A method and system for detecting stability of cinnamaldehyde nanoemulsion

Abstract

The present invention belongs to the technical field of cinnamaldehyde nanoemulsion detection, and specifically relates to a cinnamaldehyde nanoemulsion stability detection method and system. The invention can effectively evaluate the stability of cinnamaldehyde nanoemulsion samples by accurately controlling and monitoring the changes in particle size and Zeta potential value during the aging process. By setting reasonable fluctuation thresholds and fluctuation nodes, the aging abnormality of the sample can be discovered in time, so that corresponding treatment measures can be taken to ensure the stability and effectiveness of the cinnamaldehyde nanoemulsion samples during storage and use. In addition, the detection system of the present invention can automatically adjust the monitoring frequency according to the detection results, and monitor samples with disordered distribution characteristics more frequently to ensure that potential stability problems are discovered in time and reduce the risk of loss caused by sample instability.

Description

Technical Field

[0001] The invention belongs to the technical field of cinnamaldehyde nanoemulsion detection, and particularly relates to a cinnamaldehyde nanoemulsion stability detection method and system. Background Art

[0002] With the deepening of the research on cinnamaldehyde nanoemulsion, its application in food, medicine, cosmetics and other fields is becoming more and more extensive. In order to ensure the stability of cinnamaldehyde nanoemulsion during storage and use, its stability needs to be accurately tested and evaluated to ensure the long-term stability of cinnamaldehyde nanoemulsion products and avoid unnecessary losses and risks.

[0003] However, existing detection methods often require long-term storage and multiple measurements of samples to obtain more accurate stability evaluation results, and are unable to monitor the stability changes of cinnamaldehyde nanoemulsions in real time, resulting in certain limitations in practical applications. Based on this, this scheme provides a cinnamaldehyde nanoemulsion stability detection method to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a cinnamaldehyde nanoemulsion stability detection method and system, which can monitor the stability change of the cinnamaldehyde nanoemulsion in real time and can quickly and accurately evaluate its stability, thereby improving the detection efficiency of the cinnamaldehyde nanoemulsion.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A method for detecting the stability of cinnamaldehyde nanoemulsion, comprising:

[0007] Obtaining a cinnamaldehyde nanoemulsion sample, and performing an aging treatment on the cinnamaldehyde nanoemulsion sample to simulate stability changes under actual storage conditions;

[0008] Simulating the storage environment temperature of the cinnamaldehyde nanoemulsion sample and recording it as a precondition parameter;

[0009] Measuring the particle size of the cinnamaldehyde nanoemulsion sample after the aging treatment and recording it as the first condition parameter, and the Zeta potential value of the cinnamaldehyde nanoemulsion sample and recording it as the second condition parameter;

[0010] According to the precondition parameter, the first condition parameter and the second condition parameter, the stability of the cinnamaldehyde nanoemulsion is detected to obtain the storage state of the cinnamaldehyde nanoemulsion, wherein the storage state includes a stable state and an unstable state;

[0011] Under the stable state, recording the particle size change amplitude and Zeta potential value of the cinnamaldehyde nanoemulsion;

[0012] In the unstable state, the unstable nodes of the cinnamaldehyde nanoemulsion are recorded, and the fluctuation range of the precondition parameter is determined according to the unstable nodes.

[0013] In a preferred embodiment, the particle size is measured using a dynamic light scattering technique, and the Zeta potential value is measured using an electrophoretic light scattering technique.

[0014] In a preferred embodiment, the step of subjecting the cinnamaldehyde nanoemulsion sample to an aging treatment to simulate the stability change under actual storage conditions comprises:

[0015] Obtaining multiple batches of cinnamaldehyde nanoemulsion samples;

[0016] Placing multiple batches of the cinnamaldehyde nanoemulsion samples in thermostats at different temperatures for aging treatment to simulate storage conditions at different temperatures;

[0017] A monitoring period is constructed, and a plurality of sampling nodes are set within the monitoring period, and the particle size and Zeta potential value of the cinnamaldehyde nanoemulsion sample at each sampling node are collected.

[0018] In a preferred embodiment, the step of measuring the particle size of the cinnamaldehyde nanoemulsion sample after the aging treatment comprises:

[0019] Obtaining the particle sizes of the cinnamaldehyde nanoemulsion samples of the same batch at the same temperature, and recording them as the parameters to be evaluated;

[0020] Comparing each of the parameters to be evaluated, and outputting the comparison result as a particle size fluctuation amount;

[0021] Obtaining a standard fluctuation amount, and comparing the standard fluctuation amount with a particle size fluctuation amount;

[0022] When all the particle size fluctuations are less than or equal to the standard fluctuation, it indicates that the particle size fluctuations of the cinnamaldehyde nanoemulsion samples in the same batch are normal, and the parameters to be evaluated are summed and averaged to obtain the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment;

[0023] When the particle size fluctuation is greater than the standard fluctuation, it indicates that the particle size fluctuation of the cinnamaldehyde nanoemulsion sample in the same batch is abnormal, and the abnormal fluctuation is screened out, and then the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment is determined based on the abnormal fluctuation.

[0024] In a preferred embodiment, the step of determining the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment based on the abnormal fluctuation amount comprises:

[0025] Obtaining abnormal fluctuation amounts and abnormal fluctuation nodes of the cinnamaldehyde nanoemulsion samples of the same batch;

[0026] Counting the number of abnormal fluctuation nodes and recording them as parameters to be evaluated;

[0027] An evaluation threshold is obtained, and the parameter to be evaluated is compared with the evaluation threshold. When the parameter to be evaluated is greater than the evaluation threshold, the aging treatment of the cinnamaldehyde nanoemulsion sample is determined to be abnormal, and the aging treatment of the cinnamaldehyde nanoemulsion sample is re-executed. Otherwise, the parameter to be evaluated under the abnormal fluctuation node is screened out, and the parameters to be evaluated outside the abnormal fluctuation are summed and averaged to obtain the particle size of the cinnamaldehyde nanoemulsion sample after the aging treatment.

[0028] In a preferred embodiment, the step of detecting the stability of the cinnamaldehyde nanoemulsion according to the precondition parameter, the first condition parameter and the second condition parameter comprises:

[0029] Obtaining the initial particle size and initial Zeta potential value of the cinnamaldehyde nanoemulsion sample;

[0030] Obtaining the first condition parameter and the second condition parameter under each of the precondition parameters;

[0031] Comparing the first condition parameter and the second condition parameter with the initial particle size and the initial Zeta potential value of the cinnamaldehyde nanoemulsion sample, respectively, to obtain a first fluctuation parameter and a second fluctuation parameter;

[0032] Obtaining a first allowable fluctuation threshold and a second allowable fluctuation threshold, and comparing them with the first fluctuation parameter and the second fluctuation parameter respectively;

[0033] Recording the time point when the first fluctuation parameter and the second fluctuation parameter reach the first allowable fluctuation threshold and the second allowable fluctuation threshold as an unstable node;

[0034] A backtracking period is constructed according to the unstable node, and an orderliness analysis is performed on the first fluctuation parameter and the second fluctuation parameter within the backtracking period to determine the correlation characteristics between the first condition parameter and the second condition parameter and the stability of the cinnamaldehyde nanoemulsion.

[0035] In a preferred embodiment, the step of performing an order analysis on the first fluctuation parameter and the second fluctuation parameter within the retrospective period to determine the correlation characteristics between the first condition parameter and the second condition parameter and the stability of the cinnamaldehyde nanoemulsion comprises:

[0036] Acquire the first fluctuation parameter and the second fluctuation parameter;

[0037] Obtaining a trend evaluation function, and inputting the first fluctuation parameter and the second fluctuation parameter into the trend evaluation function respectively, to obtain a first fluctuation trend value and a second fluctuation trend value;

[0038] Obtaining a verification function, and randomly selecting a plurality of groups of adjacent first fluctuation parameters and second fluctuation parameters, and inputting the randomly selected adjacent first fluctuation parameters and second fluctuation parameters into the verification function to obtain first verification parameters and second verification parameters;

[0039] Obtaining a first verification threshold and a second verification threshold, and comparing the first verification threshold with a first verification parameter, and comparing the second verification threshold with a second verification parameter;

[0040] When the first verification parameter is greater than a first verification threshold, or the second verification parameter is greater than a second verification threshold, the correlation feature between the first condition parameter and the second condition parameter and the stability of the cinnamaldehyde nanoemulsion is recorded as a disordered distribution feature, otherwise, it is recorded as an ordered distribution feature;

[0041] Wherein, the monitoring frequency of the stability of the cinnamaldehyde nanoemulsion under the disordered distribution characteristics is greater than the monitoring frequency of the stability of the cinnamaldehyde nanoemulsion under the ordered distribution characteristics.

[0042] In a preferred solution, the step of determining the fluctuation range of the precondition parameter according to the unstable node includes:

[0043] Acquire a first condition parameter and a second condition parameter of the unstable node;

[0044] If the first condition parameter or the second condition parameter is a disordered distribution feature, the precondition parameter under the unstable node is offset, and the offset result is recorded as an edge fluctuation point;

[0045] If both the first condition parameter and the second condition parameter are ordered distribution characteristics, the unstable node is recorded as an edge fluctuation point;

[0046] There are two edge fluctuation points, which correspond to the upper temperature limit and the lower temperature limit respectively, and the range between the two edge fluctuation points is recorded as the fluctuation range of the precondition parameter.

[0047] The present invention also provides a cinnamaldehyde nanoemulsion stability detection system, using the above-mentioned cinnamaldehyde nanoemulsion stability detection method, comprising:

[0048] A sample acquisition module, wherein the sample acquisition module is used to acquire a cinnamaldehyde nanoemulsion sample and perform an aging treatment on the cinnamaldehyde nanoemulsion sample to simulate stability changes under actual storage conditions;

[0049] A simulation module, the simulation module is used to simulate the storage environment temperature of the cinnamaldehyde nanoemulsion sample and record it as a precondition parameter;

[0050] A parameter calibration module, wherein the parameter calibration module is used to measure the particle size of the cinnamaldehyde nanoemulsion sample after the aging treatment and record it as a first condition parameter, and the Zeta potential value of the cinnamaldehyde nanoemulsion sample and record it as a second condition parameter;

[0051] A sample detection module, wherein the sample detection module is used to detect the stability of the cinnamaldehyde nanoemulsion according to the precondition parameter, the first condition parameter and the second condition parameter, and obtain the storage state of the cinnamaldehyde nanoemulsion, wherein the storage state includes a stable state and an unstable state;

[0052] A stabilization processing module, wherein the stabilization processing module is used to record the particle size change amplitude and Zeta potential value of the cinnamaldehyde nanoemulsion under the stable state;

[0053] An unstable processing module is used to record the unstable nodes of the cinnamaldehyde nanoemulsion in the unstable state, and determine the fluctuation range of the precondition parameter according to the unstable nodes.

[0054] And, an electronic device, the electronic device comprising:

[0055] at least one processor;

[0056] and a memory communicatively coupled to the at least one processor;

[0057] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned cinnamaldehyde nanoemulsion stability detection method.

[0058] The technical effects achieved by the present invention are:

[0059] The present invention can effectively evaluate the stability of the cinnamaldehyde nanoemulsion sample by accurately controlling and monitoring the changes in the particle size and Zeta potential value of the cinnamaldehyde nanoemulsion sample during the aging treatment process. By setting reasonable fluctuation thresholds and fluctuation nodes, the aging abnormality of the sample can be discovered in time, so that corresponding treatment measures can be taken to ensure the stability and effectiveness of the cinnamaldehyde nanoemulsion sample during storage and use. In addition, the detection system of the present invention can automatically adjust the monitoring frequency according to the detection results, and monitor the samples with disordered distribution characteristics more frequently to ensure that potential stability problems are discovered in time and reduce the risk of loss caused by sample instability. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a schematic flow chart of the method of the present invention;

[0061] Figure 2It is a schematic diagram of the system module of the present invention;

[0062] Figure 3 It is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0063] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0064] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0065] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0066] See also Figure 1 As shown, the present invention provides a method for detecting the stability of cinnamaldehyde nanoemulsion, comprising:

[0067] S1. obtaining a cinnamaldehyde nanoemulsion sample, and subjecting the cinnamaldehyde nanoemulsion sample to an aging treatment to simulate stability changes under actual storage conditions;

[0068] S2, simulating the storage environment temperature of the cinnamaldehyde nanoemulsion sample and recording it as a precondition parameter;

[0069] S3, measuring the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment, and recording it as the first condition parameter, and the Zeta potential value of the cinnamaldehyde nanoemulsion sample, and recording it as the second condition parameter;

[0070] S4, detecting the stability of the cinnamaldehyde nanoemulsion according to the precondition parameter, the first condition parameter and the second condition parameter to obtain the storage state of the cinnamaldehyde nanoemulsion, wherein the storage state includes a stable state and an unstable state;

[0071] S5. Under the stable state, the particle size change amplitude and Zeta potential value of the cinnamaldehyde nanoemulsion are recorded;

[0072] S6. Under the unstable state, the unstable nodes of the cinnamaldehyde nanoemulsion are recorded, and the fluctuation range of the precondition parameters is determined based on the unstable nodes.

[0073] As described in the above steps S1-S6, with the deepening of the research on cinnamaldehyde nanoemulsion, its application in the food industry, the pharmaceutical field and the cosmetics industry is becoming more and more extensive. However, the stability of nanoemulsion has always been one of the key factors restricting its wide application. In this embodiment, it is necessary to first obtain cinnamaldehyde nanoemulsion samples, and then age these samples to simulate the possible changes in stability under actual storage conditions. Aging treatment refers to placing the samples under specific temperature conditions for a period of time to observe the possible changes in the storage process. Specifically, the storage environment temperature of the cinnamaldehyde nanoemulsion sample is simulated, and the storage environment temperature is recorded as a precondition parameter. Subsequently, the particle size of the cinnamaldehyde nanoemulsion sample after temperature aging treatment is measured, and the particle size value is recorded as the first condition parameter. The particle size measurement adopts dynamic light scattering technology. The change in particle size usually reflects the aggregation or dispersion state of particles in the nanoemulsion system. In addition, it is also necessary to measure the cinnamaldehyde The Zeta potential value of the nanoemulsion sample is recorded as the second condition parameter. The Zeta potential value is measured using electrophoretic light scattering technology. The Zeta potential reflects the charge state of the particle surface and affects the interaction force between the particles. The stability of the cinnamaldehyde nanoemulsion is detected based on the precondition parameter, the first condition parameter, and the second condition parameter to obtain the storage state of the cinnamaldehyde nanoemulsion. The storage state can be divided into a stable state and an unstable state. In the stable state, the particle size change amplitude and Zeta potential value of the cinnamaldehyde nanoemulsion are recorded, which helps to understand the stability change of the nanoemulsion during storage. In the unstable state, the unstable nodes of the cinnamaldehyde nanoemulsion are recorded. The unstable node refers to a specific time point that occurs during the storage process. At this time, the stability of the nanoemulsion changes significantly, and the fluctuation range of the precondition parameter is determined based on these unstable nodes, which provides a corresponding reference for the subsequent preparation and storage of cinnamaldehyde nanoemulsion.

[0074] In a preferred embodiment, the step of subjecting the cinnamaldehyde nanoemulsion sample to an aging treatment to simulate the stability change under actual storage conditions comprises:

[0075] S101, obtaining multiple batches of cinnamaldehyde nanoemulsion samples;

[0076] S102, placing multiple batches of cinnamaldehyde nanoemulsion samples in thermostats at different temperatures for aging treatment to simulate storage conditions at different temperatures;

[0077] S103, constructing a monitoring period, setting a plurality of sampling nodes within the monitoring period, and collecting the particle size and Zeta potential value of the cinnamaldehyde nanoemulsion sample at each sampling node.

[0078] As described in the above steps S101-S103, in order to study the stability changes of cinnamaldehyde nanoemulsion samples under actual storage conditions, cinnamaldehyde nanoemulsion samples were first obtained from multiple batches to ensure the diversity of samples, and then different batches of cinnamaldehyde nanoemulsion samples were placed in thermostats at different temperatures for aging treatment. This process is intended to simulate the stability changes of cinnamaldehyde nanoemulsion samples when stored under different temperature conditions. In order to more accurately monitor the stability changes of samples during the aging process, a corresponding monitoring period was constructed. During the monitoring period, multiple sampling nodes were also set to ensure that the changes of samples during the aging process can be fully captured. At each sampling node, cinnamaldehyde nanoemulsion samples were collected, and their particle size and Zeta potential values ​​were accurately measured. Through these data, the stability changes of samples during the aging process can be analyzed in detail, thereby providing a scientific basis for the stability under actual storage conditions.

[0079] In a preferred embodiment, the step of measuring the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment comprises:

[0080] S301, obtaining the particle sizes of the same batch of cinnamaldehyde nanoemulsion samples at the same temperature, and recording them as parameters to be evaluated;

[0081] S302, comparing each parameter to be evaluated, and outputting the comparison result as a particle size fluctuation amount;

[0082] S303, obtaining a standard fluctuation amount, and comparing the standard fluctuation amount with a particle size fluctuation amount;

[0083] When all particle size fluctuations are less than or equal to the standard fluctuation, it indicates that the particle size fluctuations of the cinnamaldehyde nanoemulsion samples in the same batch are normal, and the parameters to be evaluated are summed and averaged to obtain the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment;

[0084] When the particle size fluctuation is greater than the standard fluctuation, it indicates that the particle size fluctuation of the cinnamaldehyde nanoemulsion samples in the same batch is abnormal, and the abnormal fluctuation is screened out, and then the particle size of the cinnamaldehyde nanoemulsion samples after aging treatment is determined based on the abnormal fluctuation.

[0085] As described in the above steps S301-S303, in order to measure the particle size of the cinnamaldehyde nanoemulsion sample after the aging treatment, it is first necessary to obtain several samples from the same batch of cinnamaldehyde nanoemulsion samples, and measure the particle size of the cinnamaldehyde nanoemulsion samples under the same temperature conditions. After the measurement is completed, it is recorded and calibrated as the parameter to be evaluated, and then the parameter to be evaluated is compared and analyzed accordingly. By comparison, the particle size fluctuation amount between the particle sizes of each cinnamaldehyde nanoemulsion sample can be obtained. In order to ensure the accuracy of the measurement result, it is necessary to introduce a pre-set standard fluctuation amount. The standard fluctuation amount is set according to historical experience or experimental data, and is used to measure whether the sample particle size fluctuation is within the normal range. The particle size fluctuation amount actually measured is compared with the standard fluctuation amount. A comparative analysis is performed. If after comparison, it is found that the particle size fluctuation of all samples is less than or equal to the standard fluctuation, then it indicates that the particle size fluctuation of the cinnamaldehyde nanoemulsion samples from the same batch is normal. At this time, all the parameters to be evaluated will be summed and their average values ​​will be calculated. The average value will be used as the final particle size of the cinnamaldehyde nanoemulsion samples after aging treatment. However, if it is found during the comparison process that the particle size fluctuation of the cinnamaldehyde nanoemulsion samples is greater than the standard fluctuation, then this indicates that the particle size fluctuation of the cinnamaldehyde nanoemulsion samples from the same batch is abnormal. At this time, it is necessary to further screen and analyze those samples with abnormal fluctuations to determine whether the particle size of the cinnamaldehyde nanoemulsion samples after aging treatment is accurate, so as to make a more accurate assessment of the quality of the samples.

[0086] In a preferred embodiment, the step of determining the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment according to the abnormal fluctuation amount comprises:

[0087] Obtain the abnormal fluctuation amount and abnormal fluctuation nodes of the same batch of cinnamaldehyde nanoemulsion samples;

[0088] Count the number of abnormal fluctuation nodes and record them as parameters to be evaluated;

[0089] An evaluation threshold is obtained, and the parameter to be evaluated is compared with the evaluation threshold. When the parameter to be evaluated is greater than the evaluation threshold, the aging treatment of the cinnamaldehyde nanoemulsion sample is determined to be abnormal, and the aging treatment of the cinnamaldehyde nanoemulsion sample is re-executed. Otherwise, the parameter to be evaluated under the abnormal fluctuation node is screened out, and the parameters to be evaluated outside the abnormal fluctuation are summed and averaged to obtain the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment.

[0090] In this embodiment, when the particle size fluctuation of the cinnamaldehyde nanoemulsion sample is greater than the standard fluctuation, firstly, the abnormal fluctuation data is obtained from the cinnamaldehyde nanoemulsion samples of the same batch, and the abnormal fluctuation nodes where the fluctuation occurs are recorded. Then, the number of abnormal fluctuation nodes is counted, and the statistical results are recorded as the parameter to be evaluated. Then, a pre-set evaluation threshold is introduced. The evaluation threshold is set according to previous experience or experimental data, and is used to determine whether the aging treatment of the cinnamaldehyde nanoemulsion sample is normal. If the parameter to be evaluated is greater than the evaluation threshold, it means that an abnormal situation has occurred during the aging treatment of the cinnamaldehyde nanoemulsion sample, and the aging treatment step needs to be re-executed. If the parameter to be evaluated is less than or equal to the evaluation threshold, this indicates that the aging treatment process is normal. When the aging treatment is confirmed to be normal, the parameters to be evaluated under the abnormal fluctuation nodes are screened out, that is, these abnormal fluctuation data are excluded. Subsequently, the remaining parameters to be evaluated that are not affected by the abnormal fluctuation are summed and their average value is calculated. The average value will represent the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment.

[0091] In a preferred embodiment, the step of detecting the stability of the cinnamaldehyde nanoemulsion according to the precondition parameter, the first condition parameter and the second condition parameter comprises:

[0092] S401, obtaining the initial particle size and initial Zeta potential value of the cinnamaldehyde nanoemulsion sample;

[0093] S402, obtaining a first condition parameter and a second condition parameter under each precondition parameter;

[0094] S403, comparing the first condition parameter and the second condition parameter with the initial particle size and the initial Zeta potential value of the cinnamaldehyde nanoemulsion sample, respectively, to obtain a first fluctuation parameter and a second fluctuation parameter;

[0095] S404, obtaining a first allowable fluctuation threshold and a second allowable fluctuation threshold, and comparing them with the first fluctuation parameter and the second fluctuation parameter respectively;

[0096] S405, recording the time point at which the first fluctuation parameter and the second fluctuation parameter reach the first allowable fluctuation threshold and the second allowable fluctuation threshold as an unstable node;

[0097] S406: construct a backtracking period according to the unstable node, and perform orderliness analysis on the first fluctuation parameter and the second fluctuation parameter in the backtracking period to determine the correlation characteristics between the first condition parameter and the second condition parameter and the stability of the cinnamaldehyde nanoemulsion.

[0098] As described in the above steps S401-S406, when the stability of the cinnamaldehyde nanoemulsion is detected, the initial particle size and initial Zeta potential value of the cinnamaldehyde nanoemulsion sample are first obtained as the basis for subsequent comparison. The initial particle size refers to the average particle size of the nanoemulsion particles when they are not affected by any external conditions, and the initial Zeta potential value reflects the charge state on the surface of the nanoemulsion particles. Then, the first condition parameter and the second condition parameter under each precondition parameter are collected. Each precondition parameter remains unchanged during the experiment, while the first condition parameter and the second condition parameter will change during the experiment, thereby affecting the stability of the nanoemulsion. Then, the first condition parameter and the second condition parameter are compared with the initial particle size and the initial Zeta potential value of the cinnamaldehyde nanoemulsion sample, respectively, to obtain the first fluctuation parameter and the second fluctuation parameter, so as to reflect the stability of the nanoemulsion under different conditions. The changes in the particle size and potential of the nanoemulsion are then analyzed, and the first allowable fluctuation threshold and the second allowable fluctuation threshold are introduced, and compared with the first fluctuation parameter and the second fluctuation parameter respectively. The allowable fluctuation threshold refers to the maximum fluctuation range that the nanoemulsion can accept under specific conditions. If this range is exceeded, it is considered that the stability of the nanoemulsion is affected. During the experiment, the time points when the first fluctuation parameter and the second fluctuation parameter reach the first allowable fluctuation threshold and the second allowable fluctuation threshold are recorded. These time points are called unstable nodes. The unstable nodes are reference points for evaluating the stability of the nanoemulsion. Finally, a backtracking period is constructed based on the unstable nodes, and the orderliness analysis of the first fluctuation parameter and the second fluctuation parameter within the backtracking period is performed. Through this analysis, the correlation characteristics between the first condition parameter and the second condition parameter and the stability of the cinnamaldehyde nanoemulsion can be determined, providing a basis for optimizing the storage of the nanoemulsion.

[0099] In a preferred embodiment, the step of performing an order analysis on the first fluctuation parameter and the second fluctuation parameter within the retrospective period to determine the correlation characteristics between the first condition parameter and the second condition parameter and the stability of the cinnamaldehyde nanoemulsion comprises:

[0100] Obtaining a first fluctuation parameter and a second fluctuation parameter;

[0101] Obtain a trend evaluation function, and input the first fluctuation parameter and the second fluctuation parameter into the trend evaluation function respectively to obtain a first fluctuation trend value and a second fluctuation trend value;

[0102] Obtaining a verification function, and randomly selecting a plurality of groups of adjacent first fluctuation parameters and second fluctuation parameters, and inputting the randomly selected adjacent first fluctuation parameters and second fluctuation parameters into the verification function to obtain first verification parameters and second verification parameters;

[0103] Obtaining a first verification threshold and a second verification threshold, and comparing the first verification threshold with a first verification parameter, and comparing the second verification threshold with a second verification parameter;

[0104] When the first verification parameter is greater than the first verification threshold, or the second verification parameter is greater than the second verification threshold, the correlation feature between the first condition parameter and the second condition parameter and the stability of the cinnamaldehyde nanoemulsion is recorded as a disordered distribution feature, otherwise, it is recorded as an ordered distribution feature;

[0105] Among them, the monitoring frequency of the stability of cinnamaldehyde nanoemulsion under disordered distribution characteristics is greater than the monitoring frequency of the stability of cinnamaldehyde nanoemulsion under ordered distribution characteristics.

[0106] As mentioned above, after the lookback period is determined, it is first necessary to obtain the first fluctuation parameter and the second fluctuation parameter in the lookback period to understand the fluctuation of the cinnamaldehyde nanoemulsion in the lookback period, and then the preset trend evaluation function can be introduced to evaluate the trend of the first fluctuation parameter and the second fluctuation parameter, wherein the expression of the trend evaluation function is: , where Indicates the first fluctuation trend value or the second fluctuation trend value, Indicates the length of the lookback period. represents the number of the first fluctuation parameter or the second fluctuation parameter, Represents the first fluctuation parameter or the second fluctuation parameter. After the first fluctuation trend value and the second fluctuation trend value are determined, the correlation between them and the stability of the cinnamaldehyde nanoemulsion can be analyzed. Specifically, a preset verification function needs to be introduced. Based on the verification function, multiple groups of adjacent first fluctuation parameters and second fluctuation parameters are randomly selected, and the adjacent first fluctuation parameters or second fluctuation parameter pairs are input into the verification function to obtain the first verification parameter and the second verification parameter. The expression of the verification function is: , where represents the first verification parameter or the second verification parameter, Indicates the first or second fluctuation parameter in the adjacent position. Indicates the first or second fluctuation parameter in the adjacent position. Indicates the time length between adjacent first fluctuation parameters or second fluctuation parameters. In addition, it is necessary to obtain the first verification threshold and the second verification threshold, and compare the first verification threshold with the first verification parameter to determine whether the first fluctuation parameter and the second fluctuation parameter are within the normal fluctuation range. When the first verification parameter is greater than the first verification threshold, or the second verification parameter is greater than the second verification threshold, the correlation characteristics between the first condition parameter and the second condition parameter and the stability of the cinnamaldehyde nanoemulsion are manifested as disordered distribution characteristics. On the contrary, if the first verification parameter is less than or equal to the first verification threshold, or the second verification parameter is less than or equal to the second verification threshold, then the correlation characteristics are manifested as ordered distribution characteristics. Finally, different monitoring strategies will be adopted according to the difference between the disordered distribution characteristics and the ordered distribution characteristics. Under the disordered distribution characteristics, the monitoring frequency of the stability of the cinnamaldehyde nanoemulsion will be greater than the monitoring frequency under the ordered distribution characteristics, because the disordered distribution characteristics indicate that the stability of the cinnamaldehyde nanoemulsion may be affected by more uncertain factors, and more frequent monitoring is required to ensure its stability, while the ordered distribution characteristics mean that the stability of the cinnamaldehyde nanoemulsion is relatively stable, and the monitoring frequency can be appropriately reduced.

[0107] In a preferred embodiment, the step of determining the fluctuation range of the precondition parameter according to the unstable node includes:

[0108] Obtain the first condition parameter and the second condition parameter under the unstable node;

[0109] If the first condition parameter or the second condition parameter is a disordered distribution feature, the precondition parameter under the unstable node is offset, and the offset result is recorded as an edge fluctuation point;

[0110] If both the first condition parameter and the second condition parameter have ordered distribution characteristics, the unstable node is recorded as an edge fluctuation point;

[0111] Among them, there are two edge fluctuation points, corresponding to the upper temperature limit and the lower temperature limit respectively, and the range between the two edge fluctuation points is recorded as the fluctuation range of the precondition parameter.

[0112] In this implementation, after the unstable node is output, it is first necessary to obtain the first condition parameter and the second condition parameter under the unstable node, as well as the distribution characteristics of the first condition parameter and the second condition parameter. If it is found that the first condition parameter or the second condition parameter has a disordered distribution characteristic, then it is necessary to perform an offset processing on the precondition parameter under the unstable node. The purpose of the offset processing is to better understand and control the fluctuation range of the parameter and ensure the accuracy of the analysis result. After the processing is completed, the offset result is recorded as an edge fluctuation point. On the other hand, if the first condition parameter and the second condition parameter are both ordered distribution characteristics, it means that the parameter values ​​are arranged according to a certain rule or order within a certain range. In this case, the unstable node is directly recorded as an edge fluctuation point. The parameters with ordered distribution characteristics are usually easier to analyze and predict. Therefore, the process of recording edge fluctuation points is relatively simple. Here, it should be noted that the existence of the edge fluctuation point is to identify the fluctuation range of the precondition parameter. In this implementation, there are two edge fluctuation points, corresponding to the upper temperature limit and the lower temperature limit, respectively. The range between the two edge fluctuation points is the fluctuation range of the precondition parameter, which helps managers better understand and control the parameter fluctuation under the unstable node, thereby providing a basis for subsequent decision-making and adjustment.

[0113] See also Figure 2 A cinnamaldehyde nanoemulsion stability detection system, using the above-mentioned cinnamaldehyde nanoemulsion stability detection method, comprises:

[0114] A sample acquisition module, which is used to obtain cinnamaldehyde nanoemulsion samples and perform aging treatment on the cinnamaldehyde nanoemulsion samples to simulate stability changes under actual storage conditions;

[0115] A simulation module, which is used to simulate the storage environment temperature of the cinnamaldehyde nanoemulsion sample and record it as a precondition parameter;

[0116] A parameter calibration module, the parameter calibration module is used to measure the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment and record it as a first condition parameter, and the Zeta potential value of the cinnamaldehyde nanoemulsion sample and record it as a second condition parameter;

[0117] A sample detection module, the sample detection module is used to detect the stability of the cinnamaldehyde nanoemulsion according to the precondition parameter, the first condition parameter and the second condition parameter, and obtain the storage state of the cinnamaldehyde nanoemulsion, wherein the storage state includes a stable state and an unstable state;

[0118] A stabilization processing module, which is used to record the particle size change amplitude and Zeta potential value of the cinnamaldehyde nanoemulsion in a stable state;

[0119] The unstable processing module is used to record the unstable nodes of the cinnamaldehyde nanoemulsion in an unstable state, and determine the fluctuation range of the precondition parameters according to the unstable nodes.

[0120] In the above, the system includes a sample acquisition module, a simulation module, a parameter calibration module, a sample detection module, a stabilization processing module and an unstable processing module. The main function of the sample acquisition module is to obtain the cinnamaldehyde nanoemulsion sample. After the cinnamaldehyde nanoemulsion sample is obtained, it will be aged to simulate the possible changes in stability under actual storage conditions. The purpose of the aging treatment is to better understand the stability performance of the cinnamaldehyde nanoemulsion after long-term storage. The role of the simulation module is to simulate the ambient temperature conditions that the cinnamaldehyde nanoemulsion sample may encounter during storage. By simulating these conditions, the system can record the temperature data as a precondition parameter, thereby providing an important reference basis for subsequent stability analysis. The parameter calibration module is responsible for detailed physical parameter measurement of the cinnamaldehyde nanoemulsion sample that has been aged. Specifically, the particle size of the cinnamaldehyde nanoemulsion sample will be measured and recorded as the first condition parameter. In addition, The Zeta potential value of the sample will be measured and recorded as the second condition parameter. The sample detection module will conduct a comprehensive evaluation of the stability of the cinnamaldehyde nanoemulsion based on the precondition parameter, the first condition parameter and the second condition parameter. Through the analysis of these parameters, the system can determine the storage state of the cinnamaldehyde nanoemulsion, that is, whether it is in a stable state or an unstable state. After the stabilization processing module determines that the cinnamaldehyde nanoemulsion is in a stable state, the stabilization processing module will further record the particle size change amplitude and Zeta potential value of the cinnamaldehyde nanoemulsion, which is helpful to understand the stability change trend of the cinnamaldehyde nanoemulsion in a stable state and provide a scientific basis for long-term storage. When the cinnamaldehyde nanoemulsion is determined to be in an unstable state, the unstable processing module records the unstable node of the cinnamaldehyde nanoemulsion. By analyzing the unstable node, the system can determine the fluctuation range of the specific precondition parameter that causes the instability of the cinnamaldehyde nanoemulsion, and provide guidance for improving storage conditions.

[0121] See also Figure 3 , an electronic device, the electronic device comprising:

[0122] at least one processor;

[0123] and a memory communicatively coupled to the at least one processor;

[0124] The memory stores a computer program executable by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the above-mentioned cinnamaldehyde nanoemulsion stability detection method.

[0125] In the above, the processor of the electronic device can be any general or special microprocessor, microcontroller, digital signal processor (DSP), or other types of processors. The memory can include random access memory (RAM), read-only memory (ROM), flash memory, or other types of storage devices. The computer program can include a series of instructions that enable the processor to execute the various steps of the above-mentioned cinnamaldehyde nanoemulsion stability detection method.

[0126] The electronic device may further include an arithmetic unit and an input / output (I / O) interface, wherein the arithmetic unit is used to perform various mathematical operations and logical operations, and the input / output (I / O) interface is used to communicate with external devices such as a keyboard, a mouse, a display, a printer, etc. In addition, the electronic device may also include a network interface for connecting to a local area network (LAN), a wide area network (WAN), the Internet, or other types of networks to facilitate remote data transmission and remote control.

[0127] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0128] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. A method for detecting the stability of cinnamaldehyde nanoemulsion, characterized in that: include: Obtaining a cinnamaldehyde nanoemulsion sample, and performing an aging treatment on the cinnamaldehyde nanoemulsion sample to simulate stability changes under actual storage conditions; Simulating the storage environment temperature of the cinnamaldehyde nanoemulsion sample and recording it as a precondition parameter; Measuring the particle size of the cinnamaldehyde nanoemulsion sample after the aging treatment and recording it as the first condition parameter, and the Zeta potential value of the cinnamaldehyde nanoemulsion sample and recording it as the second condition parameter; According to the precondition parameter, the first condition parameter and the second condition parameter, the stability of the cinnamaldehyde nanoemulsion is detected to obtain the storage state of the cinnamaldehyde nanoemulsion, wherein the storage state includes a stable state and an unstable state; Under the stable state, recording the particle size change amplitude and Zeta potential value of the cinnamaldehyde nanoemulsion; In the unstable state, recording the unstable nodes of the cinnamaldehyde nanoemulsion, and determining the fluctuation range of the precondition parameter according to the unstable nodes; Wherein, the step of detecting the stability of the cinnamaldehyde nanoemulsion according to the precondition parameter, the first condition parameter and the second condition parameter comprises: Obtaining the initial particle size and initial Zeta potential value of the cinnamaldehyde nanoemulsion sample; Obtaining the first condition parameter and the second condition parameter under each of the precondition parameters; Comparing the first condition parameter and the second condition parameter with the initial particle size and the initial Zeta potential value of the cinnamaldehyde nanoemulsion sample, respectively, to obtain a first fluctuation parameter and a second fluctuation parameter; Obtaining a first allowable fluctuation threshold and a second allowable fluctuation threshold, and comparing them with the first fluctuation parameter and the second fluctuation parameter respectively; Recording the time point when the first fluctuation parameter and the second fluctuation parameter reach the first allowable fluctuation threshold and the second allowable fluctuation threshold as an unstable node; A backtracking period is constructed according to the unstable node, and an orderliness analysis is performed on the first fluctuation parameter and the second fluctuation parameter within the backtracking period to determine the correlation characteristics between the first condition parameter and the second condition parameter and the stability of the cinnamaldehyde nanoemulsion; The step of performing an order analysis on the first fluctuation parameter and the second fluctuation parameter within the retrospective period to determine the correlation characteristics between the first condition parameter and the second condition parameter and the stability of the cinnamaldehyde nanoemulsion comprises: Acquire the first fluctuation parameter and the second fluctuation parameter; Obtain a trend evaluation function, and input the first fluctuation parameter and the second fluctuation parameter into the trend evaluation function respectively to obtain a first fluctuation trend value and a second fluctuation trend value, wherein the expression of the trend evaluation function is: , where Indicates the first fluctuation trend value or the second fluctuation trend value, Indicates the length of the lookback period, represents the number of the first fluctuation parameter or the second fluctuation parameter, represents the first fluctuation parameter or the second fluctuation parameter; A verification function is obtained, and multiple groups of adjacent first fluctuation parameters and second fluctuation parameters are randomly selected, and the randomly selected adjacent first fluctuation parameters and second fluctuation parameters are input into the verification function to obtain the first verification parameter and the second verification parameter, wherein the expression of the verification function is: , where represents the first verification parameter or the second verification parameter, Indicates the first or second fluctuation parameter in the adjacent position. Indicates the first or second fluctuation parameter in the adjacent position. Indicates the time length between adjacent first fluctuation parameters or second fluctuation parameters; Obtaining a first verification threshold and a second verification threshold, and comparing the first verification threshold with a first verification parameter, and comparing the second verification threshold with a second verification parameter; When the first verification parameter is greater than a first verification threshold, or the second verification parameter is greater than a second verification threshold, the correlation feature between the first condition parameter and the second condition parameter and the stability of the cinnamaldehyde nanoemulsion is recorded as a disordered distribution feature, otherwise, it is recorded as an ordered distribution feature; Wherein, the monitoring frequency of the stability of the cinnamaldehyde nanoemulsion under the disordered distribution characteristics is greater than the monitoring frequency of the stability of the cinnamaldehyde nanoemulsion under the ordered distribution characteristics; The step of determining the fluctuation range of the precondition parameter according to the unstable node comprises: Acquire a first condition parameter and a second condition parameter of the unstable node; If the first condition parameter or the second condition parameter is a disordered distribution feature, the precondition parameter under the unstable node is offset, and the offset result is recorded as an edge fluctuation point; If both the first condition parameter and the second condition parameter are ordered distribution characteristics, the unstable node is recorded as an edge fluctuation point; There are two edge fluctuation points, which correspond to the upper temperature limit and the lower temperature limit respectively, and the range between the two edge fluctuation points is recorded as the fluctuation range of the precondition parameter.

2. A cinnamaldehyde nanoemulsion stability detection method according to claim 1, characterized in that: The particle size measurement adopts dynamic light scattering technology, and the zeta potential value measurement adopts electrophoretic light scattering technology.

3. A method for detecting the stability of cinnamaldehyde nanoemulsion according to claim 1, characterized in that: The step of subjecting the cinnamaldehyde nanoemulsion sample to an aging treatment to simulate the stability change under actual storage conditions comprises: Obtaining multiple batches of cinnamaldehyde nanoemulsion samples; Placing multiple batches of the cinnamaldehyde nanoemulsion samples in thermostats at different temperatures for aging treatment to simulate storage conditions at different temperatures; A monitoring period is constructed, and a plurality of sampling nodes are set within the monitoring period, and the particle size and Zeta potential value of the cinnamaldehyde nanoemulsion sample at each sampling node are collected.

4. A method for detecting the stability of cinnamaldehyde nanoemulsion according to claim 1, characterized in that: The step of measuring the particle size of the cinnamaldehyde nanoemulsion sample after the aging treatment comprises: Obtaining the particle sizes of the cinnamaldehyde nanoemulsion samples of the same batch at the same temperature, and recording them as the parameters to be evaluated; Comparing each of the parameters to be evaluated, and outputting the comparison result as a particle size fluctuation amount; Obtaining a standard fluctuation amount, and comparing the standard fluctuation amount with a particle size fluctuation amount; When all the particle size fluctuations are less than or equal to the standard fluctuation, it indicates that the particle size fluctuations of the cinnamaldehyde nanoemulsion samples in the same batch are normal, and the parameters to be evaluated are summed and averaged to obtain the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment; When the particle size fluctuation is greater than the standard fluctuation, it indicates that the particle size fluctuation of the cinnamaldehyde nanoemulsion sample in the same batch is abnormal, and the abnormal fluctuation is screened out, and then the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment is determined based on the abnormal fluctuation.

5. A method for detecting the stability of cinnamaldehyde nanoemulsion according to claim 4, characterized in that: The step of determining the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment according to the abnormal fluctuation amount comprises: Obtaining abnormal fluctuation amounts and abnormal fluctuation nodes of the cinnamaldehyde nanoemulsion samples of the same batch; Counting the number of abnormal fluctuation nodes and recording them as parameters to be evaluated; An evaluation threshold is obtained, and the parameter to be evaluated is compared with the evaluation threshold. When the parameter to be evaluated is greater than the evaluation threshold, the aging treatment of the cinnamaldehyde nanoemulsion sample is determined to be abnormal, and the aging treatment of the cinnamaldehyde nanoemulsion sample is re-executed. Otherwise, the parameter to be evaluated under the abnormal fluctuation node is screened out, and the parameters to be evaluated outside the abnormal fluctuation are summed and averaged to obtain the particle size of the cinnamaldehyde nanoemulsion sample after the aging treatment.

6. A cinnamaldehyde nanoemulsion stability detection system, characterized in that: The method for detecting the stability of the cinnamaldehyde nanoemulsion according to any one of claims 1 to 5 comprises: A sample acquisition module, wherein the sample acquisition module is used to acquire a cinnamaldehyde nanoemulsion sample and perform an aging treatment on the cinnamaldehyde nanoemulsion sample to simulate stability changes under actual storage conditions; A simulation module, the simulation module is used to simulate the storage environment temperature of the cinnamaldehyde nanoemulsion sample and record it as a precondition parameter; A parameter calibration module, wherein the parameter calibration module is used to measure the particle size of the cinnamaldehyde nanoemulsion sample after the aging treatment and record it as a first condition parameter, and the Zeta potential value of the cinnamaldehyde nanoemulsion sample and record it as a second condition parameter; A sample detection module, wherein the sample detection module is used to detect the stability of the cinnamaldehyde nanoemulsion according to the precondition parameter, the first condition parameter and the second condition parameter, and obtain the storage state of the cinnamaldehyde nanoemulsion, wherein the storage state includes a stable state and an unstable state; A stabilization processing module, wherein the stabilization processing module is used to record the particle size change amplitude and Zeta potential value of the cinnamaldehyde nanoemulsion under the stable state; An unstable processing module is used to record the unstable nodes of the cinnamaldehyde nanoemulsion in the unstable state, and determine the fluctuation range of the precondition parameter according to the unstable nodes.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the cinnamaldehyde nanoemulsion stability detection method according to any one of claims 1 to 5.

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