CN110659806A

CN110659806A - Rosa resource evaluation method

Info

Publication number: CN110659806A
Application number: CN201910805896.3A
Authority: CN
Inventors: 程曦; 黄丛林; 罗昌; 陈东亮; 刘华; 芦瑶; 冯焱; 苏国辉; 黄敦辉; 陈菲
Original assignee: BEIJING AGRICULTURAL BIOLOGICAL TECHNOLOGY Research CENTRE
Current assignee: Beijing Academy of Agriculture and Forestry Sciences
Priority date: 2019-08-29
Filing date: 2019-08-29
Publication date: 2020-01-07

Abstract

The invention belongs to the field of rosa planting application, and provides a rosa resource evaluation method which comprises the steps of constructing a hierarchical analysis model, constructing a judgment matrix according to the constructed hierarchical analysis model and a resource evaluation target, calculating a sorting weight value according to the constructed judgment matrix, calculating a weighted sorting weight value according to the calculated sorting weight value, calculating a weighted score according to the actual score of each element of a scheme layer of the hierarchical analysis model and the calculated weighted sorting weight value, and evaluating resources by using the weighted score; the hierarchical analysis model comprises: a decision layer for performing selected application evaluation or comprehensive evaluation on the rosa plants; a criterion layer for evaluating the specific application of the rosa plants; protocol layer, alignment layer specific applications evaluate the selected biological trait. The method provided by the invention is used for scientifically evaluating the rosa plants according to the needs, and conveniently, quickly and accurately selecting the varieties which need different applications and comprehensive applications.

Description

Rosa resource evaluation method

Technical Field

The invention belongs to the field of planting application of rosa plants, and particularly relates to a rosa resource evaluation method.

Background

Rosa plants have a long history, and China is one of the main origins of Rosa plants. There are 82 rosa plants recorded in the Chinese plant journal, and the native seeds account for 41 percent of the world. There are about one hundred or more wild species of rose, which are progenitors of cultivars. The seed source composition of the cultivated varieties is mixed, and almost all modern Chinese rose varieties are bred for hundreds of years under different hybridization backgrounds.

In recent years, floral traits have been increasingly emphasized in the breeding direction of rosa plants in addition to ornamental properties. The main floral substances of the European rose are phenethyl alcohol and monoterpene alcohol, and the floral substances are different among different varieties. The rose is one of the oldest natural perfumes in the world in China, and the rose essential oil extracted from fresh flowers of the rose is world-name precious essential oil. The method fully explores and utilizes excellent germplasm resources, particularly wild resources, accelerates the cultivation speed of new species, and is a fundamental and necessary way for the industrial development of rosa plants (mainly rosa aromatic plants) mainly comprising roses in China. Plant germplasm resource evaluation work comprises biological identification, economic character evaluation, stress resistance, insect pest resistance identification and the like, and is the basis for effective utilization of resources, wherein the biological character evaluation and floral substance component analysis and identification can provide a basis for breeding of rosa plants and molecular biological research of key characters, however, an objective and stable resource evaluation method for rosa plants does not exist at present.

Disclosure of Invention

In order to solve the above problems in the prior art, the present invention provides a rosa resource evaluation method, including the steps of constructing a hierarchical analysis model, constructing a judgment matrix according to the constructed hierarchical analysis model and a resource evaluation target, calculating a ranking weight value according to the constructed judgment matrix, calculating a weighted ranking weight value according to the calculated ranking weight value, calculating a weighted score of each variety according to an actual score of each element of a scheme layer of the hierarchical analysis model and the calculated weighted ranking weight value, and evaluating resources by using the weighted score of each variety;

wherein, the hierarchical analysis model comprises the following three layers:

a decision layer, namely performing selected application evaluation or comprehensive evaluation on the rosa plants;

a criterion layer, namely carrying out specific application evaluation on the rosa plants;

protocol layer, i.e., evaluating the selected biological trait for a specific application of the criteria layer.

The method provided by the invention integrates the subjective selection evaluation target and the elements for objective evaluation, creatively utilizes the analytic hierarchy process, can carry out scientific and objective evaluation on the rosa plants according to the needs, obtains a conclusion which can meet the needs of subjective selection, fully utilizes the objective character data of the variety, reduces a large amount of complicated labor, and can conveniently, quickly and accurately select the variety which needs different applications and comprehensive applications.

Drawings

Fig. 1 is a landscape application evaluation hierarchical analysis model of example 1.

Fig. 2 is a schematic diagram of the flowering stages of Chinese roses, and the numbers at the lower right corner in each small graph are flower levels, wherein the number of 0: unrooted flower buds; level 1: half-opened flower buds; and 2, stage: a fully bloomed bud; and 3, level: a flower which is opened at first; 4, level: full blossoming flowers; and 5, stage: flowers in the end; and 6, level: aged flowers.

Fig. 3 is a floral application evaluation hierarchy analysis model of example 2.

Fig. 4 is a comprehensive evaluation hierarchical analysis model of garden application evaluation and floral application evaluation in example 3.

Detailed Description

In order to make the technical solution, objects and advantages of the present invention more apparent, the present invention will be described in further detail by referring to specific embodiments and drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.

Various reagents, materials and the like used in the following examples are commercially available products unless otherwise specified; unless otherwise specified, all the tests and detection methods used in the following examples are conventional in the art and can be obtained from textbooks, tool books or academic journals.

In the following examples, various numerical calculations were performed using the software for the analytic hierarchy process (yaahp standard edition) available from Shanxi Meta decision software technology, Inc.

Example 1

This example is presented to illustrate the landscape application evaluation. In this example, 55 rosa species (see table 1) were subjected to investigation and analysis of biological properties, and the investigation population was all foreign transplanted species, and the initial resource evaluation of the transplanted species focused on the traits of adaptability after introduction (traits related to vigor) and traits related to difficulty in harvesting (traits related to skin prickling). The variety with stronger adaptability is suitable for popularization and planting and can also be used as excellent breeding resources.

TABLE 1 biological trait survey varieties

According to the specificity, consistency and stability test guide of new plant varieties, Rosa (Guidelines for the products of tests for the diseases, unity and stability-Rose (Rosa L.) DUS test guide) is used for carrying out the investigation of biological traits, and the traits related to the growth vigor comprise the length of top lobule leaves, the width of the top lobule leaves and the length between 5-7 knots; the properties of the skin thorn include the shape of the lower part of the skin thorn, the total number of the skin thorn of 5-7 sections, the number of the long skin thorn of 5-7 sections, the number of the short skin thorn of 5-7 sections and the existence of the thorn hair. Wherein, long skin thorn means skin thorn with length more than 3mm, short skin thorn means skin thorn with length not more than 3 mm.

Each plant was investigated for 3 current-year shoots, and a total of 3 plants were selected as biological replicates. The subjects were 55 varieties in table 1, and two surveys were conducted in 2018, month 5 and month 10.

And respectively calculating the weighted scores of 55 varieties by using an Analytic Hierarchy Process (AHP) and an Analytic Hierarchy Process (AHP) software, and further evaluating and selecting the varieties with excellent comprehensive character performance.

The specific analytic hierarchy process used in this example is as follows:

1. building a hierarchical analysis model

As shown in fig. 1, the constructed hierarchical analysis model is divided into three layers: a decision layer, namely, resource application evaluation on rosa plants (the uppermost "garden application evaluation" layer in fig. 1); a criterion layer, namely evaluating the rose plants in terms of growth potential condition and harvesting difficulty (a 'growth potential condition evaluation' layer and a 'harvesting difficulty evaluation' layer in figure 1); protocol layer, i.e. 8 biological traits investigated (see in particular the eight trait layers at the bottom in fig. 1). The constructed hierarchical analysis model is input into the hierarchical analysis software (yaahp standard edition) purchased from Shanxi Meta decision software science and technology Co., Ltd, so as to facilitate the subsequent calculation.

2. Constructing a decision matrix

According to the evaluation target, the importance of any two elements in the same layer to the previous layer is compared by combining the importance degree of objective evaluation elements to the previous layer, and the relative importance of any two elements in the same layer to the previous layer is assigned by numerical values 1, 3, 5, 7 and 9 according to the relative importance degree of each element to the previous layer. Wherein 1 represents "equally important", 3 represents "slightly important", 5 represents "comparatively important", 7 represents "very important", and 9 represents "absolutely important"; in a comparison where the reciprocal of the numbers 1, 3, 5, 7, 9 is two elements, the latter element is more important, and the degree of importance is as described above. If necessary, the numerical values 2, 4, 6, and 8 can also be used to represent the median values of two adjacent judgments 1, 3, 5, 7, and 9, respectively, and the reciprocal of the numerical values 2, 4, 6, and 8 is the latter element in the comparison of the two elements, and the degree of importance is as described above. In the present invention, the meaning of the relative importance assignment to the previous level between two elements is the same as that here, and will not be described again. For example, as shown in table 2, for the criterion layer "evaluation of growth potential conditions", the importance of the "length between 5-7 nodes" to the "evaluation of growth potential conditions" is assigned to 7, since the importance of the "length between 5-7 nodes" to the "evaluation of growth potential conditions" is "important" between two elements of the solution layer "width between top leaves" and "length between 5-7 nodes" to the "evaluation of growth potential conditions" of the criterion layer; in contrast, the importance of "apical leaflet width" relative to "internode length between 5 and 7" for the "evaluation of the growth potential status" of the criterion layer was assigned to 1/7.

The constructed judgment matrices are shown in tables 2 to 4. Specifically, the method comprises the following steps:

according to the importance degree of the "growth potential condition evaluation" of the solution layer when the traits (top leaflet length, top leaflet width, 5-7 internode length) related to the growth potential are aligned in the solution layer, the relative importance of the growth potential condition evaluation between any two traits is assigned, thereby constituting the judgment matrix shown in table 2.

According to the importance degree of the "harvesting difficulty evaluation" of the layer in the scheme layer aligned with the properties of the skin pricks (the shape of the lower part of the skin pricks, the total number of the 5-7 sections of skin pricks, the number of the 5-7 sections of long skin pricks, the number of the 5-7 sections of short skin pricks and the presence or absence of the pricked hair), the relative importance of the "harvesting difficulty evaluation" between any two properties is assigned, so that the judgment matrix shown in the table 3 is formed.

According to the importance degree of the 'growth potential condition evaluation' and the 'harvesting difficulty evaluation' of the criterion layer to the 'garden application evaluation' of the decision layer, the relative importance of the 'growth potential condition evaluation' and the 'harvesting difficulty evaluation' to the 'garden application evaluation' of the decision layer is assigned, so that a judgment matrix shown in the table 4 is formed.

The numerical values of the judgment matrices shown in tables 2 to 4 are inputted into software (the term "software" herein refers to "analytic hierarchy process software (yaahp standard edition)" available from shanxi meta-decision software technology ltd) to be processed next.

3. Consistency check and calculation of rank weight values

And (3) checking consistency: in order to ensure the reliability of the result, consistency check is also required to be performed on the judgment matrixes in tables 2 to 4, that is, each element is ensured to be consistent when being compared with each other. The principle of the consistency check is as follows: when the maximum feature root (λ max) of the determination matrix is equal to the number (n) of elements in the determination matrix (λ max ═ n), or λ max is slightly larger than n ((λ max-n)/n ≦ 5%), it is determined that the determination matrix is consistent. As shown in tables 2 to 4, λ max was slightly larger than or equal to n, and satisfactory consistency was achieved.

Specifically, after the numerical values of the judgment matrices shown in tables 2 to 4 are input into software, the consistency check of the judgment matrices is automatically performed by the software. If the matrix has defects or is inconsistent, the software displays the errors in an output window, all the matrix inconsistency can be corrected by clicking a mark correction button on a toolbar, and a right-click popup menu can be clicked on a corresponding node on the hierarchical structure tree to select 'automatic consistency adjustment'.

Calculating a ranking weight value: the ranking weight value Wi of the importance of each element in the next level to the previous level respectively is calculated by the software (i.e. clicking on the software "calculate results" page). Specifically, according to the judgment matrix of table 2, the ranking weight values Wi of the three properties related to the growth potential condition evaluation for the "growth potential condition evaluation" of the criterion layer are calculated; calculating to obtain sorting weight values Wi of five properties related to the harvesting difficulty evaluation of the criterion layer according to the judgment matrix in the table 3; according to the judgment matrix of table 4, the ranking weight values Wi of the "growth potential condition evaluation" and the "harvesting difficulty evaluation" of the criterion layer for the "garden application evaluation" of the decision layer are calculated (see the numerical values of the "Wi" column on the rightmost side of tables 2 to 4, respectively).

Table 2 "evaluation of growth potential conditions" criterion layer-plan layer judgment matrix (λ max ═ 3.0000)

Table 3 criteria layer-scheme layer judgment matrix of "easy and difficult to harvest evaluation" (λ max ═ 5.0420)

Table 4 decision layer-criterion layer judgment matrix (λ max ═ 2.0000)

Description of the drawings: in tables 2 to 4, except for the rightmost column "Wi", the corresponding judgment matrices are constructed.

4. Calculating a weighted ranking weight value

And (4) according to each sorting weight value (Wi) obtained by calculation in the step (3), calculating the weighted sorting weight value of each element in the scheme layer to the decision layer respectively. The calculation method comprises the following steps: the product of the ranking weight value of each element of the scheme layer relative to the criterion layer and the ranking weight value of the element of the corresponding criterion layer relative to the decision layer is the weighted ranking weight value of each element in the scheme layer relative to the decision layer. For example, the weighted ranking weight value of the plan layer "top leaflet length" with respect to the decision layer "garden application rating" is equal to the product of the ranking weight value Wi (value 0.1111, see table 2) of the "top leaflet length" with respect to the criterion layer "growth potential condition rating" and the ranking weight value Wi (value 0.3333, see table 4) of the criterion layer "growth potential condition rating" with respect to the decision layer "garden application rating" (0.1111 × 0.3333 ═ 0.0370). Specific results are shown in table 5.

TABLE 5 weighted ranking weight values for each element in the solution layer for the decision layer

5. Computing weighted scores and resource valuations

And (4) according to the weighted sorting weight values of the elements in the scheme layer obtained in the step (4) for the decision layer, calculating to obtain the weighted score of each variety by combining the actual scores of the elements in the scheme layer of each screened variety. Specifically, the actual scores of the elements of the plan layer of each screened item are input to the software, and the weighted scores of the items are obtained by clicking a "weighted calculation" button in the software. And sorting according to the weighted scores of the varieties to screen out the target material.

The actual scores of the individual elements of the solution layer are shown in tables 6 and 7. For example, as shown in table 7, the actual score of the plan layer element "5 to 7 internodes long" of the variety "hansa" is 2.43 (i.e., the length value of "5 to 7 internodes long" of "hansa" is 2.43cm), and this value is input to the software, and all other plan layer elements are operated in the same manner.

Table 6 shows the actual scores of the characteristics "shape of the lower part of the skin prick, total number of skin pricks at 5 to 7 knots, number of long skin pricks at 5 to 7 knots, number of short skin pricks at 5 to 7 knots, and presence or absence of skin pricks" in the plan layer element (characteristic) of the criteria layer "evaluation of difficulty in harvesting". In table 6, the numerical values of "shape of lower part of skin thorn" are as follows: no skin thorn (0), flat straight thorn (1), inclined straight thorn (2), bent thorn (3) and hook thorn (4). The numerical values of "the presence or absence of bristles" are as follows in order: no thorn hair (0) and thorn hair (1).

These 55 varieties were ranked according to the number of skin pricks and classified into the following 5 classes (see table 6): no skin prick (I), less skin prick (II), medium skin prick number (III), more skin prick number (IV) and more skin prick number (V). The investigation result shows that 4 varieties of 'purple branch of pink flower', 'rosa davurica', 'purple branch of white flower' and 'purple branch of single petal' are thornless varieties (I); the 23 varieties have fewer skin pricks (II); the total number of skin pricks for 'blue dream', 'benjamington' and 'marmoros' exceeds 100. The shapes of the lower parts of the skin thorns are concentrated on No. 2 thorns and No. 3 thorns, the number of the materials with No. 2 thorns is 20, and the number of the materials with No. 3 thorns is 22.

TABLE 6 actual score of protocol layer elements for "evaluation of difficulty in harvesting" (statistical results of skin prick test)

Table 7 shows the actual scores of three traits, "tip leaflet length, tip leaflet width, and internode length between 5 and 7 knots," which are the plan layer elements (traits) of the criterion layer "evaluation of vigor condition.

As can be seen from the results in Table 7, the internode lengths of 5-7 nodes are mainly distributed from 2.43cm to 11.91cm, and the distribution is relatively continuous. Dividing 55 varieties into 3 types according to the internode length of 5 th to 7 th, wherein the internode is shorter (2.43cm-4.33 cm); II is longer internode (4.59cm-8.14 cm); III is internode length (8.48cm-11.91 cm). The results show that the roses and roses have shorter internodes, and the original foreign hybrid perfume rose has longer internodes; the internodes of the fragrant Chinese rose 'Zipaoyyu belt' from abroad are compact and have larger leaves; the rosewood rose, the single purple branch, the pink purple branch and the white purple branch have shorter internodes and are non-thorn varieties.

TABLE 7 actual score of protocol layer elements for "evaluation of growth potential conditions

(statistical result of growth potential-related trait survey, unit: cm)

The results of the weighted scores and the grades of the respective varieties obtained by the calculation are shown in table 8.

TABLE 8 weighted calculation results and ranking

The grades of the varieties are classified according to the calculated weighted score results of the varieties, and the grades can be roughly classified into the following 4 grades (see table 8): grade I, 15 varieties in total, which are easy to plant and harvest, have strong growth potential, compact internodes, no skin prick or less skin prick, and are not easy to hurt people in the shape of the lower part; grade II, 22 varieties in total, which are easy to plant and harvest and have moderate growth potential; grade III, 15 varieties in total, moderate growth vigor and inconvenient planting and harvesting; IV stage: the total 3 varieties belong to hybrid rose, the growth vigor is moderate, and the planting and harvesting are inconvenient.

According to the weighting score results of various varieties, the materials with higher economic value, namely the rosa davurica pall, the rosa kuroshi, the rosa bulgaricus, the rosa damascena, the rosa michaux, the rosa spinalis, the rosa chrysosporium and the rosa yunnanensis, belong to the level with moderate skin thorn and stronger growth potential, belong to the level I and the level II, and the rosa davurica pall and the rosa chinensis with multiple fragrances with stronger garden application value are also distributed in the level I and the level II.

Example 2

This example is presented to illustrate floral application evaluation. In this example, flower fragrance substances of 59 rosa plants (59 species shown in table 18) were measured, and the measured flower fragrance substances were preliminarily evaluated using the measured flower fragrance substances as evaluation indexes, and classified into oil resources, aromatherapy resources, and health care ornamental resources.

The sampling was done according to the criteria established by male horses (2005) on the number of flowering stages of the rose (shown in fig. 2 as 0, 1, 2, 3, 4, 5 and 6 stages), and the sampling was done on 3-4 stage flowers. The flower fragrance is measured by sampling from 1 month 10 in 2018, sampling is carried out from 9 am in fine days, branches with better quality of 3-5 flowers are selected from each plant, the lower branches are obliquely cut, the branches are inserted into a container filled with clear water and taken back to a laboratory, and the flower fragrance material measurement is carried out 22-24 hours after the bottle is inserted.

The manual SPME sample injector and 100 μm PDMS extraction head are products of Supelco, Inc., USA. The experimental apparatus was DSQ, gas chromatography-mass spectrometer (GC/MS).

The gas chromatography detection method comprises the following steps: before collecting the volatile gas, the solid phase micro-extraction head is aged for 30 minutes at 200 ℃ in a gas chromatography sample inlet, and other impurities adsorbed in the extraction head are oxidized and decomposed. 3g of petals are weighed by an electronic balance and placed in a collection bottle, 20 mu L of ethyl decanoate (1mol/L) diluted by n-hexane is added at the same time, a cover is covered, the aged solid phase micro-extraction head is inserted into the collection bottle, and the solid phase micro-extraction head is adsorbed for 30 minutes in water bath at 40 ℃. And taking the solid phase micro-extraction head after adsorption out of the bottle, inserting the solid phase micro-extraction head into a gas chromatography-mass spectrometer, desorbing the solid phase micro-extraction head for 1min at the temperature of 200 ℃, and starting the gas chromatography-mass spectrometer to collect and record data. Chromatographic conditions are as follows: the sample injection amount is 0.2 mu L, the carrier gas is He, and the flow rate is 1 mL/min; the initial temperature of the column temperature is 50 ℃, the column temperature is kept for 1min, then the temperature is increased to 180 ℃ at the speed of 5 ℃/min, and finally the temperature is increased to 230 ℃ at the speed of 10 ℃/min and the column temperature is kept for 20 min. Mass spectrum conditions: the GC/MS interface temperature is 250 ℃, the ion temperature source is 200 ℃, the ionization mode EI is adopted, and the electron energy is 70 eV.

Determination and quantification of volatiles: and (3) separating the volatile matters through gas chromatography to form different chromatographic peaks, searching and analyzing component substances by using an NIST (NiST spectral library), and inquiring and determining the components of the flower fragrance substances by combining literature data. And performing semi-quantitative calculation by adopting an internal standard method, namely calculating the ratio of the peak area of the internal standard substance to the peak area of each ion flow chromatographic spectrum, and calculating the mass concentration of the component to be measured after correction. According to the formula: the mass concentration of each component is the peak area of each component × mass concentration of internal standard × f/peak area of internal standard, and the mass concentration of each component is calculated. Wherein f is a correction factor for the internal standard substance of each component, and f is 1.

Firstly, constructing a hierarchical analysis model

The 59 varieties are evaluated by applying an analytic hierarchy process, the flower fragrance substance components and the concentration of the 59 varieties are mainly considered, and three types of candidate varieties, namely (oil resources) varieties close to the fragrance of the essential oil extraction varieties, (aromatherapy resources) varieties suitable for aromatherapy and ornamental flower (health-care ornamental resources) varieties used for both health care and landscaping, are screened out based on the difference of the flower fragrance substance components and the concentrations.

In the evaluation of the oil resource, the (P1) nerol mass concentration, (P2) geraniol mass concentration, (P3) citronellol mass concentration, and (P4) phenethyl alcohol mass concentration are used as evaluation indexes with reference to the international standard of rose oil.

In the resource evaluation of aromatherapy, the (P1) nerol mass concentration, (P2) geraniol mass concentration, (P3) citronellol mass concentration, (P4) phenethyl alcohol mass concentration, (P5) (E) -3-hexen-1-ol acetate mass concentration, (P6) TMB mass concentration, (P7) phenethyl acetate mass concentration, (P8) geranyl acetate mass concentration, (P9) citronellyl acetate mass concentration, (P10) neryl acetate mass concentration, (P11) hexyl acetate mass concentration, (P12) DMT mass concentration, (P13) phenol-derived substance mass concentration, and (P14) mass concentration of monoterpene alcohol and ester substance are used as evaluation indexes.

In the evaluation of health-care ornamental resources, the quality concentration of (P15) terpenes, (P16) beta, alpha and gamma-limonene, (P17) geranylene (ABD) is used as an evaluation index to screen out health-care flowers with anticancer, anti-inflammatory and antibacterial effects.

For the application evaluation of the oil resources, the aromatherapy resources and the health care ornamental resources, the same hierarchical analysis model (as shown in figure 3, the decision layer is 'flower fragrance application evaluation', the criterion layer is 'oil resource evaluation, aromatherapy resource evaluation and health care ornamental resource evaluation', and the scheme layer is P1-P17 in figure 3) is used, and for the evaluation of different applications, the relative importance of each element in the two-to-two comparison of the criterion layer is changed, so that the screening of different application value materials is completed.

Secondly, establishing a judgment matrix, checking consistency, calculating a sorting weight value and calculating a weighted sorting weight value

1. Evaluation of oil resources

(1) And constructing a judgment matrix

Table 9 shows a criterion layer-plan layer judgment matrix for oil resource evaluation, and table 10 shows a decision layer-criterion layer judgment matrix for oil resource evaluation. The meaning of assignment is the same as that in example 1, and will not be described herein.

TABLE 9 criterion layer-scheme layer judgment matrix for oil resource evaluation (λ max: 4.0000)

Injecting: the reference numerals "P1-P17" in tables 9-17 refer to scheme layer elements, and the specific meanings are as described above or shown in FIG. 3.

TABLE 10 decision-making layer-criterion layer evaluation judgment matrix for oil resource evaluation (λ max: 3.0142)

(2) Consistency check and calculation of rank weight values

The method of the consistency check is the same as in example 1. And (4) automatically carrying out consistency check on the judgment matrix through software.

Calculating a ranking weight value: the ranking weight value Wi of the importance of each element in the next level to the previous level is calculated by software, and the calculation result is shown in the numerical value of the rightmost column "Wi" in tables 9 and 10.

(3) Calculating the weighted sorting weight value

The method of calculating the weighted ranking weight values is the same as in example 1. Specific results are shown in table 11.

TABLE 11 scheme layer element weighted ranking weight for oil resource evaluation

2. Resource assessment of aromatherapy

(1) And constructing a judgment matrix

Table 12 shows a criteria layer-scheme layer decision matrix for aromatherapy resource evaluation; table 13 shows a decision-level-criteria-level decision matrix for aromatherapy resource evaluation, increasing the importance value for criteria-level aromatherapy resource evaluation and decreasing the importance values for the other two elements of criteria-level. The meaning of assignment is the same as that in example 1, and will not be described herein.

TABLE 12 criterion layer-scheme layer judgment matrix for aromatherapy resource evaluation (λ max: 14.0718)

TABLE 13 decision level for aromatherapy resource evaluation-criteria level decision matrix (λ max: 3.0649)

(2) Consistency check and calculation of rank weight values

Calculating a ranking weight value: the ranking weight value Wi of the importance of each element in the next level to the previous level is calculated by software, and the calculation result is referred to the numerical value of the rightmost column "Wi" in tables 12 and 13.

(3) Calculating the weighted sorting weight value

The method of calculating the weighted ranking weight values is the same as in example 1. Specific results are shown in table 14.

TABLE 14 scheme layer element weighted ranking weight values for aromatherapy resource evaluation

3. Evaluation of health-care ornamental resources

(1) And constructing a judgment matrix

Table 15 shows a criterion layer-scheme layer judgment matrix for evaluation of healthcare-type ornamental resources; table 16 shows a decision layer-criterion layer decision matrix for evaluation of the healthcare ornamental resources, which increases the importance value of evaluation of the healthcare ornamental resources of the criterion layer and decreases the importance values of the other two elements of the criterion layer. The meaning of assignment is the same as that in example 1, and will not be described herein.

TABLE 15 criterial layer-plan layer judgment matrix (λ max: 3.0000) for evaluation of health care type ornamental resources

TABLE 16 decision layer-criterion layer judgment matrix for evaluation of health-care type ornamental resources (λ max: 3.0649)

(2) Consistency check and calculation of rank weight values

Calculating a ranking weight value: the ranking weight value Wi of the importance of each element in the next level to the previous level is calculated by software, and the calculation result is shown in the numerical values of the rightmost column "Wi" in tables 15 and 16.

(3) Calculating the weighted sorting weight value

The method of calculating the weighted ranking weight values is the same as in example 1. Specific results are shown in table 17.

TABLE 17 weighted ranking weight values of scheme layer elements for health care type ornamental resource evaluation

Thirdly, calculating the weighted score and the resource evaluation

The method of calculating the weighted score is the same as in example 1. That is, the weighted score of each variety is calculated based on the weighted ranking weight value of each element in the scheme layer of the oil resource evaluation, the aromatherapy resource evaluation and the health care ornamental resource evaluation obtained in the second step for the decision layer, and in combination with the actual score of each element in the scheme layer of each screened variety in the oil resource evaluation, the aromatherapy resource evaluation and the health care ornamental resource evaluation. Specifically, the actual scores of the elements of the plan layer of each screened item are input to the software, and the weighted scores of the items are obtained by clicking a "weighted calculation" button in the software.

The actual score (i.e., mass concentration of each substance in. mu.g/L) data for each element of the protocol layer for each selected variety in the three resource evaluations are shown in Table 18 (including Table 18-1, Table 18-2, Table 18-3, and Table 18-4).

And sorting according to the weighted scores of all varieties to screen out the target material.

TABLE 18-1 actual scores for project level elements for three resource evaluations

TABLE 18-2 actual scores for project level elements for three resource evaluations

TABLE 18-3 actual scores for project level elements for three resource evaluations

TABLE 18-4 actual scores for project level elements for three resource evaluations

1. Weighted scoring and ranking of individual varieties for oil resource assessment

According to the weighted scores obtained by calculation (see table 19), 59 varieties are divided into three grades, the grade I (2.73-3.78) is an essential oil extraction (oil resource) alternative variety, 18 varieties are provided, the fragrance substance concentration reaches the international standard of rose oil, and the fragrance type is similar to the fragrance type of rosa damascena, so that the rosa damascena can be used as an oil resource similar to the fragrance type of rosa damascena. Class II (2.02-2.60), with higher target floral material concentrations, can be tried as an essential oil extract variety. The III-grade (1.03-1.98) target flower fragrance substance has low concentration, is a variety with a large difference with the fragrance of Rosa damascena, and is not suitable for extracting essential oil.

TABLE 19 weighted scores and grades for each variety for oil resource assessment

2. Weighted scoring and ranking of individual varieties for aromatherapy resource assessment

Based on the calculated weighted scores (see Table 20), 59 varieties were classified into three grades, and the grade I (2.53-3.35) was suitable for aromatherapy, and had certain health promotion effect and aromatic flavor, and was 20 varieties in total. The II grade (2.23-2.48) can be used as aromatherapy variety, and has certain health promotion effect. Class III (1.13-2.20) floral material concentrations are typical, light or non-fragrant, and are not typically aromatherapy varieties.

TABLE 20 weighted scores and ratings for each variety used for aromatherapy resource assessment

3. Weighted scoring and grading of various varieties for health-care ornamental resource evaluation

According to the calculated weighted score (see table 21), 59 varieties are divided into three grades, i grade (2.90-3.80), and 20 varieties have good health care effect. Class II (2.01-2.87) has certain health promotion effect. Class III (1.05-1.88), weak health-care effect or no health-care effect.

TABLE 21 weighted scores and ratings for each variety for healthcare-based ornamental resource evaluation

Fourth, evaluation of floral application

And (4) according to the weighted score calculation result of the step three, comprehensively evaluating the oil resources, the aromatherapy resources and the health care ornamental resources, and obtaining the results shown in table 22.

TABLE 22 floral application evaluation of Rosa plants

From the data of table 22 it can be seen that:

1. the following varieties can be used as oil resources, aromatherapy resources and health care ornamental resources: 'Newman sister', 'Walleron old garden', 'college', 'sweet horse car'.

2. In addition to the four varieties listed in the above 1 which can be used as both oil resources, aromatherapy resources and health care ornamental resources, the following varieties can be used as both oil resources and aromatherapy resources: ' sweet dream ', ' welcome ', ' Yunnan red ', ' love ', ' Wenchester church ', ' apple tart ', ' purple gown and jade belt ', ' Margaret royal ' ', ' Shakebia ' and ' islands of the stick '.

3. In addition to the four varieties listed in the above 1 which can be used as an oil resource, an aromatherapy resource and a health care ornamental resource at the same time, the following varieties can be used as an aromatherapy resource and a health care ornamental resource at the same time: 'real Zeus', 'Saint Ezebrajia' and 'Abelian ratio'.

4. The oil resource is prominent, but the variety which is not the best as the aromatherapy resource and the health care ornamental resource is: 'fragrant garden', 'fragrant concubine', 'metaphor', 'fire phoenix'.

5. The aromatherapy resources are prominent, but the varieties which are not the best as oil resources and health care ornamental resources are: aurora sighing sigh, love, and white hana.

6. The method is prominent as a health care type ornamental resource, but the variety which is not the best as an aromatherapy resource and an oil resource is as follows: the formula of the Chinese herbal medicine comprises the following components of ' blue storm ', ' blue Eden garden ', ' Inward ', ' autumn rouge ', ' Gelam castle ', ' unknown fur ', Stefan ancient castle ', ' Tianfang night Tan ', ' silver happiness celebration ', ' Ponbach rose ', ' Clay Austin ', ' mutton tallow perfume ' and ' Dalvenn '.

Of the 59 varieties, 'sweet dream', 'fragrant love', 'fragrant imperial concubine', 'love song', 'fire phoenix', 'drunk reddish', 'Beijing red' are rose varieties having proprietary intellectual property rights in China. In the flower fragrance application evaluation, except for Beijing infrared, the related indexes of flower fragrance substances of other varieties are relatively good, and the flower fragrance application evaluation can be used for landscaping and multifunctional flowers for popularization and planting.

Example 3

This example is presented to illustrate the combined evaluation of landscape application evaluation and floral application evaluation. This example is a comprehensive evaluation of the materials of 16 varieties (see table 29) of example 1 and example 2, which were screened for resource coincidence.

Wherein, six properties of the length of the top lobule, the width of the top lobule, the length between 5-7 nodes, the shape of the lower part of the skin thorn, the total number of the 5-7 nodes of the skin thorn and the existence of the thorn hair are selected as evaluation indexes in the aspect of garden application. In the aspect of floral application evaluation, seven properties of (P1) nerol mass concentration, (P2) geraniol mass concentration, (P3) citronellol mass concentration, (P4) phenethyl alcohol mass concentration, (P13) phenolic derivative mass concentration, (P14) monoterpene alcohol and ester substance mass concentration, and (P15) terpene mass concentration are selected as evaluation indexes.

Firstly, constructing a hierarchical analysis model:

the constructed hierarchical analysis model is shown in fig. 4 and divided into three layers: a decision layer, namely 'comprehensive evaluation' of rosa plants; a criterion layer, namely 'garden application evaluation' and 'floral application evaluation'; scheme level, i.e. the 13 traits described above (13 traits in the bottom row in fig. 4).

1. Evaluation of landscape applications

(1) And constructing a judgment matrix

Table 23 shows a criterion layer-scheme layer judgment matrix for the garden application evaluation, and table 24 shows a decision layer-criterion layer judgment matrix for the garden application evaluation. The meaning of assignment is the same as that in example 1, and will not be described herein.

TABLE 23 criterion layer-scheme layer judgment matrix for Garden application evaluation (λ max: 6.1954)

TABLE 24 decision level for Garden application evaluation-criteria level evaluation judgment matrix (λ max: 2.0000)

(2) Consistency check and calculation of rank weight values

Calculating a ranking weight value: the ranking weight value Wi of the importance of each element in the next level to the previous level is calculated by software, and the calculation result is referred to the numerical value of the rightmost column "Wi" in tables 23 and 24.

(3) Calculating the weighted sorting weight value

The method of calculating the weighted ranking weight values is the same as in example 1. Specific results are shown in table 25.

TABLE 25 weighted ranking weight values for schema layer elements for landscape application evaluation

2. Floral application evaluation

(1) And constructing a judgment matrix

Table 26 shows a criterion layer-scheme layer judgment matrix of the floral application evaluation, and table 27 shows a decision layer-criterion layer judgment matrix of the floral application evaluation. The meaning of assignment is the same as that in example 1, and will not be described herein.

TABLE 26 criterion layer-scheme layer judgment matrix for floral application evaluation (λ max: 7.0170)

TABLE 27 decision level for floral application evaluation-criteria level evaluation judgment matrix (λ max: 2.0000)

(2) Consistency check and calculation of rank weight values

Calculating a ranking weight value: the ranking weight value Wi of the importance of each element in the next level to the previous level is calculated by software, and the result of the calculation is referred to the numerical value of the rightmost column "Wi" in tables 26 and 27.

(3) Calculating the weighted sorting weight value

The method of calculating the weighted ranking weight values is the same as in example 1. Specific results are shown in table 28.

TABLE 28 floral application evaluated scheme layer element weighted ranking weight values

Thirdly, calculating the weighted score and resource evaluation of each variety

The method of calculating the weighted score is the same as in example 1. That is, according to the weighted ranking weight values of the elements in the scheme layers of the garden application evaluation and the flower fragrance application evaluation respectively obtained in the second step, the weighted scores of the varieties are calculated by combining the actual scores of the elements in the scheme layers of each screened variety in the garden application evaluation and the flower fragrance application evaluation. Specifically, the actual scores of the elements of the plan layer of each screened item are input to the software, and the weighted scores of the items are obtained by clicking a "weighted calculation" button in the software.

Actual score data for each element of the program layer of each screened variety in the landscape application evaluation and floral application evaluation are found in table 29 (data extracted from tables 6 and 7) and table 30 (data extracted from table 18).

TABLE 29 actual scores for schema layer elements of landscape application evaluation

TABLE 30 actual scores for floral application evaluated regimen layer elements

The calculated weighted score results and the grades of the respective varieties are shown in table 31.

TABLE 31 weighted score and ranking of the varieties for the Integrated evaluation

The 16 varieties are classified into three grades according to the calculated weighted scores (see table 31): grade I, 6 varieties in total, namely excellent varieties with strong growth vigor, moderate skin thorn number and rich flower fragrance substances; wherein the Yunnan red wine is an edible rose variety, has stronger economic value, stronger growth potential and easier harvesting; 'ai li' is a relatively good fragrant rose, 'sweet dream' and one of 'fragrant love' parents is 'ai li', in this embodiment, the performance of the filial generation exceeds that of the parents, 'wenchester church' in which the concentration of terpene substances is relatively high, sesquiterpene compounds have a certain health care effect, and the variety can be considered as health care ornamental flowers. Grade II, 5 varieties are in total. Grade III, 5 varieties are in total.

China has abundant rosa germplasm resources, because of long-term allopatric introduction and crossbreeding, the varieties circulate in different areas to have different names, and under the genetic background of high crossbreeding, partial loss or incomplete record of genetic relationship causes the total understanding of the varieties to be quite unclear, and the problem of legal dispute caused by the unclear genetic relationship also emerges in recent years. The investigation of biological traits can be used for preliminarily analyzing genetic relationship, and the investigation of biological traits is helpful for preliminarily grasping the form of each resource, and the understanding of the form and other characteristics of the resource is helpful for developing subsequent research work. The preliminary evaluation of the material can obtain seeds or varieties which have strong adaptability and are easy to harvest and plant, and lays a foundation for parent selection.

Aromatic breeding is a breeding trend in recent years, parent matching is very important in the breeding process, floral substance determination is carried out on floral varieties at home and abroad, materials are evaluated according to main substances of the floral substances, and theoretical basis can be provided for aromatic and functional flower breeding. The deep analysis of the flower fragrance can provide a theoretical basis for parent matching for breeding work and can also provide thought help for the extraction and selection period of the flower fragrance substances. The invention provides a reliable method support for establishing a rosa germplasm resource evaluation system, breeding new varieties and popularizing high-quality varieties in the future, and opens up a new idea for correctly selecting and utilizing resource materials in the perfume industry.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A rosa resource evaluation method is characterized by comprising the steps of constructing a hierarchical analysis model, constructing a judgment matrix according to the constructed hierarchical analysis model and a resource evaluation target, calculating a sorting weight value according to the constructed judgment matrix, calculating a weighted sorting weight value according to the calculated sorting weight value, calculating a weighted score of each variety according to an actual score of each element of a scheme layer of the hierarchical analysis model and the calculated weighted sorting weight value, and evaluating resources by using the weighted score of each variety;

wherein, the hierarchical analysis model comprises the following three layers:

2. The method of claim 1, further comprising the step of performing a consistency check after said constructing a decision matrix and before said calculating a ranking weight value.

3. A method according to claim 1 or 2, wherein the hierarchical analysis model comprises a landscape application evaluation hierarchical analysis model as follows:

a decision layer: evaluating the garden application;

a criterion layer: evaluating the growth potential condition and harvesting difficulty;

scheme layer: the biological traits selected for evaluating the growth potential condition are as follows: the length of the top leaflet, the width of the top leaflet and the length between 5-7 knots; the harvesting difficulty and easiness evaluation selected biological characters are as follows: the shape of the lower part of the skin thorn, the total number of the skin thorn of 5-7 sections, the number of the long skin thorn of 5-7 sections, the number of the short skin thorn of 5-7 sections and the existence of the thorn hair.

4. The method according to claim 3, wherein in the garden application evaluation hierarchy analysis model, the judging matrix comprises:

criterion layer-scheme layer judgment matrix for' growth potential condition evaluation

Criterion layer-scheme layer judgment matrix of' difficulty and easiness in harvesting evaluation

Decision level-criteria level decision matrix

In the judgment matrix, the values 1, 3, 5, 7 and 9 represent the relative importance degrees of any two elements in the same level to the previous level; where 1 represents "equally important", 3 represents "slightly important", 5 represents "comparatively important", 7 represents "very important", and 9 represents "absolutely important", the reciprocal of the numbers 1, 3, 5, 7, 9 being the latter element in the comparison of the two elements being more important, and the degree of importance being as described above.

5. The method according to claim 1 or 2, wherein the hierarchical analysis model comprises a floral application evaluation hierarchical analysis model as follows:

a decision layer: evaluating the application of the flower fragrance;

a criterion layer: evaluating oil resources, aromatic therapy resources and health-care ornamental resources;

scheme layer: the biological properties selected by the oil resource evaluation are as follows: nerol mass concentration, geraniol mass concentration, citronellol mass concentration and phenethyl alcohol mass concentration; the biological traits selected for the aromatherapy resource evaluation are as follows: nerol mass concentration, geraniol mass concentration, citronellol mass concentration, phenethyl alcohol mass concentration, (E) -3-hexen-1-ol acetate mass concentration, TMB mass concentration, phenethyl acetate mass concentration, geranyl acetate mass concentration, citronellyl acetate mass concentration, neryl acetate mass concentration, hexyl acetate mass concentration, DMT mass concentration, phenolic derivative substance mass concentration, monoterpene alcohol and ester substance mass concentration; the biological characters selected by the evaluation of the health-care ornamental resources are as follows: terpene mass concentration, beta, alpha, gamma-myrcene mass concentration, and geranylene (ABD) mass concentration.

6. The method according to claim 5, wherein in the floral application evaluation hierarchy analysis model, the decision matrix comprises: an oil resource evaluation judgment matrix, an aromatherapy resource evaluation judgment matrix and a health care ornamental resource evaluation judgment matrix, wherein:

the oil resource evaluation judgment matrix comprises:

criterion layer-scheme layer judgment matrix for oil resource evaluation

Decision layer-criterion layer evaluation judgment matrix for oil resource evaluation

The aromatherapy resource evaluation judgment matrix comprises:

criterion layer-scheme layer judgment matrix for aromatherapy resource evaluation

Decision layer-criterion layer judgment matrix for aromatherapy resource evaluation

The evaluation judgment matrix of the health-care ornamental resources comprises:

criterion layer-scheme layer judgment matrix for health-care ornamental resource evaluation

Decision layer-criterion layer judgment matrix for health-care ornamental resource evaluation

Wherein, P1-P17 are respectively:

p1: nerol mass concentration, P2: geraniol mass concentration, P3: citronellol mass concentration, P4: phenylethanol mass concentration, P5: (E) -3-hexen-1-ol acetate mass concentration, P6: TMB mass concentration, P7: phenylethanol acetate mass concentration, P8: geranyl acetate mass concentration, P9: citronellyl acetate mass concentration, P10: neryl acetate mass concentration, P11: mass concentration of hexyl acetate, P12: mass concentration of DMT, P13: amount concentration of phenolic derivative, P14: mass concentration of monoterpene alcohol and esters, P15: mass concentration of terpenes, P16: mass concentrations of beta, alpha and gamma-limonene, P17: germacrene (ABD) mass concentration;

7. The method of claim 6, wherein:

sequentially calculating the weighted scores of the candidate varieties according to the oil resource evaluation judgment matrix and the operation of claim 1, and evaluating the oil resources according to the weighted scores;

sequentially calculating the weighted scores of the candidate varieties according to the aromatherapy resource evaluation judgment matrix and the operation of claim 1, and carrying out aromatherapy resource evaluation according to the weighted scores;

sequentially calculating the weighted scores of the candidate varieties according to the health care type ornamental resource evaluation judgment matrix and the operation of claim 1, and evaluating the health care type ornamental resources according to the weighted scores;

and comprehensively evaluating the flower fragrance application according to the results of the oil resource evaluation, the aromatherapy resource evaluation and the health care ornamental resource evaluation.

8. The method according to claim 1 or 2, wherein the hierarchical analysis model comprises a landscape application evaluation and floral application evaluation combined evaluation hierarchical analysis model as follows:

a decision layer: comprehensively evaluating;

a criterion layer: garden application evaluation and floral application evaluation;

scheme layer: the biological characters selected by the application evaluation of the garden are as follows: the length of the top leaflet, the width of the top leaflet, the length between 5-7 nodes, the shape of the lower part of the skin thorn, the total number of the 5-7 nodes of the skin thorn and the existence of the thorn hair; the floral application evaluates selected biological traits as follows: nerol mass concentration, geraniol mass concentration, citronellol mass concentration, phenethyl alcohol mass concentration, phenol derivative mass concentration, monoterpene alcohol and ester substance mass concentration, and terpene substance mass concentration.

9. The method according to claim 8, wherein in the comprehensive evaluation level analysis model of garden application evaluation and floral application evaluation, the judgment matrix comprises a garden application evaluation judgment matrix and a floral application evaluation judgment matrix, wherein:

the garden application evaluation judgment matrix comprises:

criterion layer-scheme layer judgment matrix for garden application evaluation

Decision layer-criterion layer evaluation judgment matrix for garden application evaluation

The flower fragrance application evaluation judgment matrix comprises:

criterion layer-scheme layer judgment matrix for floral application evaluation

Decision layer-criterion layer evaluation judgment matrix for flower fragrance application evaluation

Wherein, P1-P4 and P13-P15 are respectively:

p1: nerol mass concentration, P2: geraniol mass concentration, P3: citronellol mass concentration, P4: phenylethanol mass concentration, P13: amount concentration of phenolic derivative, P14: mass concentration of monoterpene alcohol and esters, P15: mass concentration of terpenes;

10. The method according to claim 4, 6 or 8, wherein the median of two adjacent judgments 1, 3, 5, 7, 9 is also represented by the values 2, 4, 6, 8 in the judgment matrix, and the reciprocal of the values 2, 4, 6, 8 is the latter element in the comparison of the two elements, and the degree of importance is as described above.