Method for optimizing process parameters of hpp non-thermal sterilization based on microbial inactivation kinetics

Abstract

This invention belongs to the field of food processing technology and discloses a method for optimizing HPP non-thermal sterilization process parameters based on microbial inactivation mechanics. The method includes: collecting Zeta potentials at different pressures and temperatures, and obtaining the safe operating range through fitting analysis; conducting experimental analysis and data fitting on the product stock solution to obtain the pretreatment formula and initial process parameters; performing non-thermal sterilization according to the pretreatment formula and initial process parameters, and recording the actual pressure and temperature at each time point; analyzing and obtaining the charge risk exposure based on the actual pressure and temperature at each time point; and optimizing and updating the process parameters based on the charge risk exposure and the next batch of product stock solution. This invention quantifies the actual weakening effect of charge risk on sterilization effect, forming a self-iterable process learning closed loop.

Description

Technical Field

[0001] This invention relates to the field of food processing technology, and more specifically, to a method for optimizing HPP non-thermal sterilization process parameters based on microbial inactivation mechanics. Background Technology

[0002] High hydrostatic pressure (HPP) non-thermal sterilization technology is widely used in liquid and semi-solid foods such as juices, sauces, dairy products, and minced meat. The industry generally relies on pressure-time models based on the homogeneity of microbial dissociation for process optimization. However, in actual production, especially when handling complex food systems rich in protein and lipids, perplexing sterilization failures occasionally occur: process parameters theoretically calculated to achieve sterilization intensities of 5-log or higher result in the presence of spoilage microorganisms or pathogens during large-scale production, leading to product spoilage within the shelf life and even safety hazards. Traditional analysis often attributes this to strain variation or equipment inhomogeneity. Under high pressure, amphoteric molecules such as proteins and phospholipids in food undergo conformational changes, and their surface charge characteristics change dynamically. In particular, when the pressure rises to a certain threshold, the isoelectric point of some proteins may shift, causing interfaces that are negatively charged at normal pH to become neutral or even positively charged instantaneously under high pressure. This reversal causes negatively charged microbial cells, originally dispersed in the system due to electrostatic repulsion, to be strongly adsorbed and anchored at these newly formed positively charged interfaces. This anchoring is not a simple physical attachment; it embeds microbial cells within a dense protective layer composed of denatured proteins or aggregated lipids, much like donning a pressure-buffered armor. Consequently, the applied macroscopic hydrostatic pressure cannot be effectively transmitted to the anchored microbial cells, resulting in the actual pressure they withstand being far lower than the equipment's set value, leading to a significant decrease in inactivation efficiency. Traditional offline microbial validation and online pressure monitoring based on homogeneous models cannot capture or warn of this risk, making the resulting sterilization failure sudden and unpredictable, thus becoming a technical bottleneck in high-value-added food HPP processing.

[0003] In view of this, the present invention proposes a method for optimizing HPP non-thermal sterilization process parameters based on microbial inactivation mechanics to solve the above problems. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a method for optimizing HPP non-thermal sterilization process parameters based on microbial inactivation mechanics, comprising:

[0005] Zeta potentials under different pressures and temperatures are collected. Through fitting analysis, charge state prediction functions are obtained. The charge state prediction functions are solved and divided to obtain the safe operating area.

[0006] Collect the product stock solution, and conduct experimental analysis and data fitting on the product stock solution in conjunction with the safe operating area to obtain the pretreatment formula and initial process parameters;

[0007] The HPP equipment performs non-thermal sterilization based on the pretreatment formula and initial process parameters, and records the actual pressure and temperature at each time point. Based on the actual pressure and temperature at each time point, the zeta potential change trajectory during the treatment process is calculated using the charge state prediction function. The charge risk exposure is obtained by integral calculation based on the zeta potential change trajectory and the zeta potential threshold.

[0008] Based on the charge risk exposure, the actual microbial survival rate of the product after HPP equipment treatment, the charge state prediction function, and the safe operating area, the process parameters are optimized and updated in conjunction with the next batch of product concentrate.

[0009] Furthermore, methods for obtaining the charge state prediction function include:

[0010] Zeta potentials were collected under different pressures and corresponding temperatures;

[0011] With pressure and corresponding temperature as independent variables and Zeta potential as dependent variable, a second-order response surface model is used to fit the charge state prediction function.

[0012] The trajectory line where the charge state prediction function value equals 0 is taken as the charge reversal critical line. A Zeta potential threshold is set, and the area where the charge state prediction function value is lower than the Zeta potential threshold is defined as the safe operating area. The Zeta potential threshold is set according to the charge reversal critical line, the preset process fluctuation range, and the preset safety margin.

[0013] Furthermore, methods for obtaining the pretreatment formulation include:

[0014] The pH value of the product stock solution was measured, and acid-base titration and reverse calculation were performed in combination with the charge state prediction function and the preset process fluctuation range to obtain the pH adjustment target value that ensures the Zeta potential remains below the Zeta potential threshold under the preset worst-case conditions. Different concentrations of ionic strength regulators were added to the sampled product stock solution, and the quantitative relationship equation between ionic strength and Zeta potential was obtained by measuring the Zeta potential. Based on the pH adjustment target value and the quantitative relationship equation, the pretreatment formula was obtained through synergistic optimization calculation.

[0015] Furthermore, methods for obtaining the target pH adjustment value include:

[0016] The worst combination of process conditions is calculated based on the preset process window center point and the preset process fluctuation range; the product concentrate is loaded into the high-pressure chamber of the HPP equipment; the HPP equipment is set to the worst combination of process conditions.

[0017] A pH adjuster of known concentration is added dropwise to the product stock solution in the HPP equipment to obtain a mixed solution. The current pH value and Zeta potential of the mixed solution are measured. The pH value corresponding to the current Zeta potential of the mixed solution simultaneously meeting the measurement consistency condition and the model validation condition is recorded. The measurement consistency condition is that the absolute value of the difference between the current Zeta potential of the mixed solution and the Zeta potential threshold is not higher than the allowable error value. The model validation condition is that the model predicted Zeta potential is obtained by substituting the current pressure and temperature of the mixed solution into the charge state prediction function, and the absolute value of the difference between the model predicted Zeta potential and the Zeta potential threshold is not higher than the allowable error value.

[0018] Calculate the difference between the corresponding pH value and the remaining pH value to obtain the target pH adjustment value.

[0019] Furthermore, methods for obtaining the quantitative relationship between ionic strength and zeta potential include:

[0020] Take the first volume of the product stock solution into a beaker, and use a pH adjuster of known concentration to adjust the pH value of the product stock solution to the target pH value; and mark it as sample 0, and measure the Zeta potential of sample 0;

[0021] Prepare N identical containers, numbered 1 to N respectively; add a second volume of product stock solution to each container; add different volumes of pre-prepared electrolyte stock solution to each container in sequence, so that the final concentration of electrolyte in the N containers is distributed in a preset gradient, and obtain samples with different electrolyte concentrations;

[0022] For each sample in the container, the pH was adjusted to the target value using a known concentration of pH adjuster. At a uniformly set temperature, the zeta potential of the sample in each container was measured. The electrolyte concentration and zeta potential of each sample were recorded. The ionic strength was calculated based on the electrolyte concentration and the concentrations and valence states of all ions in the electrolyte. A scatter plot was drawn with the ionic strength as the abscissa and the zeta potential as the ordinate. The data points in the scatter plot were fitted with the zeta potential of sample 0 to obtain a quantitative relationship equation.

[0023] Furthermore, methods for obtaining the pretreatment formulation include:

[0024] Substituting the Zeta potential threshold into the quantitative relationship equation, the ion strength threshold is obtained by solving the equation.

[0025] If the ionic strength threshold is not higher than the maximum ionic strength, the preliminary plan is to adjust the target pH value and the ionic strength threshold.

[0026] If the ionic strength threshold is higher than the maximum ionic strength, a chelating agent is added to the product with the added pH adjustment target value and ionic strength threshold to obtain the minimum chelating agent concentration that ensures the Zeta potential of the product with added chelating agent is not higher than the Zeta potential threshold; the preliminary scheme is then the pH adjustment target value, ionic strength threshold, and minimum chelating agent concentration.

[0027] According to the preliminary plan, a pretreated sample with the preliminary plan formulation is prepared using the product stock solution. The pretreated sample is then subjected to charge verification and sensory texture verification. If both charge verification and sensory texture verification are passed, the preliminary plan is adopted as the pretreated formulation.

[0028] Furthermore, methods for obtaining initial process parameters include:

[0029] Based on the safe operating zone and the biomechanics of microbial quiescence, a sterilization efficiency function and a robustness function are constructed, and initial process parameters are obtained through multi-objective optimization.

[0030] Furthermore, methods for obtaining initial process parameters include:

[0031] Obtain all combinations of process parameters in the safe operating area as a set of candidate points;

[0032] Based on the microbial inactivation mechanics model, functions corresponding to the scale parameters and shape parameters of candidate points are constructed. The maximum value of the logarithmic decrease of candidate points within the maximum allowable time is calculated based on the functions corresponding to the scale parameters and shape parameters to obtain the bactericidal efficiency function.

[0033] Calculate the shortest Euclidean distance from each process point within the safe operating area to the boundary of the safe operating area, calculate the maximum value of the shortest Euclidean distance, and obtain the robustness function;

[0034] Define the constraints, including: the pressure is within the allowable pressure range, the temperature is within the allowable temperature range, and the sterilization efficiency function value is not lower than the minimum sterilization requirement value;

[0035] The sterilization efficiency function and robustness function are normalized, and then a comprehensive scoring function is constructed using a weighted comprehensive scoring method. The comprehensive score is calculated for each candidate point that meets the constraints.

[0036] Sort the candidates by comprehensive score in descending order and select the process parameters corresponding to the candidate with the highest score as the initial process parameters.

[0037] Furthermore, methods for obtaining charge risk exposure include:

[0038] The HPP equipment performs non-thermal sterilization based on the pretreatment formula and initial process parameters, and records the actual pressure and temperature at each time point. Based on the actual pressure and temperature at each time point, and combined with the charge state prediction function, it calculates the Zeta potential at each time point during the treatment process. When the Zeta potential is not higher than the Zeta potential threshold, the charge risk exposure at the corresponding time point is 0. When the Zeta potential is greater than the Zeta potential threshold but not higher than 0, the difference between the Zeta potential and the Zeta potential threshold is calculated to obtain the instantaneous charge risk exposure at the corresponding time point. When the Zeta potential threshold is greater than 0, the penalty product of the Zeta potential and the penalty coefficient is calculated, and then the difference between the penalty product and the Zeta potential threshold is calculated to obtain the instantaneous charge risk exposure at the corresponding time point. The instantaneous charge risk exposure at each time point for a single batch of products is integrated over the treatment time to obtain the charge risk exposure.

[0039] Furthermore, methods for optimizing and updating process parameters include:

[0040] After the product is processed by the HPP equipment, it undergoes standard microbial testing to obtain the microbial concentration of the product. Based on the initial inoculation concentration, the actual sterilization efficiency value, i.e. the actual log reduction value, is calculated.

[0041] The sterilization efficiency function is corrected based on the charge risk exposure to obtain the corrected theoretically predicted sterilization efficiency value. Based on U batch data pairs, the undetermined coefficients in the corrected sterilization efficiency function are fitted to obtain the updated sterilization efficiency function. Each batch data pair includes the charge risk exposure and the actual sterilization efficiency value corresponding to the batch.

[0042] The process parameters for the next batch of products are updated based on the updated sterilization efficiency function, charge state prediction function, and safe operating zone, combined with the method used to obtain the initial process parameters.

[0043] The technical effects and advantages of this invention are as follows: The method for optimizing HPP non-thermal sterilization process parameters based on microbial inactivation mechanics.

[0044] This invention establishes a real-time predictive model of the interfacial charge state of food matrices under high pressure, accurately delineating process no-go zones prone to microbial anchoring risks. It systematically analyzes the electrochemical pretreatment of the matrix and the high-pressure sterilization process: First, based on the predictive model, it derives a matrix pH and ionic strength control formula that fundamentally avoids charge reversal, eliminating the interfacial chemical conditions for microbial anchoring at the source. Then, within the charge-safe zone, it synergistically optimizes pressure and temperature parameters to ensure sterilization efficiency and process robustness. Finally, by analyzing and quantifying the actual weakening effect of charge risk on sterilization, it forms a self-iterable process learning closed loop. By monitoring and controlling the interfacial Zeta potential of the food matrix during high-pressure processes, it prevents the risk of abnormally decreasing microbial inactivation efficiency caused by approaching or reaching the charge reversal point. This significantly reduces the possibility of sudden sterilization failures and food safety hazards caused by this mechanism, while ensuring the sensory quality of the product. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the process parameter optimization method for HPP non-thermal sterilization based on microbial inactivation mechanics of the present invention.

[0046] Figure 2 This is a schematic diagram of the method for obtaining the quantitative relationship equation between ionic strength and Zeta potential according to the present invention.

[0047] Figure 3 This is a schematic diagram of the method for obtaining initial process parameters according to the present invention;

[0048] Figure 4 This is a schematic diagram of the method for obtaining charge risk exposure according to the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Please see Figure 1 As shown, this embodiment provides a method for optimizing HPP non-thermal sterilization process parameters based on microbial inactivation mechanics, including:

[0051] Zeta potentials under different pressures and temperatures are collected. Through fitting analysis, charge state prediction functions are obtained. The charge state prediction functions are solved and divided to obtain the safe operating area.

[0052] Methods for obtaining charge state prediction functions include:

[0053] By connecting a Zeta potential meter or high-pressure impedance spectroscopy system to a high-pressure visual cell, data is collected at different pressures covering the HPP process range. At least five pressure points uniformly covering the HPP process range can be selected, such as 100, 200, 300, 400, 500, and 600 MPa, along with the corresponding zeta potentials at the corresponding temperatures, to obtain the raw dataset. The pressure range can cover all actual pressure ranges for HPP non-thermal sterilization of different products. The pressure generation and control unit of the high-pressure visual cell or high-pressure impedance spectroscopy system covers the entire pressure range involved in the HPP process to be optimized. High-pressure in-situ measurement directly captures the actual changes in interfacial charge under pressure and temperature, transforming the abstract risk of charge reversal into measurable zeta potential data. High-pressure in-situ zeta potential measurement can be achieved using commercial high-pressure dynamic light scattering accessories or custom-designed high-pressure electrochemical cells. For clear, low-turbidity liquid samples, optical measurement is feasible; for complex systems with high turbidity, high viscosity, or containing particles, indirect characterization using high-pressure impedance spectroscopy based on flow potential or electroacoustic effects can be employed. Measurement signals may be interfered with by factors such as the light transmittance of the high-pressure window and fluid convection. Therefore, repeated measurements, background subtraction, and signal averaging are necessary to improve data reliability. This method does not rely on a single, perfect online measurement. Its core lies in calibrating the charge state prediction function using a limited but critical set of high-pressure experimental points. The subsequent trajectory of the charge state prediction function is derived from this model based on the pressure and corresponding temperature data recorded by the equipment. This embodiment does not require real-time online measurement of the Zeta potential on the production line; its charge state monitoring is based on a pre-established prediction model and real-time pressure and temperature data.

[0054] Using pressure and corresponding temperature as independent variables and Zeta potential as the dependent variable, a second-order response surface model is used to fit the charge state prediction function. The physical meaning of the output value of the charge state prediction function is the predicted Zeta potential under the given pressure and temperature conditions. The function coefficients corresponding to the charge state prediction function are fitted using the least squares method; for example, the charge state prediction function... ,in, , , , , and These are the function coefficients; For pressure; For the corresponding temperature; the charge state prediction function extrapolates experimental data into a global prediction tool, which can predict whether the matrix will approach the charge reversal point under any process conditions.

[0055] The trajectory line where the charge state prediction function equals 0 is taken as the charge reversal critical line. A Zeta potential threshold is set to ensure that the system's Zeta potential remains safely away from the charge reversal critical line within a preset process fluctuation range. The Zeta potential threshold can be set according to the following principles: First, the position of the charge reversal critical line near the expected process window is determined through preliminary experiments. Then, the Zeta potential threshold should be set to a more negative value than the Zeta potential value on the charge reversal critical line. Its negative offset, i.e., the safety margin, should cover the maximum positive fluctuation of the Zeta potential caused by the preset process fluctuations ±ΔP and ±ΔT. This offset can be estimated using the charge state prediction function. For example, to estimate the maximum possible positive offset of the Zeta potential under the worst-case fluctuation, the Zeta potential threshold can be set as the negative of the sum of the maximum possible positive offset and the additional safety margin. For example, based on experience, the Zeta potential threshold can be initially set to -15mV to -25mV, and then fine-tuned through subsequent verification.

[0056] If the Zeta potential threshold is set to -20mV, the region where the charge state prediction function value is below the Zeta potential threshold is defined as the safe operating zone. The charge reversal critical line is the theoretical red line for anchoring; the safe operating zone is a safety buffer zone set for this red line. This provides a visualized lightning protection map for process design, fundamentally avoiding operations in high-incidence areas of anchoring. When the system's Zeta potential approaches zero, the HPP inactivation efficiency of microorganisms deviates significantly from the prediction based on the homogeneous model, exhibiting protected characteristics. This embodiment effectively eliminates such anomalies in inactivation efficiency by adjusting the Zeta potential away from this critical region, ensuring the robustness of the process.

[0057] Collect the product stock solution, and conduct experimental analysis and data fitting on the product stock solution in conjunction with the safe operating area to obtain the pretreatment formula and initial process parameters;

[0058] Methods for obtaining pretreatment formulations and initial process parameters include:

[0059] While HPP (Heat-Free Processing) technology is widely used in liquid and semi-solid foods, it is not designed for solid products that are ultimately consumed. Solid products, such as meat, are rich in protein and lipids and are more susceptible to charge reversal.

[0060] When the product is non-solid, if the product contains only liquid and is non-aerobic, then the product is used directly as the product stock solution; if the product contains gas, then it is used as the product stock solution after standing and defoaming.

[0061] When the product is semi-solid, gentle mechanical stirring is used to achieve uniform dispersion, resulting in the product concentrate. If the product contains solid additives, such as beverages containing fruit particles, it is passed through a food-grade sieve with the first aperture to remove the solid additives, obtaining the product concentrate. The first aperture is generally set to 6-8 mesh, corresponding to 2.0mm-3.0mm, which can remove most solid additives and is much larger than the size of matrix colloids or protein particles to ensure that the matrix itself passes through smoothly. This can be adjusted according to the actual situation.

[0062] When the product is solid, obtain the edible portion. The product may contain inedible parts, such as bones and shells, which need to be removed beforehand. Use a pre-cooled food-grade grinder or chopper to cut the edible portion into small pieces. The pre-cooling temperature is generally set to 4℃. Add this to the grinder along with an equal mass of sterile, deionized water or the product's own broth. Under ice bath conditions, perform intermittent grinding, such as grinding for 10 seconds, pausing for 20 seconds, repeating 3-5 times until a completely homogeneous slurry is formed. The slurry temperature should not exceed 10℃. Pass the slurry through a food-grade sieve with a second aperture to remove uncrushed connective tissue, etc., to obtain a homogeneous paste. This homogeneous paste is used as the product concentrate. The second aperture is generally set to 10-20 mesh, corresponding to 1.0mm-2.0mm, which can remove most uncrushed connective tissue, etc., while allowing homogenized protein, fat, and other mixtures to pass through smoothly. This can be adjusted according to actual conditions.

[0063] The pH value of the product stock solution was measured, and acid-base titration and reverse calculation were performed using a charge state prediction function and a preset process fluctuation range to obtain the target pH value that ensures the Zeta potential remains below the Zeta potential threshold even under the preset worst-case conditions. Different concentrations of ionic strength regulators were added to the sampled product stock solution, and the quantitative relationship equation between ionic strength and Zeta potential was obtained through Zeta potential measurement. pH and ionic strength are the most effective and engineered means of controlling the interfacial charge of the product. Determining the target pH value through titration under worst-case conditions ensures charge safety even under the worst-case process fluctuations. Establishing the quantitative relationship equation provides a dose-effect relationship for quantitative control. This step shifts the problem from how to avoid danger to how to make the system safe. Based on the target pH value and the quantitative relationship equation, a pretreatment formula was obtained through co-optimization calculations. The purpose of obtaining the pretreatment formula is to actively adjust the initial electrochemical state of the food matrix to a position far from its charge reversal point under high voltage. This is equivalent to pre-eliminating the interfacial chemical conditions upon which microorganisms depend for anchoring before HPP treatment. By using worst-case titration, collaborative optimization calculations, and verification, theoretical charge safety requirements are transformed into specific and executable production formulas, which is the key to implementing preventive measures on the production line.

[0064] Methods for obtaining target pH adjustment values ​​include:

[0065] The worst process condition combination is calculated based on the preset process window center point and the preset process fluctuation range. Generally, the superposition value of the preset process window center point and the preset process fluctuation range is set as the worst process condition combination. The preset process window center point is the expected pressure and expected temperature of the planned HPP non-thermal sterilization process, and the preset process fluctuation range is the maximum allowable fluctuation value of pressure and temperature determined based on the equipment control accuracy and historical data.

[0066] The product concentrate is loaded into the high-pressure chamber of the HPP equipment; the HPP equipment is set to the worst combination of process conditions.

[0067] Using a micro-dosing system integrated within or connected to the HPP device, a pH adjuster of known concentration is added dropwise to the product stock solution within the HPP device. After each addition, the HPP device is allowed to stabilize again to obtain a mixed solution. The current pH value and Zeta potential of the mixed solution are measured. The pH value corresponding to the current Zeta potential of the mixed solution simultaneously meeting the measurement fit condition and the model validation condition is recorded. The measurement fit condition is that the absolute value of the difference between the current Zeta potential of the mixed solution and the Zeta potential threshold is not higher than the allowable error value. The model validation condition is that the current pressure and temperature of the mixed solution are substituted into the charge state prediction function to calculate the model predicted Zeta potential, and the absolute value of the difference between the model predicted Zeta potential and the Zeta potential threshold is not higher than the allowable error value. The allowable error value is set based on the instrument accuracy of the Zeta potential meter and the process requirements. Generally, it should not be lower than the instrument accuracy nor higher than the process requirements, with the smaller of the two being preferred, such as ±0.5mV. Restabilization within the HPP equipment requires meeting three conditions: Condition 1: Temperature fluctuations do not exceed the temperature fluctuation threshold for more than 30 seconds; Condition 2: The change in pH value between two consecutive measurements is less than the pH fluctuation threshold, with each pH measurement spaced 30 seconds apart; Condition 3: The average change in Zeta potential over 60 seconds is less than the Zeta potential fluctuation threshold. The temperature fluctuation threshold is set according to the calibration accuracy of commonly used temperature sensors in food and biological laboratories, typically ±0.1℃ to... ±0.3℃ allows setting the temperature fluctuation threshold to ±0.5℃, significantly higher than the calibration accuracy. This ensures that the observed temperature changes accurately reflect the system state, rather than measurement noise. The pH fluctuation threshold is set based on the pH meter's accuracy. A well-calibrated laboratory pH meter typically achieves short-term repeatability of ±0.001 pH in buffer solutions. However, in complex food matrices, due to factors such as liquid junction potential and protein adsorption, actual measurement repeatability is usually ±0.01-0.02 pH. Therefore, the pH fluctuation threshold can be set to 0.02 pH. The Zeta potential fluctuation threshold is set based on the Zeta potential meter's accuracy. Generally, commercially available Zeta potential meters have a standard deviation of ±0.5-2 mV for the repeatability of homogeneous dispersion systems. Therefore, the Zeta potential fluctuation threshold can be set to ±1 mV.

[0068] To ensure absolute safety, a margin pH value is set during the actual production process of the product. The difference between the corresponding pH value and the margin pH value is calculated to obtain the target pH adjustment value. The margin pH value is usually set to 0.2-0.3 pH units, depending on the manufacturer's actual requirements.

[0069] In the above steps, three identical product stock solutions can be prepared. One product stock solution is used for acid-base titration to analyze the target pH adjustment value. The second and third product stock solutions are used to adjust the pH of the product stock solution to the target pH adjustment value under the worst process condition combination and the preset process window center point, respectively. The corresponding Zeta potential is then measured to verify whether the corresponding Zeta potential is not greater than the Zeta potential threshold. If the verification is successful, the target pH adjustment value obtained from the analysis of the first product stock solution is output. Otherwise, the acid-base titration is repeated to analyze the new target pH adjustment value until the verification is successful.

[0070] Reference Figure 2 Methods for obtaining the quantitative relationship between ionic strength and zeta potential include:

[0071] Take a first volume, such as 50 mL of the product stock solution, in a beaker. The first volume needs to be sufficient for multiple measurements and can be adjusted according to the actual situation. Under the preset stirring speed, use a pH adjuster of known concentration to adjust the pH value of the product stock solution to the target pH value and mark it as sample 0. Measure the Zeta potential of sample 0. Specifically, measure the Zeta potential of sample 0 K times, and take the average of the K measurements as the Zeta potential of sample 0. K should be no less than three times to improve measurement accuracy. The preset stirring speed can preferably be 200±50 rpm for 30 minutes, mainly used to mix the pH adjuster and the product stock solution evenly, and can be adjusted according to the actual situation.

[0072] Prepare N clean, identical containers, such as beakers or centrifuge tubes, and number them from 1 to N.

[0073] Add a second volume, such as 45 mL of the product stock solution, to each container. The second volume can be adjusted according to the actual situation. If it is less than 50 mL, electrolyte stock solution and deionized water can be added to make the final total volume of each sample 50 mL.

[0074] Different volumes of electrolyte stock solution were added to each container sequentially to achieve a predetermined gradient distribution of electrolyte concentrations in the N containers, such as 0.05M, 0.1M, 0.2M, 0.3M, 0.4M, 0.5M, 0.75M, and 1.0M, respectively, to obtain samples with different electrolyte concentrations. To ensure consistent matrix concentrations, pure water was added to make the total volume of each sample the same. The types and proportions of electrolytes in the electrolyte stock solution were consistent with the exogenous electrolytes added to the product during actual production, and the types and proportions of exogenous electrolytes were determined based on actual production conditions.

[0075] For each sample in the container, the pH is adjusted to the target value using a known concentration of pH adjuster at a preset stirring speed. The preset stirring speed is preferably 200±50 rpm for 30 minutes, mainly used to mix the pH adjuster with the product concentrate evenly, and can be adjusted according to the actual situation. This step is crucial to ensure that all samples are compared under the exact same pH standard.

[0076] If the sample produces aggregates due to the influence of electrolytes, centrifugation at 3000 rpm for 2 minutes can be performed to remove large aggregates and ensure that the supernatant can be used for Zeta potential measurement. This centrifugation step is only one of the conventional techniques, and the corresponding treatment or adjustment can be selected according to the actual situation to ensure that the sample can be used for Zeta potential measurement.

[0077] For each sample in a container, the Zeta potential of the samples in N containers is measured sequentially at a uniformly set temperature, typically 25°C. K independent measurements are performed on each container, and the average value of the K measurements is taken as the Zeta potential of the corresponding sample.

[0078] Record the electrolyte concentration and corresponding Zeta potential for each sample. Calculate the ionic strength based on the electrolyte concentration and the concentrations and valence states of all ions in the electrolyte. ,in, ions The concentration; ions The valence states are determined; a scatter plot is drawn with ionic strength on the x-axis and Zeta potential on the y-axis.

[0079] By fitting the data points in the scatter plot with the Zeta potential of sample 0, a quantitative relationship equation can be obtained. Generally, the relationship between Zeta potential and ionic strength conforms to the electrostatic double-layer compression theory and can be fitted using the following empirical formula: (e.g., at ionic strength...) Zeta potential prediction value ,in, This is the Zeta potential when the ionic strength is 0, i.e., the Zeta potential of sample 0; This is the asymptotic value of the Zeta potential when the ionic strength is extremely high. It is a mathematical constant; It is the attenuation constant; The ionic strength of the electrolyte solution is used as the basis for determining the asymptotic value of the Zeta potential and the decay constant when the ionic strength is extremely high by performing nonlinear regression fitting on the data points in the scatter plot, thus obtaining a quantitative relationship equation.

[0080] Methods for obtaining pretreatment formulations include:

[0081] Substituting the Zeta potential threshold into the quantitative relationship equation, the ion strength threshold is obtained by solving the equation.

[0082] If the ionic strength threshold is not higher than the maximum ionic strength, the preliminary plan is to set a target pH value and an ionic strength threshold. The maximum ionic strength is a pre-set limit value during product processing, which can be obtained according to the manufacturer's pre-set product quality standards or determined through consumer sensory acceptance experiments. For example, a series of samples with different concentrations of each ion can be prepared, and for each ion concentration, 5-15 concentration gradients can be set. Each ion can be tasted by no less than 50 target consumer evaluators, and the acceptance rating method can be used for selection. The lowest ionic concentration that more than half of the target consumer evaluators judge as unacceptable can be selected. The maximum ionic strength can be calculated based on the lowest ionic concentration of each ion and the ionic valence state.

[0083] If the ionic strength threshold is higher than the maximum ionic strength, a chelating agent is added to the product with the added pH adjustment target value and ionic strength threshold. The minimum chelating agent concentration that ensures the zeta potential of the product with added chelating agent does not exceed the zeta potential threshold is obtained. Specifically, when adding one chelating agent, 5-15 chelating agent concentration gradients can be set according to the regulatory limit concentration of the chelating agent corresponding to the product. Different concentration gradients of chelating agents are added to the product stock solution with the added ionic strength threshold, and the corresponding zeta potential is measured. A concentration-potential curve is plotted with the chelating agent concentration as the vertical axis and the corresponding zeta potential as the horizontal axis. The curve function is obtained by fitting using the least squares method. The fitting process is existing technology and will not be described in detail. The minimum chelating agent concentration that ensures the zeta potential does not exceed the zeta potential threshold is calculated based on the curve function.

[0084] When adding Y chelating agents, where Y > 1, the concentration range of each chelating agent is determined based on regulatory limits and preliminary experiments. A central composite design is used to generate A experimental points, each corresponding to a combination of concentrations of the Y chelating agents. The central composite design is a commonly used experimental design method and is existing technology, so it will not be described in detail here.

[0085] For each experimental point, the test sample was precisely prepared using the product concentrate, ensuring that the pH of the test sample was adjusted to the target pH value and the ionic strength was increased to the maximum ionic strength; Y chelating agents were added according to the concentration of Y chelating agents corresponding to the experimental point; the zeta potential value of the test sample was measured and recorded as the response value;

[0086] The concentration combinations and response values ​​of Y chelating agents at all experimental points were collected. Using the measured Zeta potential as the response value and the concentration of each chelating agent as the independent variable, a second-order response surface model was obtained by fitting multiple linear regression. The steps for fitting the second-order response surface model have been detailed in the method for obtaining the charge state prediction function and are existing technology, so they will not be elaborated further. With the core constraint that the predicted Zeta potential value is not higher than the Zeta potential threshold and the objective of minimizing the total amount of chelating agent added, the minimum chelating agent concentration corresponding to each chelating agent was obtained by solving the problem using the data collected at each experimental point within the experimental concentration range of each chelating agent.

[0087] The initial plan is to set a target pH value, an ionic strength threshold, and a minimum chelating agent concentration. If the minimum chelating agent concentration does not comply with the product's corresponding addition regulations, the target pH value will be adjusted by a preset step size, such as 0.1-0.2 pH units, in the direction that makes the Zeta potential more negative, and then the ionic strength threshold will be recalculated.

[0088] According to the preliminary plan, pretreated samples with the preliminary plan formulation are precisely prepared using the product concentrate. The pretreated samples are then subjected to charge verification and sensory texture verification under worst-case process conditions. If both charge verification and sensory texture verification are passed, the preliminary plan is adopted as the pretreated formulation.

[0089] If the charge verification fails, the ion strength threshold should be slightly increased without exceeding the maximum ion strength. The increase in the ion strength threshold is usually perceptible to the senses but will not cause a drastic change. This is a common step size for engineering testing, such as 5%–10%. Alternatively, within the regulatory limits, the chelating agent concentration can be slightly increased. The amount of chelating agent used is usually quite sensitive, so the step size can be appropriately reduced to make the addition amount safer and more compliant, such as 1%–5%. If the sensory texture verification fails, the ion strength threshold should be slightly increased without exceeding the maximum ion strength, such as 5%–10%, or the chelating agent type can be changed and the verification can be repeated. Verification must be repeated after each adjustment. If the re-verification still fails, the pH adjustment target value should be adjusted by a preset step size within the allowable range of process adjustments, in the direction that makes the Zeta potential more negative. The preset step size needs to be greater than the instrument error. The typical resolution of a commercial pH meter is 0.01, and the accuracy is ±0.02. It can be set to 0.1-0.2 pH units. The step size of 0.1 is much greater than the instrument error, which can reliably detect the pH value. Moreover, the pH value change of 0.1-0.2 is sufficient to cause a significant change in the Zeta potential, which is an effective adjustment range. Furthermore, the preset step size is achievable in food processing and will not cause excessive impact on the product. Alternatively, the worst-case process conditions can be changed, i.e., the preset process fluctuation range can be reduced, and then the validation can be performed again. This involves analyzing historical data from existing equipment during stable operation, calculating the standard deviations of historical pressure and temperature, and setting the reduced preset process fluctuation range based on these standard deviations, such as setting it to two or three times the standard deviation. If the validation still fails, the optimal charge validation result in the formulation that meets sensory texture validation criteria will be output, i.e., the ionic strength threshold and minimum chelating agent concentration corresponding to the maximum Zeta potential measurement (i.e., the minimum absolute value) but still not exceeding the Zeta potential threshold, and a secondary sterilization prompt will be given. All chelating agent concentrations added to the product must comply with the relevant regulations for product addition.

[0090] Specifically, the charge stability verification steps under worst-case process conditions are as follows:

[0091] The pretreated sample is loaded into the high-pressure chamber of the HPP equipment; the HPP equipment is set to the worst process condition combination; the zeta potential after non-thermal sterilization treatment in the HPP equipment is measured. If the measured zeta potential is not higher than the zeta potential threshold, the charge verification is passed; otherwise, the charge verification is not passed.

[0092] The sensory texture verification steps are as follows:

[0093] Based on the product type, the original product solution and pretreated samples are measured and analyzed to obtain quality index values. The quality indexes for non-solid products include viscosity, brightness, red-green value, and yellow-blue value; the quality indexes for solid products include hardness, elasticity, and water retention.

[0094] For liquid and semi-solid products, viscosity was measured using a rotational viscometer at the standard shear rate; lightness, red-green value, and yellow-blue value were measured using a colorimeter; the standard shear rate was determined through rheological pre-experiments, which were performed by measuring the product concentrate at 0.1 s⁻¹. -1 —1000s -1 The viscosity curve within the specified range is used to determine the standard shear rate, which is the shear rate corresponding to the apparent viscosity decreasing to approximately 80%–90% of the initial viscosity. Alternatively, it can be set based on typical shear conditions during oral processing and swallowing, such as 50 seconds. -1 —100s -1 And it remained consistent in all comparative experiments.

[0095] For solid products, hardness, elasticity, and water retention are obtained through texture profile analysis. Specifically, the pretreated sample is equilibrated at a standard temperature for at least 2 hours, and then cut into at least 5 identical sample blocks. During cutting, the cutting direction must be consistent; for example, the transverse and longitudinal grains of meat must be consistent, avoiding obvious tendons, pores, and other defects. The standard temperature is the product's optimal storage temperature, such as 4℃±1℃. The sample block is placed in the center of the texture analyzer stage, and each sample block undergoes two compression cycles under pre-set test conditions. The pre-set test conditions can be: pre-test speed set to 1.0 mm / s, test speed set to 1.0 mm / s, return speed set to 1.0 mm / s, compression deformation set to 50% (i.e., the probe is pressed down to 50% of the initial height of the sample block), pause time between two compressions set to 3.0 seconds, trigger force set to 0.049 N, and data acquisition rate set to 200 Hz. Compression is performed using a cylindrical flat-bottom probe. The test conditions can be adjusted according to the product, but the test conditions for the same group of sample blocks must be consistent.

[0096] The pressure versus time curve during the compression process is collected by the instrument, and the maximum peak force in the first compression cycle is obtained as the hardness. The elasticity is obtained by the ratio of the height recovered by the sample between two compressions, that is, the height difference between the height reached by the sample at the beginning of the second compression and the height of the lowest point after compression, to the deformation height of the first compression, that is, the distance of the probe pressing down.

[0097] Take an initial mass M of 2.0 ± 0.5 g of the pretreated sample taken for water holding capacity determination, place it in a centrifuge tube sieve pre-lined with quantitative filter paper, place the centrifuge tubes symmetrically into the centrifuge, set the centrifugation conditions and start centrifugation. The centrifugation conditions can be set as follows: centrifugal force 10000 × g, temperature 4℃, time 20 minutes, where g is the acceleration due to gravity.

[0098] Use tweezers to remove the sample from the quantitative filter paper after centrifugation to obtain wet filter paper. Weigh the wet filter paper. Calculate the weight difference between the wet filter paper and the quantitative filter paper. Calculate the ratio of the weight difference to M, and then multiply by 100% to obtain the water retention capacity.

[0099] Calculate whether the difference between the quality index values ​​of the pretreated sample and the original product solution is within the preset threshold range. If so, the verification passes; otherwise, the verification fails. The preset threshold range is determined by referring to the current national standards, industry standards, enterprise internal control standards, or quality agreements of major customers for the product, specifying the allowable tolerance range for relevant physical properties such as viscosity, hardness, and color. If there is no direct specification, the index range corresponding to qualified products in the documents can be analyzed, and half the width of the index range corresponding to qualified products or the maximum allowable deviation from the nominal value can be taken as the threshold. For example, if a yogurt company's standard specifies that the qualified viscosity range is 1500±200 mPa·s, then the threshold can be set to ±13.3% (200 / 1500).

[0100] Charge verification and sensory texture verification are cross-validations of theory and practice. Charge verification confirms the effectiveness of preventative measures under actual high-voltage conditions; sensory texture verification ensures that the sacrifices made for safety do not compromise the core quality of the product. Dual verification is a critical quality gate for a solution to move from the laboratory to the production line.

[0101] Based on the safe operating zone and the biomechanics of microbial quiescence, a sterilization efficiency function and a robustness function are constructed, and initial process parameters are obtained through multi-objective optimization.

[0102] Reference Figure 3 Methods for obtaining initial process parameters include:

[0103] All combinations of process parameters within the safe operating zone are obtained as a set of candidate points; the optimization search is strictly limited to the safe operating zone, which fundamentally ensures that any selected pressure and temperature combination will not trigger charge reversal.

[0104] Based on microbial inactivation mechanics, functions corresponding to the scale and shape parameters of candidate sites are constructed. The maximum logarithmic decrease of candidate sites within the maximum allowable time is calculated using these functions to obtain the bactericidal efficiency function. ,in, This is a function for sterilization efficiency; The maximum allowable time can be set according to the microbial growth time; For scale parameters; The shape parameter; the scale parameter and shape parameter are obtained through fitting experimental data. Specifically, in the safe operating area, at least 5 combinations of process parameters are selected, and for each combination of process parameters... Design at least 5 different pressure holding time points. , For the first The pressure corresponding to each combination of process parameters For the first Temperature corresponding to a combination of process parameters; The value ranges from 1 to 5, corresponding to the number of combinations of process parameters. The values ​​range from 1 to 5, corresponding to the number of pressure holding time points. These five different pressure holding time points should cover the range from the expected reduction of microbial count to one-tenth of the original count to the microbial count reaching or exceeding the minimum sterilization requirement. In addition, an initial control sample was set up that did not undergo any high-pressure treatment, i.e., the pressure holding time was 0.

[0105] The pre-defined target microorganism, such as a stationary culture of Listeria monocytogenes ATCC 19115, is inoculated into the pretreated matrix of the test product at a known concentration and thoroughly mixed to ensure a consistent initial microbial concentration, thus obtaining the inoculated sample. Pretreatment means that the pH has been adjusted to the target value and the ionic strength has been determined.

[0106] The inoculated samples are aliquoted into multiple flexible, sterile sample bags and heat-sealed. The sealing temperature is determined based on the bag material and thickness, typically at 160℃±20℃ for 3±0.5 seconds. A second heat seal is performed 1 cm above the first sealing line, creating a double seal. Each bag has a uniform volume; for example, 5 mL. The specific parameters are used for each combination. Prepare at least 3 parallel sample bags.

[0107] For each treated sample bag and the untreated initial sample (i.e., the sample bag with a holding time of 0), the standard microbial plate count method was followed: according to 10 -1 10 -2 10 -3 …the dilution sequence is used for serial dilution. Based on experience or preliminary experiments, select 2 to 3 consecutive dilutions that are expected to produce 30 to 300 colonies each and spread them on agar plates. Incubate at 37°C for 24 to 48 hours. The standard microbial plate counting method is an existing technology and the specific operation steps will not be described in detail.

[0108] The colony-forming units (CFUs) on each plate were counted to estimate the number of viable bacteria per unit volume. The ratio of CFUs to the inoculated sample volume in the sample bag was calculated to obtain the concentration of viable microorganisms per milliliter of sample. .

[0109] Calculate the logarithm of the ratio of the surviving microbial concentration per milliliter of sample to the initial microbial concentration to obtain the logarithmic survival rate of microorganisms under each treatment condition. The initial microbial concentration is the arithmetic mean of the microbial concentrations of all initial control samples.

[0110] Obtain each process point The experimental dataset below The Weibull model was used for nonlinear regression fitting; for example, the logarithmic survival rate of microorganisms. ;

[0111] Furthermore, empirical models for P and T are established for both scale and shape parameters. These empirical models are fitted using a second-order response surface model; for example, the scale parameter function... ,in, , , , , and The coefficients are the scaling parameter function coefficients; For pressure; For the corresponding temperature; the scale parameter, as a time parameter, is always positive, and it often has an exponential relationship with pressure and temperature. Direct linear fitting may lead to negative predictions, violating physical meaning. Therefore, a natural logarithmic transformation is performed on δ before modeling. Shape parameter function ,in, , , , , and The coefficients of the shape parameter function are given; the coefficients of the scale parameter function and the coefficients of the shape parameter function are obtained by fitting using multiple linear regression.

[0112] Calculate the shortest Euclidean distance from each process point within the safe operating area to the boundary of the safe operating area, calculate the maximum value of the shortest Euclidean distance, and obtain the robustness function;

[0113] The constraints are defined as follows: pressure and temperature are within the allowable pressure and temperature ranges, respectively. The lower limit of the allowable pressure range is determined based on the microbial inactivation kinetic model, according to the minimum pressure required to reduce the number of microorganisms to at least one-tenth of their original number; the upper limit is the smaller of the upper limit of the conventional operating pressure recommended by the high-pressure equipment manufacturer and the upper limit of the physical tolerance pressure of the product and its packaging; the lower limit of the allowable temperature range is the lowest temperature to prevent harmful phase changes such as freezing during high-pressure processing; the upper limit is the highest temperature that does not cause thermal damage to the product and meets the auxiliary sterilization requirements, usually not exceeding 45°C; the sterilization efficiency function value is not lower than the minimum sterilization requirement value; the minimum sterilization requirement value is directly referenced from the applicable national mandatory food safety standards, based on the category of the target product, specifying the minimum logarithmic reduction value required for a specific target microorganism. For example, for ready-to-eat meat products, the requirements for Listeria monocytogenes are set with reference to the "National Food Safety Standard for Microbiological Examination of Food - Examination of Listeria monocytogenes" GB4789.30-2016.

[0114] The sterilization efficiency function and robustness function are normalized, and then a comprehensive scoring function is constructed using a weighted comprehensive scoring method. A comprehensive score is calculated for each candidate point that meets the constraints. The weighting coefficients are set according to the actual production strategy. If the actual production strategy focuses more on safety, the weighting weight corresponding to the robustness function is increased.

[0115] The process parameters corresponding to the candidate points ranked first in descending order of comprehensive scores are selected as the initial process parameters. The top A candidate points can be output as backup process parameters. If the actual survival rate of microorganisms is higher than the survival rate threshold in later tests, process parameters can be selected sequentially from the backup process parameters for non-thermal sterilization. Under the premise of safety, the optimization algorithm simultaneously pursues maximum sterilization efficiency and maximum process robustness, that is, the farther the operation point is from the charge reversal boundary, the better, so as to obtain process parameters that are both effective and reliable.

[0116] The HPP equipment performs non-thermal sterilization based on the pretreatment formula and initial process parameters, and records the actual pressure and temperature at each time point. Based on the actual pressure and temperature at each time point, the zeta potential change trajectory during the treatment process is calculated using the charge state prediction function. The charge risk exposure is obtained by integral calculation based on the zeta potential change trajectory and the zeta potential threshold.

[0117] Reference Figure 4 Methods for obtaining charge risk exposure include:

[0118] The HPP equipment performs non-thermal sterilization based on the pretreatment formula and initial process parameters, and records the actual pressure and temperature at each time point. Based on the actual pressure and temperature at each time point, and combined with the charge state prediction function, it calculates the Zeta potential at each time point during the treatment process. When the Zeta potential is not higher than the Zeta potential threshold, the charge risk exposure at the corresponding time point is 0. When the Zeta potential is greater than the Zeta potential threshold but not higher than 0, the difference between the Zeta potential and the Zeta potential threshold is calculated to obtain the instantaneous charge risk exposure at the corresponding time point. When the Zeta potential threshold is greater than 0, the penalty product of the Zeta potential and the penalty coefficient is calculated, and then the difference between the penalty product and the Zeta potential threshold is calculated to obtain the instantaneous charge risk exposure at the corresponding time point. The penalty coefficient is a number not less than 1, used to penalize the surge in risk caused by the reversal zone, and can be set based on empirical values. The instantaneous charge risk exposure at each time point for a single batch of products is integrated over the treatment time to obtain the charge risk exposure.

[0119] Based on the charge risk exposure, the actual microbial survival rate of the product after HPP equipment treatment, the charge state prediction function, and the safe operating area, the process parameters are optimized and updated in conjunction with the next batch of product concentrate.

[0120] Methods for optimizing and updating process parameters include:

[0121] After the product is processed by the HPP equipment, it undergoes standard microbial testing to obtain the microbial concentration of the product. Based on the initial inoculation concentration, the actual sterilization efficiency value, i.e., the actual logarithmic reduction value, is calculated.

[0122] The sterilization efficiency function is corrected based on the charge risk exposure level to obtain a corrected theoretically predicted sterilization efficiency value. The undetermined coefficients in the corrected sterilization efficiency function are fitted using U batch data pairs (charge risk exposure level and actual microbial survival rate of the product). U can be set to 3-5 based on experience. The actual microbial survival rate of the product is used as the sterilization efficiency function value to obtain an updated sterilization efficiency function. For example, if the sterilization efficiency function is corrected by combining charge risk exposure level, the resulting updated sterilization efficiency function is: ,in, This refers to the amount of charge risk exposure. The charge protection effect coefficient is used to quantify the degree to which charge risk weakens the sterilization effect. Data on the charge risk exposure of U batches and the actual microbial survival rate of the product are substituted into the updated sterilization efficiency function to obtain the charge protection effect coefficient, thus yielding the updated sterilization efficiency function. Through posterior analysis, the actual process fluctuation trajectory is transformed into charge risk exposure, which is correlated with the observed decrease in sterilization effect. The charge protection effect coefficient is used to quantify for the first time the specific degree of weakening of microbial inactivation efficiency by charge anchoring protection. The inactivation mechanics model is corrected using charge risk exposure and the charge protection effect coefficient, enabling the model to more accurately predict the actual sterilization effect under anchoring risk. This corrected model guides the next round of process optimization, giving the system the ability to learn from actual production and continuously approach the optimal solution.

[0123] Based on the updated sterilization efficiency function, charge state prediction function, and safe operating zone, the process parameters for the next batch of products are updated in conjunction with the method steps used to obtain the initial process parameters.

[0124] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0125] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing process parameters of HPP non-thermal sterilization based on microbial inactivation kinetics, characterized in that, include: Zeta potentials under different pressures and temperatures are collected. Through fitting analysis, charge state prediction functions are obtained. The charge state prediction functions are solved and divided to obtain the safe operating area. Collect the product stock solution, and conduct experimental analysis and data fitting on the product stock solution in conjunction with the safe operating area to obtain the pretreatment formula and initial process parameters; The HPP equipment performs non-thermal sterilization based on the pretreatment formula and initial process parameters, and records the actual pressure and actual temperature at each time point. Based on the actual pressure and temperature at each time point, the trajectory of Zeta potential change during the treatment process is calculated using the charge state prediction function; the charge risk exposure is obtained by integral calculation based on the Zeta potential change trajectory and the Zeta potential threshold. Based on the charge risk exposure, the actual microbial survival rate of the product after HPP equipment treatment, the charge state prediction function, and the safe operating area, the process parameters are optimized and updated in conjunction with the next batch of product concentrate.

2. The method for optimization of HPP non-thermal sterilization process parameters based on microbial inactivation kinetics as claimed in claim 1, wherein, Methods for obtaining charge state prediction functions include: Zeta potentials were collected under different pressures and corresponding temperatures; With pressure and corresponding temperature as independent variables and Zeta potential as dependent variable, a second-order response surface model is used to fit the charge state prediction function. The trajectory line where the charge state prediction function value equals 0 is taken as the charge reversal critical line. A Zeta potential threshold is set, and the area where the charge state prediction function value is lower than the Zeta potential threshold is defined as the safe operating area. The Zeta potential threshold is set according to the charge reversal critical line, the preset process fluctuation range, and the preset safety margin.

3. The method for optimization of HPP non-thermal sterilization process parameters based on microbial inactivation kinetics as claimed in claim 1, wherein, Methods for obtaining pretreatment formulations include: The pH value of the product stock solution was measured, and acid-base titration and reverse calculation were performed in combination with the charge state prediction function and the preset process fluctuation range to obtain the pH adjustment target value that ensures the Zeta potential remains below the Zeta potential threshold under the preset worst-case conditions. Different concentrations of ionic strength regulators were added to the sampled product stock solution, and the quantitative relationship equation between ionic strength and Zeta potential was obtained by measuring the Zeta potential. Based on the pH adjustment target value and the quantitative relationship equation, the pretreatment formula was obtained through synergistic optimization calculation.

4. The method for optimization of HPP non-thermal sterilization process parameters based on microbial inactivation kinetics as claimed in claim 3, wherein, Methods for obtaining target pH adjustment values ​​include: The worst combination of process conditions is calculated based on the preset process window center point and the preset process fluctuation range; the product concentrate is loaded into the high-pressure chamber of the HPP equipment; the HPP equipment is set to the worst combination of process conditions. A pH adjuster of known concentration is added dropwise to the product stock solution in the HPP equipment to obtain a mixed solution. The current pH value and Zeta potential of the mixed solution are measured. The pH value corresponding to the current Zeta potential of the mixed solution simultaneously meeting the measurement consistency condition and the model validation condition is recorded. The measurement consistency condition is that the absolute value of the difference between the current Zeta potential of the mixed solution and the Zeta potential threshold is not higher than the allowable error value. The model validation condition is that the model predicted Zeta potential is obtained by substituting the current pressure and temperature of the mixed solution into the charge state prediction function, and the absolute value of the difference between the model predicted Zeta potential and the Zeta potential threshold is not higher than the allowable error value. Calculate the difference between the corresponding pH value and the remaining pH value to obtain the target pH adjustment value.

5. The method for optimization of HPP non-thermal sterilization process parameters based on microbial kinetics of inactivation as claimed in claim 3, wherein, Methods for obtaining the quantitative relationship between ionic strength and zeta potential include: Take the first volume of the product stock solution into a beaker, and use a pH adjuster of known concentration to adjust the pH value of the product stock solution to the target pH value; and mark it as sample 0, and measure the Zeta potential of sample 0; Prepare N identical containers, numbered 1 to N respectively; add a second volume of product stock solution to each container; add different volumes of pre-prepared electrolyte stock solution to each container in sequence, so that the final concentration of electrolyte in the N containers is distributed in a preset gradient, and obtain samples with different electrolyte concentrations; For each sample in the container, the pH was adjusted to the target value using a known concentration of pH adjuster. At a uniformly set temperature, the zeta potential of the sample in each container was measured. The electrolyte concentration and zeta potential of each sample were recorded. The ionic strength was calculated based on the electrolyte concentration and the concentrations and valence states of all ions in the electrolyte. A scatter plot was drawn with the ionic strength as the abscissa and the zeta potential as the ordinate. The data points in the scatter plot were fitted with the zeta potential of sample 0 to obtain a quantitative relationship equation.

6. The method for optimizing HPP non-thermal sterilization process parameters based on microbial inactivation mechanics according to claim 3, characterized in that, Methods for obtaining pretreatment formulations include: Substituting the Zeta potential threshold into the quantitative relationship equation, the ion strength threshold is obtained by solving the equation. If the ionic strength threshold is not higher than the maximum ionic strength, the preliminary plan is to adjust the target pH value and the ionic strength threshold. If the ionic strength threshold is higher than the maximum ionic strength, a chelating agent is added to the product with the added pH adjustment target value and ionic strength threshold to obtain the minimum chelating agent concentration that ensures the Zeta potential of the product with added chelating agent is not higher than the Zeta potential threshold; the preliminary scheme is then the pH adjustment target value, ionic strength threshold, and minimum chelating agent concentration. According to the preliminary plan, a pretreated sample with the preliminary plan formulation is prepared using the product stock solution. The pretreated sample is then subjected to charge verification and sensory texture verification. If both charge verification and sensory texture verification are passed, the preliminary plan is adopted as the pretreated formulation.

7. The method for optimization of HPP non-thermal sterilization process parameters based on microbial inactivation kinetics as claimed in claim 1, wherein, Methods for obtaining initial process parameters include: Based on the safe operating zone and the biomechanics of microbial quiescence, a sterilization efficiency function and a robustness function are constructed, and initial process parameters are obtained through multi-objective optimization.

8. The method for optimization of HPP non-thermal sterilization process parameters based on microbial kinetics of inactivation as claimed in claim 7, wherein, Methods for obtaining initial process parameters include: Obtain all combinations of process parameters in the safe operating area as a set of candidate points; Based on the microbial inactivation mechanics model, functions corresponding to the scale parameters and shape parameters of candidate points are constructed. The maximum value of the logarithmic decrease of candidate points within the maximum allowable time is calculated based on the functions corresponding to the scale parameters and shape parameters to obtain the bactericidal efficiency function. Calculate the shortest Euclidean distance from each process point within the safe operating area to the boundary of the safe operating area, calculate the maximum value of the shortest Euclidean distance, and obtain the robustness function; Define the constraints, including: the pressure is within the allowable pressure range, the temperature is within the allowable temperature range, and the sterilization efficiency function value is not lower than the minimum sterilization requirement value; The sterilization efficiency function and robustness function are normalized, and then a comprehensive scoring function is constructed using a weighted comprehensive scoring method. The comprehensive score is calculated for each candidate point that meets the constraints. Sort the candidates by comprehensive score in descending order and select the process parameters corresponding to the candidate with the highest score as the initial process parameters.

9. The method for optimizing HPP non-thermal sterilization process parameters based on microbial inactivation mechanics according to claim 1, characterized in that, Methods for obtaining charge risk exposure include: The HPP equipment performs non-thermal sterilization based on the pretreatment formula and initial process parameters, and records the actual pressure and temperature at each time point. Based on the actual pressure and temperature at each time point, and combined with the charge state prediction function, it calculates the Zeta potential at each time point during the treatment process. When the Zeta potential is not higher than the Zeta potential threshold, the charge risk exposure at the corresponding time point is 0. When the Zeta potential is greater than the Zeta potential threshold but not higher than 0, the difference between the Zeta potential and the Zeta potential threshold is calculated to obtain the instantaneous charge risk exposure at the corresponding time point. When the Zeta potential threshold is greater than 0, the penalty product of the Zeta potential and the penalty coefficient is calculated, and then the difference between the penalty product and the Zeta potential threshold is calculated to obtain the instantaneous charge risk exposure at the corresponding time point. The instantaneous charge risk exposure at each time point for a single batch of products is integrated over the treatment time to obtain the charge risk exposure.

10. The method for optimization of HPP non-thermal sterilization process parameters based on microbial kinetics of inactivation as claimed in claim 9, wherein, Methods for optimizing and updating process parameters include: After the product is processed by the HPP equipment, it undergoes standard microbial testing to obtain the microbial concentration of the product. Based on the initial inoculation concentration, the actual sterilization efficiency value, i.e. the actual log reduction value, is calculated. The sterilization efficiency function is corrected based on the charge risk exposure to obtain the corrected theoretically predicted sterilization efficiency value. Based on U batch data pairs, the undetermined coefficients in the corrected sterilization efficiency function are fitted to obtain the updated sterilization efficiency function. Each batch data pair includes the charge risk exposure and the actual sterilization efficiency value corresponding to the batch. The process parameters for the next batch of products are updated based on the updated sterilization efficiency function, charge state prediction function, and safe operating zone, combined with the method used to obtain the initial process parameters.

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