CN119269345B - Cinnamaldehyde nanoemulsion stability detection method and system - Google Patents
Cinnamaldehyde nanoemulsion stability detection method and system Download PDFInfo
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Abstract
The invention belongs to the technical field of cinnamaldehyde nanoemulsion detection, and particularly relates to a method and a system for detecting stability of cinnamaldehyde nanoemulsion. According to the invention, the grain diameter and Zeta potential value change of the cinnamaldehyde nanoemulsion sample in the aging treatment process can be accurately controlled and monitored, the stability of the cinnamaldehyde nanoemulsion sample can be effectively evaluated, the aging abnormality of the sample can be timely found by setting a reasonable fluctuation threshold value and a fluctuation node, so that corresponding treatment measures are taken, the stability and the effectiveness of the cinnamaldehyde nanoemulsion sample in the storage and use processes are ensured, in addition, the detection system can automatically adjust the monitoring frequency according to the detection result, and the samples under unordered distribution characteristics are monitored more frequently, so that the potential stability problem can be timely found, and the loss risk caused by the unstable samples is reduced.
Description
Technical Field
The invention belongs to the technical field of cinnamaldehyde nanoemulsion detection, and particularly relates to a method and a system for detecting stability of cinnamaldehyde nanoemulsion.
Background
Along with the deep research of cinnamaldehyde nanoemulsion, the application of the cinnamaldehyde nanoemulsion in the fields of foods, medicines, cosmetics and the like is more and more extensive, and in order to ensure the stability of the cinnamaldehyde nanoemulsion in the storage and use processes, the stability of the cinnamaldehyde nanoemulsion needs to be accurately detected and evaluated, so that the long-term stability of the cinnamaldehyde nanoemulsion product is ensured, and unnecessary losses and risks are avoided.
However, the existing detection method often needs to store a sample for a long time and measure the sample for multiple times to obtain a relatively accurate stability evaluation result, and the stability change of the cinnamaldehyde nanoemulsion cannot be monitored in real time, so that a certain limitation exists in practical application.
Disclosure of Invention
The invention aims to provide a cinnamaldehyde nanoemulsion stability detection method and system, which can monitor the stability change of cinnamaldehyde nanoemulsion in real time and evaluate the stability of the cinnamaldehyde nanoemulsion rapidly and accurately, so that the detection efficiency of the cinnamaldehyde nanoemulsion is improved.
The technical scheme adopted by the invention is as follows:
A cinnamaldehyde nanoemulsion stability detection method comprises the following steps:
Obtaining a cinnamaldehyde nanoemulsion sample, performing aging treatment on the cinnamaldehyde nanoemulsion sample, and simulating stability change under actual storage conditions;
simulating the storage environment temperature of the cinnamaldehyde nanoemulsion sample, and recording the storage environment temperature as a precondition parameter;
Measuring the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment, recording the particle size as a first condition parameter, and recording the Zeta potential value of the cinnamaldehyde nanoemulsion sample as a second condition parameter;
detecting the stability of the cinnamaldehyde nanoemulsion according to the pre-condition parameters, the first condition parameters and the second condition parameters to obtain a storage state of the cinnamaldehyde nanoemulsion, wherein the storage state comprises a stable state and an unstable state;
recording the particle size variation amplitude and the Zeta potential value of the cinnamaldehyde nanoemulsion in the stable state;
And recording an unstable node of the cinnamaldehyde nanoemulsion in the unstable state, and determining the fluctuation range of the precondition parameters according to the unstable node.
In a preferred embodiment, the particle size measurement uses a dynamic light scattering technique and the Zeta potential measurement uses an electrophoretic light scattering technique.
In a preferred embodiment, the step of aging the cinnamaldehyde nanoemulsion sample to simulate a change in stability under actual storage conditions comprises:
obtaining cinnamaldehyde nanoemulsion samples of a plurality of batches;
Placing a plurality of cinnamaldehyde nanoemulsion samples in different batches into thermostated containers with different temperatures respectively for ageing treatment, and simulating storage conditions at different temperatures;
And constructing a monitoring period, setting a plurality of sampling nodes in the monitoring period, and collecting the particle size and Zeta potential value of the cinnamaldehyde nanoemulsion sample under each sampling node.
In a preferred embodiment, the step of measuring the particle size of the aged cinnamaldehyde nanoemulsion sample includes:
obtaining particle sizes of the cinnamaldehyde nanoemulsion samples in the same batch at the same temperature, and recording the particle sizes as parameters to be evaluated;
Comparing the parameters to be evaluated, and outputting the comparison result as particle size fluctuation;
obtaining standard fluctuation quantity and comparing the standard fluctuation quantity with particle size fluctuation quantity;
When the fluctuation amount of the particle size is smaller than or equal to the standard fluctuation amount, the particle size fluctuation of the cinnamaldehyde nanoemulsion sample in the same batch is 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 amount is larger than the standard fluctuation amount, the abnormal particle size fluctuation of the cinnamaldehyde nanoemulsion sample in the same batch is indicated, the abnormal fluctuation amount is screened out, and the particle size of the aged cinnamaldehyde nanoemulsion sample is determined according to the abnormal fluctuation amount.
In a preferred embodiment, the step of determining the particle size of the cinnamaldehyde nanoemulsion sample after the aging treatment according to the abnormal fluctuation amount includes:
obtaining abnormal fluctuation quantity and abnormal fluctuation nodes of the cinnamaldehyde nanoemulsion samples in the same batch;
counting the number of the abnormal fluctuation nodes and recording the number as a parameter to be evaluated;
And acquiring an evaluation threshold value, comparing the parameter to be evaluated with the evaluation threshold value, judging that the aging treatment of the cinnamaldehyde nanoemulsion sample is abnormal when the parameter to be evaluated is larger than the evaluation threshold value, and re-executing the aging treatment of the cinnamaldehyde nanoemulsion sample, otherwise, screening out the parameter to be evaluated under the abnormal fluctuation node, and summing and averaging the parameters to be evaluated except the abnormal fluctuation to obtain the particle size of the aged cinnamaldehyde nanoemulsion sample.
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 includes:
Obtaining the initial particle size and the initial Zeta potential value of the cinnamaldehyde nanoemulsion sample;
acquiring a first condition parameter and a second condition parameter under each pre-condition parameter;
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;
Acquiring a first allowable fluctuation threshold and a second allowable fluctuation threshold, and comparing the first allowable fluctuation threshold and the second allowable fluctuation threshold with a first fluctuation parameter and a second fluctuation parameter respectively;
Recording the time points when the first fluctuation parameter and the second fluctuation parameter reach a first allowable fluctuation threshold value and a second allowable fluctuation threshold value as unstable nodes;
and constructing a backtracking period according to the unstable node, carrying out order analysis on the first fluctuation parameter and the second fluctuation parameter in the backtracking period, and determining the correlation characteristic between the first condition parameter and the second condition parameter and the cinnamaldehyde nanoemulsion stability.
In a preferred scheme, the step of performing an order analysis on the first fluctuation parameter and the second fluctuation parameter in the backtracking period to determine correlation characteristics between the first condition parameter and the second condition parameter and the cinnamaldehyde nanoemulsion stability includes:
acquiring the first fluctuation parameter and the second fluctuation parameter;
Acquiring a trend evaluation function, and respectively inputting the first fluctuation parameter and the second fluctuation parameter into the trend evaluation function to obtain a first fluctuation trend value and a second fluctuation trend value;
acquiring a check function, 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 check function to obtain first check parameters and second check parameters;
acquiring a first check threshold value and a second check threshold value, comparing the first check threshold value with a first check parameter, and comparing the second check threshold value with a second check parameter;
When the first check parameter is larger than a first check threshold or the second check parameter is larger than a second check threshold, recording the association characteristic between the first condition parameter and the second condition parameter and the cinnamaldehyde nanoemulsion stability as unordered distribution characteristic, otherwise recording as ordered distribution characteristic;
And the monitoring frequency of the stability of the cinnamaldehyde nanoemulsion under the unordered distribution characteristic is greater than that of the cinnamaldehyde nanoemulsion under the ordered distribution characteristic.
In a preferred embodiment, the step of determining the fluctuation range of the precondition parameter according to the unstable node includes:
Acquiring a first condition parameter and a second condition parameter under the unstable node;
If the first condition parameter or the second condition parameter is the unordered distribution characteristic, performing offset processing on the pre-condition parameter under the unstable node, and recording an offset result as an edge fluctuation point;
if the first condition parameters and the second condition parameters are orderly distributed, recording the unstable node as an edge fluctuation point;
the two edge fluctuation points respectively correspond to an upper temperature limit and a lower temperature limit, and the range between the two edge fluctuation points is recorded as the fluctuation range of the precondition parameters.
The invention also provides a cinnamaldehyde nanoemulsion stability detection system, and the cinnamaldehyde nanoemulsion stability detection method comprises the following steps:
the sample acquisition module is used for acquiring a cinnamaldehyde nanoemulsion sample, performing aging treatment on the cinnamaldehyde nanoemulsion sample and simulating stability change under actual storage conditions;
The simulation module is used for simulating the storage environment temperature of the cinnamaldehyde nanoemulsion sample and recording the storage environment temperature as a precondition parameter;
The parameter calibration module is used for measuring the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment, recording the particle size as a first condition parameter, and recording the Zeta potential value of the cinnamaldehyde nanoemulsion sample as a second condition parameter;
The sample detection module is used for detecting the stability of the cinnamaldehyde nanoemulsion according to the pre-condition parameters, the first condition parameters and the second condition parameters to obtain a storage state of the cinnamaldehyde nanoemulsion, wherein the storage state comprises a stable state and an unstable state;
The stabilizing treatment module is used for recording the grain diameter change amplitude and the Zeta potential value of the cinnamaldehyde nanoemulsion in the stable state;
the unstable processing module is used for recording unstable nodes of the cinnamaldehyde nanoemulsion in the unstable state and determining fluctuation ranges of precondition parameters according to the unstable nodes.
And an electronic device, the electronic device comprising:
At least one processor;
and a memory communicatively coupled to the at least one processor;
Wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the cinnamaldehyde nanoemulsion stability detection method described above.
The invention has the technical effects that:
according to the invention, the grain diameter and Zeta potential value change of the cinnamaldehyde nanoemulsion sample in the aging treatment process can be accurately controlled and monitored, the stability of the cinnamaldehyde nanoemulsion sample can be effectively evaluated, the aging abnormality of the sample can be timely found by setting a reasonable fluctuation threshold value and a fluctuation node, so that corresponding treatment measures are taken, the stability and the effectiveness of the cinnamaldehyde nanoemulsion sample in the storage and use processes are ensured, in addition, the detection system can automatically adjust the monitoring frequency according to the detection result, and the samples under unordered distribution characteristics are monitored more frequently, so that the potential stability problem can be timely found, and the loss risk caused by the unstable samples is reduced.
Drawings
FIG. 1 is a schematic flow diagram of the method of the present invention;
FIG. 2 is a schematic diagram of a system module of the present invention;
fig. 3 is a schematic view of the structure of the electronic device of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one preferred embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Referring to fig. 1, the invention provides a method for detecting stability of cinnamaldehyde nanoemulsion, which comprises the following steps:
s1, obtaining a cinnamyl aldehyde nanoemulsion sample, performing aging treatment on the cinnamyl aldehyde nanoemulsion sample, and simulating stability change under actual storage conditions;
S2, simulating the storage environment temperature of the cinnamaldehyde nanoemulsion sample, and recording the storage environment temperature as a precondition parameter;
S3, measuring the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment, recording the particle size as a first condition parameter, and recording the Zeta potential value of the cinnamaldehyde nanoemulsion sample as a second condition parameter;
s4, detecting the stability of the cinnamaldehyde nanoemulsion according to the pre-condition parameters, the first condition parameters and the second condition parameters to obtain a storage state of the cinnamaldehyde nanoemulsion, wherein the storage state comprises a stable state and an unstable state;
s5, recording the particle size change amplitude and the Zeta potential value of the cinnamaldehyde nanoemulsion in a stable state;
And S6, recording an unstable node of the cinnamaldehyde nanoemulsion in an unstable state, and determining the fluctuation range of the precondition parameters according to the unstable node.
As described in the above steps S1 to S6, as the research of cinnamaldehyde nanoemulsion is advanced, its application in the food industry, the pharmaceutical field and the cosmetic industry is becoming more and more widespread, however, the problem of nanoemulsion stability is always one of the key factors restricting its wide application, in this embodiment, first, it is necessary to obtain samples of cinnamaldehyde nanoemulsion, and then subject these samples to aging treatment for simulating the variation that may occur in stability under actual storage conditions, aging treatment means that the samples are left under specific temperature conditions for a period of time to observe the variation that may occur during storage, in particular, to simulate the storage environment temperature of the cinnamaldehyde nanoemulsion samples, and record the storage environment temperature as a pre-condition parameter, then, measure the particle size of the cinnamaldehyde nanoemulsion samples after temperature aging treatment, and record the particle size value as a first condition parameter, the particle size measurement adopts a dynamic light scattering technology, the change of the particle size usually reflects the aggregation or dispersion state of particles in a nanoemulsion system, in addition, the Zeta potential value of a cinnamaldehyde nanoemulsion sample is required to be measured and recorded as a second condition parameter, the Zeta potential value measurement adopts an electrophoretic light scattering technology, the Zeta potential reflects the charge state of the particle surface and influences the interaction force among particles, the stability of the cinnamaldehyde nanoemulsion is detected according to the pre-condition parameter, the first condition parameter and the second condition parameter, so that the storage state of the cinnamaldehyde nanoemulsion is obtained, the storage state can be divided into a stable state and an unstable state, the particle size change amplitude and the Zeta potential value of the cinnamaldehyde nanoemulsion are recorded under the stable state, the stability change condition of the nanoemulsion in the storage process can be known, and in an unstable state, recording unstable nodes of the cinnamaldehyde nanoemulsion, wherein the unstable nodes refer to specific time points in the storage process, the stability of the nanoemulsion is obviously changed, and the fluctuation range of the pre-condition parameters is determined according to the unstable nodes, so that corresponding reference is provided for the preparation and storage of the subsequent cinnamaldehyde nanoemulsion.
In a preferred embodiment, the step of aging the cinnamaldehyde nanoemulsion sample to simulate stability change under actual storage conditions comprises the steps of:
S101, obtaining cinnamaldehyde nanoemulsion samples of a plurality of batches;
S102, respectively placing a plurality of cinnamaldehyde nanoemulsion samples in different batches into thermostated containers with different temperatures for ageing treatment, and simulating storage conditions at different temperatures;
S103, constructing a monitoring period, setting a plurality of sampling nodes in the monitoring period, and collecting the particle size and Zeta potential value of the cinnamaldehyde nanoemulsion sample under each sampling node.
In order to study the stability change of the cinnamaldehyde nanoemulsion sample under the actual storage condition, as described in the steps S101-S103, first, the cinnamaldehyde nanoemulsion sample is obtained from a plurality of batches, so as to ensure the diversity of the sample, then the cinnamaldehyde nanoemulsion samples of different batches are respectively placed in thermostats with different temperatures for aging treatment, the process aims at simulating the stability change condition of the cinnamaldehyde nanoemulsion sample when stored under different temperature conditions, in order to more accurately monitor the stability change of the sample in the aging process, a corresponding monitoring period is constructed, a plurality of sampling nodes are further arranged in the monitoring period so as to ensure that the change of the sample in the aging process can be comprehensively captured, and at each sampling node, the cinnamaldehyde nanoemulsion sample is collected, the particle size and Zeta potential value of the cinnamaldehyde nanoemulsion sample are accurately measured, and the stability change of the sample in the aging process can be analyzed in detail through the data, so that a scientific basis is provided for the stability of the sample under the actual storage condition.
In a preferred embodiment, the step of measuring the particle size of the aged cinnamaldehyde nanoemulsion sample comprises:
S301, obtaining particle sizes of cinnamaldehyde nanoemulsion samples in the same batch at the same temperature, and recording the particle sizes as parameters to be evaluated;
s302, comparing each parameter to be evaluated, and outputting a comparison result as particle size fluctuation;
S303, acquiring standard fluctuation quantity, and comparing the standard fluctuation quantity with particle size fluctuation quantity;
when the fluctuation amount of all particle sizes is smaller than or equal to the standard fluctuation amount, the particle size fluctuation of the cinnamaldehyde nanoemulsion samples in the same batch is normal, and the particle sizes of the cinnamaldehyde nanoemulsion samples after aging treatment are obtained by summing the parameters to be evaluated and averaging;
When the fluctuation of the particle size is larger than the standard fluctuation, the abnormal fluctuation of the particle size of the cinnamaldehyde nanoemulsion sample in the same batch is indicated, the abnormal fluctuation is screened out, and the particle size of the cinnamaldehyde nanoemulsion sample after the aging treatment is determined according to the abnormal fluctuation.
In order to measure the particle sizes of the cinnamaldehyde nanoemulsion samples subjected to the aging treatment, as described in the above steps S301-S303, firstly, a plurality of samples are required to be obtained from the same batch of cinnamaldehyde nanoemulsion samples, particle size measurement is performed on the cinnamaldehyde nanoemulsion samples under the same temperature condition, the measurement is completed, the measurement is recorded and calibrated as parameters to be evaluated, then, corresponding comparison analysis is performed on the parameters to be evaluated, the particle size fluctuation amount among the particle sizes of the cinnamaldehyde nanoemulsion samples can be obtained through comparison, in order to ensure the accuracy of the measurement result, a preset standard fluctuation amount is required to be introduced, the standard fluctuation amount is set according to historical experience or experimental data, the particle size fluctuation amount obtained through actual measurement is compared with the standard fluctuation amount, if the particle size fluctuation amount of all samples is found to be smaller than or equal to the standard fluctuation amount after the comparison, then, the particle size fluctuation amount of all the cinnamaldehyde nanoemulsion samples under the same time is normal, the parameters to be evaluated are summed, the average value of all the parameters to be evaluated can be obtained, the average value of the cinnamaldehyde nanoemulsion samples can be obtained after the aging treatment, the measurement is more accurate, however, if the sample size fluctuation amount of the cinnamaldehyde has more than the standard fluctuation amount is found during the process, and the measurement is more accurate, and the quality of the measurement is more than the measurement is carried out, and the measurement is more accurate, and the measurement is more than the measurement is carried out.
In a preferred embodiment, the step of determining the particle size of the aged cinnamaldehyde nanoemulsion sample according to the abnormal fluctuation amount comprises the following steps:
obtaining abnormal fluctuation quantity of cinnamaldehyde nanoemulsion samples in the same batch and abnormal fluctuation nodes;
counting the number of abnormal fluctuation nodes and recording the number as a parameter to be evaluated;
And (3) acquiring an evaluation threshold value, comparing the parameter to be evaluated with the evaluation threshold value, judging that the aging treatment of the cinnamaldehyde nanoemulsion sample is abnormal when the parameter to be evaluated is larger than the evaluation threshold value, and re-executing the aging treatment of the cinnamaldehyde nanoemulsion sample, otherwise, screening out the parameter to be evaluated under the abnormal fluctuation node, and summing and averaging the parameter to be evaluated except the abnormal fluctuation to obtain the particle size of the cinnamaldehyde nanoemulsion sample after the aging treatment.
In this embodiment, when the fluctuation amount of the particle size of the cinnamaldehyde nanoemulsion sample is larger than the standard fluctuation amount, abnormal fluctuation amount data of the cinnamaldehyde nanoemulsion sample in the same batch is firstly obtained, abnormal fluctuation nodes of the fluctuation are recorded, the number of the abnormal fluctuation nodes is counted, the counted result is recorded as a parameter to be evaluated, a preset evaluation threshold value is introduced, the evaluation threshold value is set according to the past experience or experimental data and is used for judging whether the aging treatment of the cinnamaldehyde nanoemulsion sample is normal, if the parameter to be evaluated is larger than the evaluation threshold value, the abnormal condition occurs in the aging treatment process of the cinnamaldehyde nanoemulsion sample, the aging treatment step needs to be re-executed, if the parameter to be evaluated is smaller than or equal to the evaluation threshold value, the condition that the aging treatment process is normal is confirmed, the parameter to be evaluated under the abnormal fluctuation nodes is sieved, namely the abnormal fluctuation data is eliminated, then, the remaining parameter to be evaluated which is not influenced by the abnormal fluctuation is summed, and the average value is calculated, and the average value representing the particle size of the treated cinnamaldehyde nanoemulsion sample is aged.
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:
S401, obtaining the initial particle size and the initial Zeta potential value of a cinnamaldehyde nanoemulsion sample;
s402, acquiring a first condition parameter and a second condition parameter under each pre-condition parameter;
S403, respectively 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 to obtain a first fluctuation parameter and a second fluctuation parameter;
S404, acquiring a first allowable fluctuation threshold and a second allowable fluctuation threshold, and comparing the first allowable fluctuation threshold and the second allowable fluctuation threshold with a first fluctuation parameter and a second fluctuation parameter respectively;
s405, recording the time points when the first fluctuation parameter and the second fluctuation parameter reach a first allowable fluctuation threshold value and a second allowable fluctuation threshold value as unstable nodes;
S406, constructing a backtracking period according to the unstable node, and carrying out order analysis on the first fluctuation parameter and the second fluctuation parameter in the backtracking period to determine the correlation characteristic between the first condition parameter and the second condition parameter and the cinnamaldehyde nanoemulsion stability.
As described in the above steps S401-S406, when the stability of cinnamaldehyde nanoemulsion is detected, firstly, the initial particle size and the initial Zeta potential value of the cinnamaldehyde nanoemulsion sample are obtained, as the basis of the subsequent comparison, the initial particle size refers to the average particle size of the nanoemulsion particles when the nanoemulsion particles are not affected by any external condition, the initial Zeta potential value reflects the charge state of the surfaces of the nanoemulsion particles, then, the first condition parameters and the second condition parameters under all the precondition parameters are collected, the first condition parameters and the second condition parameters are kept unchanged in the experimental process, the first condition parameters and the second condition parameters are changed in the experimental process, so as to affect the stability of the nanoemulsion, then, the first condition parameters and the second condition parameters are compared with the initial particle size and the initial Zeta potential value of the cinnamaldehyde nanoemulsion sample, so as to obtain the first fluctuation parameters and the second fluctuation parameters, which reflect the variation conditions of the nanoemulsion particle sizes and the potentials under different conditions, then, the first allowable fluctuation threshold and the second allowable fluctuation threshold are introduced, and compared with the first fluctuation parameters and the second fluctuation parameters respectively, the first fluctuation parameters and the second fluctuation parameters refer to the allowable fluctuation threshold and the allowable fluctuation parameters under the specific fluctuation parameters, the specific fluctuation parameters are calculated in a certain range, the first fluctuation parameters and the allowable fluctuation parameters are calculated, the first fluctuation parameters and the second fluctuation parameters are calculated and the allowable fluctuation parameters are calculated, the maximum fluctuation time is calculated, and the time is not stable, and the time is calculated, and the stable time period is calculated, and the stable, and the time period is not stable, and the stable time period is calculated and the time-stable, and the time period is calculated and stable and fluctuation time, the correlation characteristics between the first condition parameter and the second condition parameter and the stability of the cinnamaldehyde nanoemulsion can be determined, and a basis is provided for optimizing the storage of the nanoemulsion.
In a preferred embodiment, the step of performing an order analysis on the first fluctuation parameter and the second fluctuation parameter in the backtracking period to determine a correlation characteristic between the first condition parameter and the second condition parameter and the cinnamaldehyde nanoemulsion stability includes:
Acquiring a first fluctuation parameter and a second fluctuation parameter;
Acquiring a trend evaluation function, and respectively inputting a first fluctuation parameter and a second fluctuation parameter into the trend evaluation function to obtain a first fluctuation trend value and a second fluctuation trend value;
acquiring a check function, 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 check function to obtain first check parameters and second check parameters;
Acquiring a first check threshold value and a second check threshold value, comparing the first check threshold value with a first check parameter, and comparing the second check threshold value with a second check parameter;
When the first check parameter is larger than the first check threshold or the second check parameter is larger than the second check threshold, recording the association characteristic between the first condition parameter and the second condition parameter and the cinnamaldehyde nanoemulsion stability as unordered distribution characteristic, otherwise recording as ordered distribution characteristic;
wherein the monitoring frequency of the stability of the cinnamaldehyde nanoemulsion under the unordered distribution characteristic is greater than the monitoring frequency of the stability of the cinnamaldehyde nanoemulsion under the ordered distribution characteristic.
After the backtracking period is determined, the first fluctuation parameter and the second fluctuation parameter in the backtracking period need to be acquired first, so that the fluctuation condition of the cinnamaldehyde nanoemulsion in the backtracking period can be known, and then a preset trend evaluation function can be introduced, so that the trend of the first fluctuation parameter and the trend of the second fluctuation parameter can be evaluated, wherein the expression of the trend evaluation function is as follows: In which, in the process, Representing either the first or second surge trend value,The length of time of the backtracking period is indicated,Representing the number of first fluctuation parameters or second fluctuation parameters,After the first fluctuation parameter or the second fluctuation parameter is determined, the correlation between the first fluctuation trend value and the second fluctuation trend value and the stability of the cinnamaldehyde nanoemulsion can be analyzed, specifically, a preset check function is required to be introduced, a plurality of groups of adjacent first fluctuation parameters and second fluctuation parameters are randomly selected based on the check function, and the adjacent first fluctuation parameters or second fluctuation parameter pairs are input into the check function to obtain the first check parameter and the second check parameter, wherein the expression of the check function is as follows: In which, in the process, Representing either the first check parameter or the second check parameter,Representing the first fluctuation parameter or the second fluctuation parameter of the position in the adjacent bit,Representing the first fluctuation parameter or the second fluctuation parameter of the position in front of the adjacent bit,Representing the time length between adjacent first fluctuation parameters or second fluctuation parameters, and further, obtaining a first check threshold value and a second check threshold value, and comparing the first check threshold value with the first check parameter, so as to judge whether the first fluctuation parameters and the second fluctuation parameters are in a normal fluctuation range, when the first check parameter is larger than the first check threshold value or the second check parameter is larger than the second check threshold value, the correlation characteristic between the first condition parameter and the second condition parameter and the stability of cinnamaldehyde nanoemulsion is represented as an unordered distribution characteristic, conversely, if the first check parameter is smaller than or equal to the first check threshold value or the second check parameter is smaller than or equal to the second check threshold value, the correlation characteristic is represented as an ordered distribution characteristic, finally, according to the difference between the unordered distribution characteristic and the ordered distribution characteristic, different monitoring strategies can be adopted, and under the unordered distribution characteristic, the monitoring frequency of the stability of cinnamaldehyde nanoemulsion is larger than the monitoring frequency under the ordered distribution characteristic, because the distribution characteristic indicates that the stability of cinnamaldehyde nanoemulsion may be influenced by more uncertain factors, and the monitoring of the first condition parameter and the second condition parameter and the stability of cinnamaldehyde nanoemulsion is required to be more frequently monitored, so that the stability of the cinnamaldehyde nanoemulsion is relatively stable.
In a preferred embodiment, the step of determining the fluctuation range of the preconditioning parameters from the unstable node comprises:
acquiring a first condition parameter and a second condition parameter under an unstable node;
if the first condition parameter or the second condition parameter is the unordered distribution characteristic, performing offset processing on the pre-condition parameter under the unstable node, and recording an offset result as an edge fluctuation point;
If the first condition parameters and the second condition parameters are orderly distributed, recording the unstable nodes as edge fluctuation points;
Two edge fluctuation points respectively correspond to an upper temperature limit and a lower temperature limit, and the range between the two edge fluctuation points is recorded as the fluctuation range of the precondition parameters.
In this embodiment, after the output of the unstable node, first condition parameters and second condition parameters under the unstable node and distribution characteristics of the first condition parameters and the second condition parameters need to be acquired first, if the first condition parameters or the second condition parameters are found to have disordered distribution characteristics, offset processing needs to be performed on pre-condition parameters under the unstable node, the purpose of the offset processing is to better understand and control the fluctuation range of the parameters, ensure the accuracy of the analysis result, record the offset result as edge fluctuation points after the processing is completed, on the other hand, if the first condition parameters and the second condition parameters are both ordered distribution characteristics, it means that parameter values are arranged in a certain rule or order within a certain range, in this case, the unstable node is directly recorded as edge fluctuation points, the parameters of the ordered distribution characteristics are generally easier to analyze and predict, therefore, the process of recording the edge fluctuation points is relatively simple, and here, it needs to be explained that the existence of the edge fluctuation points is to identify the fluctuation range of the pre-condition parameters, in this embodiment, the edge fluctuation points are respectively corresponding to the upper temperature limit and lower temperature limit points, and the lower temperature limit ranges are better understood by a person, and the decision-making control of the parameters is better.
Referring to fig. 2, a cinnamaldehyde nanoemulsion stability detection system, using the above method for detecting cinnamaldehyde nanoemulsion stability, includes:
The sample acquisition module is used for acquiring a cinnamaldehyde nanoemulsion sample, performing aging treatment on the cinnamaldehyde nanoemulsion sample and simulating stability change under actual storage conditions;
the simulation module is used for simulating the storage environment temperature of the cinnamaldehyde nanoemulsion sample and recording the storage environment temperature as a precondition parameter;
the parameter calibration module is used for measuring the particle size of the cinnamaldehyde nanoemulsion sample after aging treatment, recording the particle size as a first condition parameter, and recording the Zeta potential value of the cinnamaldehyde nanoemulsion sample as a second condition parameter;
The sample detection module is used for detecting the stability of the cinnamaldehyde nanoemulsion according to the pre-condition parameters, the first condition parameters and the second condition parameters to obtain a storage state of the cinnamaldehyde nanoemulsion, wherein the storage state comprises a stable state and an unstable state;
The stabilizing treatment module is used for recording the grain diameter change amplitude and the Zeta potential value of the cinnamaldehyde nanoemulsion in a stable state;
The unstable processing module is used for recording unstable nodes of the cinnamaldehyde nanoemulsion in an unstable state and determining the fluctuation range of the precondition parameters according to the unstable nodes.
In the above, the system comprises a sample acquisition module, a simulation module, a parameter calibration module, a sample detection module, a stabilization processing module and an unsteady processing module, wherein the main function of the sample acquisition module is to acquire a cinnamaldehyde nanoemulsion sample, and then to perform aging processing to simulate the possible change of stability under the actual storage condition, the purpose of the aging processing is to better understand the stability performance of the cinnamaldehyde nanoemulsion after long-term storage, the simulation module is to simulate the environmental temperature conditions possibly encountered by the cinnamaldehyde nanoemulsion sample during storage, the system can record temperature data as pre-condition parameters by simulating these conditions, thereby providing important reference basis for the subsequent stability analysis, the parameter calibration module is to perform detailed physical parameter measurement on the cinnamaldehyde nanoemulsion sample after the subsequent stability analysis, and to record the grain size of the cinnamaldehyde nanoemulsion sample as a first condition parameter, and to measure the Zeta potential value of the sample, and to record as a second condition parameter, the sample detection module is to better understand the stability performance of the cinnamaldehyde nanoemulsion after the long-term storage, the stable state of the cinnamaldehyde nanoemulsion can be further determined by the analysis of the pre-condition parameter, the stable state of the cinnamaldehyde nanoemulsion can be further determined by the stable state, namely, the stable state of the stable state can be realized by the stable state of the cinnamaldehyde nanoemulsion can be obtained by the pre-condition parameter, and the stable state of the stable state can be further, and the stable state can be determined by the stable state storage state analysis of the stable state of the cinnamaldehyde, and the stable state can be obtained by the stable state, and the stable state can be obtained by the stable state after the stable state, and the stable state, the unsteady processing module records the unsteady node of the cinnamaldehyde nanoemulsion when the cinnamaldehyde nanoemulsion is judged to be in an unsteady state, and the system can determine the fluctuation range of specific pre-condition parameters which cause the instability of the cinnamaldehyde nanoemulsion by analyzing the unsteady node, so that guidance is provided for improving the storage condition.
Referring to fig. 3, an electronic device includes:
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 execute the cinnamaldehyde nanoemulsion stability detection method.
In the foregoing, the processor of the electronic device may be any general purpose or special purpose microprocessor, microcontroller, digital Signal Processor (DSP), or other type of processor. The memory may include Random Access Memory (RAM), read Only Memory (ROM), flash memory, or other types of storage devices. The computer program may contain a series of instructions that enable a processor to perform the steps of the cinnamaldehyde nanoemulsion stability detection method described above.
The electronic device may further include an operator for performing various mathematical operations and logical operations, and an input/output (I/O) interface for communicating 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), wide Area Network (WAN), the Internet, or other type of network to facilitate remote data transmission and remote control.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, apparatus, article, or method that comprises the element.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention. Structures, devices and methods of operation not specifically described and illustrated herein, unless otherwise indicated and limited, are implemented according to conventional means in the art.
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| 全反式和顺式虾青素纳米乳液的物化特征、稳定性和生物利用率比较研究;周乐松 等;食品工业科技;20241028;第1-18页 * |
| 周乐松 等.全反式和顺式虾青素纳米乳液的物化特征、稳定性和生物利用率比较研究.食品工业科技.2024,第1-18页. * |
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