Jinhua ham taste optimization method based on metabonomics and enzyme activity regulation and control technology

Abstract

The invention discloses a Jinhua ham taste optimization method based on metabonomics and an enzyme activity regulation and control technology. The method belongs to the field of meat quality control, and comprises the following optimization steps: carrying out sensory scoring on samples in different processing stages, and carrying out non-targeted analysis by using hydrophilic interaction liquid chromatography and MS / MS; analyzing and mining the metabonomics data; screening out differential metabolites in the processing process by applying a significance analysis p value, a difference multiple and a variable weight value; combining activity change results of cathepsin B and cathepsin L in the processing process; the activity of cathepsin B and cathepsin L is regulated and controlled by using a PF-DBO optimization algorithm, samples are collected and analyzed, and the influence of enzyme activity change and the change of the enzyme activity change on the content of the characteristic taste marker are evaluated. By precisely regulating and controlling the activity of cathepsin B and cathepsin L, the problem of complex relationship between taste substance change and cathepsin activity in the Jinhua ham processing process is solved; the control strategy enables the overall quality of the Jinhua ham to be significantly improved.

Description

Technical Field

[0001] The present invention belongs to the field of meat quality control and relates to a method for optimizing the flavor of Jinhua ham based on metabolomics and enzyme activity regulation technology; specifically, it relates to a method for regulating the activities of cathepsins B and L to improve the quality of Jinhua ham. Background Art

[0002] Jinhua ham is made primarily from free-range, locally raised "Liangtouwu" pigs. The production process is complex, involving multiple steps, including curing, air-drying, and fermentation. This lengthy process involves a series of biochemical reactions that ultimately create Jinhua ham's distinctive flavor and texture.

[0003] The flavoring substances of Jinhua ham include a variety of components, among which cathepsins B and L play a crucial role in flavor formation. They are primarily responsible for degrading muscle protein, producing small peptides and free amino acids that are the main components of ham flavor. In actual production, various processing parameters such as raw materials, processing temperature, sodium chloride content, and processing time have a significant impact on cathepsin activity. The selection of these parameters is based on long-term practical experience and accumulated traditional knowledge, and fine-tuning any one of them can have a significant impact on the quality of the final product. Given the complexity and constant variability of these factors, attempting to adjust them one by one would not only result in significant economic and human costs, but also may not achieve the desired results. Therefore, how to accurately adjust processing parameters to regulate the activity of cathepsins B and L is key to improving the quality and taste of Jinhua ham.

[0004] Metabolomics technology is a systematic analytical method that can reveal the differences in metabolite composition between different hams at the molecular level. The metabolomic characteristics of Jinhua ham at different processing stages also vary greatly. These changes are reflected in the aroma, taste and appearance of the ham. Using metabolomics technology, high-throughput metabolome analysis of dry-cured ham samples can be performed to identify key metabolites that affect the quality of ham. Compared with traditional sensory evaluation methods, metabolomics provides a more comprehensive and objective evaluation method, which helps to deeply understand the chemical changes and quality characteristics of ham production. Although many literatures have reported the changes in aroma components during the processing of Jinhua ham, there are relatively few studies on the changes in flavor substances during this processing. Summary of the Invention

[0005] To address these issues, the present invention proposes a method for optimizing the flavor of Jinhua ham based on metabolomics and enzyme activity regulation technology. This method addresses the complex challenge of understanding the relationship between changes in flavor compounds and the activity of cathepsins B and L during Jinhua ham processing. By screening for differential metabolites that effectively distinguish different stages of ham processing, the method further reveals the regulatory mechanisms by which cathepsins degrade or generate flavor compounds during ham production. This method provides a theoretical basis and technical support for improving the quality of Jinhua ham and promotes the optimization and innovative application of Jinhua ham production processes.

[0006] The technical solution of the present invention is: the method for optimizing the flavor of Jinhua ham based on metabolomics and enzyme activity regulation technology of the present invention comprises the following steps:

[0007] Step (1): Evaluators performed sensory scoring on Jinhua ham samples at different processing stages using hydrophilic interaction liquid chromatography and non-targeted MS / MS analysis;

[0008] Step (2): Analyze and mine the metabolomics data obtained in step (1), including statistical methods such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA);

[0009] Step (3): Apply significance analysis p-value, fold change (FC) and variable weight value (VIP) to screen out the differential metabolites in the processing of Jinhua ham, and use targeted metabolomics technology to detect the content of differential metabolites;

[0010] Step (4): Combined with the results of changes in cathepsin B and L activity during Jinhua ham processing, correlation analysis was performed with metabolites, especially key flavor compounds in ham, including total free amino acid content, total peptide content, total organic acid content, and total metabolite content. The differential metabolites in the samples were correlated with the levels of cathepsin B and L respectively;

[0011] Step (5): Utilize the PF-DBO optimization algorithm to regulate the activities of cathepsins B and L, adjust the process parameters of Jinhua ham, collect and analyze samples, detect changes in the activities of cathepsins B and L and the content of characteristic flavor markers in the samples, and evaluate the effects of different process parameters on changes in enzyme activity and their effects on changes in the content of characteristic flavor markers.

[0012] Furthermore, in step (1), the evaluator follows professional standards and scoring rules, and combines his or her own sensory experience and professional knowledge to comprehensively evaluate the ham and give corresponding scores and opinions. The hydrophilic interaction liquid chromatography selects a HILIC column suitable for separating polar metabolites, adds ammonium acetate and adjusts the pH value, and injects the sample into the liquid chromatography system. The MS / MS non-targeted analysis performs mass spectrometry detection on the separated metabolites and collects primary and secondary mass spectrometry data. The collected mass spectrometry data is matched with the database to perform qualitative and quantitative analysis of the metabolites.

[0013] Furthermore, in step (2), the metabolomics data is baseline corrected, peak detected, and normalized, and the PCA is visualized in the form of a scatter plot, a two-dimensional plot, etc. In the PLS-DA model, the expression level of the metabolite is used as the independent variable (X) and the processing stage is used as the dependent variable (Y).

[0014] Furthermore, in step (3), the chi-value standard used in the differential metabolite screening is that the P value (P-value) of the one-way analysis of variance (ANOVA) is less than 0.05; for each group of sample comparisons, the Euclidean distance matrix (EDM) of the quantitative values ​​of the differential metabolites is calculated, and the differential metabolites are clustered using the complete linkage method.

[0015] Furthermore, in step (4), the correlation analysis selects the Pearson correlation coefficient for analysis and controls the variables.

[0016] Furthermore, in step (5), the PF-DBO optimization algorithm is implemented as follows:

[0017] S1. Establish a quantitative indicator reflecting the quality of Jinhua ham, providing a clear direction for subsequent process parameter optimization. This objective function will become the basis for optimizing process parameters using the PF-DBO optimization algorithm. It reflects the quality changes of Jinhua ham at different processing stages and can guide the adjustment of process parameters to optimize the overall quality of the ham.

[0018] S2. PF algorithm is used to optimize the DBO optimization algorithm. The DBO algorithm is selected to optimize the activities of cathepsin B and L in the processing of Jinhua ham. This algorithm has a fast convergence speed and high solution accuracy, and can effectively handle nonlinear and multivariable optimization problems.

[0019] By simulating the natural behavior of dung beetles, the DBO algorithm is able to strike a balance between global search and local exploitation, which is crucial for finding the optimal enzyme activity level to improve ham quality;

[0020] S3. By applying the optimized DBO algorithm, the optimal parameters in the processing of Jinhua ham can be calculated, such as the optimal activity range of cathepsins B and L, the appropriate processing temperature and time, etc.

[0021] These parameters will be used to adjust the actual processing technology to ensure that the flavor of the ham is improved while reducing the salt content;

[0022] In this way, not only can the market competitiveness of Jinhua ham be improved, but also consumers can be provided with healthier and more delicious food choices.

[0023] Furthermore, the objective function of step S1 is as follows:

[0024]

[0025] Where x represents the vector of process parameters, such as temperature, humidity, time, etc.; Q(x) is the taste quality score of Jinhua ham, obtained through sensory evaluation, which is the part that the objective function hopes to maximize; E B (x) and E L (x) represents the activities of cathepsin B and L under given process parameters; E B * and E L * Represent the ideal activity values ​​of cathepsin B and L, respectively. B max and E L max represents the maximum activity of cathepsin B and L within the feasible process parameter range; C(x) represents the cost function of the process parameters, reflecting the cost consumption during the production process; D(x) represents the feasibility function of the process parameters, which is used to ensure that the selected parameters are feasible in actual production, such as avoiding extreme conditions. w1, w2, w3, w4, and w5 are the weights of each component in the objective function and need to be adjusted according to actual conditions.

[0026] Furthermore, the step S2 specifically includes:

[0027] In the late iteration, if the individual at the global best position falls into a local optimum, it is easy to guide other individuals to search in the direction of the local optimum. In the Pathfinder Algorithm (PFA), the pathfinder is in a multi-directional update state in the late iteration to avoid falling into the local optimum. Inspired by this, in the dung beetle stealing stage, the global best position is updated generation by generation to prevent the individual at that position from falling into the local optimum too early. At the same time, the influence of the average position individual on other individuals is taken into account, making the algorithm's search direction more comprehensive.

[0028] S2.1: Select the global optimal position xb Individuals act as pathfinders, simulating their behavior in the process of searching for and optimizing the activity of cathepsin B and L through the pathfinder position update method in PFA. Their positions are updated only when the taste quality of the ham is better than that of the current generation. This mechanism ensures that the algorithm continuously approaches a better solution during iteration. The moving step factor introduces randomness, allowing the algorithm to flexibly explore in the solution space. The random vector determines the multidirectionality of the pathfinder's movement, further enhancing the algorithm's search capability. The pathfinder's position is updated using the pathfinder position update method in PFA. When the fitness value of the new generation of pathfinders is lower than that of the current generation, the pathfinder's position is updated. To update the pathfinder's position:

[0029] x b (t+1)=x b (t)+2m(x b (t)-x b (t-1))+S

[0030]

[0031] Where x b (t-1)), x b (t) and x b (t+1) represents the position of the dung beetle at the t-1, t, and t+1 generations respectively; m is the step size factor of the dung beetle, which is a random number in [0,1]; u is a 1×d random vector, whose elements are all random numbers in [-1,1]; S is the step size of the dung beetle, and its multidirectionality is determined by the value of u;

[0032] S2.2: Calculate the average position x of dung beetles m ,By randomly choosing whether to adopt this useful information with a probability of 0.5, the algorithm can explore new possibilities while maintaining the current optimal solution;

[0033] The combination of constant values ​​and random vectors provides randomness for the position updates of the thieving dung beetles, which helps the algorithm escape from the local optimal solution and find a better combination of ham processing parameters.

[0034] Based on this position and the position of the pathfinder dung beetle, a new method for updating the position of the dung beetle individual is defined, and it occurs with a probability of 0.5. Randomly choose whether to use this useful information for extraction to balance optimality and stability. Change the individual update method of the dung beetle stealing stage to:

[0035]

[0036] Where x i(t) represents the position of the i-th thieving dung beetle individual in generation t; R is a constant value, which is set to 0.5; h represents an independent random vector of size 1×d;

[0037] S2.3: The goals of optimizing cathepsin B and L activities and flavor production by adjusting process parameters are closely linked;

[0038] By simulating the natural behavior of dung beetles, we can find more refined combinations of ham processing parameters to further improve the quality of ham. During the egg-laying stage, a micro-adjustment strategy for the egg ball position is proposed. Based on the female dung beetle's egg-laying area and the local optimal position of the individual dung beetle, the position update formula of the egg ball leader is given as follows:

[0039]

[0040] Where, AB i (t) represents the position of the oval leader after the oval position fine-tuning strategy is updated, p1 represents the update probability;

[0041] During the feeding stage of dung beetles, a micro-adjustment strategy for their positions is proposed.

[0042] Based on the optimal foraging area and global optimal position of the dung beetle individual, the position update of the small dung beetle is given as follows:

[0043]

[0044] Where Ax i (t) represents the position of the leader of the dung beetles after the dung beetle position fine-tuning strategy is updated; p2 represents the update probability;

[0045] S2.4: By flexibly adjusting the position of the leader individual, the algorithm can more efficiently explore the solution space and find more optimal ham processing parameters;

[0046] By fine-tuning the position of dynamic boundary individuals, the contemporary leader individuals can be updated and the overlap of individuals in the dynamic boundary area can be reduced, thereby effectively improving the optimization performance of the algorithm. The individual update formula is as follows:

[0047] B i (t+1)=X * +b1×(AB i (t)-Lb * )+b2×(AB i (t)-Ub * )

[0048] x i (t+1)=Ax i (t)+C1×(Ax i (t)-Lbb )+C2×(Ax i (t)-Ub b ).

[0049] Furthermore, the step S3 specifically includes: the whole process of the dung beetle optimization algorithm (DBO) is divided into four stages, namely the dung beetle ball rolling stage, the egg laying stage, the small dung beetle feeding stage and the dung beetle stealing stage;

[0050] S3.1: When rolling their dung balls, dung beetles primarily rely on the sun for navigation, with a probability of 0.9. The intensity of sunlight indirectly affects the path of the dung beetle. α, a natural coefficient, determines whether the dung beetle deviates from its original direction, simulating the indirect effect of sunlight intensity on the dung beetle's path.

[0051] When α = -1, it means that the dung beetle individual has deviated from the original direction, which helps the algorithm explore new areas in the solution space. When α = 1, it means there is no deviation, maintaining the stability of the algorithm.

[0052] At the same time, by introducing the deflection coefficient and light intensity simulation parameters, the algorithm can more realistically simulate the behavior of dung beetles in their natural environment, thereby finding more optimal ham processing parameters;

[0053] During the ball rolling process, the position update of the dung beetle individual is expressed as follows:

[0054] x i (t+1)=x i (t)+α·v·x i (t-1)+n·Δx

[0055] Where t represents the current number of iterations, x i (t) represents the position of the dung beetle individual in the t generation, which is called the leader; α is a natural coefficient with a value of -1 or 1. When α = -1, it means that the dung beetle individual deviates from the original rolling direction, and when α = 1, it means there is no deviation. i (t) is the selection strategy; parameter v∈(0,0.2] represents the deflection coefficient, parameter n∈(0,1); Δx=|x i (t)-x w |, used to simulate the change of light intensity. The larger the value, z, the weaker the light source. w Indicates the global worst position of the current dung beetle individual;

[0056] When a dung beetle encounters an obstacle and cannot move forward during the ball rolling process, it will use a dance behavior to obtain a new ball rolling path. The probability of this happening is δ 0.1;

[0057] This helps the algorithm to break out of the dilemma when encountering a local optimal solution or obstacle and continue to approach the global optimal solution. The new direction of the rolling ball path will be determined by the value of the tangent function of the domain. At this time, the position update of the dung beetle individual is expressed as follows:

[0058] x i (t+1)=x i (t)+tanθ·|x i (t)-x i (t-1)|

[0059] Where θ∈[0,π] represents the deflection angle. When θ is 0, π / 2, or π, the position of the dung beetle individual will not be updated.

[0060] S3.2: To provide a safe habitat for offspring, dung beetles roll dung balls to a safe location for hiding. Female dung beetles then use that location as their egg-laying site to raise their offspring. By distributing eggs around the current, or local, optimal location and randomly selecting one for egg-laying, the algorithm can maintain the current optimal solution while exploring potential, better solutions around it. This mechanism helps the algorithm find more refined combinations of ham processing parameters in the solution space, further improving ham quality. Many eggs are distributed around the local optimal location, and each female dung beetle will choose only one egg to lay. The egg position is updated as follows:

[0061] B i (t+1)=x * +b1×(B i (t)-Lb * )+b2×(B i (t)-Ub * )

[0062] Where, Lb * and Ub * Represent the lower and upper boundaries of the female dung beetle's egg-laying area x * represents the local optimal position of the current dung beetle individual, where B i (t) represents the position of the i-th egg in the t-th generation, which is called the leader; b1 and b2 are two random numbers belonging to (0,1);

[0063] S3.3: By randomly updating positions within a subset of the solution space, the algorithm can explore more possibilities and discover new combinations of ham processing parameters. This mechanism helps the algorithm approach the global optimal solution while maintaining diversity. The position update method of the dung beetle individual is expressed as follows:

[0064] x i (t+1)=x i (t)+C1×(xi (t)-L b )+C2×(x i (t)-U b )

[0065] Where, L b and U b They represent the lower and upper boundaries of the optimal foraging area of ​​the dung beetle; x i (t) represents the position of the i-th dung beetle individual in generation t; C1 and C2 are two random numbers belonging to (0,1);

[0066] S3.4: By introducing the constant value R and the random vector h, the algorithm can introduce new information while maintaining the stability of the current solution, which helps to find a better combination of ham processing parameters. This mechanism enhances the algorithm's search ability, allowing the algorithm to flexibly explore the solution space and find a better solution. Some dung beetles will steal the dung balls of other dung beetles. These dung beetles are called thieving dung beetles. The position update method of thieving dung beetles is expressed as follows:

[0067] x i (t+1)=X b +R·h×(|x i (t)-x * |+|x i (t)-x b |)

[0068] Where x i (t) represents the position of the i-th thieving dung beetle individual in generation t; R is a constant value, which is set to 0.5; h represents an independent random vector of size 1×d.

[0069] Metabolomics technology is a systematic analytical method that can reveal differences in metabolite composition between different hams at the molecular level. The metabolomic characteristics of Jinhua ham at different processing stages also vary significantly, and these changes are reflected in the aroma, taste, and appearance of the ham. Using metabolomics technology, high-throughput metabolome analysis of dry-cured ham samples can be performed to identify key metabolites that affect ham quality. Compared with traditional sensory evaluation methods, metabolomics provides a more comprehensive and objective evaluation method, which helps to deeply understand the chemical changes and quality characteristics during the ham production process.

[0070] The present invention has the following beneficial effects: It discloses a method for optimizing the flavor of Jinhua ham based on metabolomics and enzyme activity regulation. By precisely regulating the activities of cathepsins B and L, the invention effectively addresses the complex relationship between changes in flavor substances and cathepsin activity during modern Jinhua ham processing. This regulation strategy significantly improves the overall quality of Jinhua ham, resulting in a richer flavor and a more refined texture, meeting consumers' expectations for high-quality Jinhua ham. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is an operational flow chart of the present invention;

[0072] Figure 2 It is a flow chart of the algorithm of the present invention. DETAILED DESCRIPTION

[0073] The specific technical solutions of the present invention are further described in detail below with reference to specific examples.

[0074] As shown in the figure, the present invention provides a method for optimizing the taste of Jinhua ham based on metabolomics and enzyme activity regulation technology; evaluators perform sensory scoring on Jinhua ham samples at different processing stages, and use hydrophilic interaction liquid chromatography and MS / MS non-targeted analysis to detect metabolites in the Jinhua ham processing process. The obtained metabolomics data are analyzed and mined, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), to determine the chemical feature differences between Jinhua ham samples at different processing stages, and significance analysis p value, fold change (FC) and variable weight value (VLI) are used to analyze the chemical feature differences between Jinhua ham samples at different processing stages. A targeted metabolomics (VIP) project (VIP) was used to screen for differential metabolites during Jinhua ham processing. Targeted metabolomics techniques were used to detect differential metabolite levels to further identify key substances that could serve as quality markers for the flavor of Jinhua ham. Combined with the results of changes in cathepsin B and L activity during Jinhua ham processing, correlation analysis was conducted with metabolites, specifically key flavor compounds in the ham, including total free amino acid content, total peptide content, total organic acid content, and total metabolite content. Correlation analysis was performed between differential metabolites in the samples and cathepsin B and L levels, identifying flavor compounds significantly correlated with changes in protease activity. Processing parameters for Jinhua ham were adjusted, and samples were collected and analyzed to detect changes in cathepsin B and L activity and the content of characteristic flavor markers. The effects of different process parameters on changes in enzyme activity and the resulting changes in the content of characteristic flavor markers were evaluated to verify the effectiveness of the proposed method for regulating protease activity.

[0075] Furthermore, to systematically assess its sensory quality, a professional team of experienced sensory evaluators conducted a detailed sensory analysis of the ham samples. This analysis covered key sensory attributes such as flavor, mouthfeel, and color, aiming to quantify and record the quality characteristics of the ham at different processing stages.

[0076] By using standardized sensory evaluation methods and tools, a series of objective sensory data can be collected. These data not only provide an intuitive evaluation of the quality of the ham, but also lay a solid data foundation for subsequent process improvements and quality control.

[0077] Furthermore, LC-MS / MS technology can identify and quantify various metabolites in Jinhua ham, including amino acids, organic acids, lipids, and other small molecules, thereby gaining a deeper understanding of the metabolic mechanisms underlying the flavor formation of Jinhua ham. It can also accurately monitor key metabolites during the processing of Jinhua ham, providing a scientific basis for regulating the activity of cathepsins B and L, and thus improving the quality of Jinhua ham.

[0078] Furthermore, through PCA, high-dimensional metabolite data can be transformed into several main components that can explain most of the variability in the data;

[0079] PLS-DA was able to distinguish ham samples at different processing stages and identify those metabolites that had a significant impact on classification;

[0080] Differential metabolite screening further applied the criteria for differential metabolite screening, such as significance analysis p-value, difference fold and variable weight value, to screen out metabolites that play a key role in the processing of Jinhua ham.

[0081] Furthermore, combined with the results of changes in the activity of cathepsins B and L during the processing of Jinhua ham, correlation analysis was performed with metabolites; in particular, the key flavor compounds in ham, such as total free amino acid content, total peptide content, total organic acid content and total metabolite content, and the correlation analysis of these compounds with the levels of cathepsins B and L revealed the important regulatory role of cathepsins in the degradation or generation of metabolites during ham production.

[0082] Furthermore, the PF-DBO optimization algorithm was used to regulate the activities of cathepsins B and L through an intelligent algorithm control module to improve the quality of Jinhua ham.

[0083] The PF-DBO optimization algorithm is implemented as follows:

[0084] S1. Establish a quantitative indicator reflecting the quality of Jinhua ham, providing a clear direction for subsequent process parameter optimization. This objective function will become the basis for optimizing process parameters using the PF-DBO optimization algorithm. It reflects the quality changes of Jinhua ham at different processing stages and can guide the adjustment of process parameters to optimize the overall quality of the ham.

[0085] S2. PF algorithm is used to optimize the DBO optimization algorithm. The DBO algorithm is selected to optimize the activities of cathepsin B and L in the processing of Jinhua ham. This algorithm has a fast convergence speed and high solution accuracy, and can effectively handle nonlinear and multivariable optimization problems.

[0086] By simulating the natural behavior of dung beetles, the DBO algorithm is able to strike a balance between global search and local exploitation, which is crucial for finding the optimal enzyme activity level to improve ham quality;

[0087] S3. By applying the optimized DBO algorithm, we can calculate the optimal parameters for Jinhua ham processing, such as the optimal activity range of cathepsins B and L, the appropriate processing temperature and time, etc. These parameters will be used to adjust the actual processing technology to ensure that the flavor of the ham is improved while reducing the salt content. In this way, we can not only enhance the market competitiveness of Jinhua ham, but also provide consumers with healthier and more delicious food choices.

[0088] Furthermore, the objective function of step S1 is as follows:

[0089]

[0090] Where x represents the vector of process parameters, such as temperature, humidity, time, etc.; Q(x) is the taste quality score of Jinhua ham, obtained through sensory evaluation, which is the part that the objective function hopes to maximize; E B (x) and E L (x) represents the activities of cathepsin B and L under given process parameters; E B * and E L * represent the ideal activity values ​​of cathepsin B and L, respectively; E B max and E L max They represent the maximum activity values ​​of cathepsin B and L within the feasible process parameter range respectively; C(x) represents the cost function of the process parameters, reflecting the cost consumption in the production process; D(x) represents the feasibility function of the process parameters, which is used to ensure that the selected parameters are feasible in actual production, such as avoiding extreme conditions; w1, w2, w3, w4, and w5 are the weights of each part in the objective function, which need to be adjusted according to actual conditions.

[0091] Furthermore, the step S2 specifically includes:

[0092] In the late iteration, if the individual at the global best position falls into a local optimum, it is easy to guide other individuals to search in the direction of the local optimum. In the Pathfinder Algorithm (PFA), the pathfinder is in a multi-directional update state in the late iteration to avoid falling into the local optimum. Inspired by this, in the dung beetle stealing stage, the present invention updates the global best position generation by generation to prevent the individual at that position from falling into the local optimum too early. At the same time, it also considers the influence of the average position individual on other individuals, making the algorithm's search direction more comprehensive.

[0093] S2.1: Select the global optimal position x b Individuals act as pathfinder dung beetles, simulating the behavior of searching for and optimizing the activities of cathepsins B and L through the pathfinder position update method in PFA;

[0094] The position of the ham is updated only when the flavor quality of the current ham is better than the current one. This mechanism ensures that the algorithm continuously approaches a better solution during the iteration process. The step size factor introduces randomness, allowing the algorithm to flexibly explore the solution space. The random vector determines the multi-directional movement of the pathfinder, further enhancing the algorithm's search capabilities.

[0095] The position of the pathfinder dung beetle is updated in the same way as the pathfinder position update in PFA. The position of the pathfinder dung beetle will be updated only when the fitness value of the new generation of pathfinder dung beetles is lower than the fitness value of the current generation of pathfinder dung beetles.

[0096] Updated Pathfinder Dung Beetle location:

[0097] x b (t+1)=x b (t)+2m(x b (t)-x b (t-1))+S

[0098]

[0099] Where x b (t-1)), x b (t) and x b (t+1) represents the position of the dung beetle at the t-1, t, and t+1 generations respectively; m is the step size factor of the dung beetle, which is a random number in [0,1]; u is a 1×d random vector, whose elements are all random numbers in [-1,1]; S is the step size of the dung beetle, and its multidirectionality is determined by the value of u;

[0100] S2.2: Calculate the average position x of dung beetles m By randomly choosing whether to adopt this useful information with a probability of 0.5, the algorithm can explore new possibilities while maintaining the current optimal solution. The combination of constant values ​​and random vectors provides randomness for the position updates of the stealing dung beetles, helping the algorithm to escape the local optimal solution and discover a better combination of ham processing parameters.

[0101] Based on this position and the position of the pathfinder dung beetle, a new method for updating the position of the dung beetle individual is defined, and it occurs with a probability of 0.5. Randomly choose whether to use this useful information for extraction to balance optimality and stability. Change the individual update method of the dung beetle stealing stage to:

[0102]

[0103] Where x i (t) represents the position of the i-th thieving dung beetle individual in generation t; R is a constant value, which is set to 0.5; h represents an independent random vector of size 1×d;

[0104] S2.3: The goals of optimizing cathepsin B and L activities and flavor production by adjusting process parameters are closely linked;

[0105] By simulating the natural behavior of dung beetles, we can find more refined combinations of ham processing parameters to further improve the quality of ham;

[0106] During the egg-laying stage, a micro-adjustment strategy for the egg ball position is proposed. Based on the female dung beetle's egg-laying area and the local optimal position of the individual dung beetle, the egg ball leader position update formula is given as follows:

[0107]

[0108] Where, AB i (t) represents the position of the oval leader after the oval position fine-tuning strategy is updated, p1 represents the update probability;

[0109] During the feeding stage of the dung beetle, a micro-adjustment strategy for the dung beetle's position is proposed. Based on the optimal foraging area and global optimal position of the dung beetle individual, the position update formula of the dung beetle is given as follows:

[0110]

[0111] Where Ax i (t) represents the position of the leader of the dung beetles after the dung beetle position fine-tuning strategy is updated; p2 represents the update probability;

[0112] S2.4: By flexibly adjusting the position of the leader individual, the algorithm can more efficiently explore the solution space and find more optimal ham processing parameters; by fine-tuning the position of the dynamic boundary individual, the algorithm can adjust and update the contemporary leader individual, which can reduce the overlap of individuals in the dynamic boundary area and effectively improve the algorithm's optimization performance. The individual update is as follows:

[0113] B i (t+1)=X * +b1×(AB i (t)-Lb * )+b2×(AB i (t)-Ub * )

[0114] x i (t+1)=Ax i (t)+C1×(Ax i (t)-Lbb )+C2×(Ax i (t)-Ub b ).

[0115] Furthermore, the step S3 specifically includes: the whole process of the dung beetle optimization algorithm (DBO) is divided into four stages, namely the dung beetle ball rolling stage, the egg laying stage, the small dung beetle feeding stage and the dung beetle stealing stage;

[0116] S3.1: When rolling their dung balls, dung beetles primarily rely on the sun for navigation, with a probability of 0.9. The intensity of sunlight indirectly affects the path of the dung beetle. α, a natural coefficient, determines whether the dung beetle deviates from its original direction, simulating the indirect effect of sunlight intensity on the dung beetle's path.

[0117] When α = -1, it means that the dung beetle individual has deviated from the original direction, which helps the algorithm explore new areas in the solution space;

[0118] When α = 1, it means there is no deviation and the stability of the algorithm is maintained;

[0119] At the same time, by introducing the deflection coefficient and light intensity simulation parameters, the algorithm can more realistically simulate the behavior of dung beetles in their natural environment, thereby finding more optimal ham processing parameters;

[0120] During the ball rolling process, the position update of the dung beetle individual is expressed as follows:

[0121] xi(t+1)=xi(t)+α·v·xi(t-1)+n·Δx

[0122] Where t represents the current number of iterations, x i (t) represents the position of the dung beetle individual in the t generation, which is called the leader; α is a natural coefficient with a value of -1 or 1. When α = -1, it means that the dung beetle individual deviates from the original rolling direction, and when α = 1, it means there is no deviation. i (t) is the selection strategy; parameter v∈(0,0.2] represents the deflection coefficient, parameter n∈(0,1); Δx=|x i (t)-x w |, used to simulate the change of light intensity. The larger the value, z, the weaker the light source. w Indicates the global worst position of the current dung beetle individual;

[0123] When a dung beetle encounters an obstacle and cannot move forward during the ball rolling process, it will use a dance behavior to obtain a new ball rolling path. The probability of this happening is δ 0.1;

[0124] This helps the algorithm to break out of the dilemma when encountering a local optimal solution or obstacle and continue to approach the global optimal solution. The new direction of the rolling ball path will be determined by the value of the tangent function of the domain. At this time, the position update of the dung beetle individual is expressed as follows:

[0125] x i (t+1)=x i (t)+tanθ·|x i (t)-x i (t-1)|

[0126] Where θ∈[0,π] represents the deflection angle. When θ is 0, π / 2, or π, the position of the dung beetle individual will not be updated.

[0127] S3.2: To provide a safe habitat for offspring, dung beetles roll the dung balls to a safe location for hiding. Female dung beetles then use that location as a place to lay eggs and raise their offspring. By distributing the eggs around the current, or local, optimal location and randomly selecting one for egg laying, the algorithm can maintain the current optimal solution while exploring potential, better solutions around it.

[0128] This mechanism helps the algorithm find more refined combinations of ham processing parameters in the solution space, further improving the quality of the ham. There are many egg balls distributed around the local optimal position. Each female dung beetle will only choose one egg ball to lay eggs. The position of the egg ball is updated as follows:

[0129] B i (t+1)=x * +b1×(B i (t)-Lb * )+b2×(B i (t)-Ub * )

[0130] Where, Lb * and Ub * Represent the lower and upper boundaries of the female dung beetle's egg-laying area x * represents the local optimal position of the current dung beetle individual, where B i (t) represents the position of the i-th egg in the t-th generation, which is called the leader; b1 and b2 are two random numbers belonging to (0,1);

[0131] S3.3: By randomly updating positions within a subset of the solution space, the algorithm can explore more possibilities and discover new combinations of ham processing parameters. This mechanism helps the algorithm approach the global optimal solution while maintaining diversity. The position update method of the dung beetle individual is expressed as follows:

[0132] x i (t+1)=xi (t)+C1×(x i (t)-L b )+C2×(x i (t)-U b )

[0133] Where, L b and U b Respectively represent the lower and upper boundaries of the optimal foraging area of ​​the dung beetle. i (t) represents the position of the i-th dung beetle individual in generation t; C1 and C2 are two random numbers belonging to (0,1);

[0134] S3.4: By introducing the constant value R and the random vector h, the algorithm can introduce new information while maintaining the stability of the current solution, which helps to find a better combination of ham processing parameters;

[0135] This mechanism enhances the algorithm's search capability, enabling it to flexibly explore the solution space and find better solutions;

[0136] Some dung beetles will steal the dung balls of other dung beetles. These dung beetles are called thieving dung beetles. The position update method of thieving dung beetles is as follows:

[0137] x i (t+1)=X b +R·h×(|x i (t)-x * |+|x i (t)-x b |)

[0138] Where x i (t) represents the position of the i-th thieving dung beetle individual in generation t; R is a constant value, which is set to 0.5; h represents an independent random vector of size 1×d.

[0139] The present invention adopts a series of scientific methods, including sensory evaluation, LC-MS / MS metabolomics analysis, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), to conduct an in-depth study on the changes of metabolites in the processing of Jinhua ham; the differential metabolites screened out by significance analysis p value not only effectively distinguish the different stages of Jinhua ham processing, but also reveal the strong negative correlation between the activity of cathepsin B and L and the changes in flavor compounds; this discovery provides a new perspective and theoretical basis for understanding the mechanism of flavor formation in Jinhua ham; the present invention uses the PF-DBO optimization algorithm to accurately regulate the activity of cathepsin B and L, and this regulation method has been used in Jinhua ham. The process of processing is particularly critical; by adjusting the process parameters, the present invention not only optimizes the activity of tissue proteases, but also promotes the generation and accumulation of flavor substances, thereby improving the quality of Jinhua ham; this process optimization strategy provides a more scientific and efficient method for the production of Jinhua ham; the innovation of the present invention lies in combining metabolomics with enzyme activity regulation technology and applying it to the quality improvement of Jinhua ham; this innovative application not only provides strong technical support for the production of Jinhua ham, but also provides ideas and methods that can be used as reference for the quality improvement and process optimization of other traditional foods; in addition, the results of the present invention can also be expanded to multiple fields such as food science, nutrition, biology, etc., and has broad application prospects.

[0140] The Jinhua ham quality improvement strategy based on metabolomics and enzyme activity regulation technology revealed in the present invention has demonstrated significant beneficial effects in quality improvement, correlation analysis, process optimization and innovative application, providing an important theoretical basis and strong technical support for the production and quality improvement of Jinhua ham.

Claims

1. A method for optimizing the flavor of Jinhua ham based on metabolomics and enzyme activity regulation technology, characterized in that: The operation steps are as follows: Step (1): The evaluators performed sensory scoring on Jinhua ham samples at different processing stages, and obtained metabolomics data using hydrophilic interaction liquid chromatography and MS / MS non-targeted analysis; Step (2): Analyze and mine the metabolomics data obtained in step (1); Step (3): using significance analysis p value, difference multiple and variable weight value to screen out the differential metabolites in the processing of Jinhua ham, and using targeted metabolomics technology to detect the content of differential metabolites; Step (4): combining the results of changes in the activities of cathepsin B and L during the processing of Jinhua ham, correlation analysis was performed with metabolites, especially key flavor compounds in ham; The differential metabolites in the samples were correlated with the levels of cathepsin B and L; Step (5): Utilize the PF-DBO optimization algorithm to regulate the activities of cathepsins B and L, adjust the process parameters of Jinhua ham, collect and analyze samples, detect changes in the activities of cathepsins B and L and the content of characteristic taste markers in the samples, and evaluate the effects of different process parameters on changes in enzyme activity and changes in the content of characteristic taste markers.

2. The method for optimizing the flavor of Jinhua ham based on metabolomics and enzyme activity regulation technology according to claim 1, characterized in that: In step (1), the evaluator performs sensory scoring on the Jinhua ham samples at different processing stages. Specifically, the evaluator follows professional standards and scoring rules, and combines his or her own sensory experience and professional knowledge to conduct a comprehensive evaluation of the ham and give corresponding scores and opinions; The hydrophilic interaction liquid chromatography selects a HILIC column suitable for separating polar metabolites, adds ammonium acetate and adjusts the pH value, and injects the sample into the liquid chromatography system; The MS / MS non-targeted analysis performs mass spectrometry detection on the separated metabolites, collects primary and secondary mass spectrometry data; matches the collected mass spectrometry data with a database, and performs qualitative and quantitative analysis of the metabolites.

3. The method for optimizing the flavor of Jinhua ham based on metabolomics and enzyme activity regulation technology according to claim 1, characterized in that: In step (2), the analysis and mining methods include statistical methods of principal component analysis (PCA) and PLS-DA; The analysis and mining of the obtained metabolomics data specifically includes: performing baseline correction, peak detection, and normalization on the metabolomics data; The PCA is visualized in the form of a scatter plot, a two-dimensional graph, etc.; In the described PLS-DA model, the expression level of metabolites was used as the independent variable and the processing stage as the dependent variable.

4. The method for optimizing the flavor of Jinhua ham based on metabolomics and enzyme activity regulation technology according to claim 1, characterized in that: In step (3), the chi-square value standard used in the differential metabolite screening is that the P value of ANOVA is less than 0.05; For each group of sample comparisons, the EDM of the quantitative values ​​of differential metabolites was calculated, and the differential metabolites were clustered using the complete linkage method.

5. The method for optimizing the flavor of Jinhua ham based on metabolomics and enzyme activity regulation technology according to claim 1, characterized in that: In step (4), the correlation analysis selects the Pearson correlation coefficient for analysis and controls its variables.

6. The method for optimizing the flavor of Jinhua ham based on metabolomics and enzyme activity regulation technology according to claim 1, characterized in that: In step (4), the compound includes total free amino acid content, total peptide content, total organic acid content and total metabolite content.

7. The method for optimizing the flavor of Jinhua ham based on metabolomics and enzyme activity regulation technology according to claim 1, characterized in that: In step (5), the implementation process of the PF-DBO optimization algorithm is as follows: S1. Establish a quantitative index that reflects the quality of Jinhua ham, which provides a clear direction for the subsequent optimization of process parameters. This objective function will become the basis for the PF-DBO optimization algorithm to optimize process parameters, reflecting the quality changes of Jinhua ham at different processing stages, and can guide the adjustment of process parameters to optimize the overall quality of ham. S2, using PF algorithm to optimize DBO optimization algorithm, select DBO algorithm to optimize cathepsin B and L activities in Jinhua ham processing, and deal with nonlinear and multivariable optimization problems; The DBO algorithm achieves a balance between global search and local exploitation by simulating the natural behavior of dung beetles; S3. By applying the optimized DBO algorithm, the optimal parameters in the processing of Jinhua ham are calculated. The above parameters will be used to adjust the actual processing technology to ensure that the flavor of the ham is improved while reducing the salt content.

8. The method for optimizing the flavor of Jinhua ham based on metabolomics and enzyme activity regulation technology according to claim 1, characterized in that: The objective function of step S1 is as follows: Where x represents the vector of process parameters; Q(x) is the taste quality score of Jinhua ham, obtained through sensory evaluation, which is the part that the objective function hopes to maximize; E B (x) and E L (x) represents the activities of cathepsin B and L under given process parameters; E B * and E L * Represent the ideal activity values ​​of cathepsin B and L, respectively; E B max and E L max They represent the maximum activity values ​​of cathepsin B and L within the feasible process parameter range, respectively; C(x) represents the cost function of process parameters, reflecting the cost consumption in the production process; D(x) represents the feasibility function of the process parameters, which is used to ensure that the selected parameters are feasible in actual production; w1, w2, w3, w4, w5 are the weights of each part in the objective function and need to be adjusted according to the actual situation.

9. The method for optimizing the flavor of Jinhua ham based on metabolomics and enzyme activity regulation technology according to claim 1, characterized in that: The step S2 specifically comprises: In the later stage of iteration, if the individual with the global best position falls into the local optimum, it is easy to guide other individuals to search in the direction of the local optimum; In PFA, the pathfinder is in a multi-directional update state in the late iteration to avoid falling into the local optimum; Inspired by this, during the dung beetle stealing stage, the global best position can be updated generation by generation to prevent the individual at that position from falling into the local optimum too early. At the same time, the influence of the average position individual on other individuals is considered to make the search direction of the algorithm more comprehensive. The details are as follows: S2.1: Select the global optimal position x b Individuals act as pathfinder dung beetles, simulating the behavior of searching for and optimizing the activities of cathepsin B and L through the pathfinder position update method in PFA; When the taste quality of the ham is better than that of the contemporary one, its position is updated; the above mechanism ensures that the algorithm approaches a better solution during the iteration process; the moving step factor introduces randomness, allowing the algorithm to flexibly explore in the solution space; the random vector determines the multi-directionality of the pathfinder's movement, enhancing the algorithm's search ability; Update the position of the pathfinder dung beetle in the way of updating the pathfinder position in PFA. When the fitness value of the new generation of pathfinder dung beetles is lower than the fitness value of the current generation of pathfinder dung beetles, the position of the pathfinder dung beetle is updated; update the position of the pathfinder dung beetle: x b (t+1)=x b (t)+2m(x b (t)-x b (t-1))+S In the formula, x b (t-1))、x b (t) and x b (t+1) represents the positions of the t-1, t and t+1 generation pathfinder dung beetles respectively; m is the moving step factor of the pathfinder dung beetle, which is a random number in [0,1]; u is a 1×d random vector, whose vector elements are all random numbers in [-1,1]; S is the moving step of the pathfinder dung beetle, and its multidirectionality is determined by the value of u; S2.2: Calculate the average position x of dung beetle individuals m , by randomly choosing whether to adopt this useful information with a probability of 0.5, the algorithm can explore new possibilities while maintaining the current optimal solution; The constant value is combined with the random vector to provide randomness for the position update of the thieving dung beetle, which helps the algorithm to jump out of the local optimal solution and find a better combination of ham processing parameters; Based on the position and the position of the pathfinder dung beetle, a new method of updating the position of the dung beetle individual is defined, and it occurs with a probability of 0.5; Randomly choose whether to use this beneficial information for extraction to balance optimality and stability, and change the individual update method of the dung beetle stealing stage to: In the formula, x i (t) represents the position of the i-th thieving dung beetle individual in generation t; R is a constant value, which is set to 0.5; h represents an independent random vector of size 1×d; S2.3: The goals of optimizing the activities of cathepsin B and L and the production of flavor substances by adjusting process parameters are closely linked; by simulating the natural behavior of dung beetles, a more refined combination of ham processing parameters can be found, and a strategy for fine-tuning the position of the egg ball is proposed during the egg-laying stage; Based on the female dung beetle's egg-laying area and the local optimal position of the individual dung beetle, the egg ball leader position update formula is given as follows: In the formula, AB i (t) represents the position of the oval leader after being updated by the oval position fine-tuning strategy, p1 represents the update probability; During the feeding stage of the dung beetle, a micro-adjustment strategy for the position of the dung beetle is proposed; based on the optimal foraging area and global optimal position of the dung beetle individual, the position update of the dung beetle is given as follows: Where Ax i (t) represents the position of the leader of the dung beetles after the dung beetle position micro-adjustment strategy is updated; p2 represents the update probability; S2.4: By flexibly adjusting the position of the leader individual, the algorithm can explore the solution space and find better ham processing parameters; by fine-tuning the position of the dynamic boundary individual, the contemporary leader individual is adjusted and updated, which can reduce the overlap of individuals in the dynamic boundary area, thereby improving the algorithm's optimization performance. The individual update is as follows: B i (t+1)=X * +b1×(AB i (t)-Lb * )+b2×(AB i (t)-Ub * ) x i (t+1)=Ax i (t)+C1×(Ax i (t)-Lb b )+C2×(Ax i (t)-Ub b )。 10. The method for optimizing the flavor of Jinhua ham based on metabolomics and enzyme activity regulation technology according to claim 1, characterized in that: In step S3, DBO is divided into four stages, namely, the dung beetle ball rolling stage, the egg-laying stage, the small dung beetle feeding stage and the dung beetle stealing stage; The specific steps are: S3.1: Dung beetles rely on the sun for navigation while rolling their dung balls. The probability of this happening is 0.

9. The intensity of sunlight indirectly affects the rolling path of the dung beetles. α, as a natural coefficient, determines whether the dung beetles deviate from the original rolling direction, simulating the indirect effect of sunlight intensity on the dung beetle path. When α = -1, it means that the dung beetle individual deviates from the original direction; When α = 1, it means there is no deviation and the stability of the algorithm is maintained; At the same time, by introducing the deflection coefficient and light intensity simulation parameters, the algorithm can simulate the behavior of dung beetles in the natural environment, thereby finding better ham processing parameters; During the rolling process, the position of the dung beetle individual is updated as follows: x i (t+l)=x i (t)+α·v·x i (t-1)+n·Δx In the formula, t represents the current iteration number, x i (t) represents the position of the dung beetle individual in the t generation, which is called the leader; α is a natural coefficient with a value of -1 or 1. When α = -1, it means that the dung beetle individual deviates from the original rolling direction, and when α = 1, it means there is no deviation. i (t) is the selection strategy; parameter v∈(0,0.2] represents the deflection coefficient, parameter n∈(0,1); Δx=|x i (t)-x w |, used to simulate the change of light intensity. The larger the value, the weaker the light source. w represents the global worst position of the current dung beetle individual; When a dung beetle encounters an obstacle and cannot move forward during the ball rolling process, it will use a dancing behavior to obtain a new ball rolling path. The probability of this happening is δ 0.1; The new ball path direction will be determined by the tangent function value of the definition domain. At this time, the position update of the dung beetle individual is expressed as follows: x i (t+1)=x i (t)+tanθ·|x i (t)-x i (t-1)| Where θ∈[0,π] represents the deflection angle. When θ is 0, π / 2 or π, the position of the dung beetle individual will not be updated. S3.2: In order to provide a safe habitat for the offspring, the dung beetle individuals will roll the dung ball to a safe place for hiding, that is, the female dung beetle individuals will use the place as a place to lay eggs and raise their offspring; By distributing eggs around the current local best position and randomly selecting one to lay eggs, the algorithm can explore potential better solutions around it while maintaining the current optimal solution; This mechanism helps the algorithm find more refined combinations of ham processing parameters in the solution space and improve the quality of the ham; There are many egg balls distributed around the local optimal position. Each female dung beetle will only choose one egg ball to lay eggs. The position of the egg ball is updated as follows: B i (t+1)=x * +b1×(B i (t)-Lb * )+b2×(B i (t)-Ub * ) Where, Lb * and Ub * Respectively represent the lower and upper boundaries of the female dung beetle's egg-laying area x * represents the local optimal position of the current dung beetle individual, where B i (t) represents the position of the i-th egg in the t-th generation, which is called the leader; b1 and b2 are two random numbers belonging to (0,1); S3.3: By randomly updating positions within a subset of the solution space, the algorithm explores more possibilities and discovers new combinations of ham processing parameters; This mechanism helps the algorithm approach the global optimal solution while maintaining diversity; the position update method of the dung beetle individual is expressed as follows: x i (t+1)=x i (t)+C1×(x i (t)-L b )+C2×(x i (t)-U b ) Where, L b and U b They represent the lower and upper boundaries of the optimal foraging area of ​​the dung beetle; x i (t) represents the position of the i-th dung beetle individual in generation t; C1 and C2 are two random numbers belonging to (0,1); S3.4: By introducing the constant value R and the random vector h, the algorithm can introduce new information while maintaining the stability of the current solution, which helps to find a better combination of ham processing parameters; this mechanism enhances the algorithm's search ability, allowing the algorithm to flexibly explore in the solution space and find a better solution; some dung beetle individuals will steal the dung balls of other dung beetles. Such dung beetle individuals are called thieving dung beetles. The position update method of the thieving dung beetle individuals is expressed as follows: x i (t+1)=X b +R·h×(|x i (t)-x * |+|x i (t)-x b |) In the formula, x i (t) represents the position of the i-th thieving dung beetle individual in generation t; R is a constant value, which is set to 0.5; h represents an independent random vector of size 1×d.

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