Morel flavor quality evaluation method and system capable of reducing computational complexity and cost

Abstract

The present application discloses a method and system for evaluating the flavor quality of morels, which can reduce the amount of computation and cost. The method comprises the following steps: measuring the relative content of volatile substances in morels to obtain measurement results; obtaining the ROAV of the volatile substances based on the measurement results; obtaining the key flavor compounds of morels based on the ROAV; obtaining the ROAV-like of the key flavor compounds using the ROAV analysis method; and performing PCA on the ROAV-like to obtain analysis results; establishing a morel flavor quality evaluation model based on the analysis results, calculating the comprehensive flavor quality score of morels to obtain the comprehensive flavor quality score; performing system cluster analysis using the ROAV-like to obtain cluster analysis results; and classifying and evaluating the flavor quality of morels of different strains based on the comprehensive flavor quality score and the cluster analysis results, thereby completing the flavor quality evaluation of morels.

Description

Technical Field

[0001] The present application relates to the field of food flavor detection, and specifically to a method and system for evaluating the flavor quality of morels that can reduce computational complexity and cost. Background Art

[0002] Morels (Morchella spp.) are a rare edible and medicinal mushroom with significant economic and scientific value. Named for their tripe-like cap, they contain a unique aromatic compound, making them highly sought after in domestic and international markets. Currently, the industry primarily uses sensory perception and yield as criteria for variety selection, and uses appearance as a quality indicator for commodity grading. However, the crucial influence of volatile flavor compounds on the quality of morels is overlooked, resulting in varying degrees of intrinsic quality. Previous studies on volatile compounds have examined the quality of edible fungi, with some researchers relying solely on ROAV values ​​to identify key flavor compounds that contribute significantly to and modify the overall flavor quality of edible fungi, while others have relied solely on the relative content of volatile compounds for comprehensive flavor evaluations such as PCA.

[0003] The overall flavor of food is determined by the content of its volatile substances and its sensory threshold (the lowest concentration at which the aroma is detected). Different volatile substances have different sensory thresholds. Some volatile substances have relatively large sensory thresholds, and even if their relative content is high, they may not necessarily have a direct effect on the flavor quality of the food. Some volatile substances have low sensory thresholds, and even if their content is very low, they may still play a key role in the flavor quality of the food. Therefore, it is one-sided and unscientific to directly use the relative content of volatile substances to evaluate the flavor quality of food. Summary of the Invention

[0004] To address the technical issues presented above, this application classifies and evaluates the flavor quality of different Morchella strains based on identified key flavor compounds, identifying similarities and differences in flavor quality between different Morchella strains. This application can improve the rigor of analytical results, reduce analytical computational complexity, and lower testing costs.

[0005] To achieve the above objectives, the present application provides a method for evaluating the flavor quality of morels that can reduce computational complexity and cost, the steps comprising:

[0006] The relative content of volatile substances in Morchella esculenta is measured to obtain the measurement results;

[0007] Based on the measurement results, obtaining the ROAV of the volatile substance;

[0008] Based on the ROAV, key flavor compounds of morel mushrooms were obtained;

[0009] Obtaining the similar ROAV of the key flavor compound using the ROAV analysis method; and performing PCA on the similar ROAV to obtain an analysis result;

[0010] Based on the analysis results, a Morchella flavor quality evaluation model is established, and a Morchella flavor quality comprehensive score is calculated to obtain a flavor quality comprehensive score;

[0011] Using the ROAV-like model, a systematic cluster analysis was performed to obtain cluster analysis results;

[0012] The flavor quality of Morchella oleracea of ​​different strains was classified and evaluated by combining the flavor quality comprehensive score and the cluster analysis result, thereby completing the flavor quality evaluation of Morchella oleracea.

[0013] Preferably, the method for obtaining the determination result includes: using GC-MS to determine the relative content of volatile substances in Morchella to obtain the determination result.

[0014] Preferably, the method for obtaining the ROAV comprises:

[0015]

[0016] Where: Ci and Ti represent the relative content and sensory threshold of volatile components, respectively; Cstan and Tstan represent the relative content and sensory threshold of the component that contributes most to the overall flavor of the sample, respectively.

[0017] Preferably, the method for obtaining the ROAV-like substance comprises: using the substances with ROAV ≥ 0.1 obtained by screening, taking the relative content and sensory threshold of the key flavor compound that contributes the most to the overall flavor quality and has the highest relative content in all the tested morel samples as the standard, and recalculating with reference to the ROAV calculation formula to obtain the ROAV-like substance with a unified calculation standard.

[0018] The present application also provides a morel flavor quality evaluation system that can reduce computational complexity and cost, comprising: a determination module, a calculation module, a calibration module, an analysis module, a construction module, a clustering module, and an evaluation module;

[0019] The measuring module is used to measure the relative content of volatile substances in Morchella esculenta to obtain a measurement result;

[0020] The calculation module is used to obtain the ROAV of the volatile substance based on the measurement result;

[0021] The calibration module is used to obtain key flavor compounds of Morchella based on the ROAV;

[0022] The analysis module is used to obtain the ROAV-like value of the key flavor compound using the ROAV analysis method; and perform PCA on the ROAV-like value to obtain an analysis result;

[0023] The construction module is used to establish a Morchella flavor quality evaluation model based on the analysis results, calculate the Morchella flavor quality comprehensive score, and obtain the flavor quality comprehensive score;

[0024] The clustering module is used to perform a system cluster analysis using the ROAV-like model to obtain a cluster analysis result;

[0025] The evaluation module is used to classify and evaluate the flavor quality of Morchella fusca of different strains by combining the flavor quality comprehensive score and the cluster analysis result, thereby completing the flavor quality evaluation of Morchella fusca.

[0026] Preferably, the workflow of the determination module includes: using GC-MS to determine the relative content of volatile substances in Morchella to obtain the determination result.

[0027] Preferably, the workflow of the calculation module includes:

[0028]

[0029] Where: Ci and Ti represent the relative content and sensory threshold of volatile components, respectively; Cstan and Tstan represent the relative content and sensory threshold of the component that contributes most to the overall flavor of the sample, respectively.

[0030] Preferably, the workflow of the calibration module includes: using the substances with ROAV≥0.1 obtained by screening, taking the relative content and sensory threshold of the key flavor compounds with the greatest contribution to the overall flavor quality and the highest relative content in all the tested morel samples as standards, and recalculating with reference to the ROAV calculation formula to obtain the quasi-ROAV with a unified calculation standard.

[0031] Compared with the prior art, the present invention has the following advantages:

[0032] The present application not only avoids the one-sidedness of comprehensively evaluating the flavor quality of morels by only considering the relative content of volatile substances, but also allows, when analyzing a large sample, first to determine the relative content of volatile substances in some samples by GC-MS, then to determine the key flavor compounds by calculating the ROAV, and then to determine the content of key volatile substances in a large number of samples, and to use the similar ROAV of the key volatile substances to perform PCA and systematic cluster analysis to evaluate the flavor quality of morels, thereby achieving the purpose of improving the scientific nature of the analysis results, reducing the amount of analysis calculations and lowering the detection cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solution of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0034] Figure 1 A schematic diagram of a method flow chart of an embodiment of the present application;

[0035] Figure 2 This is a schematic diagram of cluster analysis of Morchella resources according to an embodiment of the present application;

[0036] Figure 3 Schematic diagram of the system structure of an embodiment of the present application. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0038] In order to make the above-mentioned objectives, features and advantages of the present application more understandable, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0039] Example 1

[0040] like Figure 1 , which is a schematic diagram of the method flow of an embodiment of the present application.

[0041] Firstly, the relative content of volatile substances in Morchella is measured to obtain the measurement results.

[0042] A total of 519 volatile substances were identified through GC-MS analysis (Table 1), including 15 categories of volatile substances: heterocyclic compounds, esters, aldehydes, ketones, alcohols, aromatic hydrocarbons, hydrocarbons, volatile mushrooms, volatile phenols, acids, volatile ethers, nitrogen-containing compounds, sulfur-containing compounds, volatile amines, and halogenated hydrocarbons. Table 1 shows that the percentage content of heterocyclic compounds (mass of volatile substances / mass of extracted morels × 100%) is the highest, ranging from (0.937±0.047)% to (2.21±0.047)%, followed by esters, with a content of (0.457±0.02)% to (1.615±0.054)%, aldehydes are third, with a content of (0.281±0.001)% to (1.205±0.027)%, ketones are fourth, with a content of (0.159±0.002)% to (1.007±0.036)%, and halogenated hydrocarbons have the lowest relative content, with a content of only (0.004±0)% to (0.007±0)%.

[0043] Table 1

[0044]

[0045] The results of one-way ANOVA showed that the contents of volatile substances among the samples were significantly different (R﹤0.05). Among them, the 2,6-dimethylpyrazine contents of M1 and M4 samples were the highest, which were (10221.147±257.378)μg / kg and (6765.79±90.857)μg / kg, respectively. The phenylacetaldehyde contents of M2 and M8 samples were the highest, which were (14249.194±195.077)μg / kg and (11718.326±396.976)μg / kg, respectively. kg, M3, M5, M6, M7, M9 and M10 had the highest relative content of furanone, which were (6113.171±9.279)μg / kg, (5816.641±169.782)μg / kg, (5816.641±655.699)μg / kg, (6881.664±81.978)μg / kg, (7436.148+642.299)μg / kg and (6344.265±89.238)μg / kg, respectively.

[0046] Then, based on the measurement results, the ROAV of the volatile substances was obtained.

[0047] Based on the relative content of volatile substances and their sensory thresholds, the volatile substances that contribute most to the flavor quality of the sample are determined. The component that contributes most to the flavor of the sample is defined as ROAVstan = 100. The ROAV of the volatile substance is calculated using the following formula based on the relative content (Cstan) and sensory threshold (Tstan) of the substance.

[0048]

[0049] Where: Ci and Ti represent the relative content and sensory threshold of the volatile components, respectively; Cstan and Tstan represent the relative content and sensory threshold of the component that contributes most to the overall flavor of the sample, respectively. If ROAV ≥ 1, the component is considered to have a significant contribution to the flavor quality of the analyzed sample. If 0.1 ≤ ROAV < 1, the component is considered to have a significant modifying effect on the flavor quality of the analyzed sample. In this example, compounds with ROAV ≥ 0.1 are identified as key flavor compounds affecting the flavor quality of morels.

[0050] After reviewing the literature, we found sensory thresholds for 113 volatile compounds, which were then analyzed. The ROAVs of these 113 volatile compounds are shown in Table 2. To further analyze the flavor characteristics of morels, a heat map analysis of the ROAVs of these 113 volatile compounds was performed. The results showed that furanone, methyl hexanoate, 3-mercapto-3-methyl-1-butanol, 1-heptanol, acetophenone, 1-octanol, 2,3,4,5-tetrahydropyridine, trimethylpyrazine, nonyl acetate, hexyl hexanoate, isobutyric acid, and 2-octanol had the greatest impact on the flavor quality of morels. The overall ROAV of the 113 volatile compounds was used to analyze the flavor quality of morels. The results showed that M1 had the best overall flavor quality, followed by M4. The flavor quality rankings of the remaining samples were M7, M9, M8, M3, M2, M6, M10, and M5, respectively.

[0051] Table 2

[0052]

[0053] Based on ROAV, the key flavor compounds of morel mushroom were obtained.

[0054] Of the 113 volatile compounds, 7 had ROAVs ≥ 1, significantly contributing to the flavor quality of the 10 wild Morchella samples, while 13 had ROAVs between 0.1 and 1, significantly modifying the flavor quality of all 10 samples (Table 2). Although the volatile compound content varied significantly among Morchella samples, furanone, 1-methylnaphthalene, 4-methyl-5-ethyl-3-hydroxy-2(5H)-furanone, and (E,E)-2,4-nonadienal had ROAVs greater than 1 in all samples, significantly contributing to the flavor quality of all samples. 5-Methyl-2-heptan-4-one, (E)-2-nonenal, and benzaldehyde had ROAVs of ≥ 0.1 in all samples, indicating they were key modifiers or significant contributors to the flavor quality of all 10 samples.

[0055] In general, the contribution of the 20 key flavor compounds with ROAV greater than 0.1 to the flavor quality of the 10 wild Morchella resources is as follows: furanone > 1-methyl-tetrahydrofuranone > 5-ethyl-3-hydroxy-4-methyl-2(5H)-furanone > (E,E)-2,4-nonadienal > dimethyl trisulfide > 5-methyl-2-heptadienal 4-Keto > (E)-2-nonenal > phenylacetaldehyde > benzaldehyde > 3-thio-1-hexanol > cyclohexanecarboxaldehyde > (E)-2-undecenal > guaiacol > 2-pentylfuran > 2-hexyl-pyridine > heptanal > methyl hexanoate > cis-6-nonen-1-ol acetate > biphenyl > (E,E)-2,4-hexanedialdehyde (sorbitol) > 1-heptanol. In this example, these 20 volatile compounds were identified as key flavor compounds affecting the flavor quality of 10 wild morel resources.

[0056] The ROAV analysis method was used to obtain the similar ROAVs of key flavor compounds; and PCA was performed on the similar ROAVs to obtain the analysis results.

[0057] The key flavor compounds determined by the ROAV analysis method were used. The relative contents (Cstan') and sensory thresholds (Tstan') of the key flavor compounds with the greatest contribution to flavor quality and the highest relative contents in 10 wild morel resource samples were used as standards. The ROAV analysis method was used to recalculate the key flavor compounds, and a series of ROAV-like variables were obtained (Table 3), namely, ROAV-like variables.

[0058] PCA was performed using ROAV ≥ 0.1, ROAV ≥ 0.01, ROAV ≥ 0.001, and the ROAV-like substances of all 113 ROAV-obtained substances to extract principal components with eigenvalues ​​greater than 1. The eigenvalues ​​and variance contribution rates are shown in Table 3. The cumulative variance contribution rate of the first 8 principal components of the ROAV-like PCA of 113 volatile substances was 99.104%, and the cumulative variance contribution rates of the principal components with eigenvalues ​​greater than 1 of the PCA of substances with ROAV ≥ 0.001, ROAV ≥ 0.01, and ROAV ≥ 0.1 were 99.004%, 94.624%, and 92.347%, respectively, which all contained most of the information of the 10 wild morel resources.

[0059] Table 3

[0060]

[0061] Based on the analysis results, an evaluation model for the flavor quality of Morel Mushrooms was established, and the comprehensive flavor quality score of Morel Mushrooms was calculated to obtain the comprehensive flavor quality score.

[0062] Using the analysis results, the variance contribution rate βi (i = 1, 2, 3, ..., K) of different principal component eigenvalues ​​was used as the weighting coefficient, and the comprehensive evaluation function F = β1f1 + β2f2 + β3f3 + ... + βnfn was used to calculate the score of each sample. The flavor quality of the 10 wild morel resources was comprehensively evaluated. Based on the eigenvalues ​​and corresponding eigenvectors, the flavor quality of the 10 wild morel resources was comprehensively evaluated using four principal components. The principal component scores of each resource were calculated, and finally the flavor quality evaluation model based on PCA was obtained:

[0063] F1=49.711f1+17.642f2+9.932f3+7.531f4+5.228f5+4.223f6+2.887f6+1.949f8+0.896f9;

[0064] F2=46.934f1+19.628f2+11.264f3+6.901f4+6.092f5+3.575f6+2.640f7+1.971f8;

[0065] F3=47.511f1+17.213f2+11.581f3+8.345f4+6.091f5+3.882f6;

[0066] F4=42.767f1+21.378f2+12.617f3+9.996f4+5.588f5;

[0067] Among them, F1 is a ROAVPCA-like evaluation model based on 113 volatile substances, F2 is a ROAVPCA-like evaluation model based on substances with ROAV≥0.001, F3 is a ROAVPCA-like evaluation model based on substances with ROAV≥0.01, and F4 is a ROAVPCA-like evaluation model based on substances with ROAV≥0.01. f1-fi are the weights of each principal component, and the calculation formula is as follows:

[0068] 1. Calculate the characteristic root; calculate the principal component coefficient according to formula ②

[0069]

[0070] Where: Ri is the characteristic root of the i-th principal component, and Ei is the total eigenvalue of the i-th principal component.

[0071]

[0072] Where: Hi is the principal component coefficient of the i-th principal component, Ci is the principal component load of the i-th principal component.

[0073] 2. Calculate the weight of each principal component according to the following formula ③

[0074] fi=∑(Vi×Hi)……………………③

[0075] Where fi is the weight of the i-th principal component, and Vi is the standardized value of ROAV.

[0076] Substituting formula ③ into the Morchella flavor quality evaluation model yields comprehensive flavor quality scores for different samples. The comprehensive scores and rankings for the 10 wild Morchella resources are shown in Tables 4 and 5, respectively. Despite differences in the comprehensive scores, the flavor quality rankings for the F1 and F2 samples were identical, as were the rankings for F3 and F4. The Kappa coefficient for the rankings of F3, F4, and F1 and F2 samples was 0.778, indicating high consistency. Furthermore, the F3 and F4 rankings for the 10 wild Morchella resources were consistent with the rankings from the heat map analysis.

[0077] Table 4

[0078]

[0079] Table 5

[0080]

[0081] At the same time, ROAV-like methods were used to conduct systematic cluster analysis and obtain cluster analysis results.

[0082] The cluster analysis of 10 wild Morchella resources was carried out by using the cluster average method, with ROAV≥0.1, ROAV≥0.01, ROAV≥0.001 and the similar ROAV of all 113 ROAV substances as variables. The results are as follows: Figure 2 As shown in the figure, the clustering results for all data combinations were consistent. When the distance was approximately 0.12, the 10 Morchella samples were clustered into two categories, with M1 alone in one category and the remaining samples in one category. When the distance was approximately 0.09, the 10 Morchella samples could be clustered into four categories, with M1 alone in one category, M4, M7, and M9 in one category, M6, M3, and M8 in one category, and M2, M5, and M10 in one category.

[0083] Finally, combining the comprehensive flavor quality scores and cluster analysis results, the flavor quality of different strains of Morchella was classified and evaluated, completing the flavor quality evaluation of Morchella.

[0084] The PCA principal component scores yielded only one result, and when the distance was 0.12-0.14, the cluster analysis was consistent with the PCA results. This suggests that cluster analysis using key flavor compounds with ROAV ≥ 0.1, ROAV ≥ 0.01, ROAV ≥ 0.001, and ROAV-like systems for all 113 ROAV-derived compounds can classify the 10 wild Morchella resources, classify and evaluate the flavor quality of different Morchella strains, and determine the similarities and differences in flavor quality between different Morchella strains.

[0085] This application compares the comprehensive evaluation results of the flavor quality of morels using ROAV heat map analysis of 113 volatile substances, ROAV ≥ 0.001, ROAV ≥ 0.01, and ROAV ≥ 0.1 volatile substances similar to ROAV. It shows that the comprehensive evaluation of the flavor quality of morels using key flavor compounds with ROAV ≥ 0.1 similar to ROAV can be used to classify and evaluate the tested morels, and the amount of data involved in the analysis is minimal, which can significantly reduce the computational complexity of the comprehensive evaluation of the flavor quality of morels. At the same time, when conducting large sample analysis, some samples can be used to first determine the key flavor compounds that affect the flavor quality of morels, and then combined with the design of mixed standards to reduce GC-MS measurement indicators, thereby reducing the cost of morel flavor quality analysis. This application can provide a relatively simple comprehensive evaluation method for the flavor quality of morels, providing a reference for morel resource evaluation, domestication cultivation, and processing.

[0086] Example 2

[0087] like Figure 3 The figure shows a schematic diagram of the system structure of an embodiment of the present application, which includes: a determination module, a calculation module, a calibration module, an analysis module, a construction module, a clustering module, and an evaluation module. Among them, the determination module is used to perform GC-MS determination on the relative content of volatile substances in morels to obtain the determination results; the calculation module is used to obtain the ROAV of volatile substances based on the determination results; the calibration module is used to obtain the key flavor compounds of morels based on the ROAV; the analysis module is used to obtain the ROAV-like values ​​of key flavor compounds using the ROAV analysis method and perform PCA on the ROAV-like values ​​to obtain the analysis results; the construction module is used to establish a morel flavor quality evaluation model based on the analysis results, calculate the comprehensive flavor quality score of morels, and obtain the comprehensive flavor quality score; the clustering module is used to perform system cluster analysis using the ROAV-like values ​​to obtain the cluster analysis results; the evaluation module is used to classify and evaluate the flavor quality of morels of different strains based on the comprehensive flavor quality score and the cluster analysis results, thereby completing the flavor quality evaluation of morels.

[0088] The following will combine this embodiment to explain in detail how this application solves technical problems in actual production and research by using different data combination comparative analysis methods.

[0089] First, the relative content of volatile substances in Morchella is measured using a measurement module to obtain the measurement results.

[0090] GC-MS analysis identified 519 volatile compounds (Table 1), including 15 categories of volatile substances: heterocyclic compounds, esters, aldehydes, ketones, alcohols, aromatic hydrocarbons, hydrocarbons, volatile mushrooms, volatile phenols, acids, volatile ethers, nitrogen-containing compounds, sulfur-containing compounds, volatile amines, and halogenated hydrocarbons. Table 1 shows that heterocyclic compounds had the highest percentage (mass of volatile compounds / mass of extracted Morchella) at (0.937±0.047)% to (2.21±0.047)%, followed by esters at (0.457±0.02)% to (0.457±0.02)%.

[0091] (1.615±0.054)%, aldehydes are third, with a content of (0.281±0.001)%~(1.205±0.027)%, ketones are fourth, with a content of (0.159±0.002)%~(1.007±0.036)%, and halogenated hydrocarbons have the lowest relative content, with a content of only (0.004±0)%~(0.007±0)%.

[0092] The results of one-way ANOVA showed that the contents of volatile substances among the samples were significantly different (R<0.05). Among them, the 2,6-dimethylpyrazine contents of M1 and M4 samples were the highest, which were (10221.147±257.378)μg / kg and (6765.79±90.857)μg / kg, respectively. The phenylacetaldehyde contents of M2 and M8 samples were the highest, which were (14249.194±195.077)μg / kg and (11718.326±396.976)μg / kg, respectively. kg, M3, M5, M6, M7, M9 and M10 had the highest relative content of furanone, which were (6113.171±9.279)μg / kg, (5816.641±169.782)μg / kg, (5816.641±655.699)μg / kg, (6881.664±81.978)μg / kg, (7436.148+642.299)μg / kg and (6344.265±89.238)μg / kg, respectively.

[0093] The calculation module obtains the ROAV of the volatile substances based on the measurement results.

[0094] Based on the relative content of volatile substances and their sensory thresholds, the volatile substances that contribute most to the flavor quality of the sample are determined. The component that contributes most to the flavor of the sample is defined as ROAVstan = 100. The ROAV of the volatile substance is calculated using the following formula based on the relative content (Cstan) and sensory threshold (Tstan) of the substance.

[0095]

[0096] Where Ci and Ti represent the relative content and sensory threshold of the volatile components, respectively; Cstan and Tstan represent the relative content and sensory threshold of the component that contributes most to the overall flavor of the sample, respectively. If ROAV ≥ 1, the component is considered to have a significant contribution to the flavor quality of the analyzed sample. If 0.1 ≤ ROAV < 1, the component is considered to have a significant modifying effect on the flavor quality of the analyzed sample. In this example, compounds with ROAV ≥ 0.1 are identified as key flavor compounds affecting the flavor quality of morels.

[0097] After reviewing the literature, we found sensory thresholds for 113 volatile compounds, which were then analyzed. The ROAVs of these 113 volatile compounds are shown in Table 2. To further analyze the flavor characteristics of morels, a heat map analysis of the ROAVs of these 113 volatile compounds was performed. The results showed that furanone, methyl hexanoate, 3-mercapto-3-methyl-1-butanol, 1-heptanol, acetophenone, 1-octanol, 2,3,4,5-tetrahydropyridine, trimethylpyrazine, nonyl acetate, hexyl hexanoate, isobutyric acid, and 2-octanol had the greatest impact on the flavor quality of morels. The overall ROAV of the 113 volatile compounds was used to analyze the flavor quality of morels. The results showed that M1 had the best overall flavor quality, followed by M4. The flavor quality rankings of the remaining samples were M7, M9, M8, M3, M2, M6, M10, and M5, respectively.

[0098] The calibration module is based on ROAV to obtain the key flavor compounds of morel mushrooms.

[0099] Of the 113 volatile compounds, 7 had ROAVs ≥ 1, significantly contributing to the flavor quality of the 10 wild Morchella samples, while 13 had ROAVs between 0.1 and 1, significantly modifying the flavor quality of all 10 samples (Table 2). Although the volatile compound content varied significantly among Morchella samples, furanone, 1-methylnaphthalene, 4-methyl-5-ethyl-3-hydroxy-2(5H)-furanone, and (E,E)-2,4-nonadienal had ROAVs greater than 1 in all samples, significantly contributing to the flavor quality of all samples. 5-Methyl-2-heptan-4-one, (E)-2-nonenal, and benzaldehyde had ROAVs of ≥ 0.1 in all samples, indicating they were key modifiers or significant contributors to the flavor quality of all 10 samples.

[0100] In general, the contribution of the 20 key flavor compounds with ROAV greater than 0.1 to the flavor quality of the 10 wild Morchella resources is as follows: furanone > 1-methyl-tetrahydrofuranone > 5-ethyl-3-hydroxy-4-methyl-2(5H)-furanone > (E,E)-2,4-nonadienal > dimethyl trisulfide > 5-methyl-2-heptadienal -Ketone > (E)-2-nonenal > phenylacetaldehyde > benzaldehyde > 3-thio-1-hexanol > cyclohexanecarboxaldehyde > (E)-2-undecenal > guaiacol > 2-pentylfuran > 2-hexyl-pyridine > heptanal > methyl hexanoate > cis-6-nonen-1-ol acetate > biphenyl > (E,E)-2,4-hexanedialdehyde (sorbitol) > 1-heptanol. In this example, these 20 volatile compounds were identified as key flavor compounds affecting the flavor quality of 10 wild Morchella resources.

[0101] The analysis module uses the ROAV analysis method to obtain the ROAV-like values ​​of key flavor compounds, and performs PCA on the ROAV-like values ​​to obtain analysis results.

[0102] The key flavor compounds determined by the ROAV analysis method were used. The relative contents (Cstan') and sensory thresholds (Tstan') of the key flavor compounds with the greatest contribution to flavor quality and the highest relative contents in 10 wild morel resource samples were used as standards. The ROAV analysis method was used to recalculate the key flavor compounds, and a series of ROAV-like variables were obtained (Table 3), namely, ROAV-like variables.

[0103] PCA was performed using ROAV ≥ 0.1, ROAV ≥ 0.01, ROAV ≥ 0.001, and ROAV-like substances of all 113 ROAV-obtained substances to extract principal components with eigenvalues ​​greater than 1. The eigenvalues ​​and variance contribution rates are shown in Table 3. The cumulative variance contribution rate of the first 8 principal components of the ROAV-like PCA of the 113 substances was 99.104%, and the cumulative variance contribution rates of the principal components with eigenvalues ​​greater than 1 of the PCA of substances with ROAV ≥ 0.001, ROAV ≥ 0.01, and ROAV ≥ 0.1 were 99.004%, 94.624%, and 92.347%, respectively, which all contained most of the information of the 10 wild morel resources.

[0104] The construction module establishes a flavor quality evaluation model for morels based on the analysis results, calculates the comprehensive flavor quality score of morels, and obtains the comprehensive flavor quality score.

[0105] Using the analysis results, the variance contribution rate βi (i = 1, 2, 3, ..., K) of different principal component eigenvalues ​​was used as the weighting coefficient, and the comprehensive evaluation function F = β1f1 + β2f2 + β3f3 + ... + βnfn was used to calculate the score of each sample. The flavor quality of the 10 wild morel resources was comprehensively evaluated. Based on the eigenvalues ​​and corresponding eigenvectors, the flavor quality of the 10 wild morel resources was comprehensively evaluated using four principal components. The principal component scores of each resource were calculated, and finally the flavor quality evaluation model based on PCA was obtained:

[0106] F1=49.711f1+17.642f2+9.932f3+7.531f4+5.228f5+4.223f6+2.887f6+1.949f8+0.896f9;

[0107] F2=46.934f1+19.628f2+11.264f3+6.901f4+6.092f5+3.575f6+2.640f7+1.971f8;

[0108] F3=47.511f1+17.213f2+11.581f3+8.345f4+6.091f5+3.882f6;

[0109] F4=42.767f1+21.378f2+12.617f3+9.996f4+5.588f5;

[0110] Among them, F1 is the ROAV PCA evaluation model based on 113 substances, F2 is the ROAV PCA evaluation model based on ROAV≥0.001 substances, F3 is the ROAV PCA evaluation model based on ROAV≥0.01 substances, and F4 is the ROAV PCA evaluation model based on ROAV≥0.01 substances. f1-fi are the weights of each principal component, and the calculation formula is as follows:

[0111] 1. Calculate the characteristic root; calculate the principal component coefficient according to formula ②

[0112]

[0113] Where: Ri is the characteristic root of the i-th principal component, and Ei is the total eigenvalue of the i-th principal component.

[0114]

[0115] Where: Hi is the principal component coefficient of the i-th principal component, Ci is the principal component load of the i-th principal component.

[0116] 2. Calculate the weight of each principal component according to the following formula ③

[0117] fi=∑(Vi×Hi)……………………③

[0118] Where fi is the weight of the i-th principal component, and Vi is the standardized value of ROAV.

[0119] Substituting formula ③ into the Morchella flavor quality evaluation model yields comprehensive flavor quality scores for different samples. The comprehensive scores and rankings for the 10 wild Morchella resources are shown in Tables 4 and 5, respectively. Despite differences in the comprehensive scores, the flavor quality rankings for the F1 and F2 samples were identical, as were the rankings for F3 and F4. The Kappa coefficient for the rankings of F3, F4, and F1 and F2 samples was 0.778, demonstrating high consistency. Furthermore, the F3 and F4 rankings for the 10 wild Morchella samples were consistent with the rankings from the heat map analysis. Simultaneously, the clustering module used ROAV-like methods to perform a systematic cluster analysis, yielding cluster analysis results.

[0120] The cluster analysis of 10 wild Morchella resources was carried out by using the cluster average method, with ROAV≥0.1, ROAV≥0.01, ROAV≥0.001 and the similar ROAV of all 113 ROAV substances as variables. The results are as follows: Figure 2 As shown in the figure, the clustering results for all data combinations were consistent. When the distance was approximately 0.12, the 10 Morchella samples were clustered into two categories, with M1 alone in one category and the remaining samples in one category. When the distance was approximately 0.09, the 10 Morchella samples could be clustered into four categories, with M1 alone in one category, M4, M7, and M9 in one category, M6, M3, and M8 in one category, and M2, M5, and M10 in one category.

[0121] Finally, the evaluation module classified and evaluated the flavor quality of morels of different strains by combining the comprehensive flavor quality scores and cluster analysis results, thus completing the flavor quality evaluation of morels.

[0122] The PCA principal component scores yielded only one result, and when the distance was 0.12-0.14, the cluster analysis was consistent with the PCA results. This suggests that cluster analysis using key flavor compounds with ROAV ≥ 0.1, ROAV ≥ 0.01, ROAV ≥ 0.001, and ROAV-like systems for all 113 ROAV-derived compounds can classify the 10 wild Morchella resources, classify and evaluate the flavor quality of different Morchella strains, and determine the similarities and differences in flavor quality between different Morchella strains.

[0123] This application compares the results of the comprehensive evaluation of the flavor quality of morels using ROAV heat map analysis of 113 volatile substances, ROAV ≥ 0.001, ROAV ≥ 0.01, and ROAV ≥ 0.1 volatile substances similar to ROAV. It shows that the comprehensive evaluation of the flavor quality of morels using key flavor compounds with ROAV ≥ 0.1 similar to ROAV can be used to classify and evaluate the tested morels, and the amount of data involved in the analysis is minimal, which can significantly reduce the computational complexity of the comprehensive evaluation of the flavor quality of morels. At the same time, when conducting large sample analysis, some samples can be used to first determine the key flavor compounds that affect the flavor quality of morels, and then combined with the design of mixed standards to reduce GC-MS measurement indicators, thereby reducing the cost of morel flavor quality analysis. This application can provide a relatively simple comprehensive evaluation method for the flavor quality of morels, providing a reference for morel resource evaluation, domestication cultivation, and processing.

[0124] The embodiments described above are merely descriptions of the preferred embodiments of the present application and do not limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements made to the technical solutions of the present application by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present application.

Claims

1. A method for evaluating the flavor quality of Morchella edulis that can reduce computational complexity and cost, characterized in that the steps include: The relative content of volatile substances in Morchella esculenta is measured to obtain the measurement results; Based on the measurement results, obtaining the ROAV of the volatile substance; Based on the ROAV, key flavor compounds of morel mushrooms were obtained; The method for obtaining the ROAV includes: Where: Ci and Ti represent the relative content and sensory threshold of volatile components, respectively; Cstan and Tstan represent the relative content and sensory threshold of the component that contributes most to the overall flavor of the sample, respectively; The ROAV analysis method is used to obtain the similar ROAV of the key flavor compound; and the PCA is performed on the similar ROAV to obtain an analysis result. The method for obtaining the similar ROAV includes: using the substances with ROAV ≥ 0.1 obtained by screening, taking the relative content and sensory threshold of the key flavor compound with the greatest contribution to the overall flavor quality and the highest relative content in all the tested morel samples as the standard, and recalculating with reference to the ROAV calculation formula to obtain the similar ROAV with a unified calculation standard; Based on the analysis results, a Morchella flavor quality evaluation model is established, and a Morchella flavor quality comprehensive score is calculated to obtain a flavor quality comprehensive score; Using the ROAV-like model, a systematic cluster analysis was performed to obtain cluster analysis results; The flavor quality of Morchella oleracea of ​​different strains was classified and evaluated by combining the flavor quality comprehensive score and the cluster analysis result, thereby completing the flavor quality evaluation of Morchella oleracea.

2. The method for evaluating the flavor quality of Morchella edodes according to claim 1, wherein the method comprises: The method for obtaining the determination result comprises: using GC-MS to determine the relative content of volatile substances in Morchella oleracea to obtain the determination result.

3. A Morchella flavor quality evaluation system capable of reducing computational complexity and cost, characterized in that: include: Determination module, calculation module, calibration module, analysis module, construction module, clustering module and evaluation module; The measuring module is used to measure the relative content of volatile substances in Morchella esculenta to obtain a measurement result; The calculation module is used to obtain the ROAV of the volatile substance based on the measurement result; The calibration module is used to obtain the key flavor compounds of Morchella based on the ROAV. The workflow of the calculation module includes: Where: Ci and Ti represent the relative content and sensory threshold of volatile components, respectively; Cstan and Tstan represent the relative content and sensory threshold of the component that contributes most to the overall flavor of the sample, respectively; The analysis module is used to obtain the similar ROAV of the key flavor compound using the ROAV analysis method; and perform PCA on the similar ROAV to obtain an analysis result; the process of obtaining the similar ROAV includes: using the substances with ROAV ≥ 0.1 obtained by screening, taking the relative content and sensory threshold of the key flavor compound with the greatest contribution to the overall flavor quality and the highest relative content in all tested morel samples as the standard, and recalculating with reference to the ROAV calculation formula to obtain the similar ROAV with a unified calculation standard; The construction module is used to establish a Morchella flavor quality evaluation model based on the analysis results, calculate the Morchella flavor quality comprehensive score, and obtain the flavor quality comprehensive score; The clustering module is used to perform a system cluster analysis using the ROAV-like model to obtain a cluster analysis result; The evaluation module is used to classify and evaluate the flavor quality of Morchella fusca of different strains by combining the flavor quality comprehensive score and the cluster analysis result, thereby completing the flavor quality evaluation of Morchella fusca.

4. The Morchella flavor quality evaluation system capable of reducing computational complexity and cost according to claim 3, characterized in that: The workflow of the determination module includes: using GC-MS to determine the relative content of volatile substances in Morchella oleracea to obtain the determination result.

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