Camellia oil adulteration identification method and system

Abstract

The application provides a camellia oil adulteration identification method and system, which comprises the following steps: obtaining the content of oleic acid, beta-amyrin and campesterol in the camellia oil to be identified; determining whether the content of oleic acid is greater than a first threshold value; if yes, determining that the camellia oil to be identified is a mixture of one or more of camellia oil, high-oleic peanut oil, high-oleic sunflower oil and high-oleic rapeseed oil; if no, determining that the camellia oil to be identified is a mixture of one or more of peanut oil and soybean oil; then determining whether the content of beta-amyrin causes the displacement amount of the clustering result of the corresponding camellia oil to be less than a second threshold value; if yes, determining that the camellia oil to be identified is not adulterated camellia oil; if no, determining that the camellia oil to be identified is adulterated oil; then determining whether the content of campesterol causes the displacement amount of the clustering result of the corresponding high-oleic rapeseed oil to be less than a third threshold value, and determining whether the camellia oil to be identified is camellia oil adulterated with high-oleic rapeseed oil.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of camellia oil adulteration identification, and particularly relates to a camellia oil adulteration identification method and system. BACKGROUND

[0002] Camellia oil is a kind of edible vegetable oil obtained from seeds of Camellia oleifera.

[0003] In actual application, conventional physical and chemical detection mainly performs qualitative and quantitative analysis on the fatty acid content of vegetable oil, and oleic acid, which has a content much higher than other fatty acids in camellia oil, becomes a key characteristic index for identifying camellia oil adulteration. However, with the entry of new high-oleic vegetable oil varieties (high-oleic peanut oil, high-oleic sunflower oil, high-oleic rapeseed oil, etc.) into the market, the fatty acid discrimination index often fails. In addition, the spectral method is based on the relative intensity difference between different characteristic peaks of vegetable oil to build a model, which requires a large number of samples, and the method has low sensitivity, lacks quantitative index, and cannot be well applied to actual adulteration identification. SUMMARY

[0004] Therefore, the camellia oil adulteration identification method and system provided in the embodiments of the application aims to have high detection sensitivity when a small amount of high-oleic rapeseed oil is mixed in camellia oil.

[0005] The first aspect of the embodiments of the application provides a camellia oil adulteration identification method, which comprises the following steps.

[0006] Obtaining the oleic acid content, beta-amyrin content and campesterol content of the camellia oil to be identified;

[0007] Determining whether the oleic acid content is greater than a first threshold value;

[0008] If it is determined that the oleic acid content is greater than the first threshold value, it is determined that the camellia oil to be identified belongs to a first type of oil product, and it is determined whether the displacement amount of the beta-amyrin content that causes the clustering result of the corresponding camellia oil to be displaced is less than a second threshold value;

[0009] If it is determined that the oleic acid content is not greater than the first threshold value, it is determined that the camellia oil to be identified belongs to a second type of oil product;

[0010] If it is determined that the displacement amount of the beta-amyrin content that causes the clustering result of the corresponding camellia oil to be displaced is less than the second threshold value, it is determined that the camellia oil to be identified is not adulterated camellia oil;

[0011] if the displacement amount of the β-amyrin content causing the clustering result of the corresponding camellia oil to be displaced is not less than a second threshold value, it is determined that the camellia oil to be identified is adulterated oil, and whether the displacement amount of the campesterol content causing the clustering result of the corresponding high-oleic rapeseed oil to be displaced is less than a third threshold value is determined;

[0012] if the displacement amount of the campesterol content causing the clustering result of the corresponding high-oleic rapeseed oil to be displaced is less than the third threshold value, it is determined that the camellia oil to be identified is camellia oil adulterated with high-oleic rapeseed oil.

[0013] Further, the step of determining the first threshold value comprises:

[0014] respectively obtaining historical oleic acid content samples of camellia oil, high-oleic peanut oil, high-oleic sunflower oil, high-oleic rapeseed oil, soybean oil and peanut oil, and the number of historical oleic acid content samples of each type of oil is the same;

[0015] using a K-Means clustering algorithm to cluster the historical oleic acid content of each type of oil, and setting the k value to 2 and 6 in turn to obtain the corresponding clustering results;

[0016] determining a first center point according to the clustering result when the k value is set to 2;

[0017] According to the clustering result when the k value is set to 6, the first center point is pulled to determine a target point, and the data corresponding to the target point is the first threshold value.

[0018] Further, the step of determining the target point according to the clustering result when the k value is set to 6 and pulling the first center point comprises:

[0019] respectively calculating the first distance between the clustering result when the k value is set to 6 and the first center point, and determining the force size according to the first distance and a preset mapping relationship;

[0020] respectively determining that the line connecting the first center point to the clustering result when the k value is set to 6 is the direction of the force;

[0021] According to the size of the force and the direction of the force, mechanical simulation is performed to determine the displacement direction of the first center point;

[0022] Project the clustering result when the k value is set to 6 onto the straight line in the displacement direction respectively, and determine the second center point of the projection point located on the same side of the first center point;

[0023] Calculate the second distance between each of the second center points and the first center point, and make a difference to obtain the displacement amount;

[0024] According to the displacement direction and the displacement amount, the target point is determined.

[0025] Further, the step of judging whether the displacement amount of the β-amyrin content causing the clustering result of the corresponding camellia oil to be displaced is less than a second threshold value comprises:

[0026] Respectively obtain historical β-amyrin content samples of camellia oil, high-oleic peanut oil, high-oleic sunflower oil and high-oleic rapeseed oil, and the number of historical β-amyrin content samples of each type of oil is the same;

[0027] Randomly select a data point corresponding to a historical β-amyrin content sample as a third center point, and calculate a third distance between each data point that is not selected as a center point and the third center point;

[0028] Randomly select a new data point as a fourth center using a weighted probability distribution, wherein the probability of selecting a new data point is proportional to the third distance;

[0029] Take the third center and the fourth center as initial centers, and use a K-Means clustering algorithm to cluster the historical β-amyrin content of each type of oil to obtain a first clustering result;

[0030] Add the data corresponding to the β-amyrin content to the first clustering result for clustering processing to obtain a second clustering result;

[0031] Calculate a first displacement amount between the first clustering result and the second clustering result, and judge whether the first displacement amount is less than a second threshold value;

[0032] If yes, perform a step of determining that the camellia oil to be identified is a non-doped camellia oil.

[0033] Further, the step of judging whether the displacement amount of the β-amyrin content causing the clustering result of the corresponding camellia oil to be displaced is less than a second threshold value comprises:

[0034] Respectively obtain historical β-amyrin content samples of camellia oil, high-oleic peanut oil, high-oleic sunflower oil and high-oleic rapeseed oil, and the number of historical β-amyrin content samples of each type of oil is the same;

[0035] Randomly select a data point corresponding to a historical β-amyrin content sample as a third center point, and calculate a third distance between each data point that is not selected as a center point and the third center point;

[0036] Randomly select a new data point as a fourth center using a weighted probability distribution, wherein the probability of selecting a new data point is proportional to the third distance;

[0037] The fifth center and the sixth center are taken as initial centers, and the K-Means clustering algorithm is used to cluster the historical campesterol contents of each type of oil to obtain a third clustering result;

[0038] The data corresponding to the campesterol content is added to the third clustering result for clustering processing to obtain a fourth clustering result;

[0039] A second displacement amount between the third clustering result and the fourth clustering result is calculated, and it is determined whether the second displacement amount is less than a third threshold value;

[0040] If yes, a step of determining that the to-be-identified camellia oil is camellia oil doped with high-oleic rapeseed oil is performed.

[0041] Further, the first type of oil is at least one or a mixture of several of camellia oil, high-oleic peanut oil, high-oleic sunflower oil and high-oleic rapeseed oil.

[0042] Further, the second type of oil is at least one or a mixture of several of peanut oil and soybean oil.

[0043] A second aspect of the embodiment of the present application provides a camellia oil adulteration identification system for implementing the camellia oil adulteration identification method of the first aspect, and the system comprises:

[0044] An acquisition module is configured to acquire the oleic acid content, the β-amyrin content and the campesterol content of the to-be-identified camellia oil.

[0045] A first determination module is configured to determine whether the oleic acid content is greater than a first threshold value.

[0046] A second determination module is configured to determine that the to-be-identified camellia oil belongs to the first type of oil if it is determined that the oleic acid content is greater than the first threshold value, and determine whether a displacement amount of the clustering result of the corresponding camellia oil caused by the β-amyrin content is less than a second threshold value.

[0047] A first determination module is configured to determine that the to-be-identified camellia oil belongs to the second type of oil if it is determined that the oleic acid content is not greater than the first threshold value.

[0048] A second determination module is configured to determine that the to-be-identified camellia oil is not doped camellia oil if it is determined that the displacement amount of the clustering result of the corresponding camellia oil caused by the β-amyrin content is less than the second threshold value.

[0049] the third determination module is configured to determine that the camellia oil to be identified is camellia oil mixed with high-oleic rapeseed oil if the displacement amount of the campesterol content that causes the clustering result of the high-oleic rapeseed oil to be displaced is less than the third threshold value.

[0050] the third determination module is configured to determine that the camellia oil to be identified is camellia oil mixed with high-oleic rapeseed oil if the displacement amount of the campesterol content that causes the clustering result of the high-oleic rapeseed oil to be displaced is less than the third threshold value.

[0051] The third aspect of the embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the camellia oil adulteration identification method provided in the first aspect.

[0052] The fourth aspect of the embodiment of the present application provides an electronic device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the camellia oil adulteration identification method provided in the first aspect when executing the program.

[0053] The camellia oil adulteration identification method and system provided in the embodiment of the present application are as follows: the oleic acid content, the β-amyrin content, and the campesterol content of camellia oil to be identified are obtained; it is determined whether the oleic acid content is greater than a first threshold value; if yes, it is determined that the camellia oil to be identified is a mixture of one or more of camellia oil, high-oleic peanut oil, high-oleic sunflower seed oil, and high-oleic rapeseed oil; if no, it is determined that the camellia oil to be identified is a mixture of one or more of peanut oil and soybean oil; then it is determined whether the displacement amount of the β-amyrin content that causes the clustering result of the corresponding camellia oil to be displaced is less than a second threshold value; if yes, it is determined that the camellia oil to be identified is camellia oil without being mixed; if no, it is determined that the camellia oil to be identified is mixed oil, and it is determined whether the displacement amount of the campesterol content that causes the clustering result of the corresponding high-oleic rapeseed oil to be displaced is less than a third threshold value, so as to determine whether the camellia oil to be identified is camellia oil mixed with high-oleic rapeseed oil, thereby achieving accurate identification of camellia oil mixed with a small amount of high-oleic rapeseed oil. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 An implementation flowchart of the camellia oil adulteration identification method provided in the first embodiment of the present application is shown in FIG. 1.

[0055] Figure 2 A structural block diagram of the camellia oil adulteration identification system provided in the second embodiment of the present application is shown in FIG. 2.

[0056] Figure 3 A structural block diagram of the electronic device provided in the third embodiment of the present application is shown in FIG. 3. DETAILED DESCRIPTION

[0057] For the purpose of promoting an understanding of the application, the application will be described in greater detail below with reference to the illustrative embodiments. Several embodiments of the application are depicted in the drawings. The application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and fully convey the scope of the application to those skilled in the art.

[0058] It should be noted that when an element is referred to as being "on" another element, it can be directly on the other element or intervening elements can also be present. When an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements can also be present. As used herein the terms "vertical", "horizontal", "left", "right" and the like are merely used for the purpose of illustration.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0060] Embodiment one

[0061] According to the embodiment of the application, a camellia oil adulteration identification method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0062] In this embodiment one, a camellia oil adulteration identification method is provided, which can be used in electronic devices such as computers. Please refer to Figure 1 , Figure 1 The implementation flowchart of the camellia oil adulteration identification method provided by the embodiment one of the application is shown, which specifically includes steps S01 to S07.

[0063] In step S01, the content of oleic acid, the content of β-amyrin and the content of campesterol of the camellia oil to be identified are obtained.

[0064] Specifically, a certain amount of camellia oil to be identified is extracted, and the content of oleic acid, the content of beta-amyrin and the content of brassicasterol in the camellia oil are detected. The content of oleic acid can be detected by gas chromatography (GC), the content of beta-amyrin can be detected by gas chromatography-mass spectrometry (GC-MS), and the content of brassicasterol can be detected by gas chromatography (GC). It should be noted that in the final detection results, the unit of the content of oleic acid is %, the unit of the content of beta-amyrin is mg / kg, and the unit of the content of brassicasterol is mg / kg.

[0065] In step S02, it is determined whether the content of oleic acid is greater than a first threshold value. If yes, step S03 is performed. If no, step S04 is performed.

[0066] It should be noted that the content of oleic acid in each type of oil product has certain differences but also certain similarities. For example, the content of oleic acid in camellia oil, high-oleic peanut oil, high-oleic sunflower oil and high-oleic rapeseed oil is 68%-87%, the content of oleic acid in soybean oil is 17%-30%, and the content of oleic acid in peanut oil is 35%-60%. Therefore, in order to preliminarily classify oil products according to the content of oleic acid, it is particularly important to determine a relatively accurate first threshold value. Specifically, historical oleic acid content samples of camellia oil, high-oleic peanut oil, high-oleic sunflower oil, high-oleic rapeseed oil, soybean oil and peanut oil are obtained, and the number of historical oleic acid content samples of each type of oil is the same. It can be understood that undiluted oil products of different brands, different origins and different processes can be obtained to detect the content of oleic acid to obtain samples. The number of historical oleic acid content samples of camellia oil, high-oleic peanut oil, high-oleic sunflower oil, high-oleic rapeseed oil, soybean oil and peanut oil is the same, for example, each 1000 samples;

[0067] The K-Means clustering algorithm is used to cluster the historical oleic acid content of each type of oil, and the k value is set to 2 and 6 respectively to obtain the corresponding clustering results. When k is set to 2, the corresponding clustering results have 2 clusters. When k is set to 6, the corresponding clustering results have 6 clusters.

[0068] According to the clustering results when k is set to 2, a first center point is determined, wherein the first center point is the center point of the two clustering results.

[0069] According to the clustering result when the k value is set to 6, the first center point is pulled to determine a target point, and the data corresponding to the target point is the first threshold value. Specifically, the first distances between the clustering result when the k value is set to 6 and the first center point are calculated respectively, and the size of the force is determined according to the first distance and a preset mapping relationship. It can be understood that the first distances between the six clustering results and the first center point are calculated respectively and converted into mechanical simulation, that is, the size of the force is determined according to the first distance and the preset mapping relationship. The preset mapping relationship is a mapping relationship between distance and force size, which can be given according to artificial experience.

[0070] The line from the first center point to the clustering result when the k value is set to 6 is determined as the direction of the force. It can be understood that the direction of the force is from the first center point to the clustering result.

[0071] According to the size of the force and the direction of the force, mechanical simulation is performed to determine the offset direction of the first center point. It can be understood that an object is subjected to six different directions and different sizes of force, and then offset, that is, the offset direction.

[0072] The clustering result when the k value is set to 6 is projected onto the straight line of the offset direction respectively, and the second center point of the projection point located on the same side of the first center point is determined, that is, the first center point is taken as a base point to divide the second center point on the left side of the base point and the second center point on the right side of the base point.

[0073] The second distances between each second center point and the first center point are calculated and subtracted to obtain the offset amount.

[0074] The target point is determined according to the offset direction and the offset amount.

[0075] The purpose of the above steps is to distinguish whether the camellia oil to be identified is a first type of oil or a second type of oil. If it is a first type of oil, further identification is performed. Since a small amount of adulteration is identified, the oil belonging to the first type of oil will not be obviously changed into the oil belonging to the second type of oil, and the oil belonging to the second type of oil will not be changed into the oil belonging to the first type of oil.

[0076] In step S03, it is determined that the camellia oil to be identified belongs to the first type of oil, and it is judged whether the displacement amount of the clustering result of the K-Means clustering algorithm corresponding to the content of β-amyrin is less than the second threshold value. If yes, step S05 is performed, and if no, step S06 is performed.

[0077] The first type of oil is at least one or a mixture of several of camellia oil, high-oleic peanut oil, high-oleic sunflower seed oil, and high-oleic rapeseed oil.

[0078] In the embodiment, historical β-amyrin content samples of camellia oil, high-oleic peanut oil, high-oleic sunflower oil and high-oleic rapeseed oil are obtained, and the number of historical β-amyrin content samples of each type of oil is the same.

[0079] A data point corresponding to a historical β-amyrin content sample is randomly selected as a third center point, and a third distance between each data point x that is not selected as a center point and the third center point is calculated.

[0080] A new data point is randomly selected as a fourth center using a weighted probability distribution, wherein the probability of selecting the new data point is proportional to the third distance.

[0081] The third center and the fourth center are used as initial centers, and a K-Means clustering algorithm is used to cluster the historical β-amyrin content of each type of oil to obtain a first clustering result.

[0082] The data corresponding to the β-amyrin content is added to the first clustering result for clustering processing to obtain a second clustering result.

[0083] A first displacement amount between the first clustering result and the second clustering result is calculated, and it is determined whether the first displacement amount is less than a second threshold value.

[0084] If yes, a step of determining that the camellia oil to be identified is undiluted camellia oil is performed. It can be understood that, since the β-amyrin content in camellia oil is much greater than the β-amyrin content in other oil products, according to the above clustering algorithm, all data can be divided into two clusters, one cluster belongs to camellia oil and the other cluster belongs to other oils. When a new data point is added to the current data, i.e., the β-amyrin content data of the camellia oil to be identified, the centroid of the cluster belonging to camellia oil can change. By calculating the change amount, i.e., the first displacement amount, which can be understood as the distance between two data points, and comparing it with the second threshold value, it can be determined whether the camellia oil is adulterated. It can be understood that a large change amount indicates that the camellia oil is adulterated. The second threshold value can be manually set or continuously refined in subsequent applications.

[0085] If step S04 is performed, it is determined that the camellia oil to be identified belongs to the second type of oil.

[0086] The second type of oil is at least a mixture of one or more of peanut oil and soybean oil.

[0087] If step S05 is performed, it is determined that the camellia oil to be identified is undiluted camellia oil.

[0088] If yes, step S06 is performed to determine that the camellia oil to be identified is adulterated oil, and to determine whether the displacement amount of the campesterol content that causes the clustering result of the high-oleic rapeseed oil to be displaced is less than a third threshold value. If yes, step S07 is performed.

[0089] The adulterated oil is one or more of high-oleic peanut oil, high-oleic sunflower oil, and high-oleic rapeseed oil mixed with camellia oil.

[0090] Similarly, historical campesterol content samples of high-oleic peanut oil, high-oleic sunflower oil, and high-oleic rapeseed oil are obtained, and the number of historical campesterol content samples of each type of oil is the same.

[0091] A data point corresponding to a historical campesterol content sample is randomly selected as a fifth center point, and a fourth distance between each data point that is not selected as a center point and the fifth center point is calculated.

[0092] A new data point is randomly selected as a sixth center using a weighted probability distribution, wherein the probability of selecting the new data point is proportional to the fourth distance.

[0093] The fifth center and the sixth center are used as initial centers, and a K-Means clustering algorithm is used to cluster the historical campesterol content of each type of oil to obtain a third clustering result.

[0094] The data corresponding to the campesterol content is added to the third clustering result for clustering processing to obtain a fourth clustering result.

[0095] A second displacement amount between the third clustering result and the fourth clustering result is calculated, and it is determined whether the second displacement amount is less than a third threshold value.

[0096] If yes, a step of determining that the camellia oil to be identified is camellia oil adulterated with high-oleic rapeseed oil is performed.

[0097] According to the above clustering algorithm, all data can be divided into two clusters, one cluster belonging to high-oleic rapeseed oil and the other cluster belonging to other oils. When a new data point is added to the current data, i.e., the campesterol content data of the camellia oil to be identified, the centroid of the cluster belonging to high-oleic rapeseed oil can change. By calculating the change amount, i.e., the second displacement amount mentioned above, which can be understood as the distance between two data points, and comparing it with the third threshold value, it can be determined whether the camellia oil is adulterated with high-oleic rapeseed oil. It can be understood that a large change amount indicates that the camellia oil is adulterated with high-oleic rapeseed oil. The third threshold value can be artificially set or continuously refined in subsequent applications.

[0098] If yes, step S06 is performed to determine that the camellia oil to be identified is adulterated oil, and to determine whether the displacement amount of the campesterol content that causes the clustering result of the high-oleic rapeseed oil to be displaced is less than a third threshold value. If yes, step S07 is performed.

[0099] In summary, the camellia oil adulteration identification method in the above embodiments of the present application, the method is to obtain the content of oleic acid, the content of β-amyrin and the content of campesterol of the camellia oil to be identified; determine whether the content of oleic acid is greater than the first threshold value; if yes, determine that the camellia oil to be identified is one or a mixture of several of camellia oil, high-oleic peanut oil, high-oleic sunflower oil and high-oleic rapeseed oil; if not, determine that it is a mixture of one or several of peanut oil and soybean oil; then determine whether the content of β-amyrin causes the displacement of the clustering result of the corresponding camellia oil to be less than the second threshold value; if yes, determine that the camellia oil to be identified is not adulterated camellia oil; if not, determine that the camellia oil to be identified is adulterated oil, and determine whether the content of campesterol causes the displacement of the clustering result of the corresponding high-oleic rapeseed oil to be less than the third threshold value, to determine whether the camellia oil to be identified is camellia oil adulterated with high-oleic rapeseed oil, so as to complete the accurate identification of a small amount of high-oleic rapeseed oil in camellia oil.

[0100] Embodiment two

[0101] Please refer to Figure 2 , Figure 2 is a structural block diagram of a camellia oil adulteration identification system provided by the second embodiment of the present application, which is used to implement the above embodiments and preferred embodiments, and will not be described again. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware, or a combination of software and hardware is also possible and contemplated.

[0102] Specifically, the camellia oil adulteration identification system 200 comprises: an acquisition module 21, a first determination module 22, a second determination module 23, a third determination module 27, and a third determination module 27, wherein:

[0103] The acquisition module 21 is used to acquire the content of oleic acid, the content of β-amyrin and the content of campesterol of the camellia oil to be identified;

[0104] The first determination module 22 is used to determine whether the content of oleic acid is greater than the first threshold value, and the step of determining the first threshold value comprises:

[0105] The historical oleic acid content samples of camellia oil, high-oleic peanut oil, high-oleic sunflower oil, high-oleic rapeseed oil, soybean oil and peanut oil are acquired respectively, and the number of historical oleic acid content samples of each type of oil is the same;

[0106] The K-Means clustering algorithm is used to cluster the historical oleic acid content of each type of oil, and the k value is set to 2 and 6 in turn to obtain the corresponding clustering result;

[0107] determine the first center point according to the clustering result when the k value is set to 2;

[0108] determine the target point by pulling the first center point according to the clustering result when the k value is set to 6, the target point corresponding to the first threshold value, specifically, calculate the first distance between the clustering result when the k value is set to 6 and the first center point, and determine the force size according to the first distance and a preset mapping relationship;

[0109] determine the direction of the force as the line connecting the first center point to the clustering result when the k value is set to 6;

[0110] determine the offset direction of the first center point according to the size of the force and the direction of the force;

[0111] project the clustering result when the k value is set to 6 onto the straight line of the offset direction respectively, and determine the second center point of the projection point located on the same side of the first center point;

[0112] calculate the second distance between each second center point and the first center point, and make a difference to obtain the offset amount;

[0113] determine the target point according to the offset direction and the offset amount;

[0114] The second determination module 25 is configured to determine that the to-be-identified camellia oil is undyed camellia oil if it is judged that the displacement amount of the clustering result of the corresponding camellia oil caused by the β-amyrin content is less than the second threshold value.

[0115] The first determination module 24 is configured to determine that the to-be-identified camellia oil belongs to the second type of oil if it is judged that the oleic acid content is not greater than the first threshold value, the second type of oil being at least a mixture of one or more of peanut oil and soybean oil.

[0116] The second determination module 25 is configured to determine that the to-be-identified camellia oil is undyed camellia oil if it is judged that the displacement amount of the clustering result of the corresponding camellia oil caused by the β-amyrin content is less than the second threshold value.

[0117] The third determination module 26 is configured to determine that the to-be-identified camellia oil is a dyed oil if it is judged that the displacement amount of the clustering result of the corresponding camellia oil caused by the β-amyrin content is not less than the second threshold value, and judge whether the displacement amount of the clustering result of the corresponding high-oleic rapeseed oil caused by the campesterol content is less than the third threshold value.

[0118] The third determining module 27 is configured to determine that the camellia oil to be identified is camellia oil mixed with high-oleic acid rapeseed oil if it is determined that the content of the campesterol causes a displacement of the clustering result of the corresponding high-oleic acid rapeseed oil to be less than a third threshold value.

[0119] Further, in some optional embodiments of the present application, the second determining module 23 comprises:

[0120] The first obtaining unit is configured to obtain historical β-amyrin content samples of camellia oil, high-oleic acid peanut oil, high-oleic acid sunflower oil and high-oleic acid rapeseed oil respectively, and the number of historical β-amyrin content samples of each type of oil is the same;

[0121] The first selecting unit is configured to randomly select a data point corresponding to a historical β-amyrin content sample as a third center point, and calculate a third distance between each data point that is not selected as a center point and the third center point;

[0122] The second selecting unit is configured to randomly select a new data point as a fourth center point by using a weighted probability distribution, wherein the probability of selecting the new data point is proportional to the third distance;

[0123] The first clustering processing unit is configured to take the third center and the fourth center as initial centers, and perform clustering on the historical β-amyrin content of each type of oil by using a K-Means clustering algorithm to obtain a first clustering result;

[0124] The second clustering processing unit is configured to add the data corresponding to the β-amyrin content to the first clustering result for clustering processing to obtain a second clustering result;

[0125] The first calculating unit is configured to calculate a first displacement between the first clustering result and the second clustering result, and determine whether the first displacement is less than a second threshold value;

[0126] The first executing unit is configured to perform the step of determining that the camellia oil to be identified is camellia oil that is not mixed when it is determined that the first displacement is less than the second threshold value.

[0127] Further, in some optional embodiments of the present application, the third determining module 26 comprises:

[0128] The second obtaining unit is configured to obtain historical campesterol content samples of high-oleic acid peanut oil, high-oleic acid sunflower oil and high-oleic acid rapeseed oil respectively, and the number of historical campesterol content samples of each type of oil is the same;

[0129] The third selecting unit is configured to randomly select a data point corresponding to a historical campesterol content sample as a fifth center point, and calculate a fourth distance between each data point that is not selected as a center point and the fifth center point;

[0130] a fourth selection unit configured to randomly select a new data point as a sixth center using a weighted probability distribution, wherein a probability of selecting the new data point is proportional to the fourth distance;

[0131] a third clustering processing unit configured to cluster the historical campesterol content of each type of oil using a K-Means clustering algorithm with the fifth center and the sixth center as initial centers to obtain a third clustering result;

[0132] a fourth clustering processing unit configured to add the data corresponding to the campesterol content to the third clustering result for clustering processing to obtain a fourth clustering result;

[0133] a second calculation unit configured to calculate a second displacement between the third clustering result and the fourth clustering result, and determine whether the second displacement is less than a third threshold value;

[0134] a second execution unit configured to, when it is determined that the second displacement is less than the third threshold value, execute a step of determining that the to-be-authenticated camellia oil is camellia oil adulterated with high-oleic rapeseed oil.

[0135] Embodiment Three

[0136] In another aspect, the present application also provides an electronic device, please refer to Figure 3 , which is an electronic device in the embodiment three of the present application, comprising a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor, wherein the processor 10 implements the camellia oil adulteration identification method as described above when executing the computer program 30.

[0137] In some embodiments, the processor 10 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips, configured to run program codes or process data stored in the memory 20, such as executing access restriction programs.

[0138] The memory 20 includes at least one type of readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 20 can be an internal storage unit of the electronic device in some embodiments, such as a hard disk of the electronic device. The memory 20 can also be an external storage device of the electronic device in other embodiments, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Further, the memory 20 can include both the internal storage unit and the external storage device of the electronic device. The memory 20 can be used not only to store application software and various data of the electronic device, but also to temporarily store data that has been output or will be output.

[0139] It should be noted that, Figure 3 The illustrated structure does not limit the electronic device, and in other embodiments, the electronic device can include fewer or more components than illustrated, or combine certain components, or arrange different components.

[0140] The present application also provides a computer readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the camellia oil adulteration identification method as described above.

[0141] Those skilled in the art can understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a list of executable instructions for implementing the logic function, which can be specifically implemented in any computer readable medium for use by or in conjunction with an instruction execution system, device or apparatus, such as a computer-based system, a system including a processor or other system that can fetch and execute instructions from the instruction execution system, device or apparatus. For the present specification, the "computer readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by or in conjunction with the instruction execution system, device or apparatus, or in conjunction with these instruction execution systems, devices or apparatus.

[0142] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that is then employable by a computer. Examples of computer-readable media that are further within the spirit of the present application are a computer program product, a computer readable storage medium, and a computer.

[0143] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the embodiments described above, various steps or methods can be implemented, for example, by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following techniques, which are well known in the art, can be used to implement the application: a hybrid of the above techniques, discrete logic circuit(s) having logic gates for implementing logic functions upon request pins of the discrete logic circuit(s), programmable logic array(s) (PLAs), field programmable gate array(s) (FPGAs), etc.

[0144] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the application. In the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in one or more embodiments or examples.

[0145] The above embodiments only express several implementation manners of the application, which are described in a more specific and detailed manner, but cannot be understood as a limitation on the patent scope of the application. It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the application, which are all within the protection scope of the application. Therefore, the patent protection scope of the application should be subject to the appended claims.

Claims

1. A method for discriminating adulteration of camellia oil, characterized by, The method comprises: Obtaining the oleic acid content, β-amyrin content and campesterol content of the camellia oil to be identified; Determining whether the oleic acid content is greater than a first threshold value; If it is determined that the oleic acid content is greater than the first threshold value, it is determined that the camellia oil to be identified belongs to a first type of oil, and it is determined whether the displacement amount of the β-amyrin content causing the clustering result of the corresponding camellia oil to be displaced is less than a second threshold value, the first type of oil being at least one or a mixture of several of camellia oil, high-oleic peanut oil, high-oleic sunflower oil and high-oleic rapeseed oil; If it is determined that the oleic acid content is not greater than the first threshold value, it is determined that the camellia oil to be identified belongs to a second type of oil, the second type of oil being at least one or a mixture of several of peanut oil and soybean oil; If it is determined that the displacement amount of the β-amyrin content causing the clustering result of the corresponding camellia oil to be displaced is less than the second threshold value, it is determined that the camellia oil to be identified is not adulterated; If it is determined that the displacement amount of the β-amyrin content causing the clustering result of the corresponding camellia oil to be displaced is not less than the second threshold value, it is determined that the camellia oil to be identified is adulterated oil, and it is determined whether the displacement amount of the campesterol content causing the clustering result of the corresponding high-oleic rapeseed oil to be displaced is less than a third threshold value; If it is determined that the displacement amount of the campesterol content causing the clustering result of the corresponding high-oleic rapeseed oil to be displaced is less than the third threshold value, it is determined that the camellia oil to be identified is camellia oil adulterated with high-oleic rapeseed oil; The step of determining the first threshold value comprises: Respectively obtaining historical oleic acid content samples of camellia oil, high-oleic peanut oil, high-oleic sunflower oil, high-oleic rapeseed oil, soybean oil and peanut oil, and the number of historical oleic acid content samples of each type of oil is the same; Using a K-Means clustering algorithm to cluster the historical oleic acid content of each type of oil, and setting the k value to 2 and 6 in turn to obtain the corresponding clustering results; According to the clustering result when the k value is set to 2, a first center point is determined; According to the clustering result when the k value is set to 6, the first center point is pulled to determine a target point, and the data corresponding to the target point is the first threshold value; The step of pulling the first center point according to the clustering result when the k value is set to 6 to determine the target point comprises: Respectively calculating the first distance between the clustering result when the k value is set to 6 and the first center point, and determining the force size according to the first distance and a preset mapping relationship; Respectively determining that the line connecting the first center point to the clustering result when the k value is set to 6 is the direction of the force; According to the force size and the force direction, mechanical simulation is performed to determine the displacement direction of the first center point; The clustering result when the k value is set to 6 is projected onto a straight line in the displacement direction, and a second center point of the projection point located on the same side of the first center point is determined; The second distances between each second center point and the first center point are calculated, and the differences are obtained to obtain the displacement amount; According to the displacement direction and the displacement amount, the target point is determined.

2. The method for discriminating adulteration of camellia oil according to claim 1, characterized by, The step of determining whether the displacement amount of the β-amyrin content causing the clustering result of the corresponding camellia oil to be displaced is less than the second threshold value comprises: respectively, and the number of the historical β-amyrin content samples of each type of oil is the same; randomly selecting a data point corresponding to a historical β-amyrin content sample as a third center point, and calculating a third distance between each data point that is not selected as a center point and the third center point; randomly selecting a new data point as a fourth center by using a weighted probability distribution, wherein the probability of selecting the new data point is proportional to the third distance; taking the third center and the fourth center as initial centers, and performing clustering on the historical β-amyrin content of each type of oil by using a K-Means clustering algorithm to obtain a first clustering result; adding data corresponding to the β-amyrin content to the first clustering result for clustering processing to obtain a second clustering result; calculating a first displacement between the first clustering result and the second clustering result, and determining whether the first displacement is less than a second threshold value; if yes, performing a step of determining that the camellia oil to be identified is camellia oil without adulteration.

3. The method for discriminating adulteration of camellia oil according to claim 1, characterized by, The step of determining whether the displacement of the campesterol content to the clustering result of the corresponding high-oleic rapeseed oil is less than a third threshold value comprises: respectively, and the number of the historical β-amyrin content samples of each type of oil is the same; randomly selecting a data point corresponding to a historical β-amyrin content sample as a third center point, and calculating a third distance between each data point that is not selected as a center point and the third center point; randomly selecting a new data point as a fourth center by using a weighted probability distribution, wherein the probability of selecting the new data point is proportional to the third distance; taking the third center and the fourth center as initial centers, and performing clustering on the historical β-amyrin content of each type of oil by using a K-Means clustering algorithm to obtain a first clustering result; adding data corresponding to the β-amyrin content to the first clustering result for clustering processing to obtain a second clustering result; calculating a first displacement between the first clustering result and the second clustering result, and determining whether the first displacement is less than a second threshold value; if yes, performing a step of determining that the camellia oil to be identified is camellia oil without adulteration.

4. A system for discriminating adulteration of camellia oil, characterized by, The system for implementing the camellia oil adulteration identification method according to any one of claims 1-3 comprises: an acquisition module configured to acquire an oleic acid content, a β-amyrin content and a campesterol content of camellia oil to be identified; a first determination module configured to determine that the camellia oil to be identified belongs to a second type of oil if it is determined that the oleic acid content is not greater than the first threshold value; a second determination module configured to determine whether a displacement of the β-amyrin content to a clustering result of corresponding camellia oil is less than a second threshold value if it is determined that the oleic acid content is greater than the first threshold value; and a third determination module configured to determine that the camellia oil to be identified is camellia oil without adulteration if it is determined that the displacement of the β-amyrin content to the clustering result of the corresponding camellia oil is less than the second threshold value. The second determining module is configured to determine that the camellia oil to be identified is an undoped camellia oil if the shift amount of the clustering result of the camellia oil corresponding to the β-amyrin content is less than the second threshold value. The third determining module is configured to determine that the camellia oil to be identified is a doped oil if the shift amount of the clustering result of the camellia oil corresponding to the β-amyrin content is not less than the second threshold value, and determine whether the shift amount of the clustering result of the high-oleic rapeseed oil corresponding to the campesterol content is less than the third threshold value. The third determining module is configured to determine that the camellia oil to be identified is a doped high-oleic rapeseed oil camellia oil if the shift amount of the clustering result of the high-oleic rapeseed oil corresponding to the campesterol content is less than the third threshold value.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the camellia oil adulteration identification method of any one of claims 1-3.

6. An electronic device, comprising: The computer program is stored in the memory and executable on the processor, and the processor executes the program to implement the camellia oil adulteration identification method of any one of claims 1-3.

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