Traditional Chinese medicine genuine identification method based on artificial intelligence fusion multi-omics technology

Abstract

The invention provides a traditional Chinese medicine genuine identification method, system and device based on an artificial intelligence fusion multi-omics technology, a medium and a program product, and relates to the field of medicinal material identification. The method comprises the following steps: acquiring metallomics data and metabonomics data of a medicinal material to be detected; calculating CPS response values of key feature elements in the metal omics data; processing the metabonomics data to obtain mass spectrum data of peak information of key chemical components in the data; and inputting the CPS response values of the key characteristic elements and the key chemical component peak information into a genuine production area identification model to obtain a classification result of whether the key characteristic elements belong to genuine production areas or not. The invention also discloses a key characteristic element of Ca contained in metallomics data and key peak information of a peak 17307 contained in metabonomics data. According to the method, through combination with machine learning, rapid capturing of complex system features is realized, a high-precision classification model is established, and traceability of traditional Chinese medicine genuine features is realized.

Description

Technical Field

[0001] The present invention relates to the field of medicinal material identification, and more specifically, to a method, device, medium and program product for identifying the authenticity of traditional Chinese medicine based on artificial intelligence fusion multi-omics technology. Background Art

[0002] "Authentic medicinal materials" refer to Chinese medicinal materials with obvious regional characteristics, suitable growing environment, excellent quality, reasonable cultivation (breeding) and processing, and better quality than other origins. Take Peucedanum chinense as an example. Peucedanum chinense is a Chinese medicinal material with a long history. It has a prominent position in traditional Chinese medicine and is one of the key medicinal flavors in ancient classic prescriptions. With its unique pharmacological effects, it has played an important role in major public health events. Peucedanum chinense has a history of more than 1,500 years of medicinal use in my country and was first recorded in "Famous Doctors' Records". It has been recorded in all dynasties' herbal medicines and has been included in the "Chinese Pharmacopoeia" since 1963. After 2005, white flower Peucedanum chinense became the main variety, and purple flower Peucedanum chinense was included separately in 2010. In the current 2020 edition of the "Chinese Pharmacopoeia", Peucedanum chinense refers to the dried roots of white flower Peucedanum chinense.

[0003] Peucedanum is distributed in many provinces and regions in my country, and is most famous in the areas around Tianmu Mountain, such as Anhui, Zhejiang, and Jiangxi. In particular, "Faithful Peucedanum" is recognized as an authentic medicinal material. With the growth of market demand, Peucedanum has gradually developed from wild to simulated wild, woodland and field planting, and the main planting areas have expanded to Ningguo, Anhui, Hangzhou, Zhejiang, Chongqing, Sichuan, Guizhou and other places. However, the blind expansion of planting areas has led to uneven quality of Peucedanum, especially the key quality control indicator white flower peucedanum B is difficult to meet the standard in some production areas. Previous studies have found that the content of white flower peucedanum B is closely related to the production area. Based on this, the authenticity of Peucedanum is of great significance to its safety, effectiveness and quality controllability. Traceability of different production areas is crucial to improving the scientific nature of quality evaluation and strengthening supervision.

[0004] At present, the process of manually identifying the Radix Peucedanum using traditional technology is not only time-consuming, but also has a low recognition accuracy rate. There are also single-dimensional tests such as element fingerprints or secondary metabolites to achieve research on whether it is authentic. However, with the continuous expansion of planting areas and the uneven quality of traditional Chinese medicine, single-dimensional testing can no longer meet current needs. Therefore, it is urgent to establish an identification method that can accurately identify the authenticity of medicinal materials in order to achieve quality evaluation and scientific supervision of traditional Chinese medicines. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention provides a method, device, medium and program product for identifying the authenticity of traditional Chinese medicine based on artificial intelligence fusion multi-omics technology; the method of the present invention combines machine learning to achieve rapid capture of complex system characteristics, establish a high-precision classification model, and achieve traceability of the authenticity of traditional Chinese medicine.

[0006] The first aspect of the present application discloses a method for identifying the authenticity of traditional Chinese medicine based on artificial intelligence fusion multi-omics technology, the method comprising: 101. Obtain metallomics data and plant metabolomics data of the tested medicinal materials; 102, calculating the CPS response value of the key characteristic elements in the metallomics data; 103, processing the plant metabolomics data to obtain mass spectrometry data of key chemical component peak information in the data; 104, inputting the CPS response value of the key characteristic element and the key chemical component peak information into the authentic production area identification model to obtain a classification result of whether the wine belongs to the authentic production area.

[0007] In some embodiments, the authentic production area includes a first authentic production area and a second authentic production area; if the classification result of the authentic production area is obtained in 104, the metallomics data and metabolomics data of the medicinal material to be tested are input into the first authentic production area identification model for processing, and the classification result of whether it belongs to the first authentic production area is output; if a yes result is output, the operation is terminated, and the result that the medicinal material to be tested belongs to the first authentic production area is obtained; if a no result is output, the result that the medicinal material to be tested does not belong to the first authentic production area is obtained, and the metallomics data and metabolomics data of the medicinal material to be tested are input into the second authentic production area identification model for processing, and the classification result of whether it belongs to the second authentic production area is output; if a yes result is output, the operation is terminated, and the result that the medicinal material to be tested belongs to the second authentic production area is obtained; if a no result is output, the result that the medicinal material to be tested does not belong to the second authentic production area is obtained.

[0008] In some embodiments, the non-authentic production area includes a first non-authentic production area and a second non-authentic production area; if a classification result of not belonging to an authentic area is obtained in 104, the metallomics data and metabolomics data of the tested medicinal material are input into the first non-authentic production area identification model for processing, and a classification result of whether it belongs to the first non-authentic production area is output; if a yes result is output, the operation is terminated, and a result of the tested medicinal material belonging to the first non-authentic production area is obtained; if a no result is output, a result of not belonging to the first non-authentic production area is obtained, and the metallomics data and metabolomics data of the tested medicinal material are input into the second non-authentic production area identification model for processing, and a classification result of whether it belongs to the second non-authentic production area is output; if a yes result is output, the operation is terminated, and a result of the tested medicinal material belonging to the second non-authentic production area is obtained; if a no result is output, a result of not belonging to the second non-authentic production area is obtained; Optionally, the medicinal material to be tested includes: Peucedanum chinense; when the medicinal material to be tested is Peucedanum chinense, the non-authentic region includes Guizhou and Chongqing, Guizhou or Chongqing is the first non-authentic production area, Chongqing or Guizhou is the second non-authentic production area; Optionally, the metallomics data includes major elements and trace elements; trace elements include metal elements and non-metal elements.

[0009] Optionally, the metabolomics data is mass spectrometry data of secondary metabolites; and the mass spectrometry data is mass spectrometry data after cleaning processing to reduce redundancy and highly correlated features.

[0010] In some embodiments, the method for constructing the authentic production area identification model includes: Obtain metallomics data and plant metabolomics data of the training set of Peucedanum chinense from authentic and non-authentic areas, with authentic or non-authentic being the classification labels; Inputting the metallomics data into a first machine learning model to obtain a predicted classification result, comparing it with an authentic or non-authentic classification label, and optimizing the model according to the comparison result to obtain a metallomics model; Inputting the metabolomics data into a second machine learning model to obtain a predicted classification result, comparing it with the authentic or non-authentic classification label, and optimizing the model according to the comparison result to obtain a metabolomics model; The optimal model among the metallomics model and the metabolomics model obtained by different algorithms is selected, and the two optimal models are aggregated to obtain the authentic production area identification model; Optionally, the method for constructing the first authentic production area identification model or the second authentic production area identification model includes: Obtain metallomics data and metabolomics data of the training set of Radix Peucedanum from the first and second production areas, where the first and second production areas are authentic classification labels; Inputting the metallomics data into a first machine learning model to obtain a predicted classification result, comparing it with the authentic classification label, and optimizing the model according to the comparison result to obtain a metallomics model; inputting the metabolomics data into a second machine learning model to obtain a predicted classification result, comparing it with the authentic classification label, and optimizing the model according to the comparison result to obtain a metabolomics model; The optimal model of the metallomics model and the metabolomics model in the first authentic production area is selected, and the two optimal models are aggregated to obtain the first authentic production area identification model; the optimal model of the metallomics model and the metabolomics model in the second authentic production area is selected, and the two optimal models are aggregated to obtain the second authentic production area identification model; Optionally, the method for constructing the first non-authentic production area or the second non-authentic production area includes: Obtaining metallomics data and plant metabolomics data of the training set Peucedanum chinense from the first non-authentic production area and the second non-authentic production area, where the first non-authentic production area and the second non-authentic production area are non-authentic classification labels; Inputting the metallomics data into a first machine learning model to obtain a predicted classification result, comparing it with the non-authentic classification label, and optimizing the model according to the comparison result to obtain a metallomics model; inputting the plant metabolomics data into a second machine learning model to obtain a predicted classification result, comparing it with the non-authentic classification label, and optimizing the model according to the comparison result to obtain a metabolomics model; The optimal model of the metallomics model and the metabolomics model in the first non-authentic production area is selected, and the two optimal models are aggregated to obtain the first non-authentic production area identification model; the optimal model of the metallomics model and the metabolomics model in the second non-authentic production area is selected, and the two optimal models are aggregated to obtain the second non-authentic production area identification model; Optionally, before building the model, the training set medicinal materials are also subjected to oversampling, and the oversampling includes: duplicating or amplifying minority class data points to balance the number of samples of each category in the training data; the minority class data points include any one or more of the following: specific origin, specific ingredient content; Optionally, the aggregation method of the aggregation model includes: bootstrap aggregation, boosting, stacking, mixing, etc.

[0011] In some embodiments, the method used by the first machine learning model and / or the second machine learning model includes: RF, KNN, Rpa, SVM, XGB; Optionally, when the medicinal material to be tested is Peucedanum chinense, the regions include Zhejiang and Anhui, Zhejiang or Anhui is the first production area, and Anhui or Zhejiang is the second production area; Optionally, a model of the authentic production area using the XGB algorithm in the metallomics model and a model of the authentic production area using the KNN algorithm in the metabolomics model are selected and aggregated to obtain an aggregated model of the authentic production area; Optionally, the key characteristic element includes Ca element; Optionally, the key characteristic elements further include: Mn element and Mg element; Optionally, the peak information includes: peak 17307.

[0012] The second aspect of the present application discloses a method for identifying the authenticity of traditional Chinese medicine based on artificial intelligence fusion multi-omics technology, the method comprising: 201, obtaining CPS response values ​​and mass spectrum data of key peak information of key characteristic elements of the tested medicinal material, wherein the key characteristic elements include Ca element; the key peak information includes: peak 17307; 202, inputting the CPS response value of the key characteristic element of the target Ca element and the mass spectrum data of the target peak information into the authentic production area identification model for processing to obtain a classification result of whether it belongs to the authentic production area.

[0013] In some embodiments, the key characteristic elements further include Mn element and Mg element; Optionally, the key feature element further includes: S element, B element; the peak information includes one or more of the following: peak 16696, peak 13779, peak 19774, peak 17630; Optionally, if a classification result of belonging to an authentic production area is obtained, the Mg element and / or the Mn element and / or the B element and / or the peak 16696 and / or the peak 19774 are input into the first authentic production area identification model and the second authentic production area identification model to obtain a result of whether it belongs to the first authentic production area or the second authentic production area; Optionally, if a classification result is obtained that the product belongs to a non-authentic production area, the S element and / or the Mg element and / or the peak 13779 and / or the peak 17630 are input into the first non-authentic production area identification model and the second non-authentic production area identification model to obtain the result of whether the product belongs to the first non-authentic production area or the second non-authentic production area.

[0014] The third aspect of the present application discloses a computer device, which includes: a memory and a processor; the memory is used to store a computer program; and the processor executes the computer program to implement the steps of the above method.

[0015] A fourth aspect of the present application discloses a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0016] A fifth aspect of the present application discloses a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.

[0017] This application has the following beneficial effects: 1. This application innovatively discloses a method for identifying the authenticity of traditional Chinese medicine based on artificial intelligence fusion multi-omics technology. This method uses inorganic and organic substances to combine recognition patterns, and is used to identify the authenticity of traditional Chinese medicine with Herba Aspergi as a model. Based on metallomics and metabolomics, element fingerprints are integrated with chemical composition profiles. In the recognition process, the classification result of whether it belongs to the authentic production area is first realized according to the key characteristic elements, and then the different production areas contained in the authentic production area or non-authentic production area are further distinguished according to the classification results. For example, in this application, Herba Aspergi is taken as an example, the authentic production area recognition model is first trained through the training set samples, and the key characteristic elements obtained in the training process are directly input into the authentic production area recognition model in the application stage to obtain the result of whether it belongs to the authentic production area; if it belongs to the authentic production area, it is further input into the recognition model of different authentic production areas according to different key characteristic elements, and the result is output; if it belongs to the non-authentic production area, it is further input into the recognition model of different non-authentic production areas according to different key characteristic elements, and the result is output. The recognition result is accurate and reliable, the recognition route is clear, and it has good practicality and applicability.

[0018] 2. In the process of model construction, this application innovatively uses oversampling technology and aggregation models. Oversampling technology improves the ability of machine learning models to identify minority samples, providing more accurate and reliable solutions for practical application scenarios such as quality control of traditional Chinese medicine and efficacy evaluation. The aggregation model reduces the prediction error of a single model and improves the accuracy of the prediction; reduces the risk of overfitting and enhances the generalization ability of the model; the data source and information are sufficient, and the evaluation is more comprehensive; the prediction efficiency is significantly improved; and it has good practicality and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 is a schematic diagram of a method flow chart provided by the first aspect of an embodiment of the present invention; Figure 2 is a schematic diagram of a method flow chart provided by the second aspect of an embodiment of the present invention; Figure 3 Schematic diagram of a Chinese medicine authenticity identification system based on artificial intelligence fusion multi-omics technology provided by an embodiment of the present invention; Figure 4 It is a schematic diagram of a system for identifying the authenticity of traditional Chinese medicine based on artificial intelligence fusion multi-omics technology provided by a first aspect of another embodiment of the present invention; Figure 5is a schematic diagram of a computer device provided by an embodiment of the present invention; Figure 6 is a schematic diagram of the architecture of an exemplary computing device provided by an embodiment of the present invention; Figure 7 is a schematic diagram of a storage medium provided by an embodiment of the present invention; Figure 8 : is a schematic diagram of the chemometric analysis results of the element fingerprint profiles of authentic and non-authentic production areas provided by an embodiment of the present invention; wherein, Figure 8 A is the PCA result analysis, Figure 8 B is the PLSDA analysis result, Figure 8 C is the cluster heat map analysis result; Fig. 9 : is a schematic diagram of the chemometric analysis results of the metabolomics profiles of authentic and non-authentic production areas provided in an embodiment of the present invention; wherein, Fig. 9 A is the PCA result analysis, Fig. 9 B is the PLSDA analysis result, Fig. 9 C is the cluster heat map analysis result; Fig.10 It is a ROC curve diagram of the authentic production area established by integrating the element fingerprint provided by the embodiment of the present invention with five machine learning algorithms; Fig.11 is a schematic diagram of a correlation analysis diagram of secondary metabolites before and after correlation removal provided by an embodiment of the present invention; wherein, Fig.11 A is before removal, Fig.11 B is after removal; Fig.12 It is a ROC curve diagram of the non-authentic production area established by integrating five machine learning algorithms for secondary metabolites provided by an embodiment of the present invention; Fig.13 It is a ROC curve diagram of the authentic production area established by the aggregation model of metallomics fusion metabolomics provided in the embodiment of the present invention; Fig.14 are the top fifteen representative components or elements with outstanding importance of the authentic production area variables provided by the embodiment of the present invention; among them, Fig.14 A and B are the importance distribution of the element model. Fig.14 C and D show the importance distribution of chemical components. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0022] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

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

[0024] Figure 1 The present invention provides a method for identifying the authenticity of traditional Chinese medicine based on artificial intelligence fusion multi-omics technology. Specifically, the method comprises the following steps: 101: Obtain metallomics data and metabolomics data of the medicinal materials to be tested; In some embodiments, the metallomics data includes major elements and trace elements; trace elements include metal elements and non-metal elements.

[0025] In some embodiments, the metabolomics data is mass spectrometry data of secondary metabolites; and the mass spectrometry data is mass spectrometry data after cleaning to reduce redundancy and highly correlated features.

[0026] 102: Calculate the CPS response value of the key characteristic elements in the metallomics data; In some embodiments, the CPS response value is the response value after internal standard correction and blank background subtraction.

[0027] 103: Process the metabolomics data to obtain mass spectrometry data of key chemical component peak information in the data; 104: Inputting the CPS response value of the key characteristic element and the key chemical component peak information into an authentic production area identification model to obtain a classification result of whether the product belongs to an authentic production area.

[0028] In some embodiments, the method for constructing the authentic production area identification model includes: Obtain metallomics and metabolomics data of the training set of Radix Peucedanum from authentic and non-authentic areas, with authentic or non-authentic being the classification labels; Inputting the metallomics data into a first machine learning model to obtain a predicted classification result, comparing it with an authentic or non-authentic classification label, and optimizing the model according to the comparison result to obtain a metallomics model; Inputting the metabolomics data into a second machine learning model to obtain a predicted classification result, comparing it with the authentic or non-authentic classification label, and optimizing the model according to the comparison result to obtain a metabolomics model; The optimal model among the metallomics model and the metabolomics model obtained by different algorithms is selected, and the two optimal models are aggregated to obtain the authentic production area identification model; In some embodiments, the authentic production area includes a first authentic production area and a second authentic production area; if the classification result of the authentic production area is obtained in 104, the metallomics data and metabolomics data of the medicinal material to be tested are input into the first authentic production area identification model for processing, and the classification result of whether it belongs to the first authentic production area is output; if a yes result is output, the operation is terminated, and the result that the medicinal material to be tested belongs to the first authentic production area is obtained; if a no result is output, the result that the medicinal material to be tested does not belong to the first authentic production area is obtained, and the metallomics data and metabolomics data of the medicinal material to be tested are input into the second authentic production area identification model for processing, and the classification result of whether it belongs to the second authentic production area is output; if a yes result is output, the operation is terminated, and the result that the medicinal material to be tested belongs to the second authentic production area is obtained; if a no result is output, the result that the medicinal material to be tested does not belong to the second authentic production area is obtained.

[0029] Optionally, the method for constructing the first authentic production area identification model or the second authentic production area identification model includes: Obtain metallomics data and plant metabolomics data of the training set of Radix Peucedanum from the first and second production areas, where the first and second production areas are authentic classification labels; Inputting the metallomics data into a first machine learning model to obtain a predicted classification result, comparing it with the authentic classification label, and optimizing the model according to the comparison result to obtain a metallomics model; inputting the metabolomics data into a second machine learning model to obtain a predicted classification result, comparing it with the authentic classification label, and optimizing the model according to the comparison result to obtain a metabolomics model; The optimal model of the metallomics model and the metabolomics model in the first authentic production area is selected, and the two optimal models are aggregated to obtain the first authentic production area identification model; the optimal model of the metallomics model and the metabolomics model in the second authentic production area is selected, and the two optimal models are aggregated to obtain the second authentic production area identification model; In some embodiments, the non-authentic production area includes a first non-authentic production area and a second non-authentic production area; if a classification result of not belonging to an authentic area is obtained in 104, the metallomics data and metabolomics data of the tested medicinal material are input into the first non-authentic production area identification model for processing, and a classification result of whether it belongs to the first non-authentic production area is output; if a yes result is output, the operation is terminated, and a result of the tested medicinal material belonging to the first non-authentic production area is obtained; if a no result is output, a result of not belonging to the first non-authentic production area is obtained, and the metallomics data and metabolomics data of the tested medicinal material are input into the second non-authentic production area identification model for processing, and a classification result of whether it belongs to the second non-authentic production area is output; if a yes result is output, the operation is terminated, and a result of the tested medicinal material belonging to the second non-authentic production area is obtained; if a no result is output, a result of not belonging to the second non-authentic production area is obtained; Optionally, the method for constructing the first non-authentic production area or the second non-authentic production area includes: Obtaining metallomics data and metabolomics data of the training set of Radix Peucedanum from the first non-authentic production area and the second non-authentic production area, wherein the first non-authentic production area and the second non-authentic production area are non-authentic classification labels; Inputting the metallomics data into a first machine learning model to obtain a predicted classification result, comparing it with the non-authentic classification label, and optimizing the model according to the comparison result to obtain a metallomics model; inputting the metabolomics data into a second machine learning model to obtain a predicted classification result, comparing it with the non-authentic classification label, and optimizing the model according to the comparison result to obtain a metabolomics model; The optimal model of the metallomics model and the metabolomics model in the first non-authentic production area is selected, and the two optimal models are aggregated to obtain the first non-authentic production area identification model; the optimal model of the metallomics model and the metabolomics model in the second non-authentic production area is selected, and the two optimal models are aggregated to obtain the second non-authentic production area identification model; Optionally, the medicinal materials to be tested include: Peucedanum chinense, Peucedanum purpurogenum, Saposhnikovia divaricata, etc.; when the medicinal material to be tested is Peucedanum chinense, the non-official regions include Guizhou and Chongqing, Guizhou or Chongqing is the first non-official production area, Chongqing or Guizhou is the second non-official production area; Optionally, before building the model, the training set medicinal materials are also subjected to oversampling, and the oversampling includes: duplicating or amplifying minority class data points to balance the number of samples of each category in the training data; the minority class data points include any one or more of the following: specific origin, specific ingredient content; Optionally, the aggregation method of the aggregation model includes: bootstrap aggregation, boosting, stacking, mixing, etc.

[0030] In some embodiments, the method used by the first machine learning model and / or the second machine learning model includes: RF, KNN, Rpa, SVM, XGB; In some embodiments, when the medicinal material to be tested is Peucedanum chinense, the regions include Zhejiang and Anhui, Zhejiang or Anhui is the first region of production, and Anhui or Zhejiang is the second region of production; specifically, the model of the Guizhou region using the SVM algorithm in the metallomics model and the model of the Guizhou region using the SVM algorithm in the metabolomics model are selected to aggregate to obtain an aggregate model of the Guizhou region; the model of the Anhui region using the RF algorithm in the metallomics model and the model of the Anhui region using the KNN algorithm in the metabolomics model are selected to aggregate to obtain an aggregate model of the Anhui region, the model of the Zhejiang region using the KNN algorithm in the metallomics model and the model of the Zhejiang region using the XGB algorithm in the metabolomics model are selected to aggregate to obtain an aggregate model of the Zhejiang region; optionally, the model of the Chongqing region using the XGB algorithm in the metallomics model and the model of the Chongqing region using the SVM algorithm in the metabolomics model are selected to aggregate to obtain an aggregate model of the Chongqing region, Optionally, the key characteristic element includes Ca element; optionally, the key characteristic element also includes: Mn element, Mg element; optionally, the peak information includes: peak 17307; optionally, the key characteristic element also includes: S element, B element; optionally, the peak information includes one or more of the following: peak 16696, peak 13779, peak 19774, peak 17630.

[0031] In some embodiments, the second aspect of the present application provides a method for identifying the authenticity of traditional Chinese medicine based on artificial intelligence fusion multi-omics technology, such as Figure 2 As shown, the method includes: 201, obtaining CPS response values ​​and mass spectrum data of key peak information of key characteristic elements of the tested medicinal material, wherein the key characteristic elements include Ca element; the key peak information includes: peak 17307; 202, inputting the CPS response value of the key characteristic element of the target Ca element and the mass spectrum data of the target peak information into the authentic production area identification model for processing to obtain a classification result of whether it belongs to the authentic production area.

[0032] In some embodiments, the key characteristic elements further include Mn element and Mg element; Optionally, the key feature element further includes: S element, B element; the peak information includes one or more of the following: peak 16696, peak 13779, peak 19774, peak 17630; Optionally, if a classification result belonging to an authentic production area is obtained, the Mg element and / or the Mn element and / or the B element and / or the peak 16696 and / or the peak 19774 are input into the first authentic production area identification model and the second authentic production area identification model to obtain the result of whether it belongs to the first authentic production area or the second authentic production area; specifically, when the first authentic production area is Anhui and the second authentic production area is Zhejiang, the Mn element and the peak 16696 are input into the first authentic production area identification model to obtain the result of whether it belongs to the first authentic production area; the Mg element, the B element and the peak 19774 are input into the second authentic production area identification model to obtain the result of whether it belongs to the second authentic production area.

[0033] Optionally, if a classification result of belonging to a non-authentic production area is obtained, the S element and / or the Mg element and / or the peak 13779 and / or the peak 17630 are input into the first non-authentic production area identification model and the second non-authentic production area identification model to obtain the result of whether it belongs to the first non-authentic production area or the second non-authentic production area. Specifically, when the first non-authentic production area is Guizhou and the second non-authentic production area is Chongqing, the S element, the Mg element, and the peak 13779 are input into the first non-authentic production area identification model to obtain the result of whether it belongs to the first non-authentic production area; the Mg element and the peak 17630 are input into the second non-authentic production area identification model to obtain the result of whether it belongs to the second non-authentic production area.

[0034] In some embodiments, if the result is that the medicinal material comes from an authentic production area, it can be concluded that the medicinal material is authentic.

[0035] Figure 5 is a schematic diagram of a computer device provided by an embodiment of the present invention, such as Figure 5 As shown, the device may include: one or more processors, and one or more memories; wherein the memories store computer-readable codes, and when the computer-readable codes are run by the one or more processors, the method described above may be executed.

[0036] The processor in this embodiment can be an integrated circuit chip with signal processing capabilities. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, operations and logic block diagrams in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., which can be an X86 architecture or an ARM architecture.

[0037] In general, various example embodiments of the present disclosure may be implemented in hardware or dedicated circuits, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device. When various aspects of the disclosed embodiments are illustrated or described as block diagrams, flow charts, or using some other graphical representation, it will be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general purpose hardware or controllers or other computing devices, or some combination thereof as non-limiting examples.

[0038] For example, the method or device according to the embodiment of the present disclosure may also be implemented by Figure 6 The architecture of the computing device 3000 shown in FIG. Figure 6 As shown, the computing device 3000 may include a bus 3010, one or more CPUs 3020, a read-only memory (ROM) 3030, a random access memory (RAM) 3040, a communication port 3050 connected to a network, an input / output component 3060, a hard disk 3070, etc. The storage device in the computing device 3000, such as ROM 3030 or hard disk 3070, may store various data or files used for processing and / or communication of the method provided by the present disclosure and program instructions executed by the CPU. The computing device 3000 may also include a user interface 3080. Of course, Figure 6 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 6 One or more components of a computing device are shown.

[0039] The embodiment of the present invention also provides a computer-readable storage medium, such as Figure 7As shown, it is a schematic diagram of a storage medium provided by an embodiment of the present invention, and a computer readable instruction 4010 is stored on the computer storage medium 4020. When the computer readable instruction 4010 is executed by the processor, the method according to the embodiment of the present disclosure described with reference to the above figures can be executed. The computer readable storage medium in the embodiment of the present disclosure can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. The non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM) or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DR RAM). It should be noted that the memory of the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory. It should be noted that the memory of the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0040] The embodiments of the present disclosure further provide a computer program product or system, including a computer program, which implements the steps of the above method when executed by a processor.

[0041] In some embodiments, this embodiment also discloses a Chinese medicine authenticity identification system based on artificial intelligence fusion multi-omics technology, such as Figure 3 As shown, the system comprises: A first data acquisition module 301 is used or configured to acquire metallomics data and metabolomics data of the medicinal material to be tested; A first data processing module 302, used or configured to calculate the CPS response value of the key characteristic element in the metallomics data; A second data processing module 303 is used or configured to process the metabolomics data to obtain mass spectrometry data of key chemical component peak information in the data; The first classification result output module 304 is used or configured to input the CPS response value of the key characteristic element and the key chemical component peak information into the authentic production area identification model to obtain the classification result of whether it belongs to the authentic production area.

[0042] In some embodiments, the classification result output module 304 specifically includes: the authentic production area includes a first authentic production area and a second authentic production area; if the classification result of the authentic production area is obtained in 304, the metallomics data and metabolomics data of the tested medicinal material are input into the first authentic production area identification model for processing, and the classification result of whether it belongs to the first authentic production area is output; if the output result is yes, the operation is terminated, and the result that the tested medicinal material belongs to the first authentic production area is obtained; if the output result is no, the result that the tested medicinal material does not belong to the first authentic production area is obtained, and the metallomics data and metabolomics data of the tested medicinal material are input into the second authentic production area identification model for processing, and the classification result of whether it belongs to the second authentic production area is output; if the output result is yes, the operation is terminated, and the result that the tested medicinal material belongs to the second authentic production area is obtained; if the output result is no, the result that the tested medicinal material does not belong to the second authentic production area is obtained. The non-authentic production areas include a first non-authentic production area and a second non-authentic production area; if a classification result of not belonging to an authentic area is obtained in 304, the metallomics data and metabolomics data of the tested medicinal material are input into the first non-authentic production area identification model for processing, and a classification result of whether it belongs to the first non-authentic production area is output; if a yes result is output, the operation is terminated, and a result of the tested medicinal material belonging to the first non-authentic production area is obtained; if a no result is output, a result of not belonging to the first non-authentic production area is obtained, and the metallomics data and metabolomics data of the tested medicinal material are input into the second non-authentic production area identification model for processing, and a classification result of whether it belongs to the second non-authentic production area is output; if a yes result is output, the operation is terminated, and a result of the tested medicinal material belonging to the second non-authentic production area is obtained; if a no result is output, a result of not belonging to the second non-authentic production area is obtained.

[0043] In some embodiments, another embodiment further discloses a Chinese medicine authenticity identification system based on artificial intelligence fusion multi-omics technology, such as Figure 4 As shown, the system comprises: The second data acquisition module 401 is used or configured to acquire the mass spectrum data of the CPS response value and key peak information of the key characteristic element of the tested medicinal material, wherein the key characteristic element includes the Mg element; the key peak information includes any one or more of the following: peak 16696, peak 13779, peak 19774, peak 17630; The second classification result output module 402 is used or configured to input the CPS response value of the key characteristic element of the target Mg element and the mass spectrum data of the target peak information into the authentic production area identification model for processing to obtain a classification result of whether it belongs to the authentic production area. Specific embodiments

[0045] This study uses Peucedanum chinense as a model Chinese medicine. To address the problem of authenticity traceability and quality identification, it innovatively integrates non-targeted and targeted metallomics, plant metabolomics and machine learning technologies to build a set of efficient and accurate analysis strategies. By comprehensively analyzing the elemental fingerprint and secondary metabolite profile of Peucedanum chinense, combined with the construction of intelligent models, the authenticity of Peucedanum chinense was quickly and accurately identified. This study can provide new ideas, methods and tools for the quality evaluation of Peucedanum chinense, and set a benchmark for the improvement of the overall quality evaluation system of Chinese medicine and scientific supervision.

[0046] 1. Materials and Methods 1.1 Materials and reagents 1.1.1 Instruments: Inductively coupled plasma mass spectrometer (Shimadzu Corporation, Japan, model 2030LF); microwave digester (CEM Corporation, USA, model MARS6); electronic balance (METTLER TOLEDO, model XSE205DU); ultrapure water system (Millipore Corporation, USA, model Milli-Q Advantage A10); acid removal heater (Beijing Donghang Technology Instrument Co., Ltd., model BHW-C); ultra-high performance liquid chromatography (Thermo Scientific, model Vanquish); Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Scientific), equipped with Xcalibur3.2 software processing system and Compound Discover 2.0 software (Thermo Scientific).

[0047] 1.1.2 Reagents: chromatographic grade nitric acid (Sigma, USA); Al (aluminum), Ag (silver), As (arsenic), Ba (barium), B (boron), Be (beryllium), Ca (calcium), Cu (copper), Cd (cadmium), Co (cobalt), Cr (chromium), Fe (iron), Hg (mercury), K (potassium), Mg (magnesium), Mn (manganese), Mo (molybdenum), Na (sodium), Ni (nickel), Pb (lead), Rb (rubidium), Sr (strontium), Sb (antimony), Se (selenium), Sn (tin), Ti (titanium), Tl (thallium), V (vanadium), Zn (zinc) single element standard solutions were purchased from the National Institute of Metrology, China; the tuning solution was a mixed standard solution of Li, Y, Ce, Tl, and Co (1 μg / L); the internal standard solution was a mixed internal standard solution containing 100 μg / mL Ge (Agilent, USA) Chromatographic methanol (DiKMA, mass spectrometry grade), chromatographic acetonitrile (DiKMA, mass spectrometry grade), mass spectrometry grade formic acid (Thermo Fisher Scientific, mass spectrometry grade, batch number A117-50), and deionized water were purified by Milli-Q.

[0048] 1.1.3 Samples: This study collected 73 batches of white flower peucedanum, mainly collected from major production areas, medicinal material markets, medicinal material companies, pharmacies, etc. The main production areas are from Zhejiang, Anhui, Chongqing, Guizhou and other production areas, and some samples are from Zhejiang and Anhui authentic production areas. All samples were identified as white flower peucedanum of the Umbelliferae family by Chief Pharmacist Jin Hongyu of the China Food and Drug Inspection Institute. Peucedanum praeruptorum Dunn's dried root. The sample specimens were stored in the laboratory of the Chinese Medicine and Ethnology Testing Institute, China Food and Drug Testing Institutes under cool conditions.

[0049] 1.2 Metallomics analysis method: After the sample is crushed, it is passed through a No. 4 sieve. 0.5 g of the sample to be tested is accurately weighed and placed in a microwave digestion tank. 8.0 mL of nitric acid is added. The device is installed according to the operating procedures and digested (heating to 120 °C for 3 min, heating to 150 °C for 2 min, and heating to 200 °C for 12 min). After the digestion is completed, it is cooled to below 60 °C, and the digestion tank is placed on a heating plate at 100 °C to drive the acid until no brown-yellow acid mist is emitted. The digestion tank is taken out and cooled. The digestion solution is transferred to a 50 mL volumetric flask. The digestion tank is washed 3 times with a small amount of water. The washing solution is combined in the volumetric flask, and the volume is adjusted to the mark with water. Shake well to obtain the test solution. The reagent blank solution is prepared in the same way.

[0050] This study used the collision reaction cell mode. During the ICP-MS operation, the high-frequency power was 1.20 kW, the carrier gas (high-purity argon) flow rate was 0.70 L / min, the plasma gas flow rate was 9.0 L / min, the auxiliary gas flow rate was 1.1 L / min, the peristaltic pump speed was 0.3 r / s, the sampling depth was 7 mm, the cell gas was 6.0 mL / min, the cell voltage was -21 V, the energy filter was 7.0 V, and the nebulizer temperature was 5 °C. The elements and isotopes analyzed semi-quantitatively included 107 Ag, 27 Al, 75 As, 197 Au, 11 B. 138 8. 9 Be, 79 Br, 44 Ca, 114 Cd, 140 Ce, 35 Cl, 59 Co. 52 Cr, 133 Cs, 63 Cu, 164 Dy, 166 2. 153 Eu,56 Fe, 69 Ga, 158 Gd, 180 Hf 202 Hg, 165 Ho, 127 I. 193 Ir, 39 K. 139 La, 7 Li, 24 Mg, 55 Mn, 95 Mo, 23 Na, 93 Nb, 142 Nd, 60 You, 192 Os, 31 P. 208 Pb, 108 Pd, 141 Pr, 195 Pt, 85 Rb, 187 Re, 102 Such as 34 S. 121 Sb, 78 Se, 28 4. 152 Sm, 118 Sn, 88 Sr. 181 She 130 Te, 232 Th, 47 Ti, 205 Tl, 169 Tm, 238 U. 51 V. 184 W. 89 Y. 174 Yb, 66 Zn, 90 Zr. Accurately measure an appropriate amount of internal standard solution, place it in a volumetric flask, and dilute it to the mark with 5% nitric acid solution. 72 Ge, 6 Li, 45 Sc, 115 In, 209 Bi, 175 Lu, 103 Rh, 159The Tb concentration was 500 ng / mL, and the internal standard solution was obtained. Combining the principle of determination stability and similar mass number, a suitable internal standard was selected. The sample tube was inserted into the standard solution and sample solution in turn for determination. The CPS response value of each element was obtained by internal standard correction and subtraction of blank background.

[0051] 1.3 Metabolite profile analysis method: Take about 0.5 g of Peucedanum chinense powder (passed through No. 3 sieve), weigh it accurately, put it in a stoppered conical flask, accurately add 25 ml of methanol, stopper it tightly, weigh it, ultrasonically treat it (power 250 W, frequency 33 kHz) for 30 minutes, cool it, shake it well, and filter it to obtain the sample solution.

[0052] Chromatographic conditions: The chromatographic column was ACCQUITY UHPLC HSS T3 (2.1×100 mm, 1.8 μm), the mobile phase was acetonitrile (A)-0.1% formic acid (B), and the gradient elution program was as follows: 0-3 min, 90%-70% B; 3-7 min, 70%-50% B; 7-20 min, 50%-40% B; 20-30 min, 40%-10% B; 30-32 min, 10% B; 32-35 min, 10%-90%. The flow rate was 0.3 mL / min; the column temperature was 30 °C; and the injection volume was 2 μL.

[0053] Mass spectrometry conditions: Heated electrospray ion source (H-ESI) detection mode, positive ion mode detection, nebulizer gas is high purity nitrogen (Ar), collision gas is high purity helium (He). Sheath gas flow rate is 45 L / min, auxiliary gas flow rate is 15 L / min, spray voltage is 3.5 kV in positive ion mode, capillary temperature is 350 ℃, auxiliary temperature is 320 ℃; primary scanning mode is MS OT, mass range m / z 70~1000, resolution is 120000, injection time is 15 ms; secondary mass spectrometry scanning detector is Orbitrap (dd-MS2 OT HCD), resolution is 30000; collision energy (CE) is 15, 30, 50 eV.

[0054] 1.4 General data processing method: The multi-element CPS values ​​obtained by semi-quantitative collection were initially standardized by dividing the CPS by the sampling volume. According to the distribution of different production areas, the R language chemometrics package was used to first perform normalization, and then PCA, OPLS-DA and cluster heat map analysis were performed to obtain general chemometric characteristics through data dimensionality reduction.

[0055] The plant metabolomics processing method is as follows: the collected raw files are input into QI software (Waters Company) for pre-processing, including peak extraction and peak matching, to preliminarily obtain the ion response intensity in each sample. The R language chemometrics package is used to first perform normalization processing, and then PCA, OPLS-DA and cluster heat map analysis are performed to obtain general chemometric features through data dimensionality reduction.

[0056] 1.5 Machine Learning Modeling Methods 1.5.1 Dataset division: In the study based on the semi-quantitative element CPS response data and comprehensive metabolomics secondary metabolite data of 73 batches of Radix Peucedanum, this study carefully planned the data utilization strategy. This study explicitly used non-targeted metal group data, metabolomics data, and the combination of the two as the cornerstones for building machine learning models. The amount of secondary metabolite data is huge, and correlation removal is used before data set division to reduce metabolite-related features and improve computational efficiency. In order to ensure the generalization ability of the model and reduce the risk of overfitting, this study adopted a scientific data division method, that is, the data was randomly and evenly divided into a training set (70%) and a test set (30%). The training set was fully utilized to train and optimize the model, while the test set was used as an independent validation set to objectively evaluate the model's key performance indicators such as accuracy and error. This optimization strategy not only retains the original intention, but also further improves the rigor and reliability of the study through clear data division and scientific evaluation methods.

[0057] 1.5.2 Selection of machine learning methods: Combined with the profound previous research foundation of our research group, this study will make full use of the powerful functions of R and Python languages, focus on the binary classification algorithm framework, adopt data oversampling strategy, and deal with data imbalance. Then, six efficient and complementary machine learning algorithms, kNN (K nearest neighbor), RF (random forest), rpart (recursive partition tree), Stacking (stacked ensemble), SVM (support vector machine) and XGBoost (extreme gradient boosting) under the mlr3 framework based on R were carefully selected and integrated. The aggregation model was used to integrate the optimal model of metallomics and metabolomics. The core goal of this study was to optimize and construct an advanced model for accurately identifying the main authenticity and other factors of Radix Peucedanum. Through the in-depth analysis and application of this model, this study aims to further explore and reveal the complex scientific laws behind the quality of Radix Peucedanum, and provide strong data support and scientific basis for the quality evaluation, origin traceability and authenticity certification of Chinese herbal medicines. This optimization scheme emphasizes the diversity and complementarity of algorithm selection, as well as the scientificity and foresight of model construction on the basis of retaining the original content.

[0058] 1.5.3 Construction and evaluation of classification model: In the process of constructing and evaluating the classification model, this study adopted a 5-fold cross-validation strategy, which is an effective model evaluation method that can more comprehensively test the generalization ability of the model. This study selected accuracy (ACC) and area under the curve (AUC) as core evaluation indicators, which jointly measure the performance of the model. In particular, AUC is given a primary position in the process of model parameter optimization because it can more comprehensively reflect the classification ability of the model under different thresholds. In order to ensure the reliability and stability of model evaluation, this study further strengthened the experimental design and repeated the modeling process for 10 cycles. This measure not only helps to reduce the impact of random errors on the results, but also obtains more robust model performance evaluation results through multiple iterations. In each cycle, this study will strictly follow the 5-fold cross-validation process to ensure full use of data and fairness of model evaluation.

[0059] 1.5.4 Variable Importance Analysis: In this study, the output of the model is explained by assigning importance values ​​to the features. The fastshap package is used to calculate variable importance, and the shapviz package is used to visualize variable importance. This study introduces SHAP into the interpretation of machine learning models to calculate the contribution of each feature to the model prediction results. Through the SHAP value, we can intuitively see the impact of the feature on the model prediction results, and explain the prediction results of a single data point, which helps us understand why the model makes a certain prediction.

[0060] 1.6 Statistical Methods: This study efficiently integrated multiple professional tools such as Origin 2024 (OriginLab), Prism 8 (GraphPad Software), and Excel 2019 (Microsoft) to optimize the entire process of data processing, statistical analysis, and graphical presentation.

[0061] 2. Results 2.1 General characteristics of the element fingerprint profile of Peucedanum chinense: This study used a semi-quantitative method to monitor 66 major and trace elements in Peucedanum chinense. All trace elements basically cover metal elements and non-metal elements. PCA, PLSDA and cluster heat map analysis were performed on all elements using the R language package, covering the authenticity factors, focusing on distinguishing the characteristics of authentic and non-authentic production areas. The results are as follows Figure 8 As shown in the figure, some samples from the authentic production area and the non-authentic production area are separated, but the characteristic differentiation effect is not ideal ( Figure 8 A). Further PLSDA analysis results showed that there was a certain separation trend between authentic and non-authentic rice ( Figure 8 B). Regarding authenticity, the cluster heat map analysis results showed that no significant feature clustering effect was observed ( Figure 8C). The preliminary results of the element fingerprint profile study show that it is difficult to find the overall factor characteristics by traditional chemometric methods, and thus it is difficult to reveal the authentic characteristics, and it is necessary to explore more appropriate data identification methods.

[0062] 2.3 Small molecule metabolite profile analysis: This study used high-resolution mass spectrometry to collect the metabolite m / z and response values ​​of all samples of multiple batches of Radix Peucedanum from Anhui, Zhejiang, Chongqing, Guizhou and other production areas. After preliminary standardization and normalization, PCA, OPLSDA and cluster analysis were used to further observe the change profile and characteristics of secondary metabolites ( Fig. 9 ). At the same time, this study also incorporates authenticity into the metabolite profile to observe whether authenticity and non-authenticity can be reflected in the changes in metabolites. The PCA analysis results show that ( Fig. 9 A), there is also an overlap between the distances between authentic production areas and non-authentic production areas. The OPLSDA analysis results show that ( Fig. 9 B), the trend between authentic and non-authentic is more obvious, but there are still some overlapping sample areas. The cluster analysis results show that ( Fig. 9 C), the clustering results of authentic and non-authentic production areas showed overlap. In summary, this study preliminarily understood the variation profile of secondary metabolites in Peucedanum samples. Through the authenticity factor, fewer factor characteristics were observed, and better methods need to be explored to identify factor specificity.

[0063] 2.4 Machine learning for authentic classification of Peucedanum by integrating metallomics and metabolomics According to the preliminary chemometric analysis results of non-targeted omics, it is necessary to explore more suitable feature classification methods. Based on the preliminary analysis results of chemometrics, in order to more accurately discover and reveal the quality connotation information of Peucedanum chinense under multiple factors, this study uses machine learning in artificial intelligence as a means, takes Peucedanum chinense element fingerprints, collected metabolite characteristics and the combination of the two as the basic database, and develops and optimizes algorithms to achieve classification modeling under different factors, so as to more accurately and clearly reveal the key characteristics of Peucedanum chinense. In previous studies, this study found that due to the limited data volume of conventional experimental data of traditional Chinese medicine, it is difficult to obtain a suitable model through a suitable algorithm. When dealing with unbalanced data, the training of classification algorithms often leads to poor prediction performance. The model will significantly favor the majority class and ignore the minority class samples that are critical to many practical applications. This makes the model impractical when facing practical problems containing rare but high-priority events. To solve this problem, oversampling technology provides a method to rebalance the categories before model training. By duplicating minority class data points, oversampling achieves a balance in the training data, thereby preventing the algorithm from ignoring those important but scarce categories. Although this method has the risk of overfitting, it can effectively offset the adverse effects of unbalanced learning and enable machine learning models to handle key use cases. In this study, this study innovatively adopted data oversampling methods and ideas to effectively solve the problem of sample size imbalance. The measured values ​​are standardized and normalized, and as a data set for machine learning, they are randomly divided into training sets and test sets, with a training set and a test set ratio of 7:3. Five machine learning algorithms RF, KNN, Rpa, SVM, and XGB are used to establish and optimize the model, and AUC and ACC are used to evaluate the performance of the model. The samples cover the four major production areas of Anhui, Zhejiang, Guizhou, and Chongqing. In addition, it also includes the identification of authenticity. The authentic production areas are mainly Anhui and Zhejiang. In this study, the data of the two production areas are merged as preliminary authentic production areas.

[0064] 2.4.1 Machine learning classification modeling based on metallomics: This study followed the established machine learning modeling framework and deeply explored the application potential of the response values ​​of 66 elements obtained by semi-quantitative analysis after internal standard correction in the field of non-targeted metallomics. By constructing a preliminary model and evaluating its performance, this study obtained the detailed results shown in Table 1. In the model evaluation link, this study paid special attention to the AUC value of the test set, and supplemented it with the ACC indicator for comprehensive consideration. The data in Table 1 reveal the significant differences between the authenticity models. Specifically, the authentic production area, as a traditional high-quality production area, has a test set AUC range of 0.77 to 0.96 for its model, showing good overall performance. Further analysis of the ROC curve results of all algorithm modeling, such as Fig.10As shown. The closer the ROC curve is to the upper left corner, the better the performance of the classifier, which means that a high true positive rate is obtained while maintaining a low false positive rate. The characteristics of the ROC curve show that it can help select the best classification threshold. At the same time, combined with the ACC results of the test set for analysis, this study found that in authentic production areas, the XGB algorithm has become a suitable algorithm for building the optimal model due to its powerful integrated learning ability and robustness. In summary, this study deeply explored the construction and performance evaluation of different authentic metallomics models through a combination of semi-quantitative analysis and machine learning modeling. The significant differences between the models of each production area not only reveal the impact of regional element content characteristics on model construction, but also provide a strong basis for the subsequent development of customized models for different production areas.

[0065] Table 1 Evaluation results of the authenticity classification model of Peucedanum based on element fingerprints

[0066] 2.4.2 Machine learning classification modeling based on metabolomics: This study used high-resolution mass spectrometry to obtain mass spectrometry data of secondary metabolites of Peucedanum chinense from different production areas. Based on the distribution profile characteristics of secondary metabolites, five machine learning algorithms were further used to establish a classification model for different production areas and authenticity characteristics of Peucedanum chinense. Taking into account the complexity of secondary metabolites and the sensitivity of mass spectrometry detection, this study first performed data cleaning on the detected molecular ion peaks. Taking the compound information with a response value of more than 1×105 as the basic data set, the molecular ion peaks that are close to the mass spectrometry baseline and accurate are removed. In addition, in order to further optimize the basic data set and improve its quality, this study specifically performed correlation removal processing on complex metabolite information. The core purpose of this step is to reduce redundant and highly correlated features through careful screening, so as to improve the performance, interpretability, and computational efficiency of the machine learning model, reduce the risk of overfitting, and reduce noise and storage requirements. The analysis results before and after correlation removal are shown in the figure. Fig.11 By comparing the correlation characteristics before and after removal, this study found that the correlation between secondary metabolites after removal was lower, which significantly improved the quality of the data.

[0067] This study followed the established machine learning modeling framework, using cleaned secondary metabolite mass spectrometry data as input, and constructed a variety of algorithm models for authenticity classification through careful data set division, algorithm selection, and model parameter tuning. The preliminary model construction results based on non-targeted metabolomics and their evaluation details are shown in Table 2. When evaluating the model, this study mainly relied on the AUC value of the test set, supplemented by the characteristic analysis of the ROC curve and the accuracy (ACC) for comprehensive consideration. The data in Table 2 show that the AUC value of the authentic production area model is stable in the high level range of 0.90 to 0.96. Further in-depth analysis of the ROC curves of the models constructed by all algorithms (such as Fig.12 As shown in the figure, and combined with the accuracy of the test set (ACC) for horizontal comparison, this study found that for authentic production areas, the optimal model used the KNN algorithm.

[0068] Table 2 Evaluation results of the classification model of authenticity of Peucedanum based on secondary metabolites

[0069] 2.4.3 Establishment of machine learning aggregation model based on metallomics fusion metabolomics: Based on the classification model results of metallomics and metabolomics, this study found that although the accuracy of the model was greatly improved after data oversampling, the test set AUC and ACC of the metallomics model for some production areas such as Guizhou were low, and the stability between the algorithms based on metabolomics was poor. In order to further improve the accuracy and stability of prediction, reduce the risk of misjudgment, and increase the performance of the model in different scenarios, this study attempted to adopt the idea of ​​aggregation model and use the differences between different models to improve the accuracy and stability of the overall prediction system. Based on the models with higher accuracy obtained by the five algorithms, metallomics and metabolomics were aggregated in different authentic production areas. This further improved the prediction effect of the overall model and obtained more reliable prediction results. More importantly, this strategy combined inorganic and organic substances in Peucedanum chinense for the first time, and gained insight into the authenticity of Peucedanum chinense from the overall material level. The results of the establishment, optimization and evaluation of the aggregation model are shown in Table 3. Compared with the classification model results of metallomics and metabolomics alone (Table 2 and Table 3), this study found that the aggregate model significantly improved the AUC and ACC of authenticity. It can be seen that the aggregate model improves accuracy and robustness of prediction, thereby improving the generalization ability of the model. Compared with other production areas, the same trend is shown. Combined with the ROC curve characteristics of different production areas ( Fig.13), the ROCs between the authentic production areas are close to the left, and the overfitting phenomenon between the models is small, which further verifies the effectiveness and stability of the aggregation model. The results of the aggregation model in this study show that the strategy of combining inorganic and organic makes full use of the differences between different models. By integrating the information of metallomics and metabolomics, it can achieve effective aggregation of information in different production areas, thereby comprehensively improving the accuracy and stability of the overall prediction system and realizing the authenticity evaluation of Radix Peucedanum.

[0070] Table 3 Evaluation results of the aggregated model based on metallomics and metabolomics 3. Discussion 3.1 A new strategy for identifying the origin and authenticity of Chinese medicine by integrating metallomics and metabolomics: The quality of Chinese medicinal materials is closely related to their origin. Chinese medicinal materials from different origins differ in terms of growth environment, soil conditions, climate conditions, etc. These differences directly affect the chemical composition and metal element content of Chinese medicinal materials. Metallomics is a new research direction that has emerged in recent years. For Chinese medicine, metallomics is used to study the types, contents and distribution patterns of metal elements in Chinese medicinal materials. Metal elements play an important role in Chinese medicinal materials. They are not only the basic elements that constitute the chemical composition of Chinese medicinal materials, but also participate in the pharmacological effects of Chinese medicinal materials. Through metallomics analysis, the characteristic spectra of metal elements in Chinese medicinal materials can be revealed, providing important information for origin identification. Plant Chinese medicine metabolomics is the study of the types, contents and changes of small molecule metabolites in Chinese medicinal materials. Metabolites are chemical substances produced by Chinese medicinal materials during growth, development and metabolism. They can reflect the physiological state and metabolic pathways of Chinese medicinal materials. Through metabolomics analysis, the metabolic fingerprint of Chinese medicinal materials can be obtained, further revealing the origin characteristics of Chinese medicinal materials. The new strategy of using machine learning metallomics and metabolomics integrated model to identify the origin of traditional Chinese medicine is an innovative and scientific method. It combines the advantages of metallomics, metabolomics and artificial intelligence, covers inorganic and organic substances, and reveals the characteristics of the origin of traditional Chinese medicine in a more comprehensive, in-depth and intelligent way. Therefore, through the integrated analysis of metallomics and metabolomics, the origin of traditional Chinese medicine can be more accurately identified, thereby providing a scientific basis for the quality control and efficacy evaluation of traditional Chinese medicines. This innovative strategy has broad application prospects in the quality control, efficacy evaluation and protection of traditional Chinese medicine resources of traditional Chinese medicines. This study perfectly applied this strategy to Radix Peucedanum, which can integrate 66 trace elements with rich secondary metabolite information, and realize the traceability of the origin and authenticity of Radix Peucedanum with the help of the intelligent algorithm of the machine learning aggregation model. Therefore, this strategy is comprehensive, accurate and scientific in the traceability of the origin and authenticity of Radix Peucedanum, reflecting the quality connotation and scientific laws of Radix Peucedanum.

[0071] 3.2 Variable importance of inorganic elements and chemical components in the identification of the origin of Peucedanum chinense: In the field of machine learning and deep learning, the interpretability of the model occupies a pivotal position. Although complex models such as deep neural networks and integrated models (such as XGBoost, LightGBM) perform well in predictive accuracy, they are often like "black boxes" and their internal decision-making logic is elusive. In order to solve this problem, SHAP (Shapley AdditiveexPlanations) came into being. As an effective tool, it reveals and explains the output results of the model by assigning importance values ​​to each feature. SHAP is used to calculate the contribution of each feature to the model prediction results. This study further adopted Shaply additive interpretation to calculate all possible arrangements between variables, and then calculated the average contribution of each variable. In this study, the fastshap package was used to calculate variable importance, and the shapviz package was used to visualize variable importance. In the interpretation results of the authentic production area prediction model ( Fig.14 ), this study found that Ca and peak 17307 have the greatest impact on the model, and major elements Mg and Mn are also one of the most influential features. Fig.14 B and Fig.14 D further reveals the visualization of the SHAP values ​​of the 15 most influential features.

[0072] Furthermore, in this study, the fastshap package was used to calculate the importance of variables, and the shapviz package was used to visualize the importance of variables. The calculated results of the Anhui production area show that in the element model of the Anhui production area, it can be intuitively seen that Mn has the greatest impact on the model prediction results. In the chemical composition of the Anhui production area, this study found that peak 16696 has the greatest impact on the model prediction results. In the interpretation of the Guizhou production area model, S and Mg elements are the two most influential constant elements, and the characteristic importance of peak 13779 is much greater than that of other peaks. In the Zhejiang production area, B, Mg, and peak 19774 have a greater impact on the model prediction results. In the Chongqing production area, Mg and peak 17630 have become the variable features with the greatest impact on the model. In a horizontal comparison of the Guizhou, Anhui, Zhejiang, and Chongqing production areas, the SHAP values ​​in the summary diagrams of their elements and chemical compositions have their own characteristics, which also better reflects the characteristic variability between production area samples.

[0073] 3.3 Advantages of oversampling technology in the classification of origin and authenticity of Peucedanum chinense When dealing with datasets containing imbalanced categories, the training process of classification algorithms does face severe challenges, which applies not only to general classification problems but also to the field of Chinese medicine testing. Since the number of majority class samples (such as common Chinese medicine ingredients or types) far exceeds that of minority classes (such as rare or specific Chinese medicine ingredients, medicinal material varieties or quality grades), the model is prone to serious bias towards the majority class during training, thereby ignoring those minority class samples that are scarce but crucial for practical application scenarios such as Chinese medicine quality control and efficacy evaluation. Take Peucedanum chinense as an example. As a Chinese medicine, Peucedanum chinense may be affected by factors such as quality, ingredient content and origin. However, in the actual Chinese medicine testing process, since the number of majority class samples (such as common qualified Peucedanum chinense) is large, while the number of minority class samples (such as Peucedanum chinense of specific origin and specific ingredient content) is scarce, this may cause the trained model to perform poorly in identifying these minority class samples. In order to effectively address this challenge, oversampling technology can also be applied to the field of Chinese medicine testing. By duplicating or amplifying minority data points (such as samples of Radix Peucedanum with specific origins and specific ingredient contents), the number of samples of each category in the training data tends to be balanced. In this way, the algorithm will no longer easily ignore those important but scarce categories during the training process, so that it can learn the features and information in the data more comprehensively and improve the model's recognition ability for minority samples. Of course, oversampling technology also has a certain risk of overfitting. In the field of traditional Chinese medicine testing, this may cause the model to overfit the minority samples in the training data, and perform poorly in actual applications. In order to reduce this risk, reasonable parameter settings and cross-validation can be adopted. For example, the model performance can be optimized by adjusting the oversampling ratio, selecting appropriate machine learning algorithms and parameters, and other methods. In general, the application of oversampling technology in the field of traditional Chinese medicine testing can significantly offset the negative impact of unbalanced learning, improve the recognition ability of machine learning models for minority samples, and provide more accurate and reliable solutions for practical application scenarios such as traditional Chinese medicine quality control and efficacy evaluation. At the same time, this also provides new ideas and methods for data processing and the application of machine learning algorithms in the field of traditional Chinese medicine testing.

[0074] 3.4 Advantages of the aggregate model in predicting the origin and authenticity of Peucedanum chinense: The aggregate model has significant advantages in predicting the origin and authenticity of Peucedanum chinense. These advantages are mainly reflected in the following aspects: (1) Improving prediction accuracy. By integrating the results of multiple prediction models, the aggregate model can combine the advantages of different models and reduce the prediction error of a single model, thereby improving the prediction accuracy. In the prediction of the origin and authenticity of Peucedanum chinense, different models may make predictions based on different characteristics (such as climate, soil, ecological environment, etc.). The aggregate model can comprehensively consider these characteristics and give more accurate prediction results. (2) Enhancing the generalization ability of the model. By combining multiple models, the aggregate model can reduce the risk of overfitting of a single model to specific data and improve the generalization ability of the model. This means that the aggregate model can give more reliable prediction results when faced with new and unknown origin data of Peucedanum chinense. This is of great significance for the cultivation, procurement and quality control of Chinese medicinal materials. (3) Making full use of multiple data sources and information. The origin and authenticity of Chinese medicinal materials are affected by many factors, including climate, soil, ecological environment, cultivation technology, etc. The aggregation model can integrate information from different data sources, such as meteorological data, soil data, remote sensing images, etc., as well as expert judgment based on traditional knowledge and experience, so as to more comprehensively evaluate the origin and authenticity of Peucedanum chinense. (4) Improve prediction efficiency. The aggregation model can significantly improve the prediction efficiency by processing multiple prediction models in parallel. This is of great significance for the rapid response and decision-making of the Chinese medicinal materials industry. For example, in the process of Chinese medicinal materials planting, timely and accurate prediction of the origin and authenticity of Peucedanum chinense can help guide growers to choose appropriate planting areas and cultivation techniques, and improve the yield and quality of Chinese medicinal materials. (5) Adaptability and flexibility. The aggregation model has high adaptability and flexibility. It can select different prediction models for combination according to actual needs and data characteristics, and adjust the combination method and weight. This enables the aggregation model to adapt to different application scenarios and data distributions, and provide more personalized solutions. For example, when predicting the origin and authenticity of Peucedanum chinense for a specific region or a specific variety, the aggregation model can select a more suitable prediction model for combination according to the climate, soil and other characteristics of the region.

[0075] In summary, the aggregation model has many advantages in predicting the origin and authenticity of Peucedanum chinense, which makes the aggregation model one of the indispensable tools in the Chinese herbal medicine industry. By applying the aggregation model, this study can more accurately and efficiently predict the origin and authenticity of Peucedanum chinense, providing strong support for the planting, procurement and quality control of Chinese herbal medicines.

[0076] 3.5 The correlation between the traceability of the origin of Peucedanum chinense and the quality of medicinal materials: Based on the in-depth preliminary analysis of 528 batches of Peucedanum chinense, this study not only confirmed the significant regional differences in the content and qualified rate of white flower Peucedanum chinense A and B, but also revealed the close connection between the quality of Peucedanum chinense and the characteristics of specific production areas. To further explore the scientific mechanism behind this complex relationship, this study innovatively integrated a multi-component perspective, elevated the analysis of inorganic and organic matter to a multi-level material level, and cleverly combined metallomics and metabolomics technologies, opening a new chapter in the study of the origin and quality authenticity of Peucedanum chinense.

[0077] Through the precise integration of chemometrics and machine learning methods, this study successfully constructed a dual evaluation system based on element fingerprints and secondary metabolite profiles, and comprehensively explained how these two dimensions can synergistically characterize the quality attributes of Peucedanum chinense. At the level of element fingerprint analysis, this study found that the abundance of major elements such as K, Na, Ca, and Al is not only regulated by the soil environment, but also deeply reflects the unique enrichment mechanism of Peucedanum chinense plants. This discovery highlights the key role of metallomics in revealing the characteristics of traditional Chinese medicine.

[0078] At the same time, the application of non-targeted metabolomics enabled this study to deeply analyze the rich and diverse organic small molecule compound profiles in Peucedanum chinense, especially the dominance of coumarin compounds was further confirmed. In Peucedanum chinense, the richness of coumarin compounds is an important feature of it as a plant of the Umbelliferae family. Faced with these secondary metabolites with complex structures that are difficult to capture with traditional analysis, this study innovatively used machine learning technology to effectively mine and extract key features, providing strong technical support for the quality identification and authenticity evaluation of Peucedanum chinense at the organic small molecule level.

[0079] In summary, this study not only preliminarily clarified the intrinsic relationship between the authenticity and quality of Radix Peucedanum, but also successfully verified the innovative strategy of integrating metal group and metabolome in identifying the authenticity of traditional Chinese medicine, which has made important contributions to the improvement and scientific development of the quality evaluation system of traditional Chinese medicine. In the future, this strategy is expected to be widely applied to more varieties of traditional Chinese medicine, and promote the development of the traditional Chinese medicine industry in a more precise, efficient and sustainable direction.

[0080] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0081] In general, various example embodiments of the present disclosure may be implemented in hardware or dedicated circuits, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device. When various aspects of the disclosed embodiments are illustrated or described as block diagrams, flow charts, or using some other graphical representation, it will be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general purpose hardware or controllers or other computing devices, or some combination thereof as non-limiting examples.

[0082] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0083] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0084] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0086] The exemplary embodiments of the present disclosure described in detail above are merely illustrative and not restrictive. It should be understood by those skilled in the art that various modifications and combinations may be made to these embodiments or their features without departing from the principles and spirit of the present disclosure, and such modifications should fall within the scope of the present disclosure.

Claims

1. A method for identifying the authenticity of traditional Chinese medicine based on artificial intelligence and multi-omics technology, characterized in that: The method comprises: 101, obtaining metallomics data and metabolomics data of the medicinal materials to be tested; 102, calculating the CPS response value of the key characteristic elements in the metallomics data; 103, processing the metabolomics data to obtain mass spectrometry data of key chemical component peak information in the data; 104, inputting the CPS response value of the key characteristic element and the key chemical component peak information into the authentic production area identification model to obtain a classification result of whether the wine belongs to the authentic production area.

2. The method for identifying the authenticity of traditional Chinese medicine based on artificial intelligence fusion multi-omics technology according to claim 1, characterized in that: The authentic production area includes a first authentic production area and a second authentic production area; if the classification result of belonging to the authentic area is obtained in 104, the metallomics data and metabolomics data of the tested medicinal material are input into the first authentic production area identification model for processing, and the classification result of whether it belongs to the first authentic production area is output; If the result is yes, the operation is terminated, and the result that the tested medicinal material belongs to the first production area is obtained; if the result is no, the result that the tested medicinal material does not belong to the first production area is obtained, and the metallomics data and metabolomics data of the tested medicinal material are input into the second production area identification model for processing, and the classification result of whether it belongs to the second production area is output; If the output result is yes, the operation is terminated, and the result that the tested medicinal material belongs to the second production area is obtained; if the output result is no, the result that the tested medicinal material does not belong to the second production area is obtained.

3. The method for identifying the authenticity of traditional Chinese medicine based on artificial intelligence fusion multi-omics technology according to claim 1 is characterized in that: The non-authentic production areas include a first non-authentic production area and a second non-authentic production area; if a classification result not belonging to an authentic area is obtained in 104, the metallomics data and metabolomics data of the tested medicinal material are input into the first non-authentic production area identification model for processing, and a classification result of whether the tested medicinal material belongs to the first non-authentic production area is output; if a yes result is output, the operation is terminated, and a result that the tested medicinal material belongs to the first non-authentic production area is obtained; if a no result is output, a result that the tested medicinal material does not belong to the first non-authentic production area is obtained, and the metallomics data and metabolomics data of the tested medicinal material are input into the second non-authentic production area identification model for processing, and a classification result of whether the tested medicinal material belongs to the second non-authentic production area is output; If the output result is yes, the operation is terminated, and the result that the tested medicinal material belongs to the second non-authentic production area is obtained; if the output result is no, the result that the tested medicinal material does not belong to the second non-authentic production area is obtained; Optionally, when the medicinal material to be tested is Peucedanum chinense, the non-authentic regions include Guizhou and Chongqing, Guizhou or Chongqing is the first non-authentic production area, and Chongqing or Guizhou is the second non-authentic production area; Optionally, the metallomics data includes major elements and trace elements; trace elements include metal elements and non-metal elements.

4. The method for identifying the authenticity of traditional Chinese medicine based on artificial intelligence fusion multi-omics technology according to claim 1, characterized in that: The method for constructing the authentic production area identification model includes: Obtain metallomics and metabolomics data of the training set of Radix Peucedanum from authentic and non-authentic areas, with authentic or non-authentic being the classification labels; Inputting the metallomics data into a first machine learning model to obtain a predicted classification result, comparing it with an authentic or non-authentic classification label, and optimizing the model according to the comparison result to obtain a metallomics model; Inputting the metabolomics data into a second machine learning model to obtain a predicted classification result, comparing it with the authentic or non-authentic classification label, and optimizing the model according to the comparison result to obtain a metabolomics model; The optimal model among the metallomics model and the metabolomics model obtained by different algorithms is selected, and the two optimal models are aggregated to obtain the authentic production area identification model; Optionally, the method for constructing the first authentic production area identification model or the second authentic production area identification model includes: Obtain metallomics data and metabolomics data of the training set of Radix Peucedanum from the first and second production areas, where the first and second production areas are authentic classification labels; Inputting the metallomics data into a first machine learning model to obtain a predicted classification result, comparing it with the authentic classification label, and optimizing the model according to the comparison result to obtain a metallomics model; inputting the metabolomics data into a second machine learning model to obtain a predicted classification result, comparing it with the authentic classification label, and optimizing the model according to the comparison result to obtain a metabolomics model; The optimal model of the metallomics model and the metabolomics model in the first authentic production area is selected, and the two optimal models are aggregated to obtain the first authentic production area identification model; the optimal model of the metallomics model and the metabolomics model in the second authentic production area is selected, and the two optimal models are aggregated to obtain the second authentic production area identification model; Optionally, the method for constructing the first non-authentic production area or the second non-authentic production area includes: Obtaining metallomics data and metabolomics data of the training set of Radix Peucedanum from the first non-authentic production area and the second non-authentic production area, wherein the first non-authentic production area and the second non-authentic production area are non-authentic classification labels; Inputting the metallomics data into a first machine learning model to obtain a predicted classification result, comparing it with the non-authentic classification label, and optimizing the model according to the comparison result to obtain a metallomics model; inputting the metabolomics data into a second machine learning model to obtain a predicted classification result, comparing it with the non-authentic classification label, and optimizing the model according to the comparison result to obtain a metabolomics model; The optimal model of the metallomics model and metabolomics model in the first non-authentic production area was selected, and the two optimal models were aggregated to obtain the first non-authentic production area identification model; the optimal model of the metallomics model and metabolomics model in the second non-authentic production area was selected, and the two optimal models were aggregated to obtain the second non-authentic production area identification model.

5. The method for identifying the authenticity of traditional Chinese medicine based on artificial intelligence fusion multi-omics technology according to claim 1, characterized in that: The method used by the first machine learning model and / or the second machine learning model includes: RF, KNN, Rpa, SVM, XGB; Optionally, when the medicinal material to be tested is Peucedanum chinense, the regions include Zhejiang and Anhui, Zhejiang or Anhui is the first production area, and Anhui or Zhejiang is the second production area; Optionally, a model of the authentic production area using the XGB algorithm in the metallomics model and a model of the authentic production area using the KNN algorithm in the metabolomics model are selected and aggregated to obtain an aggregated model of the authentic production area; Optionally, the key characteristic element includes Ca element; Optionally, the key characteristic elements further include: Mn element and Mg element; Optionally, the peak information includes one or more of the following: peak 17307.

6. A method for identifying the authenticity of traditional Chinese medicine based on artificial intelligence and multi-omics technology, characterized in that: The method comprises: 201, obtaining CPS response values ​​and mass spectrum data of key peak information of key characteristic elements of the tested medicinal material, wherein the key characteristic elements include Ca element; the key peak information includes: peak 17307; 202, inputting the CPS response value of the key characteristic element of the Ca element and the mass spectrum data of the target peak information into the authentic production area identification model for processing to obtain a classification result of whether it belongs to the authentic production area.

7. The method for identifying the authenticity of traditional Chinese medicine based on artificial intelligence fusion multi-omics technology according to claim 6 is characterized in that: The key characteristic elements also include: Mn element and Mg element; Optionally, the key feature element further includes: S element, B element; the peak information includes one or more of the following: peak 16696, peak 13779, peak 19774, peak 17630; Optionally, if a classification result of belonging to an authentic production area is obtained, the Mg element and / or the Mn element and / or the B element and / or the peak 16696 and / or the peak 19774 are input into the first authentic production area identification model and the second authentic production area identification model to obtain a result of whether it belongs to the first authentic production area or the second authentic production area; Optionally, if a classification result is obtained that the product belongs to a non-authentic production area, the S element and / or the Mg element and / or the peak 13779 and / or the peak 17630 are input into the first non-authentic production area identification model and the second non-authentic production area identification model to obtain the result of whether the product belongs to the first non-authentic production area or the second non-authentic production area.

8. A computer device, characterized in that: The device comprises: a memory and a processor; the memory is used to store a computer program; the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

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