Method for preparing corn superoxide dismutase

Abstract

To provide a method for preparing corn superoxide dismutase that solves the viscosity problem caused by the high starch content of corn raw materials, improves enzymatic degradation efficiency, maintains SOD activity, preserves product quality, enables mass production, and reduces production costs. [Solution] The method includes a corn pretreatment step S1, an enzymatic decomposition step S2, and an extraction and posttreatment step S3. S2 includes the steps of: placing the corn slurry into a drum screen in which the lower half of the drum is immersed in an enzymatic decomposition mixture, activating the drum screen to tumble the corn raw material in the drum while it remains immersed in the enzymatic decomposition mixture; sucking up the enzymatic decomposition mixture and centrifuging it to remove starch particles and the like; reinjecting the clarified liquid after centrifugation into the drum screen and repeating the enzymatic decomposition; and stirring and filtering the enzymatically decomposed corn slurry and collecting the filtrate.

Description

Technical Field

[0001] The present invention relates to the technical field of separation and extraction of organic substances, and relates to a method for preparing superoxide dismutase from corn.

Background Art

[0002] Superoxide dismutase (SOD) is a kind of antioxidant metal enzyme existing in vivo, which can catalyze the disproportionation of superoxide anion radicals to generate oxygen and hydrogen peroxide, plays an important role in the oxidation-antioxidation balance of the organism, and is closely related to the occurrence and onset of many diseases.

[0003] Microorganisms such as yeast, bacteria, fungi, bioengineering bacterial strains, and plant roots, leaves, seeds, or other fruits are the sources of SOD. Currently, it is common to extract SOD from plants, and many of them are extracted from plants such as garlic, corn, tomatoes, mulberry leaves, sage, and cacti.

[0004] For example, a Chinese patent application with application number CN202011634220.1 discloses an industrialized production method for extracting superoxide dismutase from Shenzhou grass (a grass resulting from a hybrid of Napier grass and Pennisetum alopecuroides). This invention provides an industrialized production method for extracting superoxide dismutase from Shenzhou grass and belongs to the technical field of separation and extraction of organic matter. This method includes the following steps: Step S1: After uniformly mixing the Shenzhou grass raw material with a phosphate buffer, a macerating enzyme and a surfactant are added, and enzymatic decomposition is performed. Step S2: After enzymatic decomposition, ultrasonic extraction and solid-liquid separation are performed to obtain a liquid. Step S3: After adding one or more of an activator, protective agent, or stabilizer to the liquid, the Shenzhou grass superoxide dismutase is recovered by filtration. This invention aims to be suitable for industrialized production, and by optimizing each step of extracting superoxide dismutase from Shenzhou grass, a highly practical industrialized production method for extracting superoxide dismutase from Shenzhou grass has been obtained.

[0005] Compared to Shinshu grass, corn is more readily available as a raw material. Many conventional methods for preparing superoxide dismutase complex enzymes using corn are known, but they have problems such as the use of chemical disinfectants such as bleach and sodium hypochlorite in the preparation process. Chemical disinfectants tend to remain and contaminate the environment during the manufacturing process, affecting the quality of the prepared superoxide dismutase complex enzyme. Furthermore, in the drying process of the product, conventional methods employ freeze-drying or spraying, but the former is costly, and the latter results in a significant loss of enzyme activity, neither of which is suitable for mass production. Conventional methods also have technical problems such as low germination rates, low enzyme yields, and small production scales. On the other hand, germinating corn in a germination device has the problem of low aeration. In addition, because the growing environment for corn is narrow, bacterial films and spoilage phenomena tend to occur on the surface of the corn, which reduces the germination rate and affects the quality of the obtained superoxide dismutase. Moreover, it is difficult to wash off because it tends to remain, increasing the workload for workers.

[0006] Furthermore, the high starch content in corn causes the liquid to become viscous during enzymatic degradation, which hinders contact between the enzyme and the cell wall, reducing the efficiency of enzymatic degradation and affecting the activity and extraction amount of SOD. [Overview of the project] [Problems that the invention aims to solve]

[0007] The embodiments of this application provide a method for preparing superoxide dismutase from corn, thereby solving the problem of viscosity caused by the high starch content when corn is used as a raw material for the production of SOD. This significantly improves the efficiency of enzymatic degradation, effectively maintains SOD activity, and ensures stable product quality. Furthermore, this technology makes mass production easier and reduces production costs. [Means for solving the problem]

[0008] The embodiments of this application provide a method for preparing corn superoxide dismutase, which specifically includes the following steps.

[0009] Step S1. Pretreatment: Germinated corn is ground to 80-120 mesh, and pH=7.8, 0.05 mol / L phosphate buffer is added to the ground germinated corn in a ratio of 1:2-4. The mixture is then uniformly mixed until it becomes a corn slurry, and sonic cell disruption is performed.

[0010] Step S2. Enzymatic decomposition Step S21: Place the ultrasonically fractured corn slurry into a drum screen, the lower half of which is immersed in the enzymatic hydrolysis mixture, and activate the drum screen to tumble the corn material inside the drum while it remains immersed in the enzymatic hydrolysis mixture.

[0011] Step S22: Aspirate the enzymatically digested mixture every 10-15 minutes and centrifuge it to remove suspended starch particles and other impurities.

[0012] Step S23: The clarified liquid after centrifugation is reinjected into the drum screen, and the enzymatic digestion is repeated 2-3 times.

[0013] Step S24: The enzymatically hydrolyzed corn slurry is uniformly stirred and filtered, the filtrate is collected, and an antioxidant is added to the filtrate at a concentration of 0.5% to 1.5% by the weight of the filtrate.

[0014] Step S3. Extraction and post-processing Step S31: The filtrate is sonicated, and one or more of the following are added to the resulting liquid: an activator, a protective agent, or a stabilizer.

[0015] Step S32. Concentration and drying: The filtrate is placed in a low-temperature vacuum concentrate and evaporated at 22-25°C for 8-15 hours to obtain a corn superoxide dismutase complex enzyme liquid. The corn superoxide dismutase complex enzyme liquid is then uniformly mixed with silicon dioxide, which accounts for 1% of the total weight, and freeze-dried in a low-temperature vacuum dryer to obtain a corn superoxide dismutase complex enzyme powder.

[0016] Furthermore, the drum axis of the drum screen is positioned horizontally, the diameter of the holes in the drum screen is smaller than the particle size of the corn pulverized particles, one end is open for raw material charging, and the other end has a discharge port.

[0017] Furthermore, the amount of corn slurry added in step S21 is 40% to 60% of the drum screen volume, the enzymatic hydrolysis mixture contains a complex enzyme and a surfactant, the complex enzyme is a mixture of pectinase, cellulase, hemicellulase and protease, the amount of complex enzyme added is 0.5 to 2% of the mass of the corn slurry, the surfactant is sodium dodecanesulfonate, the amount of surfactant added is 0.5 to 0.8% of the mass of the corn slurry, the enzymatic hydrolysis temperature is 45 to 55°C, the pH is 7.0 to 8.0, and the enzymatic hydrolysis time is 40 to 80 minutes.

[0018] Furthermore, a plurality of sets of rotating blades are provided on the drum screen, and an object dynamics model is used to simulate the flow of the target object in the stirring process to ensure that it is stirred uniformly throughout. Specifically, a plurality of blades are arranged in a staggered pattern inside the drum screen. Initially, a spiral blade shape is selected. The width of the blade occupies 10% - 20% of the diameter of the drum screen, the length of the blade is slightly shorter than half of the length of the drum screen, and the angle of the blade is 30 - 60°.

[0019] Furthermore, a light transmittance sensor is provided in the storage tank of the enzymatic hydrolysis mixture to construct a mapping relationship model between the light transmittance and the starch concentration.

[0020] Specifically, the mapping relationship model between the light transmittance and the starch concentration is quantified from past data and experimental calibration, and the quantified data is analyzed and fitted according to the actual situation.

[0021] The camera provided below the drum screen photographs the state of the raw materials inside the drum screen, performs feature extraction and recognition, calculates the proportion of starch particles immersed in the mixture, and estimates the degree of enzymatic hydrolysis.

[0022] For the collected initial image, grayscale conversion is performed and analysis by histogram is carried out.

[0023] Subsequently, preprocessing such as filtering and noise removal is performed to extract the features of the starch particles.

[0024] Morphological operations are performed to further clean up the image, remove noise, connect adjacent starch particle components, and extract the contour edges of the starch particles.

[0025] All starch particle components in the image are identified by the connected component labeling algorithm, and the area and shape features of each component are calculated.

[0026] Classify the extracted features to distinguish starch particles from other impurities.

[0027] Approximately express it as the immersion ratio by calculating the number of pixels in contact between the starch particle component and the background component of the mixed solution.

[0028] Proceed to step S221, set a threshold based on the data of the total amount of starch obtained, and start the centrifuge when the starch concentration exceeds the set threshold.

[0029] After the end of step S221, proceed to step S231, and repeat the above operations until the extraction and separation of superoxide dismutase are completed.

[0030] Furthermore, after starting the centrifuge in step S221, separate the solid precipitate, which is mainly undissolved starch particles, from the supernatant.

[0031] Furthermore, collect the solid precipitate obtained after centrifugation, dry it, weigh it using a precision balance, record the mass of the precipitate, and specifically include the following steps.

[0032] Step S2211: Compare the initial amount of corn flour with the starch content, and calculate the theoretically obtainable amount of starch.

[0033] Step S2212: Calculate the degree of progress of enzymatic hydrolysis by comparing the amount of starch in the actual precipitate with the theoretical amount of starch, and express it as a percentage. Here, the degree of completion of enzymatic hydrolysis

Number

[0034] Step S2213: Analyze the above steps and evaluate the influence of the conditions of enzymatic hydrolysis, such as temperature, pH, and enzyme amount, on the degree of progress. <​ Furthermore, after recording and analyzing the mass of the precipitate, sensors and a data recording system are added to acquire a continuous, dynamic dataset including changes in each parameter during enzymatic degradation. This allows for real-time monitoring and recording of the degree of enzymatic degradation and overall time data using raw materials from the same lot.

[0036] Furthermore, prior to the enzymatic digestion of raw materials for each lot, data from the first three lots is collected and the average value is calculated to establish a baseline standard for the enzymatic digestion operation. The baseline value is calculated using the following formula.

[0037]

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number

[0038] Furthermore, the rotation speed of the drum screen and the stirring time interval of the mixture are dynamically adjusted to achieve the optimal enzymatic digestion effect, based on the real-time degree to which starch is filtered out from the drum screen.

[0039] Based on the amount of starch filtered out in a single pass and the total cumulative amount, the distribution and concentration of starch in the mixture are analyzed, and the frequency of draining and cleaning the centrifuge is automatically adjusted.

[0040] Once stirring stops, the camera captures an image of the top of the mixture in the drum screen and converts it into a grayscale image for analyzing the distribution and concentration of starch particles.

[0041] An image processing algorithm analyzes changes in tonal values ​​in different regions of a grayscale image to determine the degree and mass of starch leaching, and then estimates its real-time degree and quantity.

[0042] We will comprehensively analyze data such as drum screen rotation speed, stirring time interval, centrifugation frequency, and image recognition results to evaluate the effectiveness of the current enzymatic degradation parameters.

[0043] Based on the evaluation results, the enzyme degradation control parameters are adjusted.

[0044] One or more technical solutions according to the embodiments of this application have at least the following technical effects or advantages.

[0045] Firstly, by performing enzymatic decomposition using a drum screen and immersing the lower half of the drum in the enzymatic decomposition mixture, the raw material is constantly immersed in the mixture. Furthermore, the diameter of the holes in the drum screen is smaller than the particle size of the corn pulverized particles, allowing starch particles to pass through the screen, thus contributing to starch removal. By using a drum screen, starch particles are effectively removed, and the enzymatic decomposition mixture is ensured to be in sufficient contact with the corn to enzymatically decompose its cell walls. The dynamic tumbling of the drum screen and the circulation and renewal of the enzymatic decomposition mixture improve the efficiency of enzymatic decomposition, reduce the impact of starch on the decomposition process, effectively solve the viscosity problem caused by high starch content when using corn as a raw material for SOD production, significantly improve the efficiency of enzymatic decomposition, effectively maintain SOD activity, and ensure stable product quality. Moreover, this technology facilitates mass production, reduces production costs, and improves economic benefits.

[0046] Secondly, by incorporating a stirring system, starch particles are uniformly dispersed in the enzymatic hydrolysis mixture, avoiding viscosity problems caused by excessively high local concentrations and improving the efficiency of contact between the enzyme and maize cells. Real-time light transmittance monitoring data from a light transmittance monitoring system reflects the release and dispersion of starch in the mixture in a timely manner, supporting the adjustment of stirring parameters. Non-invasive, real-time monitoring of the degree of enzymatic hydrolysis is achieved through image analysis and calculation of starch immersion amount, providing accurate data support for process adjustment. Total starch amount control and centrifugation in the mixture remove excess starch, reduce viscosity, and recover starch, improving the overall economic efficiency of the process.

[0047] Thirdly, by further optimizing the starch content analysis step after centrifugation, quantitative evaluation of the progress and completion of enzymatic degradation was achieved. By comparing the difference between the actual amount of starch and the theoretical amount of starch, the efficiency of the enzymatic degradation reaction can be intuitively reflected, providing data support for process optimization, improving the controllability and predictability of the production process, and enhancing product quality and production efficiency. Optimization of the stirring system, real-time monitoring of light transmittance, image analysis and calculation of starch immersion amount, total starch content control in the mixture, and centrifugation significantly improve the efficiency of enzymatic degradation in the SOD extraction process from corn raw materials. Ensuring sufficient contact between the enzyme and corn cells shortens the enzymatic degradation time and improves SOD yield. Real-time monitoring and data analysis enable precise control of the progress of enzymatic degradation and reduce manual errors. Effective starch recovery reduces waste generation and improves process economics and environmental protection.

[0048] Fourth, by introducing technologies such as continuous data monitoring, baseline establishment, active control, and image recognition, intelligent management and optimization of the enzymatic degradation process can be achieved. In addition to real-time data collection and analysis, combining this with image recognition technology allows for accurate determination of the degree of starch filtration and dynamic adjustment of enzymatic degradation control parameters accordingly. Such a closed-loop feedback mechanism not only improves the efficiency of enzymatic degradation but also ensures the stability and controllability of the production process, significantly reduces the time required for enzymatic degradation, improves the activity of the product, and lowers production costs and energy consumption. [Brief explanation of the drawing]

[0049] [Figure 1] This is a schematic diagram of a drum screen according to an embodiment of the present invention. [Figure 2] This flowchart shows how to photograph the state of the raw materials inside a drum screen, perform feature extraction and recognition, calculate the proportion of starch particles immersed in the mixture, and estimate the degree of enzymatic degradation. [Modes for carrying out the invention]

[0050] Unless otherwise specified, all technical and scientific terms used in this application have the same meaning as those commonly understood by a person skilled in the art to which the present invention pertains. In this application, terms used in the specification of the present invention are for illustrative purposes only and do not limit the invention. The term "and / or" used in this application includes any or all combination of one or more related enumerated items.

[0051] Example 1 The method for preparing corn superoxide dismutase specifically includes the following steps:

[0052] Step S1. Pretreatment Step S11 Preparation of cell wall-disrupted yeast fermentation solution: Yeast was inoculated into a culture medium and fermented at 28-32°C and pH 4.5-5.5 for 48-72 hours. Then, snail enzyme was added and reacted for 1-2 hours to obtain a cell wall-disrupted yeast fermentation solution.

[0053] The yeast inoculation amount is 5% to 10% of the culture medium volume, and the snail enzyme addition rate is 50 to 100 mg per liter of fermentation solution.

[0054] Step S12: Preparation of raw materials: Prepare an appropriate amount of raw corn, remove any impurities from the corn, and set it aside.

[0055] Step S13 Germination: Place the corn in an automatic seed washing, soaking, and germination device, wash it, soak it for 8-12 hours, and germinate it at 25-30°C for 48-60 hours until the sprouts are 3-5 mm long.

[0056] The immersion solution contains a cell wall-disrupted yeast fermentation solution, with the amount added being 1% to 5% of the corn weight. It also contains 5-10 mg / L of iron, 0.1-0.5 mg / L of selenium, 1-2 mg / L of copper, 5-10 mg / L of manganese, and 5-10 mg / L of zinc.

[0057] Step S14 Grinding: Germinated corn was ground to 80-120 mesh, and 0.05 mol / L phosphate (PBS) buffer with a pH of 7.8 was added to the ground germinated corn in a ratio of 1:2-4. The mixture was then uniformly mixed until a corn slurry was formed, and sonic cell disruption was performed.

[0058] Step S2. Enzymatic decomposition As shown in Figure 1, a drum screen is installed, the drum shaft is set horizontally, an opening for raw material charging is provided at one end, and a discharge port is provided at the other end.

[0059] The diameter of the holes in the drum screen is smaller than the particle size of the ground corn.

[0060] In step S21, the corn slurry ultrasonically fractured in step S14 was placed into a drum screen, the lower half of which was immersed in the enzymatic hydrolysis mixture. The drum screen was then activated to tumble the corn material inside the drum while it remained immersed in the enzymatic hydrolysis mixture.

[0061] The amount of corn slurry added is 40% to 60% of the drum screen's volume. The enzymatic hydrolysis mixture contains a complex enzyme and a surfactant. The complex enzyme is a mixture of pectinase, cellulase, hemicellulase, and protease, and the amount of the complex enzyme added is 0.5% to 2% of the mass of the corn slurry. The surfactant is sodium dodecanesulfonate, and the amount of the surfactant added is 0.5% to 0.8% of the mass of the corn slurry. The enzymatic hydrolysis temperature is 45 to 55°C, the pH is 7.0 to 8.0, and the hydrolysis time is 40 to 80 minutes.

[0062] Step S22 The enzymatically digested mixture was aspirated and centrifuged every 10-15 minutes to remove suspended starch particles and other impurities.

[0063] Step S23: The clarified solution after centrifugation was reinjected into the drum screen, and enzymatic digestion was repeated 2-3 times.

[0064] Step S24 The enzymatically hydrolyzed corn slurry was uniformly stirred and filtered, the filtrate was collected, and an antioxidant of 0.5% to 1.5% by weight of the filtrate was added to the filtrate.

[0065] Step S3. Extraction and post-processing Step S31 The filtrate was sonicated, and one or more of the following were added to the resulting liquid: an activator, a protective agent, or a stabilizer.

[0066] The ultrasonic frequency is 20-30 kHz, the power is 10-20 W, the ultrasonic operation is paused for 5-10 seconds every 15-20 seconds, the total ultrasonic time is 10-20 minutes, and the temperature during the extraction process is 30-60°C. The activators are manganese chloride and ferrous chloride, the protective agent is a protein disulfide bond protectant, and the stabilizer is trehalose or burdock oligosaccharide.

[0067] Step S32 Concentration and drying: The filtrate was placed in a low-temperature vacuum concentrator and evaporated at 22-25°C for 8-15 hours to obtain a corn superoxide dismutase complex enzyme liquid.

[0068] A liquid solution of corn superoxide dismutase complex enzyme and silicon dioxide, which makes up 1% of the total weight, were uniformly mixed and freeze-dried in a low-temperature vacuum drying apparatus to obtain a powder of corn superoxide dismutase complex enzyme.

[0069] The technical invention according to the embodiments of this application has at least the following technical effects or advantages.

[0070] Corn contains approximately 48-73% starch. When producing SOD from corn, especially in industrial production, the release of starch particles during the enzymatic degradation process can make the liquid viscous, hindering contact between the enzyme and the cell wall, leading to uneven enzyme distribution, reduced efficiency of enzymatic degradation, increased release of impurities, and ultimately affecting SOD activity and extractability.

[0071] Enzymatic digestion is performed using a drum screen, and the lower half of the drum is immersed in the enzyme digestion mixture, ensuring that the raw material is constantly submerged in the mixture. Furthermore, the diameter of the holes in the drum screen is smaller than the particle size of the corn pulverized particles, allowing starch particles to pass through the screen, thus contributing to starch removal. The drum screen effectively removes starch particles while ensuring sufficient contact between the enzyme digestion mixture and the corn, leading to enzymatic digestion of its cell walls. Dynamic tumbling by the drum screen and the circulation and renewal of the enzyme digestion mixture improve the efficiency of enzymatic digestion, reduce the impact of starch on the digestion process, effectively solve the viscosity problem caused by high starch content when using corn as a raw material for SOD production, significantly improve the efficiency of enzymatic digestion, effectively maintain SOD activity, and ensure stable product quality. Moreover, this technology facilitates mass production, reduces production costs, and improves economic benefits.

[0072] The preparation of superoxide dismutase according to this embodiment can effectively extract SOD from corn and improve extraction efficiency. Adjusting the process parameters in this embodiment can maintain SOD activity, reduce losses during the extraction process, and improve the purity and activity of the product. By optimizing the pretreatment, enzymatic decomposition, extraction, and posttreatment steps, the consumption of raw materials and energy in the production process can be reduced, thereby lowering production costs. By optimizing the extraction and purification processes, high purity and activity of the SOD in the product can be ensured, meeting the demand for SOD products in fields such as food, cosmetics, healthcare products, and agriculture.

[0073] Example 2 In Example 1 described above, dynamic tumbling by the drum screen and circulation and renewal of the enzymatic degradation mixture improved the efficiency of enzymatic degradation, reduced the influence of starch on enzymatic degradation, effectively solved the viscosity problem caused by the high starch content when using corn as a raw material for SOD production, significantly improved the efficiency of enzymatic degradation, and effectively maintained the activity of SOD. Further improvements were made based on step S2 of Example 1 to further improve the efficiency of enzymatic degradation and enhance the activity of SOD.

[0074] Step S211: Multiple sets of rotating blades were installed on the drum screen, and the flow of the target material during the mixing process was simulated using an object dynamics model to ensure uniform mixing throughout.

[0075] Specifically, multiple blades were arranged in a staggered pattern within the drum screen. Initially, spiral blades were selected, and to cover the main mixing area without increasing resistance, the blade width was set to 10% to 20% of the drum screen diameter, the blade length to be slightly shorter than half the drum screen length, and the blade angle to 30 to 60°. Using CFD (Computational Fluid Dynamics), the geometric size of the drum screen, blade design parameters, physical properties such as raw material density and viscosity, and conditions such as mixing speed were input, and the trajectory of the raw material flow and mixing effect within the drum screen were simulated. Based on the simulation results, parameters such as the number, shape, arrangement, and rotation speed of the blades were adjusted to achieve a uniform mixing effect. The optimized parameters will be used in the design of the drum screen in actual production.

[0076] A light transmittance sensor was installed in the storage tank of the enzyme-degraded mixture, and a mapping relationship model between light transmittance and starch concentration was constructed.

[0077] Specifically, the mapping relationship model between light transmittance and starch concentration is quantified from historical data and experimental calibration, and the quantified data is analyzed and fitted to actual conditions; therefore, a detailed explanation is omitted in this specification.

[0078] As an example, samples of mixed solutions with different starch concentrations were collected through experiments, and their light transmittance was measured. Through data analysis and fitting, a quadratic polynomial regression model expressed as follows was obtained.

number

number

[0079] Step S212: A camera positioned below the drum screen photographed the state of the raw materials inside the drum screen, performed feature extraction and recognition, calculated the proportion of starch particles immersed in the mixture, and estimated the degree of enzymatic degradation.

[0080] During the mixing process, when stirring stopped, a high-resolution 16320×9200 camera captured an image of the top of the mixed liquid inside the drum screen. Within 10 seconds of the stirring stopping, a grayscale image of the top of the mixed liquid inside the drum screen was captured.

[0081] As shown in Figure 2, the steps of photographing the state of the raw materials inside the drum screen, performing feature extraction and recognition, calculating the proportion of starch particles immersed in the mixture, and estimating the degree of enzymatic degradation include the following steps.

[0082] In some examples, the steps of photographing the state of the raw materials inside the drum screen, performing feature extraction and recognition, calculating the proportion of starch particles immersed in the mixture, and estimating the degree of enzymatic degradation specifically include the following steps.

[0083] The collected initial images are grayscaled and analyzed using histograms.

[0084] Specifically, the initial color images are converted to grayscale images. Histogram analysis is performed on the grayscale images, that is, the number of pixels for different tonal values ​​within the image is counted. A histogram is a two-dimensional graph where the horizontal axis represents tonal values ​​and the vertical axis represents the number of pixels for that tonal value. Because starch particles are present, the grayscale histogram shows a significant peak in the high tonal value region around 255. Starch particles are usually brighter than the background, so they show high tonal values ​​in the grayscale image. While looking at the histogram, we look for a significant peak in the high tonal value region of 180 to 255. This peak should be much higher than the number of pixels for other tonal values ​​and relatively narrow in width. This means that the pixels corresponding to these tonal values ​​are likely to be starch particles. Based on the grayscale histogram, the trough between the starch particle peak and the background peak in the histogram is selected as the threshold for distinguishing the starch-concentrated region from the background of the mixture (for example, if the histogram shows a significant starch particle peak around a grayscale value of 200, and the background peak is mainly concentrated below 100, the threshold for distinguishing the starch-concentrated region from the background would be set to 150).

[0085] Next, preprocessing is performed to remove noise through filtering, and the characteristics of the starch particles are extracted.

[0086] Specifically, feature extraction from an image begins with color partitioning. Because there is a difference in color between the starch particles and the background of the mixture, the image is divided into K clusters based on the RGB values ​​of the color features of the pixel points, and each cluster is assigned a unique label. Once the partitioning is complete, each pixel point will have a cluster label indicating which cluster it belongs to. By analyzing the color features of each cluster and finding the cluster that most closely matches the color features of the starch particles, the starch particles can be separated from the background.

[0087] Morphological operations are applied to further clean up the image, removing noise, concatenating adjacent starch particle components, and extracting the contour edges of the starch particles.

[0088] Morphological operations include shrinking, dilating, opening, and closing. Shrinking removes small noise spots at the edges of starch particles, making the edges smaller. Dilating fills small voids inside the starch particles, making the edges larger. Opening can further clean up the image while maintaining the overall shape of the starch particles. Closing is mainly used to connect adjacent starch particle components to form a complete connected component.

[0089] Specifically, a circle is selected as the basic structural element, and its size is adjusted according to the actual size of the starch particles. The structural element is slid across the image, and for each position, if all pixels within the structural element belong to a starch particle cluster, the pixel located in the center of the structural element is kept; otherwise, this pixel is set as the background. Using the same structural element as in the shrinking process, the structural element is slid across the image, and for each position, if any pixel within the structural element belongs to a starch particle cluster, the pixel located in the center of the structural element is set as the starch particle. The shrinking process reduces the range of the target component and removes small noise spots at the edges. The opening process is an expansion process after shrinking; first, the image is shrunk to remove small noise spots and burrs, and then the resulting expansion is applied to restore the shrunk portion while keeping the noise spots and burrs removed. Closing is a process that involves expansion followed by contraction. First, the image is expanded to fill the small voids within the starch particles. Then, the expanded image is contracted to remove the extra edges created during expansion and to connect adjacent starch particle components to form a complete connected component. A single contraction or expansion process may not yield the desired cleanup result. In this case, multiple iterative operations on the image, i.e., repeatedly performing contraction, expansion, opening, and closing processes until the requirements are met, can be considered. For contour edge extraction, an edge detection algorithm using the Canny method is used, and the related operations refer to prior art and are omitted from this specification.

[0090] The linked component labeling algorithm identifies all starch particle components in the image and calculates the area and shape characteristics of each component.

[0091] We select the Wo-Pass algorithm to apply to the processing of binary images for connected component analysis.

[0092] Specifically, an output image is created that is the same size as the input image and has an integer data type (for storing labels). Typically, background pixels are labeled 0, and foreground (i.e., starch particle) pixels are initially labeled with a temporary value (which is then replaced with a single label). The entire image is scanned pixel by pixel, starting from the top-left corner. For each foreground pixel (i.e., starch particle pixel), its 4-connected or 8-connected neighbors are examined. If there are foreground pixels whose neighbors do not have labels assigned, they are classified into the same connected component as the current pixel and given the same temporary label. If there are foreground pixels whose neighbors have different temporary labels assigned, these different labels are recorded for later processing. After the first scan is complete, the equivalent relationships between multiple temporary labels are recorded, indicating which temporary labels represent the same connected component.

[0093] Based on the equivalence relationships recorded in the first scan, each connected component is assigned a unique identifier. This is typically achieved by traversing all provisional labels and replacing them with the smallest label in the equivalent class in which they exist. After the second scan, a labeled image is output, with each connected component assigned a unique identifier. The area is calculated by counting the number of pixels within each connected component. Various shape features can be calculated, such as perimeter (calculated by traversing the boundary pixels of the connected components), circularity (estimated from the relationship between area and perimeter), and aspect ratio (calculated from the length and width of the minimum circumscribing rectangle).

[0094] The extracted characteristics are classified to distinguish starch particles from other impurities.

[0095] Specifically, connected component analysis is used to obtain shape features such as area, perimeter, and circularity of each component as input features. A set of class-known image samples containing starch particles and other impurities is collected, and connected component analysis is performed on each sample to extract corresponding features. Since the dimensions and ranges of different features may differ, it is necessary to standardize the features so that each feature contributes fairly to the classifier. An SVM classifier is trained using the standardized feature dataset. During training, the SVM identifies a hyperplane that maximizes the spacing between different classes. Connected component analysis is performed on new image samples to extract corresponding features, and these features are then processed using the same standardization method. When the standardized features are input to the trained SVM classifier, the classifier outputs a probability or determination value that each sample belongs to starch particles or other impurities. Based on the classifier's output, the class of each sample is determined. For the probability output, a threshold can be set to determine the final class assignment. For the determination value output, the class can be determined directly based on its sign (positive or negative). When distinguishing the starch particles described above from other impurities, the standardization process refers to a series of transformation operations on the shape characteristics of each component extracted from linked component analysis, such as area, perimeter, and circularity. The specific steps involved are described in the prior art and are omitted from this specification.

[0096] Step S213: The number of pixels in contact between the starch particle component and the background component of the mixed solution is calculated and approximately expressed as the immersion ratio.

[0097] Specifically, each starch particle component is traversed, and the number of edge pixels in contact with the background of the mixture is counted. These pixel counts are converted to actual surface area based on pixel size and image resolution. The total number of pixels for each starch particle component is calculated and similarly converted to actual surface area.

[0098] The immersion ratio is defined as the ratio of the surface area of ​​starch particles in contact with the mixture to the total surface area of ​​the starch particles; that is, immersion ratio = surface area corresponding to the number of pixels in contact / total surface area of ​​starch particles. Before performing this step, it is necessary to take images of the starch mixture at different enzymatic degradation points by controlling experimental conditions such as enzyme concentration, temperature, and pH in the laboratory. Then, the above processing is applied to these images to calculate the immersion ratio of the starch particles at each point in time. Based on the experimental data, a relationship model between the immersion ratio of starch particles and the time of enzymatic degradation is constructed. The already constructed relationship model is applied. In the actual production process, images of the raw material state in the drum are collected in real time. The degree of enzymatic degradation is estimated based on the currently calculated immersion ratio of starch particles.

[0099] Step S221: A threshold is set based on the data of the total amount of starch obtained, and the centrifuge is activated when the starch concentration exceeds the set threshold.

[0100] Specifically, depending on the actual situation, the system is set to start when the concentration for the first activation reaches x%, initiating the first extraction and separation of superoxide dismutase. The second activation threshold is set to y% (y>x), and the third activation threshold to z% (z>y), and in this way, superoxide dismutase is ultimately extracted and separated. In other words, when the total amount of starch obtained in real time exceeds this threshold, it is considered that the starch concentration in the mixture has reached the standard for further purification or processing.

[0101] For example, suppose the initial concentration of superoxide dismutase is 80%, and the goal is to purify it to 99.9%. In the first purification, a threshold was set to activate the device when the concentration reached 85%. The device was then activated, and purification began. Purification was stopped when the concentration approached 90%, resulting in a final concentration of 88%. In the second purification, a threshold was set to activate the device when the concentration reached 90%. The device was activated, and purification continued. Purification was stopped when the concentration approached 95%, resulting in a final concentration of 92.5%. In the third purification, a threshold was set to activate the device when the concentration reached 95%. The device was activated, and the final purification was performed. Purification was stopped when the concentration approached the target purity of 99.9%, resulting in a final concentration of 99.8%.

[0102] Step S231: Repeat the above procedure until the extraction and separation of superoxide dismutase is complete.

[0103] The technical invention according to the embodiments of this application has at least the following technical effects or advantages.

[0104] By incorporating a stirring system, starch particles are uniformly dispersed in the enzymatic hydrolysis mixture, avoiding viscosity problems caused by excessively high local concentrations and improving the efficiency of contact between the enzyme and maize cells. Real-time light transmittance monitoring data from a light transmittance monitoring system reflects the release and dispersion of starch in the mixture in a timely manner, supporting the adjustment of stirring parameters. Non-invasive, real-time monitoring of the degree of enzymatic hydrolysis is achieved through image analysis and calculation of starch immersion amount, providing accurate data support for process adjustment. Total starch amount control and centrifugation in the mixture remove excess starch, reduce viscosity, and recover starch, improving the overall economic efficiency of the process.

[0105] Example 3 In Example 2 described above, the efficiency of enzymatic degradation in the SOD extraction process from corn raw material was significantly improved by installing a stirring system, real-time monitoring of light transmittance, image analysis and calculation of the amount of starch immersion, and control of the total amount of starch in the mixture and centrifugation. To further improve the efficiency of enzymatic degradation, step S221 is further improved based on Example 2.

[0106] The mixture after enzymatic decomposition was centrifuged using a high-speed centrifuge to separate the solid precipitate from the supernatant water.

[0107] The solid precipitate consists mainly of undissolved starch particles.

[0108] The solid precipitate obtained after centrifugation was collected, dried, and weighed using a precision balance, and the mass of the precipitate was recorded.

[0109] The accuracy of the balance is at least 0.01g, or 1 / 100th of a gram.

[0110] Specifically, the following:

[0111] Step S2211: Compare the initial amount of corn flour with its starch content and calculate the theoretically obtainable amount of starch.

[0112] Specifically, Mn is the mass of the separated corn starch particle precipitate, and P is the percentage of known corn starch content, which can usually be obtained from the product specifications or previous analyses. The theoretical starch content is Ms = Mn × P, and this serves as a standard for evaluating the degree of enzymatic degradation.

[0113] Step S2212: The degree of enzymatic degradation is calculated and expressed as a percentage by comparing the actual amount of starch in the precipitate with the theoretical amount of starch. Here, the degree of completion of enzymatic degradation

number

[0114] Step S2213: Analyze the steps above and evaluate the effect of enzymatic degradation conditions such as temperature, pH, and enzyme amount on the degree of progress.

[0115] The technical invention according to the embodiments of this application has at least the following technical effects or advantages.

[0116] By further optimizing the starch content analysis step after centrifugation, we achieved quantitative evaluation of the progress and completion of enzymatic degradation. By comparing the difference between the actual amount of starch and the theoretical amount of starch, we can intuitively reflect the efficiency of the enzymatic degradation reaction, provide data support for process optimization, improve the controllability and predictability of the production process, and enhance product quality and production efficiency.

[0117] By optimizing the stirring system, monitoring light transmittance in real time, performing image analysis and calculating the amount of starch immersion, and controlling the total amount of starch in the mixture and using centrifugation, the efficiency of enzymatic degradation in the SOD extraction process from corn raw materials is significantly improved.

[0118] Ensuring sufficient contact between enzymes and maize cells shortens the enzymatic degradation time and improves SOD yield. Real-time monitoring and data analysis enable precise control over the progress of enzymatic degradation, reducing human error. Effective starch recovery reduces waste generation and improves process economics and environmental protection.

[0119] Example 4 In Example 3 described above, further optimization of centrifugation and starch content analysis enabled quantitative evaluation of the progress and completion of enzymatic degradation, improving the controllability and predictability of the production process, and enhancing product quality and production efficiency. Further improvements will be made based on Example 3 to further improve the efficiency of enzymatic degradation.

[0120] By adding sensors and a data recording system, a continuous, dynamic dataset including changes in each parameter during enzymatic degradation can be acquired. The degree of enzymatic degradation and overall time data for raw materials from the same lot can be monitored and recorded in real time.

[0121] Collect data from the first three batches, calculate the average, and establish a baseline standard for the enzymatic degradation process.

[0122] The baseline value can be calculated using the following formula.

number

number

[0123] The rotation speed of the drum screen and the stirring time interval of the mixture are dynamically adjusted to achieve the optimal enzymatic digestion effect, based on the real-time rate at which starch is filtered out from the drum screen.

[0124] Specifically, the rotation speed of the drum screen can be initially set within the range of 200 to 600 rpm depending on conditions such as the viscosity, water content, and gelatinization temperature of the starch raw material. The rotation speed of the drum screen is dynamically adjusted according to the real-time degree of starch filtration. Subsequent adjustments to the rotation speed are made every 10 to 30 minutes at a rate of 50 rpm or less each time. The first stirring time is 5 minutes or longer. In the initial stages of enzymatic digestion, the stirring time interval can be appropriately shortened to every 5 to 10 minutes to promote contact and reaction between the enzyme and starch. In the middle stages of enzymatic digestion, as the reaction progresses, the stirring time interval can be appropriately extended to every 15 to 20 minutes to reduce energy consumption and wear on the equipment. In the final stages of enzymatic digestion, the stirring time interval can be extended or stirring can be stopped when the reaction approaches its endpoint to avoid the effect of excessive stirring on the starch structure.

[0125] Based on the amount of starch filtered out in a single pass and the total cumulative amount, the distribution and concentration of starch in the mixture are analyzed, and the frequency of draining and washing the centrifuge is automatically adjusted.

[0126] Specifically, a data system will be constructed to monitor the amount of starch filtered out during each centrifugation cycle in real time using a weighing device, and to record the cumulative amount. The volume of the mixture will be estimated from the amount of raw material charged and discharged from the centrifuge, and the starch concentration will be calculated (C=M / V, where C is the concentration, M is the mass of starch, and V is the volume of the mixture). The concentration will be calculated every 10 minutes, in synchronization with the monitoring of the filtered amount. Initially, the upper limit of the concentration will be set to 12% and the lower limit to 8%, and samples will be collected from different locations every 0.5 hours for concentration analysis. If the concentration exceeds the upper limit, the frequency of drain cleaning of the centrifuge will automatically increase by 20% until the concentration falls below the upper limit. If the concentration falls below the lower limit, the frequency will automatically decrease by 10% until the concentration rises above the lower limit. The initial cleaning frequency is once every 30 minutes. For example, if the concentration continuously rises to 12.5%, the cleaning frequency will automatically be adjusted to once every 24 minutes. After adjustment, if the concentration drops to 10%, maintain the current frequency or make minor adjustments as needed. If the concentration remains below 8%, the frequency will automatically adjust to once every 33 minutes.

[0127] Once stirring stops, the camera captures an image of the top of the mixture in the drum screen and converts it into a grayscale image for analyzing the distribution and concentration of starch particles.

[0128] The captured color image is converted to a grayscale image, and in the grayscale image, the color value of each pixel is converted to a brightness value in the range of 0 to 255.

[0129] An image processing algorithm analyzes changes in tonal values ​​in different regions of a grayscale image to determine the degree and mass of starch leaching, and then estimates its real-time degree and quantity.

[0130] Specifically, in the starch filtration process, the grayscale image is first divided into multiple ROI regions, and the tonal values ​​of the pixels within each region are extracted. These tonal values ​​directly reflect the presence and concentration of starch particles. By statistically analyzing the average tonal value and distribution of tonal values ​​for each ROI, the average concentration and distribution of starch particles can be determined. After continuous filtration, the number of starch particles in the mixture decreases, resulting in lower tonal values. By comparing the grayscale image and the changes in tonal values ​​at different time points, the trend and rate of starch filtration can be analyzed. Specifically, a continuous decrease in tonal values ​​indicates that filtration is progressing effectively. If the tonal values ​​do not change or decrease much, it means that filtration has reached a steady state or needs adjustment. To estimate the amount of starch filtration, a mathematical model is constructed based on experimental data and experience to convert the change in tonal values ​​into the amount of starch filtration. This usually involves constructing a linear or nonlinear relationship between tonal values ​​and starch concentration. By continuously capturing and analyzing images, the degree of starch leaching is obtained in real time, and the amount of remaining starch to be leached is predicted based on the current degree of leaching and the trend of changes in grayscale values. This prediction process requires appropriate prediction and correction by referring to past data and experience.

[0131] We will comprehensively analyze data such as drum screen rotation speed, stirring time interval, centrifugation frequency, and image recognition results to evaluate the effectiveness of the current enzymatic degradation parameters.

[0132] The evaluation of the effectiveness of current enzymatic degradation parameters includes the following steps: The efficiency of enzymatic degradation is evaluated to determine whether the desired efficiency can be achieved with the current enzymatic degradation parameters, or whether there is a significant improvement compared to past data.

[0133] The quality of the product is evaluated, and it is checked whether the product meets the requirements for purity, yield, structure, etc. Energy consumption is evaluated, and it is analyzed whether the energy consumption is reasonable with the current enzymatic degradation parameters and whether there is room for energy saving. Operational stability is evaluated, and it is determined whether the enzymatic degradation process can proceed stably without severe fluctuations or abnormalities.

[0134] Based on the evaluation results, the enzyme degradation control parameters are adjusted.

[0135] Enzyme degradation control parameters include drum screen rotation speed, stirring time interval, and centrifugation frequency.

[0136] As an example, we simulated the following implementation scenario following the steps outlined above.

[0137] We are currently enzymatically digesting the fourth batch of corn flour. Data from the first three batches has already been collected, and baseline standards have been calculated. The baseline data for the first three batches shows a baseline enzymatic digestion time of 120 minutes, a baseline drum screen rotation speed of 300 rpm, a baseline stirring interval of 10 minutes, and a baseline centrifugation frequency of once every 30 minutes. The real-time monitoring data (hypothetical data) for the fourth batch shows an initial time of 0 minutes, a current time of 60 minutes, and a current starch filtration rate of 40%. Image recognition results indicate a medium-high concentration of starch particles. When the starch particle concentration exceeds a certain threshold, the stirring and centrifugation frequency will be adjusted from every 30 minutes to every 25 minutes.

[0138] The system recorded the current degree of enzymatic degradation and time data in real time, comparing the current degree to the baseline. It found that the current filtration rate of 40% of starch within 60 minutes was slightly faster than the baseline rate. Based on the real-time monitoring data and image recognition results, the system determined that the current concentration of starch particles was high and that additional stirring was needed to accelerate the enzymatic degradation reaction. Because the rate of starch filtration was faster than expected, the system decided to perform the next centrifugation operation first, adjusting the centrifugation frequency from every 30 minutes to every 25 minutes, without changing the washing volume, and ensuring that the starch was effectively separated without wasting excess water. Each time stirring stopped, an image of the mixture in the drum screen was taken, converted to a grayscale image, and processed. The grayscale image was analyzed using an image processing algorithm to check the degree of starch filtration and compare it to a preset threshold. When the filtration amount approached the predetermined upper limit, the system prepared for the next centrifugation operation. The system continuously monitored the degree of enzymatic degradation and dynamically adjusted control parameters based on real-time data and image recognition results. When the filtration volume approached a predetermined upper limit, the system prepared for the next centrifugation operation.

[0139] Simulation results showed that, through active control and real-time monitoring, the final enzymatic degradation time for the fourth batch was reduced to 105 minutes, 15 minutes faster than the baseline time.

[0140] Furthermore, because the enzymatic degradation process has become more efficient, the activity of the products (such as glucose) is expected to increase. While specific values ​​need to be measured experimentally, activity is generally proportional to the efficiency of enzymatic degradation.

[0141] The technical invention according to the embodiments of this application has at least the following technical effects or advantages.

[0142] By introducing technologies such as continuous data monitoring, baseline establishment, active control, and image recognition, intelligent management and optimization of the enzymatic degradation process are achieved. In addition to real-time data collection and analysis, combining this with image recognition technology allows for accurate determination of the degree of starch filtration and dynamic adjustment of enzymatic degradation control parameters accordingly. Such a closed-loop feedback mechanism not only improves the efficiency of enzymatic degradation but also ensures the stability and controllability of the production process, significantly reduces the time required for enzymatic degradation, improves the activity of the product, and lowers production costs and energy consumption.

[0143] The above description represents only preferred embodiments of the present invention and does not limit it. Those skilled in the art may have various modifications and changes to the present invention. Any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and principles of the present invention are protected within the scope of the present invention.

Claims

1. A method for preparing corn superoxide dismutase, Step S1 is a pretreatment in which germinated corn is crushed to 80-120 mesh, pH = 7.8, 0.05 mol / L phosphate buffer is added to the crushed germinated corn in a ratio of 1:2-4, and the mixture is uniformly mixed until it becomes a corn slurry, followed by ultrasonic cell disruption. Step S2 is enzymatic decomposition, Step S3, which is extraction and post-processing, The aforementioned step S2 is, Step S21 involves placing the ultrasonically crushed corn slurry into a drum screen, the lower half of which is immersed in an enzymatic hydrolysis mixture, and activating the drum screen to tumble the corn raw material inside the drum while it remains immersed in the enzymatic hydrolysis mixture. Step S22 involves aspirating the enzymatically decomposed mixture every 10 to 15 minutes and centrifuging it to remove suspended starch particles and other impurities. Step S23 involves reinjecting the clarified liquid after centrifugation into a drum screen and repeating the enzymatic decomposition two to three times. Step S24 includes uniformly stirring and filtering the enzymatically hydrolyzed corn slurry, collecting the filtrate, and adding an antioxidant to the filtrate at a concentration of 0.5% to 1.5% by weight of the filtrate. Step S3 is, Step S31 involves adding one or more of the following to the liquid obtained by ultrasonically treating the filtrate: an activator, a protective agent, or a stabilizer. Step S32, which involves concentration and drying, includes: placing the filtrate in a low-temperature vacuum concentrate and evaporating it at 22-25°C for 8-15 hours to obtain a corn superoxide dismutase complex enzyme liquid; uniformly mixing the corn superoxide dismutase complex enzyme liquid with silicon dioxide, which accounts for 1% of the total weight, and freeze-drying it in a low-temperature vacuum dryer to obtain a corn superoxide dismutase complex enzyme powder. A method for preparing corn superoxide dismutase, characterized by the following:

2. The drum axis of the drum screen is positioned horizontally, the diameter of the holes in the drum screen is smaller than the particle size of the ground corn, one end is open for raw material charging, and the other end has a discharge port. The method for preparing corn superoxide dismutase according to feature 1.

3. The amount of corn slurry added in step S21 is 40% to 60% of the drum screen volume, the enzymatic hydrolysis mixture contains a complex enzyme and a surfactant, the complex enzyme is a mixture of pectinase, cellulase, hemicellulase and protease, the amount of complex enzyme added is 0.5% to 2% of the mass of the corn slurry, the surfactant is sodium dodecanesulfonate, the amount of surfactant added is 0.5% to 0.8% of the mass of the corn slurry, the enzymatic hydrolysis temperature is 45 to 55°C, the pH is 7.0 to 8.0, and the enzymatic hydrolysis time is 40 to 80 minutes. The method for preparing corn superoxide dismutase according to feature 1.

4. Multiple sets of rotating blades are provided on the drum screen, and an object dynamics model is used to simulate the flow of the target material during the mixing process to ensure uniform mixing throughout. Specifically, multiple blades are arranged in a staggered pattern within the drum screen, with a spiral blade shape initially selected. The width of the blades occupies 10% to 20% of the diameter of the drum screen, the length of the blades is slightly less than half the length of the drum screen, and the angle of the blades is 30 to 60°. The method for preparing corn superoxide dismutase according to feature 1.

5. A light transmittance sensor is installed in the storage tank of the enzyme-digested mixture, and a mapping relationship model between light transmittance and starch concentration is constructed. Specifically, The mapping relationship model between light transmittance and starch concentration was quantified from historical data and experimental calibration, and the quantified data was analyzed and fitted to actual conditions. A camera positioned below the drum screen photographs the state of the raw materials inside the drum screen, performs feature extraction and recognition, calculates the proportion of starch particles immersed in the mixture, and estimates the degree of enzymatic decomposition. The collected initial images were grayscaled and analyzed using histograms. Next, preprocessing is performed using filtering and noise reduction to extract the characteristics of the starch particles. Morphological operations are applied to further clean up the image, removing noise, concatenating adjacent starch particle components, and extracting the contour edges of the starch particles. The linked component labeling algorithm identifies all starch particle components in the image and calculates the area and shape characteristics of each component. The extracted characteristics are classified, and the starch particles are distinguished from other impurities. By calculating the number of pixels in contact between the starch particle component and the background component of the mixed solution, it can be approximately expressed as an immersion ratio. The process proceeds to step S221, where a threshold is set based on the acquired total amount of starch data, and when the starch concentration exceeds the set threshold, the centrifuge is activated. After step S221 is completed, proceed to step S231 and repeat the above operation until the extraction and separation of superoxide dismutase is complete. The method for preparing corn superoxide dismutase according to feature 1.

6. After activating the centrifuge in step S221, the solid precipitate, which mainly consists of undissolved starch particles, is separated from the supernatant water. The method for preparing corn superoxide dismutase according to feature 5.

7. The steps of collecting the solid precipitate obtained after centrifugation, drying it, weighing it using a precision balance, and recording the mass of the precipitate are as follows: Step S2211 involves comparing the initial amount of corn flour with its starch content to calculate the amount of starch that should theoretically be obtained, Step S2212 involves calculating the degree of enzymatic degradation by comparing the actual amount of starch in the precipitate with the theoretical amount of starch, and expressing it as a percentage. Step S2213 includes analyzing the above steps and evaluating the effect of conditions such as temperature, pH, and enzyme amount on the degree of enzymatic degradation, Here, the degree of completion of enzymatic degradation Therefore, Ma is the actual amount of starch measured by weighing after drying. The method for preparing corn superoxide dismutase according to feature 6.

8. After recording and analyzing the mass of the precipitate, sensors and a data recording system are further installed to obtain a continuous, dynamic dataset including changes in each parameter during enzymatic degradation. This allows for real-time monitoring and recording of the degree of enzymatic degradation and overall time data using raw materials from the same lot. The method for preparing corn superoxide dismutase according to feature 7.

9. Before enzymatic hydrolysis of raw materials for each lot, data from the first three lots is collected and the average value is calculated to establish a baseline standard for the enzymatic hydrolysis operation. The baseline value is calculated using the following formula: n represents the number of data points for which the mean is calculated, and in this case, it means the number of data points in the first three lots. batch_i This represents the data value of the i-th lot, This represents the sum of all data values ​​from the first lot to the nth lot. This formula means that the average of these data values ​​is calculated as the baseline value by summing the data values ​​of the first three lots and dividing that sum by the number of lots. The method for preparing corn superoxide dismutase according to feature 8.

10. The rotation speed of the drum screen and the stirring time interval of the mixture are dynamically adjusted to achieve the optimal enzymatic digestion effect, in accordance with the real-time degree to which starch is filtered out from the drum screen. Based on the amount of starch filtered out in a single pass and the total cumulative amount, the distribution and concentration of starch in the mixture are analyzed, and the frequency of draining and washing the centrifuge is automatically adjusted. Once stirring stops, the camera captures an image of the top of the mixture in the drum screen, converting it to a grayscale image for analyzing the distribution and concentration of starch particles. An image processing algorithm analyzes the changes in tonal values ​​in different regions of a grayscale image to determine the degree and mass of starch leaching, and estimates its real-time degree and quantity. By comprehensively analyzing data such as drum screen rotation speed, stirring time interval, centrifugation frequency, and image recognition results, we evaluate the effectiveness of the current enzymatic degradation parameters. Based on the evaluation results, adjust the enzyme degradation control parameters. The method for preparing corn superoxide dismutase according to feature 9.

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