Method for determining optimal umami substrate concentration for aroma enhancement and composition thereof

Abstract

The present application belongs to the field of food flavor chemistry and sensory evaluation technology, and provides a method for determining the optimal umami substrate concentration for aroma enhancement and a composition thereof. The method first determines the sensory recognition threshold of the umami substrate and the half-maximum intensity concentration of the aroma substance to be tested. Then, the umami enhancement intensity before and after adding a fixed concentration of aroma substance is determined through sensory evaluation. The umami enhancement intensity and substrate concentration data are fitted using a nonlinear single-peak response model to construct a synergistic umami curve. By analyzing the peak center parameter, half-peak full width parameter and asymmetric decay factor of the curve, the optimal umami substrate concentration for the aroma substance to exert the maximum synergistic effect can be accurately locked, and the effective concentration range of the synergistic effect can be quantitatively evaluated. The present application breaks through the cognitive misunderstanding of traditional high-concentration substrate umami enhancement, confirms that the low-concentration threshold region is the optimal synergistic window, and provides a scientific quantitative guidance tool for the food industry to develop low-cost and high-umami products.

Description

Technical Field

[0001] This invention belongs to the field of food flavor chemistry and sensory evaluation technology, specifically relating to a method for determining the optimal concentration of umami matrix for aroma enhancement and its composition. Background Technology

[0002] Umami, as the fifth basic taste, is a core indicator for evaluating the sensory quality of meat products, seasonings, fermented foods, and snack foods. From a flavor mechanism perspective, umami is primarily triggered by non-volatile substances such as glutamate (e.g., monosodium glutamate, MSG), 5'-nucleotides (e.g., disodium inosinate, disodium guanylate, GMP), and specific short-chain umami peptides. These substances generate a pleasant sensory experience by activating T1R1 / T1R3 heterodimer receptors in the oral cavity. In modern food industry, to meet consumer demand for high umami intensity, traditional solutions mainly rely on additive strategies, i.e., directly increasing the amount of MSG or nucleotides added, or using high-cost natural extracts (e.g., yeast extract, enzymatically hydrolyzed meat extract). However, this traditional approach faces a difficult-to-reconcile dual challenge: 1) Health risks and regulatory restrictions: High concentrations of umami agents are usually accompanied by high sodium intake. With the global trend advocating for salt reduction, sodium reduction, and clean labeling, simply increasing the amount of umami agents is no longer a sustainable technological approach. 2) Cost pressure and diminishing marginal returns: The production costs of flavor nucleotides (I+G) and highly active umami peptides are high. More importantly, according to the Weber-Fechner Law, the improvement in sensory intensity increases logarithmically with increasing matrix concentration. This means that in the high concentration range, investing several times the cost often only brings negligible sensory improvement, resulting in extremely low economic benefits.

[0003] In recent years, cutting-edge research in sensory science has revealed a highly promising strategy—Cross-modal Olfactory-Gustatory Integration. This mechanism is not a simple physical superposition, but rather based on a complex physiological and neural integration process, specifically manifested at the following two levels: 1) Central neural integration mechanism: When volatile aroma compounds (such as heptanal in meat aroma and 2-methylbutanal in malt aroma) are released during oral chewing and enter the nasal cavity through the retronasal olfactory pathway, the brain binds this specific olfactory signal with the umami signal perceived by the tongue. If this aroma frequently co-occurs with umami in past dietary experiences (i.e., it exhibits perceptual congruency), the orbitofrontal cortex of the cerebral cortex integrates these two signals into an enhanced overall perception, thereby significantly amplifying the intensity evaluation of umami without increasing the concentration of umami substances. 2) Peripheral receptor regulatory mechanism: Recent molecular biology studies have shown that certain aroma molecules (such as some hydrophobic aldehydes) not only act on olfactory receptors but can also act as positive allosteric regulators, directly binding to the transmembrane domains or hydrophobic subpockets of umami receptors (T1R1 / T1R3). This binding can stabilize the active conformation of the receptor (such as the closed state), lowering the energy threshold required for receptor activation, thereby enabling even low concentrations of glutamate molecules to efficiently activate the receptor and generate a strong umami signal.

[0004] Although the mechanism of aroma enhancement has been confirmed in academia, there are still serious technical blind spots in the actual industrial formulation research and development and production applications, which prevents this technology from moving from the laboratory to standardized applications: 1) Lack of scientific quantitative guidance models: Current flavor formulation design mainly relies on experience and lacks standardized mathematical models to quantitatively describe the functional relationship between aroma substance concentration, umami matrix concentration, and synergistic gain value. Researchers often determine the amount to add through blind trial and error, which is inefficient and has poor reproducibility.

[0005] 2) Linear Thinking Pitfall: A common misconception in the industry is that higher aroma concentration equates to stronger umami enhancement. However, receptor kinetic studies show that the synergistic effect is not linearly enhanced. When the concentration of umami matrix is ​​too high, causing receptors to saturate, or when the aroma concentration is too high, resulting in off-flavor inhibition, the synergistic effect will decrease sharply or even disappear. Current R&D models often test at high matrix concentrations, precisely falling into the failure zone of the synergistic effect, leading to the erroneous conclusion that the aroma enhancement effect is not significant.

[0006] 3) Lack of optimal synergy assessment: To date, there is no method to accurately answer the question of at what concentration of umami matrix can the addition of trace amounts of aroma substances achieve the highest input-output ratio. This technological gap has resulted in many product formulations failing to achieve the expected synergistic effect from expensive flavorings, leading to significant waste of raw materials.

[0007] Therefore, there is an urgent need to establish an evaluation method and model based on scientific quantitative data that can break through the limitations of traditional empiricism, accurately pinpoint the optimal matrix concentration range for maximizing synergistic effects, and thus provide strong technical support for the food industry to develop low-cost and high-umami products. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a method for determining the optimal concentration of umami-enhancing matrix and its composition, thereby resolving the issues in the prior art. The technical solution adopted by this invention is as follows: A method for determining the optimal concentration of umami base for aroma enhancement, comprising: Step 1, benchmark determination: Determine the sensory recognition threshold of umami base; Step 2, Aroma Calibration: Measure the half-maximum intensity concentration of the aroma substance to be tested in the umami matrix, and set the half-maximum intensity concentration value as the fixed addition concentration of the aroma substance; Step 3, Construction of the collaborative evaluation system: Prepare umami matrix solutions with different concentrations as control groups, and add aroma substances to the same umami matrix solution as experimental groups; the concentration of aroma substances in the experimental groups is set to a fixed concentration. Step 4, Sensory Quantification: Perform sensory evaluation on the experimental group and the control group, and calculate the difference in umami enhancement intensity; Step 5, Model Fitting: The logarithmic or absolute value of the umami matrix concentration is used as the x-axis, and the difference in umami enhancement intensity is used as the y-axis. A nonlinear unimodal response model is used for fitting to obtain characteristic parameters that characterize the distribution of the synergistic effect, and the optimal concentration is determined based on the characteristic parameters.

[0009] Furthermore, the nonlinear unimodal response model in step 5 is a peak function model with center location and span evaluation functions, including one or more of the following: Gaussian model, standard Lorentz model, pseudo Voigt model, or log-normal distribution model.

[0010] Furthermore, the nonlinear unimodal response model in step 5 includes at least three characteristic parameters: The peak location parameter Xc is used to characterize the matrix concentration coordinates when the synergistic effect reaches its maximum value. The response width parameter B is used to characterize the concentration range effectively covered by the synergistic effect; when the response width parameter value B ≥ 3.5, the aroma substance is defined as a wide-range flavor enhancer; when the response width parameter value B ≤ 2.2, the aroma substance is defined as a narrow-range flavor enhancer. The asymmetric correction parameter S is used to characterize the differential decay rate of the model caused by sensory odor masking or saturation after exceeding the optimal concentration.

[0011] The nonlinear single-peak response model constructs a cooperative response curve with an inverted V-shaped characteristic based on three characteristic parameters.

[0012] Furthermore, the nonlinear unimodal response model in step 5 adopts a sensory-modified asymmetric Lorentz model, expressed as: in, To enhance the umami intensity; x is the logarithmic value of concentration based on the threshold. The morphological index is used to control the sharpness of the peak; A represents the area under the curve correlation constant. B represents the baseline background noise value; S represents the response width parameter; and S represents the asymmetric correction parameter.

[0013] Furthermore, the umami matrix is ​​selected from one or more of monosodium glutamate, disodium inosinate, disodium succinate, or umami peptides.

[0014] Furthermore, the aroma compounds are aliphatic aldehydes.

[0015] A synergistic flavor-enhancing composition comprising an umami matrix and an aliphatic aldehyde aroma substance, wherein the concentration ratio of the two is determined according to the method described above.

[0016] This invention offers the following advantages: Firstly, it introduces a nonlinear unimodal response model to scientifically quantify the cross-modal aroma-umami synergistic effect. This method not only overcomes the traditional misconception of linearity in high-concentration matrix umami enhancement, but also accurately identifies the optimal synergistic concentration point (Xc) near the low-concentration threshold region, significantly reducing industrial trial-and-error and raw material costs. Furthermore, by analyzing characteristic parameters such as the synergistic width (B-value), this invention establishes a classification and evaluation standard (wide-domain or narrow-domain) for aroma-umami enhancement characteristics, thus providing an irreplaceable quantitative guidance tool for cost reduction, efficiency improvement, and precise formulation design in various complex foods. Attached Figure Description

[0017] Figure 1The differential intensity curves of three aldehydes in four umami matrices and the evaluation results of sensitivity coefficients based on the Weber-Fechner law are presented. Figures A-C show the differential intensity calibration curves of heptanal, Z-4-heptenal, and 2-methylbutanal combined with the four umami matrices (MSG, IMP, WSA, and PR-7), respectively. Figure D shows a bar chart comparing the sensory sensitivity coefficients (α) of 12 experimental combinations derived from Weber-Fechner fitting. The horizontal axis labels represent different cross-combinations of umami matrices and aldehydes. Uppercase letters represent umami substances (M: MSG; I: IMP; W: WSA; P: PR-7), and numbers represent aldehyde types (1: heptanal; 2: Z-4-heptenal; 3: 2-methylbutanal). Different lowercase letters (ac) above the bars indicate statistically significant differences in sensitivity coefficients caused by the addition of different types of aldehydes within the same umami matrix group (p<0.05).

[0018] Figure 2 This is a dose-response curve of aroma synergy fitted based on a sensory-modified asymmetric Lorentz model. The figure shows the fitting results of cross-combinations of three aldehydes (heptanal, Z-4-heptenal, 2-methylbutanal) and four umami matrices (MSG, IMP, WSA, PR-7). The x-axis of each sub-plot represents the logarithmic value of the umami matric concentration relative to the recognition threshold, i.e., log2(C / C). TV When the horizontal axis is 0, it indicates that the matrix concentration is its recognition threshold concentration; the vertical axis represents the difference in umami intensity before and after adding a fixed concentration of aroma substances. Intensity). The core feature parameter values ​​(Ri) extracted by this combination fitting are labeled within each sub-frame. 2 X c B, γ and S).

[0019] Figure 3 This diagram illustrates the molecular binding characteristics and mechanism of the synergistic complexes. Figure A shows the three-dimensional binding pattern of the representative synergistic combination (heptanal-IMP) within the T1R1-T1R3 domain of the umami receptor. Figure B shows a frequency heatmap of the key amino acid residues and intermolecular interactions for all 12 aroma-umami combinations.

[0020] Figure 4 This is a flowchart of the present invention. Detailed Implementation

[0021] The following will be described in conjunction with embodiments of the present invention. Figures 1-4The 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.

[0022] like Figure 1 This invention proposes a method for optimizing umami matrix concentration and evaluating aroma enhancement based on synergistic response curves, comprising the following steps: Step 1, Benchmark Measurement: Determine the sensory recognition threshold (C) of the umami base. TV (including but not limited to MSG, IMP, disodium succinate WSA, umami peptide PR-7, etc.) TV Since the area near the threshold is the most sensitive region of the sensory system and also the region with the highest signal-to-noise ratio for cross-modal synergistic effects, a coverage layer is constructed using the threshold as an anchor point, starting from below the threshold (e.g., 0.25×C). TV From the high concentration saturation region (e.g., 8×C) TV A series of umami matrix solution systems with varying concentration gradients were used as the basis for synergistic evaluation.

[0023] Step 2, Aroma Calibration: The half-maximal intensity concentration (HIC) of the aroma substance to be tested in the umami matrix is ​​determined, and this HIC value is set as the fixed addition concentration of the aroma substance in the synergistic evaluation system. To eliminate interference caused by differences in the threshold values ​​of different aroma substances, this invention requires determining the HIC or threshold value of the aroma substance to be tested (such as various aliphatic aldehydes) in the umami matrix. In subsequent synergistic evaluations, the amount of aroma substance added is fixed at this equivalent physiological stimulus intensity level to ensure the comparability of the evaluation results.

[0024] Step 3, Construction of collaborative evaluation system: Set up a series of umami matrix solutions covering the gradient from subthreshold to high concentration saturation region as control group, and add the aroma substances of the same series of solutions at a fixed concentration as experimental group; Step 4, Sensory Quantification: Perform sensory evaluation on the experimental and control groups, and calculate the difference in umami enhancement intensity before and after the addition of aroma ( Intensity); Step 5, Model Fitting: The logarithmic or absolute value of the umami matrix concentration is used as the x-axis, and the difference in umami enhancement intensity is used as the y-axis. The umami enhancement intensity and matrix concentration data are fitted using a nonlinear unimodal response model to obtain characteristic parameters that characterize the distribution of the synergistic effect, and the optimal umami matrix concentration is determined based on the characteristic parameters.

[0025] Furthermore, in step 5, the nonlinear unimodal response model is a peak function model with center location and span evaluation functions. The model is selected from any one or a combination of the Gaussian model, the standard Lorentzian model, the pseudo-Voigt model, or the log-normal distribution model.

[0026] Furthermore, in step 5, the nonlinear unimodal response model mathematically includes at least the following three functional dimensions of characteristic parameters: The peak localization parameter (Xc) is used to characterize the matrix concentration coordinates at which the synergistic effect reaches its maximum value. The response width parameter (B) is used to characterize the span of the concentration range effectively covered by the synergistic effect; The asymmetric correction parameter (S) is used to characterize the differentiated decay rate caused by sensory odor masking or saturation after the model exceeds the optimal concentration. The method constructs a synergistic response curve exhibiting an inverted V-shaped characteristic through the mathematical correlation combination of the above parameters.

[0027] This invention reveals that the synergistic enhancement effect of aroma compounds on umami matrix does not increase linearly in the concentration dimension, but rather exhibits an inverted V-shaped distribution characteristic centered around a threshold. Therefore, any nonlinear mathematical model capable of characterizing this unimodal evolution trend can be used as a technical means for quantitative analysis in this invention.

[0028] Furthermore, the nonlinear unimodal response model in this step is preferably a sensory-corrected asymmetric Lorentzian model, which is expressed as follows: in, To enhance the umami intensity ( Intensity). x is the logarithmic concentration value based on the threshold (log2(C / C)). TV When x=0, it means the matrix concentration equals the recognition threshold. c To determine the optimal matrix concentration at which the synergistic effect reaches its peak, if X cA value <0 indicates that the synergistic effect is optimal in the region below the threshold. B represents the full width at half maximum (FWHM), characterizing the effective concentration range of the synergistic effect. A larger B value indicates a wider umami enhancement window for the aroma compound (wide-range); a smaller B value indicates that the umami enhancement effect is highly dependent on a specific concentration (narrow-range). S represents the skewness factor (i.e., the asymmetric correction parameter), describing the left-right symmetry of the curve. When S>0, it indicates that the decay is faster on the high concentration side (masking effect exists). The morphological index controls the sharpness of the peak value, reflecting the sensitivity of the sensory response. A represents the area under the curve correlation constant, representing the total co-potential. This represents the baseline background noise value.

[0029] Furthermore, the industrial applicability characteristics of the synergistic flavor-enhancing effect of aroma substances are evaluated based on the full width at half maximum (B) of the model parameters; when B ≥ 3.5, the aroma substance is determined to be a broad-range flavor-enhancing aroma, which has a stable flavor-enhancing effect within a relatively wide matrix concentration range; when B ≤ 2.2, the aroma substance is determined to be a narrow-range flavor-enhancing aroma, which requires strict control of the matrix ratio.

[0030] Furthermore, it also includes a step of cross-validating the optimal umami matrix concentration (Xc) using the Hill Kinetic Model: by calculating the Hill Slope (n) at different matrix concentrations, the concentration range where the receptor response sensitivity abruptly changes to the peak is identified, and this range is cross-validated with the region of Xc fitted by the model.

[0031] Furthermore, the umami matrix is ​​selected from one or more combinations of monosodium glutamate (MSG), disodium inosinate (IMP), disodium succinate (WSA), or umami peptide (Pro-Val-Ala-Arg-Met-Cys-Arg, PR-7).

[0032] Furthermore, the aroma substance is an aliphatic aldehyde compound, preferably heptanal, Z-4-heptenal, or 2-methylbutanal.

[0033] Furthermore, step 5 also includes providing guidance for industrial applications based on the fitted model parameters: Optimal concentration lock (X) c ): X c This value represents the optimal concentration of the umami base for the aroma substance to exert its maximum synergistic umami-enhancing effect. When designing formulations, companies should prioritize controlling the concentration of the umami base within this range. c To achieve the highest cost-effectiveness of material input, it should be located nearby.

[0034] Flavor Enhancement Characteristic Determination (B Value): The suitability of aroma substances is determined using parameter B. A larger B value (broad peak) indicates that the aroma is a "broad-range flavor enhancer," suitable for general seasonings with large fluctuations in matrix concentration; a smaller B value (narrow peak) indicates that the aroma is a "narrow-range flavor enhancer," suitable for functional foods with precise formulations.

[0035] The present invention also proposes a synergistic flavor-enhancing composition comprising an umami matrix and an aliphatic aldehyde aroma substance, the concentration ratio of which is determined according to the method described above; wherein the concentration of the umami matrix is ​​near its sensory recognition threshold (CTV), and the concentration of the aroma substance is near its half-maximum intensity concentration (HIC).

[0036] The present invention proposes the following specific embodiments: Example 1: Asymmetric sensory correction fitting of 2-methylbutyraldehyde in umami matrix Experimental Background: The experiment revealed that the human sensory perception of the synergistic effect of aroma and umami is not perfectly physically symmetrical. This is especially true when the concentration exceeds the optimal equilibrium point X. c Afterwards, the receptor saturation effect leads to a noticeable trailing characteristic in the decay of sensory intensity.

[0037] Fitting the basic Lorentz model with the sensory-modified asymmetric Lorentz model: This embodiment uses the basic Lorentz model ( ) and the improved Lorentz model that includes a skewness correction term ( Data processing is performed. Where y represents the umami enhancement intensity ( Intensity), x is the logarithm of concentration relative to the threshold (log2(C / C)). TV Xc represents the optimal matrix concentration at which the synergistic effect reaches its peak, and B represents the full width at half maximum (FWHM), characterizing the effective concentration range of the synergistic effect. In the sensory-modified asymmetric Lorentz model, S is the skewness factor; when S>0, the curve extends gently to the right; when S<0, the curve extends gently to the left. X is a shape factor used to correct the sharpness of the peaks, making them more consistent with the distribution of sensory scores. c The meanings of B and A are the same as in the basic Lorentz model.

[0038] Comparative analysis of fitting results: The synergistic sensory data of 2-methylbutyraldehyde and umami bases (MSG, IMP, WSA, and PR-7) were fitted, and the results are shown in Table 1: Table 1. Comparison of fitting results between the basic Lorenz model and the sensory-modified asymmetric Lorenz model for sensory synergistic data of 2-methylbutyraldehyde and umami matrices (MSG, IMP, WSA, and PR-7).

[0039] Model fitting results analysis: Experimental data show that the basic Lorentz model exhibits some distortion when dealing with the 2-methylbutyraldehyde system. This is particularly evident in the 2-methylbutyraldehyde-IMP group, where R... 2 The R-value was only 0.9067, indicating that the simple symmetrical peak model cannot accurately capture the complex fluctuations in sensory evaluations. In contrast, the sensory-modified asymmetric Lorenz model demonstrated extremely high fitting accuracy: the R-values ​​for the four experiments were... 2 The values ​​are all distributed between 0.988 and 0.997, indicating a near-perfect fit. By introducing an asymmetric factor, this model effectively addresses the nonlinear characteristics commonly found in sensory evaluations, such as slow retreat after exceeding the optimal point or extremely short bursts of improvement, demonstrating the scientific validity of the modified model in aroma synergy research.

[0040] The revised model made key adjustments to the core feature parameters: optimal co-concentration (X). c Precise targeting of X: Corrected X c The modified model generally favors lower concentrations (e.g., the PR-7 group was corrected from -0.09 to -0.35). This indicates that 2-methylbutyraldehyde can elicit a peak effect even at extremely low concentrations of umami matrix, exhibiting a stronger low-threshold driving characteristic. In-depth analysis of the total synergistic energy (A): The A value (area under the curve / total synergistic energy) fitted by the modified model is significantly higher than that of the basic model (3-5 times higher). The A value of the PR-7 group reaches 22.4, the highest among the four matrices, indicating that the synergistic enhancement potential of 2-methylbutyraldehyde for umami peptides is severely underestimated under the traditional model. The skewness parameter S unique to the modified model provides a physical criterion for the synergistic mechanism of different matrices: Wide-range sustained-release (WSA): S value is 0.285 (highest among the four groups). This indicates that after 2-methylbutyraldehyde combines with WSA, the rate of decay of synergistic sensory intensity is the slowest with increasing matrix concentration, exhibiting excellent formulation stability and tolerance. Precision burst type (PR-7): S value is 0.032 (lowest among the four groups). This indicates that the combination exhibits extremely high symmetry and specificity, with synergistic effects concentrated at specific concentration points, belonging to a powerful and precise synergistic mode.

[0041] Shape factor ( Physiological response characteristics verified: in the modified model The values ​​are all between 1.95 and 2.45 (significantly greater than 1): this physically represents a narrow and sharp high-energy characteristic in the sensory response curve, rather than a smooth passivation characteristic. This result verifies that the binding of 2-methylbutyraldehyde to umami receptors has a significant chemosensory amplification effect; once the critical concentration is reached, the enhancement of umami will increase explosively.

[0042] Example 2: Quantitative Comparative Analysis of Co-enhancing Flavors Based on Sensory Modified Lorentz Model and Gaussian Model: To verify the scientific validity of the sensory-modified asymmetric Lorentz model used in this invention, this embodiment compares the fitting parameters of this model with those of the traditional Gaussian model for the synergistic effects of heptanal, Z-4-heptenal, and PR-7.

[0043] Model Fitting: The synergistic flavor-enhancing data generated by heptanal and Z-4-heptenal with PR-7 were fitted using both the sensory-modified asymmetric Lorentz model and the Gaussian model. The sensory-modified asymmetric Lorentz model fitting formula is the same as in Implementation Case 1. The Gaussian model fitting formula is as follows: .

[0044] It should be noted that the Lorentz model used in this embodiment aims to characterize the unimodal response physical characteristics of aroma substances to the synergistic enhancement effect of umami matrix as a function of matrix concentration. Those skilled in the art should understand that, depending on the sensory characteristics of different flavor systems, the dispersion of experimental data, or the asymmetry of sensory evaluation, the above-mentioned fitting model can be equivalently replaced or its parameters modified, and all such transformations fall within the scope of protection of this invention. Equivalent substitution of mathematical models: Any nonlinear fitting model that can exhibit central peak and effective response width characteristics can be used to replace the Lorentz model mentioned above. This includes, but is not limited to: Gaussian models, Voigt models (convolution of Lorentz and Gaussian), Pearson VII models, log-normal models, or derivative models based on the Sigmoid function. These models are effective in calculating the optimal concentration (X) of the synergistic effect. c When considering the effective concentration range (B value), it has the same technical logic as this invention.

[0045] Sensory-modified asymmetric model: Considering that the human senses may perceive umami concentration with inconsistent slopes around the threshold (i.e., a faster rise and a slower fall), the above model has been further transformed into an asymmetric Lorenz model by introducing a skewness factor or shape parameter. This type of modification only affects the goodness-of-fit (R²) of the curve. 2 Local optimization of ) and its extracted core physical parameters (X) c The legal effect of (B, Area) in assessing flavor enhancement characteristics is consistent with that of this invention.

[0046] Physical equivalence of parameters: The full width at half maximum (FWHM) B defined in this invention can be equivalently expressed as the standard deviation σ, FWHM, or inflection point spacing under different mathematical models. As long as it characterizes the concentration span of the synergistic effect from initiation to decay, it is considered an application of the technical solution of this invention.

[0047] Data preprocessing transformations: Performing logarithmic transformations, normalization, or weighted fitting on the original sensory intensity data before fitting to eliminate experimental errors or conform to the Weber-Fechner sensory law are also included within the scope of the claims of this invention.

[0048] Comparison of fitting results: The results of fitting the synergistic experimental data of heptanal, Z-4-heptenal, and PR-7 are shown in Table 2. Table 2. Comparison of fitting results of different mathematical models for sensory coordination data

[0049] Model fitting results analysis: The synergistic enhancement effect of heptanal and Z-4-heptenal on umami intensity conforms to the Lorentz peak function distribution, where the fitted peak center (X) c The study precisely defined the specific concentration points at which the two aldehydes exerted the strongest umami-enhancing effect, while the full width at half maximum (FWHM) parameter (B) quantified the effective concentration bandwidth of this synergistic effect. Combined with the correction of the asymmetry factor (S), the study fully revealed the umami sensory gain as the aldehyde concentration gradient changed. The intensity of volatile aldehydes exhibits a nonlinear dynamic characteristic of first rising and then tending to saturate or fall back, thus scientifically verifying the significant modulating effect of volatile aldehydes on the umami system at a specific above-threshold concentration.

[0050] The embodiments provided in this invention are intended to illustrate the technical concept and features of the invention, and are intended to enable those skilled in the art to understand the content of the invention and implement it accordingly. They should not be construed as limiting the scope of protection of this invention. All equivalent changes or modifications made according to the spirit and essence of this invention (such as sensory corrections or mathematical transformations of the fitting model parameters) should be covered within the scope of protection of this invention.

[0051] Example 3: Determination of the optimal concentration of heptanal for synergistic flavor enhancement of MSG: Umami Standard Identification Threshold Determination: The sensory identification threshold (C) of MSG in aqueous solution was determined using the ASTM E679-04 method. TV The concentration was 0.5 g / 100 mL (approximately 1.48 mmol / L).

[0052] Aroma compound concentration calibration: Using 3-AFC and Weber-Fechner law fitting, the half-maximum intensity concentration (HIC) of heptanal in the MSG matrix was determined to be 5.702 × 10⁻⁶. -8 mol / L. This will be used as the fixed addition amount for subsequent experiments.

[0053] Construction and determination of the synergistic evaluation system: A series of MSG solutions with concentrations covering 0.25×C were prepared. TV Up to 8×C TV .

[0054] Control group: Pure MSG solutions of the above gradient.

[0055] Experimental group: 5.702 × 10⁻⁶ mg / L MSG solution was added to each of the above concentration points. -8 heptanal at mol / L.

[0056] Evaluation: The evaluation team used a 15-cm scale to score the umami intensity of the two groups of samples and calculated the difference ( Intensity).

[0057] Model fitting and parameter determination: Plot the logarithm of MSG concentration on the x-axis, and... Using intensity as the ordinate, a nonlinear regression was performed on the experimental data using the Lorentz peak function. The fitting results show that the model's coefficient of determination R0 is... 2 A value >0.99 indicates that the model can accurately describe the synergistic freshness enhancement mechanism. The peak center parameter X is extracted from the fitted curve. c The results showed X c The corresponding MSG concentration is approximately 1.074 mmol / L.

[0058] Technical effect evaluation: Experimental results show that the synergistic flavor-enhancing effect of heptanal on MSG exhibits a typical inverted V-shaped characteristic. Optimal synergistic concentration X c The sensory recognition threshold of MSG is highly consistent with that of MSG, and the actual optimal point is slightly shifted towards lower concentrations. This indicates that when enhancing flavor with heptanal, controlling the MSG concentration below the threshold (approximately 1.05 mmol / L) yields the greatest synergistic gain, significantly outperforming the flavor enhancement effect under high MSG concentration conditions.

[0059] Application Guidelines: When using heptanal to enhance the umami flavor of broth, the concentration of monosodium glutamate (MSG) should be controlled at around 1.05 mmol / L. At this concentration, trace amounts of heptanal can provide the maximum umami enhancement, achieving the best cost-effectiveness.

[0060] Example 4: Synergistic effect analysis of the combination of 2-methylbutyraldehyde and IMP: This embodiment demonstrates the application of the method of the present invention in the development of formulations with high-intensity synergistic effects in the "branched aldehyde-nucleotide" system.

[0061] Parameter setting and system construction: Following the method described in Example 1, the sensory recognition threshold C of disodium inosinate (IMP) was determined. TVSubsequently, the HIC of 2-methylbutyraldehyde in the IMP matrix was determined to be 13.004 × 10⁻⁶. -8 The concentration was set at mol / L, and this was used as a fixed addition amount. A sensory evaluation was performed using an IMP concentration gradient system covering the area above and below the threshold.

[0062] Data fitting and feature parameter extraction: The synergistic dose-response curve was fitted using the Lorentz model, and the peak intensity parameter (Y) of the curve was extracted. max ).

[0063] Technical effect evaluation: The fitting results show that the peak value of the umami enhancement intensity Y of this combination is max The score reached 4.34 (out of 15), the highest value among all test combinations. The optimal matrix concentration X obtained from the fitting was... c It is located near the recognition threshold of IMP.

[0064] The results indicate that the combination of 2-methylbutyraldehyde and threshold concentration of IMP has an "explosive" flavor enhancement characteristic, which is suitable for the development of seasoning products that require instantaneous high-intensity umami perception and can achieve a significant flavor explosion with a low amount of nucleotides.

[0065] Example 5: Long-term synergistic evaluation of the combination of Z-4-heptenal and IMP: This embodiment focuses on IMP, using the method of the present invention to evaluate its synergistic potential with unsaturated aldehydes.

[0066] Parameter determination and system construction: The HIC of Z-4-heptenal in the IMP matrix was determined to be 17.752 × 10⁻⁶. -8 mol / L. Construct a collaborative evaluation system and collect data following the steps described above.

[0067] Data fitting and feature parameter extraction: The curve is fitted using the Lorentz model, and the area under the curve (Area) parameter is calculated.

[0068] Mechanism verification and effect evaluation: The fitting results show that the Lorentz area of ​​this combination reaches 17.494, which is significantly higher than that of other experimental groups. Meanwhile, molecular dynamics simulations corroborate this result, showing that the binding free energy of this combination (…) G bind The lowest value (-19.02 kcal / mol) indicates the formation of a stable receptor-ligand complex.

[0069] This example demonstrates that the combination of Z-4-heptenal and IMP has a "long-lasting" flavor-enhancing characteristic, with a large total synergistic effect and stable binding, making it suitable for food matrices (such as soup bases) that require flavor persistence, and can maintain a high level of flavor enhancement even when the matrix concentration fluctuates.

[0070] Example 6: Narrow-domain synergistic properties analysis of the combination of heptanal and umami peptide (PR-7): This embodiment demonstrates the application of the method of the present invention in identifying highly specific (narrow window) cooperative combinations.

[0071] Parameter determination and system construction: The HIC of heptanal in the PR-7 matrix was determined to be 5.216 × 10⁻⁶. -8 mol / L. Construct a gradient system and perform sensory evaluation.

[0072] Data fitting and feature parameter extraction: The data is fitted using the Lorentz model, with a focus on examining the full width at half maximum (FWHM) parameter of the model.

[0073] Technical Effect Evaluation: The fitting results show that the B value of this combination is extremely small, only 0.3944 mmol / L. This indicates that the synergistic flavor-enhancing effect of heptanal on PR-7 is concentrated within a very narrow concentration range, exhibiting a significant "narrow-range flavor enhancement" characteristic. In industrial applications, for such combinations, the method of this invention must be used to precisely determine the amount of PR-7 added to X. c The concentration must be carefully controlled; otherwise, the flavor-enhancing effect will rapidly diminish if the concentration is deviated from this level.

[0074] This finding has key guiding implications for the development of precision-formulated foods, such as medical foods. This example demonstrates a synergistic combination that is extremely sensitive to matrix concentration and is suitable for precision-formulated products.

[0075] Example 7: Analysis of the broad-domain synergistic properties of the Z-4-heptenal and PR-7 combination: This embodiment aims to evaluate the adaptability of aroma substances over a wide concentration range.

[0076] Parameter determination and system construction: The HIC of Z-4-heptenal in the PR-7 matrix was determined to be 18.488 × 10⁻⁶. -8 mol / L. Construct a gradient system and perform sensory evaluation.

[0077] Data fitting and feature parameter extraction: The data is fitted using the Lorentz model, and the full width at half maximum (B value) parameter is extracted.

[0078] Technical effect evaluation: The fitted curve shows that the B value of this combination is relatively large, reaching 3.815 (1.39 mmol / L), exhibiting a flat and broad inverted V-shaped curve. This indicates that Z-4-heptenal belongs to the "broad-range flavor-enhancing" aroma substance, which can adapt to large fluctuations in PR-7 concentration while maintaining a relatively stable flavor-enhancing effect.

[0079] This characteristic makes it ideal for production processes where the concentration of the substrate is difficult to control precisely, such as fermented foods (e.g., soy sauce, bean paste), and it has a very high tolerance for process errors.

[0080] Example 8: High-sensitivity synergistic evaluation of the combination of Z-4-heptenal and WSA: This embodiment evaluates the sensitivity characteristics of disodium succinate (WSA), a characteristic umami substance in shellfish, for synergistic umami enhancement.

[0081] Parameter determination and system construction: The HIC of Z-4-heptenal in the WSA matrix was determined to be 15.838 × 10⁻⁶. -8 mol / L. Construct a gradient system and perform sensory evaluation.

[0082] Data fitting and feature parameter extraction: Based on the construction of the co-curve, the sensitivity coefficient (α) is further analyzed by combining the Weber-Fechner equation.

[0083] Technical effectiveness evaluation: The analysis results show that the α of this combination is as high as 4.32, the highest among all test groups, and the rising edge of the synergistic curve is extremely steep. This indicates that the combination has extremely high response sensitivity to low concentrations of WSA.

[0084] In the development of seafood-flavored seasonings, trace amounts of Z-4-heptenal can significantly amplify the umami intensity. 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, 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 determining the optimal concentration of umami base for enhancing aroma, characterized in that, include: Step 1, benchmark determination: Determine the sensory recognition threshold of umami base; Step 2, Aroma Calibration: Determine the half-maximum intensity concentration of the aroma substance to be tested in the umami matrix, and set the half-maximum intensity concentration value as the fixed addition concentration of the aroma substance; Step 3, Construction of the collaborative evaluation system: Prepare umami matrix solutions with different concentrations as control groups, and add aroma substances to the same umami matrix solution as experimental groups; the concentration of aroma substances in the experimental groups is set to a fixed concentration. Step 4, Sensory Quantification: Perform sensory evaluation on the experimental group and the control group, and calculate the difference in umami enhancement intensity; Step 5, Model Fitting: The logarithmic or absolute value of the umami matrix concentration is used as the x-axis, and the difference in umami enhancement intensity is used as the y-axis. A nonlinear unimodal response model is used for fitting to obtain characteristic parameters that characterize the distribution of the synergistic effect, and the optimal concentration is determined based on the characteristic parameters.

2. The method for determining the optimal umami base concentration for aroma enhancement according to claim 1, characterized in that, The nonlinear unimodal response model in step 5 is a peak function model with center location and span evaluation functions, including one or more of the following: Gaussian model, standard Lorentz model, pseudo Voigt model, or log-normal distribution model.

3. The method for determining the optimal umami base concentration for aroma enhancement according to claim 1, characterized in that, The nonlinear single-peak response model in step 5 includes at least three characteristic parameters: The peak location parameter Xc is used to characterize the matrix concentration coordinates when the synergistic effect reaches its maximum value. The response width parameter B is used to characterize the span of the concentration range effectively covered by the synergistic effect; Furthermore, when the response width parameter value B ≥ 3.5, the aroma substance is defined as a broad-range umami-enhancing aroma. When the response width parameter value B ≤ 2.2, the aroma substance is determined to be a narrow-range umami-enhancing aroma. The asymmetric correction parameter S is used to characterize the differential decay rate of the model caused by sensory odor masking or saturation after exceeding the optimal concentration. The nonlinear single-peak response model constructs a cooperative response curve with an inverted V-shaped characteristic based on three characteristic parameters.

4. The method for determining the optimal umami base concentration for aroma enhancement according to claim 3, characterized in that, The nonlinear unimodal response model in step 5 adopts a sensory-modified asymmetric Lorentz model, expressed as: in, To enhance the umami intensity; x is the logarithmic value of concentration based on the threshold. The morphological index is used to control the sharpness of the peak; A represents the area under the curve correlation constant. B represents the baseline background noise value; S represents the response width parameter; and S represents the asymmetric correction parameter.

5. The method for determining the optimal umami base concentration for aroma enhancement according to claim 1, characterized in that, The umami base is selected from one or more of monosodium glutamate, disodium inosinate, disodium succinate, or umami peptides.

6. The method for determining the optimal umami matrix concentration for aroma enhancement according to claim 1, characterized in that, The aroma compounds are aliphatic aldehydes.

7. A synergistic flavor-enhancing composition, characterized in that, The synergistic flavor-enhancing composition comprises an umami matrix and aliphatic aldehyde aroma substances, and the concentration ratio of the two is as follows: The method according to any one of claims 1-6 determines the result.

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