CN112465366A - Comprehensive evaluation method for persimmon quality based on entropy weight TOPSIS model - Google Patents

Comprehensive evaluation method for persimmon quality based on entropy weight TOPSIS model Download PDF

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CN112465366A
CN112465366A CN202011400500.6A CN202011400500A CN112465366A CN 112465366 A CN112465366 A CN 112465366A CN 202011400500 A CN202011400500 A CN 202011400500A CN 112465366 A CN112465366 A CN 112465366A
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徐阳
龚榜初
程文强
吴开云
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Abstract

The invention discloses a comprehensive evaluation method for persimmon quality based on an entropy weight TOPSIS model, which comprises the following steps: (1) constructing a decision matrix; (2) standardizing evaluation indexes; (3) determining the weight of the evaluation index by an entropy weight method; (4) establishing a TOPSIS model based on entropy weight. According to the method, an entropy weight method and a TOPSIS method are combined, a TOPSIS model based on entropy weight is established, the optimal solution and the worst solution are found out, the distance between each evaluation object and the optimal solution and the worst solution is calculated, and comprehensive evaluation or sequencing is carried out according to the degree of the evaluation objects and the optimal solution, so that comprehensive evaluation of the quality of the deastringent softened persimmon is realized, and the evaluation result is more comprehensive, objective and accurate.

Description

Comprehensive evaluation method for persimmon quality based on entropy weight TOPSIS model
Technical Field
The invention relates to the technical field of persimmon quality evaluation, in particular to a comprehensive persimmon quality evaluation method based on an entropy weight TOPS IS model.
Background
Persimmon (Diospyros kaki Thunb.) belongs to Diospyros L of Ebenaceae (Ebenaceae), is native to China, has bright fruit color, is fragrant, sweet and juicy, and is rich in nutrients needed by human bodies, such as sugar, vitamin C and the like; the edible persimmon has the effects of clearing heat, moistening lung, preventing hypertension and the like, so that the persimmon is favored by more and more consumers.
The quality of the fruits is an important factor for determining the commodity of the fruits, and researches show that the persimmon resources in China have abundant genetic variation and large difference of the quality of the fruits of different varieties and resources, so that how to objectively and accurately evaluate the quality of the fruits is always an urgent problem to be solved. In the prior art, the research on the quality of the persimmon fruits focuses on single nutrient analysis, and a comprehensive evaluation system for the quality of the persimmon fruits is not formed.
Therefore, how to provide a comprehensive evaluation method for persimmon quality is a problem to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a comprehensive evaluation method for persimmon quality based on an entropy weight TOPSIS model, which combines an entropy weight method and a TOPSIS method, so that the index empowerment is more objective and accurate, and screening of persimmon resources with better comprehensive quality of fruits is facilitated.
In order to achieve the purpose, the invention adopts the following technical scheme:
a comprehensive evaluation method for persimmon quality based on an entropy weight TOPSIS model comprises the following steps:
(1) constructing a decision matrix
Constructing a decision matrix aiming at persimmon evaluation objects and evaluation indexes;
(2) standardization of evaluation index
The evaluation indexes comprise positive indexes and negative indexes, and negative indexes in the decision matrix are converted into positive indexes to obtain a standardized matrix;
(3) method for determining evaluation index weight by entropy weight method
Carrying out dimensionless treatment on the standardized matrix by adopting an efficacy coefficient method;
calculating an entropy value according to the proportion of each evaluation object under each evaluation index in the data after the dimensionless processing;
calculating index weight according to the entropy value;
(4) establishing TOPSIS model based on entropy weight
Constructing a weighting matrix according to the data subjected to non-dimensionalization processing and the entropy value;
determining the optimal solution and the worst solution in the weighting matrix aiming at each evaluation index;
and calculating the distance between each evaluation object and the optimal solution and the distance between each evaluation object and the worst solution, and evaluating the relative closeness, wherein the closer each evaluation object is to the optimal solution, the better the persimmon quality is, and the closer to the worst solution, the worse the persimmon quality is.
The entropy can reflect the disorder degree of the system, if the information entropy of a certain evaluation index is smaller, the larger the information quantity provided by the index is, the larger the effect in the comprehensive evaluation is, the larger the index weight is, and otherwise, the smaller the weight is. The method utilizes an entropy weight method to calculate the index weight, provides a basis for multi-index comprehensive evaluation, and enables the index weighting in the comprehensive evaluation system of the persimmon fruit quality to be more objective and the evaluation result to be more accurate.
Preferably, the evaluation index includes a single fruit weight, a water content, a crude protein content, a vitamin C content, a starch content, a calcium content, a phosphorus content, a potassium content, a β -carotene content, a soluble solids content, a soluble sugar content, a crude fiber content, and a tannin content.
Preferably, in step (1):
m persimmon evaluation objects are set, n evaluation indexes are set, and the evaluation object set is A ═ A1，A2，…，Am) The evaluation index set is B ═ B (B)1，B2，…，Bn)，AiTo BjHas a value of Xij(i 1, 2, …, m; j 1, 2, …, n), forming a decision matrix:
Figure BDA0002812397740000031
preferably, in step (2):
for negative type index, take xmnIs substituted into the decision matrix as a normalized value instead of xmnAnd obtaining a standardized matrix.
Preferably, in the step (3),
firstly, carrying out dimensionless processing on the standardized matrix:
Figure BDA0002812397740000032
then, an entropy value E is calculated according to a non-dimensionalization processing resultj：
Figure BDA0002812397740000033
In the formula: ejTo evaluate the entropy of the index j, dijThe specific gravity of the ith evaluation object under the jth evaluation index,
Figure BDA0002812397740000034
calculating an index weight W according to a non-dimensionalization processing result and an entropy valuej：
Figure BDA0002812397740000035
In the formula: w is not less than 0jLess than or equal to 1 and
Figure BDA0002812397740000036
preferably, in the step (4),
the weighting matrix Z is calculated as follows:
Z＝[Zij]m×n (5)
in the formula: zij＝WjDij。
Preferably, in the step (4),
setting the optimal solution as Z+The worst solution is Z-：
Figure BDA0002812397740000041
Figure BDA0002812397740000042
In the formula:
Figure BDA0002812397740000043
the distance between the evaluation object and the optimal solution is as follows:
Figure BDA0002812397740000044
the distance between the evaluation object and the worst solution is:
Figure BDA0002812397740000045
relative proximity KiThe calculation formula is as follows:
Figure BDA0002812397740000046
according to KiThe magnitude of the values sorting the different evaluation objects, KiThe larger the value, the better the evaluation object i, and conversely, the worse the evaluation object i.
According to the technical scheme, the method has the advantages that the original data are subjected to normalization processing, the decision matrix is constructed, the optimal solution and the worst solution are found out, the distance between each evaluation object and the optimal solution and the worst solution is calculated, and comprehensive evaluation or sequencing is carried out according to the degree of the evaluation objects and the optimal solution, so that comprehensive evaluation of the quality of the deastringent softened persimmon is realized, and the evaluation result is more comprehensive, objective and accurate.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
The method comprises the steps of taking farmer persimmon varieties in various regions of Zhejiang province as comprehensive evaluation objects, selecting single persimmon trees with good growth conditions, randomly picking 20 fruits without plant diseases and insect pests and with approximate sizes in the east, west, south and north directions of the periphery of a crown in the full-bearing period by taking yellowing and not softening of the persimmon fruits as picking standards, boxing and transporting the fruits back to a laboratory, and measuring various evaluation indexes after the fruits are ripe and softened. The numbers and names of the 101 selected persimmon resource samples are shown in Table 1.
TABLE 1 sample numbers and names of different farmhouse persimmon varieties
Figure BDA0002812397740000051
Figure BDA0002812397740000061
And (3) measuring the evaluation indexes of the 101 persimmon resource samples:
after the fruits are ripe and softened, weighing a single fruit by using an electronic balance, measuring the water content by using a drying method, measuring the content of soluble solid matters by using a handheld saccharimeter, taking an intermediate pulp part, homogenizing the pulp part, and then measuring indexes such as beta-carotene, crude protein, soluble sugar, starch, vitamin C, crude fiber, tannin, calcium, phosphorus, potassium and the like, sequencing single evaluation indexes of different farmer persimmon varieties according to the measurement result, and calculating the average value, wherein the result is shown in table 2.
TABLE 2 fruit evaluation index distribution Range of different farmhouse persimmon varieties
Figure BDA0002812397740000071
The Pearson correlation coefficient between single evaluation indexes of persimmon fruits was preliminarily analyzed, and the results are shown in table 3.
TABLE 3 Pearson correlation coefficient between single evaluation indexes of persimmon fruit
Figure BDA0002812397740000081
Note: indicates significant correlation at 0.05 level, indicates significant correlation at 0.01 level
The result shows that the water content is in positive correlation with the weight of a single fruit, is in negative correlation with calcium, and is in very negative correlation with potassium, soluble solid and soluble sugar; the soluble solid is in obvious negative correlation with the single fruit weight, in obvious positive correlation with phosphorus and in extremely obvious positive correlation with calcium and potassium; the soluble sugar is in extremely obvious positive correlation with the soluble solid and in obvious positive correlation with potassium; tannins are significantly negatively correlated with potassium. The correlation among the evaluation indexes is obvious, information overlapping exists to a certain extent when the fruit quality is reflected, and meanwhile, the indexes reflect the fruit quality in different aspects and cannot be mutually replaced, so that a comprehensive evaluation system is established according to each single evaluation index to scientifically and accurately evaluate the quality of the persimmon fruits.
Further, the comprehensive evaluation of the quality of the persimmon is carried out based on an entropy weight TOPSIS model:
1. constructing a decision matrix
Taking m-101 persimmon fruit samples as evaluation targets, taking the single fruit weight, the water content, the crude protein content, the vitamin C content, the starch content, the calcium content, the phosphorus content, the potassium content, the beta-carotene content, the soluble solid content, the soluble sugar content, the crude fiber content and the tannin content as evaluation indexes (n-13), and taking an evaluation target set as A-A (A-13)1，A2，…，Am) The evaluation index set is B ═ B (B)1，B2，…，Bn)，AiTo BjHas a value of Xij(i 1, 2, …, m; j 1, 2, …, n), forming a decision matrix:
Figure BDA0002812397740000091
2. standardization of evaluation index
And (3) the tannin content in the evaluation index is a negative index (the smaller the value is, the better the quality is), and the tannin content in the decision matrix is replaced by the reciprocal of the tannin content to obtain a standardized matrix.
3. Method for determining evaluation index weight by entropy weight method
Carrying out dimensionless treatment on the standardized matrix by adopting an efficacy coefficient method:
Figure BDA0002812397740000092
then, an entropy value E is calculated according to a non-dimensionalization processing resultj：
Figure BDA0002812397740000101
In the formula: ejTo evaluate the entropy of the index j, dijThe specific gravity of the ith evaluation object under the jth evaluation index,
Figure BDA0002812397740000102
calculating an index weight W according to a non-dimensionalization processing result and an entropy valuej：
Figure BDA0002812397740000103
In the formula: w is not less than 0jLess than or equal to 1 and
Figure BDA0002812397740000104
the results are shown in Table 4.
TABLE 4 entropy weight method for determining single evaluation index weight of fructus kaki
Figure BDA0002812397740000105
When the entropy weight method is used for calculating the index weight, the discrete degree of each index datum is fully considered. As can be seen from the table above, the weight of the single fruit weight is the lowest, which is 3.20%; the weight of vitamin C is the highest and reaches 14.62%, next, starch and soluble sugar are respectively 12.96% and 11.93%, the weight difference of the rest indexes is small, and the weight sequence of each evaluation index is that vitamin C is more than starch, soluble sugar, crude protein, tannin, beta-carotene, calcium, potassium, soluble solid, water content and crude fiber are more than single fruit weight.
4. Establishing TOPSIS model based on entropy weight
And (3) constructing a weighting matrix Z according to the data and the entropy value after the dimensionless processing:
Z＝[Zij]m×n (5)
in the formula: zij＝WjDij。
Determining the optimal solution and the worst solution in the weighting matrix aiming at each evaluation index:
optimal solution
Figure BDA0002812397740000111
The worst solution
Figure BDA0002812397740000112
In the formula:
Figure BDA0002812397740000113
the results are shown in Table 5.
TABLE 5 optimal and worst solutions for different evaluation indexes
Figure BDA0002812397740000121
Note: z+Representing an optimal solution; z-Represents the worst solution
Further, the distance between the evaluation object and the optimal solution is as follows:
Figure BDA0002812397740000122
the distance between the evaluation object and the worst solution is:
Figure BDA0002812397740000123
calculating the relative proximity Ki：
Figure BDA0002812397740000124
According to KiThe magnitude of the values sorting the different evaluation objects, KiThe larger the value, the better the evaluation object i, and the worse the evaluation object i, the results are shown in table 6.
TABLE 6 comprehensive evaluation of fruit quality of different farmhouse persimmon varieties
Figure BDA0002812397740000131
Figure BDA0002812397740000141
Note: d+And D-Respectively representing the distance between the evaluation object and the optimal solution and the distance between the evaluation object and the worst solution; kiIndicating relative proximity
The results show that the relative proximity K of Lian 2iMax, 0.52, second Lonicera japonica 4, KiValue 0.477, relative proximity K of Chun 4iAnd a minimum of only 0.157. The comprehensive quality sequence is positioned at the first 10 positions, namely the lotus all 2 > the lotus all 4 > Jinyun 6A > Xinchang 1 > thorough river 5 > Yongjia 9 > Ruian 2 > Xianju 1 > Huang Yan 2 > Leqing 4.
Wherein, the first 4 ranks are respectively 2 (square persimmons), 4 (milk persimmons), 6A (seedless jujube persimmons) in Jinyun and 1 (bull persimmon), which shows that the evaluation indexes of the farmer varieties are better and the comprehensive evaluation is better; the results are consistent with the results of previous studies, such as: chendengyun and the like (Zhongxian seedless jujube persimmon high-yield and high-quality cultivation and persimmon ball processing technology [ J ]. fruit trees in south China, 2008,37(1):71-72.) researches discover that seedless jujube persimmon is a famous and high-quality resource with rich nutrient content, small fruit and no kernel; researchers also show that the Bull-Xin persimmon is a locally preferred fine variety (Zhang Ganyang Yunxing Bull-Xin persimmon early high-yield cultivation technique [ J ]. Chinese fruit tree, 2003, (4): 45-46.).
Further, in order to verify the accuracy of the TOPSIS method for the comprehensive evaluation of the persimmon fruit quality, the comprehensive quality ranking and the ranking of single evaluation indexes are subjected to correlation analysis (the method is shown in ' Malele, Gaoling, Yangbeiang, and the like. ' the comprehensive quality evaluation of tomatoes and the response to organic fertilizer-water coupling thereof in the full organic nutrition mode [ J ]. the scientific and technical university report of northwest agriculture and forestry (Nature science edition), 2019,47(6):63-72. '), the results are shown in Table 7, the comprehensive quality of the persimmon fruits constructed by the entropy weight TOPSIS model is in positive correlation with most single evaluation index sequences, wherein, the vitamin C (r is 0.576), the starch (r is 0.341), the calcium (r is 0.264), the phosphorus (r is 0.352), the potassium (r is 0.298), the soluble solid (r is 0.312) and the soluble sugar (r is 0.544) are all in extremely obvious positive correlation; is in significant positive correlation with crude fiber (r ═ 0.221); positively correlated with crude protein (r ═ 0.173), beta-carotene (r ═ 0.080), tannin (r ═ 0.072); therefore, the comprehensive evaluation of the persimmon fruit quality by combining the entropy weight method and the TOPSIS method is relatively reliable.
TABLE 7 Single evaluation index ranking and K for different farmer persimmon varietiesiCorrelation of (2)
Figure BDA0002812397740000151
Note: indicates significant correlation at 0.05 level, indicates significant correlation at 0.01 level
In conclusion, the comprehensive quality of the persimmon fruits is evaluated by combining the entropy weight method and the TOPSIS method, the evaluation result is objective and accurate, and the calculation is simple, convenient and quick, namely the feasibility of applying the two methods to the comprehensive evaluation of the fruit quality is verified, and a theoretical basis is provided for the optimal selection of the fruit. In addition, the TOPSIS evaluation system can be further improved in the future, for example, evaluation indexes such as yield and disease resistance are combined, and the method can also be popularized and applied to other similar multi-index comprehensive evaluation problems.
The general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (7)

1. A comprehensive evaluation method for persimmon quality based on an entropy weight TOPSIS model is characterized by comprising the following steps:
(1) constructing a decision matrix
Constructing a decision matrix aiming at persimmon evaluation objects and evaluation indexes;
(2) standardization of evaluation index
The evaluation indexes comprise positive indexes and negative indexes, and negative indexes in the decision matrix are converted into positive indexes to obtain a standardized matrix;
(3) method for determining evaluation index weight by entropy weight method
Carrying out dimensionless treatment on the standardized matrix by adopting an efficacy coefficient method;
calculating an entropy value according to the proportion of each evaluation object under each evaluation index in the data after the dimensionless processing;
calculating index weight according to the entropy value;
(4) establishing TOPSIS model based on entropy weight
Constructing a weighting matrix according to the data subjected to non-dimensionalization processing and the entropy value;
determining the optimal solution and the worst solution in the weighting matrix aiming at each evaluation index;
and calculating the distance between each evaluation object and the optimal solution and the distance between each evaluation object and the worst solution, and evaluating the relative closeness, wherein the closer each evaluation object is to the optimal solution, the better the persimmon quality is, and the closer to the worst solution, the worse the persimmon quality is.
2. The comprehensive evaluation method of persimmon quality based on the TOPSIS model in entropy weight as claimed in claim 1,
the evaluation indexes include single fruit weight, water content, crude protein content, vitamin C content, starch content, calcium content, phosphorus content, potassium content, beta-carotene content, soluble solid content, soluble sugar content, crude fiber content and tannin content.
3. The comprehensive evaluation method of persimmon quality based on the TOPSIS model in entropy weight as claimed in claim 1,
in the step (1):
m persimmon evaluation objects are set, n evaluation indexes are set, and the evaluation object set is A ═ A1，A2，…，Am) The evaluation index set is B ═ B (B)1，B2，…，Bn)，AiTo BjHas a value of Xij(i 1, 2, …, m; j 1, 2, …, n), forming a decision matrix:
Figure FDA0002812397730000021
4. the comprehensive evaluation method of persimmon quality based on the TOPSIS model in entropy weight as claimed in claim 3,
in the step (2):
for negative type index, take xmnIs substituted into the decision matrix as a normalized value instead of xmnAnd obtaining a standardized matrix.
5. The comprehensive evaluation method of persimmon quality based on the TOPSIS model in entropy weight as claimed in claim 4,
in the step (3), the step (c),
firstly, carrying out dimensionless processing on the standardized matrix:
Figure FDA0002812397730000022
then, an entropy value E is calculated according to a non-dimensionalization processing resultj：
Figure FDA0002812397730000023
In the formula: ejTo evaluate the entropy of the index j, dijThe specific gravity of the ith evaluation object under the jth evaluation index,
Figure FDA0002812397730000024
calculating an index weight W according to a non-dimensionalization processing result and an entropy valuej：
Figure FDA0002812397730000031
In the formula: w is not less than 0jLess than or equal to 1 and
Figure FDA0002812397730000032
6. the comprehensive evaluation method of persimmon quality based on the TOPSIS model in entropy weight as claimed in claim 5,
in the step (4), the step (c),
the weighting matrix Z is calculated as follows:
Z＝[Zij]m×n (5)
in the formula: zij＝WjDij。
7. The comprehensive evaluation method of persimmon quality based on the TOPSIS model in entropy weight as claimed in claim 6,
in the step (4), the step (c),
setting the optimal solution as Z+The worst solution is Z-：
Figure FDA0002812397730000033
Figure FDA0002812397730000034
In the formula:
Figure FDA0002812397730000035
j＝1，2，…，n；
the distance between the evaluation object and the optimal solution is as follows:
Figure FDA0002812397730000036
the distance between the evaluation object and the worst solution is:
Figure FDA0002812397730000037
relative proximity KiThe calculation formula is as follows:
Figure FDA0002812397730000041
according to KiThe magnitude of the values sorting the different evaluation objects, KiThe larger the value, the better the evaluation object i, and conversely, the worse the evaluation object i.
CN202011400500.6A 2020-12-02 2020-12-02 Comprehensive evaluation method for persimmon quality based on entropy weight TOPSIS model Pending CN112465366A (en)

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