Response surface method-based chia seed antioxidant peptide preparation process optimization method and optimized preparation method

Abstract

The invention discloses a chia seed antioxidant peptide preparation process optimization method based on a response surface method and a preparation method obtained through optimization, and relates to the technical field of food science and engineering. According to the method, the response surface method is used as an integrated optimization and prediction tool and is used for accurate regulation and control of a complex enzymolysis system, efficient collaborative optimization of enzymolysis conditions is achieved, a predictable and controllable scientific process model is established, a universal optimization scheme is provided for enzymolysis conditions of different proteases, and the method is suitable for popularization and application. According to the optimization method provided by the invention, the preparation process of the chia seed antioxidant peptide is optimized, an enzymolysis condition for realizing synergistic maximization of the peptide yield and the antioxidant activity is obtained, the peptide concentration, the DPPH free radical scavenging rate and the FRAP value of the chia seed peptide prepared under the optimization condition all reach or approach theoretical maximum values, and the chia seed peptide has the advantages of high antioxidant activity and good stability. The method is obviously higher than a local optimal result obtained by a traditional single-factor method.

Description

Technical Field

[0001] This invention relates to the fields of food science and engineering technology, specifically to an optimization method for the preparation process of chia seed antioxidant peptides based on response surface methodology, and the optimized preparation method. Background Technology

[0002] Chia seeds, a high-quality plant resource gaining increasing global attention, possess unique nutritional components that demonstrate significant application potential in health foods, supplements, and even pharmaceuticals. Of particular note is the rich protein content of chia seeds, which, upon hydrolysis, yields functional peptides with substantial antioxidant activity. These antioxidant peptides, due to their natural origin, high safety, and outstanding activity, have become a research hotspot in the functional food and biopharmaceutical fields, and a key pathway to achieving high-value and in-depth utilization of chia seed resources. In the complex process of enzymatically preparing antioxidant peptides from chia seed protein, the refined optimization of enzymatic hydrolysis conditions is widely considered the core factor determining the final peptide yield and antioxidant activity.

[0003] In current technological practices, the optimization of the enzymatic hydrolysis process for chia seed antioxidant peptides mainly relies on the traditional single-factor experimental method. The basic logic of this method is to fix all process parameters except for one variable (e.g., hydrolysis time, temperature, pH, or enzyme-substrate ratio) and systematically examine only that single variable to explore its impact on peptide yield or antioxidant activity, thereby empirically determining an "optimal" process condition. This method, due to its simplicity and ease of understanding, has played a role in early studies, enabling the preliminary screening of individual factors that significantly influence the target response value, providing initial direction for subsequent process exploration.

[0004] However, with the deepening research into bio-enzymatic hydrolysis technology and the increasingly stringent requirements for product performance and process stability in industrial applications, the aforementioned traditional single-factor optimization methods have gradually revealed their inherent and insurmountable limitations at the principle level. The underlying reason is that enzymatic reactions are highly complex biochemical processes, and their kinetic behavior is not simply the sum of independent single factors, but rather determined by the intricate synergistic or antagonistic effects of multiple key parameters. The fundamental flaw of the single-factor experimental method lies in its design philosophy, which is based on the simplified assumption that the factors are independent or their interactions are negligible. However, in actual enzymatic hydrolysis systems, this assumption often contradicts reality. For example, enzyme activity may decrease rapidly after reaching a specific temperature, but its thermal stability at different pH values ​​may vary significantly; similarly, while increasing the enzyme-substrate ratio can accelerate the reaction rate, its potential catalytic efficiency cannot be fully realized if the reaction time is insufficient or the pH environment is unsuitable. Traditional single-factor experimental methods fail to quantitatively capture and analyze the complex and subtle interactions between these key parameters. Consequently, the "optimal process conditions" determined by these methods are often only locally optimal solutions on a multidimensional response surface, rather than globally optimal ones. This local optima severely limits the possibility of simultaneously maximizing peptide yield and antioxidant activity, resulting in preparation efficiency and product activity far below theoretical potential.

[0005] Furthermore, based on the failure to fully reveal the local optimum conditions of complex interactions between parameters, the stability and reproducibility of the constructed enzymatic hydrolysis process system are often difficult to guarantee effectively. When batch differences in raw materials, minor fluctuations in equipment, or subtle changes in the operating environment occur in actual production, the peptide yield and antioxidant activity are prone to significant deviations due to the lack of understanding and predictive models of the comprehensive influence of multiple factors. For example, the purity and microscopic structural differences of different batches of chia seed protein, under unoptimized process conditions, may lead to unpredictable fluctuations in enzyme efficiency and product characteristics under the same nominal parameters, thus directly restricting product quality control and uniformity in large-scale production. The lack of scientific prediction and precise control models to support this empirical optimization path makes the scale-up and transfer of the process particularly difficult, not only increasing the R&D costs and risks of industrial production, but also making it difficult to meet the market's high standards for product quality stability and batch consistency. Essentially, the single-factor method makes the enzymatic hydrolysis process like a "black box," making it difficult to achieve precise control of key parameters and accurate prediction of product performance, thus hindering the industrial application of chia seed antioxidant peptides.

[0006] In summary, existing technologies, due to their failure to systematically investigate and quantify the synergistic effects among key process parameters such as enzymatic hydrolysis time, temperature, pH, and enzyme-substrate ratio, often result in locally optimal hydrolysis conditions. This makes it impossible to simultaneously maximize peptide yield and antioxidant activity, and the process exhibits poor reproducibility and stability, significantly hindering the large-scale preparation and industrial application of chia seed antioxidant peptides. Therefore, overcoming the inherent limitations of traditional single-factor experimental methods in revealing multi-factor interactions and achieving process stability and controllability, and developing an optimized method capable of systematically investigating and quantifying the synergistic effects of multiple parameters, thereby constructing a method with scientific prediction and precise control capabilities to ultimately achieve high-efficiency, high-activity, and high-stability preparation of chia seed antioxidant peptides, has become a key challenge and an urgent technical problem for those skilled in the art. Summary of the Invention

[0007] This invention provides an optimization method for the preparation process of chia seed antioxidant peptides based on response surface methodology, and obtains a better method for preparing chia seed antioxidant peptides based on this optimization method. This method overcomes the inherent limitations of traditional single-factor experimental methods in the preparation technology of chia seed antioxidant peptides in revealing the interaction of multiple factors and achieving process stability and controllability, thereby promoting the large-scale preparation and industrial application of chia seed antioxidant peptides.

[0008] The technical solution adopted in this invention is as follows: An optimization method for the preparation process of chia seed antioxidant peptides based on response surface methodology, wherein the optimization method targets the enzymatic hydrolysis reaction using chia seed protein as a substrate and provides an optimization scheme for enzymatic hydrolysis parameters, including the following steps: (1) Single-factor preliminary experiment: Chia seed protein was used as substrate and enzymatic hydrolysis was carried out using protease. The effects of hydrolysis time, hydrolysis temperature, pH value and enzyme / substrate ratio (E / S ratio) on peptide concentration, DPPH free radical scavenging rate (DPPH-RSA value) and iron ion reduction antioxidant capacity (FRAP value) were investigated to determine the key level range of each parameter. (2) Response surface experimental design: Based on the results of step (1), a four-factor, three-level experimental model was constructed using Box-Behnken design. The independent variables were enzymatic hydrolysis time, enzymatic hydrolysis temperature, pH value, and enzyme / substrate ratio, and the response values ​​were peptide concentration, DPPH free radical scavenging rate, and iron ion reduction antioxidant capacity. Multiple sets of experiments were conducted. (3) Model building and validation: A mathematical model of response value and independent variable is established by fitting a quadratic polynomial regression. The significance and goodness of fit of the model are verified by analysis of variance. The influence of the interaction of various factors on the response value is analyzed by using three-dimensional response surface plot and contour plot. (4) Determination of optimal process: Based on model prediction and verification experiments, the global optimal process parameters for enzymatic hydrolysis of chia seed antioxidant peptides were determined.

[0009] This invention systematically solves the core technical problems of traditional single-factor optimization methods in the prior art, such as their inability to capture multi-factor interactions, difficulty in achieving global optimum, and poor process stability and controllability. The innovation of this invention lies in using response surface methodology as an integrated optimization and prediction tool for the precise control of complex enzymatic hydrolysis systems. This achieves efficient synergistic optimization of enzymatic hydrolysis conditions and establishes a predictable and controllable scientific process model, providing a universal optimization scheme for the enzymatic hydrolysis conditions of different proteases.

[0010] Preferably, in step (1), the values ​​of each single-factor variable are: enzymatic hydrolysis time 2~6 h, enzymatic hydrolysis temperature 35~55 ℃, pH value 5~9, and enzyme / substrate ratio 2000~10000 U / g.

[0011] Preferably, in step (2), the independent variable level range designed by Box-Behnken is: hydrolysis time 4~6 h, hydrolysis temperature 45~55 ℃, pH value 7~9, enzyme / substrate ratio 6000~10000 U / g.

[0012] Preferably, the expression of the mathematical model fitted in step (3) is: Y=β 0 +∑β i X i +∑β ii X i 2 +∑β ij X i X j； in, Y These are the individual response values ​​for peptide concentration, DPPH-RSA, or FRAP. X i and X j As the independent variable, β 0 For constant terms, β i The coefficients are linear. β ii The coefficient of the quadratic term, β ij For the interaction term coefficient.

[0013] Preferably, the protease used in step (1) is a flavor protease with an enzyme activity ≥200 U / mg. The optimized hydrolysis parameters are: hydrolysis time 5.69±0.5 h, hydrolysis temperature 52.34±2 ℃, pH 7.38±0.2, and hydrolysis under the conditions of enzyme / substrate ratio 8487.60±500 U / g.

[0014] An optimized method for preparing chia seed antioxidant peptides includes the following steps: chia seed pretreatment; chia seed protein extraction; enzymatic hydrolysis: enzymatic hydrolysis is performed using process parameters optimized by any of the above methods; and ultrafiltration separation of the enzymatic hydrolysate.

[0015] Furthermore, the chia seed pretreatment process is as follows: soak chia seeds in distilled water for 1-6 hours, degumm them by ultrasonic treatment, dry them, pulverize them, deesterify them in n-hexane, filter them, and dry them to obtain defatted chia seed powder.

[0016] Furthermore, the chia seed protein extraction process is as follows: the defatted chia seed powder is mixed with distilled water, the pH is adjusted to 9-11, and the mixture is stirred and extracted at 40-60℃ for 1-3 hours. The supernatant is collected by centrifugation, the pH is adjusted to 4-5 for isoelectric precipitation, the precipitate is collected after centrifugation and freeze-dried to obtain chia seed protein concentrate.

[0017] An optimized method for preparing chia seed antioxidant peptides includes the following steps: chia seed pretreatment; chia seed protein extraction; enzymatic hydrolysis: enzymatic hydrolysis is performed using process parameters optimized by any of the above methods; and ultrafiltration separation of the enzymatic hydrolysate.

[0018] Furthermore, the chia seed pretreatment process is as follows: soak chia seeds in distilled water for 1-6 hours, degumm them by ultrasonic treatment, dry them, pulverize them, deesterify them in n-hexane, filter them, and dry them to obtain defatted chia seed powder.

[0019] Furthermore, the chia seed protein extraction process is as follows: the defatted chia seed powder is mixed with distilled water, the pH is adjusted to 9-11, and the mixture is stirred and extracted at 40-60℃ for 1-3 hours. The supernatant is collected by centrifugation, the pH is adjusted to 4-5 for isoelectric precipitation, the precipitate is collected after centrifugation and freeze-dried to obtain chia seed protein concentrate. Attached Figure Description

[0020] Figure 1 The bar chart shows the peptide content measurement results of the enzymatic hydrolysate obtained by different proteases for chia seed protein, and the line chart shows the measurement results of DPPH free radical scavenging rate and FRAP value. Figure 2 The figure shows the experimental results of the effect of different enzymatic hydrolysis conditions on peptide content and antioxidant activity in the single-factor experiment. Figure 3The three-dimensional response surface plot and contour plot are shown to represent the changes in peptide content with two factors during the optimization of enzymatic hydrolysis process parameters based on response surface methodology. Among them, (A) time and temperature; (B) time and pH; (C) time and E / S; (D) temperature and pH; (E) temperature and E / S; (F) pH and E / S. Figure 4 The three-dimensional response surface plot and contour plot are shown to represent the changes of DPPH-RSA with two factors during the optimization of enzymatic hydrolysis process parameters based on response surface methodology. Among them, (A) time and temperature; (B) time and pH; (C) time and E / S; (D) temperature and pH; (E) temperature and E / S; (F) pH and E / S. Figure 5 The three-dimensional response surface plot and contour plot are shown to represent the changes in FRAP with two factors during the optimization of enzymatic hydrolysis process parameters based on response surface methodology. Among them, (A) time and temperature; (B) time and pH; (C) time and E / S; (D) temperature and pH; (E) temperature and E / S; (F) pH and E / S. Figure 6 The bar chart shows the in vitro antioxidant activity test results of ultrafiltration components with different molecular weights. In the chart, the pink bars represent DPPH-RSA values ​​and the blue bars represent FRAP values. Detailed Implementation

[0021] The present invention will be described in detail below with reference to specific embodiments and examples, thereby making the advantages and various effects of the present invention more clearly apparent. Those skilled in the art should understand that these specific embodiments and examples are for illustrative purposes only and are not intended to limit the present invention.

[0022] Throughout this specification, unless otherwise specified, the terminology used herein should be understood to have the meaning commonly used in the art. Therefore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. In the event of any conflict, this specification shall prevail.

[0023] Unless otherwise specified, all raw materials, reagents, instruments and equipment used in this invention can be purchased from the market or prepared by existing methods.

[0024] Example 1 This embodiment provides a process for preparing chia seed antioxidant peptides with optimized enzymatic hydrolysis reaction parameters, specifically including the following steps: 1. Pretreatment of Chia Seed Samples: Weigh 100g of chia seeds and add purified water at a ratio of 1:40 (w / v). Soak at room temperature for 2 hours to extract the mucilage. Then, sonicate the sample in an ultrasonic bath with a power of 750W and an amplitude of 50%, with 4-minute intervals, to remove the mucilage adhering to the seeds. After sonication, dry the mucilage-free seeds at 55℃ overnight. Next, manually separate the mucilage using a sieve and grind the seeds into powder using a grinder. Then, mix the mucilage-free chia seed powder with n-hexane at a ratio of 1:5 (w / v) and heat at 45℃ for 75 minutes to defatted the seeds. After three extraction cycles to ensure complete defatting, dry the powder at 55℃ overnight to remove residual solvent and store at 4℃ for later use.

[0025] 2. Extraction of Chia Seed Protein: Chia seed protein was separated using an alkali-soluble acid precipitation method. The treated chia seed powder was mixed with distilled water at a ratio of 1:10 (w / v), and the pH was adjusted to 10.0 with 1 M NaOH. The suspension was continuously stirred at room temperature for 60 min, then centrifuged at 4 °C and 8288 × g for 15 min. The supernatant was carefully collected for isoelectric point precipitation. In this step, the pH was lowered to 4.5 with 1 M HCl, and the mixture was stirred at room temperature for another 1 h. Centrifugation was repeated under the same conditions, the supernatant was discarded, and the precipitate was retained. Finally, the precipitate was stored at -80 °C and then freeze-dried to obtain chia seed protein concentrate.

[0026] 3. Optimization of enzymatic hydrolysis reaction parameters: (1) Screening of optimal proteases: Using chia seed protein as raw material, the effects of different proteases on the peptide content and antioxidant activity of the hydrolysate were studied under the optimal temperature and pH conditions of their respective enzymes (alkaline protease: pH 8.0, 50 ℃; neutral protease: pH 7.0, 50 ℃; pepsin: pH 3.0, 37 ℃; papain: pH 7.5, 60 ℃; trypsin: pH 7.5, 50 ℃; flavor protease: pH 7.5, 50 ℃), with a fixed hydrolysis time of 4 h and an E / S ratio of 2000 U / g. The results showed that the hydrolysate prepared by the flavor protease had the highest peptide content and antioxidant activity (see [link to relevant documentation]). Figure 1 Therefore, flavor protease was selected for subsequent experiments.

[0027] (2) Single-factor experiment: Single-factor experiments were used to preliminarily evaluate the effects of hydrolysis time (2, 3, 4, 5, 6 h), hydrolysis temperature (35, 40, 45, 50, 55 ℃), pH (5, 6, 7, 8, 9), and E / S ratio (2000, 4000, 6000, 8000, 10000 U / g) on ​​peptide concentration, FRAP value, and DPPH-RSA value, in order to obtain the optimal enzymatic hydrolysis conditions. The results showed that the optimal conditions determined by the single-factor experiments were: time 5 h, temperature 50 ℃, pH 8, and E / S 8000 U / g (see [reference missing]). Figure 2 Under these conditions, three independent parallel validation experiments were conducted, and the peptide concentration was measured to be 0.432 mg / mL, DPPH-RSA was 21.57%, and FRAP was 0.823 mM.

[0028] (3) Optimization of enzymatic hydrolysis process based on response surface methodology: Based on the single-factor experiments, the extraction conditions were optimized using Box-Behnken design. Time (A: 4, 5, 6 h), temperature (B: 45, 50, 55 ℃), pH (C: 7, 8, 9) and E / S (D: 6000, 8000, 10000 U / g) were used as independent variables, and peptide concentration (Y1), DPPH-RSA (Y2) and FRAP (Y3) were used as response variables. A total of 27 experiments were conducted, and the results are shown in Table 1.

[0029] Table 1 Box-Behnken Experimental Design and Response Values The quadratic regression model was fitted and analyzed for variance using Design-Expert 13 software. The results are shown in Tables 2, 3, and 4. The quadratic regression model equation for peptide concentration (Y1) obtained through software fitting is: Y1 = 0.4483 + 0.0068A + 0.0157B − 0.0091C − 0.0043D − 0.0055AB − 0.0100AC − 0.0108AD − 0.0220BC + 0.0125BD − 0.0082CD − 0.0120A 2 -0.0114B 2 -0.0168C 2 -0.0124D 2 The quadratic regression model equation for DPPH-RSA (Y2) is: Y2 = 23.55 + 0.9342A + 0.2967B + 0.0183C + 1.17D − 0.0500AB − 0.0500AC − 0.0375AD + 0.5775BC + 0.1525BD − 0.0475CD − 1.74A 2 -0.6467B 2-1.63C 2 -1.21D 2 The quadratic regression model equation for FRAP (Y3) is: Y3 = 0.9307 + 0.0263A − 0.0758B − 0.0097C + 0.0163D − 0.0185AB − 0.0175AC−0.0045AD − 0.0148BC − 0.0167BD−0.0443CD−0.0422A 2 -0.0300B 2 -0.0932C 2 -0.0157D 2 Where A is the enzymatic hydrolysis time (h), B is the enzymatic hydrolysis temperature (°C), C is the pH value, and D is the enzyme / substrate ratio (U / g). All three models were significant (p < 0.0001), and the lack-of-fit term was not significant (p > 0.05), indicating that the models are reliable. Models for peptide content, DPPH-RSA, and FRAP showed R... 2 The values ​​are 0.9359, 0.9777, and 0.9426 respectively. The adjusted R... 2 The values ​​were 0.8611, 0.9517, and 0.8756, respectively, indicating that the model can predict the response values ​​well. The interactions between the factors were analyzed using 3D response surface plots and 2D contour plots (see [link to analysis]). Figure 3 , 4, 5).

[0030] Table 2. Analysis of variance of the DPPH-RSA model

[0031] Table 3. Analysis of variance of the DPPH-RSA model

[0032] Table 4. Analysis of Variance of the FRAP Model

[0033] The optimal hydrolysis conditions predicted by the software optimization function were: time 5.69 h, temperature 52.34 ℃, pH 7.38, and E / S 8487.60 U / g. Three independent parallel validation experiments were conducted under these conditions, and the peptide concentration was measured to be 0.476±0.013 mg / mL, DPPH-RSA was 23.47±1.6%, and FRAP was 0.805±0.024 mM, which highly matched the predicted values ​​(see Table 5), confirming the effectiveness of the RSM optimization.

[0034] Table 5 Comparison of model predictions and experimental results

[0035] 4. Prepare chia seed protein hydrolysate under the determined optimal conditions. The hydrolysate was fractionated sequentially using ultrafiltration centrifuge tubes with molecular weight cutoffs of 10 kDa and 3 kDa to obtain three fractions: <3 kDa, 3-10 kDa, and >10 kDa. In vitro antioxidant activity assays showed that the <3 kDa fraction had significantly higher DPPH-RSA and FRAP values ​​than the other two fractions (see [link to relevant documentation]). Figure 6 It exhibits higher antioxidant activity.

[0036] Finally, it should be noted that the terms "comprising," "including," or any other variations are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make further changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0037] The embodiments described above merely illustrate specific implementation methods of this application, and while the descriptions are detailed, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various changes and improvements without departing from the concept of the technical solution of this application, and these all fall within the scope of protection of this application.

Claims

1. A method for optimizing the preparation process of antioxidant peptides from Chia seed based on response surface methodology, said optimization method provides an optimization scheme of enzymatic parameters for the enzymatic reaction with Chia seed protein as substrate, characterized in that, Comprising the following steps: (1) Single factor pre-experiment: taking chia seed protein as substrate, enzyme was used for enzymolysis reaction, the effects of enzymolysis time, enzymolysis temperature, pH value and enzyme / substrate ratio on peptide concentration, DPPH free radical scavenging rate and iron ion reducing antioxidant capacity were investigated, and the key level range of each parameter was determined; (2) Response surface experiment design: based on the results of step (1), a four-factor three-level experiment model was constructed by Box-Behnken design, with enzymolysis time, enzymolysis temperature, pH value and enzyme / substrate ratio as independent variables, and peptide concentration, DPPH free radical scavenging rate and iron ion reducing antioxidant capacity as response values, multiple experiments were conducted; (3) Model construction and verification: a mathematical model of response value and independent variable was established by quadratic polynomial regression fitting, the model significance and fitting degree were verified by variance analysis, the effects of interaction of each factor on response value were analyzed by three-dimensional response surface graph and contour graph; (4) Determination of optimal process: based on model prediction and verification experiment, the global optimal process parameters of chia seed antioxidant peptide enzymolysis were determined.

2. The optimization method of claim 1, wherein, In step (1), the value range of each single factor variable was: enzymolysis time 2-6 h, enzymolysis temperature 35-55 ℃, pH value 5-9, enzyme / substrate ratio 2000-10000 U / g.

3. The optimization method of claim 1, wherein, In step (2), the independent variable level range of Box-Behnken design was: enzymolysis time 4-6 h, enzymolysis temperature 45-55 ℃, pH value 7-9, enzyme / substrate ratio 6000-10000 U / g.

4. The optimization method of claim 1, wherein, The expression of the mathematical model established in step (3) is: Y=β 0 +∑β i X i +∑β ii X i 2 +∑β ij X i X j； wherein, Y R is the response value for each of the peptide concentration, DPPH-RSA or FRAP, X i and X j R is the response value for each of the peptide concentration, DPPH-RSA or FRAP, β 0 R is the response value for each of the peptide concentration, DPPH-RSA or FRAP, β i R is the response value for each of the peptide concentration, DPPH-RSA or FRAP, β ii R is the response value for each of the peptide concentration, DPPH-RSA or FRAP, β ij R is the response value for each of the peptide concentration, DPPH-RSA or FRAP.

5. The optimization method of claim 1, wherein, The protease used in step (1) was flavor protease with enzyme activity ≥200 U / mg, and the optimized optimal enzymolysis parameters were: enzymolysis time 5.69±0.5 h, enzymolysis temperature 52.34±2 ℃, pH 7.38±0.2, enzyme / substrate ratio 8487.60±500 U / g under hydrolysis conditions.

6. An optimized method of preparing antioxidant peptides from chia seeds, characterized by, Comprising the following steps: chia seed pretreatment; chia seed protein extraction; enzymolysis reaction: using the process parameters optimized by the method of any one of claims 1-5 for enzymolysis reaction; ultrafiltration separation of enzymolysis solution.

7. The method for preparing chia seed antioxidant peptides as described in claim 6, characterized in that, The operation process of the chia seed pretreatment is: soaking the chia seeds in distilled water for 1-6 h, degumming by ultrasonic treatment, drying, crushing, deesterifying in n-hexane, and filtering and drying to obtain defatted chia seed powder.

8. The method for preparing chia seed antioxidant peptides as described in claim 7, characterized in that, The operation process of the chia seed protein extraction is: mixing the defatted chia seed powder with distilled water, adjusting the pH to 9-11, stirring and extracting at 40-60 ℃ for 1-3 h, centrifuging to take the supernatant, adjusting the pH to 4-5 for isoelectric precipitation, centrifuging to collect the precipitate and freeze-drying to obtain a chia seed protein concentrate.

9. The method for preparing chia seed antioxidant peptides as described in claim 6, characterized in that, The ultrafiltration separation of enzymolysis solution process is: the enzymolysis solution is fractionated by a 3 kDa ultrafiltration membrane, and the component with a molecular weight <3 kDa is collected to obtain high-activity chia seed antioxidant peptides.

10. The method for preparing chia seed antioxidant peptides as described in claim 9, characterized in that, The ultrafiltration uses a regenerated cellulose membrane with a molecular weight cut-off of 3 kDa, and the operating pressure is 0.1-0.3 MPa and the temperature is 25-30 ℃.

Images