CN107657141A - A kind of construction method of black fungus quality monitoring system - Google Patents
A kind of construction method of black fungus quality monitoring system Download PDFInfo
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Abstract
The invention discloses a kind of construction method of black fungus quality monitoring system, comprise the following steps：(1) preliminary screening is carried out to all measurable black fungus index of quality using the coefficient of variation, selects the index of quality of the coefficient of variation more than 15% as the primary election index of quality；(2) carry out sieve to primary election index using principal component analytical method to subtract, determine the core index of quality；(3) by the core index of quality is initialized, positiveization and normalized, comprehensive score is calculated after determining indices weight, builds Quantitative Monitoring system.Using above method, the quality monitoring system of black fungus can be easily constructed, the integrated quality of Quantitative Monitoring agaric, grasps influence of each production link to agaric quality.
Description
Technical Field
The invention relates to the technical field of black fungus production, in particular to a construction method of a black fungus quality monitoring system.
Technical Field
Black fungus (Auricularia auricular) production has a long folk history and strong local characteristics in Heilongjiang forest regions, and is the support industry of understory economy. The Heilongjiang province is the biggest black fungus producing area in China, and the yield accounts for 60 percent of the whole country. However, the black fungus industry is generally in a state that good varieties are not available due to disorder of varieties, the production mode is not sustainable, the product quality is unstable, and the operation income is influenced, and particularly, a simple and practical quality monitoring method is not available in the black fungus variety breeding and cultivation production processes, so that the requirement of guaranteeing the production quality of the black fungus cannot be met.
Disclosure of Invention
In view of the above, the invention aims to provide a method for constructing a black fungus quality monitoring system, which is a simple and practical monitoring system for black fungus variety breeding and cultivation production.
Based on the purpose, the construction method of the black fungus quality monitoring system provided by the invention comprises the following steps:
(1) Primarily screening all measurable black fungus quality indexes by using the variation coefficient, and selecting the quality indexes with the variation coefficient more than 15% as primary selection quality indexes;
(2) Screening and reducing the primary selection index by using a principal component analysis method to determine a core quality index;
(3) And (3) initializing, normalizing and normalizing the core quality indexes, determining the weight of each index, calculating a comprehensive score, and constructing a quantitative monitoring system.
The measurable black fungus quality index comprises a fruiting body color index (Hunter scale index L, a, b, melanin color value), a structure index (dry-wet ratio, fruiting body thickness, middle layer proportion), a texture characteristic index (hardness, elasticity, chewiness, adhesiveness, shearing force), water content, water-soluble polysaccharide content, crude protein content, crude fat content, ash content, crude fiber content, carotene content, thiamine content, riboflavin content, nicotinic acid content, ascorbic acid content, amino acids (lysine, threonine, phenylalanine, methionine, isoleucine, leucine and valine, tyrosine, aspartic acid, serine, glutamic acid, glycine, alanine, histidine, arginine and proline) content, fatty acid content, mineral element index (phosphorus, calcium, magnesium, zinc, copper, iron, chromium, manganese, nickel, strontium, cobalt, strontium, tin, sulfur, selenium, germanium, sodium) content, pectin content, total phenol content, flavone content and melanin content).
The coefficient of variation and principal component analysis were calculated using SPSS for Windows Ver 13.0 commercial software.
And the primary selection index is screened and reduced by using a principal component analysis method, and the accumulated contribution rate of the selected principal component is required to be not less than 90%.
The initialization method of the core quality index is the absolute value of the distance between each core quality index value and an ideal value:
wherein x is i ' As initialization result, x i Is the value of each core quality index, x 0 Is an ideal value for each core quality index.
The comprehensive score (Y) is calculated according to the following formula:
wherein X i For the forward and normalized results, W i Is the weight of each core quality indicator.
The determination method of the core quality index weight is to establish a judgment matrix and check whether the consistency of the judgment matrix meets the requirement.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to specific embodiments below.
The construction method of the black fungus quality monitoring system comprises the following steps:
(1) Primarily screening all measurable black fungus quality indexes by using the variation coefficient, and selecting the quality indexes with the variation coefficient more than 15% as primary selection quality indexes;
(2) Screening and reducing the primary selection index by using a principal component analysis method to determine a core quality index;
(3) And (4) initializing, normalizing and normalizing the core quality indexes, determining the weight of each index, calculating a comprehensive score, and constructing a quantitative monitoring system.
The measurable black fungus quality index comprises a fruiting body color index (Hunter scale index L, a, b, melanin color value), a structure index (dry-wet ratio, fruiting body thickness, middle layer proportion), a texture characteristic index (hardness, elasticity, chewiness, adhesiveness, shearing force), water content, water-soluble polysaccharide content, crude protein content, crude fat content, ash content, crude fiber content, carotene content, thiamine content, riboflavin content, nicotinic acid content, ascorbic acid content, amino acids (lysine, threonine, phenylalanine, methionine, isoleucine, leucine and valine, tyrosine, aspartic acid, serine, glutamic acid, glycine, alanine, histidine, arginine and proline) content, fatty acid content, mineral element index (phosphorus, calcium, magnesium, zinc, copper, iron, chromium, manganese, nickel, strontium, cobalt, strontium, tin, sulfur, selenium, germanium, sodium) content, pectin content, total phenol content, flavone content and melanin content).
The coefficient of variation and principal component analysis were calculated using SPSS for Windows Ver 13.0 commercial software.
And the primary selection index is screened and reduced by using a principal component analysis method, and the accumulated contribution rate of the selected principal component is required to be not less than 90%.
The initialization method of the core quality index is the absolute value of the distance between each core quality index value and an ideal value:
wherein x is i ' As a result of initialization, x i Is the value of each core quality index, x 0 Is an ideal value for each core quality index.
The comprehensive score (Y) is calculated according to the following formula:
wherein X i For the forward and normalized results, W i Is the weight of each core quality indicator.
The determination method of the core quality index weight is to establish a judgment matrix and check whether the consistency of the judgment matrix meets the requirement.
Example 1 core quality index determination
In order to construct a monitoring system of black fungus in northeast China, 5 different black fungus varieties (varieties 1-5) are collected respectively, and measurable quality indexes of the black fungus of each variety comprise fruiting body color indexes (Hunter scale indexes L, a and b, melanin color values), structure indexes (dry-to-wet ratio, fruiting body thickness and interlayer proportion), texture characteristic indexes (hardness, elasticity, chewiness, cohesiveness and shearing force), water content, water-soluble polysaccharide content, crude protein content, crude fat content, ash content, crude fiber content, carotene content, thiamine content, riboflavin content, nicotinic acid content, ascorbic acid content, amino acid (lysine, threonine, phenylalanine, methionine, isoleucine, leucine and valine, tyrosine, aspartic acid, serine, glutamic acid, glycine, alanine, histidine, arginine and proline) content, fatty acid content, mineral element indexes (phosphorus, calcium, magnesium, zinc, copper, iron, chromium, manganese, nickel, strontium, mineral, cobalt, strontium, tin, sulfur, selenium, histidine, sodium) content, pectin content, germanium content, total phenol content and total flavone content). And (3) primarily screening all measurable black fungus quality indexes by using the variation coefficient, and selecting the quality index with the variation coefficient more than 15% as a primary selection quality index. The quality indexes which can be measured by comprehensively analyzing the black fungus show differences among 5 varieties, wherein the differences among crude protein content, crude fiber content, total flavone content and metal element Fe groups are large, the variation coefficient reaches more than 50%, and the variation degrees of the indexes, namely, hunter scale index b, melanin color value, metal element (strontium, zinc, manganese, chromium and nickel), texture parameter (hardness, chewiness, shearing force and adhesiveness), alanine content, aspartic acid content, glutamic acid content, glycine content, crude melanin content, water-soluble polysaccharide content, pectin content, crude fat content, total phenol content, saturated fatty acid content and unsaturated fatty acid content, also reach more than 15%. The other indexes show little difference among different varieties.
And (3) screening 34 primary selection quality indexes by integrating the variation coefficient and the correlation analysis result of each index among 5 varieties, performing principal component analysis, and finally determining a core evaluation index according to the principal component analysis result. Analyzing the principal components to obtain the characteristic value lambda of each principal component j Variance contribution ratio and corresponding feature vector e j . As can be seen from Table 1, the eigenvalues λ&The cumulative variance contribution rate of the first 5 principal components of gt and 1 reaches 91.56%, which shows that the first 5 principal components can represent most information of the original 34 quality indexes. Therefore, 34 quality characters of the black fungus can be synthesized into 5 main components.
The representative quality indexes of the first main component are 8 essential amino acid contents and 4 umami amino acid contents, the indexes of the amino acid contents have extremely obvious correlation, and the amino acid content is selected as the first main component.
The representative quality indexes of the second main component are crude fiber content, color indexes a and b and crude melanin content, wherein the color indexes a and b are related to the crude melanin content, and the correlation with the crude fiber content is not obvious. The variation among varieties of crude fiber content is 56.72%, the variation among varieties of color index a is 13.09%, the variation among varieties of color index b is 21.28%, the variation among varieties of crude melanin content is 15.71%, the variation among varieties of crude fiber content is the largest, and crude melanin can represent the color of black fungus most, so the crude fiber content and the crude melanin content are selected as second main components.
The third main component represents quality indexes of crude fat content and chromium content of metal element, the two indexes have a very obvious correlation, the inter-variety variation of the crude fat content is 22.50%, the inter-variety variation of the chromium content of metal element is 30.40%, the importance of the quality index, the load in the main component and the correlation between the quality indexes are comprehensively considered, and the crude fat content is selected to represent the third main component.
The fourth main component represents quality indexes of total flavone content and metal element zinc content, the two indexes have obvious correlation, the inter-species variation of the total flavone content is 50.76%, the inter-species variation of the metal element zinc content is 14.85%, the inter-species variation of the total flavone content is very high, and the total flavone content is selected to represent the fourth main component.
The fifth main component represents quality indexes of water-soluble polysaccharide content and pectin content, the correlation between the two indexes is not significant, the interspecies variation of the water-soluble polysaccharide content is 15.74%, the interspecies variation of the pectin content is 16.28%, and the water-soluble polysaccharide content and the pectin content jointly represent the fifth main component.
In summary, the core quality indexes of the black fungus quality monitoring system are as follows: lysine content, threonine content, phenylalanine content, methionine content, isoleucine content, leucine content, valine content, alanine content, aspartic acid content, glutamic acid content, glycine content, crude fiber content, crude melanin content, crude fat content, total flavonoids v, water-soluble polysaccharide content, and pectin content.
Example 2 construction of a quantitative monitoring System
The core quality indexes have different dimensions and orders of magnitude, and in order to eliminate the influence caused by different units of each index and the difference between the orders of magnitude, the standardization of the evaluation indexes is required.
First, an ideal value (x) of each quality index is determined 0 Table 1). Wherein the amino acid component, the water-soluble polysaccharide, the crude melanin and the total flavone are positive indexes, and the larger the measured value is, the better the measured value is; crude fiber and pectin are neutral indexes; crude fat is a negative indicator, with smaller measurements being better.
TABLE 1 Ideal values for the respective quality indices (unit: mg/g dry weight)
Initialization: in order to eliminate the influence of different dimensions and orders of magnitude on quality evaluation, 17 quality indexes are initialized, and the initialization method is the absolute value of the distance between each quality index value and an ideal value:
forward and normalization: the initialized value range of each quality index is x i ' > 0, the comprehensive evaluation needs to be processed in a forward direction for convenient comprehensive evaluation, and meanwhile, the comprehensive evaluation needs to be normalized for avoiding the influence of different orders of magnitude on the comprehensive evaluation. The forward and normalization methods are as follows:
after the core quality indexes are normalized, the weight coefficient of each core index is required to be determined in order to display the importance of different indexes on comprehensive evaluation. The weight determination method is to establish a judgment matrix and check whether the consistency meets the requirement. Establishing a judgment matrix (table 2) by using a 1-9 proportional scaling method to calculate the weight w of each index i . Wherein X is 1 Is lysine content, X 2 Is the threonine content, X 3 Is the phenylalanine content, X 4 Is the methionine content, X 5 For isoleucine content, X 6 Is leucine content, X 7 Is valine content, X 8 Is alanine content，X 9 Is the aspartic acid content, X 10 Is glutamic acid content, X 11 Is the glycine content, X 12 For crude melanin content, X 13 Is the water-soluble polysaccharide content, X 14 Is the total flavone content, X 15 Is the pectin content, X 16 Is a crude fiber content, X 17 Is the crude fat content. The meaning of the scale figures is the relative importance of each quality index: 1 indicates that the influence of the i factor is the same as that of the j factor; 3 indicates that the i factor has a slightly stronger influence than the j factor; 5 indicates that the i factor has a stronger influence than the j factor; 7 indicates that the influence of the i factor is obviously stronger than that of the j factor; 9 denotes that the i factor is absolutely stronger than the j factor; 2. 4, 6, 8 represent the ratio of the influence of the i factor to the j factor between the above 2 adjacent levels; 1/2, 1/3, \8230;, 1/9 indicates that the ratio of the influence of the j factor to the i factor is a ji ，a ji ＝1/a ij 。
TABLE 2 decision matrix
Determining the product (M) of each row of elements of the matrix i )
M i Root of square n i
Calculating and judging the maximum characteristic root lambda of matrix max
The consistency of the judgment matrix is checked, which shows that the judgment matrix has satisfactory consistency, and the weight of each quality index is obtained (table 3).
TABLE 3 respective quality index weights
The screened quality index is initialized, normalized and normalized to obtain X with the value of 0-X i A standardization index of 1 or less; determining each quality index weight by utilizing a judgment matrix in an analytic hierarchy process; the analysis and comprehensive evaluation of the black fungus instrument comprises the following steps of adding each standardized index and the corresponding index weight:
Claims (8)
1. a construction method of a black fungus quality monitoring system comprises the following steps:
(1) Primarily screening all measurable quality indexes of the black fungus by using the variation coefficient, and selecting the quality index with the variation coefficient more than 15% as a primary selection quality index;
(2) Screening and reducing the primary selection index by using a principal component analysis method to determine a core quality index;
(3) And (3) initializing, normalizing and normalizing the core quality indexes, determining the weight of each index, calculating a comprehensive score, and constructing a quantitative monitoring system.
2. A method for constructing a black fungus quality monitoring system according to claim 1, wherein the measurable quality index of black fungus includes a fruit body color index (hunter scale index L, a, b, melanin color number), a structural index (dry-to-wet ratio, fruit body thickness, middle layer ratio), a texture property index (hardness, elasticity, chewiness, cohesiveness, shearing force), water content, water-soluble polysaccharide content, crude protein content, crude fat content, ash content, crude fiber content, carotene content, thiamine content, riboflavin content, nicotinic acid content, ascorbic acid content, amino acids (lysine, threonine, phenylalanine, methionine, isoleucine, leucine and valine, tyrosine, aspartic acid, serine, glutamic acid, glycine, alanine, histidine, arginine and proline) content, fatty acid content, mineral element index (phosphorus, calcium, magnesium, zinc, copper, iron, chromium, manganese, nickel, strontium, cobalt, strontium, tin, sulfur, selenium, germanium, sodium) content, pectin content, total phenol content, and flavone content).
3. The method for constructing a black fungus quality monitoring system according to claim 1, wherein the coefficient of variation and the principal component analysis are calculated by SPSS for Windows Ver 13.0 commercial software.
4. The method for constructing a black fungus quality monitoring system according to claim 1, wherein the primary selection index is screened by using a principal component analysis method, and the accumulated contribution rate of the selected principal component is not less than 90%.
5. The method for constructing an Auricularia auricula quality monitoring system according to claim 1, wherein the initialization method of the core quality index is an absolute value of a distance between each core quality index value and an ideal value:
wherein x is i ' As initialization result, x i Is the value of each core quality index, x 0 Is an ideal value for each core quality index.
6. The method for constructing the black fungus quality monitoring system according to claim 1, wherein the method for forward normalization of the core quality index comprises the following steps:
wherein, X i Is the forward and normalized result, x' i max The maximum value of the result is initialized for each core quality index.
7. The method for constructing a black fungus quality monitoring system according to claim 1, wherein the comprehensive score (Y) is calculated according to the following formula:
wherein X i For the forward and normalized results, W i Is the weight of each core quality indicator.
8. A construction method of a black fungus quality monitoring system according to claim 1 or 7, wherein the determination method of the core quality index weight is implemented by establishing a judgment matrix and checking whether the consistency of the judgment matrix meets the requirement.
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