Rapid Quantitative Detection Method for Bitterness in Ganoderma lucidum

Abstract

This application provides a rapid quantitative detection method for the bitterness of Ganoderma lucidum, belonging to the technical field of traditional Chinese medicine efficacy and quality evaluation. The method includes: performing color difference detection on Ganoderma lucidum to obtain color difference characteristics; estimating the proportion of bitter triterpenes in Ganoderma lucidum based on the color difference characteristics using a target linear regression model, where the proportion of bitter triterpenes is the percentage of bitter triterpenes in total triterpenes; detecting the component content of Ganoderma lucidum to obtain the total triterpenes content; obtaining a bitterness intensity correction coefficient and a taste correction coefficient; calculating the theoretical bitterness value of Ganoderma lucidum based on the proportion of bitter triterpenes, the total triterpenes content, the bitterness intensity correction coefficient, and the taste correction coefficient; and mapping the theoretical bitterness value to the human perceived bitterness value using a target univariate linear model to obtain the predicted bitterness value. This application embodiment can achieve objective and accurate quantification of the bitterness of Ganoderma lucidum.

Description

Technical Field

[0001] This application relates to the field of efficacy and quality evaluation technology of traditional Chinese medicine, and in particular to a rapid quantitative detection method for the bitterness of Ganoderma lucidum. Background Technology

[0002] As a traditional Chinese medicine, the bitterness of Ganoderma lucidum is not only a key sensory characteristic but also an important indicator of the content of its pharmacodynamic substances (especially triterpenoids). Related technologies employ methods such as artificial sensory evaluation, single physicochemical detection (e.g., liquid chromatography), and biomimetic sensing technology (e.g., electronic tongue) to assess the bitterness of Ganoderma lucidum. However, artificial sensory evaluation methods are highly subjective, easily affected by individual differences, and difficult to standardize and scale up for quality control. While single physicochemical detection methods are accurate in qualitative and quantitative evaluation of bitterness, the pretreatment process is cumbersome, time-consuming, and labor-intensive, and the detected chemical component content cannot fully reflect the complex human taste perception experience. Although biomimetic sensing technology can objectively quantify bitterness, it is easily affected by cross-interference and signal drift from non-bitter components such as polysaccharides and amino acids when dealing with the complex matrix of traditional Chinese medicine, leading to inaccurate bitterness evaluation. Summary of the Invention

[0003] The main objective of this application is to propose a rapid quantitative detection method for the bitterness of Ganoderma lucidum, aiming to achieve objective and accurate quantification of the bitterness of Ganoderma lucidum.

[0004] To achieve the above objectives, a first aspect of this application proposes a rapid quantitative detection method for the bitterness of Ganoderma lucidum, the method comprising: Color difference detection was performed on Ganoderma lucidum to obtain color difference characteristics; The proportion of bitter triterpenes in Ganoderma lucidum is estimated based on the color difference characteristics using a target linear regression model; wherein, the proportion of bitter triterpenes is the proportion of bitter triterpenes in the total triterpenes; The total triterpenoid content was obtained by analyzing the components of the Ganoderma lucidum. Obtain the bitterness intensity correction coefficient and the taste correction coefficient; The theoretical bitterness value of Ganoderma lucidum is calculated based on the proportion of bitter triterpenes, the total triterpenes content, the bitterness intensity correction coefficient, and the taste correction coefficient. The theoretical bitterness value is mapped to the human perception of bitterness value using a target univariate linear model to obtain the predicted bitterness value.

[0005] In some embodiments, the target linear regression model is constructed according to the following steps: Obtain the true batch percentage of bitter triterpenes in Ganoderma lucidum samples; Color difference detection was performed on the batches of Ganoderma lucidum samples to obtain the batch color difference characteristics of the Ganoderma lucidum sample batches; A multiple linear regression model is constructed based on the batch color difference characteristics; The proportion of bitter triterpenes in the batch of Ganoderma lucidum samples is predicted by the multiple linear regression model based on the batch color difference characteristics. The fitting error is calculated based on the predicted proportion of bitter triterpenes in the batch of Ganoderma lucidum samples and the actual proportion of bitter triterpenes in the batch. Minimize the fitting error to update the model parameters of the multiple linear regression model, and obtain the target linear regression model.

[0006] In some embodiments, the batch color difference characteristics include index color difference characteristics of multiple preset color difference indices, and the step of constructing a multiple linear regression model based on the batch color difference characteristics includes: The correlation coefficient is calculated based on the color difference characteristics of each indicator and the proportion of bitter triterpenes in the actual batch. The target color difference index is selected from multiple preset color difference indices based on the correlation coefficient; Based on the target color difference index, construct the index variable and the regression coefficient of the index variable, and determine the random error term and intercept; The multiple linear regression model is constructed based on the indicator variable, the regression coefficient, the random error term, and the intercept.

[0007] In some embodiments, the formula for calculating the bitterness intensity correction coefficient is expressed as follows: , in, Indicates the bitterness intensity correction factor; This indicates the quantity of monomeric bitter components in a batch of Ganoderma lucidum samples; This indicates the quantity of bitter triterpenes in a batch of Ganoderma lucidum samples; ∑ represents the cumulative sum of bitter components. It represents the multiplication operation.

[0008] In some embodiments, the formula for calculating the taste correction coefficient is expressed as follows: , in, Indicates the taste correction coefficient; Indicates the first weight; The second weight is indicated by the bitterness value, the bitter aftertaste value, the astringency value, the astringency aftertaste value, the sweetness value, the umami value, and the richness value, which are obtained by detecting batches of Ganoderma lucidum samples using an electronic tongue.

[0009] In some embodiments, the method further includes: Obtain the true bitterness value of Ganoderma lucidum sample batches; Color difference detection is performed on the batches of Ganoderma lucidum samples to obtain the batch color difference characteristics. Based on the batch color difference characteristics, the target batch bitter triterpenoid ratio of the Ganoderma lucidum sample batch is estimated using the target linear regression model. The component content of the Ganoderma lucidum sample batch is detected to obtain the batch total triterpenoid content. Based on the target batch bitter triterpenoid ratio, the batch total triterpenoid content, the bitterness intensity correction coefficient, and the taste correction coefficient, the initial bitterness value of the Ganoderma lucidum sample batch is calculated. The bitterness error between the initial bitterness value and the true bitterness value is calculated. Minimize the bitterness error by updating the first weight and the second weight with a preset step size; The taste correction coefficients are updated based on the updated first weight and the updated second weight.

[0010] In some embodiments, the target univariate linear model is constructed according to the following steps: The reference bitterness value of the Ganoderma lucidum sample batch is calculated based on the proportion of bitter triterpenes in the target batch, the total triterpenes content of the batch, the bitterness intensity correction coefficient, and the updated taste correction coefficient. A univariate linear relationship is established between the reference bitterness value and the actual bitterness value to obtain the target univariate linear model.

[0011] The rapid quantitative detection method for bitterness in Ganoderma lucidum proposed in this application calculates the proportion of bitter triterpenes based on color difference characteristics, and calculates the theoretical bitterness value based on the proportion of bitter triterpenes, total triterpenes content, bitterness intensity correction coefficient, and taste correction coefficient, thereby objectively and accurately quantifying the bitterness of Ganoderma lucidum. To make the theoretical bitterness value close to the actual bitterness value perceived by the human body, a target univariate linear model is used to map the theoretical bitterness value to the human perceived bitterness value, obtaining a predicted bitterness value, thereby improving the accuracy and objectivity of bitterness evaluation.

[0012] This method can predict the bitterness of finished products during the development of food and medicine homology products, support flavor design, stabilize product flavor, and address the technical bottlenecks of flavor fluctuations and poor palatability of food and medicine homology materials.

[0013] This method has the advantage of rapid detection. It can be pre-fixed according to the place of origin through two sets of correction coefficients, and can be reused for a long time after one calibration. The model is only retested and updated when the production environment fluctuates significantly, thereby effectively improving the detection efficiency in production scenarios. Attached Figure Description

[0014] Figure 1 This is a flowchart of the rapid quantitative detection method for bitterness of Ganoderma lucidum provided in the embodiments of this application; Figure 2 This is a flowchart illustrating the construction process of the target linear regression model provided in the embodiments of this application; Figure 3 yes Figure 2 Flowchart of step S230; Figure 4 This is a heatmap of the correlation coefficients provided in the embodiments of this application; Figure 5 This is a binary linear regression residual analysis diagram of brightness provided in the embodiments of this application; Figure 6 This is a binary linear regression residual analysis diagram of chromaticity provided in the embodiments of this application; Figure 7 This is a flowchart illustrating the process of constructing the bitterness intensity correction coefficient provided in the embodiments of this application; Figure 8 This is a flowchart of the process for constructing the taste correction coefficients provided in the embodiments of this application; Figure 9 This is another flowchart of the rapid quantitative detection method for bitterness of Ganoderma lucidum provided in the embodiments of this application; Figure 10 This is a flowchart of the construction process of the target univariate linear model provided in the embodiments of this application. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0017] This application provides a rapid quantitative detection method for the bitterness of Ganoderma lucidum, aiming to achieve objective and accurate quantification of the bitterness of Ganoderma lucidum.

[0018] The rapid quantitative detection method for bitterness of Ganoderma lucidum provided in this application is specifically illustrated through the following embodiments. First, the rapid quantitative detection method for bitterness of Ganoderma lucidum in this application embodiment is described.

[0019] Figure 1 This is an optional flowchart of the rapid quantitative detection method for bitterness in Ganoderma lucidum provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S110 to S160.

[0020] Step S110: Perform color difference detection on Ganoderma lucidum to obtain color difference characteristics; Step S120: The proportion of bitter triterpenes in Ganoderma lucidum is estimated based on color difference characteristics using a target linear regression model; wherein, the proportion of bitter triterpenes is the proportion of bitter triterpenes in the total triterpenes. Step S130: Analyze the component content of Ganoderma lucidum to obtain the total triterpenoid content; Step S140: Obtain the bitterness intensity correction coefficient and the taste correction coefficient; Step S150: Calculate the theoretical bitterness value of Ganoderma lucidum based on the proportion of bitter triterpenes, total triterpenes content, bitterness intensity correction coefficient, and taste correction coefficient. Step S160: Map the theoretical bitterness value to the human perception of bitterness value using a target univariate linear model to obtain the predicted bitterness value.

[0021] In this embodiment, the Ganoderma lucidum was purchased from Baoding Xinyitang Herbal Biotechnology Co., Ltd. In step S110 of some embodiments, the surface of the Ganoderma lucidum is cleaned, and multiple sites on the cap layer (i.e., the epidermis) are selected for color difference detection to obtain the color difference characteristic value of each site. The average value of the color difference characteristic values ​​at each site is calculated to obtain the chromaticity parameter value. The hue parameter value is calculated based on the chromaticity parameter value, and the chromaticity parameter value and hue parameter value are used as the color difference characteristics. Multiple sites are evenly distributed, and the number of sites can be selected according to the actual situation, such as 5. The chromaticity parameter value is the specific value of the chromaticity parameter, which includes the lightness value (L). ), red-green value (a ), yellow-blue value (b) Color parameters include color difference (DE), etc. Color parameter values ​​are the specific values ​​that color parameters take, including chromaticity values ​​(C). ), hue angle (h°), comprehensive color index (CCI), color difference correction index (DE / L), and total ratio of red to yellow in brightness, etc.

[0022] The formula for calculating chromaticity values ​​is expressed as follows: , in, Indicates chromaticity value; Indicates red-green values; This indicates the yellow-blue value.

[0023] The formula for calculating the hue angle is expressed as: , in, It represents the hue angle.

[0024] The formula for calculating the comprehensive color index is as follows: , CCI stands for Comprehensive Color Index; This indicates the brightness value.

[0025] The formula for calculating the total ratio of red to yellow brightness is expressed as follows: , in, This represents the total ratio of red to yellow in brightness.

[0026] In step S120 of some embodiments, the color difference features are input into a target linear regression model for linear regression calculation to obtain the proportion of bitter triterpenes in Ganoderma lucidum. The proportion of bitter triterpenes is the percentage of bitter triterpenes in the total triterpenes. The target linear regression model is used to establish a multiple linear relationship between the color difference features and the proportion of bitter triterpenes.

[0027] Please see Figure 2 In some embodiments, the process of constructing the target linear regression model may include, but is not limited to, steps S210 to S260: Step S210: Obtain the true batch bitter triterpenoid percentage of Ganoderma lucidum sample batches; Step S220: Perform color difference detection on batches of Ganoderma lucidum samples to obtain batch color difference characteristics of Ganoderma lucidum sample batches; Step S230: Construct a multiple linear regression model based on batch color difference characteristics; Step S240: Based on batch color difference characteristics, the predicted proportion of bitter triterpenes in Ganoderma lucidum sample batches is estimated using a multiple linear regression model. Step S250: Calculate the fitting error based on the predicted proportion of bitter triterpenes in the batch of Ganoderma lucidum samples and the actual proportion of bitter triterpenes in the batch. Step S260: Minimize the fitting error to update the model parameters of the multiple linear regression model, and obtain the target linear regression model.

[0028] In step S210 of some embodiments, Ganoderma lucidum samples from a preset origin are selected as Ganoderma lucidum sample batches, and the Ganoderma lucidum samples are Ganoderma lucidum harvested at maturity. The Ganoderma lucidum sample batches are subjected to sample pretreatment, which includes sample thawing and transfer, metabolite extraction, centrifugation purification and sample preparation, and quality control sample preparation.

[0029] Sample thawing and transfer: Take out the test sample stored at -80℃ and place it in an ice-water mixture (ice-water bath) to thaw; accurately transfer 400μL of the thawed test sample into a 1.5mL EP centrifuge tube; all extraction reagents are pre-cooled at 4℃ before use.

[0030] Metabolite extraction: Add 400 μL of methanol-water extraction solution containing mixed internal standards to the centrifuge tube containing the sample to be tested. The volume ratio of methanol to water is set to 4:1. Vortex for 1 min to mix the sample thoroughly. Sonicate the centrifuge tube for 30 min under ice-water bath conditions, and then place it in a -40℃ environment overnight.

[0031] Centrifugation purification and sample preparation: Place the settled mixture in a refrigerated centrifuge, set the temperature to 4℃ and the speed to 12000 rpm, and centrifuge for 20 min. After centrifugation, the solid phase component settles to the bottom of the centrifuge tube, and the supernatant is obtained. Accurately pipette 150 μL of the supernatant and transfer it to a dedicated LC-MS (liquid chromatography-mass spectrometry) vial with matching support legs and inner liner for instrument analysis.

[0032] Quality control sample preparation: Take the extract supernatant of all the samples to be tested, mix them evenly in equal volumes to obtain a quality control mixed sample, which is used for subsequent instrument stability calibration and data quality control filtering.

[0033] Ultra-high performance liquid chromatography and mass spectrometry were used to separate and detect the quality control samples in order to extract the flavor substances of Ganoderma lucidum.

[0034] Component separation was performed using a Waters ACQUITY UPLC I-Class plus ultra-high performance liquid chromatography (UHPLC) system. The chromatographic column used was an ACQUITY UPLC HSS T3 column, with dimensions of 100 mm × 2.1 mm and a particle size of 1.8 μm. The column temperature was kept constant at 45 °C. The mobile phase consisted of a 0.1% formic acid aqueous solution (phase A) and acetonitrile (phase B). The mobile phase flow rate was 0.35 mL / min, and the single injection volume was 4 μL.

[0035] The mass spectrometry instrument used for detection was a Thermo QE HF high-resolution mass spectrometer equipped with an electrospray ionization (HESI) source. During detection, two sets of parameters were used to independently acquire sample mass spectrometry signals in both positive and negative ion scanning modes. The data acquisition mode was set to data-dependent scanning, and the scanning method was a combination of a first-stage full scan and a second-stage fragmentation scan. The second-stage fragmentation scan prioritized the top 10 ions to trigger fragmentation acquisition.

[0036] The elution gradient is shown in Table 1: Table 1

[0037] Mass spectrometry parameters are shown in Table 2: Table 2

[0038] Alkaloids and terpenoids are the key material basis for the bitter taste of Ganoderma lucidum. Among them, ganoderic acid H, ganoderic acid F, ganoderic acid A, 5-hydroxytryptamine, choline, and ganoderic acid C2 are the main bitter active components in Ganoderma lucidum.

[0039] Five batches of Ganoderma lucidum samples from three different production areas were selected, and 15 batches were tested. The relative contents of the main bitter substances are shown in Table 3. Here, A represents production area A; F represents production area F; S represents production area S, and the batch number is used to uniquely identify the batch.

[0040] Table 3

[0041] The true proportion of bitter triterpenes in each batch of Ganoderma lucidum samples was obtained, thus obtaining the true proportion of bitter triterpenes in each batch.

[0042] In step S220 of some embodiments, referring to step S110, color difference detection is performed on batches of Ganoderma lucidum samples to obtain batch color difference characteristics of the Ganoderma lucidum sample batches. The batch color difference characteristics include index color difference characteristics of multiple preset color difference indices. The preset color difference indices can be lightness value, red-green value, yellow-blue value, color difference value, chromaticity value, hue angle, comprehensive color index, color difference correction index, lightness-to-red-yellow ratio, etc. Lightness value, red-green value, yellow-blue value, and color difference are chromaticity parameters, while chromaticity value, hue angle, comprehensive color index, color difference correction index, and lightness-to-red-yellow ratio are color difference parameters. The index color difference characteristics are the specific values ​​of the preset color difference indices.

[0043] Color difference detection was performed on each batch to obtain the batch color difference characteristics. The chromaticity parameter values ​​for each batch are shown in Table 4, and the hue parameter values ​​for each batch are shown in Table 5.

[0044] Table 4

[0045] Table 5

[0046] In step S230 of some embodiments, to determine the correlation between the appearance color and internal quality of Ganoderma lucidum, the proportion of bitter triterpenes in the total triterpenes is used as the dependent variable (Y), and multiple preset color difference indicators such as lightness value, red-green value, yellow-blue value, color difference value, chromaticity value, hue angle, comprehensive color index, color difference correction index, and lightness-to-red-yellow ratio are used as independent variables to construct a multiple linear regression model. The multiple linear regression model is used to indicate the correlation between color difference characteristics and the proportion of bitter triterpenes.

[0047] Assuming a linear correlation exists between each color difference characteristic and the proportion of bitter triterpenes, the multiple linear regression model is defined as follows: , Where Y is the dependent variable, representing the predicted proportion of bitter triterpenes; Indicates the intercept; , ... This represents the independent variable, which corresponds to K preset color difference indicators; , ... This represents the regression coefficients corresponding to each independent variable. This represents the regression coefficient of the k-th independent variable; This represents the random error term.

[0048] In step S240 of some embodiments, the batch color difference characteristics are substituted into the independent variable X of the multiple linear regression model, and the values ​​of the regression coefficients, intercepts and random error terms corresponding to each independent variable are initialized to perform linear regression calculation, determine the predicted value of the proportion of bitter triterpenes in the batch of Ganoderma lucidum sample, and obtain the predicted proportion of bitter triterpenes in the batch.

[0049] In step S250 of some embodiments, the model parameters of the multiple linear regression model are estimated using the least squares method, and the best-fit curve is found by minimizing the sum of squared residuals (SSE). Specifically, the sum of squared residuals between the predicted batch proportion of bitter triterpenes and the actual batch proportion of bitter triterpenes in the Ganoderma lucidum sample batch is calculated to obtain the fitting error. The formula for calculating the fitting error is expressed as: , Where n represents the number of batches from the preset origin, which can be set to 5; This represents the actual value of the i-th batch; This represents the predicted value for the i-th batch.

[0050] In step S260 of some embodiments, the fitting error is minimized, and the regression coefficients and intercepts of the multiple linear regression model are updated to obtain the target linear regression model.

[0051] Through the above steps S210 to S260, a multiple linear correlation relationship between color difference characteristics and the proportion of bitter triterpenes can be established.

[0052] Please see Figure 3 In some embodiments, step S230 may include, but is not limited to, steps S310 to S340: Step S310: Calculate the correlation coefficient based on the color difference characteristics of each indicator and the proportion of bitter triterpenes in the actual batch; Step S320: Select the target color difference index from multiple preset color difference indices based on the correlation coefficient; Step S330: Construct the index variable and its regression coefficient based on the target color difference index, and determine the random error term and intercept; Step S340: Construct a multiple linear regression model based on the indicator variable, regression coefficients, random error term, and intercept.

[0053] In step S310 of some embodiments, a variable set is constructed based on each preset color difference index and the proportion of bitter triterpenes. The Pearson correlation coefficient between every two variables in the variable set is calculated to obtain a correlation coefficient matrix. The correlation coefficient between the preset color difference index and the proportion of bitter triterpenes in the actual batch can be calculated based on the index color difference characteristics of the preset color difference index and the proportion of bitter triterpenes in the actual batch. The correlation coefficient r between any two preset color difference indices is calculated based on their index color difference characteristics. The correlation coefficient ranges from -1 to 1. The larger the absolute value of the correlation coefficient, the stronger the correlation between the two variables. 1 indicates a strong positive correlation, -1 indicates a strong negative correlation, and 0 indicates no linear correlation. The correlation coefficient matrix includes L... a b C The correlation coefficients between each pair of variables in h°, CCI, DE / L, R, and the proportion of bitter triterpenes are shown in Table 6.

[0054] Table 6

[0055] The heatmap of the correlation coefficient matrix is ​​as follows Figure 4 As shown. In Figure 4 In the diagram, the color of the grid represents the degree of correlation between two variables, with red indicating a positive correlation and yellow indicating a negative correlation.

[0056] In step S320 of some embodiments, if the absolute value of the correlation coefficient between two variables is greater than or equal to a preset value such as 0.8, i.e., |r| ≥ 0.8, then the two variables are highly correlated and exhibit significant multicollinearity. The correlation coefficient matrix shows that the proportion of bitter triterpenes is related to b... C CCI and R are strongly correlated. With b The high overlap of information from CCI and R means that including multiple variables in the model can lead to severe multicollinearity, interfering with the regression results. Compared to the explanatory power of a single indicator, CCI... It can directly reflect the vibrancy of the sample's color, and has the highest degree of matching in sensory evaluation; L This can supplement information on the brightness dimension, thus improving the color representation dimension; the other independent variables either exhibit severe collinearity with the core indicators or have excessively low single-factor correlation coefficients, contributing little to the explanation of the dependent variable. Therefore, L was selected. and C The core independent variable, namely the target color difference index, is L. and C .

[0057] In step S330 of some embodiments, according to the target color difference index L and C Constructing indicator variables and ,Sure regression coefficient , regression coefficient And determine the random error term. and intercept The random error term can be set according to the actual situation, such as 0.0001.

[0058] In step S340 of some embodiments, based on index variables and , regression coefficient , regression coefficient Random error term and intercept A binary linear regression equation is established to obtain a multiple linear regression model.

[0059] By using steps S310 to S340 above, severe multicollinearity caused by multiple variables being included in the model can be avoided, thereby improving the prediction accuracy of the proportion of bitter triterpenes.

[0060] With L and C Using L as the independent variable and the proportion of bitter triterpenes as the dependent variable, a binary linear regression model is constructed. and C A binary linear regression equation was established for the core variables, and the fitting result is shown in the following formula: , And calculate the binary linear regression residuals, which are the differences between the true and predicted values. Keep C Fixed, for L The residual analysis graph of the binary linear regression is shown below. Figure 5 As shown, its residuals are within the interval [-0.02, 0.02]. Keep L... Fixed, for C The residual analysis graph of the binary linear regression is shown below. Figure 6 As shown, its residuals are within the range of [-0.02, 0.04].

[0061] The proportion of bitter triterpenes in 15 batches of Ganoderma lucidum samples was predicted using the above fitting equation. The comparison between the actual value and the predicted value is shown in Table 7.

[0062] Table 7

[0063] The formula for calculating the model's goodness of fit is expressed as: , in, The coefficient of determination is represented by SSE; SSE represents the sum of squared residuals. , This represents the actual value of the i-th batch; SST represents the predicted value of the i-th batch; SST represents the total sum of squares. , The average value is the predicted value for each batch. The fit and significance analysis are shown in Table 8, demonstrating the high reliability of the model.

[0064] Table 8

[0065] In step S130 of some embodiments, the content of Ganoderma lucidum triterpenes and sterols is detected by ultraviolet-visible spectrophotometry to obtain the total triterpenoid content. The detection results of Ganoderma lucidum triterpenes and sterols in 15 batches are shown in Table 9.

[0066] Table 9

[0067] In step S140 of some embodiments, pre-constructed bitterness intensity correction coefficients and taste correction coefficients are obtained.

[0068] In step S150 of some embodiments, the proportion of bitter triterpenes, the total triterpenes content, the bitterness intensity correction coefficient, and the taste correction coefficient are multiplied to obtain the theoretical bitterness value of Ganoderma lucidum. The formula for calculating the theoretical bitterness value is expressed as follows: Theoretical bitterness value = total triterpenoid content Bitter Triterpenoids , in, Indicates the bitterness intensity correction factor; Indicates the taste correction coefficient; It represents the multiplication operation.

[0069] In step S160 of some embodiments, the theoretical bitterness value is substituted into the independent variable of the target univariate linear model, and the theoretical bitterness value is mapped to the human perception of bitterness value through the target univariate linear model to obtain the predicted bitterness value.

[0070] Please see Figure 7 In some embodiments, the calculation process of the bitterness intensity correction coefficient may include, but is not limited to, steps S710 to S750: Step S710: Obtain the relative content and relative bitter weight of each monomeric bitter component in each batch of Ganoderma lucidum samples; Step S720: Obtain the relative content and weight of each bitter triterpenoid in the batch of Ganoderma lucidum samples; Step S730: Multiply the relative content of each monomeric bitter component by the corresponding relative bitter weight to obtain the first bitter content of each monomeric bitter component, and add up the first bitter content of each to obtain the second bitter content. Step S740: Multiply the relative content of each bitter triterpenoid by its corresponding weight to obtain the third bitter content of each bitter triterpenoid, and add up the third bitter content to obtain the fourth bitter content. Step S750: Calculate the ratio between the second bitterness content and the fourth bitterness content to obtain the bitterness intensity correction coefficient.

[0071] In steps S710 to S720 of some embodiments, in order to eliminate the interference of non-bitter components in total triterpenes and accurately quantify the bitterness perception of Ganoderma lucidum, a bitterness intensity contribution coefficient (relative bitterness weight) is introduced to calculate the weight of the main bitter components. The weighting rules are as follows: (1) Establish a benchmark, taking the most representative ganoderic acid A as a reference (its sensory threshold is about 30 µg / mL), and set its relative bitterness weight to 1; (2) Structural derivation: Combine the bitterness level and the structural similarity between ganoderic acid molecules to perform phased weighting; (3) Synergistic correction: For 5-hydroxytryptamine (which has a moderate bitterness and can amplify the bitterness perception) and choline (which provides basic bitterness), which have high content, corresponding weight correction is given according to their actual role in the flavor profile. The main bitterness contributing substances of Ganoderma lucidum sample batches are qualitatively and relatively quantitatively analyzed using non-targeted liquid chromatography-mass spectrometry. Their relative bitterness weights are weighted according to the above rules. For example, the relative bitterness weights of several triterpenoid compounds (ganoderic acid) and other bitter components with relatively rich content are shown in Table 10. The content of bitter components of the main bitter substances is shown in Table 3.

[0072] Bitter substances include monomeric bitter components and bitter triterpenes. The content and bitter weight of monomeric bitter components, and the content and weight of bitter triterpenes are obtained.

[0073] Table 10

[0074] In step S730 of some embodiments, a calculation model for the bitterness intensity correction coefficient is established by combining the relative contents of the main bitter substances in Table 3 and the relative bitterness weights in Table 10. The bitterness intensity correction coefficient characterizes the intensity ratio of all main bitter components to bitter triterpenoid components in the Ganoderma lucidum sample, and the calculation formula is expressed as follows: , in, Indicates the bitterness intensity correction factor; This indicates the quantity of monomeric bitter components in a batch of Ganoderma lucidum samples; This indicates the quantity of bitter triterpenes in a batch of Ganoderma lucidum samples; ∑ represents the cumulative sum of bitter components. It represents the multiplication operation.

[0075] Referring to the above formula, the relative content and relative bitter weight of each monomeric bitter component are multiplied to obtain the first bitter content of each monomeric bitter component, and the first bitter content of each component is added together to obtain the second bitter content.

[0076] In step S740 of some embodiments, the relative content of each bitter triterpenoid and its corresponding weight are multiplied to obtain the third bitter content of each bitter triterpenoid, and the third bitter content is added together to obtain the fourth bitter content.

[0077] In step S750 of some embodiments, the ratio between the second bitterness content and the fourth bitterness content is calculated to obtain a bitterness intensity correction coefficient.

[0078] As shown in Table 11, the bitterness intensity correction coefficient was obtained by calculating the bitterness intensity of each batch of Ganoderma lucidum samples using the above formula. The larger the value of the bitterness intensity correction coefficient, the higher the bitterness intensity of the sample.

[0079] Table 11

[0080] Through steps S710 to S750 above, the specific bitter components in the sample are detected using non-targeted liquid chromatography-mass spectrometry (LC-MS) technology, and a bitterness intensity correction coefficient is constructed in combination with the taste perception threshold to perform LC-MS bitterness correction.

[0081] Please see Figure 8 In some embodiments, the process of constructing the taste correction coefficient may include, but is not limited to, steps S810 to S850: Step S810: Obtain the electronic tongue taste detection values ​​of the Ganoderma lucidum sample batch; wherein, the electronic tongue taste detection values ​​include bitterness value, bitter aftertaste value, astringency value, astringency aftertaste value, sweetness value, umami value and richness value; Step S820: Determine the first weight and the second weight; Step S830: Calculate the first taste value based on the bitterness value, bitter aftertaste value, astringency value, astringency aftertaste value, and the first weight. Step S840: Calculate the second flavor value based on the bitterness value, bitter aftertaste value, astringency value, astringency aftertaste value, sweetness value, umami value, richness value, first weight and second weight; Step S850: Calculate the ratio between the first taste value and the second taste value to obtain the taste correction coefficient.

[0082] In step S810 of some embodiments, human senses are easily confused with bitterness, and sweetness, umami and other flavors can mask bitterness, resulting in inaccurate evaluation of bitterness. Therefore, embodiments of this application introduce a taste correction coefficient and correct it through multidimensional taste values ​​detected by electronic tongue, so as to eliminate interference from non-bitter substances and restore the true bitterness intensity of Ganoderma lucidum.

[0083] Take 10g of Ganoderma lucidum, add 150ml of water, reflux for 60min, and extract three times. Combine the filtrates, filter through a 100-mesh sieve, centrifuge, and concentrate to 1g / 45mL to obtain Ganoderma lucidum extract for electronic tongue taste characteristic detection. Add an appropriate amount of special reference solution to the electronic tongue beaker. The reference solution is prepared by accurately weighing 2.237g of potassium chloride and 0.045g of tartaric acid, dissolving in 500mL of distilled water, and then diluting to a 1000mL volumetric flask. The electronic tongue is equipped with CA0 (sour taste), CTO (salty taste), AAE (umami taste), CO0 (bitter taste), AE1 (astringent taste), and GL1 (sweet taste). Immerse the sensors in the reference solution and activate and preheat for 12h. Take 70ml of Ganoderma lucidum extract and test it. Before testing the Ganoderma lucidum sample, clean the sensor with distilled water for 120s, then test for 180s. Clean the sensor with the reference solution before testing different samples. Finally, save the test data. Each sample underwent four parallel experiments, and the average of the last three results was used for analysis. Data underwent baseline correction: the taste-free point response value was used as the baseline (sour taste -13, salty taste -6, other tastes 0); values ​​below this value were recorded as 0, and values ​​above this value were subtracted from the background value. The corrected results are shown in Table 12.

[0084] Table 12

[0085] The electronic tongue taste test values ​​of Ganoderma lucidum sample batches were obtained. The electronic tongue taste test values ​​include bitterness value, bitter aftertaste value, astringency value, astringency aftertaste value, sweetness value, umami value and richness value.

[0086] In step S820 of some embodiments, different taste response signals presented by the sample are detected using electronic tongue bionic sensing technology. Taste correction coefficients are constructed by utilizing the enhancement or masking effects between tastes. Considering that taste perception has synergistic and antagonistic effects—that is, astringency may enhance bitterness, while sweetness, umami, and richness may mask or inhibit bitterness—weighting parameters are introduced to obtain a first weight and a second weight. The first weight and the second weight are represented as α and β, respectively. The formula for calculating the taste correction coefficient is expressed as: , in, Indicates the taste correction coefficient; Indicates the first weight; The second weight is indicated by the bitterness value, the bitter aftertaste value, the astringency value, the astringency aftertaste value, the sweetness value, the umami value, and the richness value, which are obtained by detecting batches of Ganoderma lucidum samples using an electronic tongue.

[0087] The first weight is initially set to 0.5, which represents the weight by which astringency enhances bitterness; the second weight is initially set to 0.5, which represents the weight by which sweetness / umami inhibits bitterness.

[0088] In step S830 of some embodiments, referring to the numerator of the taste correction coefficient calculation formula, a first taste value is calculated based on the bitterness value, bitter aftertaste value, astringency value, astringency aftertaste value, and a first weight.

[0089] In step S840 of some embodiments, referring to the denominator of the taste correction coefficient calculation formula, a second taste value is calculated based on the bitterness value, bitter aftertaste value, astringency value, astringency aftertaste value, sweetness value, umami value, richness value, first weight and second weight.

[0090] In step S850 of some embodiments, the ratio between the first taste value and the second taste value is calculated to obtain a taste correction coefficient. A taste correction coefficient greater than 0.5 indicates enhanced bitterness, and the closer it is to 1, the stronger the effect of enhancing bitterness.

[0091] Through the above steps S810 to S850, the taste correction coefficient can be obtained.

[0092] Please see Figure 9 In some embodiments, after step S850, the rapid quantitative detection method for the bitterness of Ganoderma lucidum may also include, but is not limited to, steps S910 to S950: Step S910: Obtain the true bitterness value of the Ganoderma lucidum sample batch; Step S920: Color difference detection is performed on batches of Ganoderma lucidum samples to obtain batch color difference characteristics. Based on the batch color difference characteristics, the proportion of bitter triterpenes in the target batch of Ganoderma lucidum samples is estimated using a target linear regression model. The component content of the batch of Ganoderma lucidum samples is detected to obtain the total triterpenes content of the batch. The initial bitterness value of the batch of Ganoderma lucidum samples is calculated based on the proportion of bitter triterpenes in the target batch, the total triterpenes content of the batch, the bitterness intensity correction coefficient, and the taste correction coefficient. Step S930: Calculate the bitterness error between the initial bitterness value and the actual bitterness value; Step S940: Minimize the bitterness error and update the first and second weights with a preset step size; Step S950: Update the taste correction coefficients according to the updated first weight and the updated second weight.

[0093] In step S910 of some embodiments, the human perception of bitterness value of Ganoderma lucidum sample batches is determined based on human sensory evaluation to obtain the true bitterness value. The bitterness of each sample is scored using sensory profiling and quantitative descriptive analysis. Bitterness evaluation is performed on 15 batches of Ganoderma lucidum extract (1g / 45 mL) through evaluation objects. The descriptive terms and indicators are shown in Table 13, and the bitterness evaluation results are shown in Table 14.

[0094] Table 13

[0095] Table 14

[0096] In step S920 of some embodiments, color difference detection is performed on batches of Ganoderma lucidum samples to obtain batch color difference characteristics. Referring to step S120, the batch color difference characteristics are input into a target linear regression model for linear calculation to obtain the target batch bitter triterpenoid proportion of the Ganoderma lucidum sample batch. Referring to step S130, component content detection is performed on batches of Ganoderma lucidum samples to obtain the batch total triterpenoid content. The initial bitterness value of the Ganoderma lucidum sample batch is obtained by multiplying the target batch bitter triterpenoid proportion, the batch total triterpenoid content, the bitterness intensity correction coefficient, and the taste correction coefficient.

[0097] In step S930 of some embodiments, the sum of squared residuals between the initial bitterness value and the true bitterness value is calculated to obtain the bitterness error.

[0098] In step S940 of some embodiments, the bitterness error is minimized by updating the first weight and the second weight with a preset step size. The preset step size can be 0.1, and the first weight and the second weight can be increased or decreased according to the preset step size.

[0099] In step S950 of some embodiments, the taste correction coefficients are updated according to the updated first weight and the updated second weight, referring to the calculation formula of the taste correction coefficients in step S820. The taste correction coefficients are shown in Table 15.

[0100] Table 15

[0101] Through the above steps S910 to S950, an accurate taste correction coefficient can be obtained.

[0102] Please see Figure 10 In some embodiments, the process of constructing the target univariate linear model may include, but is not limited to, steps S1010 to S1020: Step S1010: Calculate the reference bitterness value of the Ganoderma lucidum sample batch based on the proportion of bitter triterpenes in the target batch, the total triterpenes content of the batch, the bitterness intensity correction coefficient, and the updated taste correction coefficient. Step S1020: Establish a univariate linear relationship between the reference bitterness value and the actual bitterness value to obtain the target univariate linear model.

[0103] In step S1010 of some embodiments, referring to the calculation formula of theoretical bitterness value in step S150, the proportion of bitter triterpenes in the target batch, the total triterpenes content of the batch, the bitterness intensity correction coefficient and the updated taste correction coefficient are multiplied to obtain the reference bitterness value of the Ganoderma lucidum sample batch.

[0104] In step S1020 of some embodiments, a univariate linear relationship is established between the reference bitterness value and the actual bitterness value to obtain the target univariate linear model.

[0105] A univariate linear model was used to fit the sample data (i.e., reference bitterness value and actual bitterness value), and the fitting results are shown in Table 16.

[0106] Table 16

[0107] As shown in Table 16, there is a significant linear positive correlation between the theoretical and actual values ​​for each production area group, and the coefficient of determination is... All values ​​are greater than 0.86, indicating that the linear model has high explanatory power and can be used for subsequent predictions.

[0108] To further verify the reliability of the model, the theoretical value derived from the proportion of bitter triterpenes predicted by color difference characteristics was substituted into the above model to calculate the predicted bitterness value, and compared with the actual sensory value. The analysis results are shown in Table 17.

[0109] Table 17

[0110] As shown in Table 17, the predicted values ​​are in good agreement with the actual values, and the residuals are generally less than 0.1, which confirms the accuracy of the bitterness evaluation system.

[0111] Through the above steps S1010 to S1020, a target univariate linear model can be obtained for accurate bitterness evaluation.

[0112] This application also provides a rapid quantitative detection method for the bitterness of Ganoderma lucidum, comprising: obtaining Ganoderma lucidum from a preset origin and performing color difference detection on the Ganoderma lucidum to obtain color difference characteristics; calling a corresponding target linear regression model according to the preset origin, and estimating the proportion of bitter triterpenes in Ganoderma lucidum based on the color difference characteristics through the target linear regression model; detecting the component content of Ganoderma lucidum to obtain the total triterpenes content; obtaining a bitterness intensity correction coefficient and a taste correction coefficient according to the preset origin; calculating the theoretical bitterness value of Ganoderma lucidum based on the proportion of bitter triterpenes, the total triterpenes content, the bitterness intensity correction coefficient, and the taste correction coefficient; calling a corresponding target univariate linear model according to the preset origin, and mapping the theoretical bitterness value to the human perceived bitterness value through the target univariate linear model to obtain the predicted bitterness value. The target linear regression model, the bitterness intensity correction coefficient, the taste correction coefficient, and the target univariate linear model are all constructed based on batches of Ganoderma lucidum samples from the preset origin.

[0113] It should be noted that when the production environment of Ganoderma lucidum fluctuates significantly, the target linear regression model needs to be reconstructed according to steps S210 to S260, the bitterness intensity correction coefficient needs to be recalculated according to steps S710 to S750, the taste correction coefficient needs to be recalculated according to steps S810 to S850, and the target univariate linear model needs to be reconstructed according to steps S1010 to S1020 in order to improve the detection accuracy in the production scenario.

[0114] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0115] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0116] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0117] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0118] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0119] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A rapid quantitative detection method for the bitterness of Ganoderma lucidum, characterized in that, The method includes: Color difference detection was performed on Ganoderma lucidum to obtain color difference characteristics; The proportion of bitter triterpenes in Ganoderma lucidum is estimated based on the color difference characteristics using a target linear regression model; wherein, the proportion of bitter triterpenes is the proportion of bitter triterpenes in the total triterpenes; The total triterpenoid content was obtained by analyzing the components of the Ganoderma lucidum. Obtain the bitterness intensity correction coefficient and the taste correction coefficient; The theoretical bitterness value of Ganoderma lucidum is calculated based on the proportion of bitter triterpenes, the total triterpenes content, the bitterness intensity correction coefficient, and the taste correction coefficient. The theoretical bitterness value is mapped to the human perception of bitterness value using a target univariate linear model to obtain the predicted bitterness value.

2. The method according to claim 1, characterized in that, The target linear regression model is constructed according to the following steps: Obtain the true batch percentage of bitter triterpenes in Ganoderma lucidum samples; Color difference detection was performed on the batches of Ganoderma lucidum samples to obtain the batch color difference characteristics of the Ganoderma lucidum sample batches; A multiple linear regression model is constructed based on the batch color difference characteristics; The proportion of bitter triterpenes in the batch of Ganoderma lucidum samples is predicted by the multiple linear regression model based on the batch color difference characteristics. The fitting error is calculated based on the predicted proportion of bitter triterpenes in the batch of Ganoderma lucidum samples and the actual proportion of bitter triterpenes in the batch. Minimize the fitting error to update the model parameters of the multiple linear regression model, and obtain the target linear regression model.

3. The method according to claim 2, characterized in that, The batch color difference characteristics include multiple preset color difference indices, and the construction of a multiple linear regression model based on the batch color difference characteristics includes: The correlation coefficient is calculated based on the color difference characteristics of each indicator and the proportion of bitter triterpenes in the actual batch. The target color difference index is selected from multiple preset color difference indices based on the correlation coefficient; Based on the target color difference index, construct the index variable and the regression coefficient of the index variable, and determine the random error term and intercept; The multiple linear regression model is constructed based on the indicator variable, the regression coefficient, the random error term, and the intercept.

4. The method according to claim 1, characterized in that, The formula for calculating the bitterness intensity correction coefficient is as follows: ， in, Indicates the bitterness intensity correction factor; This indicates the quantity of monomeric bitter components in a batch of Ganoderma lucidum samples; This indicates the quantity of bitter triterpenes in a batch of Ganoderma lucidum samples; ∑ represents the cumulative sum of bitter components. It represents the multiplication operation.

5. The method according to claim 1, characterized in that, The formula for calculating the taste correction coefficient is as follows: ， in, Indicates the taste correction coefficient; Indicates the first weight; The second weight is indicated by the bitterness value, the bitter aftertaste value, the astringency value, the astringency aftertaste value, the sweetness value, the umami value, and the richness value, which are obtained by detecting batches of Ganoderma lucidum samples using an electronic tongue.

6. The method according to claim 5, characterized in that, The method further includes: Obtain the true bitterness value of Ganoderma lucidum sample batches; Color difference detection is performed on the batches of Ganoderma lucidum samples to obtain the batch color difference characteristics of the batches of Ganoderma lucidum samples. The target batch bitter triterpenoid ratio of the Ganoderma lucidum sample batch is estimated based on the batch color difference characteristics using the target linear regression model. The component content of the batches of Ganoderma lucidum samples is detected to obtain the batch total triterpenoid content. The initial bitterness value of the batches of Ganoderma lucidum samples is calculated based on the target batch bitter triterpenoid ratio, the batch total triterpenoid content, the bitterness intensity correction coefficient, and the taste correction coefficient. Calculate the bitterness error between the initial bitterness value and the actual bitterness value; Minimize the bitterness error by updating the first weight and the second weight with a preset step size; The taste correction coefficients are updated based on the updated first weight and the updated second weight.

7. The method according to claim 6, characterized in that, The target univariate linear model is constructed according to the following steps: The reference bitterness value of the Ganoderma lucidum sample batch is calculated based on the proportion of bitter triterpenes in the target batch, the total triterpenes content of the batch, the bitterness intensity correction coefficient, and the updated taste correction coefficient. A univariate linear relationship is established between the reference bitterness value and the actual bitterness value to obtain the target univariate linear model.

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