Camellia oil adulteration identification method and system

Abstract

The invention provides a camellia oil adulteration identification method and system. The method comprises the following steps: acquiring the oleic acid content, beta-amyrin content and campesterol content of to-be-identified camellia oil; judging whether the oleic acid content is greater than a first threshold; if yes, determining that the to-be-identified camellia oil is one or a mixture of more of camellia oil, high-oleic-acid peanut oil, high-oleic-acid sunflower seed oil and high-oleic-acid rapeseed oil; if not, determining one or a mixture of more of peanut oil and soybean oil; then judging that the content of beta-amyrin enables the displacement of the clustering result of the corresponding camellia oil to be smaller than a second threshold value; if yes, determining that the to-be-identified camellia oil is undoped camellia oil; if not, determining that the to-be-identified camellia oil is doped oil, judging whether the displacement amount of displacement of the clustering result of the corresponding high-oleic-acid rapeseed oil caused by the campesterol content is smaller than a third threshold value or not, and determining whether the to-be-identified camellia oil is the camellia oil doped with the high-oleic-acid rapeseed oil or not.

Description

Technical Field

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

[0002] Camellia oil is an edible vegetable oil obtained from the seeds of the Camellia oleifera tree of the Theaceae family.

[0003] In practical application, conventional physical and chemical testing mainly conducts qualitative and quantitative analysis of the fatty acid content of vegetable oils. Oleic acid, a fatty acid, has become a key characteristic indicator for identifying adulterated tea oil because its content in tea oil is much higher than that in other vegetable oils. 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, fatty acid discrimination indicators often fail. In addition, the spectral method is based on the relative intensity difference between the characteristic peaks of different vegetable oils to model, which requires a large number of samples, and the method has low sensitivity and lacks quantitative indicators, so it cannot be well applied to actual adulteration identification. Summary of the invention

[0004] Based on this, an embodiment of the present invention provides a method and system for identifying adulteration of camellia oil, which aims to have a high detection sensitivity when a small amount of high oleic rapeseed oil is adulterated in camellia oil.

[0005] A first aspect of an embodiment of the present invention provides a method for identifying adulteration of camellia oil, the method comprising: 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; If it is determined that the oleic acid content is greater than the first threshold, it is determined that the camellia oil to be identified belongs to the first category of oils, and it is determined whether the displacement amount of the clustering result of the corresponding camellia oil caused by the β-amyrin content is less than the second threshold; 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 the second category of oil products; 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, it is determined that the camellia oil to be identified is unadulterated 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 not less than the second threshold, the camellia oil to be identified is determined to be adulterated oil, and it is determined 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; If it is determined that 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, it is determined that the camellia oil to be identified is camellia oil doped with high oleic rapeseed oil.

[0006] Furthermore, the step of determining the first threshold includes: 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 were obtained respectively, and the number of historical oleic acid content samples of each type of oil was the same; The K-Means clustering algorithm was used to cluster the historical oleic acid content of each type of oil, and the k value was set to 2 and 6 respectively, and the corresponding clustering results were obtained; According to the clustering result when the k value is set to 2, the 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 the target point, and the data corresponding to the target point is the first threshold.

[0007] Furthermore, the step of pulling the first center point to determine the target point according to the clustering result when the k value is set to 6 includes: respectively calculating a first distance between the clustering result when the k value is set to 6 and the first center point, and determining the magnitude of the force 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; Performing mechanical simulation according to the magnitude and direction of the force to determine the offset direction of the first center point; The clustering results when the k value is set to 6 are respectively projected onto the straight lines in the offset direction, and a second center point located on the same side of the projected point as the first center point is determined; Calculate the second distance between each of the second center points and the first center point, and make a difference to obtain an offset; The target point is determined according to the offset direction and the offset amount.

[0008] Furthermore, the step of judging whether the displacement amount of the clustering result of the corresponding camellia oil caused by the β-amyrin content is less than a second threshold comprises: The historical β-amyrin content samples of camellia oil, high oleic peanut oil, high oleic sunflower oil and high oleic rapeseed oil were obtained respectively, and the number of historical β-amyrin content samples of each type of oil was the same; Randomly select a data point corresponding to the historical β-amyrin content sample as a third center point, and calculate a third distance between each data point that is not selected as the center point and the third center point; Randomly selecting a new data point as the fourth center using a weighted probability distribution, wherein the probability of selecting the new data point is proportional to the third distance; The third center and the fourth center are used as initial centers, and the historical β-amyrin content of each type of oil is clustered using a K-Means clustering algorithm to obtain a first clustering result; Adding the 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; If yes, the step of determining that the camellia oil to be identified is unadulterated camellia oil is performed.

[0009] Furthermore, the step of judging whether the displacement amount of the clustering result of the corresponding high oleic rapeseed oil caused by the campesterol content is less than a third threshold value comprises: Obtain historical campesterol content samples of high oleic peanut oil, high oleic sunflower oil, and high oleic rapeseed oil, respectively, and the number of historical campesterol content samples of each type of oil is the same; Randomly select a data point corresponding to the historical campesterol content sample as the fifth center point, and calculate a fourth distance between each data point that is not selected as the center point and the fifth center point; Randomly selecting a new data point as the sixth center using a weighted probability distribution, wherein the probability of selecting the new data point is proportional to the fourth distance; The fifth center and the sixth center are used as initial centers, and the historical campesterol content of each type of oil is clustered using a K-Means clustering algorithm to obtain a third clustering result; Adding the data corresponding to the campesterol content to the third clustering result for clustering processing to obtain a fourth clustering result; Calculating a second displacement between the third clustering result and the fourth clustering result, and determining whether the second displacement is less than a third threshold; If so, the step of determining that the camellia oil to be identified is camellia oil mixed with high oleic rapeseed oil is performed.

[0010] Furthermore, the first category of oil products is at least one of camellia oil, high oleic peanut oil, high oleic sunflower oil and high oleic rapeseed oil, or a mixture of several of them.

[0011] Furthermore, the second type of oil is at least peanut oil and soybean oil, or a mixture of the two.

[0012] A second aspect of the embodiments of the present invention provides a camellia oil adulteration identification system, which is used to implement the camellia oil adulteration identification method described in the first aspect, and the system includes: An acquisition module, used for acquiring the oleic acid content, β-amyrin content and campesterol content of the camellia oil to be identified; A first judgment module, used to judge whether the oleic acid content is greater than a first threshold; A second judgment module is used to determine that the camellia oil to be identified belongs to the first category of oils if the oleic acid content is greater than the first threshold, and to judge whether the displacement amount of the clustering result of the corresponding camellia oil caused by the β-amyrin content is less than the second threshold; A first determination module, configured to determine that the camellia oil to be identified belongs to the second category of oil products if it is determined that the oleic acid content is not greater than a first threshold value; A second determination module is used to determine that the camellia oil to be identified is unadulterated camellia oil if the displacement amount caused by the β-amyrin content to shift the corresponding camellia oil clustering result is less than a second threshold value; a third judgment module, for determining that the camellia oil to be identified is adulterated 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 not less than the second threshold, and for determining 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; The third determination module is used to determine that the camellia oil to be identified is camellia oil doped with high oleic rapeseed oil if the displacement amount caused by the campesterol content to shift the corresponding high oleic rapeseed oil clustering result is less than a third threshold.

[0013] A third aspect of an embodiment of the present invention 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 provided in the first aspect.

[0014] A fourth aspect of an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for identifying adulteration of camellia oil provided in the first aspect is implemented.

[0015] A method and system for identifying adulteration of camellia oil provided in an embodiment of the present invention, the method obtains the oleic acid content, β-amyrin content and campesterol content of the camellia oil to be identified; determines whether the oleic acid content is greater than a first threshold; if so, determines 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 not, determines that it is a mixture of one or more of peanut oil and soybean oil; then determines that the displacement amount of the clustering result of the corresponding camellia oil caused by the β-amyrin content is less than a second threshold; if so, determines that the camellia oil to be identified is unadulterated camellia oil; if not, determines that the camellia oil to be identified is adulterated oil, and determines whether the displacement amount of the clustering result of the corresponding high oleic rapeseed oil caused by the campesterol content is less than a third threshold, determines whether the camellia oil to be identified is camellia oil doped with high oleic rapeseed oil, so as to complete the accurate identification of camellia oil doped with a small amount of high oleic rapeseed oil. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a method for identifying adulteration of camellia oil provided in Example 1 of the present invention; Figure 2 A structural block diagram of a camellia oil adulteration identification system provided in Embodiment 2 of the present invention; Figure 3 This is a structural block diagram of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0017] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0018] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0020] Embodiment 1 According to an embodiment of the present invention, an embodiment of a method for identifying adulteration of camellia oil 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 can be executed in an order different from that shown here.

[0021] In this embodiment 1, a method for identifying adulteration of camellia oil is provided, which can be used in electronic devices, such as computers. Figure 1 , Figure 1 The flowchart of the method for identifying adulteration of camellia oil provided in the first embodiment of the present invention is shown, which specifically includes steps S01 to S07.

[0022] Step S01, obtaining the oleic acid content, β-amyrin content and campesterol content of the camellia oil to be identified.

[0023] Specifically, a certain amount of camellia oil to be identified is extracted, and the oleic acid content, β-amyrin content and campesterol content therein are detected, wherein the oleic acid content can be detected by gas chromatography (GC), the β-amyrin content can be detected by gas chromatography-mass spectrometry (GC-MS), and the campesterol content can be detected by gas chromatography (GC). It should be noted that, in the final test results, the unit of oleic acid content is %, the unit of β-amyrin content is mg / kg, and the unit of campesterol content is mg / kg.

[0024] Step S02, determining whether the oleic acid content is greater than a first threshold value, if so, executing step S03, if not, executing step S04.

[0025] It should be noted that the oleic acid content of each type of oil is different, but also has certain similarities. For example, the oleic acid content of camellia oil, high oleic peanut oil, high oleic sunflower oil and high oleic rapeseed oil is 68%~87%, the oleic acid content of soybean oil is 17%~30%, and the oleic acid content of peanut oil is 35%~60%. Therefore, it is particularly important to initially classify oils according to the oleic acid content. For this purpose, a more accurate first threshold value needs to be formulated. Specifically, the classification Separately obtain 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. It is understandable that unadulterated oils of different brands, different origins, and different processes can be obtained, and their oleic acid content can be tested 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, 1000 samples each; The K-Means clustering algorithm was used to cluster the historical oleic acid content of each type of oil, and the k value was set to 2 and 6 respectively, and the corresponding clustering results were obtained. When k was set to 2, there were 2 corresponding clustering results; when k was set to 6, there were 6 corresponding clustering results; According to the clustering result when the k value is set to 2, a first center point is determined, wherein the first center point is the center point of the two clustering results; According to the clustering result when the k value is set to 6, the first center point is pulled to determine the target point, and the data corresponding to the target point is the first threshold. Specifically, the first distance between the clustering result when the k value is set to 6 and the first center point is calculated respectively, and the magnitude of the force is determined according to the first distance and the preset mapping relationship. It can be understood that the first distances between the 6 clustering results and the first center point are calculated respectively and converted into mechanical simulation, that is, the magnitude of the force is determined according to the first distance and the preset mapping relationship. The preset mapping relationship is a pre-established mapping relationship between the distance and the magnitude of the force, and the mapping relationship can be given according to artificial experience; Determine respectively the line from the first center point to the clustering result when the k value is set to 6 as the direction of the force. It can be understood that the direction of the force is the direction from the first center point to the clustering result; According to the magnitude and 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 forces of 6 different directions and magnitudes, and then deflected, which is the offset direction; The clustering results when the k value is set to 6 are projected onto the straight lines in the offset direction respectively, and the second center point of the projected point located on the same side of the first center point is determined, that is, the first center point is taken as the base point, and the second center point is divided into 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; Calculate the second distance between each of the second center points and the first center point, and make a difference to obtain an offset; The target point is determined according to the offset direction and the offset amount.

[0026] The purpose of the above steps is to distinguish whether the camellia oil to be identified is a first-category oil or a second-category oil. If it is a first-category oil, further identification is performed. Since it is a small amount of adulteration, it will not obviously change the oil belonging to the first category of oil products into the second category of oil products, nor will it change the oil belonging to the second category of oil products into the first category of oil products.

[0027] In step S03, it is determined that the camellia oil to be identified belongs to the first category of oils, and it is judged whether the displacement amount of the clustering result of the corresponding K-Means clustering algorithm caused by the β-amyrin content is less than the second threshold value. If so, step S05 is executed; if not, step S06 is executed.

[0028] The first category of oil products is at least one of camellia oil, high oleic peanut oil, high oleic sunflower oil and high oleic rapeseed oil, or a mixture of several of them.

[0029] In this embodiment, historical β-amyrin content samples of camellia oil, high oleic peanut oil, high oleic sunflower oil, and high oleic rapeseed oil are obtained respectively, and the number of historical β-amyrin content samples of each type of oil is the same; Randomly select a data point corresponding to the historical β-amyrin content sample as a third center point, and calculate a third distance between each data point x that is not selected as the center point and the third center point; Randomly selecting a new data point as the fourth center using a weighted probability distribution, wherein the probability of selecting the new data point is proportional to the third distance; The third center and the fourth center are used as initial centers, and the historical β-amyrin content of each type of oil is clustered using a K-Means clustering algorithm to obtain a first clustering result; Adding the 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; If so, the step of determining that the camellia oil to be identified is unadulterated camellia oil is executed. It can be understood that since the content of β-amyrin in camellia oil is much greater than the content of β-amyrin in other oils, according to the above-mentioned clustering algorithm, all data can be divided into two clusters, one cluster belonging to camellia oil and the other cluster belonging to other oils. When a new data point is added to the current data, that is, the β-amyrin content data of the camellia oil to be identified, the centroid of the cluster belonging to the camellia oil may change. By calculating the change, that is, the first displacement mentioned above, which can be understood as the distance between the two data points, and then compared with the second threshold, it is determined whether the camellia oil is adulterated. It can be understood that a large change means that the camellia oil is adulterated. Among them, the second threshold can be manually set or continuously improved in subsequent applications.

[0030] Step S04: determining that the camellia oil to be identified belongs to the second category of oils.

[0031] Wherein, the second type of oil products is at least peanut oil and soybean oil or a mixture of several of them.

[0032] Step S05, determining that the camellia oil to be identified is unadulterated camellia oil.

[0033] Step S06, determining that the camellia oil to be identified is adulterated oil, and judging whether the displacement amount of the clustering result of the corresponding high oleic rapeseed oil caused by the campesterol content is less than a third threshold, if so, executing step S07.

[0034] The mixed oil is a mixture of one or more of high oleic peanut oil, high oleic sunflower oil and high oleic rapeseed oil and camellia oil.

[0035] Similarly, historical campesterol content samples of high oleic peanut oil, high oleic sunflower oil, and high oleic rapeseed oil were obtained, and the number of historical campesterol content samples of each type of oil was the same; Randomly select a data point corresponding to the historical campesterol content sample as the fifth center point, and calculate a fourth distance between each data point that is not selected as the center point and the fifth center point; Randomly selecting a new data point as the sixth center using a weighted probability distribution, wherein the probability of selecting the new data point is proportional to the fourth distance; The fifth center and the sixth center are used as initial centers, and the historical campesterol content of each type of oil is clustered using a K-Means clustering algorithm to obtain a third clustering result; Adding the data corresponding to the campesterol content to the third clustering result for clustering processing to obtain a fourth clustering result; Calculating a second displacement between the third clustering result and the fourth clustering result, and determining whether the second displacement is less than a third threshold; If so, the step of determining that the camellia oil to be identified is camellia oil mixed with high oleic rapeseed oil is performed.

[0036] According to the above clustering algorithm, all the data can be divided into two clusters, one cluster belongs to high oleic rapeseed oil, and the other cluster belongs to other oils. When a new data point is added to the current data, that is, the rapesterol content data of the camellia oil to be identified, the centroid of the cluster belonging to the high oleic rapeseed oil may change. By calculating the change, that is, the second displacement mentioned above, it can be understood as the distance between the two data points, and then compared with the third threshold, it is determined whether the camellia oil is adulterated with high oleic rapeseed oil. It can be understood that a large change means that the camellia oil is adulterated with high oleic rapeseed oil. Among them, the third threshold can be manually set, and can also be continuously improved in subsequent applications.

[0037] Step S07: determining that the camellia oil to be identified is camellia oil mixed with high oleic rapeseed oil.

[0038] In summary, the camellia oil adulteration identification method in the above embodiment of the present invention obtains the oleic acid content, β-amyrin content and campesterol content of the camellia oil to be identified; determines whether the oleic acid content is greater than a first threshold; if so, determines 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 not, determines that it is a mixture of one or more of peanut oil and soybean oil; then determines 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; if so, determines that the camellia oil to be identified is unadulterated camellia oil; if not, determines that the camellia oil to be identified is adulterated oil, and determines 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, determines whether the camellia oil to be identified is camellia oil doped with high oleic rapeseed oil, so as to complete the accurate identification of camellia oil doped with a small amount of high oleic rapeseed oil.

[0039] Embodiment 2 See also Figure 2 , Figure 2 1 is a block diagram of a camellia oil adulteration identification system provided in Example 2 of the present invention. The camellia oil adulteration identification system 200 is used to implement the above-mentioned embodiments and preferred implementations, and the descriptions that have been made will not be repeated. 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, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0040] Specifically, the camellia oil adulteration identification system 200 includes: an acquisition module 21, a first judgment module 22, a second judgment module 23, a first determination module 24, a second determination module 25, a third judgment module 26 and a third determination module 27, wherein: An acquisition module 21 is used to acquire the oleic acid content, β-amyrin content and campesterol content of the camellia oil to be identified; The first judgment module 22 is used to judge whether the oleic acid content is greater than a first threshold value. The step of determining the first threshold value includes: 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 were obtained respectively, and the number of historical oleic acid content samples of each type of oil was the same; The K-Means clustering algorithm was used to cluster the historical oleic acid content of each type of oil, and the k value was set to 2 and 6 respectively, and the corresponding clustering results were obtained; According to the clustering result when the k value is set to 2, the 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 the target point, and the data corresponding to the target point is the first threshold. Specifically, the first distance between the clustering result when the k value is set to 6 and the first center point is calculated respectively, and the magnitude of the force is determined 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; Performing mechanical simulation according to the magnitude and direction of the force to determine the offset direction of the first center point; The clustering results when the k value is set to 6 are respectively projected onto the straight lines in the offset direction, and a second center point located on the same side of the projected point as the first center point is determined; Calculate the second distance between each of the second center points and the first center point, and make a difference to obtain an offset; Determining the target point according to the offset direction and the offset amount; The second judgment module 23 is used to determine that the camellia oil to be identified belongs to the first category of oil products if it is judged that the oleic acid content is greater than the first threshold value, and to judge whether 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, and the first category of oil products is at least one of camellia oil, high oleic peanut oil, high oleic sunflower oil and high oleic rapeseed oil, or a mixture of several thereof; A first determination module 24 is used to determine that the camellia oil to be identified belongs to the second category of oil products if it is determined that the oleic acid content is not greater than the first threshold value, and the second category of oil products is at least one of peanut oil and soybean oil or a mixture of several thereof; A second determination module 25 is used to determine that the camellia oil to be identified is unadulterated camellia oil if the displacement amount caused by the β-amyrin content to shift the clustering result of the corresponding camellia oil is less than a second threshold value; The third judgment module 26 is used to determine that the camellia oil to be identified is adulterated oil if 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, and to 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; The third determination module 27 is used to determine that the camellia oil to be identified is camellia oil doped with high oleic rapeseed oil if the displacement amount caused by the campesterol content to shift the corresponding high oleic rapeseed oil clustering result is less than a third threshold.

[0041] Further, in some optional embodiments of the present invention, the second determination module 23 includes: The first acquisition unit is used to respectively acquire 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; A first selection unit is used to randomly select a data point corresponding to the historical β-amyrin content sample as a third center point, and calculate a third distance between each data point not selected as the center point and the third center point; A second selection unit, configured to randomly select a new data point as a fourth center by using a weighted probability distribution, wherein a probability of selecting the new data point is proportional to the third distance; A first clustering processing unit is used to cluster the historical β-amyrin content of each type of oil using the third center and the fourth center as initial centers using a K-Means clustering algorithm to obtain a first clustering result; A second clustering processing unit, 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; a first calculation unit, 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; The first execution unit is used to execute the step of determining that the camellia oil to be identified is unadulterated camellia oil when it is determined that the first displacement is less than a second threshold.

[0042] Further, in some optional embodiments of the present invention, the third determination module 26 includes: The second acquisition unit is used to respectively acquire historical campesterol content samples of high oleic peanut oil, high oleic sunflower oil and high oleic rapeseed oil, and the number of historical campesterol content samples of each type of oil is the same; A third selection unit is used to randomly select a data point corresponding to the historical campesterol content sample as a fifth center point, and calculate a fourth distance between each data point that is not selected as the center point and the fifth center point; a fourth selection unit, configured to randomly select a new data point as a sixth center by using a weighted probability distribution, wherein a probability of selecting the new data point is proportional to the fourth distance; a third clustering processing unit, configured to use the fifth center and the sixth center as initial centers, and cluster the historical campesterol contents of various types of oils using a K-Means clustering algorithm to obtain a third clustering result; 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; 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; The second execution unit is used for executing the step of determining that the camellia oil to be identified is camellia oil mixed with high oleic rapeseed oil when it is determined that the second displacement is less than a third threshold.

[0043] Embodiment 3 Another aspect of the present invention provides an electronic device, see Figure 3 , shown is an electronic device in Embodiment 3 of the present invention, comprising a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor, wherein when the processor 10 executes the computer program 30, the method for identifying adulteration of camellia oil as described above is implemented.

[0044] In some embodiments, the processor 10 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 20, such as executing access restriction programs.

[0045] Among them, the memory 20 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as a hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of an electronic device, such as a plug-in hard disk equipped on the electronic device, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Further, the memory 20 can also include both an internal storage unit of the electronic device and an external storage device. The memory 20 can not only be used to store application software and various types of data of the electronic device, but also can be used to temporarily store data that has been output or is to be output.

[0046] It should be pointed out that Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than those shown in the figure, or combine certain components, or arrange the components differently.

[0047] The embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for identifying adulteration of camellia oil as described above is implemented.

[0048] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0049] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0050] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0051] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0052] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the attached claims.

Claims

1. A method for identifying adulteration of camellia oil, characterized in that: 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; If it is determined that the oleic acid content is greater than the first threshold, it is determined that the camellia oil to be identified belongs to the first category of oils, and it is determined whether the displacement amount of the clustering result of the corresponding camellia oil caused by the β-amyrin content is less than the second threshold; 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 the second category of oil products; 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, it is determined that the camellia oil to be identified is unadulterated 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 not less than the second threshold, the camellia oil to be identified is determined to be adulterated oil, and it is determined 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; If it is determined that 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, it is determined that the camellia oil to be identified is camellia oil doped with high oleic rapeseed oil.

2. The method for identifying adulteration of camellia oil according to claim 1, characterized in that: The step of determining the first threshold comprises: 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 were obtained respectively, and the number of historical oleic acid content samples of each type of oil was the same; The K-Means clustering algorithm was used to cluster the historical oleic acid content of each type of oil, and the k value was set to 2 and 6 respectively, and the corresponding clustering results were obtained; According to the clustering result when the k value is set to 2, the 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 the target point, and the data corresponding to the target point is the first threshold.

3. The method for identifying adulteration of camellia oil according to claim 2, characterized in that: The step of pulling the first center point to determine the target point according to the clustering result when the k value is set to 6 includes: respectively calculating a first distance between the clustering result when the k value is set to 6 and the first center point, and determining the magnitude of the force 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; Performing mechanical simulation according to the magnitude and direction of the force to determine the offset direction of the first center point; The clustering results when the k value is set to 6 are respectively projected onto the straight lines in the offset direction, and a second center point located on the same side of the projected point as the first center point is determined; Calculate the second distance between each of the second center points and the first center point, and make a difference to obtain an offset; The target point is determined according to the offset direction and the offset amount.

4. The method for identifying adulteration of camellia oil according to claim 1, characterized in that: The step of judging whether the displacement amount of the clustering result of the corresponding camellia oil caused by the β-amyrin content is less than a second threshold comprises: The historical β-amyrin content samples of camellia oil, high oleic peanut oil, high oleic sunflower oil and high oleic rapeseed oil were obtained respectively, and the number of historical β-amyrin content samples of each type of oil was the same; Randomly select a data point corresponding to the historical β-amyrin content sample as a third center point, and calculate a third distance between each data point that is not selected as the center point and the third center point; Randomly selecting a new data point as the fourth center using a weighted probability distribution, wherein the probability of selecting the new data point is proportional to the third distance; The third center and the fourth center are used as initial centers, and the historical β-amyrin content of each type of oil is clustered using a K-Means clustering algorithm to obtain a first clustering result; Adding the 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; If yes, the step of determining that the camellia oil to be identified is unadulterated camellia oil is performed.

5. The method for identifying adulteration of camellia oil according to claim 1, characterized in that: The step of judging whether the displacement amount of the clustering result of the corresponding high oleic rapeseed oil caused by the campesterol content is less than a third threshold comprises: Obtain historical campesterol content samples of high oleic peanut oil, high oleic sunflower oil, and high oleic rapeseed oil, respectively, and the number of historical campesterol content samples of each type of oil is the same; Randomly select a data point corresponding to the historical campesterol content sample as the fifth center point, and calculate a fourth distance between each data point that is not selected as the center point and the fifth center point; Randomly selecting a new data point as the sixth center using a weighted probability distribution, wherein the probability of selecting the new data point is proportional to the fourth distance; The fifth center and the sixth center are used as initial centers, and the historical campesterol content of each type of oil is clustered using a K-Means clustering algorithm to obtain a third clustering result; Adding the data corresponding to the campesterol content to the third clustering result for clustering processing to obtain a fourth clustering result; Calculating a second displacement between the third clustering result and the fourth clustering result, and determining whether the second displacement is less than a third threshold; If so, the step of determining that the camellia oil to be identified is camellia oil mixed with high oleic rapeseed oil is performed.

6. The method for identifying adulteration of camellia oil according to claim 1, characterized in that: The first category of oil products is at least one of camellia oil, high oleic peanut oil, high oleic sunflower oil and high oleic rapeseed oil, or a mixture of several of them.

7. The method for identifying adulteration of camellia oil according to claim 1, characterized in that: The second type of oil is at least peanut oil and soybean oil or a mixture of the two.

8. A camellia oil adulteration identification system, characterized in that: Used to implement the camellia oil adulteration identification method according to any one of claims 1 to 7, the system comprises: An acquisition module, used for acquiring the oleic acid content, β-amyrin content and campesterol content of the camellia oil to be identified; A first judgment module, used to judge whether the oleic acid content is greater than a first threshold; A second judgment module is used to determine that the camellia oil to be identified belongs to the first category of oils if the oleic acid content is greater than the first threshold, and to judge whether the displacement amount of the clustering result of the corresponding camellia oil caused by the β-amyrin content is less than the second threshold; A first determination module, configured to determine that the camellia oil to be identified belongs to the second category of oil products if it is determined that the oleic acid content is not greater than a first threshold value; A second determination module is used to determine that the camellia oil to be identified is unadulterated camellia oil if the displacement amount caused by the β-amyrin content to shift the corresponding camellia oil clustering result is less than a second threshold value; a third judgment module, for determining that the camellia oil to be identified is adulterated 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 not less than the second threshold, and for determining 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; The third determination module is used to determine that the camellia oil to be identified is camellia oil doped with high oleic rapeseed oil if the displacement amount caused by the campesterol content to shift the corresponding high oleic rapeseed oil clustering result is less than a third threshold.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for identifying adulteration of camellia oil as described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for identifying adulteration of camellia oil as claimed in any one of claims 1 to 7 is implemented.

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