Dogwood quality evaluation system based on analytic hierarchy process, construction method and application

Abstract

The invention discloses a dogwood quality evaluation system based on analytic hierarchy process, a construction method and application, and belongs to the technical field of dogwood quality detection. The method comprises the following steps: firstly, determining quality evaluation indexes of dogwood, and constructing an evaluation index hierarchical structure model; constructing a judgment matrix of each lower-layer element relative to an upper-layer dominating element in the hierarchical structure by adopting an analytic hierarchy process, and calculating the relative weight of the judgment matrix; secondly, performing normalization processing on the measured values of all the elements of the to-be-measured dogwood samples in different batches, and converting the normalized values into standardized scores in a range of 0-100 to obtain scoring results of all the elements; and carrying out reverse normalization processing on the scoring result of each element to obtain an evaluation element value range of the cornus officinalis quality comprehensive evaluation system. And finally, multiplying the weight of each evaluation element by the value range of the corresponding evaluation element, and accumulating to obtain a dogwood quality evaluation system. According to the system, scientific and comprehensive evaluation of the quality of the dogwood can be realized, and the problem that the quality of the dogwood cannot be objectively judged through experience is solved.

Description

Technical Field

[0001] This invention belongs to the field of Cornus officinalis quality testing technology, specifically involving a quality evaluation system for Cornus officinalis based on hierarchical analysis, its construction method, and its application. Background Technology

[0002] Cornus officinalis ( Cornus officinalis Cornus officinalis (Sieb. et Zucc.) is an important variety in the traditional Chinese medicine system, widely used in clinical treatment due to its significant efficacy in tonifying the kidneys and strengthening essence. In 2023, it was officially included in the "List of Food and Medicine with the Same Origin," a regulatory milestone that provides a legal basis for its application in the development of functional foods such as fermented fruit wines, vinegars, and health drinks. Phytochemical studies have shown that Cornus officinalis contains various bioactive components, including iridoid glycosides (monoside, loganin, etc.), flavonoids, and polyphenols. These components collectively endow it with significant pharmacological activities such as hypoglycemic effects, antioxidant effects, neuroprotective effects, and anti-inflammatory effects. Despite its broad market prospects, the industrialization of Cornus officinalis still faces severe challenges: the lack of standardized post-harvest processing procedures, an imperfect cold chain logistics system, and inconsistent quality control measures lead to unstable raw material supply, high processing loss rates (30%~40%), and poor retention of active ingredients, affecting the stability of product efficacy, threatening medication safety, and ultimately hindering the sustainable development of the industry.

[0003] The current quality evaluation system for Cornus officinalis is mainly based on the standards of the Chinese Pharmacopoeia (2020 edition), which specifies physicochemical parameters and morphological characteristics such as moisture content (≤16.0%), total ash content (≤6.0%), and active ingredient requirements (total content of mononoside and loganin ≥1.2%). Recent studies have attempted to establish a more comprehensive evaluation system, such as introducing supplementary indicators like the content of medicinal material debris and thousand-grain weight, and using principal component analysis for comprehensive multi-indicator evaluation. Regarding processing systems, modern processing research has confirmed through quantitative analysis that different parameters have a significant impact on the retention rate of active ingredients (variation range of 30%–50%), particularly revealing the crucial influence of softening time and drying temperature. However, existing processing methods still largely rely on empirical judgment, leading to significant raw material losses (35%–45%) and persistently high energy consumption (traditional drying energy consumption >120 kWh / kg). Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a quality evaluation system for Cornus officinalis based on hierarchical analysis, a construction method and an application, so as to solve the technical problem that the judgment of Cornus officinalis quality based on experience lacks objectivity.

[0005] To achieve the above objectives, the present invention employs the following technical solution: The first aspect of this invention discloses a method for constructing a quality evaluation system for Cornus officinalis based on hierarchical analysis, comprising the following steps: S1: Determine the quality evaluation indicators for Cornus officinalis, construct a hierarchical structure model of the evaluation indicators, and form an evaluation element system consisting of the target layer, the criterion layer, and the indicator layer; S2: Based on the contribution and importance of each evaluation element to the quality of Cornus officinalis, and combined with expert experience, construct a judgment matrix of each lower-level element relative to its upper-level dominant element in the hierarchical structure, and calculate its relative weight. S3: Normalize the measured values ​​of each element of different batches of Cornus officinalis samples and convert them into standardized scores in the range of 0 to 100 to obtain the score results of each element; perform reverse normalization on the score results of each element to obtain the value range of the evaluation elements of the comprehensive quality evaluation system of Cornus officinalis. S4: Multiply the weights of each evaluation element obtained in S2 by the corresponding value range of the evaluation element and sum them up to obtain the quality evaluation system of Cornus officinalis.

[0006] Preferably, in S1, the evaluation elements of the target layer are the quality of Cornus officinalis; the evaluation elements of the criterion layer are the appearance quality, quality indicators, typical components, and medicinal value of Cornus officinalis; the evaluation elements of the appearance quality of Cornus officinalis corresponding to the indicator layer are the thousand-grain weight, total color difference, and impurity rate; the evaluation elements of the quality indicators of Cornus officinalis corresponding to the indicator layer are the moisture content, ash content, percentage of water-soluble extract, total polysaccharide content, total organic acid content, total saponin content, and total flavonoid content; the evaluation elements of the typical components of Cornus officinalis corresponding to the indicator layer are the content of protocatechuic acid, monosodium glutamate, loganin, and swertiamarin; and the evaluation elements of the medicinal value of Cornus officinalis corresponding to the indicator layer are hypoglycemic effect and antioxidant effect.

[0007] In a second aspect, the present invention discloses a quality evaluation system for Cornus officinalis, which involves substituting the test values ​​of the evaluation elements of the Cornus officinalis to be tested into the comprehensive quality scoring formula of Cornus officinalis to obtain the quality evaluation results of Cornus officinalis. The comprehensive quality scoring formula for Cornus officinalis is as follows:

[0008] The evaluation elements of the Cornus officinalis to be tested consist of 18 indicators, denoted by C1 to C18. C1 is the weight of a thousand grains, C2 is the total color difference, C3 is the impurity rate, C4 is the moisture content, C5 is the ash content, C6 is the percentage of water-soluble extract, C7 is the content of total polysaccharides, C8 is the content of total organic acids, C9 is the content of total saponins, C10 is the content of total flavonoids, C11 is the content of gallic acid, C12 is the content of protocatechuic acid, C13 is the content of monoglucoside, C14 is the content of loganin, C15 is the content of swertiamarin, C16 is the content of new cornus glycosides, C17 is the hypoglycemic effect, and C18 is the antioxidant effect.

[0009] A third aspect of the present invention discloses the application of the Cornus officinalis quality evaluation system in the quality evaluation of Cornus officinalis.

[0010] In a fourth aspect, the present invention discloses a method for quality control of Cornus officinalis. The processing technology of Cornus officinalis is optimized by using the Box-Behnken response surface methodology. The scoring values ​​under different process parameters are obtained using the aforementioned Cornus officinalis quality evaluation system. The quality control of Cornus officinalis is carried out according to the processing technology corresponding to the highest comprehensive quality evaluation score.

[0011] Compared with the prior art, the present invention has the following beneficial effects: The method for constructing a quality evaluation system for Cornus officinalis based on the analytic hierarchy process (AHP) provided by this invention overcomes the lack of objectivity in traditional Chinese medicine quality evaluation, which relies on experience to judge the quality of Cornus officinalis, by introducing a multi-criteria decision analysis framework that combines systematization, quantification, and subjective judgment. This method has the following significant advantages: 1) By establishing a multi-level evaluation model consisting of a "target layer—criteria layer—indicator layer," the quality characteristics of Cornus officinalis are decomposed into multiple measurable elements (such as appearance quality, quality indicators, typical components, and medicinal value), and then hierarchically categorized according to their inherent relationships. This structured modeling approach not only comprehensively covers the key factors affecting the quality of Cornus officinalis but also clarifies the hierarchical relationships between various indicators, enhancing the scientific and systematic nature of the evaluation system; 2) By employing the analytic hierarchy process (AHP) combined with pairwise comparisons of the relative importance of each indicator by domain experts, a judgment matrix is ​​constructed, and the rationality of the judgment is ensured through consistency checks, thereby calculating the weight values ​​of each evaluation element. This method effectively integrates professional knowledge and statistical analysis techniques in the field of traditional Chinese medicine, solving the problems of arbitrary weight setting and lack of basis in traditional weighted scoring methods; 3) By normalizing the original measured values, they are converted into a standard scoring range of 0-100 points, eliminating comparison barriers caused by different units and orders of magnitude. At the same time, the reverse normalization process further clarifies the value range of each indicator in the final evaluation, facilitating subsequent dynamic adjustment and standardized application; 4) By multiplying and summing the standardized scores of each indicator with their scientifically assigned weights, a comprehensive evaluation value is obtained, which intuitively reflects the overall quality level of each batch of Cornus officinalis. This comprehensive score can be used to: classify Cornus officinalis medicinal materials into grades (such as superior, qualified, unqualified), guide the standardized planting and processing optimization of Cornus officinalis, and assist drug regulatory departments in formulating more accurate quality control standards for Cornus officinalis. The Cornus officinalis quality evaluation system obtained by the response surface methodology (RSM) shows that the relative error between the detection results and the theoretical values ​​of the Cornus officinalis quality evaluation system constructed by this method is only 0.70%, which is basically consistent with the theoretical values, indicating that the obtained Cornus officinalis quality evaluation system is accurate and reliable. Therefore, this method is expected to promote the construction of a comprehensive quality control system that runs through the entire Cornus officinalis industry chain.

[0012] Furthermore, using the average of the results from the arithmetic mean, geometric mean, and eigenvalue method as the weights of each element in the comprehensive quality evaluation of Cornus officinalis can avoid the result deviations that may be caused by different calculation methods, ensure the validity of the results, and reduce errors caused by data analysis. Attached Figure Description

[0013] Figure 1 This is a graph showing the effect of drying temperature on the overall quality score of Cornus officinalis according to the present invention; Figure 2 This is a graph showing the effect of drying time on the overall quality score of Cornus officinalis according to the present invention; Figure 3 This is a graph showing the effect of softening temperature on the overall quality score of Cornus officinalis according to the present invention; Figure 4 This is a graph showing the effect of softening time on the overall quality score of Cornus officinalis according to the present invention; Figure 5 shows the response surface and contour plot of the interaction between the two factors of the present invention on the comprehensive quality score of Cornus officinalis; where A represents softening time and softening temperature, B represents softening time and drying time, C represents softening time and drying temperature, D represents softening temperature and drying time, E represents softening temperature and drying temperature, and F represents drying time and drying temperature. Detailed Implementation

[0014] To enable those skilled in the art to understand the features and effects of this invention, the following is only a general description and definition of the terms and expressions mentioned in the specification and claims. The Analytic Hierarchy Process (AHP) employed herein, using the expert-derived pairwise comparisons to construct pairwise comparison matrices, provides a rigorous scientific solution for establishing a weighted multi-level quality evaluation system encompassing physicochemical properties, composition, and bioactivity. The Response Surface Methodology (RSM) employed can mathematically model and optimize key processing parameters (such as softening time and drying temperature), demonstrating superior experimental efficiency compared to traditional univariate methods. The Cornus officinalis quality evaluation system constructed using this method can be flexibly combined with core indicators (such as anti-inflammatory, antibacterial, and flavor indicators) to evaluate the quality of Cornus officinalis, adding indicators based on actual needs. These indicators include thousand-grain weight, total color difference, impurity rate, moisture content, ash content, percentage of water-soluble extract, total polysaccharide content, total organic acid content, total saponin content, total flavonoid content, protocatechuic acid content, monoglycoside content, loganin content, swertiamarin content, hypoglycemic effect, and antioxidant effect. The Cornus officinalis quality evaluation system constructed using this method can be flexibly adjusted according to actual needs, for example, by adding pesticide residues as a new criterion layer, or by incorporating DNA barcoding identification into the authenticity identification indicators. Furthermore, this framework is not only applicable to Cornus officinalis but can also be transferred to the construction of quality evaluation systems for other Chinese medicinal materials (such as Lycium barbarum, Salvia miltiorrhiza, and Astragalus membranaceus), demonstrating broad applicability.

[0015] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. The following embodiments use instruments and equipment conventional in the art. Experimental methods in the following embodiments, unless otherwise specified, are generally performed under conventional conditions or as recommended by the manufacturer. Various raw materials used in the following embodiments are conventional commercially available products with specifications in the art, unless otherwise stated.

[0016] I. Construction Method of Cornus officinalis Quality Evaluation System Based on Analytic Hierarchy Process (AHP) 1. Determining weights 1) The composition of the hierarchical structure of evaluation elements for Cornus officinalis As shown in Table 1, the hierarchical structure model of the comprehensive quality evaluation system of Cornus officinalis and the hierarchical structure relationship of each evaluation element are established using YAAHP statistical software according to their mutual relationship and subordinate relationship. The model is divided into target layer (A), criterion layer (B) and indicator layer (C).

[0017] Table 1. Hierarchical Structure of Comprehensive Quality Evaluation Elements for Cornus officinalis

[0018] 2) Construct the judgment matrix for each evaluation element. Based on the contribution and relative importance of each evaluation element to the quality of Cornus officinalis, the 1-9 scale method shown in Table 2 was used to compare the evaluation elements in each level in pairs. Combining expert experience and professional knowledge, judgment matrices were constructed for the target layer to the criterion layer (AB) and the criterion layer to the indicator layer (B1-C, B2-C, B3-C, B4-C) respectively (as shown in Tables 3 to 7).

[0019] Table 2. Basis for determining the values ​​of each element in the judgment matrix

[0020] Table 3 A-(B1, B2, B3, B4) Judgment Matrix

[0021] Table 4 B1-(C1, C2, C3) Judgment Matrix

[0022] Table 5 B2-(C5, C6, ..., C10) Judgment Matrix

[0023] Table 6 B3-(C11, C12, ..., C16) Judgment Matrix

[0024] Table 7 B4-(C17, C18) Judgment Matrix

[0025] 3) Consistency check The data in the judgment matrix obtained in step 2) were processed using YAAHP statistical software, and consistency tests were performed according to formulas (2-1) and (2-2): (2-1) (2-2) In the formula, λ max — The largest eigenvalue of the judgment matrix; n — the number of evaluation elements in this level; CI — consistency index; RI — average random consistency index (determined according to the "Standard Value Table of Average Random Consistency Index RI"); CR — consistency ratio; if CR < 0.1, the consistency of the judgment matrix is ​​considered acceptable; otherwise, the judgment matrix needs to be corrected.

[0026] The consistency test results show that in the AB(B1, B2, B3, B4) matrix, λ max : 4.00320, CR=0.0120; In the B1-C(C1, C2, C3) matrix, λ max : 3.0385, CR=0.0370; In the B2-C(C5, C6, ... C10) matrix, λ max : 7.3187, CR=0.0391, in the B3-C(C11, C12, ..., C16) matrix, λ max : 6.3276, CR=0.0520; In the B4-C(C17, C18) matrix, λ max The consistency ratio (CR) of all judgment matrices is 3.0385, CR = 0.0370, and the CR of all judgment matrices is below the critical criterion of 0.10. These results indicate that the relationships between the evaluation elements in the judgment matrices constructed by this method are relatively consistent, all judgment matrices pass the consistency test, their construction is reasonable, and the weight values ​​of each evaluation element calculated accordingly are effective and acceptable.

[0027] 4) Weighting of factors in the comprehensive quality evaluation of Cornus officinalis The weights of each evaluation element in the judgment matrices of Tables 3-6 were calculated using the arithmetic mean, geometric mean, and eigenvalue method, respectively. The average weights were then taken to obtain the local weights of each evaluation element relative to its next higher level (Table 8). Finally, the weights of the decision-making level (B) to the target level (A) were calculated. The weight of the indicator layer (C) to the decision layer (B) is equal to the weight of the indicator layer (C) to the target layer (A). The weight of the evaluation element (indicator layer (C)) to the target layer (A) is calculated, and then the weight values ​​of the comprehensive evaluation elements of Cornus officinalis quality are obtained (Table 9). The arithmetic mean method involves normalizing the columns of the judgment matrix, summing the rows, and then averaging the results to obtain the weights of each evaluation element. The geometric mean method involves multiplying the rows of the judgment matrix, taking the power of the sum, and then normalizing the columns to obtain the weights of each evaluation element. The eigenvalue method involves calculating the corresponding eigenvalues. Since the consistency matrix has one eigenvalue of n and all other eigenvalues ​​are 0, the eigenvector corresponding to eigenvalue n is obtained. The eigenvector is then normalized to obtain the weights of each evaluation element.

[0028] Table 8. Local weights of each evaluation element calculated using different methods.

[0029] Note: "Local weight" is a standard term in the Analytic Hierarchy Process (AHP), specifically referring to the weight of each element within the same level relative to its parent element, which is very precise.

[0030] Table 9 Weights of Comprehensive Quality Evaluation Elements for Cornus officinalis

[0031] 2. Data standardization 1) Obtaining indicator values As shown in Table 10, 24 batches of Cornus officinalis samples from different locations were collected, and the values ​​of various indicators (appearance quality, quality indicators, typical components and medicinal value) were measured. The test results are shown in Tables 11 to 15.

[0032] Table 10 Locations of Cornus officinalis Sample Collection

[0033] Table 11 Results of appearance quality (B1) test of Cornus officinalis samples

[0034] Note: Appearance quality includes the weight of 1000 grains. (n=3), chromaticity data ( (n=10) and impurity rate ( (n=3); different lowercase letters in the same column indicate significant differences (P<0.05), and all values ​​are expressed as mean ± standard deviation.

[0035] Table 12 Results of Quality Indicators (B2) of Cornus officinalis Samples

[0036] Note: Quality indicators include moisture, ash, extractives, and total organic acids (%). (n=3), total polysaccharide, total saponin, and total flavonoid content (mg / g, (n=3); different lowercase letters in the same column indicate significant differences (P<0.05), and all values ​​are expressed as mean ± standard deviation.

[0037] Table 13 Results of determination of typical components (B3) in Cornus officinalis samples

[0038] Note: Typical components include gallic acid, protocatechuic acid, monoglucoside, loganin, swertiamarin, and cornus officinalis glycosides (mg / g). (n=3); different lowercase letters in the same column indicate significant differences (P<0.05), and all values ​​are expressed as mean ± standard deviation.

[0039] Table 14 Results of the determination of medicinal value (B4) of Cornus officinalis samples (inhibition rate of α-glucosidase activity)

[0040] Note: The medicinal value includes the inhibition rate of α-glucosidase activity by Cornus officinalis samples. (n=5); different lowercase letters in the same column indicate significant differences (P<0.05), and all values ​​are expressed as mean ± standard deviation.

[0041] Table 15 Results of the determination of medicinal value (B4) of Cornus officinalis samples (effect on H2O2-induced survival rate of HepG2 cells)

[0042] Note: The medicinal value includes the effect of Cornus officinalis on the survival rate of H2O2-induced HepG2 cells. (n=4); different lowercase letters in the same column indicate significant differences (P<0.05), and all values ​​are expressed as mean ± standard deviation.

[0043] 2) Normalization processing The values ​​of each indicator obtained in step 1) are normalized according to formula (4-1):

[0044] In the formula, Y—the normalized score of each sample; X—the original data of each sample; X max —The maximum value of all sample data in this experiment; X min —The minimum value of all sample data in this experiment.

[0045] Note (X) min (×0.9) When the 2020 edition of the Chinese Pharmacopoeia has a minimum requirement, (X) min ×0.9) take the minimum required value; similarly (X max ×1.1) At the highest limit value in the 2020 edition of the Chinese Pharmacopoeia, (X min ×1.1) Take the highest limit value.

[0046] 3) Forward processing Since the total color difference, impurity rate, moisture content and ash content are negatively correlated with the quality of Cornus officinalis, the normalized data of total color difference, impurity rate, moisture content and ash content are processed according to formula (4-2) to transform them into positive correlations. Cornus officinalis samples whose measured data do not meet the requirements of the 2020 edition of the Chinese Pharmacopoeia are directly defined as unqualified samples. Then, their original measured index values ​​are converted into standardized scores in the range of 0 to 100. The score results of each index value are shown in Tables 16 to 19.

[0047]

[0048] Table 16 Appearance Quality Scoring Table for Cornus officinalis

[0049] Table 17 Quality Index Scoring Table for Cornus officinalis

[0050] Table 18 Typical Component Scoring Table of Cornus officinalis

[0051] Table 19. Scoring Table for the Medicinal Value of Cornus officinalis

[0052] 4) Determination of the value range of each evaluation element in the comprehensive quality evaluation system for Cornus officinalis Substitute the scores of each indicator obtained in step 3) into formula (4-1) for reverse normalization to obtain the value ranges of each evaluation element in the comprehensive quality evaluation system of Cornus officinalis: 1000-grain weight C1 (87.43 g~239.45 g), total color difference C2 (0~86.04 g), impurity rate C3 (0%~3.00%), moisture content C4 (8.42%~16%), ash content C5 (2.52%~6.00%), water-soluble extract C6 (50%~77.08%), total polysaccharides C7 (63.96 mg / g~132.34 mg / g), total organic acids C8 (7.08%~15.86%), total saponins C9 (10.37 mg / g~39.53 mg / g), and total flavonoids C10 (10.80 mg / g~37.93 mg / g). The following compounds were present: gallic acid C11 (0.16 mg / g~3.85 mg / g), protocatechuic acid C12 (0.01 mg / g~0.29 mg / g), monoglucoside C13 (7.03 mg / g~18.39 mg / g), loganin C14 (4.24 mg / g~16.25 mg / g), swertiamarin C15 (0.33 mg / g~1.05 mg / g), cornus glycoside C16 (0.84 mg / g~1.96 mg / g), hypoglycemic effect C17 (37.29%~59.86%), and antioxidant effect C18 (46.17%~81.01%).

[0053] This method measures Cornus officinalis samples processed in the same year from different production areas, as well as aged Cornus officinalis samples. The maximum and minimum values ​​of each measured data are recorded during normalization. The range of each data group is expanded upward and downward by 10%, which basically covers the corresponding index range of all Cornus officinalis.

[0054] 3. Overall quality score of Cornus officinalis The weights of the comprehensive quality evaluation elements of Cornus officinalis are multiplied by the values ​​of each evaluation element in the corresponding comprehensive quality evaluation system of Cornus officinalis, and then summed to construct the comprehensive scoring formula (5-2), thus obtaining the final comprehensive quality score of the sample to be tested.

[0055]

[0056] (5-2) For Cornus officinalis samples that meet the value range of each evaluation element, a score can be obtained according to the comprehensive quality evaluation system of the above formula (5-2); the higher the score, the better the overall quality of Cornus officinalis.

[0057] 4. Overall quality score of the Cornus officinalis samples to be tested Substituting the values ​​of various indicators (appearance quality, quality indicators, typical components, and medicinal value) of the 24 batches of Cornus officinalis samples from different locations shown in Table 10 into the comprehensive quality evaluation system of Formula 5-2, the scoring results of the Cornus officinalis samples are shown in Table 20: Table 20 Scoring results of Cornus officinalis samples to be tested

[0058] It can be seen that among the seven batches of aged Cornus officinalis samples, three batches did not meet the requirements of the 2020 edition of the Chinese Pharmacopoeia due to impurities, ash content, and extractive content. The remaining Cornus officinalis samples met the requirements. Overall, aged Cornus officinalis is inferior to Cornus officinalis processed in the current year. Among them, the comprehensive quality score of Cornus officinalis from the three major producing areas and surrounding areas was higher than that of other areas. The score of Cornus officinalis from townships under Danfeng County, Shangluo City, Shaanxi Province was much lower than that of other areas in Shaanxi Province and similar to that of aged products. The score of Cornus officinalis from Manghe Town, Yangcheng County, Jincheng City, Shanxi Province was relatively high, indicating better quality.

[0059] II. Quality Control of Cornus officinalis by Combining Response Surface Methodology and Cornus officinalis Quality Evaluation System 1. Preparation of single-factor experimental samples for processing Cornus officinalis The surface of the collected mature Cornus officinalis fruits was washed, softened in hot water at a certain temperature for a period of time, and the pits were removed promptly. The fruits were then dried in an electric hot air drying oven for a certain period of time to obtain dried mature Cornus officinalis pulp. Using the comprehensive quality evaluation system score of Cornus officinalis as the evaluation index, single-factor experiments were conducted to investigate the effects of softening time, softening temperature, drying time, and drying temperature on the quality of Cornus officinalis by controlling variables. The methods are shown in Table 21.

[0060] Table 21 Single-factor experimental methods for Cornus officinalis

[0061] 2. Preparation of experimental samples for response surface methodology optimization of Cornus officinalis processing Through single-factor experiments, softening time, softening temperature, drying time, and drying temperature were selected as independent variables. The optimal single-factor condition (best overall quality score) was used, and the Box-Behnken response surface methodology was designed using Design-Expert 13 software to optimize the processing technology of Cornus officinalis. The experimental design is shown in Table 22.

[0062] Table 22 Response Surface Optimization Experimental Methods for Cornus officinalis

[0063] The final process optimization conditions for the sample appearance quality, quality indicators, typical components, and medicinal value determination in the response surface methodology optimization experiment of Cornus officinalis processing were obtained after scoring by a quality evaluation system determined by the AHP method.

[0064] The results are as follows Figure 1 As shown in Figure 5, by Figure 1 It can be seen that the overall quality score of Cornus officinalis is highest when the drying temperature is 50℃. Figure 2 It can be seen that the overall quality score of Cornus officinalis is highest when the drying time is 24 hours. Figure 3 It can be seen that the overall quality score of Cornus officinalis is highest when the softening temperature is 80℃; Figure 4 It can be seen that the highest comprehensive quality score of Cornus officinalis was obtained when the softening time was 4 min. Figure 5 shows that the optimal processing conditions obtained by optimizing the processing technology of Cornus officinalis through response surface methodology are: softening temperature 80.428℃, softening time 3.831 min, drying temperature 48.748℃, and drying time 24.303 h. The highest comprehensive quality score obtained was 54.689. Considering the feasibility of actual operation, the optimal processing technology was adjusted to integers: softening temperature 80℃, softening time 4 min, drying temperature 50℃, and drying time 24 h. The regression model predicted a score of 54.526. A verification experiment was conducted under these conditions, repeated three times. The obtained Cornus officinalis samples met the requirements of the 2020 edition of the Chinese Pharmacopoeia, with a comprehensive quality score of 54.143. The relative error with the theoretical value was only 0.70%, which is basically consistent with the theoretical value, indicating that the data optimized using this model is accurate and reliable.

[0065] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for constructing a quality evaluation system for Cornus officinalis based on analytic hierarchy process, characterized in that, The method comprises the following steps: S1: determining the quality evaluation index of Cornus officinalis, constructing an evaluation index hierarchical model, and forming an evaluation element system composed of a target layer, a criterion layer, and an index layer; S2: according to the contribution and importance of each evaluation element to the quality of Cornus officinalis, combining expert experience, constructing a judgment matrix of each lower element in the hierarchical structure relative to the upper governing element, and calculating the relative weight; S3: normalizing the determination values of each element of different batches of Cornus officinalis samples to be tested, converting them into standardized scores in the range of 0-100, and obtaining the scoring results of each element; and performing reverse normalization processing on the scoring results of each element to obtain the evaluation element value range of the Cornus officinalis quality comprehensive evaluation system; S4: multiplying the weight of each evaluation element obtained in S2 by the corresponding evaluation element value range and then accumulating to obtain the Cornus officinalis quality evaluation system.

2. The method for constructing the evaluation system of the quality of the fruit of the plant of the genus of the Corni according to claim 1, characterized in that, In S1, the evaluation element of the target layer is the quality of Cornus officinalis; the evaluation element of the criterion layer is the appearance quality, quality index, typical component, and medicinal value of Cornus officinalis; the evaluation element corresponding to the appearance quality of Cornus officinalis in the index layer is the thousand-grain weight, total color difference, and impurity rate; the evaluation element corresponding to the quality index of Cornus officinalis in the index layer is the moisture content, ash content, percentage of water-soluble extract, content of total polysaccharide, content of total organic acid, content of total saponin, and content of total flavonoids; the evaluation element corresponding to the typical component of Cornus officinalis in the index layer is the content of protocatechuic acid, the content of morroniside, the content of loganin, and the content of jujuboside; and the evaluation element corresponding to the medicinal value of Cornus officinalis in the index layer is the hypoglycemic effect and the antioxidant effect.

3. The method for constructing the evaluation system of the quality of the fruit of the plant of the genus of the Corni according to claim 2, characterized in that, The hypoglycemic effect is the inhibition rate of α-glucosidase activity, and the antioxidant effect is the influence on the survival rate of H2O2-induced HepG2 cells.

4. The method for constructing the evaluation system of the quality of the fruit of the plant of the genus of the Corni according to claim 1, characterized in that, In S2, after constructing the judgment matrix of each lower element in the hierarchical structure relative to the upper governing element, consistency check is performed.

5. The method for constructing the quality evaluation system of Cornus officinalis based on analytic hierarchy process according to claim 1, characterized in that, In S2, the weights of each evaluation element in the judgment matrix are calculated by using the arithmetic mean method, the geometric mean method, and the eigenvalue method, and then averaged again to obtain the comprehensive evaluation element weight.

6. The method for constructing the quality evaluation system of Cornus officinalis based on analytic hierarchy process according to claim 1, characterized in that, In S3, the determination values of each element of the same batch of Cornus officinalis samples to be tested are normalized according to formula (4-1): In the formula, Y—score of each sample data after normalization; X—original data of each sample; X max —maximum value of all sample data in this experiment; X min —minimum value of all sample data in this experiment.

7. The method for constructing the evaluation system of the quality of the fruit of the plant of the genus of the Corni according to claim 2, characterized in that, In S3, the normalized data of the total color difference, impurity rate, moisture content, and ash content are processed in a positive direction according to formula (4-2): 。 8. A quality evaluation system for Cornus officinalis, characterized by The evaluation element detection value of the Cornus officinalis to be tested is substituted into the Cornus officinalis quality comprehensive scoring formula to obtain the Cornus officinalis quality evaluation result; The comprehensive quality score of the Cornus officinalis is calculated by the following formula: The evaluation element detection value of the to-be-tested Fructus Corni is composed of 18 indexes, respectively represented by C1-C18, C1 is thousand-grain weight, C2 is total color difference, C3 is impurity rate, C4 is moisture content, C5 is ash content, C6 is percentage of water-soluble extract, C7 is content of total polysaccharide, C8 is content of total organic acid, C9 is content of total saponin, C10 is content of total flavone, C11 is content of gallic acid, C12 is content of protocatechuic acid, C13 is content of morroniside, C14 is content of loganin, C15 is content of swertiamarin, C16 is content of cornuside, C17 is hypoglycemic effect, and C18 is antioxidant effect.

9. Application of the Fructus Corni quality evaluation system of claim 8 in quality evaluation of Fructus Corni.

10. A method for quality control of Cornus officinalis, characterized by, The processing technology of Fructus Corni is optimized by Box-Behnken response surface method, the score values under different process parameters are obtained by using the Fructus Corni quality evaluation system of claim 8, and the quality control of Fructus Corni is performed according to the processing technology corresponding to the highest comprehensive evaluation score of Fructus Corni quality.

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