A screening method for a key umami contributing peptide

Abstract

The application belongs to the technical field of key umami peptide evaluation, and provides a screening method for key umami contribution peptides. On the basis of a current potential umami peptide screening prediction tool, the application proposes an umami peptide screening tool, i.e. a combination of a plurality of factors, such as the relative abundance of potential umami peptides and the predicted umami threshold, which have an influence on umami perception, is used to evaluate the umami contribution degree of a peptide segment in a sample, so as to realize the screening of key umami contribution peptides in the sample. The application makes up for the inability to further select key umami peptides in the current umami peptide screening strategy, realizes the progress from a peptide segment-potential umami peptide-potential key umami contribution peptide, and has important theoretical significance and practical application value.

Description

Technical Field

[0001] This invention belongs to the field of key umami peptide evaluation technology, specifically relating to a method for screening key umami-contributing peptides. Background Technology

[0002] Umami can significantly enhance the flavor and quality of food, giving it a rich and delicious character, and therefore has important applications in seasonings, food additives, and other fields. However, with the increasing demand from consumers for healthier and more natural foods, how to accurately obtain and utilize umami components has become an important direction for food science research.

[0003] Umami peptides, as a novel functional substance, have attracted much attention due to their unique chemical structure and biological functions. Umami peptides are small polypeptide fragments, typically containing 2-20 amino acid residues, generated from proteins through enzymatic hydrolysis or fermentation. Compared to free amino acids, umami peptides not only release a more persistent umami flavor but also exhibit higher flavor stability and synergistic effects. Furthermore, umami peptides have wide applications in the food industry, serving as natural flavoring agents, nutritional fortifiers, or functional ingredients to meet consumers' demand for healthy foods.

[0004] Currently, the main methods for obtaining umami peptides include enzymatic hydrolysis, fermentation, and modern biomanufacturing technologies. Enzymatic hydrolysis involves selecting and optimizing specific proteases to cleave protein molecules into peptides with specific sequences; fermentation utilizes the metabolic activities of microorganisms to produce polypeptide fragments. However, existing umami peptide screening methods primarily rely on traditional physicochemical techniques (such as HPLC and MS) combined with sensory evaluation, making it difficult to comprehensively assess the umami contribution of peptides in actual samples.

[0005] It is worth noting that the contribution of umami peptides to umami flavor is not only related to their own threshold but also closely related to their content in the sample. Peptides with high thresholds but low content may not significantly enhance the overall flavor, while peptides with low thresholds but high content may play a crucial role. Although current virtual screening strategies can predict whether a peptide has umami characteristics through computational models, they do not take into account its actual content and contribution in the sample, making it difficult to screen out key umami peptides that contribute significantly to the umami intensity of the sample. Therefore, how to simultaneously consider the threshold and content of peptides to screen out key umami peptides with real practical application value has become an urgent technical challenge to be solved.

[0006] Based on the above analysis, this invention proposes a screening method for key umami-contributing peptides, aiming to overcome the problem of incomplete evaluation of umami contribution in existing technologies and provide the food industry with a more accurate and efficient screening tool. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a method for screening key umami-contributing peptides, thereby resolving the issues in the prior art. The technical solution adopted by this invention is as follows: A method for screening key umami-contributing peptides, comprising: Step 1, Multi-dimensional screening based on virtual strategies: Integrating computational simulation, cheminformatics, and sensory evaluation to construct a multi-level screening system; Step 2, constructing an umami peptide screener: Based on the umami peptides identified under the conditions of multi-enzyme hydrolysis system and Maillard reaction, the umami peptide screener integrates multiple factors to identify key umami-contributing peptides. Step 3: Based on the umami peptide screener, predict and screen key umami-contributing peptides.

[0008] Furthermore, in step 2, the umami peptide screener is represented by the following formula: in, peptide i The relative abundance; : Umami threshold of peptide i; The minimum ratio between the relative abundance of peptides in potential umami peptides and the umami prediction threshold. ; The total number of all potential umami peptides in the sample; : Peptide interaction matrix coefficients; : Global scale factor of interaction effects; : Characteristic vector representing the physicochemical properties of the sample matrix; Correction function; For umami peptide screening indicators, A higher value indicates a greater contribution of the peptide to the umami flavor of the sample.

[0009] Furthermore, peptide interaction matrix coefficients The construction process includes: Model construction: The 3D structure of receptor T1R1 / T1R3 was constructed using AlphaFold2 or homology modeling, and the energy of potential umami peptide ligands was minimized to establish a library of candidate ligand 3D structures. Co-doping simulation: Peptides from the candidate library are paired up and placed in the active pocket of the receptor for co-doping simulation; Interaction coefficient calculation: Extract the binding free energy of co-doping of the two ligands respectively. Gtotal, and the binding energy of each individual monomer docking independently. Gi and Gj; Sij Assignment and Matrixing: Defining Interaction Coefficients Sij= Gtotal - ( Gi + Gj); By statistically analyzing the energy differences of all peptide combinations, an N×N interaction matrix S is constructed; If Sij is negative, it is determined to be a synergistic enhancement effect; if it is positive, it is determined to be a spatial competition or antagonistic effect.

[0010] Furthermore, the correction function The construction process includes: The matrix feature vector F is defined as follows: the key matrix parameters affecting umami perception are hydrogen ion concentration f1, sodium chloride concentration f2, EUC value f3 and sample system viscosity f4. The feature vector F = (f1, f2, f3, f4) is constructed. Determination process: Peptide P was determined in deionized water. i The basic sensory intensity was used as a reference; the response surface methodology was used to score the umami intensity of the same peptide concentration under different combinations of F parameters by a sensory evaluation panel; the intensity variation coefficient obtained from the experiment was subjected to nonlinear regression analysis with matrix parameters to determine the coefficient of function M(F); If M(F)>1, it indicates that the sample matrix environment enhances the umami expression of the peptide; otherwise, it indicates that the matrix has a masking or inhibitory effect.

[0011] A key umami-contributing peptide of chicken enzymatic hydrolysate was screened using the aforementioned screening method for a key umami-contributing peptide; its amino acid sequences are: AAEKGVP, APEEHPT, DVGDWRKN, EEHPTLL, EGEFKGRY, and HEEGKIL.

[0012] The present invention has the following beneficial effects: This invention overcomes the problem of traditional screening methods' incomplete assessment of umami contribution, particularly the limitation of accurately identifying key umami peptides in complex food systems. By introducing a dual assessment dimension of content and activity, the practical application value and reliability of the screening results are significantly improved. Attached Figure Description

[0013] Figure 1 This is an overall framework diagram of the present invention; Figure 2 This is a graph showing the prediction results of the virtual screening strategy of this invention; Figure 3 A diagram showing the molecular docking binding sites of the key umami-contributing peptides screened for this invention with T1R1 / T1R3-VFT. Figure 4 The diagram shows the binding pattern of the key umami-contributing peptides screened for this invention with T1R1 / T1R3-VFT. Detailed Implementation

[0014] The following will be described in conjunction with embodiments of the present invention. Figures 1-4 The technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0015] like Figure 1 This invention proposes a novel concept—the Umami Peptides Selector—based on current potential umami peptide screening and prediction tools (Q-value, Umami_YYDS, Tastepeptides_DM, and molecular docking technology). This selector combines the relative abundance of potential umami peptides with multiple factors influencing umami perception, such as their predicted umami threshold, to assess the umami contribution of peptides in a sample, thereby enabling the screening of key umami-contributing peptides. This invention overcomes the limitation of current umami peptide screening strategies in further selecting key umami peptides, achieving progress from peptide to potential umami peptide to potential key umami-contributing peptide, and possesses significant theoretical and practical value.

[0016] This invention proposes a method for screening key umami-contributing peptides, comprising: Step 1, Multi-dimensional Screening Based on Virtual Strategies: By integrating computational simulation, cheminformatics, and sensory evaluation techniques, a multi-level screening system combining virtual screening with experimental verification was constructed. Specifically, this includes: Peptidomics raw data processing: 1. Detect samples using liquid chromatography-mass spectrometry (LC-MS / MS) to obtain raw mass spectrometry data of peptides. 2. Identify peptide sequences using protein databases (such as UniProt) to obtain complete sequence information of peptides in the sample and their corresponding relative abundance (A). i (Usually expressed as mass spectrometry peak area or response value).

[0017] Preliminary screening of umami potential based on machine learning: Preliminary screening of potential umami peptides is carried out using umami prediction models based on deep learning algorithms (Q value, YYDS, TPDM) combined with virtual screening technology (molecular docking).

[0018] Evaluation of the umami contribution of peptides: 1. Use the Umami_IP tool to obtain the umami prediction threshold T of potential umami peptides. i 2. Potential umami peptides were used as ligands for molecular docking with umami receptors T1R1-T1R3, and docking scores (S) were calculated. ij3. Calculate the interaction coefficient β between peptides based on the physicochemical properties (hydrophobicity, charge distribution, etc.) of the amino acid sequence; 4. If the sample provides parameters such as pH, salt concentration, and fat content, the correction function M(F) can also be used as an evaluation parameter; 5. Finally, evaluate the umami contribution of peptides based on the umami peptide screening formula. The higher the ranking, the greater its contribution to the umami of the sample.

[0019] Sensory verification: The umami threshold of the selected peptides was measured to verify the accuracy of the screening system.

[0020] This method can not only predict whether a peptide has potential umami flavor and its threshold, but also comprehensively evaluate the actual contribution of umami peptides by combining the content data in actual samples.

[0021] Step 2, construct the Umami Peptides Selector. ups Based on a multi-enzyme hydrolysis system and Maillard reaction conditions, the umami peptide screener index integrates multiple factors affecting peptide perception, such as peptide abundance and umami threshold, to achieve accurate identification of key umami peptides. This index can effectively screen peptides that have a significant contribution to umami in actual food systems, while avoiding the problems of "high intensity but low concentration" or "high concentration but low intensity" that exist in traditional screening methods. The preparation process of umami peptides identified based on a multi-enzyme hydrolysis system and Maillard reaction conditions includes: 1. Substrate pretreatment: Mix meat raw materials (such as chicken breast) with solvent (such as deionized water) in a ratio of 1:1 to 1:3, pretreat at 45-55℃, and keep stirring to ensure the system is homogeneous.

[0022] 2. Multi-enzyme combined hydrolysis: Add a complex protease (preferably containing the endonuclease Protamex and the exonuclease Flavorzyme, each added at 0.05%-0.2%) to the above system and hydrolyze at 45-55℃ for 3-6 hours. Utilize the synergistic effect of enzymes with different cleavage sites to fully release short peptides with umami potential.

[0023] 3. Maillard reaction and inactivation: Add reducing sugars (such as 1.0%-2.0% xylose, 4.0%-5.0% glucose) to the enzymatic hydrolysate and heat at 115-125℃ for 45-90 minutes. This step achieves complete enzyme inactivation and the Maillard reaction simultaneously through high temperature, using the thermal reaction to modify peptide flavor and enhance the overall umami characteristics of the sample.

[0024] 4. Product collection: After the reaction is completed, the product is subjected to vacuum freeze-drying. The resulting dried powder is sealed and stored in an ultra-low temperature (-80℃) environment for subsequent peptidomics detection and umami contribution assessment.

[0025] Step 3: Based on the umami peptide screener, predict and screen key umami-contributing peptides.

[0026] In step 2, the umami peptide screener is represented by the following formula: in, peptide i Relative abundance detected by peptidomics technology; : Umami threshold predicted by peptide i using the Umami_IP tool; The minimum ratio between the relative abundance of peptides in potential umami peptides and the umami prediction threshold. ; : The total number of all potential umami peptides in the sample. : Peptide interaction matrix coefficients, representing the influence of umami substance or peptide j on the umami contribution of target peptide i, can be preliminarily determined by the absolute value of molecular docking results; : A global scale factor for interaction effects, used to modulate the overall strength of synergistic / antagonistic effects; : Characteristic vectors representing the physicochemical properties of the sample matrix, such as pH, salt concentration, fat content, etc.; A calibration function used to describe the influence of matrix properties on peptide umami release and perception. It can be fitted to a simple linear model or a more complex nonlinear function using prior experimental data, such as... M (F)= (γ) k The weighting coefficients of each matrix factor are not required in the initial screening process. This is an indicator for the umami peptide screening tool.

[0027] in, The specific construction process includes: 1. Model Construction: The 3D structure of receptor T1R1 / T1R3 was constructed using AlphaFold2 or homology modeling, and the energy of potential umami peptide ligands was minimized to establish a library of candidate ligand 3D structures.

[0028] 2. Co-doping simulation: Peptides in the candidate library are paired (Pi and Pj) and placed in the active pocket of the receptor for co-doping simulation.

[0029] 3. Interaction coefficient calculation: Extract the binding free energy of the co-doping of the two ligands. Gtotal, and the binding energy of each individual monomer docking independently. Gi and Gj.

[0030] 4. Sij Assignment and Matrix Transformation: Define the interaction coefficients Sij = Gtotal - ( Gi + Gj). By statistically analyzing the energy differences of all peptide combinations, an N×N interaction matrix S is constructed.

[0031] 5. Logical judgment: If Sij is negative, it is judged as a synergistic enhancement effect; if it is positive, it is judged as a spatial competition or antagonistic effect.

[0032] Wherein, the correction function The construction process includes: 1. Define the matrix feature vector F: In this invention, the key matrix parameters affecting the perception of umami are defined as hydrogen ion concentration f1, sodium chloride concentration f2, EUC value f3 and sample system viscosity f4, so as to construct the feature vector F = (f1, f2, f3, f4).

[0033] 2. Determination process: 1) Benchmark test: Determine peptide P in deionized water. i 1) Using the basic sensory intensity as a reference; 2) Orthogonal experimental design: Using the response surface methodology, under different combinations of F parameters, the sensory evaluation panel will score the umami intensity of the same peptide concentration; 3) Data fitting: The intensity variation coefficient obtained from the experiment will be subjected to nonlinear regression analysis with the matrix parameters to determine the coefficient of the function M(F).

[0034] 3. Numerical analysis: If M(F)>1, it indicates that the sample matrix environment (such as appropriate salinity and nucleotides) enhances the umami expression of the peptide; otherwise, it indicates that the matrix has a masking or inhibitory effect.

[0035] In step 3, The umami value, as a key indicator for evaluating the contribution of peptides to umami, not only combines traditional indicators highly correlated with contribution, namely the relative peak area and umami intensity of peptides, but also incorporates computational simulation data, including the molecular docking score between peptides and umami receptors, the inter-peptide interaction coefficient determined by the physicochemical characteristics of amino acids (hydrophobicity, charge, etc.), and the influence of matrix properties on umami release and perception. A higher umami value indicates a greater contribution of peptides to umami in the sample.

[0036] Based on the screening method for key umami-contributing peptides proposed in this invention, a key umami-contributing peptide of chicken enzymatic hydrolysate was obtained: after ultrafiltration, peptide identification, and prediction using peptide umami prediction tools (Q value, Umami_YYDS, and Tastepeptides_DM), it was screened by an umami peptide selector (Umami Peptides Selector). ups After calculation using the formula, peptides with scores ≥ 1000 were selected as key umami-contributing peptides in the sample; their amino acid sequences were: AAEKGVP, APEEHPT, DVGDWRKN, EEHPTLL, EGEFKGRY, and HEEGKIL.

[0037] The application examples of this invention are as follows: Example 1: (1.1) Obtaining chicken enzymatic hydrolysate: Chicken breast was homogenized in deionized water (material-to-liquid ratio 1:2) and pre-incubated in a magnetic stirrer with a constant temperature water bath at 50°C and 200 rpm. Novozymes commercial enzymes (Protamex and Flavorzyme) were added to the meat paste at a ratio of 0.1% and reacted at 50°C for 4 hours. The hydrolysate was then heated at 120°C for 1 hour while magnetically stirred at 200 rpm to inactivate the enzymes. Simultaneously, the hydrolysate was mixed with 1.5% xylose and 4.5% glucose, respectively, and Maillard reactions were performed under the same heating conditions. All prepared samples were collected after freeze-drying and stored at -80°C for subsequent experiments.

[0038] (1.2) Identification of peptides in chicken enzymatic hydrolysate: Hydrolysis products and Maillard reaction products were dissolved in ultrapure water, centrifuged, and the supernatant was collected. After pre-filtration through a 0.45 μm filter membrane, short peptides were enriched using a 10 / 5 kDa ultrafiltration tube. The ultrafiltered sample was then freeze-dried under vacuum to obtain peptide powder. MS / MS scans (m / z range 350–1800) were performed using an Easy nLC 1200 / Q Exactive plus mass spectrometer in positive ion mode. The peptide sequences and molecular weights were identified, and the relative abundance of peptides was quantitatively analyzed by matching with the UniProt database using Peaks X software.

[0039] Specifically, 14,910 peptides were identified from the enzymatic hydrolysate samples, with short peptides containing 3-5 amino acids accounting for one-third of all peptides, followed by those containing 6-10 and 11-15 amino acids, indicating that this enzymatic hydrolysis method provides a sufficient pool of potential umami peptides.

[0040] Example 2: Unlike Example 1, the prediction and screening of potential umami peptides in chicken enzymatic hydrolysates includes: (2.1) Online tools predict potential umami peptides: Multiple online tools—Q-value, Umami_YYDS (https: / / tastepeptides-meta.com / cal), and Tastepeptides_DM (http: / / tastepeptides-meta.com / TPDM)—were used to predict the umami properties of the identified peptide sequences. Each tool outputs a prediction result, determining whether the peptide possesses umami characteristics. Only peptides consistently predicted by all tools to have umami characteristics were further screened and retained.

[0041] Specifically, 8604 overlapping peptide segments were ultimately selected, such as... Figure 2 As shown, it can be considered a potential candidate umami peptide.

[0042] (2.2) Molecular docking of peptides with umami receptors: The T1R1 / T1R3-VFT protein complex (UniProt ID: Q7RTX1 and Q7RTX0) was used as the docking acceptor. Using the docking software Smina, peptide molecules were placed at the center of the docking box (center coordinates: x = -23, y = 4, z = 38; docking box size: 68 Å) to simulate their interaction with the acceptor protein. The binding free energy calculated from the molecular docking experiments was used to evaluate the peptide-acceptor binding affinity. Based on the principle of minimum energy, 383 peptides with docking fractions below -7 kcal / mol were screened in one step as potential umami peptides. Currently, published patents / research on umami peptide screening only go as far as this, selecting a few from the potential umami peptides screened above based on sequence characteristics for verification; however, they do not consider the degree of contribution to umami in the sample, i.e., key umami-contributing peptides are not selected. Therefore, this invention aims to screen key umami peptides through Example 2.

[0043] Example 3: Unlike Example 2, the umami contribution of predicted potential umami peptides in chicken enzymatic hydrolysates was selected, including: (3.1) Prediction of the umami threshold of potential umami peptides: The umami threshold of the screened potential umami peptides was predicted using the online tool Umami IP (http: / / tastepeptides-meta.com / Umami_IP).

[0044] (3.2) Selection of potential key umami peptides: Based on the relative quantitative analysis results of the abundance of peptides identified in Example 1, combined with the potential umami peptides screened in Example 2 and the predicted umami threshold, an umami peptide selector is proposed. ups The concept of potential umami peptides is used to calculate a theoretical score for their contribution to a sample. The formula is as follows: in, peptide i Relative abundance detected by peptidomics technology; : Umami threshold predicted by peptide i using the Umami_IP tool; The minimum ratio between the relative abundance of peptides in potential umami peptides and the umami prediction threshold. ; : The total number of all potential umami peptides in the sample. : Peptide interaction matrix coefficients, representing the influence of umami substance or peptide j on the umami contribution of target peptide i, can be preliminarily determined by the absolute value of molecular docking results; : A global scale factor for interaction effects, used to modulate the overall strength of synergistic / antagonistic effects; : Characteristic vectors representing the physicochemical properties of the sample matrix, such as pH, salt concentration, fat content, etc.; A calibration function used to describe the influence of matrix properties on peptide umami release and perception. It can be fitted to a simple linear model or a more complex nonlinear function using prior experimental data, such as... M (F)= (γ) k (Weighting coefficients of each matrix factor), this indicator is not necessary in the early screening process. A higher value indicates a greater contribution of the peptide to the umami flavor of the sample.

[0045] First, I chose ups Chemical synthesis verification was performed on the top ten peptides, and the results showed that six of them had umami flavor. ups ≥1000, namely: AAEKGVP, APEEHPT, DVGDWRKN, EEHPTLL, EGEFKGRY and HEEGKIL.

[0046] (3.3) Sensory evaluation of potential key umami peptides: Potential key umami flavor components selected from chicken enzymatic hydrolysates were synthesized in a solid-phase manner (purity >98%) for peptide umami validation. The evaluation panel consisted of 10 sensory evaluators (5 men and 5 women, aged 22-30 years), all of whom had undergone professional sensory training according to GB / T16291.1-2012 standards. Panel members performed taste descriptive analysis and a three-point test to determine the umami threshold of each peptide in a series of synthesized peptide solutions (pH=6.5, 1:1 v / v stepwise dilution). The umami threshold for each synthesized peptide was determined as the geometric mean of the individual thresholds of the evaluation panel members.

[0047] Specifically, the screened chicken enzymatic hydrolysates all exhibited umami and sweetness, with umami recognition thresholds ranging from 0.132 to 0.344 mmol / L. The umami intensity of the six peptides ranked between 14% and 39.7% compared to the reported umami peptide thresholds (as of March 2025), as shown in Table 1.

[0048] This invention demonstrates that the concept of an umami peptide screener, proposed based on computer simulation of multiple strategies, can effectively identify key umami peptides in samples and has broad application prospects.

[0049] Table 1: Taste characteristics of synthetic peptide aqueous solutions

[0050] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, alterations, alterations, or substitutions made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for screening key umami-contributing peptides, characterized in that, include: Step 1, Multi-dimensional screening based on virtual strategies: Integrating computational simulation, cheminformatics, and sensory evaluation to construct a multi-level screening system; Step 2, constructing an umami peptide screener: Based on the umami peptides identified under the conditions of multi-enzyme hydrolysis system and Maillard reaction, the umami peptide screener integrates multiple factors to identify key umami-contributing peptides. Step 3: Based on the umami peptide screener, predict and screen key umami-contributing peptides.

2. The method for screening key umami-contributing peptides according to claim 1, characterized in that, In step 2, the umami peptide screener is represented by the following formula: in, peptide i The relative abundance; : Umami threshold of peptide i; The minimum ratio between the relative abundance of peptides in potential umami peptides and the umami prediction threshold. ; The total number of all potential umami peptides in the sample; : Peptide interaction matrix coefficients; : Global scale factor of interaction effects; : Characteristic vector representing the physicochemical properties of the sample matrix; Correction function; For umami peptide screening indicators, A higher value indicates a greater contribution of the peptide to the umami flavor of the sample.

3. The method for screening key umami-contributing peptides according to claim 2, characterized in that, peptide interaction matrix coefficients The construction process includes: Model construction: The 3D structure of receptor T1R1 / T1R3 was constructed using AlphaFold2 or homology modeling, and the energy of potential umami peptide ligands was minimized to establish a library of candidate ligand 3D structures. Co-doping simulation: Peptides from the candidate library are paired up and placed in the active pocket of the receptor for co-doping simulation; Interaction coefficient calculation: Extract the binding free energy of co-doping of the two ligands respectively. Gtotal, and the binding energy of each individual monomer docking independently. Gi and Gj; Sij Assignment and Matrixing: Defining Interaction Coefficients Sij= Gtotal - ( Gi + Gj); By statistically analyzing the energy differences of all peptide combinations, an N×N interaction matrix S is constructed; If Sij is negative, it is determined to be a synergistic enhancement effect; if it is positive, it is determined to be a spatial competition or antagonistic effect.

4. The method for screening key umami-contributing peptides according to claim 2, characterized in that, Correction function The construction process includes: The matrix feature vector F is defined as follows: the key matrix parameters affecting umami perception are hydrogen ion concentration f1, sodium chloride concentration f2, EUC value f3 and sample system viscosity f4. The feature vector F = (f1, f2, f3, f4) is constructed. Determination process: Peptide P was determined in deionized water. i The basic sensory intensity was used as a reference; the response surface methodology was used to score the umami intensity of the same peptide concentration under different combinations of F parameters by a sensory evaluation panel; the intensity variation coefficient obtained from the experiment was subjected to nonlinear regression analysis with matrix parameters to determine the coefficient of the function M(F); If M(F)>1, it indicates that the sample matrix environment enhances the umami expression of the peptide; otherwise, it indicates that the matrix has a masking or inhibitory effect.

5. A key umami-contributing peptide in chicken enzymatic hydrolysate, characterized in that, The screening method for a key umami-contributing peptide according to any one of claims 1-4 was used; the amino acid sequences of the peptides are: AAEKGVP, APEEHPT, DVGDWRKN, EEHPTLL, EGEFKGRY, and HEEGKIL.

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