A method, system, and storage medium for monitoring thermal stability of a complex emulsifier

Abstract

The application relates to the technical field of composite emulsifier monitoring, and discloses a thermal stability monitoring method and system of a composite emulsifier and a storage medium. The method comprises the following steps: performing oil-water interfacial tension dynamic measurement and particle size distribution analysis on initial emulsion of the composite emulsifier to obtain initial characteristic parameters; constructing a multi-parameter light scattering measurement platform and performing measurement parameter calibration to obtain light scattering measurement calibration parameters; loading the initial emulsion into the multi-parameter light scattering measurement system, and performing thermal cycle processing according to the light scattering measurement calibration parameters to obtain multi-angle scattering signal data; calculating thermal response characteristic data based on the multi-angle scattering signal data and the initial characteristic parameters, and calculating multi-stage thermal stability scores based on the thermal response characteristic data; and generating a formula parameter optimization scheme based on the multi-stage thermal stability scores. The application can monitor the stability change of the composite emulsifier in real time during the thermal treatment process, and improves the monitoring efficiency and the optimization accuracy of the formula parameters.

Description

Technical Field

[0001] This application relates to the field of composite emulsifier monitoring technology, and in particular to a method, system and storage medium for monitoring the thermal stability of composite emulsifiers. Background Technology

[0002] The thermal stability of composite emulsifiers has a decisive impact on product quality and safety. In industrial production, emulsified products often undergo complex heat treatment processes. These heat treatment processes may cause changes in the interfacial activity of composite emulsifiers, leading to instability in the emulsion system, manifested as problems such as increased particle size, phase separation, and structural damage.

[0003] Traditional methods for assessing emulsion stability primarily rely on single-parameter measurements (such as particle size changes or rheological properties), lacking the ability to monitor the entire heat treatment process in real time. In particular, they cannot distinguish the differentiated behavior of emulsifiers during heating, high-temperature holding, and cooling. Current assessment methods generally ignore the differences in physicochemical properties exhibited by composite emulsifiers at different temperature stages. For example, during heating, interfacial activity changes are the primary influence; during high-temperature stages, structural reorganization is the main factor; and during cooling, molecular reequilibrium is the primary factor. This neglect of stage-specific characteristics makes it difficult for assessment results to accurately reflect the true thermal stability of composite emulsifiers. Traditional methods also struggle to capture the interaction effects between parameters, resulting in low optimization efficiency and insufficient reliability of results. Summary of the Invention

[0004] This application provides a method, system, and storage medium for monitoring the thermal stability of composite emulsifiers. This application enables real-time monitoring of the stability changes of composite emulsifiers during heat treatment, improving monitoring efficiency and the accuracy of formula parameter optimization.

[0005] In a first aspect, this application provides a method for monitoring the thermal stability of a composite emulsifier, the method comprising:

[0006] The initial characteristic parameters of the oil-water interfacial tension were dynamically measured and the particle size distribution was analyzed in the initial emulsion of the composite emulsifier.

[0007] A multi-parameter light scattering measurement platform was constructed and the measurement parameters were calibrated to obtain the light scattering measurement calibration parameters;

[0008] The initial emulsion is loaded into the temperature-controlled sample cell of the multi-parameter light scattering measurement system, and thermal cycling is performed according to the light scattering measurement calibration parameters to obtain multi-angle scattering signal data.

[0009] Thermal response characteristic data are calculated based on the multi-angle scattering signal data and the initial characteristic parameters, and a multi-stage thermal stability score is calculated based on the thermal response characteristic data.

[0010] generating a formulation parameter optimization scheme based on the multi-stage thermal stability score.

[0011] In a second aspect, the present application provides a thermal stability monitoring system of a composite emulsifier, comprising:

[0012] a measurement module configured to perform oil-water interfacial tension dynamic measurement and particle size distribution analysis on the initial emulsion of the composite emulsifier to obtain initial characteristic parameters;

[0013] a parameter calibration module configured to construct a multi-parameter light scattering measurement platform and perform measurement parameter calibration to obtain light scattering measurement calibration parameters;

[0014] a thermal cycle module configured to load the initial emulsion into a temperature-controlled sample cell of the multi-parameter light scattering measurement system and perform thermal cycle processing according to the light scattering measurement calibration parameters to obtain multi-angle scattering signal data;

[0015] a calculation module configured to calculate thermal response characteristic data based on the multi-angle scattering signal data and the initial characteristic parameters, and calculate a multi-stage thermal stability score based on the thermal response characteristic data;

[0016] a generation module configured to generate a formulation parameter optimization scheme based on the multi-stage thermal stability score.

[0017] In a third aspect, a computer readable storage medium is provided, which stores instructions, when executed on a computer, cause the computer to perform the above-mentioned thermal stability monitoring method of a composite emulsifier.

[0018] In the technical scheme provided in the application, the multi-parameter light scattering measurement system integrating dynamic light scattering, static light scattering and backscattering technology realizes comprehensive detection of the particle size change, distribution width, phase separation state and aggregation degree of the emulsion system, and overcomes the limitations of the traditional single parameter evaluation method; the thermal response characteristic data is divided into three stages of surfactant change at the rising temperature interface, high temperature structure recombination and cooling rebalancing, and differential weight coefficients are used for scoring calculation, so that the evaluation result can accurately reflect the stability change rule of the composite emulsifier in the whole process of heat treatment; by applying the response surface method design optimization algorithm, a correlation model between the formula parameters (HLB value, pH value, emulsifier ratio, total addition amount) and the heat stability score is established, and the theoretical optimal formula parameter combination is obtained through mathematical analysis methods such as partial derivative solution of optimal point, Hessian matrix eigenvalue confirmation, which greatly improves the scientificity and reliability of formula optimization; in addition, the application does not need complex rheometer or thermal analysis equipment, but only through light scattering and image analysis technology, the stability change of the composite emulsifier can be monitored in real time during the heat treatment process, which significantly reduces the monitoring cost and improves the monitoring efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical scheme of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor based on these drawings.

[0020] Figure 1 An embodiment schematic diagram of the heat stability monitoring method of the composite emulsifier in the embodiment of the application;

[0021] Figure 2 An embodiment schematic diagram of the heat stability monitoring system of the composite emulsifier in the embodiment of the application. DETAILED DESCRIPTION

[0022] The embodiment of the present application provides a kind of composite emulsifier heat stability monitoring method, system and storage medium.The terms "first", "second", "third", "fourth" and the like (if exist) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence.It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein.In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] For ease of understanding, the specific process of the embodiment of the present application is described below, please refer to Figure 1 One embodiment of the heat stability monitoring method of the composite emulsifier in the embodiment of the present application comprises:

[0024] Step S101, the initial emulsion of the composite emulsifier is measured by oil-water interfacial tension and particle size distribution analysis, and initial characteristic parameters are obtained;

[0025] It can be understood that the execution subject of the present application can be a heat stability monitoring system of composite emulsifier, and can also be a terminal or a server, and the specific place is not limited.The embodiment of the present application takes the server as the execution subject for example.

[0026] Specifically, at least one of polysorbate-80, polyoxyethylene sorbitan oleate, and polyethylene glycol fatty acid ester is selected as a hydrophilic emulsifier, and at least one of sorbitan monooleate, glycerol monostearate, and beeswax is selected as a lipophilic emulsifier. The selection of these emulsifiers determines the hydrophilic-lipophilic balance (HLB value) of the composite emulsifier, which in turn affects the stability and emulsification effect of the emulsion. According to the type and proportion of the selected emulsifiers, the HLB value of the composite emulsifier is adjusted to ensure the emulsion maintains good stability under different use conditions. The selected hydrophilic emulsifier contains a hydrophilic polyoxyethylene chain, while the lipophilic emulsifier is composed of long-chain fatty acids or wax esters with lipophilicity. The combination of these components forms the basis of the composite emulsifier. The hydrophilic emulsifier is physically mixed with the lipophilic emulsifier to obtain a mixture of the composite emulsifier, and the two emulsifiers are uniformly dispersed together through physical action. The uniformity of the mixture affects the performance of the composite emulsifier, especially the stability and uniformity of the particle size distribution during the emulsification process. At least one of citrate buffer, phosphate buffer, and acetate buffer is selected as a pH buffer, and the pH value of the pH buffer is adjusted to a predetermined pH value to obtain a pH buffered aqueous phase. The selection of the pH buffer and the adjustment of the pH value are to ensure the chemical stability of the emulsion during the entire experimental process, avoid the influence of pH fluctuations on the ionization state of the emulsifier or the structure of the interfacial film, and further affect the stability of the emulsion. The composite emulsifier mixture is added to the prepared pH buffered aqueous phase, and a high-speed shear emulsifier is used for shearing. The emulsifier is dispersed in the aqueous phase through high shear force to form a stable emulsion. The high-speed shear emulsifier can rapidly and uniformly distribute the emulsifier molecules in the liquid, break up large particles in the oil phase, and form small and uniform oil droplets, thereby improving the stability of the emulsion. During the shearing process, the particle size and distribution of the emulsion will change, so the particle size distribution characteristics of the initial emulsion are measured. A laser particle size analyzer is used to measure the particle size distribution of the initial emulsion to obtain the volume-weighted mean diameter, the surface area-weighted mean diameter, and the particle size distribution width index (Span value). The volume-weighted mean diameter (D[4,3]) is calculated based on the volume weight of the particles and reflects the contribution of large particles in the sample. The surface area-weighted mean diameter (D[3,2]) is calculated based on the surface area weight of the particles and reflects the influence of small particles on the properties of the sample. The particle size distribution width index (Span value) is used to describe the width of the particle size distribution, and the smaller the Span value, the more uniform the particle size distribution. Through these measurement results, the particle size distribution characteristics of the emulsion are reflected, and the dispersion effect and stability of the emulsifier are evaluated. At the same time, an interfacial tension meter is used to dynamically measure the oil-water interfacial tension of the initial emulsion and determine the equilibrium interfacial tension value.The oil-water interfacial tension dynamic change curve reflects the spreading ability of the emulsifier at the oil-water interface. With the passage of time, the change of the interfacial tension reveals the adsorption ability of the emulsifier and the stability of the interfacial film. The equilibrium interfacial tension value refers to the tension value of the oil-water interface after the emulsion reaches a stable state. A lower interfacial tension value means that the emulsifier has better spreading ability at the oil-water interface, thereby being able to stabilize the emulsion and prevent the aggregation of oil droplets. By measuring the dynamic change of these interfacial tensions and the equilibrium interfacial tension value, the surface activity of the emulsifier and its effect in the emulsification process are reflected. The initial particle size distribution characteristics, the oil-water interfacial tension dynamic change curve and the equilibrium interfacial tension value are taken as initial characteristic parameters.

[0027] In step S102, a multi-parameter light scattering measurement platform is constructed and measurement parameter calibration is performed to obtain light scattering measurement calibration parameters.

[0028] Specifically, a helium-neon laser, a temperature-controlled sample cell, an optical path system, a multi-angle detector array, and a data acquisition and processing unit are assembled and connected to form a multi-parameter light scattering measurement platform. The helium-neon laser serves as the light source, and a laser with a wavelength of 632.8 nm is selected due to its stability and excellent beam quality, making it suitable for high-precision light scattering experiments. The temperature-controlled sample cell is used to accurately control the temperature of the sample during the entire experiment, ensuring the stability and repeatability of the experimental data. The optical path system guides the laser beam into the sample through key components such as collimators, polarizers, apertures, and beam splitters, ensuring the stability of the optical path of the laser beam. The multi-angle detector array is responsible for collecting scattering signals at different scattering angles, which will be analyzed in the subsequent data processing unit. The data acquisition and processing unit is responsible for real-time collection of signals from multiple detectors, data storage and processing, and generation of experimental results. The measurement parameters are calibrated. The positioning and optical axis adjustment of each element in the optical path system are accurately performed. The adjustment of collimators, polarizers, apertures, and beam splitters is a key step, as the adjustment of these components affects the quality and direction of the incident light beam. In particular, the collimator adjusts the laser beam to be parallel light, while the polarizer controls the polarization state of the laser beam. The aperture is used to limit the size of the light beam, and the beam splitter divides the laser beam into multiple parts, allowing the light beam to simultaneously illuminate multiple scattering angle detectors. The multi-angle detector array is debugged, and the photomultiplier tubes in the array are fixed at multiple scattering angle positions. The signal amplification factor of each photomultiplier tube is adjusted to ensure that each detector can receive sufficient signal at different scattering angles, and the signal intensity will not deviate due to inappropriate amplification factor. Adjusting the amplification factor of the photomultiplier tube ensures the sensitivity of signal acquisition and optimizes the signal-to-noise ratio, improving the reliability and accuracy of the signal. The signal intensity at each scattering angle will be different, and accurate amplification factor adjustment can ensure the measurement accuracy of the entire system. The output power of the helium-neon laser is adjusted and stabilized. Since the output power of the laser directly affects the intensity of the scattering signal, it is precisely adjusted to ensure that the power is stable and within the optimal working range. By adjusting the power of the laser and measuring the signal intensity at different detection angles using the multi-angle scattering signal acquisition system, the optimal laser power calibration value is obtained. The optimal detection range of the laser power calibration value refers to the optimal value of the laser power under the premise of ensuring that the scattering signal is strong enough and the signal-to-noise ratio is good. Through this process, it is ensured that the signal at each angle is within the optimal measurement range during actual measurement, thereby improving the accuracy of the data. To calibrate the system response, standard polystyrene latex microspheres are used. These microspheres have a known particle size, and their scattering characteristics comply with the Mie scattering theory, serving as a calibration standard for the light scattering system.The standard polystyrene latex microspheres are injected into the temperature-controlled sample cell respectively, and the multi-angle scattering signal acquisition system is used for acquisition, so as to obtain the scattering signals at different scattering angles. Through comparative analysis with the theoretical scattering curve, the calibration data of the system response are obtained. The theoretical scattering curve is based on the Mie scattering theory and describes the light scattering intensity of particles with different particle sizes at different scattering angles. By comparing the deviation between the experimental data and the theoretical data, the response of the system is accurately evaluated, and necessary adjustments are made. Based on the system response calibration data, the standard curve relationship between the scattering signal intensity and the particle size and the concentration is established. The standard curve is obtained by comparing the scattering signal intensity measured by experiment with the standard microspheres with known particle size and known concentration, so as to establish the mathematical relationship between the scattering signal and the particle size and the concentration. The standard curve relationship equation is used to convert the scattering signal into the numerical value of the particle size and the concentration, so as to provide quantitative data support for subsequent actual sample analysis. The laser power calibration value, the system response calibration data and the standard curve relationship equation are written into the memory of the data acquisition and processing unit, and the light scattering measurement calibration parameters are obtained.

[0029] In step S103, the initial emulsion is loaded into the temperature-controlled sample cell of the multi-parameter light scattering measurement system, and a thermal cycle process is performed according to the light scattering measurement calibration parameters, so as to obtain multi-angle scattering signal data.

[0030] Specifically, the initial emulsion is divided into appropriate test containers, and the containers are placed into a temperature-controlled sample cell, ensuring that the optical window of the test container is directly opposite the multi-angle detector array. The alignment of the optical window ensures that the laser beam can be transmitted from the sample, collected by the detector array after different scattering angles, and the accurate scattering signal is obtained. The setting process of the sample to be tested requires that the container be fixed and the optical window be accurately aligned to minimize errors and beam deviation, ensuring the accuracy of subsequent measurement data. According to the light scattering measurement calibration parameters in the early stage, the data acquisition parameters of the multi-angle scattering signal are set. The selection of parameters includes signal acquisition frequency, sampling time interval, and acquisition sensitivity of each scattering angle. Selecting appropriate acquisition frequency and time interval can ensure that the changes in the signal are accurately captured at each stage, especially during the thermal cycling process with large temperature changes. The setting of the acquisition parameters can determine the detail of the data and the sensitivity of the experiment. According to the thermal stability characteristics of the sample to be tested, the specific thermal cycling parameters of the heating stage, the constant temperature stage and the cooling stage in the thermal cycling process are designed. These parameters include heating rate, constant temperature holding time, cooling rate, etc. Reasonable thermal cycling design can simulate the temperature changes of the emulsion in actual application, thereby better evaluating its thermal stability. After completing the thermal cycling parameter design, start the constant temperature water bath circulation system of the temperature-controlled sample cell, and input the thermal cycling parameters into the control unit. The control unit accurately controls the temperature in the sample cell according to the input parameters, ensuring that the sample to be tested maintains within the predetermined temperature range during the entire thermal cycling process. This process is achieved through an automated temperature control system, which gradually heats the sample according to the set heating rate, maintains a stable temperature after reaching the constant temperature stage, and then cools down according to the set rate. During this process, the temperature control system needs to have high precision and stability to ensure that the sample accurately responds to the changes in external temperature at each stage, providing accurate temperature data for the collection of scattering signals. During the thermal cycling process, the multi-angle scattering signals are monitored in real time. Through the setting of data acquisition parameters, signal data at different scattering angles are collected regularly according to the set acquisition points, and at the same time, optical microscope images of the emulsion are taken. The multi-angle scattering signal can reveal the changes in particle distribution and state of the emulsion under different temperature conditions, while the optical microscope image can help observe the morphological changes of the emulsion particles, such as aggregation, dispersion or phase separation. In order to ensure the accuracy and repeatability of the data, the scattering signal data with temperature markers are repeatedly measured. These scattering signal data are repeatedly measured at different temperature stages and different time points to eliminate the influence of accidental errors and enhance the reliability of the measurement results. Assuming that E times of repeated measurements are performed, the average value of E times of measurements is calculated, effectively reducing the errors caused by equipment fluctuations, external interference or slight fluctuations of the sample. The obtained multi-angle scattering signal data include signal data at multiple time points and temperature stages.

[0031] In step S104, the thermal response feature data is calculated based on the multi-angle scattering signal data and the initial characteristic parameters, and the multi-stage thermal stability score is calculated based on the thermal response feature data.

[0032] Specifically, dynamic light scattering data from multi-angle scattering signal data is used to calculate particle size changes during thermal cycling. Dynamic light scattering obtains the average particle diameter in real time by measuring the Brownian motion of particles in the solution. As the temperature rises, the movement of particles in the emulsion intensifies, and particle aggregation or dispersion leads to changes in particle size. The data provided by dynamic light scattering can help track the trend of particle size changes with temperature, thereby revealing changes in the stability of the emulsion during thermal cycling. By calculating the rate of particle size change with temperature, the trend of particle size increase or decrease is obtained, and this data reflects the thermal stability of the emulsion during the heating, isothermal, and cooling stages. Based on static light scattering data, particle size distribution changes during thermal cycling are calculated. Static light scattering obtains detailed particle size distribution by measuring the light intensity at different scattering angles. The information provided by static light scattering reflects how the particle size distribution of the emulsion changes during heat treatment, especially the changes in the width (span value) and average diameter of the particle size distribution. Changes in particle size distribution are closely related to the stability of the emulsion; a wider particle size distribution usually indicates an increased possibility of particle aggregation or phase separation. By analyzing static light scattering data, changes in particle size distribution are quantified to further assess whether aggregation occurred in the emulsion during heat treatment. Based on backscattering data from multi-angle scattering signals, changes in emulsion stability are calculated. Backscattering technology measures the intensity of the reflected signal of scattered light to reflect whether particles in the emulsion aggregate or settle during heat treatment. The intensity of the backscattering signal is directly related to the sample's stability; an increase in intensity indicates the beginning of particle aggregation, while a decrease in intensity indicates phase separation. By analyzing backscattering data, the stability change index of the emulsion at different stages is calculated, and the overall stability of the emulsion during thermal cycling is assessed based on this. Optical microscopic images are analyzed. Images of the emulsion at different temperatures are captured using an optical microscope, and image analysis techniques are used to obtain data on morphological changes during thermal cycling. Morphological data includes particle aggregation degree, shape changes, and whether significant phase separation occurred. Changes in morphological characteristics are achieved by calculating particle aspect ratio, roundness, and aggregation degree. For example, the aspect ratio (AR) and roundness (R) of particles are calculated using image processing software, while aggregation reflects whether particles have aggregated or clustered. By tracking these morphological characteristics, changes in emulsion stability are assessed. Data on particle size variation, particle size distribution variation, emulsion stability variation, and morphological characteristic variation are normalized to eliminate differences in data magnitude under different experimental conditions, ensuring data comparability. After normalization, the data are compared with initial characteristic parameters to calculate the relative rate of change for each indicator. The relative rate of change reflects how each parameter changes relative to the initial state during heat treatment, thus helping to quantify changes in thermal stability.For example, the interface film strength change rate is represented by the change of interfacial tension data, and the particle size growth rate is calculated by the change of particle size data. The oil-water phase separation index is an important indicator to measure the dispersion stability of the emulsion, which is obtained by backscattering data or optical microscopic image analysis, and the degree of heat-induced aggregation is reflected by the aggregation degree of particles or the increase of particle size. Through the calculation of these indicators, the thermal response characteristic data of each stage is obtained. The thermal response characteristic data is divided into the heating interface activity change stage, the high temperature structure reorganization stage and the cooling rebalancing stage, and the multi-stage thermal stability score is calculated. The heating stage mainly reflects the change trend of interfacial activity and particle size when the temperature gradually increases. In this stage, the aggregation degree of particles increases, and the interface film strength decreases. The high temperature structure reorganization stage is the stage in which the internal structure of the emulsion reorganizes when the highest temperature is reached, accompanied by large changes in particle size and morphological characteristics. The cooling rebalancing stage is the stage in which the particles can be re-dispersed or stabilized after cooling, and the stability of this stage is particularly critical, which determines whether the emulsion can restore its original dispersion stability. According to the thermal response characteristic data of each stage, the stability score of each stage is calculated, and the scores are weighted and summed to obtain the final multi-stage thermal stability score, which reflects the stability of the composite emulsifier during the heat treatment process.

[0033] The thermal response characteristic data is divided into different temperature stages. These stages include the temperature rising interfacial activity change stage, the high temperature structure reorganization stage, and the cooling rebalancing stage. The temperature rising interfacial activity change stage is from the first temperature value to the second temperature value, the second temperature value to the highest temperature is the high temperature structure reorganization stage, and the highest temperature to the first temperature value is the cooling rebalancing stage. In this process, the first temperature value is always less than the second temperature value, ensuring that the temperature rises from the starting value to the set highest temperature and then gradually cools down to the initial state. Through this temperature division, the stability performance of the emulsion in the temperature rising, constant temperature, and cooling stages is analyzed respectively. Based on the above stage division, the thermal response characteristic data is subdivided to obtain the thermal response data set of each stage. In the temperature rising interfacial activity change stage, the thermal response data set mainly includes the particle size change, interfacial tension change, etc. of the emulsion in the temperature rising process. These data reflect whether the emulsion has aggregation, dispersion, etc. in the temperature rising process. For the high temperature structure reorganization stage, the thermal response data set includes the particle morphology change, particle size distribution change, etc. of the emulsion at the highest temperature. These data reveal whether the emulsion has undergone molecular structure change or particle aggregation phenomenon at high temperature. In the cooling rebalancing stage, the thermal response data set focuses on the particle size change, aggregation state change, and interfacial film recovery of the emulsion in the cooling process. These data will help analyze whether the emulsion can recover to the stable state before heat treatment or new aggregation phenomenon occurs. Stability calculation is performed on each data set to obtain the stability score of the corresponding stage. In the temperature rising stage stability calculation, the particle size change rate in the temperature rising process is analyzed. The increase of particle size indicates that the emulsion has particle aggregation or increased aggregation degree in the temperature rising process. By comparing the particle size change with the initial particle size, the particle size growth rate is obtained, and the stability score of the temperature rising stage is calculated according to this. The stability score usually considers multiple factors, such as particle size growth rate, interfacial tension change, and particle distribution uniformity, etc. The final score will reflect the stability of the emulsion in the temperature rising process. In the stability calculation of the high temperature structure reorganization stage, the structural change of the emulsion at high temperature is focused on. In this stage, the emulsion undergoes molecular structure rearrangement, leading to particle aggregation or phase separation. By analyzing the data such as particle size distribution width, particle aggregation degree, and interfacial tension change, the stability of the emulsion at high temperature is evaluated. The stability score is based on particle size change, distribution width, and phase separation index, etc. to comprehensively evaluate the thermal stability of the emulsion. For the stability calculation of the cooling rebalancing stage, whether the emulsion particles are re-dispersed or restored to the original uniform distribution state in the cooling process is analyzed. In this stage, the decrease of particle size indicates that the particles are re-dispersed, while the increase of particle size means that aggregation phenomenon occurs. By tracking the change of particle size and combining the recovery of interfacial tension, the stability score of the cooling stage is calculated.After obtaining the stability scores of each stage, according to the measurement results under different HLB values and different pH values, weight optimization calculation is performed to obtain target weight coefficients. Since different HLB values and pH values have different effects on the thermal stability of the emulsion, weight optimization calculation can help the system to assign appropriate weights to the stability scores of each stage under different conditions. Through regression analysis or response surface analysis and other methods, the weight coefficients are optimized so that the score of each stage truly reflects the importance of the stage in the overall thermal stability. According to the obtained target weight coefficients, the temperature rising stage stability score, the high temperature stage stability score and the cooling stage stability score are weighted and summed to obtain the multi-stage thermal stability score of the complex emulsifier.

[0034] Step S105, generating a formulation parameter optimization scheme based on the multi-stage thermal stability score.

[0035] Specifically, the multi-stage thermal stability score is plotted against different HLB values and pH conditions to form intuitive three-dimensional surface plots and thermal stability score distribution heat maps. Through these graphical representations, the thermal stability performance under different formulation conditions can be intuitively compared and analyzed, and the thermal stability score distribution map can be obtained. Based on the thermal stability score distribution map, multivariate regression analysis is performed to analyze the relationship between the ratio of hydrophilic emulsifiers to lipophilic emulsifiers, the total emulsifier addition amount, HLB value, and pH value, and the multi-stage thermal stability score. Multivariate regression analysis reveals how different formulation parameters affect the thermal stability score of the emulsion by constructing a mathematical model. Through regression analysis, the contribution of each formulation parameter is quantified, and the quantitative relationship between them and the thermal stability score is obtained. The experimental point matrix is determined based on the formulation parameters, and the regression coefficients are calculated based on the matrix using the least squares method. The design of the experimental point matrix needs to cover all possible combinations of formulation parameters and ensure the comprehensiveness and representativeness of the experimental data. The design of the experimental point matrix uses classical response surface methods such as Box-Behnken design or central composite design, which can effectively reduce the number of experiments while ensuring the accuracy of the model. After the experimental point matrix is designed, the data is obtained through experiments, and the least squares method is used to fit the formulation parameter correlation model to obtain the regression coefficients. The least squares method minimizes the sum of the squared errors between the experimental data and the predicted values of the regression model to obtain the optimal regression coefficients. Through these regression coefficients, a response surface equation is established to describe the relationship between the formulation parameters and the thermal stability score. The response surface equation is mathematically analyzed. By calculating the partial derivatives of the response surface equation, the sensitivity of each formulation parameter to the thermal stability score is obtained, i.e., the degree of influence on the thermal stability score when each parameter changes. Through the calculation of partial derivatives, the most sensitive formulation parameter is identified, and the formulation is adjusted accordingly to effectively improve the thermal stability score. In order to find the optimal formulation parameters, the optimal point of the response surface equation, i.e., the theoretical optimal formulation parameters, is solved by solving the system of equations. By solving these equations, the combination of formulation parameters that maximizes the thermal stability score is found. This optimal point is the theoretically optimal formulation that provides the best thermal stability performance. To ensure that the optimal point obtained is indeed an extreme point, the eigenvalues of the Hessian matrix are calculated to confirm the type of extreme value. The Hessian matrix is a matrix of second-order partial derivatives of the response surface equation, and the signs of its eigenvalues can help the system determine whether the optimal point is a maximum, minimum, or saddle point. By analyzing the eigenvalues of the Hessian matrix, if all eigenvalues are positive, it indicates that the optimal point is a minimum, i.e., the formulation parameters corresponding to this point are the most stable formulation; if all eigenvalues are negative, it indicates that the optimal point is a maximum; if the eigenvalues are mixed positive and negative, it is a saddle point. Through this analysis, the nature of the optimal point is confirmed, ensuring that the selected formulation is the most stable and best meets the thermal stability requirements.

[0036] In the embodiments of the present application, the multi-parameter light scattering measurement system integrating dynamic light scattering, static light scattering and backscattering technology is used to comprehensively detect the particle size change, distribution width, phase separation state and aggregation degree of the emulsion system, overcoming the limitations of the traditional single parameter evaluation method; the thermal response characteristic data is divided into three stages of surfactant change at the rising temperature, structure recombination at high temperature and re-equilibrium at cooling, and differential weight coefficients are used for scoring calculation, so that the evaluation results can accurately reflect the stability change rule of the complex emulsifier in the whole thermal treatment process; by using the response surface method to design the optimization algorithm, a correlation model between the formula parameters (HLB value, pH value, emulsifier ratio, total addition amount) and the thermal stability score is established, and the theoretical optimal formula parameter combination is obtained through mathematical analysis methods such as partial derivative solution of the optimal point and eigenvalue confirmation of the Hessian matrix, greatly improving the scientificity and reliability of the formula optimization; in addition, the present application does not need complex rheometer or thermal analysis equipment, but only through light scattering and image analysis technology, the stability change of the complex emulsifier can be monitored in real time during the thermal treatment process, which significantly reduces the monitoring cost and improves the monitoring efficiency.

[0037] In a specific embodiment, the process of step S101 can specifically include the following steps:

[0038] At least one is selected from polysorbate-80, polyoxyethylene sorbitan oleate, and polyethylene glycol fatty acid ester as a hydrophilic emulsifier, at least one is selected from sorbitan monooleate, glycerol monostearate, and beeswax as a lipophilic emulsifier, to obtain a complex emulsifier raw material with different HLB values;

[0039] The hydrophilic emulsifier and the lipophilic emulsifier are physically mixed to obtain a complex emulsifier mixture;

[0040] At least one is selected from citrate buffer, phosphate buffer, and acetate buffer as a pH buffer, and the pH buffer is adjusted in pH value to obtain a pH buffered aqueous phase;

[0041] The complex emulsifier mixture is added to the pH buffered aqueous phase, and a high-speed shearing emulsifier is used for shearing to obtain an initial emulsion;

[0042] A laser particle size analyzer is used to measure the volume-weighted average diameter, the surface area-weighted average diameter, and the particle size distribution width index of the initial emulsion to obtain initial particle size distribution characteristics;

[0043] An interfacial tension meter is used to measure the dynamic change curve of the oil-water interfacial tension and the equilibrium interfacial tension value of the initial emulsion, and the initial particle size distribution characteristics, the dynamic change curve of the oil-water interfacial tension, and the equilibrium interfacial tension value are taken as initial characteristic parameters.

[0044] Specifically, appropriate hydrophilic and lipophilic emulsifiers are selected. The hydrophilic emulsifier is selected from at least one of polysorbate-80, polyoxyethylene sorbitan oleate, and polyethylene glycol fatty acid ester. The role of the hydrophilic emulsifier is to form micelles or bilayer membranes in the aqueous phase through the hydrophilic polyoxyethylene segment, reduce the surface tension of the oil-water interface, and promote the dispersion of oil droplets in the aqueous phase. The lipophilic emulsifier is selected from at least one of sorbitan monooleate, glycerol monostearate, and beeswax. The lipophilic emulsifier, due to its long-chain fatty acid or wax ester structure, can form stable oil droplets in the oil phase and help disperse the oil components in the aqueous phase. When selecting emulsifiers, the mass ratio of hydrophilic emulsifier to lipophilic emulsifier is set according to the formulation target, and the ratio is 1:3 to 3:1. The ratio range determines the HLB value of the emulsifier, i.e., the balance between hydrophilicity and lipophilicity. If the desired emulsion is biased towards the oil-in-water type (O / W type), more hydrophilic emulsifier is used, and the ratio tends to 1:3; if the desired emulsion is biased towards the water-in-oil type (W / O type), more lipophilic emulsifier is used, and the ratio tends to 3:1. By adjusting this ratio, the HLB value of the emulsifier is precisely controlled, thereby affecting the stability, particle size distribution, and interfacial tension of the emulsion, among other key characteristics. The emulsifier mixture is mixed with the pH buffer. The pH buffer is selected from at least one of citrate buffer, phosphate buffer, and acetate buffer, and the role of the buffer is to adjust the pH value of the emulsion and maintain pH stability. The pH value of the buffer is adjusted to the range of 4.0 to 8.0. Acid or base is used to adjust the pH value to ensure that the activity of the emulsifier is not affected during the emulsification process. When the pH buffer is prepared, the composite emulsifier mixture is added to the buffered aqueous phase. In order to ensure that the emulsifier is uniformly dispersed in the aqueous phase, a high-speed shear emulsifier is used for emulsification. The shear speed of the high-speed shear emulsifier is set to 8000 to 12000 revolutions per minute, and the shear time is 5 to 15 minutes. Under the action of high-speed shear, the hydrophilic emulsifier forms micelles or membrane structures in the aqueous phase, while the lipophilic emulsifier is dispersed in the oil phase, forming a stable emulsion. The initial emulsion is divided into special test containers with optical measurement windows, and each test container contains 50 to 100 milliliters. A laser particle size analyzer is used to measure the particle size distribution of the initial emulsion, and the main parameters measured include the volume-weighted mean diameter (D[4,3]), the surface area-weighted mean diameter (D[3,2]), and the particle size distribution width index (Span value). These parameters reflect the distribution characteristics of the particles in the emulsion, especially during the thermal stability test, the change in particle size can effectively indicate the stability of the emulsion. An interfacial tension meter is used to measure the dynamic change of the oil-water interfacial tension of the initial emulsion. By measuring the dynamic change curve of the oil-water interfacial tension over a certain period of time and the equilibrium interfacial tension value, the spreadability and stability of the emulsifier at the oil-water interface are evaluated. The measurement temperature of the interfacial tension meter is controlled at 25°C ± 0.5°C, and the measurement is performed for 120 seconds to obtain reliable data.The initial particle size distribution characteristics, oil-water interfacial tension dynamic change curve and equilibrium interfacial tension value are taken as initial characteristic parameters. By comparison with real-time data in the thermal cycle process, the stability change of the emulsion at different temperature stages is evaluated.

[0045] In a specific embodiment, the process of performing step S102 can specifically include the following steps:

[0046] A helium-neon laser, a temperature-controlled sample cell, an optical path system, a multi-angle detector array and a data acquisition and processing unit are assembled and connected to obtain a multi-parameter light scattering measurement platform.

[0047] The collimator, polarizer, diaphragm and beam splitter in the optical path system are positioned and the optical axis is adjusted to obtain the incident light beam.

[0048] The photomultiplier tubes in the multi-angle detector array are fixed at multiple scattering angle positions respectively, and the signal amplification multiple of the photomultiplier tubes is adjusted to obtain a multi-angle scattering signal acquisition system.

[0049] The output power of the helium-neon laser is adjusted and stabilized, the signal intensity at each detection angle is measured by the multi-angle scattering signal acquisition system, and the laser power calibration value in the optimal detection range is obtained.

[0050] The standard polystyrene latex microspheres are injected into the temperature-controlled sample cell of the multi-parameter light scattering measurement platform, the scattering signals at different scattering angles are collected by the multi-angle scattering signal acquisition system, and the theoretical scattering curve is compared and analyzed to obtain system response calibration data.

[0051] Based on the system response calibration data, a standard curve relationship equation between the scattering signal intensity and the particle size and concentration is established, and the laser power calibration value, the system response calibration data and the standard curve relationship equation are written into the memory of the data acquisition and processing unit to obtain light scattering measurement calibration parameters.

[0052] Specifically, the key components are precisely assembled, including a helium-neon laser, a temperature-controlled sample cell, an optical path system, a multi-angle detector array, and a data acquisition and processing unit. The combination of these components forms the core platform for light scattering measurements. The helium-neon laser is the core component that provides the light source. A helium-neon laser with a wavelength of 632.8 nm is selected, which has stable output power (between 15-30 mW) and excellent beam quality, suitable for high-precision scattering measurements. The output beam of the laser passes through the optical path system, which includes collimators, polarizers, apertures, and beam splitters. After adjustment, a consistent and stable incident beam is formed, ensuring the accuracy of experimental data. At the same time, precise positioning and adjustment of the optical path system are performed to ensure that the collimator, polarizer, aperture, and beam splitter are positioned and adjusted accurately, ensuring that the quality and direction of the beam fully meet the experimental requirements. A multi-angle detector array is configured, with photomultiplier tubes arranged at different scattering angle positions (such as 13°, 45°, 90°, 135°, and 173°). These photomultiplier tubes are used to collect light signals at different scattering angles and convert them into electrical signals for analysis. To ensure the accuracy of the data, the signal amplification factor of each photomultiplier tube is adjusted to obtain appropriate signal intensity at different scattering angles. This adjustment process is optimized according to the performance of the photomultiplier tubes and the required sensitivity of the experiment to ensure the collection of high-quality scattering signals. At the same time, to ensure the measurement accuracy and accuracy of the system, the output power of the helium-neon laser is adjusted and stabilized. In actual measurements, the multi-angle scattering signal acquisition system measures the scattering signal intensity at different detection angles, and adjusts the laser power according to these signal intensities to ensure that the signal intensity at all angles is within the optimal detection range, ensuring that the signals collected at different scattering angles have sufficient intensity and avoiding measurement errors caused by excessively weak or strong signals. To calibrate the system, standard polystyrene latex microspheres with diameters of 100±5 nm or 1000±20 nm are used. These standard microspheres provide stable and known particle size characteristics. The standard polystyrene latex microspheres are injected into the temperature-controlled sample cell of the multi-parameter light scattering measurement platform, which has good temperature control accuracy (usually ±0.1°C), ensuring that the temperature of the sample remains stable throughout the experiment. The sample in the temperature-controlled sample cell is irradiated by the laser light source and collected by the multi-angle scattering signal acquisition system. By measuring the scattering signals of the microspheres at different scattering angles and comparing these data with the theoretical Mie scattering curve, the calibration data of the system response are obtained. The Mie scattering curve is based on the Mie theory and describes the scattering of light on particles. By comparing experimental data with the theoretical curve, the relationship between the light scattering signal and the particle size is calculated. Based on the system response calibration data, a standard curve relationship between the scattering signal intensity and the particle size and concentration is established. The standard curve is a mathematical model obtained by fitting experimental data, and the least squares method is used for data fitting.The relationship between the scattering signal intensity and the particle size and concentration. Using the data of standard microspheres, the accurate relationship between the particle size and the scattering signal is obtained by fitting the data of multiple scattering angles. The laser power calibration value, system response calibration data and standard curve relationship equation are written into the memory of the data acquisition and processing unit, which provides accurate calibration reference for subsequent scattering signal processing and data analysis, and the light scattering measurement calibration parameters are obtained.

[0053] In a specific embodiment, the process of performing step S103 can specifically include the following steps:

[0054] The initial emulsion is divided into test containers, the test containers are placed in a temperature-controlled sample cell, the optical window of the test container is aligned with the multi-angle detector array, and the sample to be measured is obtained;

[0055] According to the light scattering measurement calibration parameters, the data acquisition parameters of the multi-angle scattering signal are set, and the sample to be measured is designed to obtain thermal cycling parameters including a warming stage, a constant temperature stage and a cooling stage;

[0056] The constant temperature water bath circulation system of the temperature-controlled sample cell is started, the thermal cycling parameters are input into the control unit, the temperature of the sample to be measured is regulated according to the thermal cycling parameters, and the thermal cycling process is obtained;

[0057] During the thermal cycling process, the multi-angle scattering signal and the optical microscopic image are collected according to the data acquisition parameters, and the scattering signal data with temperature markers are obtained;

[0058] The scattering signal data with temperature markers are measured repeatedly E times, the average value of E times of measurement is calculated, and the multi-angle scattering signal data are obtained.

[0059] Specifically, the initial emulsion is divided into test containers with optical windows. The selection of test containers ensures that the material of the container can support the needs of laser scattering measurement, and the optical window of the container is aligned with the multi-angle detector array to ensure that the scattering signal is effectively collected by the multi-angle detector array. The test container is placed in a temperature-controlled sample pool to ensure accurate control of the temperature of the sample during the experiment. The design of the sample pool needs to ensure the accuracy of temperature control to prevent temperature fluctuations from affecting the reliability of the data. In this configuration, the sample to be tested is stably held in the sample pool, and the optical window of the test container is directly opposite the multi-angle detector array, so that the laser can penetrate the test sample and collect signals at multiple scattering angles. According to the light scattering measurement calibration parameters, the corresponding data acquisition parameters are set. The collection frequency and other related parameters of the scattering signal need to be optimized according to the experimental requirements. The acquisition parameters include data acquisition time interval, sampling frequency and detection angle. The sampling frequency is matched with the change frequency of the scattering signal to ensure sufficient time resolution to capture the subtle changes of the scattering signal during temperature changes. For example, during the measurement process, the sampling frequency is set to once every 30 seconds or less to monitor the signal changes in real time during the thermal cycling process. At the same time, the thermal cycling parameters are set according to the experimental design of the sample to be tested. Thermal cycling includes three stages: heating stage, constant temperature stage and cooling stage. The temperature change rate of the heating stage is set to 1-5°C / min, and the temperature is accurately controlled within the set temperature range. In the constant temperature stage, the temperature of the sample is maintained within the set range, and the duration is from 10 minutes to 120 minutes, and the specific time is selected according to the requirements of the experiment and the characteristics of the sample. In the cooling stage, the temperature change rate is set to 0.5-3°C / min, and it also needs to be accurately controlled within the set range to prevent the drastic fluctuations of the temperature from adversely affecting the measurement of the stability of the emulsion. When the thermal cycling parameters are set, start the constant temperature water bath circulation system of the temperature-controlled sample pool to ensure that the sample can maintain a constant temperature during the entire experiment. The temperature control system adjusts the temperature of the sample to be tested according to the input thermal cycling parameters to ensure that the temperature change during the entire process from heating to cooling remains within the predetermined range. The control unit receives the input thermal cycling parameters through the automatic system and adjusts the water bath temperature in the temperature-controlled sample pool according to these parameters to ensure smooth heating or cooling process. In this process, the temperature-controlled sample pool not only needs to accurately control the temperature, but also needs to feed back the temperature data in real time to ensure that each stage of the experiment is within the predetermined temperature range. During the thermal cycling process, the multi-angle scattering signals are collected in real time based on the previously set data acquisition parameters. These scattering signals are the key data for analyzing the changes in the thermal stability of the emulsion. In addition to collecting multi-angle scattering signals, optical microscopic images also need to be collected simultaneously to obtain visual data of particle morphology and aggregation state.The combination of optical microscopy and scattering signals can provide a more comprehensive understanding of the physical changes of emulsions during thermal cycling. These signals and images are labeled with temperature, which can clearly identify the corresponding scattering signal data at different temperature points. To ensure the reliability of the data, repeated measurements of the temperature-labeled scattering signal data are required. The number of repeated measurements (set as E times) depends on the experimental requirements and the volatility of the data. By taking multiple measurements, the impact of accidental errors is effectively reduced, and the accuracy of the data is improved. After all data collection is complete, the multi-angle scattering signal data obtained are used for subsequent thermal stability analysis. These data are compared with the initial characteristic parameters to help evaluate the stability changes of emulsions at different temperatures.

[0060] In a specific embodiment, the process of performing step S104 can specifically include the following steps:

[0061] Based on the dynamic light scattering data in the multi-angle scattering signal data, the particle size change data during the thermal cycling process is calculated;

[0062] Based on the static light scattering data in the multi-angle scattering signal data, the particle size distribution change data during the thermal cycling process is calculated;

[0063] Based on the backscattering data in the multi-angle scattering signal data, the emulsion stability change data during the thermal cycling process is calculated;

[0064] Image analysis is performed on the optical microscopy images in the multi-angle scattering signal data to obtain the morphological feature change data during the thermal cycling process;

[0065] The particle size change data, the particle size distribution change data, the emulsion stability change data, and the morphological feature change data are normalized, compared with the initial characteristic parameters, and the relative change rates are calculated to obtain the interface film strength change rate, the particle size growth rate, the oil-water phase separation index, and the thermal-induced aggregation degree numerical value;

[0066] The interface film strength change rate, the particle size growth rate, the oil-water phase separation index, and the thermal-induced aggregation degree numerical value are taken as the thermal response characteristic data;

[0067] The thermal response characteristic data is divided into a warming interfacial activity change stage, a high-temperature structure reorganization stage, and a cooling rebalancing stage, and a multi-stage thermal stability score is calculated.

[0068] Specifically, based on the dynamic light scattering data in the multi-angle scattering signal, the particle size change is calculated. Dynamic light scattering is a method for measuring particle size based on the fluctuation of light scattering intensity caused by the Brownian motion of particles in a liquid. During the thermal cycle, the size of the particles in the emulsion changes with the change of temperature, and dynamic light scattering helps the system to monitor the fluctuation of particle size in real time. Through the cumulants analysis method, the hydrodynamic diameter of the particles is obtained. Through calculation, it is obtained how the average diameter of the emulsion particles changes with the change of temperature during the thermal cycle, so as to monitor its stability. Based on the static light scattering data, the change of particle size distribution is calculated. Static light scattering provides particle size distribution information, and the average diameter and distribution of particles are calculated using the scattering intensity at multiple scattering angles. The change of particle size distribution helps to evaluate the stability of the emulsion, especially during the heat treatment process, the change of particle size is often a precursor of phase separation or aggregation. Commonly used calculation formulas of static light scattering include volume-weighted average diameter and surface area-weighted average diameter. These calculations can help the system understand how the particle size distribution changes during the thermal cycle, especially during the heating or cooling process, the particles increase due to heat-induced aggregation. At the same time, based on the backscattering data, the stability change of the emulsion is calculated. Backscattering technology evaluates the stability of the emulsion by measuring the reflection of scattered light in the sample. By measuring the change of backscattering intensity at different time points, the stability index TSI(t) of the emulsion is obtained. The stability index TSI(t) is obtained by integrating the backscattering signal, which can reflect the stability change of the emulsion during the heat treatment process. When the scattering intensity changes greatly, it indicates that the emulsion has undergone a more obvious structural change or phase separation. In order to further analyze the thermal response of the emulsion, optical microscopic images are analyzed. These images help the system to visually observe the changes of particle morphology and aggregation state. Morphological features include particle roundness, aspect ratio, aggregation degree, etc., which can provide additional quantitative analysis of thermal stability. In order to comprehensively evaluate the thermal stability, the thermal response feature data is divided into the heating interfacial activity change stage, the high temperature structure reorganization stage and the cooling rebalance stage. The thermal stability score is calculated according to the characteristic change of each stage. In the heating stage, the changes of particle size, PDI, stability index and phase separation index are evaluated; in the high temperature stage, the changes of aspect ratio, roundness, aggregation degree and stability index are evaluated; in the cooling stage, the changes of particle size distribution and phase separation index are evaluated. According to these calculations, the thermal stability score of each stage is obtained, and the comprehensive thermal stability score is obtained by weighted calculation. The comprehensive thermal stability score (CTSI) is calculated by the following formula:

[0069] CTSI = β1·S1 + β2·S2 + β3·S3;

[0070] S1, S2, S3 are the stability scores of the warming-up stage, the high-temperature stage and the cooling stage respectively, and β1, β2, β3 are the importance coefficients of the respective stages.

[0071] In a specific embodiment, the process of performing step of dividing the thermal response characteristic data into a warming-up interfacial activity change stage, a high-temperature structure reorganization stage and a cooling rebalancing stage, and calculating a multi-stage thermal stability score can specifically include the following steps:

[0072] defining the first temperature value to the second temperature value as the warming-up interfacial activity change stage, the second temperature value to the highest temperature as the high-temperature structure reorganization stage, and the highest temperature to the first temperature value as the cooling rebalancing stage, wherein the first temperature value is less than the second temperature value;

[0073] based on the warming-up interfacial activity change stage, the high-temperature structure reorganization stage and the cooling rebalancing stage, dividing the thermal response characteristic data to obtain a first thermal response data set of the warming-up interfacial activity change stage, a second thermal response data set of the high-temperature structure reorganization stage, and a third thermal response data set of the cooling rebalancing stage;

[0074] performing warming-up stage stability calculation on the first thermal response data set to obtain a warming-up stage stability score;

[0075] performing high-temperature stage stability calculation on the second thermal response data set to obtain a high-temperature stage stability score;

[0076] performing cooling stage stability calculation on the third thermal response data set to obtain a cooling stage stability score;

[0077] performing weight optimization calculation according to the measurement results under different HLB values and different pH values to obtain target weight coefficients;

[0078] performing weighted summation on the warming-up stage stability score, the high-temperature stage stability score and the cooling stage stability score according to the target weight coefficients to obtain a multi-stage thermal stability score.

[0079] Specifically, a temperature rising interfacial surfactant change stage, a high temperature structure reorganization stage, and a cooling rebalancing stage are defined. The temperature range of the temperature rising interfacial surfactant change stage is from a first temperature value (the initial temperature of the sample) to a second temperature value (the set temperature rising end point); the high temperature structure reorganization stage is from the second temperature value to the highest temperature of the sample, and the cooling rebalancing stage is from the highest temperature to the first temperature value (the temperature at the end of the temperature reduction). The first temperature value must be less than the second temperature value, that is, the starting temperature of the temperature rising stage is lower than the ending temperature of the temperature rising stage. Based on the above-mentioned three stages, i.e., the temperature rising interfacial surfactant change stage, the high temperature structure reorganization stage, and the cooling rebalancing stage, the division of the heat response characteristic data is carried out. The data set of each stage is processed independently, and the stability score is calculated respectively. In the temperature rising interfacial surfactant change stage, through the dynamic light scattering and static light scattering data, the key data such as the particle size change, the particle size distribution change, etc. are obtained. These data reflect the particle behavior of the emulsion in the temperature rising process, especially how the particle size changes with the increase of the temperature. In the high temperature structure reorganization stage, since the emulsion will experience a relatively intense molecular rearrangement when reaching the maximum temperature, leading to the change of the structure of the emulsifier, in this stage, the particle aggregation degree, the interfacial tension change, and the width of the particle size distribution are the focus of the analysis. The static light scattering and backscattering data are helpful to reveal the trend of the particle aggregation or stability in this stage. The analysis of the optical microscopic images can also provide the change data of the morphological characteristics, such as the particle aggregation condition, the phase separation index, etc., which are helpful to evaluate the structural change of the emulsion at high temperature. In the cooling rebalancing stage, with the decrease of the temperature, the particles in the emulsion will undergo certain recombination or redispersion, and the change trend of the particle size and the particle size distribution is still the focus of the analysis. Through the data of the dynamic light scattering and the static light scattering, the process of the particle recovery, dispersion or reaggregation is observed, so as to evaluate the final stability of the emulsion. The stability calculation is carried out on the data set of each stage. For the first heat response data set of the temperature rising interfacial surfactant change stage, the stability score of the temperature rising stage is calculated. The stability of the temperature rising stage is mainly related to the growth of the particle size, the change of the particle size distribution, and the change of the interfacial tension. Through the analysis of the measurement results under different HLB values and different pH values, the target weight coefficients of different stages are obtained. These weight coefficients are the regression analysis results based on the experimental data, which are optimized by the least square method, so as to ensure that the stability score of each stage can reasonably reflect its contribution to the overall thermal stability. Using the optimized target weight coefficients, the temperature rising stage stability score, the high temperature stage stability score, and the cooling stage stability score are weighted and summed to obtain the multi-stage thermal stability score of the emulsion. Through the weighted calculation, the comprehensive stability of the composite emulsifier in the whole heat treatment process is obtained.

[0080] In a specific embodiment, the process of performing step S105 can specifically include the following steps:

[0081] The multi-stage thermal stability score is plotted into a three-dimensional surface graph and a thermal stability grade distribution heat map according to different HLB values and pH conditions, and the thermal stability performance under different formula conditions is directly compared and analyzed to obtain a thermal stability score distribution graph;

[0082] Based on the thermal stability score distribution graph, multiple regression analysis is performed on the relationship between the formula parameters, such as the proportion of hydrophilic emulsifiers and oleophilic emulsifiers, the total emulsifier addition amount, the HLB value, and the pH value, and the multi-stage thermal stability score, to obtain a formula parameter correlation model;

[0083] According to the formula parameters, an experimental point matrix is determined, and based on the experimental point matrix, the regression coefficients in the formula parameter correlation model are calculated by the least square method to obtain a response surface equation;

[0084] The response surface equation is mathematically analyzed, the partial derivatives of each parameter on the thermal stability score are calculated, and the theoretical optimal point is obtained by solving the equation set, and the extreme value type is confirmed by calculating the eigenvalues of the Hessian matrix to generate an optimized formula parameter scheme.

[0085] Specifically, the multi-stage thermal stability scores are plotted as three-dimensional surface plots and heat stability grade distribution heatmaps for different HLB values and pH conditions, reflecting the thermal stability performance of different formulations at different temperature stages. Through these graphical representations, the stability changes of different formulations during the warming stage, high-temperature stage, and cooling stage are observed intuitively, and then it is identified which formulation combinations have better thermal stability. For example, in the three-dimensional surface plot, a three-dimensional image describing thermal stability is plotted by taking HLB value and pH value as independent variables and thermal stability score as dependent variable. Based on the thermal stability score distribution map, multiple regression analysis is performed on the formulation parameters of the composite emulsifier to find the relationship between the proportion of hydrophilic emulsifier and oleophilic emulsifier, the total emulsifier addition amount, HLB value, and pH value, and the multi-stage thermal stability score. Through multiple regression analysis, a mathematical model is established to reflect the thermal stability changes under different formulation conditions. The regression analysis uses the least squares method to obtain the best regression coefficients by minimizing the sum of squares of errors, thereby establishing a mathematical relationship between the formulation parameters and thermal stability. Through regression analysis, the contribution of each formulation parameter to the thermal stability score is obtained. According to the relationship between the formulation parameters and the thermal stability score, an experimental point matrix is determined. The design of the experimental point matrix needs to cover different value ranges of the formulation parameters to ensure that the influence of each formulation parameter on the thermal stability score is fully captured. The experimental point matrix is determined through Box-Behnken design or central composite design, which can effectively explore the interaction of formulation parameters and improve experimental efficiency. After the experimental point matrix is determined, experiments are conducted according to the regression model, and the regression coefficients in the formulation parameter correlation model are calculated through the least squares method. According to the experimental point matrix, an experimental design scheme is generated, then experiments are conducted and data are collected. The response surface equation is obtained by optimizing the regression coefficients through the least squares method. Through this equation, the relationship between the formulation parameters and the thermal stability score is described, and the optimal formulation parameters for thermal stability are found in the experimental space. Mathematical analysis is performed on the response surface equation to calculate the partial derivatives of each formulation parameter with respect to the thermal stability score, and then the influence of each parameter within different ranges on the thermal stability score is revealed. The partial derivatives help the system understand the sensitivity of the final thermal stability score to changes in a specific formulation parameter (such as HLB value or emulsifier ratio) under different formulation conditions. The theoretical optimum point is obtained by solving the system of equations. By solving the system of equations, a set of optimal formulation parameter values is obtained, which can make the thermal stability score reach the maximum value. The theoretical optimum point is calculated based on experimental data and regression model, representing the optimal combination of emulsifier formulations, which can provide the best thermal stability performance. To confirm whether this optimum point is an extreme value, the eigenvalues of the Hessian matrix are calculated to determine the type of extreme value. The Hessian matrix is a matrix composed of the second-order partial derivatives of the response surface equation, and the signs of the eigenvalues help the system determine whether the point is a maximum, minimum, or saddle point.If all the eigenvalues of the Hessian matrix are positive, it means that the optimal point is a minimum value; if all the eigenvalues are negative, it means that the optimal point is a maximum value. Through this analysis, it is ensured that the obtained optimal formulation parameters can indeed provide the best thermal stability performance. Based on the results of response surface analysis, an optimization scheme of formulation parameters is generated. By optimizing the formulation of emulsifiers, the stability of emulsions under high temperature conditions is improved, and the wide applicability of emulsions in different application scenarios is ensured.

[0086] The above describes the thermal stability monitoring method of the composite emulsifier in the embodiments of the present application, and the thermal stability monitoring system of the composite emulsifier in the embodiments of the present application is described below. Please refer to Figure 2 The thermal stability monitoring system of the composite emulsifier in the embodiments of the present application includes one embodiment:

[0087] The measurement module 201 is configured to perform oil-water interfacial tension dynamic measurement and particle size distribution analysis on the initial emulsion of the composite emulsifier to obtain initial characteristic parameters.

[0088] The parameter calibration module 202 is configured to construct a multi-parameter light scattering measurement platform and perform measurement parameter calibration to obtain light scattering measurement calibration parameters.

[0089] The thermal cycle module 203 is configured to load the initial emulsion into a temperature-controlled sample cell of the multi-parameter light scattering measurement system and perform thermal cycle processing according to the light scattering measurement calibration parameters to obtain multi-angle scattering signal data.

[0090] The calculation module 204 is configured to calculate thermal response characteristic data based on the multi-angle scattering signal data and the initial characteristic parameters, and calculate a multi-stage thermal stability score based on the thermal response characteristic data.

[0091] The generation module 205 is configured to generate an optimization scheme of formulation parameters based on the multi-stage thermal stability score.

[0092] Through the cooperation of the above-mentioned components, through the multi-parameter light scattering measurement system integrating dynamic light scattering, static light scattering and backscattering technology, the overall detection of the emulsion system particle size change, distribution width, phase separation state and aggregation degree is realized, and the limitations of the traditional single parameter evaluation method are overcome; the thermal response characteristic data is divided into three stages of surfactant change at the rising interface, high temperature structure recombination and cooling rebalancing, and differential weight coefficients are used for scoring calculation, so that the evaluation result can accurately reflect the stability change rule of the composite emulsifier in the whole heat treatment process; through the application of response surface method design optimization algorithm, the correlation model between the formula parameters (HLB value, pH value, emulsifier ratio, total addition amount) and the heat stability score is established, and through mathematical analysis methods such as partial derivative solution of the optimal point, Hessian matrix eigenvalue confirmation, the theoretical optimal formula parameter combination is obtained, which greatly improves the scientificity and reliability of the formula optimization; in addition, the present application does not need complex rheometer or thermal analysis equipment, but through light scattering and image analysis technology, the stability change of the composite emulsifier can be monitored in real time during the heat treatment process, which significantly reduces the monitoring cost and improves the monitoring efficiency.

[0093] The application further provides a computer readable storage medium, which can be a nonvolatile computer readable storage medium or a volatile computer readable storage medium, and instructions are stored in the computer readable storage medium, and when the instructions are run on a computer, the computer is caused to execute the steps of the heat stability monitoring method of the composite emulsifier.

[0094] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, system and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein.

[0095] The integrated unit, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk and various program code storage media.

[0096] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring the thermal stability of a composite emulsifier, characterized in that, The method includes: The initial characteristic parameters of the oil-water interfacial tension were dynamically measured and the particle size distribution was analyzed in the initial emulsion of the composite emulsifier. A multi-parameter light scattering measurement platform was constructed and the measurement parameters were calibrated to obtain the light scattering measurement calibration parameters; The initial emulsion is loaded into the temperature-controlled sample cell of the multi-parameter light scattering measurement platform, and thermal cycling is performed according to the light scattering measurement calibration parameters to obtain multi-angle scattering signal data. Thermal response characteristic data is calculated based on the multi-angle scattering signal data and the initial characteristic parameters, and a multi-stage thermal stability score is calculated based on the thermal response characteristic data, including: calculating particle size change data during thermal cycling based on dynamic light scattering data in the multi-angle scattering signal data; calculating particle size distribution change data during thermal cycling based on static light scattering data in the multi-angle scattering signal data; calculating emulsion stability change data during thermal cycling based on backscattering data in the multi-angle scattering signal data; performing image analysis on optical microscopic images in the multi-angle scattering signal data to obtain morphological characteristic change data during thermal cycling; normalizing the particle size change data, particle size distribution change data, emulsion stability change data, and morphological characteristic change data, comparing them with the initial characteristic parameters, calculating the relative change rate, and obtaining the values ​​of interface film strength change rate, particle size growth rate, oil-water phase separation index, and thermally induced aggregation degree; and using the values ​​of interface film strength change rate, particle size growth rate, oil-water phase separation index, and thermally induced aggregation degree as thermal response characteristic data. The thermal response characteristic data is divided into a heating interface activity change stage, a high-temperature structural reorganization stage, and a cooling rebalancing stage, and a multi-stage thermal stability score is calculated. This includes defining the period from a first temperature value to a second temperature value as the heating interface activity change stage, the period from the second temperature value to the highest temperature as the high-temperature structural reorganization stage, and the period from the highest temperature to the first temperature value as the cooling rebalancing stage, wherein the first temperature value is less than the second temperature value. Based on the heating interface activity change stage, the high-temperature structural reorganization stage, and the cooling rebalancing stage, the thermal response characteristic data is further divided to obtain a first thermal response dataset for the heating interface activity change stage and a second thermal response dataset for the high-temperature structural reorganization stage. The dataset includes a first thermal response dataset and a third thermal response dataset for the cooling and rebalancing phase. The first thermal response dataset is used to perform a heating phase stability calculation to obtain a heating phase stability score. The second thermal response dataset is used to perform a high-temperature phase stability calculation to obtain a high-temperature phase stability score. The third thermal response dataset is used to perform a cooling phase stability calculation to obtain a cooling phase stability score. Based on measurement results under different HLB and pH conditions, a weight optimization calculation is performed to obtain a target weight coefficient. Based on the target weight coefficient, the heating phase stability score, the high-temperature phase stability score, and the cooling phase stability score are weighted and summed to obtain a multi-stage thermal stability score. An optimized formulation parameter scheme is generated based on the multi-stage thermal stability score.

2. The method for monitoring the thermal stability of the composite emulsifier according to claim 1, characterized in that, The initial characteristic parameters of the composite emulsion are obtained by dynamically measuring the oil-water interfacial tension and analyzing the particle size distribution. These parameters include: At least one of polysorbate-80, polyoxyethylene dehydrated sorbitan oleate, and polyethylene glycol fatty acid ester is selected as a hydrophilic emulsifier, and at least one of sorbitan monooleate, glyceryl monostearate, and beeswax is selected as a lipophilic emulsifier to obtain composite emulsifier raw materials with different HLB values. The hydrophilic emulsifier and the lipophilic emulsifier are physically mixed to obtain a composite emulsifier mixture; At least one of citrate buffer, phosphate buffer, and acetate buffer is selected as a pH buffer, and the pH value of the pH buffer is adjusted to obtain a pH buffered aqueous phase. The composite emulsifier mixture was added to the pH buffer aqueous phase and sheared using a high-speed shear emulsifier to obtain the initial emulsion. The initial particle size distribution characteristics were obtained by measuring the volume-weighted average diameter, surface area-weighted average diameter, and particle size distribution width of the initial emulsion using a laser particle size analyzer. The initial emulsion was measured using an interfacial tensiometer to determine the dynamic change curve of the oil-water interfacial tension and the equilibrium interfacial tension value. The initial particle size distribution characteristics, the dynamic change curve of the oil-water interfacial tension, and the equilibrium interfacial tension value were used as initial characteristic parameters.

3. The method for monitoring the thermal stability of the composite emulsifier according to claim 1, characterized in that, The construction of a multi-parameter light scattering measurement platform and the execution of measurement parameter calibration to obtain light scattering measurement calibration parameters include: A multi-parameter light scattering measurement platform is obtained by assembling and connecting a helium-neon laser, a temperature-controlled sample cell, an optical path system, a multi-angle detector array, and a data acquisition and processing unit. Positioning and optical axis adjustment are performed on the collimator, polarizer, aperture, and beam splitter in the optical path system to obtain the incident beam. The photomultiplier tubes in the multi-angle detector array are fixed at multiple scattering angle positions, and the signal amplification factor of the photomultiplier tubes is adjusted to obtain a multi-angle scattering signal acquisition system. The output power of the helium-neon laser is adjusted and stabilized, and the signal intensity at each detection angle is measured by the multi-angle scattering signal acquisition system to obtain the laser power calibration value within the optimal detection range. Standard polystyrene latex microspheres were injected into the temperature-controlled sample cell of the multi-parameter light scattering measurement platform. The multi-angle scattering signal acquisition system was used to collect scattering signals at different scattering angles. The signals were compared and analyzed with the theoretical scattering curves to obtain system response calibration data. Based on the system response calibration data, a standard curve relationship equation between the scattering signal intensity and particle size and concentration is established, and the laser power calibration value, the system response calibration data, and the standard curve relationship equation are written into the memory of the data acquisition and processing unit to obtain the light scattering measurement calibration parameters.

4. The method for monitoring the thermal stability of the composite emulsifier according to claim 3, characterized in that, The process of loading the initial emulsion into the temperature-controlled sample cell of the multi-parameter light scattering measurement platform and performing thermal cycling according to the light scattering measurement calibration parameters to obtain multi-angle scattering signal data includes: The initial emulsion is dispensed into a test container, and the test container is placed in the temperature-controlled sample cell, with the optical window of the test container facing the multi-angle detector array, to obtain the sample to be tested. The data acquisition parameters for the multi-angle scattering signal are set according to the light scattering measurement calibration parameters. At the same time, thermal cycling parameters including heating stage, isothermal stage and cooling stage are designed for the sample to be tested. The constant temperature water bath circulation system of the temperature-controlled sample cell is started, the thermal circulation parameters are input into the control unit, and the temperature of the sample to be tested is adjusted according to the thermal circulation parameters to obtain the thermal circulation process; During the thermal cycle, multi-angle scattering signals and optical microscopic images are acquired according to the data acquisition parameters to obtain scattering signal data with temperature markings; The temperature-marked scattering signal data is repeatedly measured E times, and the average value of the E measurements is calculated to obtain multi-angle scattering signal data.

5. The method for monitoring the thermal stability of the composite emulsifier according to claim 1, characterized in that, The formulation parameter optimization scheme generated based on the multi-stage thermal stability score includes: The multi-stage thermal stability scores were plotted into three-dimensional surface plots and thermal stability level distribution heatmaps according to different HLB and pH conditions. The thermal stability performance under different formulation conditions was compared and analyzed intuitively to obtain thermal stability score distribution maps. Based on the aforementioned thermal stability score distribution map, a multivariate regression analysis was performed on the relationship between the formulation parameters—the ratio of hydrophilic to lipophilic emulsifiers, the total amount of emulsifier added, HLB value, and pH value—and the multi-stage thermal stability score to obtain a formulation parameter correlation model. The experimental point matrix is ​​determined based on the formula parameters, and the regression coefficients in the formula parameter correlation model are calculated using the least squares method based on the experimental point matrix to obtain the response surface equation. Mathematical analysis is performed on the response surface equations to calculate the partial derivatives of each parameter with respect to the thermal stability score. The theoretical optimal point is obtained by solving the system of simultaneous equations. At the same time, the type of extreme value is confirmed by calculating the eigenvalues ​​of the Hessian matrix, and an optimized formula parameter scheme is generated.

6. A thermal stability monitoring system for composite emulsifiers, characterized in that, For implementing the method for monitoring the thermal stability of the composite emulsifier as described in any one of claims 1-5, the thermal stability monitoring system for the composite emulsifier comprises: The measurement module is used to dynamically measure the oil-water interfacial tension and analyze the particle size distribution of the initial emulsion of the composite emulsion to obtain the initial characteristic parameters. The parameter calibration module is used to build a multi-parameter light scattering measurement platform and perform measurement parameter calibration to obtain light scattering measurement calibration parameters. The thermal cycling module is used to load the initial emulsion into the temperature-controlled sample cell of the multi-parameter light scattering measurement platform, and perform thermal cycling processing according to the light scattering measurement calibration parameters to obtain multi-angle scattering signal data. The calculation module is used to calculate thermal response characteristic data based on the multi-angle scattering signal data and the initial characteristic parameters, and to calculate a multi-stage thermal stability score based on the thermal response characteristic data; The generation module is used to generate an optimized formula parameter scheme based on the multi-stage thermal stability score.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it causes the processor to perform the method for monitoring the thermal stability of the composite emulsifier as described in any one of claims 1 to 5.

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