Detection method and detection system of A2β-casein characteristic peptide in raw milk

Abstract

The present invention relates to the field of food detection technology, specifically discloses a detection method and detection system for characteristic peptides of A2β-casein in raw milk, the method comprising: obtaining initial substrate concentration and initial trypsin concentration, and obtaining an estimated enzymatic reaction time after processing; monitoring enzymatic reaction condition data, and analyzing to obtain enzymatic reaction compensation time; collecting enzymatic conversion overview data and product degradation monitoring data of enzymatic reaction, obtaining actual enzymatic reaction time, and according to the estimated enzymatic reaction time and enzymatic reaction compensation time, comprehensively analyzing to obtain an enzymatic reaction termination judgment index; controlling the enzymatic reaction according to the enzymatic reaction termination judgment index. The present invention can accurately detect the A2β-casein content in dairy products by screening and synthesizing characteristic peptides of bovine A2β-casein, has high specificity, sensitivity, recovery rate and precision, and effectively reduces the impact of problems such as inaccurate high performance liquid chromatography detection.

Description

Technical Field

[0001] The present invention relates to the technical field of food detection, and specifically to a detection method and a detection system for A2β-casein characteristic peptides in raw milk. Background Art

[0002] Milk is known as the perfect food in nature due to its comprehensive nutritional components. The protein in milk is mainly casein, accounting for 80% of the total protein. Among them, the primary form of β-casein is type A2, and then variants such as type A1 are derived due to gene mutation. When type A1 is digested in the human body, β-casomorphin 7 is produced, which may cause health problems. In contrast, type A2 β-casein is more easily digested and absorbed, promoting the increasing expansion of the A2 dairy product market. Given the increasing association between milk consumption and adverse reactions in susceptible populations, research on the detection of A2 milk has also shown an increasing trend.

[0003] For example, the invention patent with the publication number CN110346443B is a method for detecting the content of A2β-casein in milk. The method includes: cooling and centrifuging the milk to obtain a reserve solution to be detected; diluting the reserve solution to be detected with a sample diluent to obtain a diluted reserve solution, wherein the sample diluent contains a concentration of sodium dihydrogen phosphate of 5 - 15 mmol / L, 100 - 150 mmol / L of tris(hydroxymethyl)aminomethane, 6 - 10 mol / L of urea, 30 - 50 mmol / L of disodium ethylenediaminetetraacetate, and 0.05 - 0.15% of dithiothreitol, and the pH value of the sample diluent is 7.5 - 8.6; and detecting the diluted reserve solution by capillary electrophoresis to determine the content of A2β-casein in the milk.

[0004] For example, the invention patent with the publication number CN106198692B is a method for detecting A1β-casein and A2β-casein in milk. The method includes: (1) heating, centrifuging, freezing, and thawing the sample to be detected in sequence to obtain a reserve solution to be detected; (2) pre-treating the reserve solution to be detected to obtain a test solution, wherein the pre-treatment includes mixing the reserve solution to be detected with a pre-treatment solution; and (3) detecting the test solution by capillary electrophoresis to determine whether A1β-casein and A2β-casein are contained in the milk.

[0005] However, in the process of implementing the inventive technical solution in the embodiments of the present application, it is found that the above technologies have at least the following technical problems: Currently, according to the existing casein analysis and detection methods, such as high-performance liquid chromatography or capillary electrophoresis, only the α-, β-, and κ-types of casein can be detected. Due to the lack of A1-type or A2-type β-casein reference substances in the market, quantitative detection of A1β-casein and A2β-casein cannot be achieved. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a method and a detection system for detecting A2β-casein characteristic peptides in raw milk, which can effectively solve the problems involved in the above-mentioned background art.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: In the first aspect of the present invention, a method for detecting A2β-casein characteristic peptides in raw milk is provided, including: obtaining the initial substrate concentration and the initial trypsin concentration, and obtaining the predicted enzymatic hydrolysis reaction time through processing.

[0008] Monitoring the enzymatic hydrolysis reaction condition data, comprehensively analyzing to obtain the evaluation value of the influence of the enzymatic hydrolysis reaction process deviation, and matching the enzymatic hydrolysis reaction compensation time according to the evaluation value of the influence of the enzymatic hydrolysis reaction process deviation.

[0009] Monitoring the general data of enzymatic conversion in the enzymatic hydrolysis reaction, obtaining the enzymatic hydrolysis deficiency evaluation index through processing, collecting the product degradation monitoring data of the enzymatic hydrolysis reaction, obtaining the enzymatic hydrolysis overevaluation index through processing, obtaining the actual enzymatic hydrolysis reaction time, and comprehensively analyzing according to the predicted enzymatic hydrolysis reaction time and the enzymatic hydrolysis reaction compensation time to obtain the enzymatic hydrolysis reaction termination determination index.

[0010] Controlling the enzymatic hydrolysis reaction according to the enzymatic hydrolysis reaction termination determination index.

[0011] As a further method, the specific process of obtaining the predicted enzymatic hydrolysis reaction time is as follows: obtaining the initial substrate concentration and the initial trypsin concentration, and extracting the enzymatic hydrolysis reaction rate influence factors corresponding to the preset initial substrate concentration and trypsin concentration from the enzymatic hydrolysis reaction database, and comprehensively analyzing to obtain the enzymatic hydrolysis reaction matching index.

[0012] Comparing the enzymatic hydrolysis reaction matching index with the preset enzymatic hydrolysis reaction matching index intervals in the enzymatic hydrolysis reaction database, and matching to obtain the corresponding predicted enzymatic hydrolysis reaction time.

[0013] As a further method, the specific process of matching the enzymatic hydrolysis reaction compensation time according to the evaluation value of the influence of the enzymatic hydrolysis reaction process deviation is as follows: comprehensively analyzing according to the enzymatic hydrolysis reaction condition data to obtain the evaluation value of the influence of the enzymatic hydrolysis reaction process deviation, and the enzymatic hydrolysis reaction condition data includes the reaction environment pH value and the reaction environment temperature.

[0014] Comparing the evaluation value of the influence of the enzymatic hydrolysis reaction process deviation with the compensation time corresponding to the enzymatic hydrolysis reaction process deviation influence evaluation value intervals stored in the enzymatic hydrolysis reaction database, and matching to obtain the enzymatic hydrolysis reaction compensation time.

[0015] As a further method, the processing obtains an insufficient enzymatic hydrolysis evaluation index, and the specific process is as follows: The enzymatic conversion profile data of the enzymatic hydrolysis reaction includes the substrate concentration and the expected product concentration.

[0016] Based on the substrate concentration and the expected product concentration, the insufficient enzymatic hydrolysis evaluation index is obtained through comprehensive analysis.

[0017] As a further method, the processing obtains an excessive enzymatic hydrolysis evaluation index, and the specific process is as follows: The product degradation monitoring data of the enzymatic hydrolysis reaction includes the number of non-expected peptide species and the acid value of the solution.

[0018] Based on the number of non-expected peptide species and the acid value of the solution, the excessive enzymatic hydrolysis evaluation index is obtained through comprehensive analysis.

[0019] As a further method, the comprehensive analysis obtains an enzymatic hydrolysis reaction termination determination index, and the specific analysis process is as follows: The expected enzymatic hydrolysis reaction time and the enzymatic hydrolysis reaction compensation time are added together, and the sum result is marked as the enzymatic hydrolysis reaction required time.

[0020] The actual enzymatic hydrolysis reaction time is obtained, the difference between the enzymatic hydrolysis reaction required time and the actual enzymatic hydrolysis reaction time is calculated, and the difference result is marked as the enzymatic hydrolysis reaction reference index.

[0021] The enzymatic hydrolysis reaction reference index is compared with each enzymatic hydrolysis reaction termination determination influencing factor corresponding to the enzymatic hydrolysis reaction reference index interval stored in the enzymatic hydrolysis reaction database, and the enzymatic hydrolysis reaction termination determination influencing factors corresponding to the insufficient enzymatic hydrolysis evaluation index and the excessive enzymatic hydrolysis evaluation index are matched.

[0022] Based on the insufficient enzymatic hydrolysis evaluation index and the excessive enzymatic hydrolysis evaluation index, the enzymatic hydrolysis reaction termination determination index is obtained through comprehensive analysis.

[0023] As a further method, the control of the enzymatic hydrolysis reaction according to the enzymatic hydrolysis reaction termination determination index is as follows: The threshold value of the enzymatic hydrolysis reaction termination determination index is obtained from the enzymatic hydrolysis reaction database, the enzymatic hydrolysis reaction termination determination index is compared with the threshold value of the enzymatic hydrolysis reaction termination determination index. If the enzymatic hydrolysis reaction termination determination index is greater than or equal to the threshold value of the enzymatic hydrolysis reaction termination determination index, the enzymatic hydrolysis reaction is terminated.

[0024] The second aspect of the present invention provides a detection system for A2β-casein characteristic peptides in raw milk, including: an enzymatic hydrolysis reaction time prediction module for obtaining the initial substrate concentration and the initial trypsin concentration and obtaining the expected enzymatic hydrolysis reaction time through processing.

[0025] An enzymatic hydrolysis reaction time compensation module for monitoring the enzymatic hydrolysis reaction condition data, obtaining an evaluation value of the influence of the enzymatic hydrolysis reaction process deviation through comprehensive analysis, and obtaining the enzymatic hydrolysis reaction compensation time according to the evaluation value of the influence of the enzymatic hydrolysis reaction process deviation.

[0026] The enzymatic hydrolysis reaction effect evaluation module is used to monitor the data of the enzymatic conversion profile of the enzymatic hydrolysis reaction, obtain the enzymatic hydrolysis deficiency evaluation index after processing, collect the data of the product degradation monitoring of the enzymatic hydrolysis reaction, obtain the enzymatic hydrolysis over - evaluation index after processing, obtain the actual enzymatic hydrolysis reaction time, and comprehensively analyze according to the expected enzymatic hydrolysis reaction time and the enzymatic hydrolysis reaction compensation time to obtain the enzymatic hydrolysis reaction termination determination index.

[0027] The enzymatic hydrolysis reaction termination control module is used to control the enzymatic hydrolysis reaction according to the enzymatic hydrolysis reaction termination determination index.

[0028] The enzymatic hydrolysis reaction database is used to store the data related to the enzymatic hydrolysis reaction.

[0029] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0030] (1) By providing a detection method and a detection system for A2β - casein characteristic peptides in raw milk, the present invention can accurately detect the content of A2β - casein in dairy products by screening and synthesizing bovine A2β - casein characteristic peptides, with high specificity, sensitivity, recovery rate and precision, effectively reducing the influence caused by losses in the sample pretreatment process and inaccurate high - performance liquid chromatography detection.

[0031] (2) By comprehensively analyzing the initial substrate concentration and the initial trypsin concentration, the present invention can predict the enzymatic hydrolysis reaction time. Accurately predicting the enzymatic hydrolysis time can help plan the experimental process in advance, reasonably arrange experimental steps and resources, and avoid waste of resources caused by too long or too short waiting time. At the same time, the expected enzymatic hydrolysis time helps to control the degree of the enzymatic hydrolysis reaction, avoid over - digestion or under - digestion, and ensure that the product has good analyzability and repeatability.

[0032] (3) By comprehensively analyzing the reaction environment temperature and the reaction environment pH value, the present invention can timely discover and handle abnormal situations, ensure that the enzymatic hydrolysis reaction is always carried out under the optimal conditions, help to maximize the catalytic activity of the enzyme, and thus improve the enzymatic hydrolysis reaction efficiency. Adjusting the required time of the enzymatic hydrolysis reaction in real - time according to the environmental temperature and pH value can optimize the physical and chemical properties of the product, reduce the excessive consumption of raw materials, improve the utilization rate of raw materials, and improve the quality and stability of the product.

[0033] (4) By monitoring and analyzing the substrate concentration and the expected product concentration, the present invention can timely understand the progress of the enzymatic hydrolysis reaction, thereby optimizing the reaction conditions and improving the reaction efficiency, helping to reduce the waste of the substrate and the loss of the product, and ensuring that the product meets the expected specifications and quality standards. Brief Description of the Drawings

[0034] The present invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can be obtained based on the following drawings without creative efforts.

[0035] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0036] Figure 2 It is a schematic diagram of the connection of system modules of the present invention.

[0037] Figure 3 It is a schematic diagram of the functional relationship between the over-enzyme digestion evaluation index and the enzyme digestion reaction termination determination index involved in the embodiment of the present invention. Detailed implementation manners

[0038] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0039] Refer to Figure 1 As shown, the first aspect of the present invention provides a method for detecting A2β-casein characteristic peptides in raw milk, including: obtaining the initial substrate concentration and the initial trypsin concentration, and obtaining the predicted enzyme digestion reaction time through processing.

[0040] Monitor the enzyme digestion reaction condition data, comprehensively analyze to obtain the evaluation value of the influence of the enzyme digestion reaction process deviation, and match the enzyme digestion reaction compensation time according to the evaluation value of the influence of the enzyme digestion reaction process deviation.

[0041] Monitor the general data of the enzymatic conversion of the enzyme digestion reaction, obtain the under-enzyme digestion evaluation index through processing, collect the product degradation monitoring data of the enzyme digestion reaction, obtain the over-enzyme digestion evaluation index through processing, obtain the actual enzyme digestion reaction time, and comprehensively analyze to obtain the enzyme digestion reaction termination determination index according to the predicted enzyme digestion reaction time and the enzyme digestion reaction compensation time.

[0042] Control the enzyme digestion reaction according to the enzyme digestion reaction termination determination index.

[0043] Specifically, the process of obtaining the predicted enzyme digestion reaction time is as follows: obtain the initial substrate concentration and the initial trypsin concentration, and extract the enzyme digestion reaction rate influence factor corresponding to the preset initial substrate concentration and trypsin concentration from the enzyme digestion reaction database, and comprehensively analyze to obtain the enzyme digestion reaction matching index.

[0044] It should be understood that in this embodiment, the initial substrate concentration refers to the concentration of the sample solution to be enzymatically hydrolyzed obtained after the target dairy product has been pretreated (such as denaturation, ultrafiltration, etc.) before the trypsin enzymatic hydrolysis step. This concentration directly affects the efficiency of enzymatic hydrolysis, the types and quantities of products, as well as the sensitivity and accuracy of subsequent analysis steps. Specifically, the initial substrate concentration refers to the concentration of milk proteins in the sample. During the enzymatic hydrolysis process, trypsin will recognize and cleave specific peptide bonds in the sample, thereby generating a series of smaller peptide fragments. An excessively high initial substrate concentration may lead to incomplete enzymatic hydrolysis or overly complex products, making it difficult to analyze; while an excessively low initial substrate concentration may reduce the detection sensitivity and increase errors.

[0045] It should be understood that in this embodiment, trypsin is a widely used protease that can specifically cleave peptide bonds in proteins. During the detection of A2β-casein, through the enzymatic hydrolysis of trypsin, complex protein samples can be decomposed into smaller peptide fragments, and these peptide fragments contain characteristic peptides that can be used for subsequent analysis. The concentration of trypsin is crucial for the effect of the enzymatic hydrolysis reaction. An excessively low concentration may result in incomplete enzymatic hydrolysis, and the proteins in the sample may not be fully decomposed into the required peptide fragments; while an excessively high concentration may introduce additional enzymatic cleavage sites, leading to overly fragmented peptide fragments, which is not conducive to subsequent analysis and detection.

[0046] In a specific embodiment, a preset reference initial substrate concentration and a reference trypsin concentration are obtained from the enzymatic hydrolysis reaction database, and an enzymatic hydrolysis reaction matching index is comprehensively analyzed. The specific numerical expression is:

[0047]

[0048] In the formula, represents the enzymatic hydrolysis reaction matching index, C(Y) 1 represents the initial substrate concentration, C(M) 1 represents the initial trypsin concentration, C(Y) 0 represents the preset reference initial substrate concentration, C(M) 0 represents the preset reference trypsin concentration, ω 1 represents the enzymatic hydrolysis reaction rate influence factor corresponding to the preset initial substrate concentration, ω 2 represents the enzymatic hydrolysis reaction rate influence factor corresponding to the preset trypsin concentration.

[0049] It should be understood that in this embodiment, the value range of the enzymatic reaction rate influencing factor corresponding to the initial substrate concentration and the trypsin concentration is [0, +∞). Based on the relationship between the initial substrate concentration and the trypsin concentration and the enzymatic reaction matching index in the historical data of the enzymatic reaction, a mapping set of the initial substrate concentration and the trypsin concentration and the corresponding enzymatic reaction rate influencing factors is constructed. According to the initial substrate concentration and the initial trypsin concentration, the enzymatic reaction rate influencing factor corresponding to the initial substrate concentration and the enzymatic reaction rate influencing factor corresponding to the trypsin concentration are obtained from the mapping set.

[0050] Table 1 Example data of enzymatic reaction matching index

[0051]

[0052] As shown in Table 1, in a specific embodiment, C(Y) 0 = 100 μg / mL, C(M) 0 = 5 μg / mL, ω 1 = 1.5, ω 2 = 0.8. In this embodiment, the enzymatic reaction matching index is a reference index obtained by comprehensively considering the initial substrate concentration and the initial trypsin concentration, and is used to reflect the required time length of the enzymatic reaction. The enzymatic reaction matching index is jointly determined by the initial substrate concentration and the initial trypsin concentration. The greater the initial substrate concentration and the greater the initial trypsin concentration, the greater the corresponding enzymatic reaction matching index, indicating that the required time for the enzymatic reaction is shorter.

[0053] It should be understood that enzymatic reactions follow the Michaelis-Menten equation. When the enzyme concentration remains unchanged, when the initial substrate concentration is low, the reaction rate increases with the increase of the initial substrate concentration; but when the initial substrate concentration increases to a certain extent, the increase in the reaction rate will gradually slow down until it reaches a maximum value. At this time, increasing the initial substrate concentration further will not increase the reaction rate anymore. Therefore, when the initial substrate concentration is low, due to the slow reaction rate, the time required to complete the enzymatic reaction is longer. As the initial substrate concentration increases, the reaction rate increases, so the time required to complete the enzymatic reaction will be correspondingly shortened.

[0054] It should be understood that in this embodiment, increasing the initial substrate concentration and the initial trypsin concentration can synergistically increase the reaction rate. A higher initial substrate concentration provides more reactant molecules, while a higher trypsin concentration provides more catalytic sites. By comprehensively analyzing the initial substrate concentration and the initial trypsin concentration, the enzymatic reaction time can be predicted. Accurately predicting the enzymatic reaction time can help plan the experimental process in advance, reasonably arrange the experimental steps and resources, and avoid waste of resources caused by too long or too short waiting time. Predicting the enzymatic reaction time helps control the degree of the enzymatic reaction, avoid over-digestion or under-digestion, and ensure that the product has good analyzability and repeatability.

[0055] Compare the enzymatic hydrolysis reaction matching index with each preset enzymatic hydrolysis reaction matching index range in the enzymatic hydrolysis reaction database to obtain the corresponding predicted enzymatic hydrolysis reaction time.

[0056] It should be understood that in this embodiment, there is a one-to-one correspondence between each preset enzymatic hydrolysis reaction matching index range in the enzymatic hydrolysis reaction database and the predicted enzymatic hydrolysis reaction time. By obtaining the range of the enzymatic hydrolysis reaction matching index, the corresponding predicted enzymatic hydrolysis reaction time can be obtained.

[0057] In a specific embodiment, construct a mapping set between the enzymatic hydrolysis reaction matching index range and the predicted enzymatic hydrolysis reaction time according to historical experimental data, obtain the actual enzymatic hydrolysis reaction matching index, and then determine the range to which the actual enzymatic hydrolysis reaction matching index belongs, and obtain the predicted enzymatic hydrolysis reaction time corresponding to this enzymatic hydrolysis reaction matching index range from the mapping set.

[0058] Specifically, according to the evaluation value of the influence of the enzymatic hydrolysis reaction process deviation, the enzymatic hydrolysis reaction compensation time is obtained. The specific process is as follows: According to the enzymatic hydrolysis reaction condition data, the evaluation value of the influence of the enzymatic hydrolysis reaction process deviation is comprehensively analyzed. The enzymatic hydrolysis reaction condition data includes the pH value of the reaction environment and the temperature of the reaction environment.

[0059] In a specific embodiment, extract the enzymatic hydrolysis reaction rate influence factor corresponding to the preset environmental temperature and environmental pH value from the enzymatic hydrolysis reaction database, and obtain the preset reference reaction environment pH value, reference reaction environment temperature, allowable deviation pH value, and allowable deviation temperature, and comprehensively analyze to obtain the evaluation value of the influence of the enzymatic hydrolysis reaction process deviation. The specific numerical expression is:

[0060]

[0061] In the formula, represents the evaluation value of the influence of the enzymatic hydrolysis reaction process deviation, pH(t) represents the pH value of the reaction environment at time t, Q(t) represents the temperature of the reaction environment at time t, pH 0 represents the reference reaction environment pH value, Q 0 represents the reference reaction environment temperature, ΔpH represents the preset allowable deviation pH value, ΔQ represents the preset allowable deviation temperature, t represents the time variable, t ∈ [t1, t2], t1 represents the start time point of the enzymatic hydrolysis reaction, t2 represents the current time point of the enzymatic hydrolysis reaction, ψ 1 represents the enzymatic hydrolysis reaction rate influence factor corresponding to the preset environmental pH value, ψ 2 represents the enzymatic hydrolysis reaction rate influence factor corresponding to the preset environmental temperature.

[0062] It should be understood that in this embodiment, the reference reaction environment pH value and the reference reaction environment temperature are the preset optimal reaction conditions. The closer the actual reaction environment pH value and the reaction environment temperature are to the corresponding reference values, the greater the enzymatic hydrolysis reaction rate, and the less the corresponding required time for the enzymatic hydrolysis reaction. In this embodiment, the evaluation value of the enzymatic hydrolysis reaction process deviation is used to quantitatively evaluate the deviation degree between the actual reaction conditions and the preset reference reaction conditions, providing a data basis for the correction of the required time for the enzymatic hydrolysis reaction. In this embodiment, the greater the deviation of the reaction environment pH value and the reaction environment temperature from the corresponding reference values, the greater the evaluation value of the enzymatic hydrolysis reaction process deviation, indicating that the enzymatic hydrolysis reaction requires more time to fully react.

[0063] It should be understood that in this embodiment, the value range of the enzymatic hydrolysis reaction rate influence factor corresponding to the environment temperature and the environment pH value is between 0 and 1. Based on the relationship between the reaction environment pH value and the reaction environment temperature in the historical data and the evaluation value of the enzymatic hydrolysis reaction process deviation, a mapping set of the reaction environment pH value and the reaction environment temperature and the corresponding enzymatic hydrolysis reaction rate influence factors is constructed. Input the real-time reaction environment pH value and the reaction environment temperature, and obtain the enzymatic hydrolysis reaction rate influence factor corresponding to the environment pH value and the enzymatic hydrolysis reaction rate influence factor corresponding to the environment temperature from the mapping set.

[0064] It should be understood that in this embodiment, the reaction environment temperature is monitored by a temperature sensor. The reaction environment temperature will affect the enzymatic hydrolysis reaction time. Taking the reference reaction environment temperature as the standard, as the temperature increases, the collision frequency between the enzyme and the substrate increases, thereby increasing the reaction rate and shortening the enzymatic hydrolysis reaction time; when the temperature exceeds the reference reaction environment temperature, the structure of the enzyme will change, resulting in a decrease or even inactivation of the activity. At this time, the reaction rate will decrease, and the enzymatic hydrolysis reaction time will be prolonged or even unable to proceed.

[0065] It should be understood that in this embodiment, the reaction environment pH value is monitored by a pH meter. In the enzymatic hydrolysis reaction, the pH value will also affect the enzymatic hydrolysis reaction time by affecting the enzyme activity. The greater the deviation of the reaction environment pH value from the optimal pH value of the enzyme, the longer the required time for the enzymatic hydrolysis reaction.

[0066] It should be understood that in this embodiment, the change in the environment temperature will affect the ionization degree of the solution, and thus affect the reaction environment pH value. By comprehensively analyzing the reaction environment temperature and the reaction environment pH value, abnormal situations can be discovered and processed in a timely manner, ensuring that the enzymatic hydrolysis reaction is always carried out under the optimal conditions, which helps to maximize the catalytic activity of the enzyme, thereby improving the enzymatic hydrolysis reaction efficiency. Adjusting the required time for the enzymatic hydrolysis reaction in real time according to the environment temperature and pH value can optimize the physical and chemical properties of the product, reduce the excessive consumption of raw materials, improve the utilization rate of raw materials, and improve the quality and stability of the product.

[0067] Compare the evaluation value of the enzymatic hydrolysis reaction process deviation with the compensation time corresponding to each evaluation value interval of the enzymatic hydrolysis reaction process deviation stored in the enzymatic hydrolysis reaction database to obtain the enzymatic hydrolysis reaction compensation time.

[0068] In a specific embodiment, construct a mapping set between the evaluation value interval of the enzymatic hydrolysis reaction process deviation and the enzymatic hydrolysis reaction compensation time according to historical experimental data, obtain the actual evaluation value of the enzymatic hydrolysis reaction process deviation, and then determine the interval to which the actual evaluation value of the enzymatic hydrolysis reaction process deviation belongs, and obtain the enzymatic hydrolysis reaction compensation time corresponding to this evaluation value interval of the enzymatic hydrolysis reaction process deviation from the mapping set.

[0069] Specifically, obtain the enzymatic hydrolysis deficiency evaluation index. The specific process is as follows: The general data of the enzymatic conversion of the enzymatic hydrolysis reaction includes the substrate concentration and the expected product concentration.

[0070] It should be understood that in this embodiment, the substrate concentration refers to the real-time concentration of milk protein during the enzymatic hydrolysis reaction, and the expected product concentration refers to the concentration of the characteristic peptides obtained by enzymatic hydrolysis of milk protein. In this embodiment, the substrate concentration and the expected product concentration can be monitored in real time by an online near-infrared spectrometer.

[0071] In this embodiment, the characteristic peptide refers to the specific peptide segment of the target protein.

[0072] Based on the substrate concentration and the expected product concentration, comprehensively analyze to obtain the enzymatic hydrolysis deficiency evaluation index.

[0073] In a specific embodiment, obtain the theoretical maximum concentration of the expected product concentration corresponding to the substrate concentration from the enzymatic hydrolysis reaction database, and extract the enzymatic hydrolysis deficiency evaluation influence factor corresponding to the preset substrate concentration and expected product concentration from the enzymatic hydrolysis reaction database, and comprehensively analyze to obtain the enzymatic hydrolysis deficiency evaluation index. The specific numerical expression is:

[0074]

[0075] In the formula, νZ represents the enzymatic hydrolysis deficiency evaluation index, θC(Y) represents the substrate concentration, C(Y) 1 represents the initial substrate concentration, θC(T) represents the expected product concentration, C(T) 0 represents the theoretical maximum concentration of the expected product concentration, χ 1 represents the enzymatic hydrolysis deficiency evaluation influence factor corresponding to the preset substrate concentration, χ 2 represents the enzymatic hydrolysis deficiency evaluation influence factor corresponding to the preset expected product concentration.

[0076] It should be understood that in this embodiment, the enzymatic hydrolysis deficiency evaluation index is used to quantitatively evaluate the inadequacy of the enzymatic hydrolysis reaction. The enzymatic hydrolysis deficiency evaluation index is jointly determined by the substrate concentration and the expected product concentration. The higher the substrate concentration and the lower the expected product concentration, the higher the corresponding enzymatic hydrolysis deficiency evaluation index, indicating that the enzymatic hydrolysis reaction is more inadequate and a longer enzymatic hydrolysis reaction time needs to be reserved.

[0077] It should be understood that in this embodiment, the value range of the influencing factor of enzymatic hydrolysis deficiency corresponding to the substrate concentration and the expected product concentration is between 0 and 1. Based on the relationship between the substrate concentration and the expected product concentration in the historical data and the enzymatic hydrolysis deficiency evaluation index, a mapping set of the substrate concentration and the expected product concentration and the corresponding influencing factors of enzymatic hydrolysis deficiency is constructed. By inputting the real-time substrate concentration and the expected product concentration, the influencing factor of enzymatic hydrolysis deficiency corresponding to the substrate concentration and the influencing factor of enzymatic hydrolysis deficiency corresponding to the expected product concentration are obtained from the mapping set.

[0078] In a specific embodiment, as the enzymatic hydrolysis reaction proceeds, the substrate is gradually converted into the expected product. By comparing the decrease in the substrate concentration and the increase in the expected product concentration, the inadequacy of the enzymatic hydrolysis reaction can be evaluated. If the decrease in the substrate concentration is less than expected or the increase in the expected product concentration is less than expected, it indicates that the reaction is inadequate. By monitoring and analyzing the substrate concentration and the expected product concentration, the progress of the enzymatic hydrolysis reaction can be understood in a timely manner, thereby optimizing the reaction conditions and improving the reaction efficiency, helping to reduce the waste of the substrate and the loss of the product, and ensuring that the product meets the expected specifications and quality standards.

[0079] Specifically, the process of obtaining the over-enzymatic hydrolysis evaluation index is as follows: The product degradation monitoring data of the enzymatic hydrolysis reaction includes the number of non-expected peptide species and the acid value of the solution.

[0080] It should be understood that in this embodiment, the number of non-expected peptide species refers to the number of peptide species other than the characteristic peptides. When the enzymatic hydrolysis reaction is excessive, the enzyme may cleave at non-specific sites, generating non-expected peptides. The appearance of non-expected peptides increases the complexity of the product and may lead to difficulties in data interpretation. The acid value of the solution is an index to measure the content of free fatty acids. As the enzymatic hydrolysis reaction proceeds, the content of free fatty acids gradually increases, resulting in an increase in the acid value of the solution. When the enzymatic hydrolysis reaction is excessive, the increase in the acid value is particularly obvious.

[0081] It should be understood that in this embodiment, the number of non-expected peptide species can be monitored by an online near-infrared spectrometer, and the acid value of the solution can be monitored by an electrochemical sensor.

[0082] Based on the number of non-expected peptide species and the acid value of the solution, the over-enzymatic hydrolysis evaluation index is comprehensively analyzed and obtained.

[0083] In a specific embodiment, the critical number of unexpected peptide species and the critical acid value of the solution are obtained from the enzymatic hydrolysis reaction database, and the enzymatic hydrolysis over - evaluation impact factors corresponding to the preset number of unexpected peptide species and the acid value of the solution are extracted from the enzymatic hydrolysis reaction database. Through comprehensive analysis, the enzymatic hydrolysis over - evaluation index is obtained, and the specific numerical expression is:

[0084]

[0085] In the formula, νG represents the enzymatic hydrolysis over - evaluation index, NT 1 represents the number of unexpected peptide species, NT 0 represents the critical number of unexpected peptide species, PS 1 represents the acid value of the solution, PS 0 represents the critical acid value of the solution, φ 1 represents the enzymatic hydrolysis over - evaluation impact factor corresponding to the preset number of unexpected peptide species, φ 2 represents the enzymatic hydrolysis over - evaluation impact factor corresponding to the preset acid value of the solution.

[0086] It should be understood that in this embodiment, the enzymatic hydrolysis over - evaluation index is used to quantitatively evaluate the degree of over - enzymatic hydrolysis. The enzymatic hydrolysis over - evaluation index is jointly determined by the number of unexpected peptide species and the acid value of the solution. The higher the number of unexpected peptide species and the acid value of the solution, the higher the corresponding enzymatic hydrolysis over - evaluation index, indicating that there are signs of over - enzymatic hydrolysis and the enzymatic hydrolysis reaction needs to be terminated in a timely manner.

[0087] It should be understood that in this embodiment, the value range of the enzymatic hydrolysis over - evaluation impact factors corresponding to the number of unexpected peptide species and the acid value of the solution is between 0 and 1. Through the relationship between the number of unexpected peptide species and the acid value of the solution in historical data and the enzymatic hydrolysis over - evaluation index, a mapping set of the number of unexpected peptide species and the acid value of the solution and the corresponding enzymatic hydrolysis over - evaluation impact factors is constructed. Input the real - time number of unexpected peptide species and the acid value of the solution, and obtain the enzymatic hydrolysis over - evaluation impact factor corresponding to the number of unexpected peptide species and the enzymatic hydrolysis over - evaluation impact factor corresponding to the acid value of the solution from the mapping set.

[0088] Specifically, through comprehensive analysis, the enzymatic hydrolysis reaction termination determination index is obtained. The specific analysis process is as follows: The predicted enzymatic hydrolysis reaction time and the enzymatic hydrolysis reaction compensation time are summed, and the sum result is marked as the enzymatic hydrolysis reaction required time.

[0089] The actual enzymatic hydrolysis reaction time is obtained, the difference between the enzymatic hydrolysis reaction required time and the actual enzymatic hydrolysis reaction time is calculated, and the difference result is marked as the enzymatic hydrolysis reaction reference index.

[0090] Compare the enzymatic hydrolysis reaction reference index with each enzymatic hydrolysis reaction termination determination influencing factor corresponding to the enzymatic hydrolysis reaction reference index intervals stored in the enzymatic hydrolysis reaction database, and match to obtain the enzymatic hydrolysis reaction termination determination influencing factors corresponding to the insufficient enzymatic hydrolysis evaluation index and the excessive enzymatic hydrolysis evaluation index.

[0091] In a specific embodiment, construct a mapping set between the enzymatic hydrolysis reaction reference index intervals and the enzymatic hydrolysis reaction termination determination influencing factors corresponding to the insufficient enzymatic hydrolysis evaluation index and the excessive enzymatic hydrolysis evaluation index according to historical experimental data, obtain the actual enzymatic hydrolysis reaction reference index, and then determine the interval to which the actual enzymatic hydrolysis reaction reference index belongs, and obtain the enzymatic hydrolysis reaction termination determination influencing factors corresponding to the insufficient enzymatic hydrolysis evaluation index and the excessive enzymatic hydrolysis evaluation index corresponding to this enzymatic hydrolysis reaction reference index interval from the mapping set.

[0092] It should be understood that in this embodiment, the value ranges of the enzymatic hydrolysis reaction termination determination influencing factors corresponding to the insufficient enzymatic hydrolysis evaluation index and the excessive enzymatic hydrolysis evaluation index are between 0 and 1. Based on the relationship between the number of unexpected peptide species and the solution acid value and the excessive enzymatic hydrolysis evaluation index under different enzymatic hydrolysis reaction reference index conditions in historical data, construct a mapping set between the insufficient enzymatic hydrolysis evaluation index and the excessive enzymatic hydrolysis evaluation index and the corresponding enzymatic hydrolysis reaction termination determination influencing factors, input the real-time insufficient enzymatic hydrolysis evaluation index and excessive enzymatic hydrolysis evaluation index, and obtain the enzymatic hydrolysis reaction termination determination influencing factor corresponding to the insufficient enzymatic hydrolysis evaluation index and the enzymatic hydrolysis reaction termination determination influencing factor corresponding to the excessive enzymatic hydrolysis evaluation index from the mapping set.

[0093] Based on the insufficient enzymatic hydrolysis evaluation index and the excessive enzymatic hydrolysis evaluation index, comprehensively analyze to obtain the enzymatic hydrolysis reaction termination determination index.

[0094] In a specific embodiment, the numerical expression of the enzymatic hydrolysis reaction termination determination index is:

[0095]

[0096] In the formula, δ represents the enzymatic hydrolysis reaction termination determination index, νZ represents the insufficient enzymatic hydrolysis evaluation index, νG represents the excessive enzymatic hydrolysis evaluation index, τ 1 represents the enzymatic hydrolysis reaction termination determination influencing factor corresponding to the preset insufficient enzymatic hydrolysis evaluation index, τ 2 represents the enzymatic hydrolysis reaction termination determination influencing factor corresponding to the preset excessive enzymatic hydrolysis evaluation index.

[0097] Such as Figure 3 shown, in a specific embodiment, τ 1 = 0.5, τ 2= 0.5. When νZ = 0.3, the functional relationship between the over - enzymatic hydrolysis evaluation index and the enzymatic hydrolysis reaction termination determination index is shown as curve a; when νZ = 0.5, the functional relationship between the over - enzymatic hydrolysis evaluation index and the enzymatic hydrolysis reaction termination determination index is shown as curve b; when νZ = 0.7, the functional relationship between the over - enzymatic hydrolysis evaluation index and the enzymatic hydrolysis reaction termination determination index is shown as curve c. In this embodiment, the enzymatic hydrolysis reaction termination determination index is used to quantitatively evaluate the degree of the termination requirement of the enzymatic hydrolysis reaction. The enzymatic hydrolysis reaction termination determination index is jointly determined by the under - enzymatic hydrolysis evaluation index and the over - enzymatic hydrolysis evaluation index. The smaller the under - enzymatic hydrolysis evaluation index and the larger the over - enzymatic hydrolysis evaluation index, the larger the corresponding enzymatic hydrolysis reaction termination determination index, indicating that the enzymatic hydrolysis reaction needs to be terminated more.

[0098] Specifically, the enzymatic hydrolysis reaction is controlled according to the enzymatic hydrolysis reaction termination determination index. The specific process is as follows: Obtain the enzymatic hydrolysis reaction termination determination index threshold from the enzymatic hydrolysis reaction database, compare the enzymatic hydrolysis reaction termination determination index with the enzymatic hydrolysis reaction termination determination index threshold. If the enzymatic hydrolysis reaction termination determination index is greater than or equal to the enzymatic hydrolysis reaction termination determination index threshold, perform a termination operation on the enzymatic hydrolysis reaction.

[0099] Refer to Figure 2 As shown, the second aspect of the present invention provides a detection system for A2β - casein characteristic peptides in raw milk, including: An enzymatic hydrolysis reaction time prediction module, used to obtain the initial substrate concentration and the initial trypsin concentration, and obtain the predicted enzymatic hydrolysis reaction time through processing.

[0100] An enzymatic hydrolysis reaction time compensation module, used to monitor the enzymatic hydrolysis reaction condition data, comprehensively analyze to obtain the evaluation value of the influence of the enzymatic hydrolysis reaction process deviation, and match the enzymatic hydrolysis reaction compensation time according to the evaluation value of the influence of the enzymatic hydrolysis reaction process deviation.

[0101] An enzymatic hydrolysis reaction effect evaluation module, used to monitor the enzymatic conversion profile data of the enzymatic hydrolysis reaction, obtain the under - enzymatic hydrolysis evaluation index through processing, collect the product degradation monitoring data of the enzymatic hydrolysis reaction, obtain the over - enzymatic hydrolysis evaluation index through processing, obtain the actual enzymatic hydrolysis reaction time, and comprehensively analyze to obtain the enzymatic hydrolysis reaction termination determination index according to the predicted enzymatic hydrolysis reaction time and the enzymatic hydrolysis reaction compensation time.

[0102] An enzymatic hydrolysis reaction termination control module, used to control the enzymatic hydrolysis reaction according to the enzymatic hydrolysis reaction termination determination index.

[0103] An enzymatic hydrolysis reaction database, used to store enzymatic hydrolysis reaction - related data, including indicators such as the reference initial substrate concentration, the reference trypsin concentration, and the critical solution acid value. The data in the enzymatic hydrolysis reaction database is obtained by collecting the experimental data of multiple enzymatic hydrolysis reactions.

[0104] In a specific embodiment, the present invention provides a characteristic peptide for detecting the content of A2β-casein in bovine dairy products by the external standard method. The amino acid sequence of the characteristic peptide is as shown in SEQ ID NO.1: IHPFAQTQSLVYPFPGPIPNSLPQNIPPLTQTPVVVPPFLQPEVMGVSK.

[0105] After detection, bovine dairy products without A2β-casein do not contain peptide segments with the same amino acid sequence as this characteristic peptide segment. This characteristic peptide segment is obtained by chemical synthesis. After chemical synthesis and purification, the purity is greater than 95%, and it is used as a peptide standard. The A2β-casein described in the present invention refers to bovine A2β-casein.

[0106] The present invention also provides a kit for detecting the content of A2β-casein in bovine dairy products by the external standard method. The kit includes a characteristic peptide with the amino acid sequence as shown in SEQ ID NO.1.

[0107] The present invention also provides the application of the above-mentioned kit in detecting the content of A2β-casein in bovine dairy products by the external standard method.

[0108] The present invention also provides a method for detecting the content of A2β-casein in bovine dairy products by the external standard method. The method uses the above-mentioned characteristic peptide. The specific method includes the following steps:

[0109] (1) Pretreatment of the sample: Take the sample to be detected, and after denaturation, ultrafiltration, and trypsin digestion in sequence, terminate the reaction, and desalt through a C18 desalting column to obtain a sample solution;

[0110] (2) Sample detection: Use high performance liquid chromatography-mass spectrometry (HPLC-MS) technology to detect the sample solution obtained in step (1) to obtain the peak area;

[0111] (3) Obtaining the characteristic equation of the standard curve: Prepare working solutions of the standard curve of the characteristic peptide with different series of concentrations, respectively obtain standard solutions of the characteristic peptide with each concentration through the pretreatment method described in step (1), and then detect them respectively using the method described in (2) to obtain the characteristic equation of the standard curve of the characteristic peptide;

[0112] (4) Calculation of the content of the A2β-casein characteristic peptide: Substitute the peak area obtained in step (2) into the characteristic equation of the standard curve obtained in step (3) to obtain the concentration of the characteristic peptide in the sample, and then substitute the concentration of the characteristic peptide into the content calculation formula to obtain the content of the A2β-casein characteristic peptide in the sample.

[0113] Further defined, the denaturation in step (1) is to sequentially add dithiothreitol (DTT) and iodoacetamide (IAA).

[0114] Adding dithiothreitol can hydrolyze disulfide bonds and disrupt the spatial structure of proteins; adding iodoacetamide can completely denature proteins.

[0115] In the trypsin digestion process of step (1), by taking advantage of the specific action of trypsin on arginine (R) and lysine (K), the A2β-casein peptide segment molecules with a molecular weight of several hundred to thousands of daltons can be hydrolyzed, so as to select the characteristic peptide segments unique to A2β-casein. After synthesis and purification, highly pure characteristic peptide segments are obtained and applied to this detection method.

[0116] Further limited, the ultrafiltration in step (1) is to perform ultrafiltration successively with a 100 mM Tris-HCl buffer solution with a urea content of 8 M and a 50 mM NH4HCO3 solution.

[0117] Further limited, a 10% formic acid solution is added in the termination reaction of step (1).

[0118] Further limited, the detection conditions of the high performance liquid chromatography in step (2) are: a silane-based C18 column, with a column length of 100 mm, a column inner diameter of 2.1 mm, a packing particle size of 1.7 μm, and a pore diameter The column temperature is 40 °C, the mobile phase A is 0.1% formic acid-water; the mobile phase B is 0.1% formic acid-acetonitrile, the flow rate of the mobile phase is 0.3 mL / min, and the sample temperature is 40 °C.

[0119] Further limited, the detection conditions of the mass spectrometry in step (2) are: electrospray ionization, multiple reaction monitoring, curtain gas 35 psi, ionization voltage 5500 V, capillary temperature 550 °C, spray gas GS1 55 psi; auxiliary heating gas GS2 60 psi.

[0120] Further limited, the characteristic equation of the standard curve in step (3) is obtained by the following method: Prepare standard curve working solutions with different series of concentrations of A2β-casein characteristic peptides, and then obtain the peak areas corresponding to the standard solutions of characteristic peptides with each concentration through the pretreatment method and detection method described in step (1) and step (2) in sequence. Draw a standard curve based on the relationship between the standard solutions of characteristic peptides with each concentration and the peak areas; and then obtain the characteristic equation of the standard curve Y = kX + b; where Y is the peak area of the A2β-casein characteristic peptide, X is the concentration of the A2β-casein characteristic peptide, with the unit of nmol / mL; k is the slope of the standard curve; b is the intercept of the standard curve.

[0121] Further limited, the content calculation formula in step (4) is Cx = na × M × N × 10 -10; where Cx is the content of A2 β-casein in the sample to be tested, with the unit of g / 100g; na is the concentration of the A2 β-casein characteristic peptide in the sample to be tested; M is the relative molecular mass of the A2 β-casein characteristic peptide, which is 5319.21; N is the sample dilution factor.

[0122] Further defined, the dairy products include 6 kinds of A2 β-casein-rich milk (Samples 1-6, where Sample 1 is raw milk from a genotyped A2 cow) and commercially available ordinary milk (Samples 7-9).

[0123] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with specific embodiments. The experimental methods used in the following embodiments are all conventional methods unless otherwise specified. The materials, reagents, methods and instruments used are all conventional materials, reagents, methods and instruments in the art unless otherwise specified, and those skilled in the art can obtain them through commercial channels.

[0124] The instrument equipment and chemical reagents used in the present invention are as follows:

[0125] Ultra-high performance liquid chromatography tandem quadrupole mass spectrometry (UPLC-TQ-MS, Waters, USA); ME204E precision electronic analytical balance (Mettler Toledo, USA); CF1524R high-speed refrigerated microcentrifuge (Scilogex, USA); Concentratorplus vacuum centrifugal concentrator (Eppendorf, Germany); bovine trypsin and Tris-HCl are both purchased from Sigma-Aldrich; iodoacetamide (IAA), dithiothreitol (DTT), urea, ammonium bicarbonate, and formic acid are all purchased from Aladdin; C18 desalting column (Thermo Fisher Scientific (China) Co., Ltd.), ultrafiltration tube (Sartorius, Germany); A2 β-casein characteristic peptide standard is purchased from Hefei Guopeptide Biotechnology Co., Ltd.

[0126] The A2 β-casein characteristic peptide standard used has a purity greater than 95% and has been verified by HPLC and MS. The amino acid sequence of the A2 β-casein characteristic peptide is as shown in SEQ ID NO.1: IHPFAQTQSLVYPFPGPIPNSLPQNIPPLTQTPVVVPPFLQPEVMGVSK.

[0127] The conditions of high performance liquid chromatography and mass spectrometry used in the following embodiments are:

[0128] Chromatographic conditions:

[0129] Silane-based C18 column, column length: 100 mm, column inner diameter: 2.1 mm, packing particle size: 1.7 μm, pore size: Column temperature: 40 °C, Mobile phase A: 0.1% formic acid - water; Mobile phase B: 0.1% formic acid - acetonitrile.

[0130] The mobile phase gradient elution program is shown in Table 2.

[0131] Table 2 Mobile phase gradient elution program

[0132]

[0133] Mobile phase flow rate: 0.3 mL / min;

[0134] Sample temperature: 40 °C;

[0135] Injection volume: 10 μL.

[0136] Mass spectrometry conditions: Electrospray ionization (ESI+); Mass spectrometry scan mode: Multiple reaction monitoring (MRM); Curtain gas: 35 psi; Ionization voltage: 5500 V; Capillary temperature: 550 °C; Declustering voltage: 32 V; Spray gas GS1: 55 psi; Auxiliary heating gas GS2: 60 psi. The main reference mass spectrometry parameters of the A2β-casein characteristic peptide are shown in Table 3.

[0137] Table 3 Main reference mass spectrometry parameters of the A2β-casein characteristic peptide

[0138]

[0139] Example 1: The detection of the content of the A2β-casein characteristic peptide in raw milk is as follows.

[0140] (1) Preparation of the characteristic peptide standard curve working solutions with different series of concentrations:

[0141] Take 1 mg of the A2β-casein characteristic peptide standard to a 10 mL volumetric flask, dilute to 10 mL with deionized water, and mix well. This is the A2β-casein characteristic peptide standard solution. Take 20 μL, 40 μL, 50 μL, 100 μL, 200 μL, and 400 μL of the above A2β-casein characteristic peptide standard solution to 1 mL volumetric flasks respectively and dilute to 1 mL with Wahaha water to obtain 6 characteristic peptide standard curve working solutions with different series of concentrations.

[0142] (2) Pretreatment of the characteristic peptide standard curve working solutions with different series of concentrations:

[0143] Take 100 μL of the characteristic peptide standard curve working solutions with 6 different series of concentrations obtained in step (2) into 2 mL centrifuge tubes for pretreatment: Add 100 μL of dithiothreitol (DTT) (final concentration 10 mM) to the centrifuge tubes, mix well, incubate at 56 °C for 30 min for reduction, and after cooling to room temperature, add 100 μL of iodoacetamide (IAA) (final concentration 60 mM) to the centrifuge tubes, and keep for 30 min at room temperature in the dark for alkylation; Add the sample to an ultrafiltration tube (10 kDa), and centrifuge at 14000×g for 15 min to obtain a precipitate. Then add 300 μL of buffer (8 M urea dissolved in 100 mM Tris-HCl, pH 8.5) to the ultrafiltration tube, and centrifuge at 14000×g for 15 min to obtain a precipitate. This step is repeated twice. Subsequently, add 200 μL of 50 mM NH4HCO3 solution to the ultrafiltration tube, and centrifuge at 14000×g for 15 min to obtain a precipitate. This step is also repeated twice; Then, add 500 μL of 1 mg / mL trypsin solution to the ultrafiltration tube and digest overnight at 37 °C; Finally, add 10 μL of 10% formic acid solution to terminate the digestion, then centrifuge at 14000×g for 15 min to collect the digested solution and desalt it using a C18 desalting column. After drying by rotation at room temperature, store at -20 °C until analysis. Reconstitute with mobile phase A (one-thousandth formic acid aqueous solution) to obtain the sample to be measured.

[0144] (3) Detection of the characteristic peptide concentrations in the characteristic peptide standard curve working solutions with different series of concentrations:

[0145] Use high performance liquid chromatography - mass spectrometry (HPLC - MS) technology to detect the samples to be measured of the 6 different series of concentrations of the characteristic peptide standard curve working solutions obtained in step (3). The content of A2β - casein characteristic peptides in the standard is shown in Table 4.

[0146] Table 4 Content of A2β - casein characteristic peptides in the standard

[0147]

[0148] (4) Obtaining the characteristic equation of the standard curve:

[0149] From the peak areas corresponding to the samples to be measured of the characteristic peptide standard curve working solutions with different series of concentrations obtained in step (4), draw a standard curve according to the relationship between the concentration of the characteristic peptide standard curve working solution and the peak area; and then obtain the characteristic equation of the standard curve Y = kX + b; where, Y is the peak area of A2β - casein characteristic peptide, X is the concentration of A2β - casein characteristic peptide, with the unit of nmol / mL; k is the slope of the standard curve; b is the intercept of the standard curve.

[0150] A linear investigation was carried out to obtain a linear equation for the characteristic peptide of A2β-casein. The linear equation is Y = 5.40338e4X + -6.09683e4, and its correlation coefficient is R2 = 0.99792.

[0151] (5) Pretreatment of raw milk to be tested:

[0152] Take a certain amount of raw milk sample 1 and centrifuge it for defatting at 5000 rpm. Take 100 μL of the defatted milk sample into a centrifuge tube. Add an equal volume of dithiothreitol DTT (final concentration 10 mM) to the centrifuge tube, mix well and incubate at 56 °C for 30 min for reduction. After cooling to room temperature, add 100 μL of iodoacetamide IAA (final concentration 60 mM) to the centrifuge tube and keep it for 30 min at room temperature in the dark for alkylation; add the sample to an ultrafiltration tube (10 kDa) and centrifuge at 14000×g for 15 min to obtain a precipitate. Then add 300 μL of buffer (8 M urea dissolved in 100 mM Tris-HCl, pH 8.5) to the ultrafiltration tube and centrifuge at 14000×g for 15 min to obtain a precipitate. This step is repeated twice. Subsequently, add 200 μL of 50 mM NH4HCO3 solution to the ultrafiltration tube and centrifuge at 14000×g for 15 min to obtain a precipitate. This step is also repeated twice; then add 500 μL of 1 mg / mL trypsin solution to the ultrafiltration tube and digest overnight at 37 °C; finally, add 10 μL of 10% formic acid solution to terminate the digestion, and then centrifuge at 14000×g for 15 min to collect the digested solution and desalt it using a C18 desalting column. After drying at room temperature by rotation, store it at -20 °C until analysis. Reconstitute it with mobile phase A (aqueous solution of one-thousandth formic acid) to obtain the sample to be tested.

[0153] (6) Detection of the content of characteristic peptides in raw milk:

[0154] Use high performance liquid chromatography - mass spectrometry coupling technology to detect the sample to be tested obtained in step (5) and obtain the peak area.

[0155] (7) Calculation of the content of A2β-casein characteristic peptides in raw milk:

[0156] Substitute the peak area obtained in step (6) into the characteristic equation of the standard curve obtained in step (4) to obtain the concentration of the characteristic peptide in the sample. Then substitute the concentration of the characteristic peptide into the content calculation formula to obtain the content of A2β-casein characteristic peptides in raw milk sample 1.

[0157] The content calculation formula is Cx = na × M × N × 10-10; where Cx is the content of A2β-casein in the sample to be tested, with the unit of g / 100 g; na is the concentration of A2β-casein characteristic peptides in the sample to be tested; M is the relative molecular mass of A2β-casein characteristic peptides, which is 5319.21; N is the sample dilution factor.

[0158] The concentration of A2β-casein characteristic peptide in raw milk of sample 1 was detected to be 2.24E+05 nmol / mL, and the content of A2β-casein characteristic peptide was 1.19 ± 0.01 g / 100 g.

[0159] Example 2: The detection of A2β-casein content in commercially available milk of different brands is as follows.

[0160] (1) Pretreatment of the sample to be tested:

[0161] Take a certain amount of samples of A2β-casein milk of different commercially available brands and centrifuge them for degreasing at 5000 rpm. Take 100 μL of milk sample into a centrifuge tube, add an equal volume of dithiothreitol DTT (final concentration 10 mM) to the centrifuge tube, mix well and incubate at 56 °C for 30 min for reduction. After cooling to room temperature, add 100 μL of iodoacetamide IAA (final concentration 60 mM) to the centrifuge tube and keep it for 30 min for alkylation in the dark at room temperature; add the sample to an ultrafiltration tube (10 kDa) and centrifuge at 14000×g for 15 min to obtain a precipitate. Then add 300 μL of buffer solution (8 M urea dissolved in 100 mM Tris-HCl, pH 8.5) to the ultrafiltration tube and centrifuge at 14000×g for 15 min to obtain a precipitate. This step is repeated twice. Subsequently, add 200 μL of 50 mM NH4HCO3 solution to the ultrafiltration tube and centrifuge at 14000×g for 15 min to obtain a precipitate. This step is also repeated twice; then add 500 μL of 1 mg / mL trypsin solution to the ultrafiltration tube and digest overnight at 37 °C; finally, add 10 μL of 10% formic acid solution to terminate the digestion, and then centrifuge at 14000×g for 15 min to collect the digested solution and desalt it using a C18 desalting column. After drying at room temperature by rotation, store it at -20 °C until analysis. Reconstitute it with mobile phase A (aqueous solution of one-thousandth formic acid) to obtain the sample to be tested.

[0162] (2) Detection of the content of characteristic peptide in A2β-casein milk:

[0163] Use high performance liquid chromatography-mass spectrometry (HPLC-MS) technology to detect the sample to be tested obtained in step (1) and obtain the peak area.

[0164] (3) Calculation of the A2β-casein content in milk:

[0165] Substitute the peak area obtained in step (2) into the standard curve characteristic equation obtained in step (4) of Example 1 to obtain the concentration of the characteristic peptide in the sample, and then substitute the concentration of the characteristic peptide into the content calculation formula to obtain the A2β-casein content in the sample.

[0166] The content calculation formula is Cx = na × M × N × 10-10; where Cx is the content of A2β-casein in the sample to be tested, with the unit of g / 100g; na is the concentration of the A2β-casein characteristic peptide in the sample to be tested; M is the relative molecular mass of the A2β-casein characteristic peptide, which is 5319.21; and N is the sample dilution factor.

[0167] The concentrations and contents of the A2β-casein characteristic peptide in each brand of milk are shown in Table 5 after detection.

[0168] Table 5 A2β-casein characteristic peptide and its content in each brand of milk

[0169]

[0170] Example 3: Detection of the A2β-casein content in ordinary milk is as follows.

[0171] (1) Pretreatment of the sample to be tested:

[0172] Take a certain amount of commercially available ordinary milk samples and centrifuge them for defatting at 5000 rpm. Take 100 μL of the milk sample into a centrifuge tube, add an equal volume of dithiothreitol DTT (final concentration 10 mM) to the centrifuge tube, mix well and incubate at 56 °C for 30 min for reduction. After cooling to room temperature, add 100 μL of iodoacetamide IAA (final concentration 60 mM) to the centrifuge tube and keep it for 30 min in the dark at room temperature for alkylation; add the sample to an ultrafiltration tube (10 kDa) and centrifuge at 14000×g for 15 min to obtain a precipitate. Then add 300 μL of buffer solution (8 M urea dissolved in 100 mM Tris-HCl, pH 8.5) to the ultrafiltration tube and centrifuge at 14000×g for 15 min to obtain a precipitate. Repeat this step twice. Subsequently, add 200 μL of 50 mM NH4HCO3 solution to the ultrafiltration tube and centrifuge at 14000×g for 15 min to obtain a precipitate. Repeat this step twice as well; then add 500 μL of 1 mg / mL trypsin solution to the ultrafiltration tube and digest overnight at 37 °C; finally, add 10 μL of 10% formic acid solution to terminate the digestion, and then centrifuge at 14000×g for 15 min to collect the digested solution and desalt it using a C18 desalting column. After drying at room temperature by rotation, store it at -20 °C until analysis. Redissolve it with mobile phase A (one-thousandth formic acid aqueous solution) to obtain the sample to be tested.

[0173] (2) Detection of the characteristic peptide content in ordinary milk:

[0174] Use high performance liquid chromatography - mass spectrometry (HPLC - MS) technology to detect the sample to be tested obtained in step (1) and obtain the peak area.

[0175] (3) Calculation of the A2β-casein content in ordinary milk:

[0176] Substitute the peak area obtained in step (2) into the standard curve characteristic equation obtained in step (4) of Example 1 to obtain the concentration of the characteristic peptide in the sample, and then substitute the concentration of the characteristic peptide into the content calculation formula to obtain the content of A2β-casein in the sample.

[0177] The content calculation formula is Cx = na × M × N × 10-10; where Cx is the content of A2β-casein in the sample to be tested, with the unit of g / 100g; na is the concentration of the A2β-casein characteristic peptide in the sample to be tested; M is the relative molecular mass of the A2β-casein characteristic peptide, which is 5319.21; N is the sample dilution factor.

[0178] The concentrations and contents of the A2β-casein characteristic peptide in different ordinary milks were detected as shown in Table 6.

[0179] Table 6 A2β-casein characteristic peptides and their contents in milk of each brand

[0180]

[0181]

[0182] In a specific embodiment, the present invention provides a detection method and a detection system for A2β-casein characteristic peptides in raw milk. By screening and synthesizing bovine A2β-casein characteristic peptides and using high performance liquid chromatography-mass spectrometry (HPLC-MS) technology, the quantitative detection of A2β-casein in dairy products is realized. The characteristic peptides and detection methods provided by the present invention can accurately detect the content of A2β-casein in dairy products, and have high specificity, sensitivity, recovery rate and precision, which have far-reaching significance for both food safety and market supervision.

[0183] In this embodiment, the specific action of trypsin on arginine (R) and lysine (K) is utilized to select the characteristic peptide segments that only belong to A2β-casein. After synthesis and purification, the characteristic peptide segments with a purity greater than 95% are applied to this detection method to ensure the accuracy of the method.

[0184] This embodiment adopts the method of using characteristic peptide segments as external standards in combination with HPLC-MS technology to effectively reduce the influence caused by losses in the sample pretreatment process and inaccurate HPLC detection.

[0185] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for monitoring the production process of A2β-casein characteristic peptide in raw milk, characterized in that: The following steps are involved: (1) Processing to obtain the estimated enzymatic reaction time: (1-1) The initial substrate concentration and the initial trypsin concentration are obtained, and the preset reference initial substrate concentration, the preset reference trypsin concentration, and the enzymatic reaction rate influencing factors corresponding to the preset initial substrate concentration and the trypsin concentration are extracted from the enzymatic reaction database, and the enzymatic reaction matching index is obtained by comprehensive analysis, and its numerical expression is: In the formula, represents the enzymatic reaction matching index, C(Y)1 represents the initial substrate concentration, C(M)1 represents the initial trypsin concentration, C(Y)0 represents the preset reference initial substrate concentration, C(M)0 represents the preset reference trypsin concentration, ω1 represents the enzymatic reaction rate influence factor corresponding to the preset initial substrate concentration, and ω2 represents the enzymatic reaction rate influence factor corresponding to the preset trypsin concentration; (1-2) comparing the enzymatic reaction matching index with each enzymatic reaction matching index interval preset in the enzymatic reaction database, and matching to obtain the corresponding estimated enzymatic reaction time; (2) Processing to obtain enzymatic reaction compensation time: (2-1) Monitor the enzymatic reaction condition data, obtain the reaction environment pH value and reaction environment temperature, extract the enzymatic reaction rate influencing factors corresponding to the preset environment temperature and environment pH value from the enzymatic reaction database, as well as the preset reference reaction environment pH value, reference reaction environment temperature, allowable deviation pH value and allowable deviation temperature, and comprehensively analyze to obtain the enzymatic reaction process deviation impact assessment value, whose numerical expression is: In the formula, represents the evaluation value of the deviation impact of the enzymatic hydrolysis process, pH(t) represents the pH value of the reaction environment at time t, Q(t) represents the temperature of the reaction environment at time t, pH0 represents the reference pH value of the reaction environment, Q0 represents the reference temperature of the reaction environment, ΔpH represents the preset allowable deviation pH value, ΔQ represents the preset allowable deviation temperature, t represents the time variable, t∈[t1, t2], t1 represents the start time point of the enzymatic hydrolysis reaction, t2 represents the current time point of the enzymatic hydrolysis reaction, ψ1 represents the enzymatic hydrolysis reaction rate influence factor corresponding to the preset environmental pH value, and ψ2 represents the enzymatic hydrolysis reaction rate influence factor corresponding to the preset environmental temperature; (2-2) comparing the enzymatic reaction process deviation impact assessment value with the compensation time corresponding to each enzymatic reaction process deviation impact assessment value interval stored in the enzymatic reaction database, and matching to obtain the enzymatic reaction compensation time; (3) Processing to obtain the enzymatic reaction termination judgment index: (3-1) The enzymatic conversion profile data of the enzymatic hydrolysis reaction is monitored to obtain the substrate concentration and the expected product concentration. The corresponding theoretical maximum concentration of the expected product concentration and the enzymatic hydrolysis deficiency assessment influencing factors corresponding to the preset substrate concentration and expected product concentration are extracted from the enzymatic hydrolysis reaction database. The enzymatic hydrolysis deficiency assessment index is obtained by comprehensive analysis. The numerical expression is: Wherein, vZ represents the enzymatic insufficiency evaluation index, θC(Y) represents the substrate concentration, C(Y)1 represents the initial substrate concentration, θC(T) represents the expected product concentration, C(T)0 represents the theoretical maximum concentration of the expected product concentration, χ1 represents the enzymatic insufficiency evaluation factor corresponding to the preset substrate concentration, and χ2 represents the enzymatic insufficiency evaluation factor corresponding to the preset expected product concentration; Among them, the expected product concentration is the concentration of the A2β-casein characteristic peptide; (3-2) The degradation monitoring data of the enzymatic reaction products are collected to obtain the number of unexpected peptides and the acid value of the solution. The critical number of unexpected peptides and the critical acid value of the solution are obtained from the enzymatic reaction database, as well as the enzymatic over-evaluation influencing factors corresponding to the preset number of unexpected peptides and the acid value of the solution. The enzymatic over-evaluation index is obtained through comprehensive analysis. Its numerical expression is: Wherein, vG represents the over-evaluation index of enzymatic hydrolysis, NT1 represents the number of unexpected peptides, NT0 represents the critical number of unexpected peptides, PS1 represents the solution acid value, PS0 represents the critical solution acid value, φ1 represents the over-evaluation impact factor of enzymatic hydrolysis corresponding to the preset number of unexpected peptides, and φ2 represents the over-evaluation impact factor of enzymatic hydrolysis corresponding to the preset solution acid value; (3-3) summing the estimated enzymatic reaction time and the enzymatic reaction compensation time, and marking the sum result as the enzymatic reaction required time; (3-4) obtaining the actual enzymatic reaction time, subtracting the required enzymatic reaction time from the actual enzymatic reaction time, and marking the difference result as an enzymatic reaction reference indicator; (3-5) Compare the enzymatic hydrolysis reaction reference index with each enzymatic hydrolysis reaction termination judgment influencing factor corresponding to each enzymatic hydrolysis reference index interval stored in the enzymatic hydrolysis reaction database, match the enzymatic hydrolysis termination judgment influencing factors corresponding to the enzymatic hydrolysis insufficient evaluation index and the enzymatic hydrolysis excessive evaluation index, and comprehensively analyze the enzymatic hydrolysis termination judgment index based on the enzymatic hydrolysis insufficient evaluation index and the enzymatic hydrolysis excessive evaluation index, and its numerical expression is: In the formula, δ represents the enzymatic reaction termination judgment index, vZ represents the enzymatic reaction insufficient evaluation index, vG represents the enzymatic reaction excessive evaluation index, τ1 represents the enzymatic reaction termination judgment influence factor corresponding to the preset enzymatic reaction insufficient evaluation index, and τ2 represents the enzymatic reaction termination judgment influence factor corresponding to the preset enzymatic reaction excessive evaluation index; (4) Controlling the enzymatic reaction according to the enzymatic reaction termination judgment index: The enzymatic hydrolysis reaction termination judgment index threshold is obtained from the enzymatic hydrolysis reaction database, and the enzymatic hydrolysis reaction termination judgment index is compared with the enzymatic hydrolysis reaction termination judgment index threshold. If the enzymatic hydrolysis reaction termination judgment index is greater than or equal to the enzymatic hydrolysis reaction termination judgment index threshold, the enzymatic hydrolysis reaction is terminated.

2. A system using the monitoring method according to claim 1, comprising: The enzymatic reaction time estimation module is used to obtain the initial substrate concentration and the initial trypsin concentration, and extract relevant data from the enzymatic reaction database, and obtain the estimated enzymatic reaction time after processing; The enzymatic reaction time compensation module is used to monitor the enzymatic reaction condition data, obtain the reaction environment pH value and reaction environment temperature, and extract relevant data from the enzymatic reaction database, and comprehensively analyze the enzymatic reaction process deviation impact assessment value, and match the enzymatic reaction compensation time according to the enzymatic reaction process deviation impact assessment value; The enzymatic reaction effect evaluation module is used to monitor the enzymatic conversion profile data of the enzymatic reaction, obtain the substrate concentration and the expected product concentration, and extract relevant data from the enzymatic reaction database, and obtain the insufficient enzymatic reaction evaluation index after processing; collect the product degradation monitoring data of the enzymatic reaction, obtain the excessive enzymatic reaction evaluation index after processing, and obtain the actual enzymatic reaction time, and obtain the enzymatic reaction reference index according to the actual enzymatic reaction time, the expected enzymatic reaction time and the enzymatic reaction compensation time, and compare the enzymatic reaction reference index with each enzymatic reaction termination judgment influencing factor corresponding to each enzymatic reaction reference index interval stored in the enzymatic reaction database, and obtain the enzymatic reaction termination judgment index through comprehensive analysis according to the insufficient enzymatic reaction evaluation index and the excessive enzymatic reaction evaluation index; The enzymolysis reaction termination control module is used to control the enzymolysis reaction according to the enzymolysis reaction termination judgment index.

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