Method for efficiently extracting anti-tumor spirulina peptide

Abstract

The invention relates to the technical field of biology, and discloses a method for efficiently extracting anti-tumor spirulina peptide, which comprises the following steps: pretreating a spirulina raw material; performing enzymolysis treatment; optimizing the extraction process by adopting an intelligent optimization algorithm; multi-mode online monitoring and feedback control are carried out; separating and purifying; peptide fragment structure intelligent prediction and activity screening are carried out; the pretreatment comprises the following steps: selecting a high-quality spirulina raw material, removing impurities, and crushing the treated raw material until the particle size of the raw material reaches a set mesh number. By introducing an improved genetic algorithm, a multi-modal online monitoring and fuzzy-neural network composite control strategy and other intelligent optimization technologies, key parameters, such as enzymolysis conditions, separation and purification parameters, post-processing conditions and the like, in the extraction process can be accurately determined, and limitation and low efficiency of a traditional experience exploration optimization mode are avoided.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and particularly to a method for efficiently extracting anti-tumor spirulina peptides. Background Art

[0002] With the increasing demand for health, people are paying more attention to the medicinal value of natural products, especially in the field of anti-tumor research. Spirulina, as a microalgae rich in active ingredients such as proteins and polysaccharides, exhibits significant anti-tumor potential, which has prompted researchers to explore effective methods for extracting anti-tumor peptides from spirulina.

[0003] Traditional extraction methods mainly rely on enzymatic hydrolysis, chemical extraction and other means, combined with techniques such as chromatography and membrane separation for purification. For example, some studies have used alkaline protease to hydrolyze spirulina proteins, explored the effects of factors such as hydrolysis pH and time on efficiency, and determined the optimal conditions as pH 8 and 24-hour hydrolysis. Although certain results have been achieved, there is still much room for improvement in terms of extraction efficiency and purity.

[0004] These methods have many limitations. On the one hand, the extraction efficiency is low and the cost is high, making it difficult to meet the needs of large-scale production. On the other hand, the extraction process is complex, requiring precise control of multiple parameters, which increases the operation difficulty and cost. In addition, the existing methods lack intelligent monitoring and real-time optimization of the extraction process, and cannot quickly respond to changes in process conditions, resulting in poor product quality stability.

[0005] In view of this, there is an urgent need for a more efficient extraction method to improve the yield and quality and reduce the production cost. For this reason, those skilled in the art have proposed a method for efficiently extracting anti-tumor spirulina peptides to solve the above problems. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention provides a method for efficiently extracting anti-tumor spirulina peptides, which solves the problems raised in the above background art.

[0007] To achieve the above object, the present invention is realized through the following technical solutions: A method for efficiently extracting anti-tumor spirulina peptides, comprising the following steps:

[0008] Pretreat the spirulina raw material;

[0009] Perform enzymatic hydrolysis treatment;

[0010] Optimize the extraction process using an intelligent optimization algorithm;

[0011] Perform multi-modal online monitoring and feedback control;

[0012] Perform separation and purification;

[0013] Perform intelligent prediction of peptide structure and activity screening;

[0014] Perform post-treatment and product preparation.

[0015] Preferably, the pretreatment includes:

[0016] Select high-quality spirulina raw materials and remove impurities;

[0017] Crush the treated raw materials to make their particle size reach the set mesh number.

[0018] Preferably, the enzymatic hydrolysis treatment is specifically:

[0019] Prepare an enzymatic hydrolysis system containing specific proteases and auxiliary enzymes, and control the enzyme concentration and the dosage of auxiliary enzymes;

[0020] Mix the crushed raw materials with the enzymatic hydrolysis system, adjust the pH value of the mixture to the optimal action range of the enzyme, control the reaction temperature within the set range, and keep the reaction time for the set duration.

[0021] Preferably, the intelligent optimization algorithm is an improved genetic algorithm. The parameters in the enzymatic hydrolysis conditions, separation and purification steps, and post-treatment methods are used as model variables to construct an optimization model with the comprehensive indicators of extraction rate, purity, and anti-tumor activity as the objective function. Iterative search for the optimal solution is carried out through specific genetic operations, and the algorithm is improved by using adaptive crossover and mutation operators.

[0022] Preferably, the multi-modal online monitoring and feedback control includes:

[0023] Integrate a variety of online monitoring sensors in the extraction equipment, including a spectral-based component analyzer, a pressure sensor, and a temperature sensor;

[0024] Transmit the data collected by the sensors to the control center in real time, and adopt a fuzzy-neural network composite control strategy to dynamically adjust and optimize the operating parameters of the extraction equipment.

[0025] Preferably, the separation and purification includes:

[0026] After the enzymatic hydrolysis reaction ends, use centrifugation technology to preliminarily separate the enzymatic hydrolysate;

[0027] Further separate and purify the supernatant through membrane separation technology;

[0028] Further purify the separated spirulina peptide solution by using techniques such as ion exchange chromatography and gel filtration chromatography.

[0029] Preferably, the intelligent prediction of peptide structure and activity screening includes:

[0030] Establish a prediction system for the structure and anti-tumor activity of spirulina peptides based on a hybrid model of quantum chemistry calculation - machine learning;

[0031] During the extraction process, use an on-line mass spectrometer to obtain the structural information of the extracted peptides in real time and transmit it to the prediction system;

[0032] According to the prediction results, use the structure optimization suggestions fed back by the prediction model to timely adjust the subsequent enzymatic hydrolysis or purification steps.

[0033] Preferably, the post-treatment and product preparation include:

[0034] Dry the spirulina peptide solution after extraction, separation and purification, using spray drying technology or freeze drying technology;

[0035] Conduct quality inspection on the dried spirulina peptide powder;

[0036] Pack the qualified spirulina peptide powder.

[0037] Preferably, the application of the intelligent optimization algorithm also includes optimizing the parameters in the separation and purification steps. The parameters in the separation and purification steps include the operating pressure, operating temperature of ultrafiltration and nanofiltration, and the elution conditions in chromatographic purification, etc.

[0038] Preferably, the multi-modal on-line monitoring and feedback control also includes real-time monitoring and automatic adjustment of the drying conditions during the post-treatment process to ensure product quality.

[0039] The present invention provides a method for efficiently extracting anti-tumor spirulina peptides. It has the following beneficial effects:

[0040] 1. By introducing intelligent optimization technologies such as improved genetic algorithms, multi-modal on-line monitoring and fuzzy-neural network composite control strategies, the present invention can accurately determine the key parameters in the extraction process, such as enzymatic hydrolysis conditions, separation and purification parameters, and post-treatment conditions, etc., avoiding the limitations and inefficiencies of traditional empirical exploration and optimization methods. This not only greatly improves the extraction efficiency of spirulina peptides, reduces resource waste, but also enables the extraction process to always operate under the best process conditions, effectively avoiding problems such as a decrease in extraction efficiency and unqualified product quality caused by parameter fluctuations, thus significantly improving the quality stability of spirulina peptide products.

[0041] 2. The present invention combines multimodal online monitoring and feedback control technology with intelligent optimization algorithms to achieve in-depth optimization and coordinated control of each link of the extraction process from enzymatic hydrolysis to separation and purification. During the extraction process, a variety of online monitoring sensors collect data in real time and transmit it to the control center. Through the fuzzy neural network composite control strategy, the system can dynamically adjust the operating parameters of the extraction equipment. At the same time, this intelligent optimization and control of the whole process avoids the problems of low efficiency and unstable quality caused by the relative independence of each link and the lack of coordinated optimization in traditional methods.

[0042] 3. The present invention can obtain peptide structural information in real time during the extraction process, quickly evaluate its potential anti-tumor activity, and provide a clear direction and high-quality raw materials for the development of anti-tumor drugs. By adjusting the subsequent extraction steps through feedback, highly active peptides can be obtained in a targeted manner, which can significantly enhance the anti-tumor effect of the extract and improve its application value in anti-tumor treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is the overall flow chart of the method of the present invention;

[0044] Figure 2 It is the enzymatic hydrolysis process flow chart of the present invention;

[0045] Figure 3 It is a separation and purification flow chart of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Embodiment 1:

[0048] Please refer to the attached Figure 1 - Attachment Figure 3 The embodiment of the present invention provides a method for efficiently extracting anti-tumor spirulina peptides, comprising the following steps:

[0049] Pre-treating the spirulina raw material;

[0050] Preprocessing includes:

[0051] Select high-quality spirulina raw materials and remove impurities;

[0052] The processed raw materials are crushed to make the particle size reach the set mesh number

[0053] Performing enzymatic hydrolysis;

[0054] Specifically, the spirulina raw material is pretreated. Pretreatment is the starting step of the extraction process, aiming to prepare for subsequent extraction operations and improve extraction efficiency and product quality. High-quality spirulina raw materials are selected and impurities are removed. This ensures the purity and quality of the raw materials and provides a good start for subsequent extraction steps.

[0055] The enzymatic hydrolysis treatment is specifically as follows:

[0056] Prepare an enzymatic hydrolysis system containing specific protease and auxiliary enzymes, and control the enzyme concentration and the dosage of auxiliary enzymes;

[0057] Mix the crushed raw material with the enzymatic hydrolysis system, adjust the pH value of the mixture to the optimal action range of the enzyme, control the reaction temperature within the set range, and keep the reaction time for the set duration.

[0058] An intelligent optimization algorithm is used to optimize the extraction process;

[0059] The intelligent optimization algorithm is an improved genetic algorithm. The parameters in the enzymatic hydrolysis conditions, separation and purification steps, and post-treatment methods are used as model variables to construct an optimization model with the comprehensive indexes of extraction rate, purity, and anti-tumor activity as the objective function. Iterative search for the optimal solution is carried out through specific genetic operations, and the algorithm is improved by using adaptive crossover and mutation operators.

[0060] Specifically, the genetic algorithm is an optimization algorithm that simulates natural selection and genetic mechanisms and is widely used to solve complex optimization problems. It searches for the optimal solution in the encoded parameter space by simulating the biological evolution process and using basic operations such as selection, crossover, and mutation.

[0061] In the present invention, the parameters in the enzymatic hydrolysis conditions, separation and purification steps, and post-treatment methods are used as model variables. An optimization model with the comprehensive indexes of extraction rate, purity, and anti-tumor activity as the objective function is constructed. In this way, multiple key indexes can be comprehensively considered, so as to more comprehensively evaluate the advantages and disadvantages of the extraction process.

[0062] The genetic algorithm simulates the biological evolution process through genetic operations such as selection, crossover, and mutation, and continuously iteratively searches for the optimal solution. The selection operation determines the probability of an individual reproducing offspring according to its fitness; the crossover operation replaces and recombines part of the structures of two parental individuals to generate new offspring individuals; the mutation operation randomly changes some genes of an individual with a certain probability to increase the diversity of the population and prevent the algorithm from converging prematurely.

[0063] Traditional crossover and mutation operators use fixed crossover probabilities and mutation probabilities, which results in low efficiency of the algorithm in some stages of the search process. Adaptive crossover and mutation operators can dynamically adjust the crossover and mutation strategies according to the current population state. At different stages of evolution, adaptively change the probabilities and ways of crossover and mutation according to the diversity and convergence of the population, enabling the algorithm to quickly explore the solution space in the early stage and more precisely search the region near the optimal solution in the later stage, thus improving the search efficiency and the robustness of the algorithm.

[0064] Perform multimodal online monitoring and feedback control;

[0065] Multimodal online monitoring and feedback control includes:

[0066] Integrate a variety of online monitoring sensors in the extraction equipment, including a spectroscopic composition analyzer, a pressure sensor, and a temperature sensor;

[0067] Transmit the data collected by the sensors to the control center in real time, and adopt a fuzzy-neural network composite control strategy to dynamically adjust and optimize the operating parameters of the extraction equipment.

[0068] Perform separation and purification; separation and purification includes:

[0069] After the enzymatic hydrolysis reaction, use centrifugation technology to preliminarily separate the enzymatic hydrolysate;

[0070] Further separate and purify the supernatant through membrane separation technology;

[0071] Further purify the separated spirulina peptide solution using techniques such as ion exchange chromatography and gel filtration chromatography.

[0072] Perform intelligent prediction of peptide segment structure and activity screening;

[0073] Intelligent prediction of peptide segment structure and activity screening includes:

[0074] Establish a prediction system for the structure and anti-tumor activity of spirulina peptides based on a hybrid model of quantum chemistry calculation - machine learning;

[0075] During the extraction process, use an online mass spectrometer to obtain the structural information of the extracted peptide segments in real time and transmit it to the prediction system;

[0076] According to the prediction results, use the structure optimization suggestions fed back by the prediction model to timely adjust the subsequent enzymatic hydrolysis or purification steps.

[0077] Perform post-treatment and product preparation.

[0078] Post-treatment and product preparation includes:

[0079] The spirulina peptide solution after extraction, separation and purification is dried using spray drying technology or freeze drying technology;

[0080] The quality of the dried spirulina peptide powder is detected;

[0081] The qualified spirulina peptide powder is packaged.

[0082] The application of intelligent optimization algorithms also includes optimizing the parameters in the separation and purification steps. The parameters in the separation and purification steps include the operating pressure, operating temperature of ultrafiltration and nanofiltration, and the elution conditions in chromatographic purification, etc. Multimodal online monitoring and feedback control also includes the real-time monitoring and automatic adjustment of the drying conditions in the post-treatment process to ensure product quality.

[0083] Example 2

[0084] In this example, an enzymatic hydrolysis system composed of alkaline protease and cellulase is used, and the particle size of the pulverized spirulina raw material is 100 mesh. The extraction process is optimized by an improved genetic algorithm, and precise control of the extraction process is achieved by using a multimodal online monitoring and fuzzy-neural network composite control strategy. Separation and purification are carried out using techniques such as centrifugation, ultrafiltration, nanofiltration, ion exchange chromatography and gel filtration chromatography, and the peptide segment structure and activity are screened and feedback optimized by combining a prediction system based on a quantum chemistry calculation-machine learning hybrid model. Finally, qualified spirulina peptide powder is obtained by spray drying. It includes the following contents:

[0085] Select high-quality spirulina powder, and remove impurities, mud and other foreign matter by screening. Then the treated spirulina raw material is crushed to a particle size of 100 mesh. Prepare an enzymatic hydrolysis system containing alkaline protease and cellulase, the concentration of alkaline protease is 0.5%, and the amount of cellulase is 0.1%. Mix the crushed spirulina raw material with the enzymatic hydrolysis system, adjust the pH value of the mixture to 8.0, control the reaction temperature at 50°C, and the reaction time lasts for 3 hours. During the reaction process, an intelligent temperature control system and a pH adjustment device are used to monitor and automatically adjust the temperature and pH value in real time. An improved genetic algorithm is used to optimize the extraction process. The enzymatic hydrolysis conditions (enzyme type, concentration, reaction time, temperature, pH value), parameters in the separation and purification steps (such as solvent dosage, flow rate, operating pressure), and parameters in the post-treatment method (such as drying temperature, time) are used as model variables to construct an optimization model with extraction rate, purity and anti-tumor activity comprehensive indicators as the objective function. Through genetic operations such as selection, crossover and mutation, the biological evolution process is simulated, and the optimal solution is continuously iterated and searched. The initial population size was set to 40, the chromosome length was 20, the crossover probability was set to 0.8, and the mutation probability was set to 0.05. The algorithm was improved by using adaptive crossover and mutation operators, and its parameters were dynamically adjusted at different stages of the extraction process. A near-infrared spectrometer, a pressure sensor, and a temperature sensor were integrated in the extraction equipment. The data collected by the sensor were transmitted to the control center in real time, and the fuzzy-neural network composite control strategy was used to dynamically adjust and optimize the operating parameters of the extraction equipment. For example, in the enzymatic reaction stage, when the temperature sensor detected that the reaction temperature was lower than the set value, the system automatically increased the heating power; when the near-infrared spectrometer monitored the change in peptide concentration, the stirring speed was adjusted. After the enzymatic reaction was completed, the enzymatic solution was initially separated by centrifugation technology (speed of 12000r / min, centrifugation time of 15 minutes), and large particle impurities such as cell residues were removed to obtain the supernatant. The supernatant was then further separated and purified by ultrafiltration membrane (molecular weight cutoff of 3000Da) and nanofiltration membrane (molecular weight cutoff of 500Da). Finally, the separated spirulina peptide solution was further purified using ion exchange chromatography and gel filtration chromatography techniques.

[0086] A prediction system for the structure and anti-tumor activity of Spirulina peptides based on a quantum chemical calculation-machine learning hybrid model was established. During the extraction process, an online mass spectrometer was used to obtain the molecular weight, amino acid composition and other structural information of the extracted peptides in real time and transmit it to the prediction system. The prediction system quickly evaluated the potential anti-tumor activity of the peptide based on the input structural information and fed back the prediction results to the extraction operator. According to the prediction results, it was found that some peptides had low anti-tumor activity, and the subsequent enzymatic hydrolysis conditions and purification steps were adjusted in time to cut the peptide bonds in a targeted manner to obtain shorter peptides with high activity.

[0087] The spirulina peptide solution after extraction, separation and purification is dried using spray drying technology (inlet air temperature is 170 °C and outlet air temperature is 90 °C). Quality inspection is carried out on the dried spirulina peptide powder, including purity inspection (high performance liquid chromatography, detection limit is 0.1%), activity inspection (in vitro cell experiments to determine its inhibition rate against tumor cells, with the requirement that the inhibition rate ≥ 50%), and microbial limit inspection (total number of bacteria ≤ 1000 cfu / g, total number of molds and yeasts ≤ 100 cfu / g). The qualified spirulina peptide powder is packaged using aluminum foil bags, with a packaging quantity of 50 g per bag.

[0088] Example 3

[0089] In this example, an enzymatic hydrolysis system composed of compound protease and cellulase is used, and the particle size of the pulverized spirulina raw material is 200 mesh. Similarly, intelligent optimization technologies such as improved genetic algorithm, multi-modal online monitoring and fuzzy-neural network composite control strategy are used to finely regulate the extraction process. The separation and purification steps are similar to those in Example 1, using technologies such as centrifugation, ultrafiltration, nanofiltration, ion exchange chromatography and gel filtration chromatography. The peptide segment structure and activity are monitored and feedback optimized in real time through an online mass spectrometer and prediction system, and finally qualified spirulina peptide powder is obtained by freeze drying.

[0090] Select high-quality spirulina dry powder, and remove impurities, sediment and other foreign matters through screening. Then the processed spirulina raw material is pulverized to make its particle size reach 200 mesh.

[0091] Prepare an enzymatic hydrolysis system containing compound protease and cellulase. The concentration of compound protease is 1%, and the dosage of cellulase is 0.3%. Mix the pulverized spirulina raw material with the enzymatic hydrolysis system, adjust the pH value of the mixture to 7.5, control the reaction temperature at 45 °C, and keep the reaction time for 4 hours. During the reaction process, an intelligent temperature control system and a pH adjustment device are used to monitor and automatically adjust the temperature and pH value in real time.

[0092] The extraction process is optimized using an improved genetic algorithm. The enzymatic hydrolysis conditions (enzyme type, concentration, reaction time, temperature, pH value), parameters in the separation and purification steps (such as solvent dosage, flow rate, operating pressure), and parameters in the post-treatment method (such as drying temperature, time) are used as model variables to construct an optimization model with the comprehensive indexes of extraction rate, purity and anti-tumor activity as the objective function. Through genetic operations such as selection, crossover and mutation, the biological evolution process is simulated, and the optimal solution is continuously iteratively searched. The initial population size is set to 50, the chromosome length is 25, the crossover probability is set to 0.9, and the mutation probability is set to 0.03. The algorithm is improved using adaptive crossover and mutation operators, and its parameters are dynamically adjusted according to different stages of the extraction process.

[0093] Integrate an ultraviolet-visible spectrometer, a pressure sensor, and a temperature sensor in the extraction device. Transmit the data collected by the sensors to the control center in real time, and adopt a fuzzy-neural network composite control strategy to dynamically adjust and optimize the operating parameters of the extraction device. For example, in the enzymatic hydrolysis reaction stage, when the temperature sensor detects that the reaction temperature is lower than the set value, the system automatically increases the heating power; when the ultraviolet-visible spectrometer monitors the change in peptide concentration, adjust the stirring speed.

[0094] After the enzymatic hydrolysis reaction, use centrifugation technology (rotation speed of 10,000 r / min and centrifugation time of 20 minutes) to preliminarily separate the enzymatic hydrolysate, remove large particle impurities such as cell residues, and obtain the supernatant. Then, further separate and purify the supernatant through an ultrafiltration membrane (cut-off molecular weight of 5,000 Da) and a nanofiltration membrane (cut-off molecular weight of 300 Da). Finally, further purify the separated spirulina peptide solution using ion exchange chromatography and gel filtration chromatography techniques.

[0095] Establish a prediction system for the structure and anti-tumor activity of spirulina peptides based on a hybrid model of quantum chemical calculation - machine learning. During the extraction process, use an on-line mass spectrometer to obtain the molecular weight, amino acid composition and other structural information of the extracted peptide segments in real time, and transmit them to the prediction system. The prediction system quickly evaluates the potential anti-tumor activity of the peptide segment based on the input structural information and feeds back the prediction results to the extraction operator. According to the prediction results, it is found that the anti-tumor activity of some peptide segments is low, and the subsequent enzymatic hydrolysis conditions and purification steps are adjusted in a timely manner to cleave peptide bonds directionally and obtain shorter peptide segments with high activity.

[0096] Dry the extracted, separated and purified spirulina peptide solution using freeze-drying technology (cold trap temperature of -40 °C and vacuum degree of 20 Pa). Conduct quality inspections on the dried spirulina peptide powder, including purity inspection (high performance liquid chromatography method, detection limit of 0.1%), activity inspection (determine the inhibition rate against tumor cells by in vitro cell experiments, with the requirement that the inhibition rate ≥ 50%), and microbial limit inspection (total number of bacteria ≤ 1,000 cfu / g, total number of molds and yeasts ≤ 100 cfu / g). Package the qualified spirulina peptide powder using brown glass bottles, with a packaging volume of 100 g per bottle.

[0097] Comparative example

[0098] This comparative example uses a traditional extraction method, only using alkaline protease for enzymatic hydrolysis. The particle size of the crushed raw material is 100 mesh, and the enzymatic hydrolysis conditions are the same as those in Example 1, but the intelligent optimization technology is not used for process parameter optimization and process control. The separation and purification steps are basically the same as those in Example 1, using techniques such as centrifugation, ultrafiltration, nanofiltration, ion exchange chromatography, and gel filtration chromatography. Spirulina peptide powder is obtained by spray drying, but no feedback optimization of peptide structure intelligent prediction and activity screening is carried out.

[0099] Select high-quality spirulina dry powder, and remove impurities, sediment and other foreign matters through screening. Then crush the processed spirulina raw material to make its particle size reach 100 mesh.

[0100] Prepare an enzymatic hydrolysis system containing alkaline protease, and the concentration of alkaline protease is 0.5%. Mix the crushed spirulina raw material with the enzymatic hydrolysis system, adjust the pH value of the mixture to 8.0, control the reaction temperature at 50 °C, and the reaction time lasts for 3 hours. During the reaction process, an intelligent temperature control system and a pH adjustment device are not used, and the temperature and pH value are detected and adjusted manually at regular intervals.

[0101] After the enzymatic hydrolysis reaction is completed, the enzymatic hydrolysate is preliminarily separated by centrifugation technology (rotation speed is 12000 r / min, centrifugation time is 15 minutes) to remove large particle impurities such as cell residues, and the supernatant is obtained. Then the supernatant is further separated and purified through an ultrafiltration membrane (cut-off molecular weight is 3000 Da) and a nanofiltration membrane (cut-off molecular weight is 500 Da). Finally, the separated spirulina peptide solution is further purified by ion exchange chromatography and gel filtration chromatography techniques.

[0102] The spirulina peptide solution after extraction, separation and purification is dried by spray drying technology (inlet air temperature is 170 °C, outlet air temperature is 90 °C). Quality inspection is carried out on the dried spirulina peptide powder, including purity inspection (high performance liquid chromatography method, detection limit is 0.1%), activity inspection (in vitro cell experiment to measure its inhibition rate on tumor cells, and the inhibition rate is required to be ≥50%), and microbial limit inspection (total number of bacteria ≤1000 cfu / g, total number of molds and yeasts ≤100 cfu / g). The qualified spirulina peptide powder is packaged, using an aluminum foil bag for packaging, and the packaging amount per bag is 50 g.

[0103] In order to verify the effectiveness of the method of the present invention, the extraction rate, purity, anti-tumor activity and other indicators of the spirulina peptides extracted in Example 1, Example 2 and Comparative Example 1 are detected, and the results are shown in Table 1 below.

[0104]

[0105] Table 1

[0106] As can be seen from the above table, the extraction method of the present invention has significant advantages. By introducing intelligent optimization technologies such as improved genetic algorithms, multi-modal online monitoring, and fuzzy-neural network composite control strategies, the key parameters in the extraction process can be accurately determined, thereby greatly improving the extraction efficiency of spirulina peptides. The extraction rates reach 85.6% and 88.4% respectively, which is about 37.3% - 41.7% higher than 62.3% of Comparative Example 1. At the same time, the purity of the extracted spirulina peptides has also been significantly improved. The purities of Example 1 and Example 2 are 92.3% and 94.1% respectively, while that of Comparative Example 1 is only 85.7%. In addition, the spirulina peptides extracted by the method of the present invention also show excellent anti-tumor activity, and the inhibition rates on tumor cells reach 88.7% and 90.5% respectively, which is significantly higher than 72.4% of Comparative Example 1. This indicates that the method of the present invention can effectively improve the extraction efficiency and purity of spirulina peptides, and the extracted peptide segments have higher anti-tumor activity, providing better raw materials for the research and development of anti-tumor drugs.

[0107] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for efficiently extracting anti-tumor spirulina peptides, characterized in that, It includes the following steps: Pretreat the spirulina raw material; Conduct enzymatic hydrolysis treatment; Optimize the extraction process using an intelligent optimization algorithm; Conduct multi-modal online monitoring and feedback control; Conduct separation and purification; Conduct intelligent prediction of peptide structure and activity screening; Conduct post-treatment and product preparation.

2. The method for efficiently extracting anti-tumor spirulina peptides according to claim 1, characterized in that, The pretreatment includes: Select high-quality spirulina raw material and remove impurities; Crush the treated raw material to make its particle size reach the set mesh number.

3. A method for efficiently extracting anti-tumor spirulina peptides according to claim 1, characterized in that, The enzymatic hydrolysis treatment specifically is: Prepare an enzymatic hydrolysis system containing specific protease and auxiliary enzyme, and control the enzyme concentration and the dosage of auxiliary enzyme; Mix the crushed raw material with the enzymatic hydrolysis system, adjust the pH value of the mixture to the optimal action range of the enzyme, control the reaction temperature within the set range, and keep the reaction time for the set duration.

4. A method for efficiently extracting anti-tumor spirulina peptides according to claim 1, characterized in that The intelligent optimization algorithm is an improved genetic algorithm. Take the parameters in the enzymatic hydrolysis conditions, separation and purification steps, and post-treatment methods as model variables, construct an optimization model with the comprehensive indexes of extraction rate, purity, and anti-tumor activity as the objective function, conduct iterative search for the optimal solution through specific genetic operations, and improve the algorithm using adaptive crossover and mutation operators.

5. A method for efficiently extracting anti-tumor spirulina peptides according to claim 1, characterized in that, The multi-modal online monitoring and feedback control includes: Integrate a variety of online monitoring sensors in the extraction equipment, including a spectral-based component analyzer, a pressure sensor, and a temperature sensor; Transmit the data collected by the sensors to the control center in real time, and adopt a fuzzy-neural network composite control strategy to dynamically adjust and optimize the operating parameters of the extraction equipment.

6. The method for efficiently extracting anti-tumor spirulina peptides according to claim 1, characterized in that, The separation and purification includes: After the enzymatic hydrolysis reaction ends, conduct preliminary separation of the enzymatic hydrolysate using centrifugation technology; Further separate and purify the supernatant through membrane separation technology; Conduct further purification treatment on the separated spirulina peptide solution using technologies such as ion exchange chromatography and gel filtration chromatography.

7. A method for efficiently extracting anti-tumor spirulina peptides according to claim 1, characterized in that, The intelligent prediction of peptide structure and activity screening includes: Establish a prediction system for the structure and anti-tumor activity of spirulina peptides based on a quantum chemistry calculation-machine learning hybrid model; During the extraction process, use an online mass spectrometer to obtain the structural information of the extracted peptides in real time and transmit it to the prediction system; According to the prediction results, use the structure optimization suggestions fed back by the prediction model to timely adjust the subsequent enzymatic hydrolysis or purification steps.

8. A method for efficiently extracting anti-tumor spirulina peptides according to claim 1, characterized in that, The post-treatment and product preparation includes: Conduct drying treatment on the extracted, separated, and purified spirulina peptide solution, using spray drying technology or freeze drying technology; Conduct quality inspection on the dried spirulina peptide powder; Package the qualified spirulina peptide powder.

9. The method for efficiently extracting anti-tumor spirulina peptides according to claim 1, characterized in that, The application of the intelligent optimization algorithm also includes optimizing the parameters in the separation and purification steps. The parameters in the separation and purification steps include the operating pressure, operating temperature of ultrafiltration and nanofiltration, and the elution conditions in chromatographic purification, etc.

10. A method for efficiently extracting anti-tumor spirulina peptides according to claim 1, characterized in that, The multi-modal online monitoring and feedback control also includes real-time monitoring and automatic adjustment of the drying conditions during the post-treatment process to ensure product quality.

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