Method and system for detecting A2 beta-casein characteristic peptide in raw milk

Abstract

The invention relates to the technical field of food detection, and particularly discloses a detection method and a detection system for A2 beta-casein characteristic peptide in raw milk, the method comprises the following steps: obtaining initial substrate concentration and initial trypsin concentration, and processing to obtain predicted enzymolysis reaction time; monitoring and analyzing enzymolysis reaction condition data to obtain enzymolysis reaction compensation time; collecting enzymatic conversion general situation data and product degradation monitoring data of the enzymolysis reaction to obtain actual enzymolysis reaction time, and performing comprehensive analysis according to the predicted enzymolysis reaction time and enzymolysis reaction compensation time to obtain an enzymolysis reaction termination judgment index; and controlling the enzymolysis reaction according to the enzymolysis reaction termination judgment index. By screening and synthesizing the bovine A2 beta-casein characteristic peptide, the content of A2 beta-casein in dairy products can be accurately detected, the method has relatively high specificity, sensitivity, recovery rate and precision, and the influence caused by inaccurate high performance liquid chromatography detection and other problems is effectively reduced.

Description

Technical Field

[0001] The invention relates to the technical field of food detection, in particular to a detection method and a detection system for A2 beta-casein characteristic peptide in raw milk. Background Art

[0002] Cow's milk is known as the perfect food of nature for its comprehensive nutritional composition. The protein in cow's milk is mainly casein, accounting for 80% of the total protein. The original form of β-casein is A2, and later variants such as A1 were derived due to gene mutation. A1 produces β-casomorphin 7 when digested in the human body, which may cause health problems. In contrast, A2 β-casein is easier to digest and absorb, which has led to the expansion of the A2 dairy market. Given the increasing association between milk consumption and adverse reactions in susceptible populations, research on A2 milk testing is also on the rise.

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

[0004] For example, the invention patent with the announcement number CN106198692B is a method for detecting A1β-casein and A2β-casein in cow's milk. The method comprises: (1) heating, centrifuging, freezing and thawing the sample to be tested in sequence to obtain a test stock solution; (2) pre-treating the test stock solution to obtain a test solution, wherein the pre-treatment comprises mixing the test stock solution with a pre-treatment solution; and (3) detecting the test solution by capillary electrophoresis to determine whether the cow's milk contains A1β-casein and A2β-casein.

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application discovered that the above technology has at least the following technical problems: currently, according to the existing casein analysis and detection methods, only the α-, β- and κ-types of casein can be detected using high performance liquid chromatography or capillary electrophoresis, etc. Due to the lack of A1 or A2 β-casein standard substances on the market, quantitative detection of A1 β-casein and A2 β-casein cannot be achieved. Summary of the invention

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

[0007] To achieve the above objectives, the present invention is implemented through the following technical scheme: The first aspect of the present invention provides a method for detecting A2β-casein characteristic peptide in raw bovine milk, comprising: obtaining an initial substrate concentration and an initial trypsin concentration, and obtaining an estimated enzymatic reaction time through processing.

[0008] The enzymatic hydrolysis reaction condition data is monitored, and a comprehensive analysis is performed to obtain an evaluation value of the impact of the enzymatic hydrolysis reaction process deviation. Based on the evaluation value of the impact of the enzymatic hydrolysis reaction process deviation, the enzymatic hydrolysis reaction compensation time is matched.

[0009] The enzymatic conversion profile data of the enzymatic hydrolysis reaction are monitored and processed to obtain the insufficient enzymatic hydrolysis assessment index. The product degradation monitoring data of the enzymatic hydrolysis reaction are collected and processed to obtain the excessive enzymatic hydrolysis assessment index. The actual enzymatic hydrolysis reaction time is obtained, and based on the expected enzymatic hydrolysis reaction time and the enzymatic hydrolysis reaction compensation time, a comprehensive analysis is performed to obtain the enzymatic hydrolysis reaction termination judgment index.

[0010] The enzymatic hydrolysis reaction is controlled according to the enzymatic hydrolysis reaction termination judgment index.

[0011] As a further method, the processing obtains the estimated enzymatic reaction time, and the specific process is: obtaining the initial substrate concentration and the initial trypsin concentration, and extracting the enzymatic reaction rate influencing factors corresponding to the preset initial substrate concentration and trypsin concentration from the enzymatic reaction database, and comprehensively analyzing to obtain the enzymatic reaction matching index.

[0012] The enzymatic hydrolysis reaction matching index is compared with each enzymatic hydrolysis reaction matching index interval preset in the enzymatic hydrolysis reaction database, and the corresponding estimated enzymatic hydrolysis reaction time is obtained by matching.

[0013] As a further method, the enzymatic hydrolysis reaction compensation time is matched according to the enzymatic hydrolysis reaction process deviation impact assessment value. The specific process is: according to the enzymatic hydrolysis reaction condition data, the enzymatic hydrolysis reaction process deviation impact assessment value is obtained by comprehensive analysis. The enzymatic hydrolysis reaction condition data includes the reaction environment pH value and the reaction environment temperature.

[0014] The enzymatic hydrolysis reaction process deviation impact assessment value is compared with the compensation time corresponding to each enzymatic hydrolysis reaction process deviation impact assessment value interval stored in the enzymatic hydrolysis reaction database, and the enzymatic hydrolysis reaction compensation time is obtained by matching.

[0015] As a further method, the processing obtains the enzymatic insufficiency assessment index, and the specific process is: the enzymatic conversion profile data of the enzymatic reaction includes substrate concentration and expected product concentration.

[0016] Based on the substrate concentration and expected product concentration, the enzymatic insufficiency assessment index was obtained through comprehensive analysis.

[0017] As a further method, the processing obtains the enzymatic over-evaluation index, and the specific process is: the degradation monitoring data of the product of the enzymatic reaction includes the number of unexpected peptide species and the acid value of the solution.

[0018] The over-enzyme hydrolysis evaluation index was obtained by comprehensive analysis based on the number of unexpected peptide species and the acid value of the solution.

[0019] As a further method, the comprehensive analysis obtains the enzymatic reaction termination judgment index, and the specific analysis process is: summing the expected enzymatic reaction time and the enzymatic reaction compensation time, and marking the sum result as the enzymatic reaction required time.

[0020] The actual enzymatic reaction time is obtained, the required enzymatic reaction time is subtracted from the actual enzymatic reaction time, and the difference result is marked as the enzymatic reaction reference indicator.

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

[0022] According to the insufficient enzymatic hydrolysis evaluation index and the excessive enzymatic hydrolysis evaluation index, the enzymatic hydrolysis reaction termination judgment index was obtained through comprehensive analysis.

[0023] As a further method, the enzymatic hydrolysis reaction is controlled according to the enzymatic hydrolysis reaction termination judgment index. The specific process is: obtaining the enzymatic hydrolysis reaction termination judgment index threshold from the enzymatic hydrolysis reaction database, comparing the enzymatic hydrolysis reaction termination judgment index with the enzymatic hydrolysis reaction termination judgment index threshold, and 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.

[0024] The second aspect of the present invention provides a detection system for characteristic peptides of A2β-casein in raw bovine milk, comprising: an enzymatic reaction time estimation module for obtaining an initial substrate concentration and an initial trypsin concentration, and obtaining an estimated enzymatic reaction time after processing.

[0025] The enzymatic reaction time compensation module is used to monitor the enzymatic reaction condition data, comprehensively analyze and obtain 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.

[0026] The enzymatic hydrolysis reaction effect evaluation module is used to monitor the enzymatic conversion profile data of the enzymatic hydrolysis reaction, obtain the insufficient enzymatic hydrolysis evaluation index after processing, collect the product degradation monitoring data of the enzymatic hydrolysis reaction, obtain the excessive enzymatic hydrolysis evaluation index after processing, obtain the actual enzymatic hydrolysis reaction time, and obtain the enzymatic hydrolysis reaction termination judgment index through comprehensive analysis based on the expected enzymatic hydrolysis reaction time and the enzymatic hydrolysis reaction compensation time.

[0027] The enzymolysis reaction termination control module is used to control the enzymolysis reaction according to the enzymolysis reaction termination judgment index.

[0028] The enzymatic hydrolysis reaction database is used to store 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) The present invention provides a method and system for detecting characteristic peptides of A2β-casein in raw bovine milk. By screening and synthesizing characteristic peptides of bovine A2β-casein, the content of A2β-casein in bovine dairy products can be accurately detected. The method has high specificity, sensitivity, recovery rate and precision, and effectively reduces the loss in the sample pretreatment process and the impact caused by inaccurate high performance liquid chromatography detection.

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

[0032] (3) The present invention can timely discover and handle abnormal situations by comprehensively analyzing the reaction environment temperature and the reaction environment pH value, ensuring that the enzymatic hydrolysis reaction is always carried out under the most suitable conditions, which helps to maximize the catalytic activity of the enzyme, thereby improving the efficiency of the enzymatic hydrolysis reaction. By adjusting the required time of the enzymatic hydrolysis reaction in real time according to the ambient temperature and pH value, the physical and chemical properties of the product can be optimized, the excessive consumption of raw materials can be reduced, the utilization rate of raw materials can be improved, and the quality and stability of the product can be improved.

[0033] (4) The present invention can timely understand the progress of the enzymatic hydrolysis reaction by monitoring and analyzing the substrate concentration and the expected product concentration, thereby optimizing the reaction conditions and improving the reaction efficiency, helping to reduce the waste of substrate and the loss of product, and ensuring that the product meets the expected specifications and quality standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[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 system module connection of the present invention;

[0037] Figure 3 It is a schematic diagram of the functional relationship between the over-enzyme hydrolysis evaluation index and the enzymatic hydrolysis reaction termination judgment index involved in the embodiment of the present invention. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] Reference Figure 1 As shown, the first aspect of the present invention provides a method for detecting characteristic peptides of A2β-casein in raw bovine milk, comprising: obtaining an initial substrate concentration and an initial trypsin concentration, and obtaining an estimated enzymatic reaction time through processing.

[0040] The enzymatic hydrolysis reaction condition data is monitored, and a comprehensive analysis is performed to obtain an evaluation value of the impact of the enzymatic hydrolysis reaction process deviation. Based on the evaluation value of the impact of the enzymatic hydrolysis reaction process deviation, the enzymatic hydrolysis reaction compensation time is matched.

[0041] The enzymatic conversion profile data of the enzymatic hydrolysis reaction are monitored and processed to obtain the insufficient enzymatic hydrolysis assessment index. The product degradation monitoring data of the enzymatic hydrolysis reaction are collected and processed to obtain the excessive enzymatic hydrolysis assessment index. The actual enzymatic hydrolysis reaction time is obtained, and based on the expected enzymatic hydrolysis reaction time and the enzymatic hydrolysis reaction compensation time, a comprehensive analysis is performed to obtain the enzymatic hydrolysis reaction termination judgment index.

[0042] The enzymatic hydrolysis reaction is controlled according to the enzymatic hydrolysis reaction termination judgment index.

[0043] Specifically, the estimated enzymatic reaction time is obtained by processing, and the specific process is: obtaining the initial substrate concentration and the initial trypsin concentration, and extracting the enzymatic reaction rate influencing factors corresponding to the preset initial substrate concentration and trypsin concentration from the enzymatic reaction database, and comprehensively analyzing to obtain the enzymatic reaction matching index.

[0044] It should be understood that the initial substrate concentration in this embodiment refers to the concentration of the sample solution to be hydrolyzed obtained after the target dairy product is pretreated (such as denaturation, ultrafiltration, etc.) before the trypsin hydrolysis step. This concentration directly affects the efficiency of the hydrolysis, the type and quantity of the product, and the sensitivity and accuracy of the subsequent analysis steps. Specifically, the initial substrate concentration refers to the concentration of milk protein in the sample. During the hydrolysis process, trypsin will recognize and cut specific peptide bonds in the sample to generate a series of smaller peptides. Too high an initial substrate concentration may result in incomplete hydrolysis or the product is too complex and difficult to analyze; and too low an initial substrate concentration may reduce the detection sensitivity and increase errors.

[0045] It should be understood that trypsin in this embodiment is a widely used protease that can specifically cut peptide bonds in proteins. In the process of detecting A2β-casein, complex protein samples can be decomposed into smaller peptides through the enzymatic hydrolysis of trypsin, and these peptides contain characteristic peptides that can be used for subsequent analysis. The concentration of trypsin is crucial to the effect of the enzymatic hydrolysis reaction. Too low a concentration may lead to incomplete enzymatic hydrolysis, and the protein in the sample cannot be fully decomposed into the required peptides; while too high a concentration may introduce additional enzymatic hydrolysis sites, resulting in excessive fragmentation of the peptides, which is not conducive to subsequent analysis and detection.

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

[0047]

[0048] 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 Indicates the preset reference initial substrate concentration, C(M) 0 represents the preset reference trypsin concentration, ω 1 Indicates the enzymatic reaction rate influencing factor corresponding to the preset initial substrate concentration, ω 2 Indicates the factor affecting the enzymatic reaction rate 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, +∞). Through the relationship between the initial substrate concentration and the trypsin concentration and the enzymatic reaction matching index in the enzymatic reaction history data, a mapping set of the initial substrate concentration and the trypsin concentration and the corresponding enzymatic reaction rate influencing factor 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 of enzymatic reaction matching index data

[0051]

[0052] As shown in Table 1, in a specific embodiment, C(Y) 0 =100 μg / mL, C(M) 0 =5μg / 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 length of time required for the enzymatic reaction. The enzymatic reaction matching index is jointly determined by the initial substrate concentration and the initial trypsin concentration. The larger the initial substrate concentration and the initial trypsin concentration, the larger the corresponding enzymatic reaction matching index, indicating that the enzymatic reaction requires a longer time.

[0053] It should be understood that the enzymatic reaction follows the Michaelis equation. When the enzyme concentration remains unchanged and the initial substrate concentration is low, the reaction rate increases with the increase of the initial substrate concentration. However, when the initial substrate concentration increases to a certain extent, the increase of the reaction rate will gradually slow down until it reaches a maximum value. At this time, if the initial substrate concentration is increased, the reaction rate will no longer increase. Therefore, when the initial substrate concentration is low, the time required to complete the enzymatic reaction is longer due to the slow reaction rate. As the initial substrate concentration increases, the reaction rate increases, so the time required to complete the enzymatic reaction will be shortened accordingly.

[0054] It should be understood that, in the present embodiment, increasing the initial substrate concentration and the initial trypsin concentration can synergistically improve the reaction rate, and 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 enzymolysis reaction time can be predicted, and accurately predicting the enzymolysis time can help plan the experimental process in advance, reasonably arrange the experimental steps and resources, and avoid the waste of resources caused by the waiting time being too long or too short. It is estimated that the enzymolysis time helps to control the degree of the enzymolysis reaction, avoids over-digestion or under-digestion, and ensures that the product has good analyzability and repeatability.

[0055] The enzymatic hydrolysis reaction matching index is compared with each enzymatic hydrolysis reaction matching index interval preset in the enzymatic hydrolysis reaction database, and the corresponding estimated enzymatic hydrolysis reaction time is obtained by matching.

[0056] It should be understood that the preset enzymatic reaction matching index intervals in the enzymatic reaction database of this embodiment have a one-to-one correspondence with the estimated enzymatic reaction time, and the corresponding estimated enzymatic reaction time can be obtained by obtaining the interval range to which the enzymatic reaction matching index belongs.

[0057] In a specific embodiment, a mapping set between the enzymatic hydrolysis reaction matching index interval and the expected enzymatic hydrolysis reaction time is constructed based on historical experimental data, the actual enzymatic hydrolysis reaction matching index is obtained, and then the interval to which the actual enzymatic hydrolysis reaction matching index belongs is determined, and the expected enzymatic hydrolysis reaction time corresponding to the enzymatic hydrolysis reaction matching index interval is obtained from the mapping set.

[0058] Specifically, according to the evaluation value of the impact of the enzymatic hydrolysis process deviation, the enzymatic hydrolysis reaction compensation time is matched, and the specific process is: according to the enzymatic hydrolysis reaction condition data, the evaluation value of the impact of the enzymatic hydrolysis process deviation is obtained by comprehensive analysis, and the enzymatic hydrolysis reaction condition data includes the reaction environment pH value and the reaction environment temperature.

[0059] In a specific embodiment, the enzymatic reaction rate influencing factors corresponding to the preset ambient temperature and ambient pH value are extracted from the enzymatic reaction database, and the preset reference reaction environment pH value, reference reaction environment temperature, allowable deviation pH value and allowable deviation temperature are obtained, and the enzymatic reaction process deviation impact assessment value is obtained by comprehensive analysis. The specific numerical expression is:

[0060]

[0061] In the formula, represents the evaluation value of the deviation 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, and pH 0 Indicates the pH value of the reference reaction environment, 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 Indicates the enzymatic reaction rate influencing factor corresponding to the preset environmental pH value, ψ 2 Indicates the factor affecting the enzymatic reaction rate corresponding to the preset ambient temperature.

[0062] It should be understood that, in the present embodiment, reference reaction environment pH value and reference reaction environment temperature are preset optimal reaction conditions, when actual reaction environment pH value and reaction environment temperature are closer to corresponding reference values, enzymolysis reaction rate is larger, and corresponding enzymolysis reaction time requirement is less. In the present embodiment, enzymolysis reaction process deviation affects the evaluation value for quantitatively evaluating the deviation degree of actual reaction conditions from preset reference reaction conditions, and provides data basis for the correction of enzymolysis reaction time requirement. In the present embodiment, when reaction environment pH value and reaction environment temperature deviate from corresponding reference values ​​more, enzymolysis reaction process deviation affects the evaluation value larger, indicating that enzymolysis reaction requires more time to fully react.

[0063] It should be understood that in this embodiment, the value range of the enzymatic reaction rate influencing factor corresponding to the ambient temperature and the ambient pH value is between 0 and 1. Through the relationship between the reaction environment pH value and the reaction environment temperature in the historical data and the enzymatic reaction process deviation impact assessment value, a mapping set of the reaction environment pH value and the reaction environment temperature and the corresponding enzymatic reaction rate influencing factor is constructed, and the real-time reaction environment pH value and the reaction environment temperature are input to obtain the enzymatic reaction rate influencing factor corresponding to the ambient pH value and the enzymatic reaction rate influencing factor corresponding to the ambient temperature from the mapping set.

[0064] It should be understood that in this embodiment, the temperature of the reaction environment is monitored by a temperature sensor. The reaction environment temperature will affect the enzymatic reaction time. With 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 reaction time; when the temperature exceeds the reference reaction environment temperature, the structure of the enzyme will change, resulting in reduced activity or even inactivation, at which time the reaction rate will decrease, the enzymatic reaction time will be extended or even cannot be carried out.

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

[0066] It should be understood that the change of ambient temperature in the present embodiment will affect the degree of ionization of the solution, and then affect the reaction environment pH value. By comprehensively analyzing the reaction environment temperature and the reaction environment pH value, it is possible to promptly discover and handle abnormal conditions, ensure that the enzymolysis reaction is always carried out under the most suitable conditions, and contribute to maximizing the catalytic activity of the enzyme, thereby improving the enzymolysis reaction efficiency. According to the real-time adjustment of the enzymolysis reaction demand time of ambient temperature and pH value, the physical and chemical properties of the product can be optimized, the excessive consumption of raw materials can be reduced, the utilization rate of raw materials can be improved, and the quality and stability of the product can be improved.

[0067] The enzymatic hydrolysis reaction process deviation impact assessment value is compared with the compensation time corresponding to each enzymatic hydrolysis reaction process deviation impact assessment value interval stored in the enzymatic hydrolysis reaction database, and the enzymatic hydrolysis reaction compensation time is obtained by matching.

[0068] In a specific embodiment, a mapping set between the enzymatic hydrolysis reaction process deviation impact assessment value interval and the enzymatic hydrolysis reaction compensation time is constructed based on historical experimental data, and the actual enzymatic hydrolysis reaction process deviation impact assessment value is obtained, and then the interval to which the actual enzymatic hydrolysis reaction process deviation impact assessment value belongs is determined, and the enzymatic hydrolysis reaction compensation time corresponding to the enzymatic hydrolysis reaction process deviation impact assessment value interval is obtained from the mapping set.

[0069] Specifically, the enzymatic insufficiency evaluation index is obtained by processing, and the specific process is: the enzymatic conversion profile data of the enzymatic reaction includes the substrate concentration and the expected product concentration.

[0070] It should be understood that the substrate concentration in this embodiment refers to the real-time concentration of milk protein during the enzymatic hydrolysis reaction, and the expected product concentration refers to the concentration of 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 expected product concentration, the enzymatic insufficiency assessment index was obtained through comprehensive analysis.

[0073] In a specific embodiment, the theoretical maximum concentration of the expected product concentration corresponding to the substrate concentration is obtained from the enzymatic reaction database, and the enzymatic insufficiency evaluation factor corresponding to the preset substrate concentration and the expected product concentration is extracted from the enzymatic reaction database, and the enzymatic insufficiency evaluation index is obtained by comprehensive analysis. The specific numerical expression is:

[0074]

[0075] In the formula, νZ represents the enzymatic insufficiency evaluation index, θC(Y) represents the substrate concentration, and 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 Indicates the impact factor of insufficient enzymatic hydrolysis corresponding to the preset substrate concentration, χ 2 It indicates the impact factor of insufficient enzymatic hydrolysis assessment corresponding to the preset expected product concentration.

[0076] It should be understood that the insufficient enzymolysis evaluation index in this embodiment is used to quantitatively evaluate the inadequacy of the enzymolysis reaction. The insufficient enzymolysis evaluation index is 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 insufficient enzymolysis evaluation index, indicating that the enzymolysis reaction is more insufficient and a longer enzymolysis reaction time needs to be reserved.

[0077] It should be understood that in this embodiment, the value range of the insufficient enzymatic hydrolysis assessment impact factor corresponding to the substrate concentration and the expected product concentration is between 0 and 1. Through the relationship between the substrate concentration and the expected product concentration and the insufficient enzymatic hydrolysis assessment index in the historical data, a mapping set of the substrate concentration and the expected product concentration and the corresponding insufficient enzymatic hydrolysis assessment impact factor is constructed, and the real-time substrate concentration and the expected product concentration are input to obtain the insufficient enzymatic hydrolysis assessment impact factor corresponding to the substrate concentration and the insufficient enzymatic hydrolysis assessment impact factor corresponding to the expected product concentration from the mapping set.

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

[0079] Specifically, the enzymatic over-evaluation index is obtained by processing, and the specific process is: the product degradation monitoring data of the enzymatic reaction includes the number of unexpected peptide species and the acid value of the solution.

[0080] It should be understood that the number of unexpected peptides in this embodiment refers to the number of peptides other than the characteristic peptides. When the enzymatic reaction is excessive, the enzyme may cut at non-specific sites to produce unexpected peptides. The appearance of unexpected peptides increases the complexity of the product and may cause difficulties in data interpretation. The acid value of the solution refers to an indicator for measuring the content of free fatty acids. As the enzymatic reaction proceeds, the free fatty acid content gradually increases, resulting in an increase in the acid value of the solution. When the enzymatic reaction is excessive, the increase in the acid value is particularly obvious.

[0081] It should be understood that in this embodiment, the number of unexpected 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] The over-enzyme hydrolysis evaluation index was obtained by comprehensive analysis based on the number of unexpected peptide species and the acid value of the solution.

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

[0084]

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

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

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

[0088] Specifically, the enzymatic reaction termination judgment index is obtained through comprehensive analysis. The specific analysis process is: summing the expected enzymatic reaction time and the enzymatic reaction compensation time, and marking the sum result as the enzymatic reaction required time.

[0089] The actual enzymatic reaction time is obtained, the required enzymatic reaction time is subtracted from the actual enzymatic reaction time, and the difference result is marked as the enzymatic reaction reference indicator.

[0090] The enzymatic hydrolysis reaction reference index is compared with each enzymatic hydrolysis reaction termination judgment influencing factor corresponding to each enzymatic hydrolysis reaction reference index interval stored in the enzymatic hydrolysis reaction database, and the enzymatic hydrolysis reaction termination judgment influencing factors corresponding to the enzymatic hydrolysis insufficient evaluation index and the enzymatic hydrolysis excessive evaluation index are matched.

[0091] In a specific embodiment, a mapping set between the enzymatic hydrolysis reaction reference index interval and the enzymatic hydrolysis termination judgment influencing factors corresponding to the insufficient enzymatic hydrolysis evaluation index and the excessive enzymatic hydrolysis evaluation index is constructed based on historical experimental data to 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 judgment influencing factors corresponding to the insufficient enzymatic hydrolysis evaluation index and the excessive enzymatic hydrolysis evaluation index corresponding to the enzymatic hydrolysis reaction reference index interval from the mapping set.

[0092] It should be understood that in this embodiment, the value range of the influencing factors for the termination of enzymatic hydrolysis corresponding to the insufficient enzymatic hydrolysis assessment index and the excessive enzymatic hydrolysis assessment index is between 0 and 1. Through the relationship between the number of unexpected peptide types and the acid value of the solution and the excessive enzymatic hydrolysis assessment index under different enzymatic hydrolysis reference index conditions in the historical data, a mapping set of the insufficient enzymatic hydrolysis assessment index and the excessive enzymatic hydrolysis assessment index and the corresponding factors for the termination of enzymatic hydrolysis is constructed, and the real-time insufficient enzymatic hydrolysis assessment index and the excessive enzymatic hydrolysis assessment index are input to obtain the factors for the termination of enzymatic hydrolysis corresponding to the insufficient enzymatic hydrolysis assessment index and the factors for the termination of enzymatic hydrolysis corresponding to the excessive enzymatic hydrolysis assessment index from the mapping set.

[0093] According to the insufficient enzymatic hydrolysis evaluation index and the excessive enzymatic hydrolysis evaluation index, the enzymatic hydrolysis reaction termination judgment index was obtained through comprehensive analysis.

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

[0095]

[0096] In the formula, δ represents the index for determining the termination of the enzymatic reaction, νZ represents the evaluation index for insufficient enzymatic reaction, νG represents the evaluation index for excessive enzymatic reaction, and τ 1 Indicates the influence factor of enzymatic reaction termination judgment corresponding to the preset enzymatic insufficiency evaluation index, τ 2 It indicates the influencing factor of enzymatic reaction termination judgment corresponding to the preset enzymatic over-evaluation index.

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

[0098] Specifically, the enzymatic hydrolysis reaction is controlled according to the enzymatic hydrolysis reaction termination judgment index. The specific process is: obtaining the enzymatic hydrolysis reaction termination judgment index threshold from the enzymatic hydrolysis reaction database, comparing the enzymatic hydrolysis reaction termination judgment index with the enzymatic hydrolysis reaction termination judgment index threshold, and 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.

[0099] Reference Figure 2 As shown, the second aspect of the present invention provides a detection system for characteristic peptides of A2β-casein in raw bovine milk, comprising: an enzymatic reaction time estimation module for obtaining an initial substrate concentration and an initial trypsin concentration, and obtaining an estimated enzymatic reaction time after processing.

[0100] The enzymatic reaction time compensation module is used to monitor the enzymatic reaction condition data, comprehensively analyze and obtain 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.

[0101] The enzymatic hydrolysis reaction effect evaluation module is used to monitor the enzymatic conversion profile data of the enzymatic hydrolysis reaction, obtain the insufficient enzymatic hydrolysis evaluation index after processing, collect the product degradation monitoring data of the enzymatic hydrolysis reaction, obtain the excessive enzymatic hydrolysis evaluation index after processing, obtain the actual enzymatic hydrolysis reaction time, and obtain the enzymatic hydrolysis reaction termination judgment index through comprehensive analysis based on the expected enzymatic hydrolysis reaction time and the enzymatic hydrolysis reaction compensation time.

[0102] The enzymolysis reaction termination control module is used to control the enzymolysis reaction according to the enzymolysis reaction termination judgment index.

[0103] The enzymatic reaction database is used to store enzymatic reaction related data, including reference initial substrate concentration, reference trypsin concentration, critical solution acid value and other indicators. The data in the enzymatic reaction database is obtained by collecting data from multiple enzymatic reaction experiments.

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

[0105] After testing, it was found that bovine milk products that do not contain A2β-casein do not contain a peptide segment that is consistent with the amino acid sequence of the characteristic peptide segment. The characteristic peptide segment is obtained by chemical synthesis, and after chemical synthesis and purification, the purity is greater than 95%, and is used as a peptide segment 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 dairy products by an external standard method, wherein the kit comprises a characteristic peptide having an amino acid sequence as shown in SEQ ID NO.1.

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

[0108] The present invention also provides a method for detecting A2β-casein in dairy products by an external standard method, wherein the method utilizes the above-mentioned characteristic peptide and specifically comprises the following steps:

[0109] (1) Sample pretreatment: Take the sample to be tested, denature, ultrafilter, and trypsinize it in sequence, terminate the reaction, and desalt it through a C18 desalting column to obtain a sample solution;

[0110] (2) Sample detection: The sample solution obtained in step (1) is detected by high performance liquid chromatography-mass spectrometry to obtain the peak area;

[0111] (3) Obtaining the characteristic equation of the standard curve: preparing characteristic peptide standard curve working solutions of different series of concentrations, respectively obtaining characteristic peptide standard solutions of different concentrations by the pretreatment method described in step (1), and then respectively detecting by the method described in step (2) to obtain the characteristic equation of the standard curve of the characteristic peptide;

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

[0113] It is further defined that the denaturation in step (1) is performed by sequentially adding dithiothreitol (DTT) and iodoacetamide (IAA).

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

[0115] The trypsin hydrolysis process in step (1) utilizes the characteristic that trypsin acts specifically on arginine (R) and lysine (K), and can hydrolyze A2β-casein peptide molecules with a molecular weight of several hundred to thousands of Daltons, thereby selecting characteristic peptides unique to A2β-casein. After synthesis and purification, high-purity characteristic peptides are obtained and applied to this detection method.

[0116] It is further defined that the ultrafiltration in step (1) is performed by sequentially using a 100 mM Tris-HCl buffer solution having a urea content of 8 M and a 50 mM NH4HCO3 solution.

[0117] It is further defined that in step (1), 10% formic acid solution is added to terminate the reaction.

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

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

[0120] It is further defined that the characteristic equation of the standard curve in step (3) is obtained by the following method: A2β-casein characteristic peptide is prepared into standard curve working solutions of different series of concentrations, and then the peak areas corresponding to the characteristic peptide standard solutions of each concentration are obtained by the pretreatment method and the detection method described in step (1) and step (2) in turn, and a standard curve is obtained based on the relationship between the characteristic peptide standard solutions of each concentration and the peak areas; and then the characteristic equation of the standard curve Y=kX+b is obtained; wherein Y is the peak area of ​​the A2β-casein characteristic peptide, X is the concentration of the A2β-casein characteristic peptide, in nmol / mL; k is the slope of the standard curve; and b is the intercept of the standard curve.

[0121] Further, the content calculation formula in step (4) is Cx=na×M×N×10 -10; Wherein Cx is the A2β-casein content of the sample to be tested, the unit is 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 multiple.

[0122] It is further defined that the bovine dairy products include 6 types of A2 β-casein-enriched milk (samples 1-6, wherein sample 1 is raw milk from a cow genotyped as A2) and commercially available ordinary milk (samples 7-9).

[0123] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments. The experimental methods used in the following embodiments are conventional methods unless otherwise specified, and the materials, reagents, methods and instruments used are conventional materials, reagents, methods and instruments in the art unless otherwise specified, and can be obtained by those skilled in the art through commercial channels.

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

[0125] Ultra-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 were purchased from Sigma-Aldrich; iodoacetamide (IAA), dithiothreitol (DTT), urea, ammonium bicarbonate, and formic acid were purchased from Aladdin; C18 desalting column (Thermo Fisher Scientific (China) Co., Ltd.), ultrafiltration tube (Sartorius, Germany); A2β-casein characteristic peptide standard was purchased from Hefei Guopeptide Biotechnology Co., Ltd.

[0126] The A2β-casein characteristic peptide standard used has a purity greater than 95% and is verified by HPLC and MS. The amino acid sequence of the A2β-casein characteristic peptide is shown in SEQ ID NO.1: IHPFAQTQSLVYPFPGPIPNSLPQNIPPLTQTPVVVPPFLQPEVMGVSK.

[0127] The conditions of HPLC and mass spectrometry used in the following examples are:

[0128] Chromatographic conditions:

[0129] Silane C18 column, column length: 100 mm, column inner diameter: 2.1 mm, filler 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]

[0134] Mobile phase flow rate: 0.3 mL / min;

[0135] Sample temperature: 40°C;

[0136] Injection volume: 10 μL.

[0137] Mass spectrometry conditions: electrospray ionization (ESI+); mass spectrometry scanning 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 A2β-casein characteristic peptide are shown in Table 3.

[0138] Table 3 Main reference mass spectrometry parameters of A2β-casein characteristic peptides

[0139]

[0140] Example 1: The content of A2β-casein characteristic peptide in raw milk was detected as follows.

[0141] (1) Preparation of characteristic peptide standard curve working solutions of different concentrations:

[0142] Take 1 mg of 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 a 1 mL volumetric flask and dilute to 1 mL with Wahaha water to obtain 6 different series of characteristic peptide standard curve working solutions.

[0143] (2) Pretreatment of characteristic peptide standard curve working solution with different concentrations:

[0144] Take 100 μL of the six different concentrations of the characteristic peptide standard curve working solution obtained in step (2) into a 2 mL centrifuge tube for pretreatment: add 100 μL of dithiothreitol DTT (final concentration 10 mM) to the centrifuge tube, mix well and incubate at 56°C for 30 min, cool to room temperature, add 100 μL of iodoacetamide IAA (final concentration 60 mM) to the centrifuge tube, and keep it in the dark at room temperature for 30 min 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 to the ultrafiltration tube. The solution was washed (8M urea dissolved in 100mMTris-HCl, pH8.5), centrifuged at 14000×g for 15min, and the precipitate was obtained. This step was repeated twice. Subsequently, 200μL of 50mMNH4HCO3 solution was added to the ultrafiltration tube, and the precipitate was obtained by centrifugation at 14000×g for 15min. This step was also repeated twice; then, 500μL of 1mg / mL trypsin solution was added to the ultrafiltration tube, and digested at 37°C overnight; finally, 10μL of 10% formic acid solution was added to terminate the digestion, and then the digested solution was collected by centrifugation at 14000×g for 15min and desalted using a C18 desalting column. After being spin-dried at room temperature, it was stored at -20°C until analysis. Redissolved with mobile phase A (one thousandth formic acid aqueous solution) to obtain the sample to be tested.

[0145] (3) Characteristic peptide concentration detection of characteristic peptide standard curve working solution with different concentrations:

[0146] The samples of the 6 different series of concentrations of the characteristic peptide standard curve working solutions obtained in step (3) were tested using high performance liquid chromatography-mass spectrometry. The content of the A2β-casein characteristic peptide in the standard is shown in Table 4.

[0147] Table 4 Content of A2β-casein characteristic peptide in standard products

[0148]

[0149] (4) Obtaining the characteristic equation of the standard curve:

[0150] The peak areas corresponding to the samples to be tested of the characteristic peptide standard curve working solutions of different series of concentrations are obtained by step (4), and a standard curve is obtained according to the relationship between the concentration and the peak area of ​​the characteristic peptide standard curve working solutions of each concentration; and then the characteristic equation of the standard curve is obtained: Y=kX+b; wherein Y is the peak area of ​​the A2β-casein characteristic peptide, X is the concentration of the A2β-casein characteristic peptide, in nmol / mL; k is the slope of the standard curve; and b is the intercept of the standard curve.

[0151] A linear investigation was conducted and a linear equation for the characteristic peptide of A2β-casein was obtained, which was Y=5.40338e4X+-6.09683e4 and a correlation coefficient of R2=0.99792.

[0152] (5) Pretreatment of raw milk to be tested:

[0153] A certain amount of raw milk sample 1 was centrifuged and defatted at 5000 rpm, 100 μL of the defatted milk sample was taken into a centrifuge tube, an equal volume of dithiothreitol DTT (final concentration 10 mM) was added to the centrifuge tube, mixed and incubated at 56°C for 30 min for reduction, cooled to room temperature, 100 μL of iodoacetamide IAA (final concentration 60 mM) was added to the centrifuge tube, and kept at room temperature in the dark for 30 min for alkylation; the sample was added to an ultrafiltration tube (10 kDa), and centrifuged at 14000 × g for 15 min to obtain a precipitate, and then 300 μL of buffer ( 8M urea was dissolved in 100mMTris-HCl, pH8.5), centrifuged at 14000×g for 15min, and a precipitate was obtained. This step was repeated twice. Subsequently, 200μL of 50mMNH4HCO3 solution was added to the ultrafiltration tube, and the solution was centrifuged at 14000×g for 15min to obtain a precipitate. This step was also repeated twice. Then, 500μL of 1mg / mL trypsin solution was added to the ultrafiltration tube, and the solution was digested at 37°C overnight. Finally, 10μL of 10% formic acid solution was added to terminate the digestion, and then the digested solution was collected by centrifugation at 14000×g for 15min and desalted using a C18 desalting column. After being dried at room temperature, it was stored at -20°C until analysis. The sample to be tested was re-dissolved with mobile phase A (one thousandth formic acid aqueous solution) to obtain the sample to be tested.

[0154] (6) Detection of characteristic peptide content in raw milk:

[0155] The sample obtained in step (5) is tested using high performance liquid chromatography-mass spectrometry to obtain the peak area.

[0156] (7) Calculation of the content of A2β-casein characteristic peptide in raw milk:

[0157] Substitute the peak area obtained in step (6) into the characteristic equation of the standard curve obtained in step (4) to obtain the characteristic peptide concentration in the sample, and then substitute the characteristic peptide concentration into the content calculation formula to obtain the A2β-casein characteristic peptide content in raw milk sample 1.

[0158] The content calculation formula is Cx=na×M×N×10-10; where Cx is the A2β-casein content of the sample to be tested, and the unit is 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 multiple.

[0159] The detection results showed that the concentration of A2β-casein characteristic peptide in raw milk of sample 1 was 2.24E+05nmol / mL, and the content of A2β-casein characteristic peptide was 1.19±0.01g / 100g.

[0160] Example 2: The A2β-casein content in different brands of milk on the market was detected as follows.

[0161] (1) Pretreatment of samples to be tested:

[0162] A certain amount of samples of A2β-casein milk of different brands on the market were centrifuged and defatted at 5000rpm, 100μL of milk sample was taken into a centrifuge tube, an equal volume of dithiothreitol DTT (final concentration 10mM) was added to the centrifuge tube, mixed and incubated at 56°C for 30min for reduction, cooled to room temperature, 100μL of iodoacetamide IAA (final concentration 60mM) was added to the centrifuge tube, and kept at room temperature in the dark for 30min for alkylation; the sample was added to an ultrafiltration tube (10kDa), and centrifuged at 14000×g for 15min to obtain a precipitate, and then 300μL of iodoacetamide IAA was added to the ultrafiltration tube. L buffer (8M urea dissolved in 100mMTris-HCl, pH8.5), centrifuged at 14000×g for 15min, and obtained a precipitate. This step was repeated twice. Subsequently, 200μL of 50mMNH4HCO3 solution was added to the ultrafiltration tube, and centrifuged at 14000×g for 15min to obtain a precipitate. This step was also repeated twice; then, 500μL of 1mg / mL trypsin solution was added to the ultrafiltration tube, and digested at 37°C overnight; finally, 10μL of 10% formic acid solution was added to terminate the digestion, and then the digested solution was collected by centrifugation at 14000×g for 15min and desalted using a C18 desalting column. After being spin-dried at room temperature, it was stored at -20°C until analysis. Redissolved with mobile phase A (one thousandth formic acid aqueous solution) to obtain the sample to be tested.

[0163] (2) Detection of characteristic peptide content in A2β-casein milk:

[0164] The sample obtained in step (1) is tested using high performance liquid chromatography-mass spectrometry to obtain the peak area.

[0165] (3) Calculation of A2β-casein content in milk:

[0166] Substitute the peak area obtained in step (2) into the characteristic equation of the standard curve obtained in step (4) of Example 1 to obtain the characteristic peptide concentration in the sample, and then substitute the characteristic peptide concentration into the content calculation formula to obtain the A2β-casein content in the sample.

[0167] The content calculation formula is Cx=na×M×N×10-10; where Cx is the A2β-casein content of the sample to be tested, and the unit is 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 multiple.

[0168] The concentration and content of A2β-casein characteristic peptide in each brand of milk are shown in Table 5.

[0169] Table 5 Characteristic peptides and contents of A2β-casein in milk of various brands

[0170]

[0171] Example 3: The A2β-casein content in ordinary milk is detected as follows.

[0172] (1) Pretreatment of samples to be tested:

[0173] A certain amount of commercially available ordinary milk samples were taken for centrifugal degreasing at 5000rpm, 100μL of milk sample was taken into a centrifuge tube, an equal volume of dithiothreitol DTT (final concentration 10mM) was added to the centrifuge tube, mixed and incubated at 56°C for 30min for reduction, cooled to room temperature, 100μL of iodoacetamide IAA (final concentration 60mM) was added to the centrifuge tube, and kept at room temperature in the dark for 30min for alkylation; the sample was added to an ultrafiltration tube (10kDa) and centrifuged at 14000×g for 15min to obtain a precipitate, and then 300μL of buffer was added to the ultrafiltration tube. Solution (8M urea dissolved in 100mMTris-HCl, pH8.5), centrifuged at 14000×g for 15min, and obtained a precipitate. This step was repeated twice. Subsequently, 200μL of 50mMNH4HCO3 solution was added to the ultrafiltration tube, and centrifuged at 14000×g for 15min to obtain a precipitate. This step was also repeated twice; then, 500μL of 1mg / mL trypsin solution was added to the ultrafiltration tube, and digested at 37°C overnight; finally, 10μL of 10% formic acid solution was added to terminate the digestion, and then the digested solution was collected by centrifugation at 14000×g for 15min and desalted using a C18 desalting column. After being spin-dried at room temperature, it was stored at -20°C until analysis. Redissolved with mobile phase A (one thousandth formic acid aqueous solution) to obtain the sample to be tested.

[0174] (2) Detection of characteristic peptide content in ordinary milk:

[0175] The sample obtained in step (1) is tested using high performance liquid chromatography-mass spectrometry to obtain the peak area.

[0176] (3) Calculation of A2β-casein content in ordinary milk:

[0177] Substitute the peak area obtained in step (2) into the characteristic equation of the standard curve obtained in step (4) of Example 1 to obtain the characteristic peptide concentration in the sample, and then substitute the characteristic peptide concentration into the content calculation formula to obtain the A2β-casein content in the sample.

[0178] The content calculation formula is Cx=na×M×N×10-10; where Cx is the A2β-casein content of the sample to be tested, and the unit is 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 multiple.

[0179] The concentration and content of A2β-casein characteristic peptide in different ordinary milks are shown in Table 6.

[0180] Table 6 Characteristic peptides and contents of A2β-casein in milk of various brands

[0181]

[0182] In a specific embodiment, the present invention provides a detection method and a detection system for A2β-casein characteristic peptides in raw bovine milk, screens and synthesizes bovine A2β-casein characteristic peptides, and uses high performance liquid chromatography-mass spectrometry to achieve quantitative detection of A2β-casein in bovine dairy products. The characteristic peptide and detection method provided by the present invention can accurately detect the A2β-casein content in bovine dairy products, and have high specificity, sensitivity, recovery rate and precision, which has far-reaching significance both for food safety and for market supervision.

[0183] In this embodiment, the characteristic of trypsin that specifically acts on arginine (R) and lysine (K) is utilized to select a characteristic peptide segment that belongs only to A2β-casein. The characteristic peptide segment with a purity greater than 95% is obtained through synthesis and purification and is applied to this detection method to ensure the accuracy of the method.

[0184] This embodiment uses a method of using characteristic peptides as external standards in combination with high performance liquid chromatography-mass spectrometry technology to effectively reduce the impact of problems such as loss during sample pretreatment and inaccurate high performance liquid chromatography detection.

[0185] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. 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 detecting characteristic peptides of A2β-casein in raw milk, characterized in that: include: The initial substrate concentration and the initial trypsin concentration are obtained, and the estimated enzymatic reaction time is obtained after processing; Monitor the enzymatic hydrolysis reaction condition data, comprehensively analyze and obtain the enzymatic hydrolysis reaction process deviation impact assessment value, and match the enzymatic hydrolysis reaction compensation time according to the enzymatic hydrolysis reaction process deviation impact assessment value; The enzymatic conversion profile data of the enzymatic hydrolysis reaction is monitored and processed to obtain the insufficient enzymatic hydrolysis evaluation index; the product degradation monitoring data of the enzymatic hydrolysis reaction is collected and processed to obtain the excessive enzymatic hydrolysis evaluation index; the actual enzymatic hydrolysis reaction time is obtained; and based on the expected enzymatic hydrolysis reaction time and the enzymatic hydrolysis reaction compensation time, a comprehensive analysis is performed to obtain the enzymatic hydrolysis reaction termination judgment index; The enzymatic hydrolysis reaction is controlled according to the enzymatic hydrolysis reaction termination judgment index.

2. The method for detecting the characteristic peptide of A2β-casein in raw milk according to claim 1, characterized in that: The treatment is performed to obtain the estimated enzymatic reaction time, and the specific process is as follows: Obtaining the initial substrate concentration and the initial trypsin concentration, and extracting the enzymatic reaction rate influencing factors corresponding to the preset initial substrate concentration and trypsin concentration from the enzymatic reaction database, and comprehensively analyzing to obtain the enzymatic reaction matching index; The enzymatic hydrolysis reaction matching index is compared with each enzymatic hydrolysis reaction matching index interval preset in the enzymatic hydrolysis reaction database, and the corresponding estimated enzymatic hydrolysis reaction time is obtained by matching.

3. The method for detecting the characteristic peptide of A2β-casein in raw milk according to claim 1, characterized in that: According to the evaluation value of the enzymatic hydrolysis reaction process deviation, the enzymatic hydrolysis reaction compensation time is matched, and the specific process is as follows: Comprehensively analyzing the enzymatic hydrolysis reaction condition data to obtain an enzymatic hydrolysis reaction process deviation impact assessment value, wherein the enzymatic hydrolysis reaction condition data includes a reaction environment pH value and a reaction environment temperature; The enzymatic hydrolysis reaction process deviation impact assessment value is compared with the compensation time corresponding to each enzymatic hydrolysis reaction process deviation impact assessment value interval stored in the enzymatic hydrolysis reaction database, and the enzymatic hydrolysis reaction compensation time is obtained by matching.

4. The method for detecting the characteristic peptide of A2β-casein in raw milk according to claim 1, characterized in that: The enzymatic insufficiency evaluation index is obtained by the treatment, and the specific process is as follows: Enzymatic conversion profile data of the enzymatic hydrolysis reaction, including substrate concentration and expected product concentration; Based on the substrate concentration and expected product concentration, the enzymatic insufficiency assessment index was obtained through comprehensive analysis.

5. The method for detecting the characteristic peptide of A2β-casein in raw milk according to claim 1, characterized in that: The enzymatic over-evaluation index is obtained by the treatment, and the specific process is as follows: Degradation monitoring data of the products of the enzymatic hydrolysis reaction, including the number of unexpected peptide species and the acid value of the solution; The over-enzyme hydrolysis evaluation index was obtained by comprehensive analysis based on the number of unexpected peptide species and the acid value of the solution.

6. The method for detecting the characteristic peptide of A2β-casein in raw milk according to claim 5, characterized in that: The comprehensive analysis obtains the enzyme hydrolysis reaction termination judgment index, and the specific analysis process is as follows: The estimated enzymatic reaction time and the enzymatic reaction compensation time are summed up, and the sum result is marked as the enzymatic reaction required time; 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; The enzymatic hydrolysis reaction reference index is compared with each enzymatic hydrolysis reaction termination judgment influencing factor corresponding to each enzymatic hydrolysis reaction reference index interval stored in the enzymatic hydrolysis reaction database, and the enzymatic hydrolysis termination judgment influencing factors corresponding to the enzymatic hydrolysis insufficient evaluation index and the enzymatic hydrolysis excessive evaluation index are matched; According to the insufficient enzymatic hydrolysis evaluation index and the excessive enzymatic hydrolysis evaluation index, the enzymatic hydrolysis reaction termination judgment index was obtained through comprehensive analysis.

7. The method for detecting characteristic peptide of A2β-casein in raw milk according to claim 6, characterized in that: The enzymatic reaction is controlled according to the enzymatic reaction termination judgment index, and the specific process is as follows: 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.

8. The method for detecting characteristic peptide of A2β-casein in raw milk according to claim 5, characterized in that: The specific numerical expression of the enzymatic over-evaluation index is: Wherein, νG represents the over-evaluation index of enzymatic hydrolysis, NT1 represents the number of unexpected peptide types, NT0 represents the critical number of unexpected peptide types, 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 peptide types, and φ2 represents the over-evaluation impact factor of enzymatic hydrolysis corresponding to the preset solution acid value.

9. The method for detecting characteristic peptide of A2β-casein in raw milk according to claim 6, characterized in that: The specific numerical expression of the enzyme hydrolysis reaction termination judgment index is: In the formula, δ represents the enzymatic hydrolysis reaction termination judgment index, νZ represents the enzymatic hydrolysis insufficient evaluation index, νG represents the enzymatic hydrolysis excessive evaluation index, τ1 represents the enzymatic hydrolysis reaction termination judgment influencing factor corresponding to the preset enzymatic hydrolysis insufficient evaluation index, and τ2 represents the enzymatic hydrolysis reaction termination judgment influencing factor corresponding to the preset enzymatic hydrolysis excessive evaluation index.

10. A system for detecting the characteristic peptide of A2β-casein in raw milk according to claims 1-9, characterized in that: include: The enzymatic reaction time estimation module is used to obtain the initial substrate concentration and the initial trypsin concentration, and obtain the estimated enzymatic reaction time after processing; The enzymatic reaction time compensation module is used to monitor the enzymatic reaction condition data, comprehensively analyze and obtain the evaluation value of the enzymatic reaction process deviation impact, and match the enzymatic reaction compensation time according to the evaluation value of the enzymatic reaction process deviation impact; The enzymatic hydrolysis reaction effect evaluation module is used to monitor the enzymatic conversion profile data of the enzymatic hydrolysis reaction, obtain the enzymatic hydrolysis deficiency evaluation index after processing, collect the product degradation monitoring data of the enzymatic hydrolysis reaction, obtain the enzymatic hydrolysis excess evaluation index after processing, obtain the actual enzymatic hydrolysis reaction time, and obtain the enzymatic hydrolysis reaction termination judgment index through comprehensive analysis based on the expected enzymatic hydrolysis reaction time and the enzymatic hydrolysis reaction compensation time; An enzymatic reaction termination control module is used to control the enzymatic reaction according to the enzymatic reaction termination judgment index; The enzymatic reaction database is used to store enzymatic reaction related data, including reference initial substrate concentration, reference trypsin concentration, critical solution acid value and other indicators. The data in the enzymatic reaction database is obtained by collecting data from multiple enzymatic reaction experiments.

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