative or semi-quantitative results are derived from peak areas in the characteristic peak data package, calculated by comparison with a standard curve or by normalization. The SAR diagram section visualizes a dynamic component network model, using nodes of different shapes and colors to distinguish core active ingredient clusters, potential auxiliary ingredient clusters, and ordinary nodes, and using different types of lines to indicate regulatory pathways. The confidence level rating section provides a comprehensive confidence level for each qualitative or quantitative conclusion in the report based on matching degree, validation experimental data, and model consistency.

[0052] See Figure 5In the activity test analysis of the experiment, this figure presents the dose-dependent effects of betaine concentrate, wolfberry polysaccharide concentrate, and their mixture on testosterone secretion in mouse testicular interstitial cells. Specifically, the sample concentration (μg / mL) is plotted on the x-axis, and the relative value of testosterone secretion (%) is plotted on the y-axis. Testosterone is a core physiological indicator closely related to the effects of tonifying the kidney and replenishing essence, and its physiological functions directly align with the reproductive function regulation and the pursuit of abundant essence and qi targeted by tonifying the kidney and replenishing essence. The set of target proteins and classical pathways related to tonifying the kidney and replenishing essence as defined in the rules of pharmacological a priori knowledge partially involve the synthesis, secretion, and regulation of testosterone. When the core functional component cluster exerts its effects by acting on these targets or pathways, it often directly affects the related physiological metabolism of testosterone. In the targeted validation experiment, testosterone is a key indicator for evaluating whether the core functional component cluster effectively exerts its effects of tonifying the kidney and replenishing essence. The three curves correspond to the effect changes of the three treatment groups: with increasing concentration, the relative testosterone secretion of each group showed an upward trend and gradually stabilized; among them, the mixed group had the strongest promoting effect, followed by the betaine concentrate, and the Lycium barbarum polysaccharide concentrate had a relatively weaker effect. The half-maximal effective concentration (EC50) of each group is marked by dashed lines in the figure: the EC50 of the betaine concentrate is 85.3 μg / mL, the EC50 of the Lycium barbarum polysaccharide concentrate is 120.7 μg / mL, and the EC50 of the mixed group is 65.8 μg / mL, which shows that the mixed group has better activity (lower EC50 value) and also verifies the synergistic effect of the two components in promoting testosterone secretion.

[0053] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0054] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for component analysis of a kidney-tonifying and essence-boosting compound, characterized in that, The method includes: Physical disruption and multi-solvent gradient extraction were performed on the original sample of the kidney-tonifying and essence-benefiting compound to be tested to generate a multidimensional digital fingerprint of the compound; the kidney-tonifying and essence-benefiting compound to be tested was selected from one or more of the following components: deer antler peptide, maca powder, cistanche, ginseng powder, eucommia male flower pollen, polygonatum powder, deer blood peptide, oyster peptide powder, wolfberry powder, burdock root powder, L-arginine, taurine, zinc lactate, vitamin B1, and vitamin B2. Obtain an extract containing multiple components, and simultaneously acquire morphological images and spectral scanning data of the original sample; Supercritical chromatography is performed on the extract of the multi-stage components to capture all characteristic peaks that appear during the separation process. The retention time, peak area and mass-to-charge ratio of each characteristic peak are spatiotemporally aligned and fused with the morphological image and spectral scanning data to generate a multidimensional digital fingerprint of the kidney-tonifying and essence-nourishing compound to be tested. The multidimensional digital fingerprint is input into a preset compound basic feature library for matching and expansion. Each feature peak is associated with the candidate compound name, chemical formula and known biological activity description. Based on the association results, a dynamic component network model is constructed with feature peaks as nodes and inter-peak association strength and activity synergy as edges. Pharmacological prior knowledge rules are injected into the dynamic component network model. The nodes and edges in the model are traversed, and the strength of structure-activity relationships and metabolic pathway correlations between nodes are calculated. Core functional component clusters and potential auxiliary component clusters are identified, and the regulatory pathways between component clusters are labeled, including: The pharmacological prior knowledge rules define the set of target proteins, classical pathways of action, and known combination patterns of active ingredients related to the kidney-tonifying and essence-replenishing effects. The bioactivity descriptions of nodes in the dynamic component network model are matched with the target protein set and classical pathways to calculate the efficacy weight of each node in the context of tonifying the kidney and replenishing essence. Traverse the dynamic component network model to find the node clusters with high efficacy weights, and the subnetworks formed by connecting these nodes through high-weight edges. Mark such subnetworks as core efficacy component clusters. Around the core functional component cluster, identify the set of nodes that are connected to it through regulatory pathway edges and that enhance the stability or activity of the core cluster nodes, and mark them as potential auxiliary component clusters. The regulatory pathways specifically refer to the relationships between components that promote absorption, reduce metabolism, synergistically enhance effects, or antagonize transformation. Based on the identified core functional component clusters and regulatory pathways, targeted validation experiments were designed to obtain experimental feedback data. The experimental feedback data was then used to perform parameter correction and confidence assessment on the dynamic component network model, ultimately generating a structured analysis report.

2. The method for component analysis of a kidney-tonifying and essence-boosting compound according to claim 1, characterized in that, The original sample of the kidney-tonifying and essence-benefiting compound to be tested is subjected to physical disruption and multi-solvent gradient extraction to generate a multidimensional digital fingerprint of the compound, including: The specific steps of the multi-solvent gradient extraction include: The physically broken original sample was first extracted using a non-polar solvent to separate the lipid-soluble components into an extract and residue. The residue was subjected to a second extraction using a polar solvent to separate the water-soluble component extract and the secondary residue. The secondary residue was subjected to a third extraction using a mixed solvent to separate the intermediate polar component extract. The lipid-soluble component extract, water-soluble component extract, and intermediate polar component extract were concentrated and preliminarily purified to obtain a three-stage extract for analysis. The morphological images capture the surface texture and color distribution of the sample using macro imaging technology, while the spectral scanning data obtains the overall chemical bond vibration information of the sample using a near-infrared spectrometer.

3. The method for component analysis of a kidney-tonifying and essence-boosting compound according to claim 2, characterized in that, The specific process for performing supercritical chromatography separation is as follows: The three-stage extract to be analyzed was sequentially injected into a supercritical fluid chromatography system, and separation was performed using a combination of programmed pressure increase and gradient temperature change. The separated effluents are monitored online by an ultraviolet detector and a mass spectrometer. The ultraviolet detector records the characteristic ultraviolet absorption curve of each effluent component, and the mass spectrometer records the corresponding molecular ion peak and fragment ion information. The retention time, UV absorption curve, and mass-to-charge ratio information corresponding to the same elution component are bound together to form a characteristic peak data package; All feature peak data packets are arranged in order of retention time, and pixel region mapping is performed with the morphological image. Band feature association is performed with the spectral scan data to complete the assembly of the multidimensional digital fingerprint.

4. The method for component analysis of a kidney-tonifying and essence-boosting compound according to claim 3, characterized in that, The specific steps for constructing the dynamic component network model include: Each feature peak data packet in the multidimensional digital fingerprint is compared with the standard spectrum in the compound basic feature library; When the similarity exceeds a preset threshold, the feature peak corresponding to the feature peak data package is marked as an identified peak, and the compound name, chemical formula and biological activity description of the corresponding entry in the compound basic feature library are inherited. When the similarity is lower than a preset threshold, the feature peaks corresponding to the feature peak data packets are marked as unidentified peaks. Based on their mass-to-charge ratio information and fragmentation patterns, the fragmentation pattern database in the library is called to infer structural fragments and generate inferred compound categories and activity descriptions. Using all characteristic peaks as nodes, and the proximity of each characteristic peak on the chromatogram, the correlation of mass spectrometry fragments, and the inferred metabolic transformation relationship as edges, a component relationship network is initialized. The determined attributes of the identified peaks and the inferred attributes of the unidentified peaks are used as node attributes. The proximity, correlation and transformation relationship strength are quantified as edge weights to complete the construction of the dynamic component network model.

5. The method for component analysis of a kidney-tonifying and essence-boosting compound according to claim 4, characterized in that, The design and execution of the targeted validation experiment include: Based on the identified core functional component clusters, the corresponding components are selectively enriched from the three-stage extract to be analyzed to prepare a verification sample; Design bioactivity tests at the cellular or in vitro enzyme levels, apply the validation samples to specific cell models or enzyme targets related to kidney tonification and essence replenishment, and detect changes in key biomarkers. The experimental feedback data obtained includes dose-response curves, half-maximal effective concentrations, and percentages of activation or inhibition of specific pathways. The activity intensity data from the experimental feedback data is fed back to the efficacy weight attribute of the corresponding node in the dynamic component network model, and the confidence scores of the corresponding node and associated edges are adjusted according to the consistency between the activity data and the predicted activity.

6. The method for component analysis of a kidney-tonifying and essence-boosting compound according to claim 5, characterized in that, The structured analysis report shall include at least the following parts: The structured analysis report includes qualitative and quantitative results of the components, structure-activity relationship diagrams, and confidence ratings; The qualitative and quantitative results section lists detailed information on all identified and unidentified peaks, including retention time, presumed or confirmed compound name, quantitative or semi-quantitative results, and associated bioactivity descriptions. The structure-activity relationship diagram visually displays the dynamic component network model, highlighting the core functional component clusters, potential auxiliary component clusters, and regulatory pathways. The confidence rating section provides a comprehensive confidence level for each qualitative or quantitative conclusion in the report, based on the degree of matching, validation experimental data, and model consistency. The quantitative or semi-quantitative results are derived from the peak area in the characteristic peak data package, which is calculated by comparing it with the standard curve or by normalization.

7. The method for component analysis of a kidney-tonifying and essence-boosting compound according to claim 6, characterized in that, The step of matching the bioactivity descriptions of nodes in the dynamic component network model with the target protein set and classical pathways, and calculating the efficacy weight of each node in the context of tonifying the kidney and replenishing essence, specifically includes: A standardized mapping dictionary is established between the set of kidney-tonifying and essence-nourishing target proteins, classical pathways of action, and bioactivity descriptions of known effective components as defined in the pharmacological prior knowledge rules. Each mapping relationship in the mapping dictionary is associated with an initial matching score. Traverse each node in the dynamic component network model to obtain the bioactivity description text of the node; Natural language processing is performed on the bioactivity description text to extract keywords or phrases related to the target, pathway and pharmacological effects. The extracted keywords or phrases are matched with the standardized mapping dictionary to identify kidney-tonifying and essence-boosting target proteins and / or classical pathways related to the bioactivity description of the node. Based on the number of successfully matched target protein set elements, the number of action pathways, and the corresponding initial matching scores, and taking into account the connectivity of the node in the model, the preliminary efficacy weight of the node is calculated. Based on the efficacy component combination patterns defined in the pharmacological prior knowledge rules, the preliminary efficacy weights of multiple nodes existing in the same combination pattern are synergistically enhanced and corrected to obtain the final efficacy weight of each node in the context of tonifying the kidney and replenishing essence. The update mechanism of the compound basic feature library includes: When the structured analysis report confirms the accurate structure or new activity of the unidentified peak, a new standard entry is generated. The new standard entry includes at least the final determined multidimensional digital fingerprint features of the unidentified peak, compound structure information, and verified bioactivity data; The new standard entries will be added to the basic feature library of compounds for matching and expansion in subsequent analysis of other kidney-tonifying and essence-boosting compounds.

8. A component analysis system for a kidney-tonifying and essence-benefiting compound, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the step of the component analysis method of the kidney-tonifying and essence-nourishing compound as described in any one of claims 1 to 7.

Images