Method, device, equipment and medium for tracing cyperus esculentus producing area by combining support vector machine with elemental analysis

Abstract

The invention provides a cyperus esculentus producing area tracing method, device and equipment based on support vector machine combined element analysis and a medium. The method comprises the following steps: collecting element content data of cyperus esculentus from different producing areas, measuring the data and dividing the data into a training set and a prediction set; the element content data comprises primary element content data and secondary element content data; preprocessing the element content data to obtain preprocessed data; carrying out model operation on the preprocessed data in the training set by utilizing chemometrics software and adopting a support vector machine model to obtain a production place traceability model, and verifying the production place prediction model by adopting the preprocessed data in the prediction set; carrying out model parameter optimization on the origin traceability model by adopting the pre-processing data in the training set to obtain model parameters; and then the preprocessing data in the prediction set is used for calculation under the origin traceability model with model parameters, so that the cyperus esculentus origin traceability is realized, the cyperus esculentus origin traceability can be realized, and the prospect is wide.

Description

Technical Field

[0001] The present invention relates to the technical field of cyperus rotundus, and in particular to a method, device, equipment and medium for tracing the origin of cyperus rotundus by combining a support vector machine with element analysis. Background Art

[0002] Cyperus oleiferus is an important oil crop, and producing cyperus oleiferus oil is one of the main ways to utilize cyperus oleiferus. At present, food traceability technology refers to the ability to trace the origin of food during the process from raw materials, processing, finished products to sales. Food origin traceability technology is one of its important contents. It is an effective means to protect regional products and product quality, and also protect the interests of consumers, prevent extortion and deception, maintain fair competition in the market, and reduce unnecessary losses.

[0003] Moreover, there are great differences in the composition of cyperus rotundus from different origins. Understanding the origin of cyperus rotundus is crucial for making better use of cyperus rotundus resources. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method, device, equipment and medium for tracing the origin of cyperus oleifera by combining support vector machine with elemental analysis. By combining support vector machine with elemental analysis and training with experimental data, a prediction model with a high origin tracing recognition rate can be obtained, which is of great significance for tracing the origin of cyperus oleifera.

[0005] To achieve this object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for tracing the origin of cyperus oleifera by combining a support vector machine with element analysis, the method comprising:

[0007] (1) Collecting element content data of cyperus oleifera from different origins, measuring the data and dividing them into a training set and a prediction set; the element content data includes main element content data and secondary element content data; (2) preprocessing the element content data of step (1) to obtain preprocessed data; (3) using chemometrics software and a support vector machine model to perform model calculation on the preprocessed data in the training set to obtain an origin tracing model, and using the preprocessed data in the prediction set to verify the origin prediction model; (4) using the preprocessed data in the training set to optimize the model parameters of the origin tracing model to obtain the model parameters; and then using the preprocessed data in the prediction set to calculate under the origin tracing model with the model parameters to achieve origin tracing of cyperus oleifera.

[0008] It should be noted that cyperus oleifera is a crop with a relatively uniform distribution of ingredients, which contains 20-30% fat, 25-30% starch, 6-8% protein and 8-10% cellulose. Since the contents of different components vary greatly in different origins, the quality of cyperus oleifera from different origins is different, and the extraction methods of valuable substances between cyperus oleifera from different origins may also be different. Therefore, the present invention develops an origin tracing method for cyperus oleifera, which can provide a basis for quality management of cyperus oleifera and extraction of valuable substances.

[0009] Unlike ordinary oil crops, since jatropha contains more protein and cellulose in addition to fat, its origin traceability cannot be carried out in the same way as conventional oil crops. Research has found that only by combining element content analysis with support vector machine model prediction can a high recognition rate of origin traceability be achieved.

[0010] Preferably, the elements in the element content data in step (1) include any one or a combination of at least two of Li, Be, B, Mg, Al, Ti, V, Cr, Mn, Fe, Ni, Co, Cu, Zn, Ga, As, Sr, Cd, Sn, Sb, Ti, Pb, Bi, Na, K or Ca, wherein typical but non-limiting combinations are a combination of Li and Be, a combination of V and Be, a combination of Li and Cr, a combination of Mn and Be, a combination of Li and Ni, a combination of Co and Cu, a combination of Ga and Be, and a combination of Bi and Sr.

[0011] Preferably, the main element content data includes any one of B, Mg, Al, Ti, V, Fe, Ni, Zn, Sr, Na, K or Ca, or a combination of at least two of them, wherein typical but non-limiting combinations are a combination of B and Mg, a combination of Al and Mg, a combination of B and Ti, a combination of V and Mg, a combination of Fe and Ni, a combination of Zn and Ca, and a combination of Na and K.

[0012] Preferably, the secondary element content data includes any one or a combination of at least two of Li, Co, Cd or Cu, wherein typical but non-limiting combinations are a combination of Li and Co, a combination of Cd and Co, a combination of Li and Cd, and a combination of Cu and Co.

[0013] It is worth noting that the present invention divides elements into three categories: one is the main elements with variance values ​​greater than 80%, one is the secondary elements with variance values ​​between 60% and 80% and less than 80%, and the third is other elements with variance values ​​less than 60%.

[0014] In the process of model prediction, the present invention uses a larger weight for the main element with a variance greater than 80%, uses a medium weight for the secondary element, and does not perform prediction evaluation or uses a smaller weight for other elements.

[0015] Generally, the ratio of the weight of the main element content data to the weight of the secondary element content data is 4.0 to 5.0:1, for example, it can be 4.0:1, 4.1:1, 4.2:1, 4.3:1, 4.4:1, 4.5:1, 4.6:1, 4.7:1, 4.8:1, 4.9:1 or 5.0:1, etc.

[0016] The present invention preferably sets the ratio of the weight of the main element content data to the weight of the secondary element content data within the above range, so that the final prediction model has a better origin recognition rate.

[0017] Preferably, the ratio of the data volume of the training set and the prediction set is (1.5-2.5):1, for example, it can be 1.5:1, 1.6:1, 1.7:1, 1.8:1, 2.0:1, 2.2:1, 2.3:1, 2.4:1 or 2.5:1, etc.

[0018] Preferably, the method for collecting element content data of cyperus oleifera from different origins comprises obtaining the data by inductively coupled plasma mass spectrometry.

[0019] Compared with qualitative analysis methods such as infrared and Raman, the present invention preferably uses inductively coupled plasma mass spectrometry to detect the specific element content in cyperus oleifera, which has higher accuracy.

[0020] Preferably, the origin of the cyperus oleifera includes any one of Yunnan, Xinjiang, Hebei or Hunan, or a combination of at least two of them.

[0021] Preferably, the pretreatment in step (2) includes UV scaling.

[0022] The UV scaling process described in the present invention refers to standardization or normalization of data, where U represents “mean” and V represents “standard deviation”.

[0023] Preferably, the UV scaling process in step (2) is performed using a kernel function.

[0024] Preferably, the kernel function includes any one of a Gaussian kernel function, a linear kernel function or a polynomial kernel function, or a combination of at least two of them, preferably a linear kernel function.

[0025] The linear kernel function used in the present invention is shown as follows:

[0026]

[0027] Among them, K(x i , x j ) is the sample point x i and x j The kernel function value between is the sample point x i The transpose of x j That is the sample point x j The original data.

[0028] Preferably, the chemometrics software in step (3) includes chempattern.

[0029] Preferably, the model operation also includes eliminating sample data that does not meet the identification requirements.

[0030] The present invention preferably uses Mahalanobis distance analysis to eliminate abnormal samples.

[0031] Preferably, the verification method in step (3) includes leave-one-out cross-validation.

[0032] The present invention preferably adopts a leave-one-out cross-validation method, which has the effect of requiring a small amount of data samples and making the prediction more accurate.

[0033] Preferably, the method for tracing the origin of cyperus oleifera further comprises: using the EXACT function and the COUNTIFS function to count the correct data on the origin tracing of cyperus oleifera, and then obtaining the origin recognition rate of cyperus oleifera in the prediction set.

[0034] In a second aspect, the present invention provides a device for tracing the origin of cyperus oleifera by using the support vector machine combined with element analysis described in the first aspect, the device comprising:

[0035] The data collection module is used to collect element content data of cyperus rotundus from different origins.

[0036] The data classification module divides the element content data into main element content data and secondary element content data according to the variance values ​​of different data.

[0037] The model building module is used to use the support vector machine model to perform model operations on the pre-processed data in the training set to obtain an origin traceability model.

[0038] The calculation module is used to optimize the model parameters of the origin tracing model using the pre-processed data in the training set to obtain the model parameters.

[0039] The prediction module is used to predict the origin of cyperus oleifera based on the origin traceability model.

[0040] In a third aspect, the present invention provides an electronic device, the electronic device comprising:

[0041] at least one processor; and

[0042] a memory communicatively connected to the at least one processor; wherein,

[0043] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for tracing the origin of jatropha using support vector machine combined with element analysis as described in the first aspect.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a processor to implement the method for tracing the origin of jojoba beans by combining support vector machine with element analysis as described in the first aspect when executed.

[0045] Compared with the prior art, the present invention has at least the following beneficial effects:

[0046] The method for tracing the origin of cyperus oleifera by combining support vector machine with elemental analysis provided by the present invention is simple and quick to operate, adopts chemometrics software for model calculation and optimization, and can identify the origin of cyperus oleifera quickly, accurately and at low cost. The recognition rate of the origin of cyperus oleifera by the optimized prediction model reaches 100%, and the method is suitable for large-scale promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 These are the determination results of 26 elements in Xinjiang No. 85 sample in the present invention.

[0048] Figure 2 It is the optimal pattern classification and recognition result in Example 1 of the present invention.

[0049] Figure 3 This is a schematic diagram of an electronic device provided in Example 3.

[0050] Figure 3 In it, 10-electronic device, 11-processor, 12-ROM, 13-RAM, 14-bus, 15-I / O interface, 16-input unit, 17-output unit, 18-storage unit, 19-communication unit. DETAILED DESCRIPTION

[0051] For the convenience of understanding the present invention, the present invention lists the following embodiments. It should be understood by those skilled in the art that the embodiments are only to help understand the present invention and should not be regarded as specific limitations of the present invention.

[0052] Example 1

[0053] This embodiment provides a method for tracing the origin of cyperus rotundus by combining support vector machine with element analysis. The method for tracing the origin of cyperus rotundus comprises the following steps:

[0054] (1) Collecting element content data of cyperus oleifera from different origins, measuring the data and dividing them into a training set and a prediction set; the element content data includes main element content data and secondary element content data.

[0055] Specifically include:

[0056] S101. Detecting the element content data of cyperus oleifera from different origins by using an inductively coupled plasma mass spectrometer.

[0057] A total of 128 samples of Cyperus oleifera were collected from four provinces, namely Hunan, Yunnan, Xinjiang and Hebei. All samples were numbered in the order of collection time, washed and dried, crushed evenly by a high-speed grinder, and the whole powder was retained. After classification and storage at 4°C, the contents of 26 elements (Li, Be, B, Mg, Al, Ti, V, Cr, Mn, Fe, Ni, Co, Cu, Zn, Ga, As, Sr, Cd, Sn, Sb, Ti, Pb, Bi, Na, K and Ca) of all samples of Cyperus oleifera were collected using an inductively coupled plasma mass spectrometer (ICP-MS). The instrument operating conditions are shown in GB5009.268-2016, which are as follows: 0.5 g of solid powder sample was weighed and placed in a microwave digestion inner tank, 10 mL of nitric acid was added, the tank was covered and placed for 1 h or overnight, the tank cover was tightened, and digestion was performed according to the standard steps of the microwave digestion instrument. After completing sample pretreatment and the instrument reaches normal measurement requirements, edit the measurement method, select the corresponding internal standard element according to the color properties of the element to be measured, and measure the m / z of the element and the internal standard element. Figure 1 shown.

[0058] S102. Perform variance analysis on the element content data of cyperus oleifera from different origins, record the variance value greater than 80% as the main element, the variance value between 60% and 80% and less than 80% as the secondary element, and the variance value less than 60% as other elements; the ratio of the weight of the main element content data to the weight of the secondary element content data is 4.44:1, where the main element content data includes B, Mg, Al, Ti, V, Fe, Ni, Zn, Sr, Na, K and Ca; the secondary element content data includes Li, Co, Cd and Cu; the rest are other elements. And divide the measured data into a training set and a prediction set at a ratio of about 2:1.

[0059] (2) Preprocessing the element content data described in step (1) to obtain preprocessed data.

[0060] The linear kernel function was used to UV scale the element content data, and representative samples were set in the chemometrics software to remove sample points that did not meet the identification requirements. Specifically, there were 128 total samples, and 85 samples were designed as a training set, 42 samples as a prediction set, and 1 sample as a validation set.

[0061] (3) Using chemometrics software and a support vector machine model to perform model calculations on the preprocessed data in the training set, an origin traceability model was obtained. The preprocessed data in the prediction set was then used to verify the origin prediction model. The results are shown in Table 1.

[0062] Table 1

[0063]

[0064] It can be concluded from Table 1 that in SVM mode, the data preprocessing is selected as variable UV scaling, Gaussian kernel function type, 80% variance explanation, leave-one-out cross validation, and the recognition rate of the training set can reach 1-25.5814%=74.4186%. The recognition results in this mode are shown in Table 2. It can be seen from Table 2 that the predicted recognition result is only 25%, and the model needs to be further optimized.

[0065] Table 2

[0066]

[0067]

[0068] (4) The model parameters of the origin tracing model are optimized using the pre-processed data in the training set to obtain the model parameters; the pre-processed data in the prediction set are then used to calculate under the origin tracing model with the model parameters to achieve the origin tracing of Cyperus oleifera. The data with correct origin identification are statistically analyzed using the EXACT function and the COUNTIFS function to obtain the origin identification rate of Cyperus oleifera in the prediction set.

[0069] Finally, only the main elements were used for analysis. The data preprocessing was homogenization. The linear kernel function was used for identification. The results of classification and identification are shown in Table 3 and Figure 2 shown.

[0070] Table 3

[0071]

[0072]

[0073] It can be seen from Table 3 that after adopting the optimal recognition mode and optimizing the parameters, the recognition rate of the four provinces can reach 100%.

[0074] In this example, elements of cyperus oleifera samples from four provinces were determined, and model training was performed through chemometrics software to obtain the best classification and recognition model for origin traceability: support vector machine (SVM) and corresponding optimal model parameters, thereby completing the rapid and accurate identification of the origin of each cyperus oleifera sample in the prediction set.

[0075] Example 2

[0076] The present embodiment provides a method for tracing the origin of cyperus rotundus by combining support vector machine with elemental analysis. The method for tracing the origin of cyperus rotundus is the same as that of Embodiment 1, except that the element content data obtained by inductively coupled ion mass spectrometry is replaced by the element content data detected by inductively coupled plasma emission spectroscopy.

[0077] Since the present embodiment uses inductively coupled plasma emission spectroscopy to detect the element data content, compared with inductively coupled ion mass spectrometry, the detection accuracy of Cyperus oleifera is relatively low.

[0078] Comparative Example 1

[0079] This comparative example provides a method for tracing the origin of cyperus oleifera, wherein the method for tracing the origin of cyperus oleifera adopts two material content analyses obtained by analyzing the crude fat and starch contents in cyperus oleifera as training and verification data.

[0080] The prediction model obtained in this comparative example has a poor effect on the traceability of the origin of cyperus oleifera. Even if different models, kernel functions and data preprocessing methods are used for optimization calculation, it is difficult to obtain a model with a high recognition rate. The recognition rate is lower than 80%, as shown in Table 4.

[0081] Table 4

[0082]

[0083] Comparative Example 2

[0084] This comparative example provides a method for tracing the origin of cyperus oleifera, which is the same as Example 1 except that the support vector machine model is replaced by the orthogonal partial least squares method.

[0085] Even after optimization, the prediction model obtained in this comparative example has a poor traceability effect on the origin of Cyperus oleifera, with a recognition rate of less than 40%.

[0086] Example 3

[0087] This embodiment provides an electronic device, such as Figure 3, which is a schematic diagram of the structure of an electronic device 10 for implementing an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0088] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory ROM12, a random access memory RAM13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory ROM12 or the computer program loaded from the storage unit 18 to the random access memory RAM13. In RAM13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, ROM12 and RAM13 are connected to each other through a bus 14; the I / O interface 15 is also connected to the bus 14.

[0089] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0090] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as the origin tracing method of cyperus oleifera by combining support vector machine with element analysis.

[0091] In some embodiments, the method for tracing the origin of cyperus oleiferus by combining support vector machine with elemental analysis can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via ROM12 and / or communication unit 19. When the computer program is loaded into RAM13 and executed by processor 11, one or more steps of the method for tracing the origin of cyperus oleiferus by combining support vector machine with elemental analysis described above can be executed. Alternatively, in other embodiments, processor 11 can be configured to execute the method for tracing the origin of cyperus oleiferus by combining support vector machine with elemental analysis in any other appropriate manner (e.g., by means of firmware).

[0092] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0093] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0094] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0095] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0096] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0097] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0098] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0099] The present invention illustrates the detailed features of the present invention through the above embodiments, but the present invention is not limited to the above detailed features, that is, it does not mean that the present invention must rely on the above detailed features to be implemented. Those skilled in the art should understand that any improvement to the present invention, equivalent replacement of the technical features selected by the present invention, addition of auxiliary technical features, selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

Claims

1. A method for tracing the origin of Cyperus oleifera by combining support vector machine with element analysis, characterized in that: The method for tracing the origin of cyperus oleifera comprises: (1) collecting element content data of cyperus oleifera from different origins, measuring the data and dividing them into a training set and a prediction set; the element content data includes main element content data and secondary element content data; (2) preprocessing the element content data in step (1) to obtain preprocessed data; (3) Using chemometrics software and a support vector machine model to perform model calculations on the preprocessed data in the training set, a provenance traceability model was obtained, and the provenance prediction model was verified using the preprocessed data in the prediction set; (4) The preprocessed data in the training set is used to optimize the model parameters of the origin tracing model to obtain the model parameters; and then the preprocessed data in the prediction set is used to calculate under the origin tracing model with the model parameters to achieve the origin tracing of the cyperus oleifera.

2. The method for tracing the origin of Cyperus oleifera according to claim 1, characterized in that: The elements in the element content data in step (1) include any one or a combination of at least two of Li, Be, B, Mg, Al, Ti, V, Cr, Mn, Fe, Ni, Co, Cu, Zn, Ga, As, Sr, Cd, Sn, Sb, Ti, Pb, Bi, Na, K or Ca; Preferably, the main element content data includes any one or a combination of at least two of B, Mg, Al, Ti, V, Fe, Ni, Zn, Sr, Na, K or Ca; Preferably, the secondary element content data includes any one of Li, Co, Cd or Cu, or a combination of at least two of them.

3. The method for tracing the origin of cyperus oleifera according to claim 1 or 2, characterized in that: The ratio of the data volume of the training set and the prediction set is (1.5-2.5):

1.

4. The method for tracing the origin of cyperus oleifera according to any one of claims 1 to 3, characterized in that: The method for collecting element content data of cyperus oleifera from different origins includes obtaining the data by inductively coupled plasma mass spectrometry.

5. The method for tracing the origin of Cyperus oleifera according to any one of claims 1 to 4, characterized in that: The pretreatment in step (2) includes UV scaling; Preferably, the UV scaling process in step (2) is performed using a kernel function; Preferably, the kernel function includes any one of a Gaussian kernel function, a linear kernel function or a polynomial kernel function, or a combination of at least two of them, preferably a linear kernel function.

6. The method for tracing the origin of Cyperus oleifera according to any one of claims 1 to 5, characterized in that: The chemometrics software in step (3) includes chempattern; Preferably, the model operation also includes eliminating sample data that does not meet the identification requirements.

7. The method for tracing the origin of Cyperus oleifera according to any one of claims 1 to 6, characterized in that: The verification method in step (3) includes leave-one-out cross-validation; Preferably, the method for tracing the origin of cyperus oleifera further comprises: using the EXACT function and the COUNTIFS function to count the correct data on the origin tracing of cyperus oleifera, and then obtaining the origin recognition rate of cyperus oleifera in the prediction set.

8. A method for tracing the origin of Cyperus oleifera by using the support vector machine combined with element analysis according to any one of claims 1 to 7, characterized in that: The device comprises: Data collection module, used to collect element content data of Cyperus oleifera from different origins; The data classification module divides the element content data into main element content data and secondary element content data according to the variance values ​​of different data; A model building module is used to use a support vector machine model to perform model operations on the pre-processed data in the training set to obtain an origin tracing model; A calculation module, used to optimize the model parameters of the origin tracing model using the pre-processed data in the training set to obtain the model parameters; The prediction module is used to predict the origin of cyperus oleifera based on the origin traceability model.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for tracing the origin of cyperus oleifera by combining support vector machine with elemental analysis as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for tracing the origin of cyperus oleifera by combining support vector machine with element analysis as described in any one of claims 1 to 7 when executed.

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