Soybean enzymolysis protein peptide preparation process optimization system and method

Abstract

The invention discloses a preparation process optimization system and method of soybean enzymolysis protein peptides, and relates to the technical field of bio-enzymes, initial enzymolysis condition evaluation is carried out on soybean protein raw materials, an initial enzymolysis parameter combination is obtained, an experimental enzymolysis reaction is carried out, a hydrolysis degree change curve is obtained, whether the hydrolysis degree increase rate reaches a preset threshold value or not is judged, and if yes, the soybean enzymolysis protein peptides are obtained. If the peptide molecular weight distribution meets the target range, continuing the enzymolysis reaction, and when the peptide molecular weight distribution meets the target range, separating and purifying an enzymolysis product, recording a final enzymolysis parameter combination, judging whether a preset functional index is met or not, and if so, taking the final enzymolysis parameter combination as an optimized preparation process; if not, dynamically adjusting a preset threshold value or a target range; and if not, adjusting parameters in the initial enzymolysis parameter combination to form an optimized enzymolysis parameter combination. The functional activity and the stability of a protein peptide product are remarkably improved, and the production cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of bioenzyme technology, specifically to an optimized system and method for preparing soybean enzymatically hydrolyzed protein peptides. Background Technology

[0002] Soy protein peptides, as a functional food ingredient with diverse physiological activities, have seen increasingly widespread application in health foods, pharmaceutical intermediates, and special nutritional products in recent years. Studies have shown that soy protein peptides possess various physiological functions, including antioxidant, blood pressure-lowering, blood lipid-lowering, immune-regulating, and anti-fatigue effects. These functions are closely related to the peptide's molecular weight distribution, specific amino acid sequence, and spatial configuration. Currently, soy protein peptides are mainly prepared through enzymatic hydrolysis of soy protein isolate or concentrate, and the preparation process directly affects the product's functional properties and bioactivity.

[0003] However, existing technologies suffer from three key problems: First, the enzymatic hydrolysis process lacks an intermediate evaluation mechanism, meaning product quality can only be assessed after the entire process is completed, hindering timely adjustments. Second, process optimization typically employs a "one-step" strategy, neglecting the complex relationship between changes in the degree of hydrolysis and the final product's functional characteristics. Third, the lack of a closed-loop design for adjusting process parameters in reverse order based on product functional characteristics makes it difficult to determine targeted adjustments when product functionality fails to meet standards. The core of these problems lies in the lack of multi-level logical judgment and branch decision-making mechanisms in existing technologies, preventing precise control across the entire chain from "process control to molecular characteristics to functional characteristics," ultimately leading to unstable product quality and unsatisfactory functional activity.

[0004] Therefore, this invention proposes an optimized system and method for the preparation of soybean enzymatically hydrolyzed protein peptides. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art. To this end, this invention proposes an optimized system and method for the preparation of soybean enzymatically hydrolyzed protein peptides, which significantly improves the functional activity and stability of protein peptide products while reducing production costs.

[0006] To achieve the above objectives, this application proposes an optimized method for preparing soybean enzymatically hydrolyzed protein peptides, comprising the following steps:

[0007] An optimized method for preparing soybean enzymatically hydrolyzed protein peptides, characterized by comprising the following steps:

[0008] Step 1: Evaluate the initial enzymatic hydrolysis conditions of soybean protein raw materials to obtain the initial enzymatic hydrolysis parameter combination, which includes enzyme type, enzyme activity level and reaction environment parameters;

[0009] Step 2: Conduct an experimental enzymatic hydrolysis reaction based on the initial enzymatic hydrolysis parameter combination, monitor the change trend of the degree of hydrolysis during the enzymatic hydrolysis process, and obtain the degree of hydrolysis change curve;

[0010] Step 3: Analyze the hydrolysis degree change curve to determine whether the hydrolysis degree growth rate has reached a preset threshold. If it has reached the preset threshold, proceed to Step 4; otherwise, proceed to Step 5.

[0011] Step 4: Continue the enzymatic hydrolysis reaction under the current enzymatic hydrolysis conditions and monitor the peptide molecular weight distribution in real time. When the peptide molecular weight distribution meets the target range, proceed to Step 6.

[0012] Step 5: Adjust the parameters in the initial enzymatic hydrolysis parameter combination to form an optimized enzymatic hydrolysis parameter combination. The optimized enzymatic hydrolysis parameter combination is achieved by changing the enzyme type ratio or adjusting the reaction environment parameters. Return to Step 2.

[0013] Step 6: Separate and purify the enzymatic hydrolysis products to obtain the target soybean protein peptide product, and record the final enzymatic hydrolysis parameter combination;

[0014] Step 7: Evaluate the functional characteristics of the target soybean protein peptide product and determine whether it meets the preset functional indicators. If it does, use the final enzymatic hydrolysis parameter combination as the optimized preparation process; if it does not, dynamically adjust the preset threshold or the target range and return to Step 1.

[0015] The initial enzymatic hydrolysis condition evaluation of soybean protein raw materials to obtain the initial enzymatic hydrolysis parameter combination includes the following steps:

[0016] Step 11: Select soybean protein raw materials and pre-treat them, including defatting, impurity removal and pH adjustment.

[0017] Step 12: Screen for potentially applicable protease species and establish an enzyme species evaluation system;

[0018] Step 13: Determine the suitable reaction conditions for each candidate enzyme, including suitable pH, suitable temperature and suitable substrate concentration;

[0019] Step 14: Evaluate the enzyme activity level of each candidate enzyme and establish standard enzyme activity units;

[0020] Step 15: Based on the enzyme type evaluation system, the suitable reaction conditions, and the enzyme activity level, determine the initial enzymatic hydrolysis parameter combination, including enzyme type, enzyme activity level, and reaction environment parameters, to form the initial enzymatic hydrolysis parameter combination.

[0021] The process of obtaining the hydrolysis degree change curve includes the following steps:

[0022] Step 21: Prepare the enzymatic hydrolysis reaction system, including the preparation of substrate solution, enzyme solution and buffer system;

[0023] Step 22: Establish an enzymatic hydrolysis reaction apparatus, including a constant temperature water bath, an automatic pH adjustment system, and a stirring system;

[0024] Step 23: Start the enzymatic hydrolysis reaction in the enzymatic hydrolysis reaction apparatus and take samples for analysis at preset time intervals;

[0025] Step 24: Measure the change in degree of hydrolysis during the enzymatic hydrolysis process, using the pH-stat method or OPA method for real-time monitoring, and calculate the real-time degree of hydrolysis;

[0026] Step 25: Based on the real-time collected hydrolysis degree, plot the hydrolysis degree change curve and analyze the enzymatic hydrolysis kinetic characteristics.

[0027] The analysis of the hydrolysis degree change curve and the determination of the hydrolysis degree growth rate include the following steps:

[0028] Step 31: Calculate the hydrolysis degree growth rate by differentiating the hydrolysis degree change curve, and establish a hydrolysis degree growth rate evaluation model;

[0029] Step 32: Set a preset threshold for the degree of hydrolysis growth rate based on the minimum acceptable value of the degree of hydrolysis growth rate in the intermediate transition stage;

[0030] Step 33: Determine whether the rate of increase in degree of hydrolysis has reached the preset threshold.

[0031] The real-time monitoring of peptide molecular weight distribution includes the following steps:

[0032] Step 41: Based on the enzymatic hydrolysis conditions determined in Step 3 to have reached the preset threshold, maintain the enzymatic hydrolysis reaction to obtain a stable enzymatic hydrolysis reaction system;

[0033] Step 42: Based on the sample in the stable enzymatic hydrolysis reaction system, the molecular weight distribution of peptides is measured and analyzed to obtain characteristic parameters of molecular weight distribution;

[0034] Step 43: Based on the molecular weight distribution characteristic parameters, set the molecular weight distribution requirements for the target product to obtain the molecular weight distribution range of the target peptide;

[0035] Step 44: Based on the continuous measurement results of the molecular weight distribution characteristic parameters, analyze the trend of peptide molecular weight distribution changes and obtain a molecular weight distribution prediction model;

[0036] Step 45: Based on the molecular weight distribution prediction model and the target peptide molecular weight distribution range, evaluate the current peptide molecular weight distribution and obtain the peptide molecular weight distribution conformity judgment result.

[0037] The process of forming the optimized combination of enzymatic hydrolysis parameters includes the following steps:

[0038] Step 51: Based on the hydrolysis degree change curve and the peptide molecular weight distribution, analyze the limiting factors of enzymatic hydrolysis and obtain the limiting factor identification results;

[0039] Step 52: Based on the results of the identification of limiting factors, adjust the enzyme type ratio to obtain an optimized enzyme system composition scheme;

[0040] Step 53: Based on the results of the limiting factor identification, adjust and design the reaction environment parameters to obtain an optimized reaction condition scheme;

[0041] Step 54: Based on the optimized enzyme system composition scheme and the optimized reaction condition scheme, the enzymatic hydrolysis parameters are systematically adjusted to obtain the optimized enzymatic hydrolysis parameter combination;

[0042] The recording of the final enzymatic hydrolysis parameter combination includes the following steps:

[0043] Step 61: Based on heat treatment or pH adjustment, terminate the enzymatic hydrolysis reaction in step four that conforms to the molecular weight distribution range of the target peptide to obtain a pre-treated enzymatic hydrolysate.

[0044] Step 62: Based on the ultrafiltration-nanofiltration tandem process, the pre-treated enzymatic hydrolysate is subjected to membrane separation to obtain pre-fractionated peptide components;

[0045] Step 63: Based on chromatographic separation technology, the peptide components are further purified to obtain a high-purity target peptide product;

[0046] Step 64: Based on concentration and drying technology, the high-purity target peptide product is subjected to final processing to obtain the finished soybean protein peptide product;

[0047] Step 65: Based on the full process parameter records, the preparation conditions of the finished soybean protein peptide product are systematically organized to obtain the final enzymatic hydrolysis parameter combination and process database.

[0048] The evaluation of the functional characteristics of the target soybean protein peptide product and the determination of whether it meets the preset functional indicators include the following steps:

[0049] Step 71: Based on standard physicochemical analysis methods, perform basic characteristic determination on the finished soybean protein peptide product to obtain product functional characteristic data;

[0050] Step 72: Based on the product functional characteristic data, compare and analyze it against the preset functional indicators to obtain the functional indicator compliance assessment results;

[0051] Step 73: Based on the functional indicator compliance assessment results, determine whether the product meets the preset functional indicators and obtain process optimization decisions.

[0052] The method of dynamically adjusting the preset threshold or the target range includes:

[0053] Based on the indicators that do not meet the standards in the functional indicator compliance assessment results, the preset threshold in step three is quantitatively adjusted to obtain the adjusted preset threshold.

[0054] Based on the indicators that do not meet the standards in the functional indicator compliance assessment results, the target range in step four is quantitatively adjusted to obtain the adjusted target range, and the process is restarted from step one.

[0055] This application proposes an optimization system for the preparation process of soybean enzymatically hydrolyzed protein peptides, including an enzymatic hydrolysis parameter combination collection module, a hydrolysis degree change curve collection module, a growth rate judgment module, an enzymatic hydrolysis parameter optimization module, and a separation and purification module; wherein, the modules are connected by electrical means.

[0056] The enzymatic hydrolysis parameter combination collection module evaluates the initial enzymatic hydrolysis conditions of soybean protein raw materials, obtains the initial enzymatic hydrolysis parameter combination, and sends the initial enzymatic hydrolysis parameter combination to the hydrolysis degree change curve collection module.

[0057] The hydrolysis degree change curve collection module performs an experimental enzymatic hydrolysis reaction based on the initial enzymatic hydrolysis parameter combination, monitors the hydrolysis degree change trend during the enzymatic hydrolysis process, obtains the hydrolysis degree change curve, and sends the hydrolysis degree change curve to the growth rate judgment module.

[0058] The growth rate judgment module analyzes the hydrolysis degree change curve and determines whether the hydrolysis degree growth rate has reached a preset threshold. If the preset threshold is reached, the module proceeds to the separation and purification module; otherwise, the module proceeds to the enzymatic hydrolysis parameter optimization module.

[0059] The enzymatic hydrolysis parameter optimization module adjusts the parameters in the initial enzymatic hydrolysis parameter combination to form an optimized enzymatic hydrolysis parameter combination.

[0060] The separation and purification module continues the enzymatic hydrolysis reaction under the current conditions and monitors the peptide molecular weight distribution in real time. When the peptide molecular weight distribution meets the target range, the enzymatic hydrolysis product is separated and purified to obtain the target soybean protein peptide product. The final enzymatic hydrolysis parameter combination is recorded, and the functional characteristics of the target soybean protein peptide product are evaluated to determine whether it meets the preset functional indicators. If it does, the final enzymatic hydrolysis parameter combination is used as the optimized preparation process; if it does not, the preset threshold or the target range is dynamically adjusted, and the process is transferred back to the enzymatic hydrolysis parameter combination collection module.

[0061] Compared with the prior art, the beneficial effects of the present invention are:

[0062] This invention evaluates the initial enzymatic hydrolysis conditions of soybean protein raw materials to obtain an initial combination of enzymatic hydrolysis parameters; conducts experimental enzymatic hydrolysis reactions based on the initial parameters, monitoring the trend of hydrolysis degree changes; analyzes the hydrolysis degree change curve to determine whether the hydrolysis rate has reached a preset threshold; continues enzymatic hydrolysis under suitable conditions and monitors peptide molecular weight distribution; adjusts enzymatic hydrolysis parameters as needed to form an optimized combination; separates and purifies the enzymatic hydrolysis products to obtain the target product; and finally evaluates the functional characteristics of the product to verify the optimization effect. The decision to continue enzymatic hydrolysis or adjust parameters is made by determining whether the hydrolysis degree increase rate has reached a preset threshold; the second decision point is in the product evaluation stage, where the decision to confirm successful optimization or dynamically adjust process parameter thresholds is made by determining whether the functional characteristics meet preset indicators. This "process first, function second" dual judgment mechanism forms a complete closed-loop optimization system, giving process adjustments clear direction and precision; and establishes a dual monitoring system based on the hydrolysis degree change curve and peptide molecular weight distribution, realizing real-time optimization and control of the enzymatic hydrolysis process. This optimizes the soybean protein peptide preparation process, significantly improving the functional activity and stability of the product and reducing production costs. Attached Figure Description

[0063] Figure 1 This is a flowchart of the optimized preparation process of soybean enzymatic hydrolysis protein peptides in Example 1 of the present invention;

[0064] Figure 2 This is a simulation diagram of a hydrolysis degree variation curve corresponding to the first-order kinetic model in Embodiment 1 of the present invention;

[0065] Figure 3 This is a diagram showing the module connection relationship of the soybean enzymatic hydrolysis protein peptide preparation process optimization system in Example 2 of the present invention. Detailed Implementation

[0066] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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.

[0067] Example 1

[0068] like Figure 1 As shown, the optimized preparation process of soybean enzymatic hydrolysate peptides includes the following steps:

[0069] An optimized method for preparing soybean enzymatically hydrolyzed protein peptides, characterized by comprising the following steps:

[0070] Step 1: Evaluate the initial enzymatic hydrolysis conditions of soybean protein raw materials to obtain the initial enzymatic hydrolysis parameter combination, which includes enzyme type, enzyme activity level and reaction environment parameters;

[0071] Step 2: Conduct an experimental enzymatic hydrolysis reaction based on the initial enzymatic hydrolysis parameter combination, monitor the change trend of the degree of hydrolysis during the enzymatic hydrolysis process, and obtain the degree of hydrolysis change curve;

[0072] Step 3: Analyze the hydrolysis degree change curve to determine whether the hydrolysis degree growth rate has reached a preset threshold. If it has reached the preset threshold, proceed to Step 4; otherwise, proceed to Step 5.

[0073] Step 4: Continue the enzymatic hydrolysis reaction under the current enzymatic hydrolysis conditions and monitor the peptide molecular weight distribution in real time. When the peptide molecular weight distribution meets the target range, proceed to Step 6.

[0074] Step 5: Adjust the parameters in the initial enzymatic hydrolysis parameter combination to form an optimized enzymatic hydrolysis parameter combination. The optimized enzymatic hydrolysis parameter combination is achieved by changing the enzyme type ratio or adjusting the reaction environment parameters. Return to Step 2.

[0075] Step 6: Separate and purify the enzymatic hydrolysis products to obtain the target soybean protein peptide product, and record the final enzymatic hydrolysis parameter combination;

[0076] Step 7: Evaluate the functional characteristics of the target soybean protein peptide product and determine whether it meets the preset functional indicators. If it does, use the final enzymatic hydrolysis parameter combination as the optimized preparation process; if it does not, dynamically adjust the preset threshold or the target range and return to Step 1.

[0077] The initial enzymatic hydrolysis condition evaluation of soybean protein raw materials to obtain the initial enzymatic hydrolysis parameter combination includes the following steps:

[0078] Step 11: Select suitable soybean protein raw materials and pre-treat them, including defatting, impurity removal and pH adjustment.

[0079] Specifically, the soybean protein raw material can be selected from soy protein isolate, soy protein concentrate, or defatted soy flour. The pretreatment first involves defatting, using n-hexane solvent at a 1:5 (w / v) ratio, extracted three times at room temperature with stirring, each time for 30 minutes, to remove fat components that may affect enzymatic hydrolysis efficiency. The impurity removal process involves centrifugation to remove insoluble impurities, obtaining a clear protein solution. The pH is adjusted using a 0.1 mol / L NaOH or HCl solution to a neutral range of 6.8–7.2, creating a suitable initial environment for the subsequent enzymatic hydrolysis reaction.

[0080] Step 12: Screen for potentially applicable protease species and establish an enzyme species evaluation system;

[0081] Specifically, the screening of protease types is based on the enzyme's functional characteristics and the molecular weight distribution requirements of the target peptide, mainly examining trypsin, papain, alkaline protease, neutral protease, and complex protease. The evaluation system employs an orthogonal experimental design method, setting up an L9(34) orthogonal array. Factors examined include enzyme type, enzyme dosage, reaction temperature (e.g., 45℃, 55℃, 65℃), and reaction time (e.g., 2h, 4h, 6h). By measuring the degree of proteolysis (DH) and peptide molecular weight distribution under different conditions, 3-5 potentially applicable proteases are screened as candidate enzymes.

[0082] Step 13: Determine the suitable reaction conditions for each candidate enzyme, including suitable pH, suitable temperature and suitable substrate concentration;

[0083] Specifically, the optimal pH value is determined using the pH gradient method. Within the pH range of 4.0 to 10.0, different pH buffer systems are set up at 0.5 intervals, and enzyme activity is measured under each pH condition. A pH-enzyme activity curve is plotted to determine the optimal pH value. The optimal temperature is determined within the range of 30-80℃, with enzyme activity measured at 5℃ intervals. A temperature-enzyme activity curve is plotted to determine the optimal temperature. The optimal substrate concentration is determined by setting a substrate concentration gradient within the range of 5%-20% (w / v), measuring the initial reaction rate at each concentration, and using the Lineweaver-Burk double reciprocal plot method to determine the Km and Vmax values, thereby determining the optimal substrate concentration to be 10-15% (w / v).

[0084] Step 14: Evaluate the enzyme activity level of each candidate enzyme and establish standard enzyme activity units;

[0085] Specifically, the enzyme activity assessment employs a standard substrate method, using casein or hemoglobin as the standard substrate. Under suitable conditions, the amount of tyrosine or tryptophan released per unit time is measured, and the enzyme activity unit is calculated. The standard enzyme activity unit is defined as the amount of enzyme required to release 1 μmol of tyrosine equivalent per minute by hydrolyzing the standard substrate under standard conditions (suitable pH, suitable temperature). A standard curve is established by measuring the absorbance change at a wavelength of 280 nm using ultraviolet spectrophotometry, and the specific activity (U / mg) of each candidate enzyme is calculated.

[0086] Step 15: Based on the enzyme type evaluation system, the suitable reaction conditions, and the enzyme activity level, determine the initial enzymatic hydrolysis parameter combination, including enzyme type, enzyme activity level, and reaction environment parameters, to form the initial enzymatic hydrolysis parameter combination.

[0087] Specifically, the enzyme type is selected as a single enzyme or a complex enzyme system consisting of 2-3 enzymes mixed in a specific ratio, with preference given to enzymes that exhibit high hydrolysis degree and peptide molecular weight distribution as expected in preliminary experiments. The enzyme activity level is set at 50-200 U / g substrate, and the required addition amount is calculated based on the specific activity of different enzymes. The reaction environment parameters include pH value, temperature, substrate concentration, reaction time, and stirring rate. The pH value is set according to the suitable pH of the selected enzyme; the temperature is set according to the suitable temperature of the selected enzyme, typically 45-60℃; the substrate concentration is 10-15% (w / v); the reaction time can be initially set to 4-6 hours; and the stirring rate is set within the range of 200-300 rpm. The initial combination of enzymatic hydrolysis parameters serves as the basic conditions for the experimental enzymatic hydrolysis reaction in step two.

[0088] Furthermore, obtaining the hydrolysis degree change curve includes the following steps:

[0089] Step 21: Prepare the enzymatic hydrolysis reaction system, including the preparation of substrate solution, enzyme solution and buffer system;

[0090] Specifically, the substrate solution is prepared using the soybean protein raw material pretreated in step 11, dissolved in deionized water at the substrate concentration determined in step 15, and stirred thoroughly. The enzyme solution is prepared by accurately weighing the corresponding mass of enzyme preparation according to the enzyme activity level determined in step 15, dissolving it in a small amount of deionized water, and preparing it fresh for immediate use. The buffer system is selected based on the suitable pH value of the selected enzyme. Commonly used buffer systems include phosphate-buffered saline (PBS) with a pH of 6.0–8.0, Tris-HCl buffer with a pH of 7.0–9.0, or acetate-sodium acetate buffer with a pH of 4.0–6.0, at a concentration of 0.05–0.1 mol / L.

[0091] Step 22: Establish an enzymatic hydrolysis reaction apparatus, including a constant temperature water bath, an automatic pH adjustment system, and a stirring system.

[0092] Specifically, the constant temperature water bath is a digitally controlled constant temperature water bath with a temperature control accuracy of ±0.1℃. The set temperature is based on the suitable temperature determined in step 15. The automatic pH adjustment system consists of a pH electrode, a pH controller, and a micro-pump. It monitors the pH value of the reaction system in real time. When the pH deviates from the set value by ±0.2 units, it automatically adds 0.5mol / L NaOH or HCl solution for adjustment. The stirring system uses a magnetic stirrer with a stirring speed set to 200-300rpm to ensure uniform mixing of the reaction system without generating excessive shear force. The reaction device is also equipped with a sampling port for convenient periodic sampling and analysis.

[0093] Step 23: Start the enzymatic hydrolysis reaction in the enzymatic hydrolysis reaction apparatus and take samples for analysis at preset time intervals;

[0094] Specifically, before initiating the enzymatic hydrolysis reaction, the substrate solution is preheated to a set temperature, the pH is adjusted to a suitable value, and then a predetermined amount of enzyme solution is quickly added, recording this as the reaction start time (t=0). Sampling analysis is performed at time intervals of 0, 15, 30, 60, 90, 120, 180, 240, 300, and 360 minutes, with 2-5 mL samples taken each time. The enzyme is immediately inactivated by heat treatment in a 95°C water bath for 5 minutes, and after cooling, the supernatant is collected by centrifugation for subsequent analysis. For long-term enzymatic hydrolysis with a reaction time exceeding 6 hours, the sampling interval can be appropriately extended to once every 2-4 hours.

[0095] Step 24: Measure the change in degree of hydrolysis during the enzymatic hydrolysis process, using the pH-stat method or OPA method for real-time monitoring, and calculate the real-time degree of hydrolysis;

[0096] Specifically, the pH-stat method is based on the principle that the release of amino and carboxyl groups during protein hydrolysis alters the pH of the solution. It calculates the degree of hydrolysis by recording the volume of alkali or acid added to maintain a constant pH. The formula for calculating the degree of hydrolysis (DH) is:

[0097] ;

[0098] Where B is the volume of the added alkaline solution in mL, Nb is the concentration of the alkaline solution in mol / L, α is the degree of dissociation, which is related to pH and temperature, Mp is the protein mass in g, and htot is the total number of peptide bonds in the protein in mmol / g protein.

[0099] The OPA method, also known as the phthalaldehyde method, is based on the principle of reacting OPA reagent with primary amino groups to generate fluorescent products. The degree of hydrolysis is calculated by measuring the absorbance at a wavelength of 340 nm. The formula for calculating the degree of hydrolysis is:

[0100] ;

[0101] Where Nt is the amino nitrogen content at time t, in mg / g protein, N0 is the amino nitrogen content before hydrolysis, in mg / g protein, and Ntotal is the amino nitrogen content after complete hydrolysis, in mg / g protein.

[0102] Step 25: Based on the real-time collected hydrolysis degree, plot the hydrolysis degree change curve and analyze the enzymatic hydrolysis kinetic characteristics.

[0103] Specifically, the hydrolysis degree variation curve is plotted with reaction time on the x-axis and hydrolysis degree DH on the y-axis. Nonlinear regression analysis is used to fit the hydrolysis degree variation curve; commonly used kinetic models include the first-order kinetic model and the double-exponential model. The expression for the first-order kinetic model is:

[0104] ;

[0105] in, denoted as t, where DHmax is the maximum degree of hydrolysis (in %), k is the hydrolysis rate constant (in min^-1), and t is the reaction time (in min).

[0106] The expression for the double exponential model is:

[0107] ;

[0108] Where A and B are the contribution coefficients of the two hydrolysis stages, and k1 and k2 are the hydrolysis rate constants of the fast and slow stages, respectively.

[0109] like Figure 2 The figure shown is a schematic diagram of a hydrolysis degree variation curve corresponding to the first-order kinetic model.

[0110] By analyzing the fitting parameters, the trend of the hydrolysis degree growth rate dDH / dt over time is determined, providing a basis for judging the hydrolysis degree growth rate in step three.

[0111] Furthermore, the analysis of the hydrolysis degree change curve and the determination of the hydrolysis degree growth rate include the following steps:

[0112] Step 31: Calculate the hydrolysis degree growth rate by differentiating the hydrolysis degree change curve, and establish a hydrolysis degree growth rate evaluation model.

[0113] Specifically, for the first-order kinetic model, the formula for calculating the hydrolysis degree growth rate is:

[0114] ;

[0115] For the double exponential model, the formula for calculating the hydrolysis degree growth rate is:

[0116] = ;

[0117] The hydrolysis degree growth rate evaluation model is based on the characteristics of the hydrolysis degree growth rate changing over time. It sets key time points t1, t2, and t3, corresponding to the initial rapid hydrolysis stage, the intermediate transition stage, and the later slow hydrolysis stage, respectively. By calculating the hydrolysis degree growth rate values ​​r1, r2, and r3 at these three time points, the efficiency and completion of the enzymatic hydrolysis process are evaluated.

[0118] Step 32: Set a preset threshold for the degree of hydrolysis growth rate based on the minimum acceptable value of the degree of hydrolysis growth rate in the intermediate transition stage;

[0119] Specifically, the preset threshold is usually set to the minimum acceptable value of the hydrolysis degree growth rate at the intermediate transition stage t2, denoted as rthreshold. According to empirical values and pre-experiment results, rthreshold is usually set to 0.05 - 0.1% / min. When r2 ≥ rthreshold, it indicates that the enzymatic hydrolysis efficiency meets the requirements; when r2 < rthreshold, it indicates that the enzymatic hydrolysis efficiency is insufficient and the enzymatic hydrolysis parameters need to be adjusted.

[0120] Preferably, the judgment criterion can also consider the decay characteristic of the hydrolysis degree growth rate, and the decay coefficient λ is defined as , and the smaller the λ value, the more significant the decrease in the later hydrolysis rate. When λ < 0.3, it indicates that the enzymatic hydrolysis reaction is approaching the equilibrium state and the reaction can be considered terminated; when λ ≥ 0.3, it indicates that the enzymatic hydrolysis reaction still has the potential to continue.

[0121] Step 33: Determine whether the hydrolysis degree growth rate reaches the preset threshold.

[0122] Specifically, in the process of the determination, first calculate the hydrolysis degree growth rate r2 at t2, and then compare it with the preset threshold rthreshold. If r2 ≥ rthreshold, it is determined that the hydrolysis degree growth rate reaches the preset threshold and proceed to step four; if r2 < rthreshold, it is determined that the hydrolysis degree growth rate does not reach the preset threshold and proceed to step five.

[0123] Furthermore, the real-time monitoring of the peptide molecular weight distribution includes the following steps:

[0124] Step 41: Based on the enzymatic hydrolysis conditions determined to reach the preset threshold in step three, perform a maintenance treatment on the enzymatic hydrolysis reaction to obtain a stable enzymatic hydrolysis reaction system;

[0125] Specifically, the maintenance treatment means that after it is determined in step three that the hydrolysis degree growth rate reaches the preset threshold, keep the current combination of enzymatic hydrolysis parameters unchanged and continue the enzymatic hydrolysis reaction. The stable enzymatic hydrolysis reaction system includes an automatic sampling device, a rapid analysis module, and a data processing unit. The automatic sampling device extracts a small amount of 1 - 2 mL sample from the reaction system at a preset time interval, and the preset time interval is usually 30 - 60 minutes. After rapid heat inactivation at 95°C for 5 minutes and centrifugation at 12000×g for 5 minutes, the supernatant is transported to the rapid analysis module. The rapid analysis module uses high performance liquid chromatography HPLC or gel filtration chromatography GFC technology to analyze the peptide molecular weight distribution in real time.

[0126] Step 42: Based on the samples in the stable enzymatic hydrolysis reaction system, measure and analyze the peptide molecular weight distribution to obtain molecular weight distribution characteristic parameters;

[0127] Specifically, the peptide molecular weight distribution was determined using gel filtration chromatography or high-performance liquid chromatography (HPLC). Gel filtration chromatography used a Sephadex G-25 or 1.6 × 60 cm G-15 column, with a mobile phase of 0.02 mol / L phosphate buffer at pH 7.0, a flow rate of 0.5 mL / min, and detection wavelengths of 220 nm and 280 nm. HPLC used a TSK G2000SWXL column (7.8 × 300 mm, 5 μm particle size, 125 Å pore size), with a mobile phase of 0.1% trifluoroacetic acid aqueous solution and 0.1% trifluoroacetic acid acetonitrile solution. The 0.1% trifluoroacetic acid aqueous solution was phase A, and the 0.1% trifluoroacetic acid acetonitrile solution was phase B. The gradient elution program was: 0–5 min, 5% B; 5–25 min, 5–40% B; 25–30 min, 40–5% B; the flow rate was 0.8 mL / min, and the detection wavelength was 214 nm.

[0128] The molecular weight distribution characteristics include: average molecular weight (Mw), molecular weight distribution index (PDI), and percentage of peptides within a specific molecular weight range, where the molecular weight ranges are P < 1 kDa, P 1-3 kDa, P 3-5 kDa, and P > 5 kDa, respectively. Average molecular weight The calculation formula is:

[0129] ;

[0130] Where Ai is the peak area of ​​the peptide segment with a molecular weight of Mi. Molecular weight distribution index. The calculation formula is:

[0131] ;

[0132] Where Mn is the number-average molecular weight, calculated using the following formula: Percentage of peptides within a specific molecular weight range The calculation formula is:

[0133] ;

[0134] Where Axy is the sum of the peak areas of peptides with molecular weight in the range of xy, and Attotal is the sum of the peak areas of all peptides.

[0135] Step 43: Based on the molecular weight distribution characteristic parameters, set the molecular weight distribution requirements for the target product to obtain the molecular weight distribution range of the target peptide;

[0136] Specifically, the target peptide molecular weight distribution range is determined based on the expected functional characteristics of the product. For soybean protein peptides with antioxidant activity, the target molecular weight distribution is typically: P<1kDa≥50%, P1-3kDa≥30%, P3-5kDa≤15%, P>5kDa≤5%, with the average molecular weight Mw controlled within the range of 500-1500 Da. For soybean protein peptides with antihypertensive activity, the target molecular weight distribution is typically: P<1kDa≥60%, P1-3kDa≥25%, P3-5kDa≤10%, P>5kDa≤5%, with the average molecular weight Mw controlled within the range of 300-1000 Da.

[0137] The judgment criterion adopts a multi-parameter comprehensive scoring method, setting the weight coefficients of each feature parameter as w1, w2, ..., wn, where wn is the weight coefficient of the nth feature parameter, and n is the number of feature parameters. The feature parameters are P<1kDa, P1-3kDa, P3-5kDa, P>5kDa and the average molecular weight Mw, i.e., n=5. The comprehensive score S is calculated as follows:

[0138] ;

[0139] Wherein, P<1kDa_target and P1-3kDa_target are the target percentages of small molecule peptides, P3-5kDa_max and P>5kDa_max are the maximum allowable percentages of large molecule peptides, and Mw_target is the target average molecular weight. When S is greater than or equal to the preset scoring threshold, the peptide molecular weight distribution is determined to be within the target range.

[0140] Step 44: Based on the continuous measurement results of the molecular weight distribution characteristic parameters, analyze the trend of peptide molecular weight distribution changes and obtain a molecular weight distribution prediction model;

[0141] Specifically, the trend of peptide molecular weight distribution is obtained by continuously monitoring the characteristic parameters of molecular weight distribution at different time points and plotting parameter-time curves. For each characteristic parameter, such as P<1kDa, Mw, etc., a multinomial regression or exponential regression model is used to fit its trend over time. Taking P<1kDa as an example, its trend can be fitted by the following model:

[0142] ;

[0143] Where a is the initial value, a+b is the limit value when t approaches infinity, c is the rate constant, and t is the reaction time.

[0144] The prediction model predicts the characteristic parameters of molecular weight distribution at future time points based on the obtained parameter change trends. The model can estimate the remaining reaction time required to achieve the target molecular weight distribution, thus optimizing production efficiency. The accuracy of the prediction model is evaluated using the root mean square error (RMSE) and the coefficient of determination (R²). A model is considered reliable if RMSE < 5% and R² > 0.95.

[0145] Step 45: Based on the molecular weight distribution prediction model and the target peptide molecular weight distribution range, evaluate the current peptide molecular weight distribution and obtain the peptide molecular weight distribution conformity judgment result.

[0146] Specifically, the evaluation process is based on the judgment criteria established in step 43, calculating the comprehensive score S of the current peptide molecular weight distribution, and comparing it with a preset score threshold. If S is greater than or equal to the preset score threshold, the peptide molecular weight distribution is determined to be within the target range, the enzymatic hydrolysis reaction is terminated, and the process proceeds to step six; if S < the preset score threshold, the enzymatic hydrolysis reaction continues, and the process returns to step 41.

[0147] The judgment also considers the reaction time factor, setting a maximum allowable reaction time tmax, typically 8-12 hours. If the reaction time t ≥ tmax but S is still less than the preset scoring threshold, the prediction model results are analyzed: if the prediction is that S can reach a value greater than or equal to the preset scoring threshold within a reasonable additional time (e.g., less than 2 hours), the reaction time is appropriately extended; otherwise, the reaction is terminated, the current parameter combination is recorded as an unsuitable condition, and the process proceeds to step five to readjust the parameters. The judgment process uses a decision tree structure to ensure the systematic nature and traceability of the judgment.

[0148] Furthermore, the formation of the optimized enzymatic hydrolysis parameter combination includes the following steps:

[0149] Step 51: Based on the hydrolysis degree change curve and the peptide molecular weight distribution, analyze the limiting factors of enzymatic hydrolysis and obtain the limiting factor identification results;

[0150] Specifically, the limiting factor analysis, based on the hydrolysis degree change curve in step three and the peptide molecular weight distribution in step four, uses a multidimensional analysis method to identify key factors affecting enzymatic hydrolysis efficiency and product quality. The hydrolysis degree change curve analysis focuses on three parameters: initial hydrolysis rate, maximum hydrolysis degree, and hydrolysis equilibrium time. A low initial hydrolysis rate, typically <1% / min, indicates insufficient enzyme activity or low enzyme-substrate affinity; a low maximum hydrolysis degree, typically <20%, indicates limited cleavage sites or substrate structure hindering enzyme contact; and a long hydrolysis equilibrium time, typically >8 hours, indicates limited reaction kinetics.

[0151] The peptide molecular weight distribution analysis focused on the generation rate of small peptides (P < 1 kDa) and the residual rate of large peptides (P > 5 kDa). A low generation rate of small peptides indicates insufficient endokinetic activity of the enzyme; a high residual rate of large peptides indicates insufficient exokinetic activity. Principal component analysis (PCA) or partial least squares regression (PLS) was used to establish a correlation model between limiting factors and enzymatic hydrolysis parameters, providing a basis for parameter adjustment.

[0152] Step 52: Based on the results of the identification of limiting factors, adjust the enzyme type ratio to obtain an optimized enzyme system composition scheme;

[0153] Specifically, the enzyme composition adjustment is based on the limiting factors identified in step 51, and the composition of single enzymes or complex enzyme systems is adjusted accordingly. For cases with low initial hydrolysis rates, the proportion of highly active endonucleases such as trypsin and alkaline proteases is increased; for cases with low maximum hydrolysis rates, the proportion of broad-spectrum neutral proteases is increased; for cases with low small peptide generation rates, the proportion of exonucleases or carboxypeptidases is increased.

[0154] The optimized enzyme system composition scheme employs a response surface methodology (RSM) design experiment to establish a mathematical model relating enzyme species ratios to key response variables such as degree of hydrolysis and percentage of small peptides. Taking a complex enzyme system of three enzymes (A, B, and C) as an example, a three-factor Box-Behnken design is used, with factor levels set as the proportions of enzyme A, B, and C. The proportions of enzyme A and B can be set to 20-60%, and the proportion of enzyme C can be set to 10-40%. The constraint is that the sum of the proportions of the three enzymes is 100%. Through model analysis, the optimal enzyme species ratio is determined, with an expected improvement in hydrolysis efficiency of 10-30%.

[0155] Step 53: Based on the results of the limiting factor identification, adjust and design the reaction environment parameters to obtain an optimized reaction condition scheme;

[0156] Specifically, the adjustment of reaction environment parameters includes the optimization of factors such as pH, temperature, substrate concentration, ionic strength, and additives. The pH adjustment range is ±1.0 unit. Based on the analysis results in step 51, if it is necessary to improve endonuclease activity, the pH should be adjusted towards the enzyme's optimal pH; if it is necessary to improve exonuclease activity, the pH can be appropriately deviated from the optimal pH by 0.5-1.0 unit. The temperature adjustment range is ±10℃. Generally, increasing the temperature by 5-10℃ within the enzyme's thermal stability range can accelerate the reaction, but enzyme inactivation must be monitored.

[0157] In the optimized reaction conditions, substrate concentration adjustment is based on substrate inhibition analysis. If significant substrate inhibition exists, the substrate concentration is reduced to 5-8% (w / v); if no significant inhibition exists, it can be increased to 15-20% (w / v) to improve production efficiency. Ionic strength adjustment is achieved by adding 0-0.5 mol / L NaCl or KCl, as some enzymes exhibit enhanced activity at specific ionic strengths. Additive optimization considers adding reducing agents, metal ions, or surfactants to promote loose protein structure or enhance enzyme activity.

[0158] Step 54: Based on the optimized enzyme system composition scheme and the optimized reaction condition scheme, the enzymatic hydrolysis parameters are systematically adjusted to obtain the optimized enzymatic hydrolysis parameter combination;

[0159] Specifically, the system adjustment is implemented using either a single-factor variable method or an orthogonal experimental design method to systematically evaluate the impact of parameter adjustments on the enzymatic hydrolysis effect. The single-factor variable method adjusts each parameter sequentially, one parameter at a time while keeping others constant, measuring the changes in the degree of hydrolysis and peptide molecular weight distribution before and after the adjustment to determine the optimal level. The orthogonal experimental design method considers the interaction of multiple parameters simultaneously, employing... or An orthogonal array design experiment was used, and the significance and optimal combination of each parameter were determined through analysis of variance.

[0160] The optimized enzymatic hydrolysis parameter combination includes: optimized enzyme type ratio, optimized enzyme dosage, optimized pH value, optimized temperature, optimized substrate concentration, optimized reaction time, and optimized additive combination. The optimized enzyme type ratio is typically trypsin:papain:aminopeptidase = 4:4:2. The optimized enzyme dosage is usually 1-3% of the substrate mass. The optimized pH range is usually 6.5-8.5. The optimized temperature is usually 45-65℃. The optimized substrate concentration is usually 8-15% (w / v). The optimized reaction time is usually 4-8 hours. The optimized parameter combination is expected to increase the degree of hydrolysis by 15-40% and the percentage of small molecule peptides (P<1kDa) by 20-50%. Step 55: Based on the optimized enzymatic hydrolysis parameter combination, record and organize the parameter adjustment results to obtain an optimized parameter record table, and return to step two for verification.

[0161] Specifically, the record-keeping process uses a standardized parameter recording table to record in detail the values ​​before and after adjustment, the adjustment range, and the expected effects of each parameter. The parameter recording table includes the following: enzyme type and ratio (name and proportion of each enzyme), enzyme activity level (U / g substrate), pH value, temperature (°C), substrate concentration (%, w / v), reaction time (h), stirring rate (rpm), additives (name and concentration), and other special treatment measures.

[0162] Returning to step two refers to re-executing steps two through four with the optimized enzymatic hydrolysis parameter combination to verify the optimization effect. During verification, the focus is on whether the hydrolysis degree increase rate reaches the preset threshold and whether the peptide molecular weight distribution meets the target range. If the verification results meet the requirements, proceed to step six; if they still do not meet the requirements, further analyze the reasons and return to step 51 for further optimization until a satisfactory enzymatic hydrolysis parameter combination is obtained. Optimization cycles typically do not exceed three times. If the target is still not achieved after three optimizations, the rationality of the target setting needs to be reassessed, or the soybean protein raw material should be replaced.

[0163] Furthermore, the recording of the final enzymatic hydrolysis parameter combination includes the following steps:

[0164] Step 61: Based on heat treatment or pH adjustment, terminate the enzymatic hydrolysis reaction in step four that conforms to the molecular weight distribution range of the target peptide to obtain a pre-treated enzymatic hydrolysate.

[0165] Specifically, the enzymatic hydrolysis reaction is terminated using either heat treatment or pH adjustment. Heat treatment involves rapidly heating the hydrolysis solution to 90-95°C and maintaining this temperature for 10-15 minutes to inactivate the protease. pH adjustment involves adjusting the pH of the reaction solution to the enzyme inactivation range, typically pH < 3.0 or pH > 10.0, and maintaining this temperature for 30 minutes. The preliminary treatment includes centrifugation to remove unhydrolyzed proteins and inactivated enzyme protein precipitates, collecting the supernatant containing the target protein peptide. The supernatant is then filtered through a 0.45 μm filter membrane to remove residual particulate matter, yielding a clear, pre-treated hydrolysate, i.e., the crude hydrolysate.

[0166] Step 62: Based on the ultrafiltration-nanofiltration tandem process, the pre-treated enzymatic hydrolysate is subjected to membrane separation to obtain pre-fractionated peptide components;

[0167] Specifically, the membrane separation process employs a series of ultrafiltration and nanofiltration steps. The ultrafiltration process uses an ultrafiltration membrane with a molecular weight cutoff (MWCO) of 5 kDa, and performs tangential flow filtration at a pressure of 0.2-0.3 MPa to remove large peptides with a molecular weight greater than 5 kDa and unhydrolyzed proteins; the permeate is then collected. The nanofiltration process uses a nanofiltration membrane with an MWCO of 200-300 Da, and is carried out at a pressure of 0.5-0.8 MPa to retain peptides with a molecular weight greater than 200-300 Da, while simultaneously removing small molecule impurities such as salts and free amino acids.

[0168] The fractionation process can be designed with multi-stage membrane separation procedures according to the requirements of the target product. For example, for products rich in small molecule peptides, a three-stage membrane separation of 5kDa ultrafiltration → 1kDa ultrafiltration → 300Da nanofiltration can be used to collect peptides in the 1-5kDa and <1kDa molecular weight ranges, respectively. For medium molecular weight peptides with stronger functionality, a two-stage membrane separation of 10kDa ultrafiltration → 3kDa ultrafiltration can be used to collect peptides in the 3-10kDa molecular weight range. The temperature is controlled at 10-15℃ during the membrane separation process to reduce peptide degradation and microbial contamination.

[0169] Step 63: Based on chromatographic separation technology, the peptide components are further purified to obtain a high-purity target peptide product;

[0170] Specifically, the chromatographic separation and purification process selects appropriate chromatographic techniques based on the characteristics of the target peptide. For peptides with specific functional activities, affinity chromatography is used for selective separation; for peptide products requiring high purity, tandem purification using ion exchange chromatography and reversed-phase high-performance liquid chromatography (RP-HPLC) is employed.

[0171] The affinity chromatography employs affinity packing material coupled with specific ligands (such as ACE inhibitor target proteins). Samples are loaded under low ionic strength conditions to bind to peptides with specific activities, followed by elution with high ionic strength buffer or pH gradient. The ion exchange chromatography uses DEAE-Sepharose or CM-Sepharose packing material, selecting anion or cation exchange mode based on the isoelectric point of the target peptide, and separating different peptides by elution using pH gradient or salt concentration gradient. The reversed-phase high-performance liquid chromatography uses a C18 column with 0.1% trifluoroacetic acid aqueous solution and 0.1% trifluoroacetic acid acetonitrile solution as the mobile phase. The gradient elution program is: 0-5 min, 5% B; 5-35 min, 5-50% B; 35-40 min, 50-5% B; flow rate is 1.0 mL / min; and detection wavelengths are 214 nm and 280 nm.

[0172] Step 64: Based on concentration and drying technology, the high-purity target peptide product is subjected to final processing to obtain the finished soybean protein peptide product.

[0173] Specifically, the concentration process employs rotary evaporation or vacuum concentration technology, concentrating under reduced pressure at 40-50°C to a solid content of 20-30% (w / v). The drying process selects either spray drying or freeze drying technology based on the product morphology requirements. Spray drying uses process parameters of inlet temperature 180-200°C, outlet temperature 80-90°C, and atomization pressure 0.2-0.3 MPa to obtain a powdered product. Freeze drying uses process parameters of pre-freezing, primary drying, and secondary drying to obtain a loose, porous product.

[0174] The final product is packaged in a sealed, moisture-proof, and light-proof container, labeled with information such as product name, batch number, production date, shelf life, molecular weight distribution, and protein content. The product is stored at 4°C or -20°C, and the shelf life is determined based on stability test results, typically ranging from 12 to 24 months.

[0175] Step 65: Based on the full process parameter records, the preparation conditions of the finished soybean protein peptide product are systematically organized to obtain the final enzymatic hydrolysis parameter combination and process database.

[0176] Specifically, the final enzymatic hydrolysis parameter combination refers to the set of process parameters that, after optimization in steps two through five, can stably produce soybean protein peptide products that meet the target requirements. The final parameter combination is detailed in the following: soybean protein raw material specifications, pretreatment method, enzyme type and ratio, enzyme dosage, pH value, temperature, substrate concentration, reaction time, stirring rate, additive combination, termination method, separation and purification process parameters, and final product indicators.

[0177] The process database adopts a relational database structure, establishing a parameter-performance correlation model to support new product development and process optimization. The database comprises four main components: raw material data table, enzymatic hydrolysis parameter table, product performance table, and process correlation table, with data in each table linked by a unique batch number. The process database supports multidimensional queries and data mining functions, enabling the reverse derivation of optimal process parameters based on target product performance, thereby improving R&D efficiency and product consistency.

[0178] The evaluation of whether the functional characteristics of the target soybean protein peptide product meet the preset functional indicators includes the following steps:

[0179] Step 71: Based on standard physicochemical analysis methods, perform basic characteristic determination on the finished soybean protein peptide product to obtain product functional characteristic data.

[0180] Specifically, the basic physicochemical properties include parameters such as protein content, moisture content, ash content, pH value, solubility, emulsification, foaming ability, and antioxidant activity. Protein content was determined using the Kjeldahl method with a conversion factor of 6.25; moisture content was determined using the constant weight drying method at 105℃; ash content was determined using the ignition method at 550℃; pH value was determined using the potentiometric method; solubility was determined by centrifugation to measure the nitrogen solubility index (NSI) under different pH conditions; emulsification was evaluated using the emulsification activity index (EAI) and emulsification stability index (ESI); foaming ability was evaluated using the foaming capacity (FC) and foam stability (FS); and antioxidant activity was evaluated using DPPH free radical scavenging ability, ABTS free radical scavenging ability, and ferric ion reducing ability (FRAP).

[0181] The product functional characteristic data includes quantitative indicators such as protein content, moisture content, ash content, pH value, NSI, EAI, ESI, DPPH free radical scavenging rate, ABTS free radical scavenging rate, and FRAP value, which provide basic data for subsequent functional indicator judgment.

[0182] Step 72: Based on the product functional characteristic data, compare and analyze it against the preset functional indicators to obtain the functional indicator compliance assessment results.

[0183] Specifically, the preset functional indicators are set according to product positioning, including main functional activity indicators and auxiliary functional characteristic indicators. Taking antioxidant soybean protein peptides as an example, the main functional activity indicators are: DPPH free radical scavenging rate ≥80%, ABTS free radical scavenging rate ≥85%, hydroxyl free radical scavenging rate ≥70%, and FRAP value. The auxiliary functional characteristics are: average molecular weight ≤1000Da, P<1kDa≥60%, solubility≥95%, bitterness score≤2 points.

[0184] The comparative analysis employs standardization and a comprehensive scoring method, standardizing each functional indicator before conducting a comprehensive evaluation. The standardization formula is as follows:

[0185] ;

[0186] Where Si is the standardized score of indicator i, Xi is the measured value of indicator i, and Xmin and Xmax are the minimum allowable value and maximum expected value of indicator i, respectively. (Comprehensive Score) The calculation formula is:

[0187] ;

[0188] Where Wi is the weighting coefficient of index i. .

[0189] Step 73: Based on the functional indicator compliance assessment results, determine whether the product meets the preset functional indicators and obtain process optimization decisions;

[0190] Specifically, the judgment process is based on the comprehensive score S value obtained in step 72, which is compared with a preset score threshold. If S ≥ the preset score threshold, the product is determined to meet the preset functional indicators, and the final enzymatic hydrolysis parameter combination in step six is ​​used as the optimized preparation process; if S < the preset score threshold, the product is determined to not meet the preset functional indicators, and the preset threshold in step three or the target range in step four needs to be adjusted, returning to step one to restart the optimization process.

[0191] The process optimization decision not only considers the overall score, but also analyzes the compliance status of each individual indicator, identifies the functional characteristics that need to be improved, and provides targeted guidance for parameter adjustment.

[0192] Furthermore, the methods for dynamically adjusting the preset threshold in step three or the target range in step four include:

[0193] Based on the indicators that do not meet the standards in the functional indicator compliance assessment results, the preset threshold in step three is quantitatively adjusted to obtain the adjusted preset threshold.

[0194] Specifically, the quantitative adjustment is based on the degree of difference between the unmet indicator and the target value, and modifies the preset threshold using a proportional adjustment method. The adjustment formula for the preset threshold rthreshold for the hydrolysis degree growth rate is:

[0195] rthreshold_new=rthreshold_old×(1-α×ΔS);

[0196] Where rthreshold_new is the adjusted preset threshold, rthreshold_old is the original preset threshold, α is the adjustment coefficient, usually taken as 0.2~0.5, and ΔS is the difference in comprehensive score, which is calculated by subtracting the comprehensive score S from the preset score threshold.

[0197] If the main functional activity indicators fail to meet the standards, and the measured value is lower than 80% of the target value, the preset threshold is reduced by 20-30%, the enzymatic hydrolysis time is extended, and the degree of hydrolysis is increased. If the measured value is between 80-95% of the target value, the preset threshold is reduced by 10-20%. If the measured value is close to the target value, the preset threshold is fine-tuned by 5-10%. For example, if the original preset threshold rthreshold_old is 0.08% / min and the comprehensive score S is 0.75, then ΔS=0.1, and α=0.3 is taken. The adjusted preset threshold rthreshold_new is calculated to be 0.08×(1-0.3×0.1)=0.0776% / min, which is approximately 0.078% / min, a reduction of 2.5%.

[0198] Based on the indicators that do not meet the standards in the functional indicator compliance assessment results, the target range in step four is quantitatively adjusted to obtain the adjusted target range, and the process is restarted from step one.

[0199] Specifically, the quantitative adjustment is based on the type and severity of the non-compliance indicators, and the target range of peptide molecular weight distribution is modified accordingly. For small molecule peptides with a percentage P < 1 kDa that is insufficient, the formula for adjusting the target range is:

[0200] P<1kDa_target,new=P<1kDa_target,old×(1+β×ΔP)

[0201] Where P<1kDa_target,new is the adjusted target value, P<1kDa_target,old is the original target value, β is the adjustment coefficient, usually taken as 0.1-0.3, and ΔP is the percentage difference, which is the ratio of the difference between the target value and the measured value to the target value.

[0202] For cases where the average molecular weight Mw is too high, the formula for adjusting the target range is:

[0203] Mw_target,new=Mw_target,old×(1-γ×ΔM);

[0204] Where Mw_target,new is the adjusted target value, Mw_target,old is the original target value, γ is the adjustment coefficient, usually taken as 0.1-0.2, ΔM is the relative difference of molecular weight, the difference between the measured value and the target value is calculated, and the ratio of this difference to the target value is the relative difference of molecular weight.

[0205] For example, in the original target range, P<1kDa_target,old is 60%, and the measured value is 48%. Then ΔP=(60-48) / 60=0.2. Taking β=0.2, the adjusted target value P<1kDa_target,new=60×(1+0.2×0.2)=62.4%, which is an increase of 2.4 percentage points. In the original target range, Mw_target,old is 1000Da, and the measured value is 1200Da. Then ΔM=(1200-1000) / 1000=0.2. Taking γ=0.15, the adjusted target value Mw_target,new=1000×(1-0.15×0.2)=970Da, which is a decrease of 30Da.

[0206] The "return to step one" refers to re-executing the complete process from steps one to seven, carrying the adjusted preset threshold and target range, until an optimized preparation process that meets the preset functional indicators is obtained. This quantitative dynamic adjustment mechanism ensures that the final soybean protein peptide product possesses stable and reliable functional properties.

[0207] Example 2

[0208] like Figure 3 As shown, the soybean enzymatic hydrolysis protein peptide preparation process optimization system includes an enzymatic hydrolysis parameter combination collection module, a hydrolysis degree change curve collection module, a growth rate judgment module, an enzymatic hydrolysis parameter optimization module, and a separation and purification module; wherein, each module is connected to the other by electrical means.

[0209] The enzymatic hydrolysis parameter combination collection module evaluates the initial enzymatic hydrolysis conditions of soybean protein raw materials, obtains the initial enzymatic hydrolysis parameter combination, and sends the initial enzymatic hydrolysis parameter combination to the hydrolysis degree change curve collection module.

[0210] The hydrolysis degree change curve collection module performs an experimental enzymatic hydrolysis reaction based on the initial enzymatic hydrolysis parameter combination, monitors the hydrolysis degree change trend during the enzymatic hydrolysis process, obtains the hydrolysis degree change curve, and sends the hydrolysis degree change curve to the growth rate judgment module.

[0211] The growth rate judgment module analyzes the hydrolysis degree change curve and determines whether the hydrolysis degree growth rate has reached a preset threshold. If the preset threshold is reached, the module proceeds to the separation and purification module; otherwise, the module proceeds to the enzymatic hydrolysis parameter optimization module.

[0212] The enzymatic hydrolysis parameter optimization module adjusts the parameters in the initial enzymatic hydrolysis parameter combination to form an optimized enzymatic hydrolysis parameter combination.

[0213] The separation and purification module continues the enzymatic hydrolysis reaction under the current conditions and monitors the peptide molecular weight distribution in real time. When the peptide molecular weight distribution meets the target range, the enzymatic hydrolysis product is separated and purified to obtain the target soybean protein peptide product. The final enzymatic hydrolysis parameter combination is recorded, and the functional characteristics of the target soybean protein peptide product are evaluated to determine whether it meets the preset functional indicators. If it does, the final enzymatic hydrolysis parameter combination is used as the optimized preparation process; if it does not, the preset threshold or the target range is dynamically adjusted, and the process is transferred back to the enzymatic hydrolysis parameter combination collection module.

[0214] In addition, the parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of the corresponding technical solutions in the prior art have not been described in detail, so as to avoid excessive elaboration.

[0215] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0216] The preset parameters or preset thresholds mentioned above are all set by those skilled in the art based on actual conditions or obtained through large-scale data simulation.

[0217] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for optimizing the preparation process of soy enzymatic protein peptides, characterized in that, The method comprises the following steps: Step one, initial enzyme hydrolysis condition evaluation of soybean protein raw material, to obtain initial enzyme hydrolysis parameter combination; Step two, based on the initial enzyme hydrolysis parameter combination, to carry out experimental enzyme hydrolysis reaction, to monitor the degree of hydrolysis change trend in the enzyme hydrolysis process, to obtain the degree of hydrolysis change curve; Step three, to analyze the degree of hydrolysis change curve, to determine whether the degree of hydrolysis growth rate reaches the preset threshold value, if it reaches the preset threshold value, to enter step four, if it does not reach the preset threshold value, to enter step five; Step four, to continue enzyme hydrolysis reaction under the current enzyme hydrolysis condition, and to monitor the peptide molecular weight distribution in real time, when the peptide molecular weight distribution meets the target range, to enter step six; Step five, to adjust the parameters in the initial enzyme hydrolysis parameter combination, to form the optimized enzyme hydrolysis parameter combination, to return to step two; Step six, to separate and purify the enzyme hydrolysis product, to obtain the target soybean protein peptide product, to record the final enzyme hydrolysis parameter combination; Step seven, to evaluate the functional characteristics of the target soybean protein peptide product, to determine whether it meets the preset functional index, if it meets, to take the final enzyme hydrolysis parameter combination as the optimized preparation process, if it does not meet, to dynamically adjust the preset threshold value or the target range, to return to step one.

2. The method for optimizing the preparation process of soybean enzymatic protein peptides according to claim 1, characterized in that, The initial enzyme hydrolysis condition evaluation of soybean protein raw material, to obtain the initial enzyme hydrolysis parameter combination comprises the following steps: Step 11: selecting soybean protein raw material and pretreating, including defatting, impurity removing and pH value adjusting operation; Step 12: screening potential suitable protease species, to establish enzyme species evaluation system; Step 13: determining suitable reaction conditions of each candidate enzyme species, including suitable pH value, suitable temperature and suitable substrate concentration; Step 14: evaluating enzyme activity level of each candidate enzyme species, to establish standard enzyme activity unit; Step 15: based on the enzyme species evaluation system, the suitable reaction conditions and the enzyme activity level, to determine the initial enzyme hydrolysis parameter combination.

3. The method for optimizing the preparation process of soybean enzymatic protein peptides according to claim 2, characterized in that, The degree of hydrolysis change curve comprises the following steps: Step 21: preparing enzyme hydrolysis reaction system, including configuring substrate solution, enzyme solution and buffer system; Step 22: establishing enzyme hydrolysis reaction device, including constant temperature water bath, pH automatic adjusting system and stirring system; Step 23: starting enzyme hydrolysis reaction in the enzyme hydrolysis reaction device, to take sample analysis according to preset time interval; Step 24: measuring the degree of hydrolysis change in the enzyme hydrolysis process, to monitor in real time by pH-stat method or OPA method, to calculate real-time degree of hydrolysis; Step 25: according to the real-time collected degree of hydrolysis, to draw the degree of hydrolysis change curve, to analyze enzyme hydrolysis kinetics characteristics.

4. The method for optimizing the preparation process of soybean enzymatic protein peptides according to claim 3, characterized in that, The analysis of the degree of hydrolysis change curve, to determine the degree of hydrolysis growth rate comprises the following steps: Step 31: calculating the degree of hydrolysis growth rate by derivation of the degree of hydrolysis change curve, to establish the degree of hydrolysis growth rate evaluation model; Step 32: setting the preset threshold value of the degree of hydrolysis growth rate according to the minimum acceptable value of the degree of hydrolysis growth rate in the intermediate transition stage; Step 33: determining whether the degree of hydrolysis growth rate reaches the preset threshold value.

5. The method for optimizing the preparation process of soybean enzymatic protein peptides according to claim 4, characterized in that, The real-time monitoring of the peptide molecular weight distribution comprises the following steps: Step 41: based on the determination in step three that the enzyme hydrolysis condition reaches the preset threshold value, maintaining the enzyme hydrolysis reaction to obtain a stable enzyme hydrolysis reaction system; Step 42: based on the sample in the stable enzyme hydrolysis reaction system, determining and analyzing the molecular weight distribution of the peptide to obtain molecular weight distribution characteristic parameters; Step 43: based on the molecular weight distribution characteristic parameters, setting the molecular weight distribution requirements of the target product to obtain the target peptide molecular weight distribution range; Step 44: based on the continuous determination results of the molecular weight distribution characteristic parameters, analyzing the change trend of the peptide molecular weight distribution to obtain a molecular weight distribution prediction model; Step 45: based on the molecular weight distribution prediction model and the target peptide molecular weight distribution range, evaluating the current peptide molecular weight distribution to obtain a peptide molecular weight distribution compliance judgment result.

6. The method for optimizing the preparation process of soybean enzymatic protein peptides according to claim 5, characterized in that, The forming of the optimized enzyme hydrolysis parameter combination includes the following steps: Step 51: based on the degree of hydrolysis change curve and the peptide molecular weight distribution, analyzing the enzyme hydrolysis limiting factors to obtain a limiting factor identification result; Step 52: based on the limiting factor identification result, adjusting and designing the enzyme type ratio to obtain an optimized enzyme system composition scheme; Step 53: based on the limiting factor identification result, adjusting and designing the reaction environment parameters to obtain an optimized reaction condition scheme; Step 54: based on the optimized enzyme system composition scheme and the optimized reaction condition scheme, adjusting the enzyme hydrolysis parameters to obtain an optimized enzyme hydrolysis parameter combination.

7. The method for optimizing the preparation process of soybean enzymatic protein peptides according to claim 6, characterized in that, The recording of the final enzyme hydrolysis parameter combination includes the following steps: Step 61: based on heat treatment or pH adjustment method, terminating the enzyme hydrolysis reaction in step four that meets the target peptide molecular weight distribution range to obtain a preliminary processed enzyme hydrolysis liquid; Step 62: based on the ultrafiltration-nanofiltration series process, performing membrane separation treatment on the preliminary processed enzyme hydrolysis liquid to obtain a preliminary fractionated peptide segment component; Step 63: based on the chromatographic separation technology, further purifying the peptide segment component to obtain a high-purity target peptide product; Step 64: based on the concentration and drying technology, finally processing the high-purity target peptide product to obtain a finished soy protein peptide product; Step 65: based on the whole-process process parameter record, systematically organizing the preparation conditions of the finished soy protein peptide product to obtain the final enzyme hydrolysis parameter combination and process database.

8. The method for optimizing the preparation process of soybean enzymatic protein peptides according to claim 7, characterized in that, The evaluation of the functional characteristics of the target soy protein peptide product to determine whether it meets the preset functional indicators includes the following steps: Step 71: based on the standard physicochemical analysis method, determining the basic characteristics of the finished soy protein peptide product to obtain product functional characteristic data; Step 72: based on the product functional characteristic data, comparing and analyzing the preset functional indicators to obtain a functional indicator compliance evaluation result; Step 73: based on the functional indicator compliance evaluation result, determining whether the product meets the preset functional indicators to obtain a process optimization decision.

9. The method for optimizing the preparation process of soybean enzymatic protein peptides according to claim 8, characterized in that, The dynamic adjustment of the preset threshold value or the target range includes: Based on the non-compliance indicators in the functional index compliance evaluation results, the preset threshold of step three is quantitatively adjusted to obtain an adjusted preset threshold; Based on the non-compliance indicators in the functional index compliance evaluation results, the target range in step four is quantitatively adjusted to obtain an adjusted target range, and the optimization process is restarted from step one.

10. A system for optimizing a preparation process of soybean enzymatic protein peptides, for implementing the method for optimizing a preparation process of soybean enzymatic protein peptides according to any one of claims 1-9, characterized in that, The enzyme hydrolysis parameter combination collecting module, the degree of hydrolysis change curve collecting module, the growth rate judging module, the enzyme hydrolysis parameter optimization module, and the separation and purification module are included, and each module is connected through electrical connection; The enzyme hydrolysis parameter combination collecting module evaluates the initial enzyme hydrolysis conditions of the soybean protein raw material to obtain an initial enzyme hydrolysis parameter combination, and sends the initial enzyme hydrolysis parameter combination to the degree of hydrolysis change curve collecting module; The degree of hydrolysis change curve collecting module performs a trial enzyme hydrolysis reaction based on the initial enzyme hydrolysis parameter combination, monitors the degree of hydrolysis change trend during the enzyme hydrolysis process, obtains a degree of hydrolysis change curve, and sends the degree of hydrolysis change curve to the growth rate judging module; The growth rate judging module analyzes the degree of hydrolysis change curve, judges whether the degree of hydrolysis growth rate reaches a preset threshold, and if the preset threshold is reached, it is transferred to the separation and purification module, and if it is not reached, it is transferred to the enzyme hydrolysis parameter optimization module; The enzyme hydrolysis parameter optimization module adjusts the parameters in the initial enzyme hydrolysis parameter combination to form an optimized enzyme hydrolysis parameter combination; The separation and purification module continues the enzyme hydrolysis reaction under the current enzyme hydrolysis conditions and real-time monitors the peptide molecular weight distribution, and when the peptide molecular weight distribution meets the target range, the enzyme hydrolysis product is separated and purified to obtain a target soybean protein peptide product, the final enzyme hydrolysis parameter combination is recorded, the functional characteristics of the target soybean protein peptide product are evaluated, and it is judged whether the preset functional indicators are met. If it is met, the final enzyme hydrolysis parameter combination is used as the optimized preparation process; if it is not met, the preset threshold or the target range is dynamically adjusted, and the enzyme hydrolysis parameter combination collecting module is re-transferred.

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