Method and system for monitoring thermal stability of compound emulsifier and storage medium

Abstract

The invention 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 an initial emulsion of the compound emulsifier to obtain initial characteristic parameters; constructing a multi-parameter light scattering measurement platform and executing measurement parameter calibration to obtain light scattering measurement calibration parameters; loading the initial emulsion into a multi-parameter light scattering measurement system, and executing thermal cycle treatment 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 scattered signal data and the initial characteristic parameters, and calculating a multi-stage thermal stability score based on the thermal response characteristic data; and generating a formula parameter optimization scheme based on the multi-stage thermal stability score. The stability change of the compound emulsifier can be monitored in real time in the heat treatment process, and the monitoring efficiency and the optimization accuracy of formula parameters are improved.

Description

Technical Field

[0001] This application relates to the technical field of compound emulsifier monitoring, and particularly to a method, system, and storage medium for monitoring the thermal stability of compound emulsifiers. Background Art

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

[0003] Traditional methods for evaluating emulsification stability mainly rely on single-parameter measurements (such as particle size changes or rheological properties) and lack the ability to monitor the entire heat treatment process in real time. In particular, they are unable to distinguish the differential behavior of emulsifiers during the heating, high-temperature holding, and cooling processes. Current evaluation methods generally ignore the differences in the physicochemical properties of compound emulsifiers at different temperature stages. For example, during the heating stage, it is mainly affected by changes in interfacial activity, during the high-temperature stage, it is mainly affected by structural reorganization, and during the cooling stage, it is mainly affected by molecular re-equilibration. This neglect of stage characteristics results in evaluation results that are difficult to accurately reflect the true thermal stability performance of compound emulsifiers. Traditional methods are difficult to capture the interaction effects between parameters, leading to low optimization efficiency and insufficient result reliability. Summary of the Invention

[0004] This application provides a method, system, and storage medium for monitoring the thermal stability of compound emulsifiers. This application can monitor the stability changes of compound emulsifiers in real time during the heat treatment process, improving the monitoring efficiency and the optimization accuracy of formulation parameters.

[0005] In a first aspect, this application provides a method for monitoring the thermal stability of compound emulsifiers. The method for monitoring the thermal stability of compound emulsifiers includes:

[0006] Performing dynamic measurement of the oil-water interfacial tension and analysis of the particle size distribution of the initial emulsion of the compound emulsifier to obtain initial characteristic parameters;

[0007] Constructing a multi-parameter light scattering measurement platform and performing measurement parameter calibration to obtain light scattering measurement calibration parameters;

[0008] Loading the initial emulsion into the temperature-controlled sample cell of the multi-parameter light scattering measurement system and performing thermal cycling treatment according to the light scattering measurement calibration parameters to obtain multi-angle scattering signal data;

[0009] Calculating thermal response characteristic data based on the multi-angle scattering signal data and the initial characteristic parameters, and calculating a multi-stage thermal stability score based on the thermal response characteristic data;

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

[0011] In a second aspect, the present application provides a thermal stability monitoring system for a composite emulsifier. The thermal stability monitoring system for the composite emulsifier includes:

[0012] A measurement module for dynamically measuring the oil-water interfacial tension and analyzing the particle size distribution of the initial emulsion of the composite emulsifier to obtain initial characteristic parameters;

[0013] A parameter calibration module for constructing a multi-parameter light scattering measurement platform and performing measurement parameter calibration to obtain light scattering measurement calibration parameters;

[0014] A thermal cycling module for loading the initial emulsion into a temperature-controlled sample cell of the multi-parameter light scattering measurement system and performing thermal cycling processing according to the light scattering measurement calibration parameters to obtain multi-angle scattering signal data;

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

[0016] A generation module for generating a formulation parameter optimization plan based on the multi-stage thermal stability score.

[0017] In a third aspect, a computer-readable storage medium is provided. Instructions are stored in the computer-readable storage medium, and when it runs on a computer, it causes the computer to execute the above-mentioned thermal stability monitoring method for a composite emulsifier.

[0018] In the technical solution provided by this application, through a multi-parameter light scattering measurement system that integrates dynamic light scattering, static light scattering, and backscattering technologies, a comprehensive detection of the particle size change, distribution width, phase separation state, and aggregation degree of the emulsion system is achieved, overcoming the limitations of traditional single-parameter evaluation methods; the thermal response characteristic data is divided into three stages: the change of interfacial activity during heating, high-temperature structure reorganization, and cooling rebalancing, and differential weight coefficients are used for scoring calculation, so that the evaluation results can accurately reflect the stability change law of the composite emulsifier throughout the heat treatment process; by applying the response surface method to design and optimize the algorithm, a correlation model between the formulation parameters (HLB value, pH value, emulsifier ratio, total addition amount) and the thermal stability score is established, and through mathematical analysis methods such as solving the optimal point by partial derivatives and confirming the eigenvalues of the Hessian matrix, the theoretical optimal formulation parameter combination is obtained, greatly improving the scientificity and reliability of formulation optimization; in addition, this application does not require complex rheometers or thermal analysis equipment, and only through light scattering and image analysis technologies, the stability change of the composite emulsifier can be monitored in real time during the heat treatment process, significantly reducing the monitoring cost and improving the monitoring efficiency. Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0020] Figure 1 It is a schematic diagram of an embodiment of the thermal stability monitoring method of the composite emulsifier in the embodiment of this application;

[0021] Figure 2 It is a schematic diagram of an embodiment of the thermal stability monitoring system of the composite emulsifier in the embodiment of this application. Detailed Embodiments

[0022] The embodiments of the present application provide a method, a system and a storage medium for monitoring the thermal stability of a composite emulsifier. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above-mentioned drawings of the present application are used to distinguish similar objects and do not necessarily 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 here can be implemented in an order different from that illustrated or described here. In addition, the term "comprising" or "having" and any variation 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 necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0023] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 , an embodiment of the method for monitoring the thermal stability of the composite emulsifier in the embodiments of the present application includes:

[0024] Step S101, dynamically measure the oil-water interfacial tension and analyze the particle size distribution of the initial emulsion of the composite emulsifier to obtain initial characteristic parameters;

[0025] It can be understood that the execution subject of the present application can be a system for monitoring the thermal stability of the composite emulsifier, or a terminal or a server, and is not specifically limited here. The embodiments of the present application will be described by taking the server as the execution subject as an example.

[0026] Specifically, at least one is selected from polysorbate-80, polyoxyethylene sorbitan oleate, and polyethylene glycol fatty acid ester as the hydrophilic emulsifier, and at least one is selected from sorbitan monooleate, glycerol monostearate, and beeswax as the 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 regulated to ensure that the emulsion maintains good stability under different usage conditions. The selected hydrophilic emulsifier contains a hydrophilic polyoxyethylene chain, while the lipophilic emulsifier is composed of a lipophilic long-chain fatty acid or wax ester. The combination of these components forms the basis of the composite emulsifier. The hydrophilic emulsifier and the lipophilic emulsifier are physically mixed to obtain a mixture of the composite emulsifier, and the two emulsifiers are uniformly dispersed together through physical action. The uniformity of the mixing affects the performance of the composite emulsifier, especially the stability and the uniformity of the particle size distribution during the emulsification process. At least one is selected from citrate buffer, phosphate buffer, and acetate buffer as the pH buffer, and by adjusting the pH value of the pH buffer to reach a predetermined pH value, pH-buffered aqueous phase is obtained. The selection of the pH buffer and the adjustment of the pH value are to ensure the chemical stability of the emulsion throughout the experiment, avoiding the impact on the ionization state of the emulsifier or the structure of the interfacial film due to pH fluctuations, which in turn affects the stability of the emulsion. The composite emulsifier mixture is added to the prepared pH-buffered aqueous phase, and shear is performed using a high-speed shear emulsifier. The emulsifier is dispersed in the aqueous phase through high shear force to form a stable emulsion. The high-speed shear emulsifier can quickly and uniformly distribute the emulsifier molecules in the liquid, break 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. Therefore, the particle size distribution characteristics of the initial emulsion are measured. The particle size distribution of the initial emulsion is measured using a laser particle size analyzer to obtain the volume-weighted average diameter, surface area-weighted average diameter, and particle size distribution width index (Span value). The volume-weighted average diameter (D[4,3]) is the average diameter calculated based on the volume weight of the particles and is used to reflect the contribution of large particles in the sample. The surface area-weighted average diameter (D[3,2]) is the average diameter calculated through 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. 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, a dynamic measurement of the oil-water interfacial tension and a determination of the equilibrium interfacial tension value of the initial emulsion are performed using an interfacial tensiometer.The dynamic change curve of the oil-water interfacial tension reflects the spreading ability of the emulsifier at the oil-water interface. As time goes by, 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 property at the oil-water interface, thus being able to stabilize the emulsion and prevent the aggregation of oil droplets. By measuring the dynamic changes of these interfacial tensions and the equilibrium interfacial tension value, the surface activity of the emulsifier and its effect during the emulsification process are reflected. The initial particle size distribution characteristics, the dynamic change curve of the oil-water interfacial tension and the equilibrium interfacial tension value are used as the initial characteristic parameters.

[0027] Step S102: Construct a multi-parameter light scattering measurement platform and perform measurement parameter calibration to obtain the 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 is used as the light source, and a laser with a wavelength of 632.8 nm is selected. Its stability and excellent beam quality make it suitable for high-precision light scattering experiments. The temperature-controlled sample cell is used to precisely control the temperature of the sample throughout the 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 a collimator, a polarizer, a diaphragm, and a beam splitter, 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, and these signals will be analyzed in the subsequent data processing unit. The data acquisition and processing unit is responsible for collecting signals from multiple detectors in real time, storing and processing the data, and generating the results required for the experiment. Calibrate the measurement parameters. Accurately position and adjust the optical axis of each component in the optical path system. The adjustment of the collimator, polarizer, diaphragm, and beam splitter are key steps because the adjustment of these components affects the quality and direction of the incident light beam. Especially the collimator, its function is to adjust the laser beam into parallel light, while the polarizer controls the polarization state of the laser beam. The diaphragm is used to limit the size of the light beam, and the beam splitter divides the laser beam into multiple parts, enabling the light beam to irradiate the detectors at multiple scattering angles simultaneously. Debug the multi-angle detector array, fix the photomultiplier tubes in the array at multiple scattering angle positions respectively, and adjust the signal amplification factor of each photomultiplier tube to ensure that each detector can receive a strong enough signal at different scattering angles, and the intensity of the signal will not deviate due to inappropriate amplification factor. Adjusting the amplification factor of the photomultiplier tube can ensure the sensitivity of signal acquisition and optimize the signal-to-noise ratio, improving the reliability and accuracy of the signal. The signal intensity at each scattering angle will be different, and precise adjustment of the amplification factor can ensure the measurement accuracy of the entire system. Adjust and stabilize the output power of the helium-neon laser. 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 laser power calibration value within the optimal detection range refers to the optimal value of the laser power on the premise of ensuring that the scattering signal is strong enough and the signal-to-noise ratio is good. Through this process, ensure that in actual measurement, the signal at each angle is within the optimal measurement range, thereby improving the accuracy of the data. To perform system response calibration, standard polystyrene latex microspheres are used. These microspheres have known particle sizes, and their scattering characteristics conform to Mie scattering theory, serving as the calibration standard for the light scattering system.By injecting standard polystyrene latex microspheres into a temperature-controlled sample cell respectively and using a multi-angle scattering signal acquisition system to collect data, scattering signals at different scattering angles are obtained. By comparing and analyzing with the theoretical scattering curve, calibration data of the system response is obtained. The theoretical scattering curve is based on 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, a standard curve relationship between the scattering signal intensity, particle size, and concentration is established. The standard curve is established by comparing the scattering signal intensity measured experimentally with standard microspheres of known particle size and known concentration, thereby establishing a mathematical relationship between the scattering signal, particle size, and concentration. The standard curve relationship equation is used to convert the scattering signal into values of particle size and concentration, providing quantitative data support for subsequent analysis of actual samples. 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 to obtain the calibration parameters for light scattering measurement.

[0029] Step S103: Load the initial emulsion into the temperature-controlled sample cell of the multi-parameter light scattering measurement system, and perform thermal cycling treatment according to the light scattering measurement calibration parameters to obtain multi-angle scattering signal data;

[0030] Specifically, the initial emulsion is sub-packed into a suitable test container, and the container is placed in a temperature-controlled sample cell, ensuring that the optical window of the test container faces the multi-angle detector array directly. The alignment of the optical window ensures that the laser beam can transmit through the sample and be effectively collected by the detector array after passing through different scattering angles, obtaining accurate scattering signals. The setup process of the sample to be tested requires minimizing errors and beam deviation to the greatest extent while ensuring the container is fixed and the optical window is accurately aligned, ensuring the accuracy of subsequent measurement data. Set the data acquisition parameters for the multi-angle scattering signals according to the pre-measured light scattering calibration parameters. The parameter selection includes signal acquisition frequency, sampling time interval, and acquisition sensitivity at each scattering angle, etc. Selecting appropriate acquisition frequency and time interval can ensure that the signal changes 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 level of the data and the sensitivity of the experiment. According to the thermal stability characteristics of the sample to be tested, design the specific thermal cycling parameters for the heating stage, constant temperature stage, and cooling stage during the thermal cycling process. These parameters include heating rate, constant temperature holding time, cooling rate, etc. A reasonable thermal cycling design can simulate the temperature changes of the emulsion in actual applications, thus 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 precisely regulates the temperature in the sample cell according to the input parameters, ensuring that the sample to be tested is maintained within a predetermined temperature range throughout the thermal cycling process. This process is achieved through an automated temperature control system, heating the sample gradually according to the set heating rate, maintaining a stable temperature during the constant temperature stage, and then cooling at the set rate. During this process, the temperature control system needs to have high precision and stability to ensure that the sample can accurately respond to external temperature changes at each stage, providing accurate temperature data for the acquisition of scattering signals. During the thermal cycling process, monitor the multi-angle scattering signals in real-time. Through the setting of data acquisition parameters, according to each set acquisition point, regularly collect the signal data at different scattering angles and simultaneously use an optical microscope to take microscopic images of the emulsion. The multi-angle scattering signals can reveal the changes in particle distribution and state of the emulsion under different temperature conditions, while the optical microscopic images help observe the morphological changes of the emulsion particles, such as particle aggregation, dispersion, or phase separation, etc. To ensure the accuracy and repeatability of the data, repeat the measurement of the scattering signal data with temperature markings. 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. Assume that E repeated measurements are carried out, calculate the average value of the E measurements, effectively reducing the errors caused by equipment fluctuations, external interference, or slight fluctuations of the sample. The obtained multi-angle scattering signal data includes signal data at multiple time points and temperature stages.

[0031] Step S104: Calculate the thermal response characteristic data based on the multi-angle scattering signal data and the initial characteristic parameters, and calculate the multi-stage thermal stability score based on the thermal response characteristic data;

[0032] Specifically, the particle size change data during the thermal cycle is calculated using the dynamic light scattering data in the multi-angle scattering signal data. Dynamic light scattering obtains the average diameter of particles in real time by measuring the Brownian motion of particles in solution. As the temperature increases, the movement of particles in the emulsion intensifies, and the aggregation or dispersion of particles leads to changes in particle size. The data provided by dynamic light scattering can help track the trend of particle size change with temperature, thereby revealing the change in the stability of the emulsion during the thermal cycle. By calculating the rate of particle size change with temperature, the trend of particle size increase or decrease is obtained, and the thermal stability of the emulsion in the heating stage, constant temperature stage, and cooling stage is reflected through this data. Based on the static light scattering data, the particle size distribution change data during the thermal cycle is calculated. Static light scattering obtains the 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 the heat treatment process, especially the change in the width (Span value) of the particle size distribution and the average diameter. The change in particle size distribution is closely related to the stability of the emulsion. A wider particle size distribution usually means an increased possibility of particle aggregation or phase separation. By analyzing the static light scattering data, the change in particle size distribution is quantified, and further evaluation is made on whether aggregation occurs in the emulsion during the heat treatment process. Based on the backscattering data in the multi-angle scattering signal data, the data on the change in emulsion stability is calculated. The backscattering technique reflects whether the particles in the emulsion aggregate or settle during the heat treatment process by measuring the intensity of the reflected signal of the scattered light. The intensity of the backscattering signal is directly related to the stability of the sample. An increase in intensity indicates that the particles in the emulsion begin to aggregate, while a decrease in intensity indicates that phase separation has occurred in the emulsion. By analyzing the backscattering data, the stability change index of the emulsion at different stages is calculated, and based on this, the overall stability of the emulsion during the thermal cycle is evaluated. The optical microscopy images are analyzed. Images of the emulsion at different temperatures are taken by an optical microscope, and combined with image analysis techniques, the data on the change in morphological characteristics during the thermal cycle is obtained. The morphological characteristic data includes the degree of particle aggregation, shape change, and whether obvious phase separation has occurred, etc. The change in morphological characteristics is achieved by calculating indexes such as the aspect ratio, roundness, and aggregation degree of particles. For example, the aspect ratio (AR) and roundness (R) of particles are calculated by image processing software, and the aggregation degree reflects whether the particles aggregate or form clusters. By tracking these morphological characteristics, the change in the stability of the emulsion is judged. The particle size change data, particle size distribution change data, emulsion stability change data, and morphological characteristic change data are normalized to eliminate the difference in data magnitude under different experimental conditions, making the data comparable. After normalization, it is compared with the initial characteristic parameters, and the relative change rate of each index is calculated. The relative change rate reflects how each parameter in the heat treatment process changes relative to the initial state, thereby helping to quantify the change in thermal stability.For example, the rate of change in interfacial film strength is represented by the change in interfacial tension data, and the particle size growth rate is calculated from the change in particle size data. The oil-water phase separation index, an important indicator for measuring the dispersion stability of the emulsion, is obtained through backscattering data or optical microscopy image analysis, while the degree of thermal-induced aggregation is reflected by the degree of particle aggregation or the increase in particle size. By calculating these indicators, the thermal response characteristic data for each stage are obtained. The thermal response characteristic data are divided into a heating interfacial activity change stage, a high-temperature structure reorganization stage, and a cooling rebalancing stage, and a multi-stage thermal stability score is calculated. The heating stage mainly reflects the changing trends of interfacial activity and particle size as the temperature of the emulsion gradually increases. During this stage, the degree of particle aggregation increases and the interfacial film strength decreases. The high-temperature structure reorganization stage is when the internal structure of the emulsion undergoes reorganization at the highest temperature, accompanied by significant changes in particle size and morphological characteristics. The cooling rebalancing stage is when the particles can redisperse or stabilize after the emulsion cools down. The stability at this stage is particularly crucial and determines whether the emulsion can regain its original dispersion stability. Based on the thermal response characteristic data for each stage, a stability score is calculated for each stage, and these 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] Divide the thermal response characteristic data according to different temperature stages. These stages include the heating interfacial activity change stage, the high-temperature structure rearrangement stage, and the cooling rebalancing stage. The heating interfacial activity change stage ranges from the first temperature value to the second temperature value. The range from the second temperature value to the highest temperature is the high-temperature structure rearrangement stage, and the range from 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 after the temperature rises from the starting value to the set highest temperature, it then gradually cools back to the initial state. Through this temperature division, analyze the stability performance of the emulsion during the heating, constant temperature, and cooling stages respectively. Based on the above stage division, subdivide the thermal response characteristic data to obtain the thermal response data set for each stage. In the heating interfacial activity change stage, the thermal response data set mainly includes information such as the particle size change and interfacial tension change of the emulsion during heating. These data reflect whether phenomena such as aggregation and dispersion occur during the heating process of the emulsion. For the high-temperature structure rearrangement stage, the thermal response data set includes changes in the particle morphology and particle size distribution of the emulsion at the highest temperature. These data reveal whether the emulsion has undergone molecular structure changes or particle aggregation phenomena at high temperatures. In the cooling rebalancing stage, the thermal response data set focuses on the particle size change, aggregation state change, and recovery of the interfacial film of the emulsion during cooling. These data will help analyze whether the emulsion can return to the stable state before heat treatment or new aggregation phenomena occur. Calculate the stability for each data set to obtain the stability score for the corresponding stage. In the stability calculation during the heating stage, analyze the change rate of the particle size during heating. An increase in the particle size indicates that particle aggregation or an increase in the degree of aggregation has occurred during the heating process of the emulsion. By comparing the change in the particle size with the initial particle size, obtain the particle size growth rate, and calculate the stability score for the heating stage based on this. The stability score usually considers multiple factors such as the particle size growth rate, the change in interfacial tension, and the uniformity of particle distribution. The final score will reflect the stability of the emulsion during the heating process. In the stability calculation during the high-temperature structure rearrangement stage, focus on the structural changes of the emulsion at high temperatures. At this stage, the emulsion undergoes molecular structure rearrangement, resulting in particle aggregation or phase separation. Evaluate the stability of the emulsion at high temperatures by analyzing data such as the calculation of the particle size distribution width, particle aggregation degree, and change in interfacial tension. The stability score is based on indicators such as particle size change, distribution width, and phase separation index, comprehensively evaluating the thermal stability of the emulsion. For the stability calculation during the cooling rebalancing stage, analyze whether the emulsion particles are redispersed or restored to the original uniform distribution state during cooling. At this stage, a decrease in the particle size indicates particle redispersion, while an increase in the particle size means the occurrence of aggregation phenomena. By tracking the change in the particle size and combining the recovery of the interfacial tension, calculate the stability score for the cooling stage.After obtaining the stability scores for each stage, based on the measurement results under different HLB values and different pH values, perform weighted optimization calculations to obtain the target weight coefficients. Since different HLB values and pH values have different effects on the thermal stability of the emulsion, the weighted optimization calculation can help the system assign appropriate weights to the stability scores of each stage under different conditions. By methods such as regression analysis or response surface analysis, optimize the weight coefficients so that the score of each stage truly reflects the importance of that stage in the overall thermal stability. According to the obtained target weight coefficients, perform a weighted sum of the stability scores in the heating stage, the high-temperature stage, and the cooling stage to obtain the multi-stage thermal stability score of the composite emulsifier.

[0034] Step S105, generate an optimized formulation parameter plan based on the multi-stage thermal stability score.

[0035] Specifically, the multi-stage thermal stability scores are plotted according to different HLB values and pH conditions to form intuitive three-dimensional surface maps and thermal stability grade distribution heat maps. Through these graphical representations, the thermal stability performance under different formulation conditions is intuitively compared and analyzed, and the thermal stability score distribution map is obtained. Based on the thermal stability score distribution map, a multivariate regression analysis is performed to analyze the relationship between the formulation parameters such as the ratio of hydrophilic emulsifier to lipophilic emulsifier, the total emulsifier addition, the HLB value and the pH value and the multi-stage thermal stability score. The 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 according to the formulation parameters, and the regression coefficient is calculated based on the matrix by 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 adopts the classical response surface method design (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 design is completed, the data is obtained through experiments, and the least squares method is used to fit the formula parameter association model to obtain the regression coefficient. The least squares method obtains the optimal regression coefficient by minimizing the sum of squares of the error between the experimental data and the predicted value of the regression model. Through these regression coefficients, the response surface equation is established to describe the relationship between the formula 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 formula parameter to the thermal stability score is obtained, that is, the degree of influence of each parameter on the thermal stability score when it changes. Through the calculation of partial derivatives, the most sensitive formula parameters are identified, and the formula is adjusted accordingly, so that the thermal stability score is effectively improved. In order to find the optimal formula parameters, the simultaneous equations are solved to find the optimal point in the response surface equation, that is, the theoretical optimal formula parameters. By solving these equations, the combination of formula parameters that maximizes the thermal stability score is found. This optimal point is the theoretically optimal formula that can provide the best thermal stability performance. In order to ensure that the optimal point obtained is indeed an extreme point, the type of extreme value is confirmed by calculating the eigenvalue of the Hessian matrix. The Hessian matrix is the matrix of the second-order partial derivatives of the response surface equation. The sign of its eigenvalue 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 means that the optimal point is a minimum, that is, the formula parameters corresponding to this point are the most stable formula; if all eigenvalues are negative, it means that the optimal point is a maximum; if there is a mixture of positive and negative eigenvalues, it is a saddle point. Through this analysis, the properties of the optimal point are confirmed to ensure that the selected formula is the most stable formula that best meets the thermal stability requirements.

[0036] In the embodiments of the present application, a multi-parameter light scattering measurement system integrating dynamic light scattering, static light scattering, and backscattering technologies has achieved a comprehensive detection of the particle size change, distribution width, phase separation state, and aggregation degree of the emulsion system, overcoming the limitations of traditional single-parameter evaluation methods; the thermal response characteristic data is divided into three stages: the change in interfacial activity during heating, the high-temperature structure reorganization, and the cooling re-equilibration, and differential weight coefficients are used for scoring calculation, enabling the evaluation results to accurately reflect the stability change law of the composite emulsifier throughout the heat treatment process; by applying the response surface method to design an optimization algorithm, a correlation model between the formulation parameters (HLB value, pH value, emulsifier ratio, total addition amount) and the thermal stability score is established, and through mathematical analysis methods such as solving the optimal point by partial derivatives and confirming the eigenvalues of the Hessian matrix, the theoretical optimal formulation parameter combination is obtained, greatly improving the scientificity and reliability of formulation optimization; in addition, the present application does not require complex rheometers or thermal analysis equipment, and only through light scattering and image analysis technologies, the stability change of the composite emulsifier can be monitored in real time during the heat treatment process, significantly reducing the monitoring cost and improving the monitoring efficiency.

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

[0038] Select at least one of polysorbate-80, polyoxyethylene sorbitan oleate, and polyethylene glycol fatty acid ester as the hydrophilic emulsifier, and select at least one of sorbitan monooleate, glycerol monostearate, and beeswax as the lipophilic emulsifier to obtain composite emulsifier raw materials with different HLB values;

[0039] Physically mix the hydrophilic emulsifier and the lipophilic emulsifier to obtain a composite emulsifier mixture;

[0040] Select at least one of citrate buffer, phosphate buffer, and acetate buffer as the pH buffer, and adjust the pH value of the pH buffer to obtain a pH buffer aqueous phase;

[0041] Add the composite emulsifier mixture to the pH buffer aqueous phase and use a high-speed shear emulsifier for shearing to obtain an initial emulsion;

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

[0043] Use an interfacial tensiometer to measure the dynamic change curve of the oil-water interfacial tension and the equilibrium interfacial tension value of the initial emulsion, and use the initial particle size distribution characteristics, the dynamic change curve of the oil-water interfacial tension, and the equilibrium interfacial tension value as the 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 chain segments, reduce the surface tension at 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, glyceryl monostearate, and beeswax. Due to its long-chain fatty acid or wax ester structure, the lipophilic emulsifier 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 the hydrophilic emulsifier to the lipophilic emulsifier is set according to the formulation target, and this ratio is 1:3 to 3:1. The ratio range determines the HLB value of the emulsifier, that is, the hydrophilic-lipophilic balance. If the properties of the required emulsion tend to be oil-in-water (O / W type), more hydrophilic emulsifier is used, and the ratio tends to 1:3; if the required emulsion tends to be water-in-oil (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 key properties such as the stability, particle size distribution, and interfacial tension of the emulsion. 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. The role of the buffer is to adjust the pH value of the emulsion and maintain the pH stability. The pH value of the buffer is adjusted to the range of 4.0 to 8.0. An acid or a base is used to adjust the pH value to ensure that the activity of the emulsifier is not affected during the emulsification process. After the pH buffer is prepared, the composite emulsifier mixture is added to the buffered aqueous phase. To ensure the uniform dispersion of the emulsifier in the aqueous phase, a high-speed shear emulsifier is used for emulsification. The shear speed of the high-speed shear emulsifier is set at 8000 to 12000 revolutions per minute, and the shear time is 5 to 15 minutes. Under the action of high-speed shearing, the hydrophilic emulsifier forms micelles or membrane structures in the aqueous phase, while the lipophilic emulsifier is dispersed in the oil phase to form a stable emulsion. The initial emulsion is dispensed into a special test container with an optical measurement window, and the filling volume of each test container is 50 to 100 milliliters. A laser particle size analyzer is used to measure the particle size distribution of the initial emulsion. The main parameters measured include the volume-weighted average diameter (D[4,3]), the surface area-weighted average 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 tensiometer 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 within a certain time and its equilibrium interfacial tension value, the spreading and stability of the emulsifier at the oil-water interface are evaluated. The measurement temperature of the interfacial tensiometer is controlled at 25°C ± 0.5°C, and the measurement is carried out for 120 seconds to obtain reliable data.The initial particle size distribution characteristics, the dynamic change curve of the oil-water interfacial tension, and the equilibrium interfacial tension value are used as the initial characteristic parameters. By comparing with the real-time data during the thermal cycle process, the stability change of the emulsion at different temperature stages is evaluated.

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

[0046] Assemble and connect 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 to obtain a multi-parameter light scattering measurement platform;

[0047] Locate and adjust the optical axis of the collimator, polarizer, aperture, and beam splitter in the optical path system to obtain an incident light beam;

[0048] Fix the photomultiplier tubes in the multi-angle detector array at multiple scattering angle positions respectively, and adjust the signal amplification factor of the photomultiplier tubes to obtain a multi-angle scattering signal acquisition system;

[0049] Adjust and stabilize the output power of the helium-neon laser, measure the signal intensities at each detection angle through the multi-angle scattering signal acquisition system, and obtain the laser power calibration value within the optimal detection range;

[0050] Inject standard polystyrene latex microspheres into the temperature-controlled sample cell of the multi-parameter light scattering measurement platform respectively, use the multi-angle scattering signal acquisition system to collect the scattering signals at different scattering angles, and compare and analyze them with the theoretical scattering curve to obtain the system response calibration data;

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

[0052] Specifically, 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, etc. The combination of these components together constitutes the core platform for light scattering measurement. 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 a stable output power (between 15 - 30 mW) and excellent beam quality, and is suitable for high-precision scattering measurement. The output beam of the laser passes through the optical path system. After being adjusted by elements such as a collimator, a polarizer, a diaphragm, and a beam splitter, a consistent and stable incident beam is formed to ensure the accuracy of experimental data. At the same time, precise positioning of the optical path system and adjustment of the optical axis are carried out, and precise operations are performed on the positioning and adjustment of the collimator, polarizer, diaphragm, and beam splitter to ensure that the quality and direction of the beam fully meet the experimental requirements. A multi-angle detector array is configured, and photomultiplier tubes are 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 the light signals into electrical signals for analysis. To ensure the accuracy of the data, the signal amplification factor of each photomultiplier tube is adjusted so as to obtain a suitable signal intensity at different scattering angles. This adjustment process is optimized according to the performance of the photomultiplier tube and the sensitivity required by the experiment to ensure high-quality acquisition of the scattering signal. At the same time, to ensure the measurement accuracy and precision of the system, the output power of the helium-neon laser is adjusted and stabilized. In actual measurement, through the multi-angle scattering signal acquisition system, the scattering signal intensities at different detection angles are measured, and the laser power is adjusted according to these signal intensities so that the signal intensities at all angles are within the optimal detection range, ensuring that the signals collected at different scattering angles have sufficient intensity and avoiding measurement errors caused by too weak or too strong signals. To calibrate the system, standard polystyrene latex microspheres are used. The diameters of these microspheres are between 100 ± 5 nm or 1000 ± 20 nm. These standard microspheres can 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. The temperature-controlled sample cell has good temperature control accuracy (usually ±0.1 °C) to ensure 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 through 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, calibration data of the system response are obtained. The Mie scattering curve is based on Mie theory and describes the situation of light scattering on particles. By comparing the experimental data and the theoretical curve, the relationship between the light scattering signal and the particle size is calculated. Based on the system response calibration data, the standard curve relationship between the scattering signal intensity, 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, 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 at multiple scattering angles. The laser power calibration value, system response calibration data, and the standard curve relationship equation are written into the memory of the data acquisition and processing unit to provide an accurate calibration reference for subsequent scattering signal processing and data analysis, and the optical scattering measurement calibration parameters are obtained.

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

[0054] The initial emulsion is dispensed into a test container, and the test container is placed in a temperature-controlled sample cell so that the optical window of the test container faces the multi-angle detector array to obtain the sample to be measured.

[0055] According to the optical scattering measurement calibration parameters, the data acquisition parameters of the multi-angle scattering signal are set. At the same time, a thermal cycle parameter including a heating stage, a constant temperature stage, and a cooling stage is designed for the sample to be measured.

[0056] The constant temperature water bath circulation system of the temperature-controlled sample cell is started, the thermal cycle parameter is input into the control unit, and the temperature of the sample to be measured is regulated according to the thermal cycle parameter to obtain the thermal cycle process.

[0057] During the thermal cycle process, the multi-angle scattering signal and the optical microscopic image are collected according to the data acquisition parameters to obtain the scattering signal data with temperature marks.

[0058] The scattering signal data with temperature marks is repeatedly measured E times, and the average value of the E measurements is calculated to obtain the multi-angle scattering signal data.

[0059] Specifically, the initial emulsion is sub-packed into a test container with an optical window. The test container is selected to ensure that the material of the container can support the requirements 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 cell to ensure that the temperature of the sample can be precisely controlled during the experiment. The design of the sample cell needs to ensure temperature control accuracy 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 cell, and the optical window of the test container faces the multi-angle detector array, enabling the laser to penetrate the test sample and collect signals at multiple scattering angles. According to the calibration parameters of the light scattering measurement, the corresponding data acquisition parameters are set. The acquisition frequency of the scattering signal and other relevant parameters need to be optimized according to the experimental requirements. The acquisition parameters include the 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 in the scattering signal during the temperature change process. For example, during the measurement, the sampling frequency is set to once every 30 seconds or shorter to monitor the signal changes during the thermal cycle in real time. At the same time, the thermal cycle parameters are set according to the experimental design of the sample to be tested. The thermal cycle includes three stages: the heating stage, the constant temperature stage, and the cooling stage. The temperature change rate in the heating stage is set to 1 °C / min to 5 °C / min and is precisely 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 varies from 10 minutes to 120 minutes, and the specific time is selected according to the experimental requirements and the characteristics of the sample. In the cooling stage, the temperature change rate is set to 0.5 °C / min to 3 °C / min and also needs to be precisely controlled within the set range to prevent the adverse impact of drastic temperature fluctuations on the measurement of the emulsion stability. When the thermal cycle parameters are set, the constant temperature water bath circulation system of the temperature-controlled sample cell is started to ensure that the sample can be maintained at a constant temperature throughout the experiment. The temperature control system adjusts the temperature of the sample to be tested according to the input thermal cycle parameters to ensure that the temperature change remains within the predetermined range during the entire process from heating to cooling. The control unit receives the input thermal cycle parameters through the automation system and adjusts the water bath temperature in the temperature-controlled sample cell according to these parameters to ensure the smooth progress of the heating or cooling process. During this process, the temperature-controlled sample cell not only needs to precisely control the temperature but also needs to provide real-time feedback of the temperature data to ensure that each stage of the experiment is within the predetermined temperature range. During the thermal cycle, based on the previously set data acquisition parameters, the multi-angle scattering signals are collected in real time. These scattering signals serve as key data for analyzing the thermal stability changes of the emulsion. In addition to collecting multi-angle scattering signals, optical microscopic images also need to be synchronously collected to obtain visual data on the particle morphology and aggregation state.By combining an optical microscope with scattering signals, a more comprehensive understanding of the physical changes occurring in the emulsion during the thermal cycle can be achieved. These signals and images are temperature-tagged, enabling the determination of the scattering signal data corresponding to different temperature points. To ensure the reliability of the data, the temperature-tagged scattering signal data needs to be measured repeatedly. The number of repeated measurements (denoted as E times) depends on the experimental requirements and the volatility of the data. By performing multiple measurements, the influence of accidental errors can be effectively reduced, improving the accuracy of the data. After all the data acquisition is completed, the obtained multi-angle scattering signal data is used for subsequent thermal stability analysis. These data are compared and analyzed with the initial characteristic parameters to help evaluate the stability changes of the emulsion at different temperatures.

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

[0061] Calculate the particle size change data during the thermal cycle based on the dynamic light scattering data in the multi-angle scattering signal data;

[0062] Calculate the particle size distribution change data during the thermal cycle based on the static light scattering data in the multi-angle scattering signal data;

[0063] Calculate the emulsion stability change data during the thermal cycle based on the backscattering data in the multi-angle scattering signal data;

[0064] Perform image analysis on the optical microscopy images in the multi-angle scattering signal data to obtain the morphological feature change data during the thermal cycle;

[0065] Normalize the particle size change data, particle size distribution change data, emulsion stability change data, and morphological feature change data, compare them with the initial characteristic parameters, calculate the relative change rate, and obtain the interfacial film strength change rate, particle size growth rate, oil-water phase separation index, and thermal-induced aggregation degree value;

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

[0067] Divide the thermal response characteristic data into a heating interfacial activity change stage, a high-temperature structure reorganization stage, and a cooling rebalancing stage, and calculate the multi-stage thermal stability score.

[0068] Specifically, based on the dynamic light scattering data in the multi-angle scattering signals, the particle size change is calculated. Dynamic light scattering is a method for measuring particle size based on the intensity fluctuations of light scattering caused by the Brownian motion of particles in a liquid. During the thermal cycling process, the size of the particles in the emulsion changes with temperature, and dynamic light scattering helps the system to monitor the fluctuations of the particle size in real time. Through the cumulant analysis method, the hydrodynamic diameter of the particles is obtained. By calculation, it is obtained how the average diameter of the emulsion particles changes with temperature during the thermal cycling process, so as to monitor its stability. Based on the static light scattering data, the change of the 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 intensities at multiple scattering angles. The change of the particle size distribution helps to evaluate the stability of the emulsion. Especially during the heat treatment process, the change of the particle size is often a precursor to phase separation or aggregation. The commonly used calculation formulas for static light scattering include volume-weighted average diameter and surface area-weighted average diameter. These calculations can help the system to understand how the particle size distribution changes during the thermal cycling process, especially during the heating or cooling process when the particles increase due to thermally induced aggregation. At the same time, based on the backscattering data, the change of the stability of the emulsion is calculated. The backscattering technique evaluates the stability of the emulsion by measuring the reflection of the scattered light in the sample. By measuring the change of the backscattering intensity at different time points, the stability index TSI(t) of the emulsion is obtained. The stability index TSI(t) can reflect the change of the stability of the emulsion during the heat treatment process through the integration of the backscattering signal. When the change of the scattering intensity is large, it indicates that obvious structural changes or phase separation have occurred in the emulsion. To further analyze the thermal response of the emulsion, the optical microscopy images are analyzed. These images help the system to visually observe the changes in the particle morphology and its aggregation state. The morphological features include the roundness, aspect ratio, aggregation degree, etc. of the particles, which can provide additional quantitative analysis for the thermal stability. To comprehensively evaluate the thermal stability, the thermal response characteristic data are divided into the heating interfacial activity change stage, the high-temperature structure reorganization stage, and the cooling rebalancing stage. The thermal stability score is calculated according to the characteristic changes in each stage. During the heating stage, the changes in particle size, PDI, stability index, and phase separation index are evaluated; during the high-temperature stage, the changes in aspect ratio, roundness, aggregation degree, and stability index are evaluated; during the cooling stage, the changes in 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 through weighted calculation. The comprehensive thermal stability score (CTSI) is calculated by the following formula:

[0069] CTSI = β1·S1 + β2·S2 + β3·S3;

[0070] Among them, S1, S2, and S3 are the stability scores of the heating stage, high-temperature stage, and cooling stage respectively, and β1, β2, and β3 are the importance coefficients of each stage.

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

[0072] Define the first temperature value to the second temperature value as the heating interface 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, where the first temperature value is less than the second temperature value;

[0073] Based on the heating interface activity change stage, the high-temperature structure reorganization stage, and the cooling rebalancing stage, divide the thermal response characteristic data to obtain a first thermal response data set for the heating interface activity change stage, a second thermal response data set for the high-temperature structure reorganization stage, and a third thermal response data set for the cooling rebalancing stage;

[0074] Perform stability calculation for the heating stage on the first thermal response data set to obtain the stability score for the heating stage;

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

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

[0077] According to the measurement results under different HLB values and different pH values, perform weight optimization calculation to obtain the target weight coefficient;

[0078] According to the target weight coefficient, perform weighted summation on the stability scores of the heating stage, the high-temperature stage, and the cooling stage to obtain the multi-stage thermal stability score.

[0079] Specifically, a temperature-rising interfacial activity change stage, a high-temperature structure reorganization stage, and a cooling rebalancing stage are defined. The temperature range of the temperature-rising interfacial activity change stage is from the first temperature value (the initial temperature of the sample) to the second temperature value (the set termination point of temperature rise); the high-temperature structure reorganization stage extends from the second temperature value to the highest temperature of the sample, and the cooling rebalancing stage starts from the highest temperature until the sample returns to the first temperature value (i.e., the temperature at the end of cooling). 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 termination temperature of the temperature-rising stage. Based on the above three divided stages, namely the temperature-rising interfacial activity change stage, the high-temperature structure reorganization stage, and the cooling rebalancing stage, the thermal response characteristic data are divided. The data sets of each stage are processed independently, and the stability scores are calculated separately. In the temperature-rising interfacial activity change stage, key data such as particle size change and particle size distribution change are obtained through dynamic light scattering and static light scattering data. These data reflect the particle behavior of the emulsion during temperature rise, especially how the particle size changes with the increase in temperature. In the high-temperature structure reorganization stage, since the emulsion undergoes relatively intense molecular rearrangement when reaching the maximum temperature, resulting in changes in the structure of the emulsifier, in this stage, the focus of analysis is the aggregation degree of particles, the change in interfacial tension, and the width of the particle size distribution. Static light scattering and backscattering data help to reveal the trend of particle aggregation or stability in this stage. The analysis of optical microscopic images can also provide data on changes in morphological characteristics, such as particle aggregation and phase separation index, which are helpful for evaluating the structural changes of the emulsion at high temperatures. In the cooling rebalancing stage, as the temperature decreases, the particles in the emulsion will undergo certain reorganization or redispersion, and the change trends of particle size and particle size distribution are still the focus of analysis. Through the data of dynamic light scattering and static light scattering, the process of particle recovery, dispersion, or reaggregation is observed, so as to evaluate the final stability of the emulsion. The stability of the data set of each stage is calculated. For the first thermal response data set in the temperature-rising interfacial activity 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 particle size, the change in particle size distribution, and the change in interfacial tension. By analyzing the measurement results under different HLB values and different pH values, the target weight coefficients of different stages are obtained. These weight coefficients are based on the regression analysis results of experimental data and are optimized by the least squares method to ensure that the stability scores of each stage can reasonably reflect their contributions to the overall thermal stability. Using the optimized target weight coefficients, the stability scores of the temperature-rising stage, the high-temperature stage, and the cooling stage are weighted and summed to obtain the multi-stage thermal stability score of the emulsion. Through weighted calculation, the comprehensive stability of the composite emulsifier during the entire heat treatment process is obtained.

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

[0081] The multi-stage thermal stability scores are plotted as three-dimensional surface plots and thermal stability grade distribution heat maps under different HLB values and pH values, and the thermal stability performances under different formulation conditions are visually compared and analyzed to obtain the thermal stability score distribution atlas;

[0082] Based on the thermal stability score distribution atlas, a multiple regression analysis is performed on the relationship between the formulation parameters of the ratio of hydrophilic emulsifier to lipophilic emulsifier, the total emulsifier addition amount, HLB value, pH value and the multi-stage thermal stability score to obtain the formulation parameter correlation model;

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

[0084] A mathematical analysis is performed on the response surface equation, the partial derivatives of each parameter with respect to the thermal stability score are calculated, and the theoretical optimal point is obtained by solving the simultaneous equations. At the same time, the eigenvalue of the Hessian matrix is calculated to confirm the type of extreme value, and a formulation parameter optimization scheme is generated.

[0085] Specifically, the multi-stage thermal stability scores are plotted as three-dimensional surface plots and thermal stability grade distribution heat maps under different HLB values and pH value conditions to reflect the thermal stability performance of different formulations at different temperature stages. Through these graphical representations, the stability changes of different formulations during the heating stage, high-temperature stage, and cooling stage can be visually observed, and then it can be identified which formulation combinations have better thermal stability. For example, in the three-dimensional surface plot, taking the HLB value and pH value as independent variables and the thermal stability score as the dependent variable, a three-dimensional image describing thermal stability is plotted. Based on the thermal stability score distribution map, multiple regression analysis is performed on the formulation parameters of the composite emulsifier to find the relationships between the ratio of hydrophilic emulsifier to lipophilic emulsifier, the total emulsifier addition amount, the HLB value, the pH value, and these formulation parameters 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 squared errors, thereby establishing the mathematical relationship between the formulation parameters and thermal stability. Through regression analysis, the contribution degree 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 can be comprehensively captured. The experimental point matrix is determined by Box-Behnken design or central composite design, and these design methods can effectively explore the interaction of the formulation parameters and improve the experimental efficiency. After the experimental point matrix is determined, experiments are carried out according to the regression model, and the regression coefficients in the formulation parameter correlation model are calculated by the least squares method. An experimental design scheme is generated according to the experimental point matrix, and then experiments are carried out and data are collected. The regression coefficients are optimized by the least squares method to obtain the response surface equation. Through this equation, the relationship between the formulation parameters and the thermal stability score is described, and the formulation parameters with the best 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 to reveal the influence degree of each parameter on the thermal stability score in different ranges. The partial derivatives help the system understand the sensitivity of a specific formulation parameter (such as the HLB value or emulsifier ratio) change to the final thermal stability score under different formulation conditions. Solving the system of simultaneous equations gives the theoretical optimum point. By solving the system of simultaneous equations, a set of optimal formulation parameter values are obtained, and these formulation parameter values can make the thermal stability score reach the maximum value. The theoretical optimum point is calculated based on the experimental data and the regression model, representing the optimal combination of the emulsifier formulation and capable of providing the best thermal stability performance. To confirm whether this optimum point is an extreme value, the type of the extreme value is confirmed by calculating the eigenvalues of the Hessian matrix. 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 this point is a maximum value, a minimum value, or a saddle point.If all the eigenvalues of the Hessian matrix are positive, it indicates that the optimal point is a minimum value; if all the eigenvalues are negative, it indicates 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 the response surface analysis, an optimization scheme for the formulation parameters is generated. By optimizing the formulation of the emulsifier, the stability of the emulsion under high-temperature conditions is improved, and the wide applicability of the emulsion in different application scenarios is ensured.

[0086] The above describes the method for monitoring the thermal stability of the composite emulsifier in the embodiments of the present application. Next, the system for monitoring the thermal stability of the composite emulsifier in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the system for monitoring the thermal stability of the composite emulsifier in the embodiments of the present application includes:

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

[0088] A parameter calibration module 202, configured to construct a multi-parameter light scattering measurement platform and perform measurement parameter calibration to obtain light scattering measurement calibration parameters;

[0089] A thermal cycle module 203, configured to load the initial emulsion into the 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] A calculation module 204, 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] A generation module 205, configured to generate an optimization scheme for the formulation parameters based on the multi-stage thermal stability score.

[0092] Through the collaborative cooperation of the above-mentioned various components, a multi-parameter light scattering measurement system integrating dynamic light scattering, static light scattering, and backscattering techniques realizes the comprehensive detection of the particle size change, distribution width, phase separation state, and aggregation degree of the emulsion system, overcoming the limitations of traditional single-parameter evaluation methods; the thermal response characteristic data is divided into three stages: the change of interfacial activity during heating, high-temperature structure reorganization, and cooling rebalancing, and differential weight coefficients are used for scoring calculation, so that the evaluation results can accurately reflect the stability change law of the composite emulsifier throughout the heat treatment process; by applying the response surface method to design an optimization algorithm, a correlation model between the formulation parameters (HLB value, pH value, emulsifier ratio, total addition amount) and the thermal stability score is established, and through mathematical analysis methods such as solving the optimal point by partial derivatives and confirming the eigenvalues of the Hessian matrix, the theoretical optimal formulation parameter combination is obtained, greatly improving the scientificity and reliability of formulation optimization; in addition, this application does not require complex rheometers or thermal analysis equipment, and only through light scattering and image analysis techniques, the stability change of the composite emulsifier can be monitored in real time during the heat treatment process, significantly reducing the monitoring cost and improving the monitoring efficiency.

[0093] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is made to execute the steps of the thermal stability monitoring method of the composite emulsifier.

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

[0095] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several 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 methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0096] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring the thermal stability of a composite emulsifier, characterized in that, The method includes: Performing dynamic measurement of the oil-water interfacial tension and analysis of the particle size distribution on the 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 temperature-controlled sample cell of the multi-parameter light scattering measurement system and performing thermal cycling treatment 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 a multi-stage thermal stability score based on the thermal response characteristic data; Generating an optimized formulation parameter scheme 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 performing dynamic measurement of the oil-water interfacial tension and analysis of the particle size distribution on the initial emulsion of the composite emulsifier to obtain initial characteristic parameters includes: Selecting at least one of polysorbate-80, polyoxyethylene sorbitan oleate, and polyethylene glycol fatty acid ester as the hydrophilic emulsifier, and selecting at least one of sorbitan monooleate, glycerol monostearate, and beeswax as the lipophilic emulsifier to obtain composite emulsifier raw materials with different HLB values; Physically mixing the hydrophilic emulsifier and the lipophilic emulsifier to obtain a composite emulsifier mixture; Selecting at least one of citrate buffer, phosphate buffer, and acetate buffer as the pH buffer, and adjusting the pH value of the pH buffer to obtain a pH buffer aqueous phase; Adding the composite emulsifier mixture to the pH buffer aqueous phase and using a high-speed shear emulsifier for shearing to obtain an initial emulsion; Measuring the volume-weighted average diameter, surface area-weighted average diameter, and particle size distribution width index of the initial emulsion using a laser particle size analyzer to obtain the initial particle size distribution characteristics; Measuring the dynamic change curve of the oil-water interfacial tension and the equilibrium interfacial tension value of the initial emulsion using an interfacial tensiometer, and taking the initial particle size distribution characteristics, the dynamic change curve of the oil-water interfacial tension, and the equilibrium interfacial tension value as the initial characteristic parameters.

3. The method for monitoring the thermal stability of the composite emulsifier according to claim 1, characterized in that, The constructing a multi-parameter light scattering measurement platform and performing measurement parameter calibration to obtain light scattering measurement calibration parameters includes: 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 to obtain a multi-parameter light scattering measurement platform; Positioning and adjusting the optical axis of the collimator, polarizer, aperture, and beam splitter in the optical path system to obtain an incident light beam; Fixing the photomultiplier tubes in the multi-angle detector array at multiple scattering angle positions respectively, and adjusting the signal amplification factor of the photomultiplier tubes to obtain a multi-angle scattering signal acquisition system; Adjusting and stabilizing the output power of the helium-neon laser, and measuring the signal intensity at each detection angle through the multi-angle scattering signal acquisition system to obtain a laser power calibration value within the optimal detection range; Standard polystyrene latex microspheres are respectively injected into the temperature-controlled sample cell of the multi-parameter light scattering measurement platform. The scattering signals at different scattering angles are collected using the multi-angle scattering signal acquisition system, and compared and analyzed with the theoretical scattering curve to obtain system response calibration data; Based on the system response calibration data, a standard curve relationship equation between the scattering signal intensity, 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, Loading the initial emulsion into the temperature-controlled sample cell of the multi-parameter light scattering measurement system and performing a thermal cycle process according to the light scattering measurement calibration parameters to obtain multi-angle scattering signal data, including: The initial emulsion is sub-packed in a test container, and the test container is placed in the temperature-controlled sample cell so that the optical window of the test container faces the multi-angle detector array to obtain a sample to be measured; Set the data acquisition parameters of the multi-angle scattering signal according to the light scattering measurement calibration parameters. At the same time, design a thermal cycle parameter for the sample to be measured that includes a heating stage, a constant temperature stage, and a cooling stage; Start the constant temperature water bath circulation system of the temperature-controlled sample cell, input the thermal cycle parameters into the control unit, and regulate the temperature of the sample to be measured according to the thermal cycle parameters to obtain a thermal cycle process; During the thermal cycle process, collect multi-angle scattering signals and optical microscopic images according to the data acquisition parameters to obtain scattering signal data with temperature marks; Measure the scattering signal data with temperature marks E times repeatedly, calculate the average value of the E measurements to obtain multi-angle scattering signal data.

5. The method for monitoring the thermal stability of the composite emulsifier according to claim 1, wherein Calculating the thermal response characteristic data based on the multi-angle scattering signal data and the initial characteristic parameters, and calculating the multi-stage thermal stability score based on the thermal response characteristic data, including: Calculating the particle size change data during the thermal cycle process based on the dynamic light scattering data in the multi-angle scattering signal data; Calculating the particle size distribution change data during the thermal cycle process based on the static light scattering data in the multi-angle scattering signal data; Calculating the emulsion stability change data during the thermal cycle process based on the backscattering data in the multi-angle scattering signal data; Performing image analysis on the optical microscopic images in the multi-angle scattering signal data to obtain the morphological characteristic change data during the thermal cycle process; Normalize the particle size change data, the particle size distribution change data, the emulsion stability change data, and the morphological characteristic change data, compare them with the initial characteristic parameters, calculate the relative change rate, and obtain the interfacial film strength change rate, particle size growth rate, oil-water phase separation index, and thermal-induced aggregation degree value; Use the interfacial film strength change rate, the particle size growth rate, the oil-water phase separation index, and the thermal-induced aggregation degree value as thermal response characteristic data; Divide the thermal response characteristic data into a heating interfacial activity change stage, a high-temperature structure reorganization stage, and a cooling rebalancing stage, and calculate the multi-stage thermal stability score.

6. The method for monitoring the thermal stability of the composite emulsifier according to claim 5, wherein The thermal response characteristic data is divided into a temperature rise interface activity change stage, a high temperature structure reorganization stage and a cooling rebalance stage, and a multi-stage thermal stability score is calculated, including: The first temperature value to the second temperature value is defined as the temperature rise interface activity change stage, the second temperature value to the maximum temperature is defined as the high temperature structure reorganization stage, and the maximum temperature to the first temperature value is defined as the cooling rebalance stage, wherein the first temperature value is less than the second temperature value; Based on the temperature-increasing interfacial activity change stage, the high-temperature structural reorganization stage, and the cooling rebalancing stage, the thermal response characteristic data are divided to obtain a first thermal response data set of the temperature-increasing interfacial activity change stage, a second thermal response data set of the high-temperature structural reorganization stage, and a third thermal response data set of the cooling rebalancing stage; Performing a temperature rise phase stability calculation on the first thermal response data set to obtain a temperature rise phase stability score; performing high temperature stage stability calculation on the second thermal response data set to obtain a high temperature stage stability score; performing cooling stage stability calculation on the third thermal response data set to obtain a cooling stage stability score; According to the measurement results under different HLB values and different pH values, weight optimization calculation is performed to obtain the target weight coefficient; According to the target weight coefficient, the temperature rise stage stability score, the high temperature stage stability score and the cooling stage stability score are weighted and summed to obtain a multi-stage thermal stability score.

7. The method for monitoring the thermal stability of the composite emulsifier according to claim 1, wherein, The method of generating a formulation parameter optimization scheme based on the multi-stage thermal stability score comprises: 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 values, and the thermal stability performance under different formulation conditions is intuitively compared and analyzed to obtain a thermal stability score distribution map; Based on the thermal stability score distribution map, a multivariate regression analysis was performed on the relationship between the formula parameters of the ratio of hydrophilic emulsifier to lipophilic emulsifier, the total emulsifier addition amount, HLB value, pH value and the multi-stage thermal stability score to obtain a formula parameter association model; Determine an experimental point matrix according to the formula parameters, and based on the experimental point matrix, calculate the regression coefficients in the formula parameter association model by the least square method to obtain a response surface equation; The response surface equation is mathematically analyzed, the partial derivatives of each parameter with respect to the thermal stability score are calculated, the simultaneous equations are solved to obtain the theoretical optimal point, and the extreme value type is confirmed by calculating the eigenvalue of the Hessian matrix to generate a formulation parameter optimization scheme.

8. A thermal stability monitoring system for a composite emulsifier, characterized in that, A method for monitoring the thermal stability of a composite emulsifier according to any one of claims 1 to 7, wherein the thermal stability monitoring system of 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 emulsifier to obtain the initial characteristic parameters; A parameter calibration module is used to construct a multi-parameter light scattering measurement platform and perform measurement parameter calibration to obtain light scattering measurement calibration parameters; A thermal cycling module, configured to load the initial emulsion into a temperature-controlled sample cell of the multi-parameter light scattering measurement system and perform thermal cycling processing according to the light scattering measurement calibration parameters to obtain multi-angle scattering signal data; 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; A generation module, configured to generate a formulation parameter optimization scheme based on the multi-stage thermal stability score.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by a processor, the processor is caused to execute the method for monitoring the thermal stability of the composite emulsifier according to any one of claims 1 to 7.

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