A method for evaluating the synergistic effect of umami based on electroencephalogram (EEG) monitoring technology

Abstract

This invention belongs to the field of food quality evaluation and provides a method for evaluating the synergistic effect of umami based on electroencephalogram (EEG) monitoring technology. This invention mainly addresses the problem that in traditional sensory evaluation methods, psychological or physiological factors such as subjective judgment of sensory personnel, forced selection or scoring, individual differences in umami perception and sensitivity may affect the accuracy of sensory experimental results. This invention discloses methods for umami sample selection and intensity assessment, EEG signal acquisition experimental paradigms, quantitative evaluation and statistical analysis, feature extraction and response difference analysis, etc. Taking the human response to umami signals as the research object and using EEG signal detection and analysis as the main method, it explores the response mechanism of typical umami compounds in the human brain, providing a new theoretical basis for the quantitative evaluation of human umami intensity, and is worthy of widespread application.

Description

Technical Field

[0001] This invention belongs to the field of food quality evaluation, specifically relating to a method for evaluating the synergistic effect of umami based on electroencephalogram (EEG) monitoring technology. Background Technology

[0002] Taste is one of the important physiological senses for evaluating food quality. In the past two decades, with the discovery of umami receptors and the development of umami evaluation methods, umami, as one of the five basic tastes, has gradually been accepted by people. However, due to the increasing discovery of umami substances and their analogues, traditional umami evaluation methods are gradually failing to meet the needs of contemporary food umami evaluation. In traditional sensory evaluation methods, subjective judgments of sensory personnel, forced choices or ratings, individual differences in umami perception, and differences in umami sensitivity, among other psychological or physiological factors, can affect the accuracy of sensory experiment results to some extent. This poses new challenges and difficulties for the quantitative evaluation of umami intensity based on human perception.

[0003] Methods for evaluating umami currently include artificial sensory analysis, intelligent sensory analysis, and biosensors. Among these, artificial sensory evaluation is the most important means of evaluating taste. Commonly used methods include descriptive taste methods, three-point test, scaling scoring, taste dilution analysis, and comparative taste dilution analysis. However, artificial sensory methods are subjective evaluation methods, and their experimental results are affected by human subjective factors. Intelligent sensory methods, such as electronic tongues, are biomimetic taste systems, but they are limited by sample type and cannot provide strong response signals to all samples. Therefore, they cannot comprehensively reflect the true human perception of umami.

[0004] Electroencephalography (EEG) is a common method for studying changes in brain waves during brain activity. It is a comprehensive reflection of the electrophysiological activity of brain nerve cells on the scalp surface. EEG has the advantages of high temporal resolution, relative time saving, quantifiable measurement results, and immunity to subjective human interference. Therefore, EEG is used to explore the brain's perception of umami. Brain response topology maps can show the topological changes in the brain's response to stimuli at different times, and can be used to explore the distribution of relevant potentials in the scalp's response to umami stimuli.

[0005] Currently, the most commonly used seasonings on the market are compound seasonings such as chicken essence, whose main component is monosodium glutamate (MSG), the sodium salt of glutamic acid, containing a small amount of glutamic acid. Disodium inosinate is often added to enhance umami flavor, utilizing the synergistic umami-enhancing effect between MSG and disodium inosinate. Studies have shown that disodium inosinate allows MSG to bind more tightly to umami receptors at their binding sites, resulting in better binding in the active pocket domain, thus achieving a synergistic effect. Glutamic acid is an acidic amino acid containing two carboxyl groups, chemically known as α-aminoglutarate, abundant in cereal proteins and playing a crucial role in protein metabolism. The umami flavor perceived in our daily diet is not "pure" umami, but rather an enhanced flavor resulting from this synergistic effect. Currently, there are few reports on the impact of this synergistic effect on the human brain. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for evaluating the synergistic effect of umami based on electroencephalogram (EEG) monitoring technology, thereby resolving the issues in the prior art. The technical solution adopted by this invention is as follows:

[0007] A method for evaluating the synergistic effect of umami based on electroencephalogram (EEG) monitoring technology includes the following steps:

[0008] S1: Umami samples were selected for taste EEG induction. The umami intensity of several umami solutions of different concentrations was evaluated using the 0-100 linear scaling method. EEG equipment was used to collect EEG signals under stimulation by umami solutions of different concentrations.

[0009] S2: A quantitative evaluation method was used to evaluate the umami intensity of different umami solutions and mixed solutions. The molar concentration of individual samples and mixed samples was kept consistent during the experiment. A 64-channel EEG device was used to collect EEG signals under different stimuli. Subsequently, all samples were statistically analyzed using mixed model analysis of variance and Bonfurney post-hoc test analysis. In this method, the sensory organ was used as a random factor, and the sample, rhythm wave, and different brain regions were used as fixed factors.

[0010] S3: Select data from a specific time period, extract features for analysis, including analyzing the energy spectrum response results to explore the location of the maximum signal response; and analyzing brain regions and rhythmic waves with taste-encoded responses by combining the average spectral response signal distribution of the brain with response topology maps to explore the differences in the brain's response to synergistic stimuli of different umami flavors.

[0011] Furthermore, in step S1, the umami sample selected is a typical umami stimulant, including monosodium glutamate, disodium succinate, and disodium inosinate.

[0012] Furthermore, in step S1, when collecting EEG signals under stimulation with different concentrations of umami solution using an EEG device, a 64-channel EEG device is used to collect EEG signals under stimulation with different concentrations of umami solution; the total duration of a single collection is 10-20 seconds, the electrode impedance is 5-20 kΩ, and the sampling rate is 200-1000 Hz.

[0013] During collection, the patient first rinsed their mouth with 8-12 mL of water and physiological saline for 8-10 seconds, then tasted 8-15 mL of umami standard solutions with different umami intensities. After 10-15 seconds, the patient spat out the solution, rinsed their mouth with water, rested for 20-30 seconds, and then tasted the next group. This process was repeated 3-6 times, and the average value was taken.

[0014] Furthermore, in step S1, when evaluating the umami intensity of several umami solutions of different concentrations, the determination of the concentration of umami agent with similar sensory intensity is obtained by combining the sensory intensity evaluation results of the umami intensity quantitative evaluation method with the 0-100 linear scaling method.

[0015] Furthermore, in step S3, the frequency and rhythm wave response are analyzed, including: importing data and electrode positioning, followed by filtering preprocessing, calculating the power spectrum value and performing analysis. The analysis indicators include the energy of each rhythm: delta wave, theta wave, alpha wave, beta wave, and gamma wave, so as to explore the influence of the taste produced by different umami solutions on different rhythm waves and regions of EEG.

[0016] Furthermore, step S3 also includes studying the effects of different umami solutions, rhythmic waves, and different brain regions on brain responses using a significant difference analysis method: the brain responses to different umami standard solutions and mixed solutions during a specific time period randomly selected from all subjects are used to obtain a topological map of the brain in response to umami stimulation, and the location and degree of umami response in each activation area are analyzed.

[0017] By combining different umami solutions, rhythmic waves, and the brain's response to different brain regions, we can determine whether the brain can distinguish between different standard umami solutions and mixed solutions.

[0018] To analyze whether there are significant differences in the brain's response to δ, θ, and α waves induced by umami stimulation from different umami standard solutions and mixed solutions; whether the synergistic effect of umami stimulation on brain response is a simple superposition between rhythmic waves; and whether synergistic effects can be observed between samples under different rhythmic waves.

[0019] Finally, a significance analysis was performed on the responses in different brain regions to explore whether there were significant differences in the EEG signal responses in different brain regions. Mathematical statistical analysis was used to compare the taste response signals induced by different frequencies and concentrations of umami with the response differences between different brain regions and different brain anatomical regions.

[0020] To establish a method for analyzing umami perception response signals in the human brain based on electroencephalography (EEG) combined with mathematical statistical methods, and to explore the feasibility of detecting umami response signals using EEG.

[0021] The present invention has the following beneficial effects:

[0022] This invention takes the human response to umami signals as the research object and uses electroencephalogram (EEG) signal detection and analysis as the main method to explore the response mechanism of typical umami compounds in the human brain. It provides a new theoretical basis for the quantitative evaluation of human umami intensity and is worthy of widespread promotion. Attached Figure Description

[0023] Figure 1 This is a flowchart of the electroencephalogram (EEG) experiment in an embodiment of the present invention;

[0024] Figure 2 The sensory intensity of each umami flavor in the sensory experiment of the embodiments of the present invention;

[0025] Figure 3 The results of variance analysis of the brain response to different sensory intensities of monosodium glutamate, disodium succinate, and disodium inosinate in the embodiments of the present invention are as follows:

[0026] Figure 4 The results of variance analysis of the brain's response to changes in the δ, θ, and α values ​​of biological rhythmic waves induced by monosodium glutamate, disodium succinate, and disodium inosinate in this embodiment of the invention are as follows:

[0027] Figure 5 These are the brain response topology results to different umami stimuli in the embodiments of the present invention. (a) is the brain response topology result of monosodium glutamate with different sensory intensities; (b) is the brain response topology result of monosodium glutamate, disodium succinate and disodium inosinate with similar sensory intensities.

[0028] Figure 6 These are the results of variance analysis of the responses of different brain regions to umami stimuli in embodiments of the present invention;

[0029] Figure 7 These are the variance analysis results of the umami intensity of different umami sensory analyses in the embodiments of the present invention;

[0030] Figure 8 This is a topological diagram of the brain's response to umami stimulation in an embodiment of the present invention: monosodium glutamate, disodium inosinate, and a mixture of monosodium glutamate and disodium inosinate;

[0031] Figure 9 The results of variance analysis on the brain response to changes in rhythmic waves δ, θ, and α caused by different umami monosodium glutamate, disodium inosinate, and mixtures of monosodium glutamate and disodium inosinate in the oral cavity in the embodiments of the present invention.

[0032] Figure 10 These are the results of variance analysis of the responses of different brain regions to umami stimuli in embodiments of the present invention;

[0033] Figure 11 These are power spectrum topographic maps of different umami-enhancing monosodium glutamate (MSG), disodium inosinate, and mixtures of MSG and disodium inosinate at 6 s in embodiments of the present invention. Detailed Implementation

[0034] The following will be based on embodiments of the present invention. Figures 1-2 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.

[0035] This invention addresses the problem that in traditional sensory evaluation methods, psychological or physiological factors such as the subjective judgment of sensory officers, forced selection or rating, and individual differences in umami perception and sensitivity can affect the accuracy of sensory experimental results. It proposes a method for evaluating the synergistic effect of umami based on electroencephalogram (EEG) monitoring technology. Umami samples for taste-induced EEG stimulation were selected, and the umami intensity of several umami solutions of different concentrations was evaluated using a 0-100 linear scaling method. The experimental paradigm for umami stimulation was as follows: 64-channel EEG equipment was used to collect EEG signals under stimulation with umami solutions of different concentrations. The total duration of each acquisition was 10–20 seconds. At the start of the experiment, after rinsing the mouth with 8–12 mL of water and saline solution for 8–10 seconds, the patient tasted 8–15 mL of umami standard solutions of different intensities, spitting it out after 10–15 seconds and rinsing with water. After a 20–30 second rest, the next group of tastings was performed. The experiment was repeated 3–6 times, and the results were averaged. S2: Quantitative evaluation methods were used to assess the umami intensity of different umami solutions and mixed solutions, maintaining consistent molar concentrations for individual and mixed samples. A 64-channel EEG device was used to collect EEG signals under different stimuli. Subsequently, mixed-model analysis of variance and Bonfurney post-hoc tests were performed on all samples, with sensory organs as random factors and samples, rhythmic waves, and different brain regions as fixed factors. S3: Data from specific time periods, such as 5–10 seconds, were selected, and features were extracted and analyzed. Feature extraction mainly involved analyzing the spectral response results to explore the location of the maximum signal response; and analyzing brain regions and rhythmic waves with taste-encoded responses by combining the average spectral response signal distribution of the brain with response topology maps. This aimed to explore the differences in the brain's response to synergistic stimuli of different umami flavors.

[0036] like Figure 1A method for evaluating the synergistic effect of umami based on electroencephalogram (EEG) monitoring technology includes the following steps:

[0037] S1: Umami samples were selected for taste-based EEG induction. The umami intensity of several umami solutions of different concentrations was evaluated using a 0-100 linear scaling method. EEG signals from different concentrations of umami solutions were acquired using a 64-channel EEG device. The total duration of each acquisition session was 10–20 seconds.

[0038] S2: A quantitative evaluation method was used to assess the umami intensity of different umami solutions and mixed solutions, maintaining consistent molar concentrations for both individual and mixed samples. A 64-channel EEG device was used to collect EEG signals under different stimuli. Subsequently, all samples underwent mixed-model analysis of variance and Bonfurney post-hoc test analysis, with sensory organs as random factors and samples, rhythmic waves, and different brain regions as fixed factors.

[0039] S3: Select data from a specific time period, such as 5–10 seconds, extract features for analysis. Feature extraction mainly involves analyzing the energy spectrum response results to explore the location of the maximum signal response; and analyzing brain regions and rhythmic waves with taste-encoded responses by combining the average spectral response signal distribution of the brain with response topology maps. This aims to explore the differences in the brain's response to synergistic stimuli of different umami flavors.

[0040] Specifically, in S2, Mixed Model ANOVA and Bonferroni Post Hoc Test are commonly used analytical methods in statistics to process data in complex experimental designs, especially in cases of repeated measures or multi-factor interactions.

[0041] Mixed-model ANOVA is suitable for experimental designs that include both fixed and random effects. Fixed effects refer to controllable experimental conditions of interest to the researcher (such as different treatment groups), while random effects refer to uncontrollable, randomly varying factors in the experiment (such as individual differences among subjects). The Bonfurney post-hoc test is a multiple comparison correction method used to further compare differences between groups after the ANOVA has been made significant.

[0042] This invention combines mixed-model ANOVA with Bonfurney post-hoc tests. First, mixed-model ANOVA is used to determine whether experimental conditions (fixed effects) or time points (repeated measures) have a significant impact on the outcome variable. Once a significant effect is identified, Bonfurney post-hoc tests are used to perform pairwise comparisons between groups to identify the specific sources of difference.

[0043] Furthermore, in step S1, the taste stimulation sample selected is a typical umami stimulant, specifically including but not limited to monosodium glutamate, disodium succinate, and disodium inosinate.

[0044] Furthermore, in step S1, the established experimental paradigm for umami stimulation specifically involves: using a 64-channel EEG device to collect EEG signals under stimulation with umami solutions of different concentrations. The total duration of a single acquisition is 10–20 seconds, the electrode impedance is 5–20 kΩ, and the sampling rate is 200–1000 Hz. At the start of the experiment, after rinsing the mouth with 8–12 mL of water and physiological saline for 8–10 seconds, the patient tastes 8–15 mL of umami standard solutions with different umami intensities, spits it out after 10–15 seconds, and rinses with water. After a 20–30 second rest, the next group of tasters begins. The experiment is repeated 3–6 times, and the average result is taken.

[0045] Furthermore, in step S1, the umami intensity of several umami solutions with different concentrations is evaluated. Specifically, the concentration of umami agent with similar sensory intensity is determined by combining the sensory intensity evaluation results of the umami intensity quantitative evaluation method with the 0-100 linear scaling method.

[0046] Furthermore, in step S3, the frequency and rhythm wave response are analyzed. Specifically, the data and electrode positioning are imported, and then preprocessed by filtering to remove noise interference, removing electrooculogram, and re-reference, etc. The power spectrum value is further calculated and analyzed. The analysis indicators include the energy of each rhythm: δ, θ, α, β and γ, so as to explore the influence of the taste produced by different umami solutions on the different rhythm waves and regions of EEG.

[0047] Furthermore, in step S3, data from a specific time period, such as 5-10 seconds, is selected for data analysis. The EEG signal data is processed, including data import and electrode localization. After preprocessing such as filtering to remove noise interference, ocular de-o ...

[0048] Further, the effects of different umami solutions, rhythmic waves, and different brain regions on brain responses were investigated using significant difference analysis. The brain responses to different umami standard solutions and mixed solutions were obtained from randomly selected time periods across all subjects, leading to a topological map of the brain's response to umami stimuli. The location and intensity of umami responses in each activated area were analyzed. The brain's ability to distinguish between different umami standard solutions and mixed solutions was further assessed by combining the responses of different umami solutions, rhythmic waves, and different brain regions. Further analysis was conducted to determine whether there were significant differences (p<0.05) in the brain's response to δ, θ, and α waves induced by umami stimuli from different standard solutions and mixed solutions. The study also examined whether the synergistic effect of umami stimuli on brain responses was a simple superposition of rhythmic waves, and whether synergistic effects could be observed between samples under different rhythmic waves. Finally, a significance analysis was performed on the responses between brain regions to explore whether there were significant differences in EEG signal responses between different brain regions (p<0.005). This study employs mathematical statistical analysis to compare the taste response signals evoked by umami at different frequencies and concentrations with the response differences between different brain regions and different anatomical areas of the brain. It aims to establish a method for analyzing umami perception response signals in the human brain based on electroencephalography (EEG) combined with mathematical statistical methods, and to explore the feasibility of using EEG to detect umami response signals.

[0049] In this embodiment, the EEG acquisition device is a 64-channel EEG acquisition system. The experimental paradigm is as follows: Figure 1 As shown in the figure. First, the acquired EEG signals were preprocessed and subjected to significant difference analysis to explore the effects of monosodium glutamate (MSG) as a savory stimulus on the brain's response to delta-gamma wave frequency (1-100 Hz) and MSG concentration. The results are shown in the figure. Figure 2 Using frequency, monosodium glutamate (MSG) concentration, and electrode location as fixed factors, a three-way ANOVA and Bonfurney post-hoc test were employed to determine the brain response locations to umami. A two-way ANOVA and Bonfurney post-hoc test were then used, with the brain response region and concentration of MSG as fixed factors, to analyze the differences in response to umami stimuli among the left, central, and right brain regions (significance level α = 0.05). The results are as follows: Figure 3 As shown, the brain can distinguish between different types and different intensities of umami stimuli of the same type (p<0.05), and can partially distinguish between monosodium glutamate, disodium succinate, and disodium inosinate at different sensory intensities. Furthermore, the brain is more sensitive to disodium succinate and disodium inosinate stimuli than to monosodium glutamate; the brain's sensitivity to different umami stimuli with similar intensities varies, with higher sensitivity to low and high umami intensities than to medium umami intensities. The differences in EEG results are more significant compared to sensory experiment results. Figure 4 As shown, all three umami flavors significantly enhanced the brain's response to delta, theta, and alpha waves (p<0.05), with the alpha wave response showing the greatest change. Disodium succinate and disodium inosinate showed the greatest enhancement of the brain's alpha wave response. Figure 5 , 6 As shown, different brain regions respond differently to savory stimuli. The parietal-occipital region, the prefrontal region, and other regions all show significant differences (p<0.001), and the parietal-occipital region > the prefrontal region > the left temporal region, the central region, and the right temporal region, while there are no significant differences between the left temporal region, the central region, and the right temporal region.

[0050] In this embodiment, based on the signal response peaks around 5 seconds for monosodium glutamate and disodium succinate, and considering that action potentials during the first 5 seconds of tasting might interfere with the EEG signal response, EEG signals from a specific time period were selected and then statistically analyzed. Three-way ANOVA and Bonfurney post-hoc tests were used to compare the effects of different umami flavors on brain response frequency bands and regions; the relationship between changes in EEG response and umami intensity; and the relationship between changes in EEG response and different types of umami. The results are as follows: Figure 7 , 8 As shown, the brain can distinguish between monosodium glutamate (MSG) and disodium inosinate, as well as mixtures of MSG and disodium inosinate, but cannot distinguish between disodium inosinate and mixtures of MSG and disodium inosinate. The brain has some ability to distinguish between MSG, disodium inosinate, and mixtures of MSG and disodium inosinate, and this ability may be related to the latency period and the levels in different brain regions. Figure 9 As shown, umami stimulation from monosodium glutamate (MSG), disodium inosinate, and mixtures of MSG and disodium inosinate elicited significant differences in brain responses to delta, theta, and alpha waves (p<0.05). The synergistic effect of umami stimulation on brain responses is not a simple superposition of rhythmic waves; its mechanism is more complex. In alpha3 waves (12-13 Hz), mixtures of MSG and disodium inosinate exhibited a synergistic effect with MSG and disodium inosinate, while no synergistic effect was found in alpha1 waves (8-9 Hz) and alpha2 waves (9-12 Hz). Further results are shown in […]. Figure 10 Significant differences were observed between the parieto-occipital region and all other regions, including the prefrontal cortex, left temporal cortex, central cortex, and right temporal cortex. Significant differences were also observed between the prefrontal cortex and the left temporal cortex, and between the prefrontal cortex and the right temporal cortex (p<0.005); similar differences were also observed between the central cortex and the left temporal cortex, and between the central cortex and the right temporal cortex (p<0.005). A mixture of monosodium glutamate and disodium inosinate also showed significant enhancement of the orbitofrontal cortex at 6-second stimulation (see...). Figure 11 Compared to monosodium glutamate or disodium inosinate, the mixture of monosodium glutamate and disodium inosinate showed a significantly increased response area and average power.

[0051] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, alterations, substitutions, or variations made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention shall fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for evaluating the synergistic effect of umami based on electroencephalogram (EEG) monitoring technology, characterized in that, Includes the following steps: S1: Umami samples were selected for taste EEG induction. The umami intensity of several umami solutions of different concentrations was evaluated using the 0-100 linear scaling method. EEG equipment was used to collect EEG signals under stimulation by umami solutions of different concentrations. S2: A quantitative evaluation method was used to evaluate the umami intensity of different umami solutions and mixed solutions. The molar concentration of individual samples and mixed samples was kept consistent during the experiment. A 64-channel EEG device was used to collect EEG signals under different stimuli. Subsequently, all samples were statistically analyzed using mixed model analysis of variance and Bonfurney post-hoc test analysis. In this method, the sensory organ was used as a random factor, and the sample, rhythm wave, and different brain regions were used as fixed factors. Individual samples include: monosodium glutamate, disodium succinate, and disodium inosinate; The mixed sample includes a mixture of monosodium glutamate and disodium inosinate; S3: Select data from a specific time period, extract features for analysis, including analyzing the energy spectrum response results to explore the location of the maximum signal response; and analyzing brain regions and rhythmic waves with taste-encoded responses by combining the average spectral response signal distribution of the brain with response topology maps to explore the differences in the brain's response to synergistic stimuli of different umami flavors. Step S3 further includes studying the effects of different umami solutions, rhythmic waves, and different brain regions on brain responses using a significant difference analysis method: the brain responses to different umami standard solutions and mixed solutions during a specific time period randomly selected from all subjects are used to obtain a topological map of the brain in response to umami stimulation, and the location and degree of umami response in each activation area are analyzed. By combining different umami solutions, rhythmic waves, and the brain's response to different brain regions, we can determine whether the brain can distinguish between different standard umami solutions and mixed solutions. To analyze whether there are significant differences in the brain's response to δ, θ, and α waves induced by umami stimulation from different umami standard solutions and mixed solutions; whether the synergistic effect of umami stimulation on brain response is a simple superposition between rhythmic waves; and whether synergistic effects can be observed between samples under different rhythmic waves. Finally, a significance analysis was performed on the responses in different brain regions to explore whether there were significant differences in the EEG signal responses in different brain regions. Mathematical statistical analysis was used to compare the taste response signals induced by different frequencies and concentrations of umami with the response differences between different brain regions and different brain anatomical regions. To establish a method for analyzing umami perception response signals in the human brain based on electroencephalography (EEG) combined with mathematical statistical methods, and to explore the feasibility of detecting umami response signals using EEG.

2. The method for evaluating the synergistic effect of umami based on electroencephalogram (EEG) monitoring technology as described in claim 1, characterized in that, In step S1, the selected umami sample is a typical umami stimulant, including monosodium glutamate, disodium succinate, and disodium inosinate.

3. The method for evaluating the synergistic effect of umami based on electroencephalogram (EEG) monitoring technology as described in claim 1, characterized in that, In step S1, when collecting EEG signals under stimulation with different concentrations of umami solution using an EEG device, a 64-channel EEG device is used to collect EEG signals under stimulation with different concentrations of umami solution; the total duration of a single collection is 10~20 s, the electrode impedance is 5~20 kΩ, and the sampling rate is 200~1000 Hz. During collection, the patient first rinsed their mouth with 8-12 mL of water and physiological saline for 8-10 seconds, then tasted 8-15 mL of umami standard solutions with different umami intensities. After 10-15 seconds, the patient spat out the solution, rinsed their mouth with water, rested for 20-30 seconds, and then tasted the next group. This process was repeated 3-6 times, and the average value was taken.

4. The method for evaluating the synergistic effect of umami based on electroencephalogram (EEG) monitoring technology as described in claim 1, characterized in that, In step S1, when evaluating the umami intensity of several umami solutions with different concentrations, the determination of the concentration of umami agent with similar sensory intensity is obtained by combining the sensory intensity evaluation results of the umami intensity quantitative evaluation method with the 0-100 linear scaling method.

5. The method for evaluating the synergistic effect of umami based on electroencephalogram (EEG) monitoring technology as described in claim 1, characterized in that, In step S3, the frequency and rhythm wave response are analyzed, including: importing data and electrode positioning, followed by filtering preprocessing, calculating the power spectrum value and analyzing it. The analysis indicators include the energy of each rhythm: delta wave, theta wave, alpha wave, beta wave, and gamma wave, so as to explore the influence of the taste produced by different umami solutions on different rhythm waves and regions of EEG.

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