Method for carrying out comprehensive quality evaluation on Chinese chestnuts of different producing areas and different varieties

Abstract

The invention provides a method for evaluating the comprehensive quality of Chinese chestnuts of different producing areas and different varieties, and belongs to the technical field of Chinese chestnut quality evaluation. The method for evaluating the comprehensive quality of the Chinese chestnuts of different producing areas and different varieties comprises the following steps: measuring indexes influencing the comprehensive quality of Chinese chestnut samples, and determining key indexes influencing the comprehensive quality of the Chinese chestnut samples by using hierarchical clustering analysis; obtaining a main component load and determining the number of main component factors by adopting a main component analysis method; constructing a chestnut comprehensive quality evaluation model by using the main component factors; and ranking the quality of each variety of Chinese chestnuts based on the comprehensive quality evaluation model. According to the Chinese chestnut comprehensive quality evaluation method based on hierarchical clustering and principal component analysis, the accuracy and standardization of Chinese chestnut comprehensive quality evaluation can be effectively improved, deviation caused by subjective factors of evaluation personnel is avoided, the comprehensive quality of Chinese chestnuts is reflected more objectively, and technical support is provided for scientific evaluation.

Description

Technical Field

[0001] The invention belongs to the technical field of chestnut quality evaluation, and specifically relates to a method for comprehensive quality evaluation of chestnuts of different varieties from different origins. Background Art

[0002] Chestnut (Castanea mollissima.cv.Banli) belongs to the genus Castanea of ​​the Fagaceae family. It is rich in protein, starch, fat, B vitamins, vitamin C, carotene, etc. Chestnut is delicious and has high nutritional value. Regular consumption has health benefits such as invigorating qi and strengthening spleen, tonifying kidney and strengthening tendons. It is known as "hardcore crops" and "woody food". Chestnut cultivation has a long history and is distributed over a vast area. There are more than 300 varieties in various places. With the prosperity of the chestnut market, many problems such as unclear grades, confusion of origins and varieties, and inferior products have been exposed, which greatly restricted the healthy development of the chestnut industry. Therefore, it is urgent to conduct a comprehensive and integrated evaluation of chestnuts from different origins and varieties to provide a theoretical basis for subsequent breeding and chestnut product development.

[0003] The comprehensive quality of chestnuts is affected by many factors. The appearance of the fruit is an important factor directly used to measure the quality of the fruit, which affects the choice of consumers. The appearance quality mainly includes fruit shape index, color, hardness, etc. The fruit color is an important criterion for evaluating the appearance quality of the fruit, which affects the attractiveness and commercial value of the fruit. The texture structure of agricultural products and their deep-processed products is one of the important attributes affecting their quality. The measured texture results can reflect the quality of fruit and vegetable agricultural products and their derivative products. With the changes in the needs of modern society, the demand for nutritious foods is increasing. The content of nutrients affects the quality of chestnuts, and the content of mineral elements also has a significant impact on the quality of chestnut fruits. At present, there are no reports in this field on how to quickly, accurately and objectively evaluate the comprehensive quality of chestnuts. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method for comprehensive quality evaluation of chestnuts of different varieties from different origins, which can avoid the deviation caused by subjective factors of judges and more objectively reflect the comprehensive quality of chestnuts.

[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0006] The present invention provides a method for comprehensive quality evaluation of chestnuts of different varieties from different origins, comprising the following steps: determining indices that affect the comprehensive quality of chestnut samples, using hierarchical cluster analysis to determine key indices that affect the comprehensive quality of chestnut samples; using principal component analysis to obtain principal component loads and determine the number of principal component factors; constructing a chestnut comprehensive quality evaluation model based on the principal component factors; and ranking the quality of each variety of chestnuts based on the comprehensive quality evaluation model.

[0007] Preferably, the different origins include Tangshan Zunhua, Tangshan Qianxi and Chengde Kuancheng; the different varieties include purple amber, aventurine pearl, Tafeng, Shuofeng, Dabanhong, Yanli, Zunyu, Yanshanhong, Short Ci, Yanshan Zaofeng, Zunda Li, Yanfeng, Shisheng Li, Yankui, Short Branch, Laoshu Li and Daqingchuang.

[0008] Preferably, the indicators that affect the comprehensive quality of chestnut samples include transverse diameter, longitudinal diameter, fruit shape index, brightness, red-green value, yellow-blue degree, browning index, hardness, cohesion, chewiness, elasticity, viscosity, moisture, fat, protein, starch, energy value, soluble solids, soluble protein, amylose, reducing sugar, soluble sugar, ascorbic acid, flavonoids, total phenols, Na, Cu, Zn, Fe, Al, Mg, Mn, K, Ca and Ni.

[0009] Preferably, when using hierarchical cluster analysis, the clustering mode is average, the distance type is correlation, and the method is distance sum.

[0010] Preferably, the key indicators include fruit shape index, browning index, starch, elasticity, cohesion, energy value, soluble solids, reducing sugar, ascorbic acid, flavonoids, Ca and Ni.

[0011] Preferably, when determining the number of main component factors, the principle for extracting the number of main component factors is: the first n main components whose eigenvalues ​​corresponding to the main components are greater than 1.

[0012] Preferably, the number of main component factors is 5.

[0013] Preferably, the principal component score model is calculated by the ratio of the principal component factor to the eigenvalue; the weight of the principal component factor = variance contribution rate / cumulative contribution rate.

[0014] Preferably, the chestnut comprehensive quality evaluation model is Q=0.317F1+0.238F2+0.178F3+0.156F4+0.120F5, wherein Q represents the comprehensive score of chestnut quality; F1-F5 represent the scores of the five main component factors respectively:

[0015] F1=0.077X1+0201X2+0.096X3+0.441X4-0.089X5-0.097X6-0.033X7+0.397X8-0.096X9+0.164X 10 -0.063X 11 +0.122X 12 ;

[0016] F2=-0.089X1+-0.075X2+0.502X3+0.138X4-0.431X5-0.081X6-0.006X7-0.081X8-0.155X9-0.228X 10 -0.192X 11 +0.151X 12 ;

[0017] F3=0.188X1-0.111X2+0.028X3-0.133X4-0.053X5-0.034X6+0.464X7+0.051X8+0.11X9-0.537X 10 +0.394X 11 -0.067X 12 ;

[0018] F4=-0.113X1-0.355X2+0.153X3-0.054X4-0.18X5+0.59X6+0.373X7-0.073X8-0.101X9+0.001X 10 -0.211X 11 -0.127X 12 ;

[0019] F5=-0.437X1-0.197X2+0.105X3-0.054X4-0.028X5-0.023X6+0.082X7-0.025X8+0.448X9+0.06X 10 +0.053X 11 +0.563X 12 ;

[0020] X1 is the standardized index of fruit shape index, X2 is the standardized index of browning index, X3 is the standardized index of elasticity, X4 is the standardized index of starch, X5 is the standardized index of Ni, X6 is the standardized index of Ca, X7 is the standardized index of reducing sugar, X8 is the standardized index of energy value, X9 is the standardized index of ascorbic acid, X 10 is the normalized index of soluble solids, X 11 is the normalized index of cohesion, X 12 It is the standardized index of semi-flavonoids.

[0021] Preferably, the standardized index is obtained by standardizing the key indicators of the measurement using SPSS software.

[0022] Beneficial effects of the present invention:

[0023] The chestnut comprehensive quality evaluation method based on hierarchical clustering and principal component analysis provided by the present invention can effectively improve the accuracy and standardization of chestnut comprehensive quality evaluation, avoid the deviation caused by subjective factors of judges, more objectively reflect the comprehensive quality of chestnuts, and provide technical support for scientific evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a schematic diagram of chestnut horizontal and vertical diameter measurement;

[0025] Figure 2 This is the result of hierarchical cluster analysis of quality indicators. DETAILED DESCRIPTION

[0026] The present invention provides a method for comprehensive quality evaluation of chestnuts of different varieties from different origins, comprising the following steps: determining indices that affect the comprehensive quality of chestnut samples, using hierarchical cluster analysis to determine key indices that affect the comprehensive quality of chestnut samples; using principal component analysis to obtain principal component loads and determine the number of principal component factors; constructing a chestnut comprehensive quality evaluation model based on the principal component factors; and ranking the quality of each variety of chestnuts based on the comprehensive quality evaluation model.

[0027] In the present invention, the different origins preferably include Tangshan Zunhua, Tangshan Qianxi and Chengde Kuancheng; the different varieties preferably include Zipo, Dongling Pearl, Tafeng, Shuofeng, Dabanhong, Yanli, Zunyu, Yanshanhong, Short Ci, Yanshan Zaofeng, Zundali, Yanfeng, Shishengli, Yankui, Short Branch, Laoshuli and Daqingchuang. In the present invention, the indicators that affect the comprehensive quality of chestnut samples include chestnut phenotypic texture indicators, color indicators, basic nutritional indexes, mineral element indicators and antioxidant component indicators, wherein the phenotypic texture indicators preferably include transverse diameter, longitudinal diameter, fruit shape index, browning index, hardness, elasticity, viscosity, cohesion, chewiness and moisture; color indicators preferably include brightness (L*), red-green value (a*) and yellow-blue degree (b*); basic nutritional component indicators preferably include starch, protein, fat, reducing sugar, energy value, soluble solids, soluble protein, straight-chain starch, soluble sugar and ascorbic acid; mineral element indicators preferably include Na, K, Ca, Mg, Cu, Zn, Fe, Al, Mn and Ni; antioxidant component indicators preferably include total phenols and flavonoids. The present invention does not specifically limit the specific determination method of the above-mentioned indicators.

[0028] In the present invention, when using hierarchical cluster analysis, the clustering mode is preferably average, the distance type is preferably correlation, and the method is preferably distance sum. According to the correlation, the key indicators affecting the comprehensive quality of the chestnut sample are automatically selected from 35 indicators that affect the comprehensive quality of the chestnut sample, and the key indicators preferably include fruit shape index, browning index, starch, elasticity, cohesion, energy value, soluble solids, reducing sugar, ascorbic acid, flavonoids, Ca and Ni.

[0029] The present invention utilizes five types of indicators, namely phenotypic texture, color, basic nutrients, antioxidant components and mineral elements, to comprehensively describe the quality of chestnuts. Key quality indicators are screened through hierarchical cluster analysis, and a comprehensive chestnut quality evaluation model is constructed using principal component analysis. This avoids the deviation caused by subjective factors of judges and reflects the comprehensive chestnut quality more objectively.

[0030] In the present invention, the principal component analysis method preferably includes the following main steps: analyzing the values ​​of 12 key indicators of 24 samples to obtain a data matrix; obtaining a principal component analysis rotation component matrix; calculating its eigenvalues ​​and corresponding standard eigenvectors; calculating the main component contribution rate and cumulative contribution rate; finding the main component load and determining the number of main components; calculating the component score coefficient matrix. When determining the number of main component factors, the principle of extracting the number of main component factors is preferably: the first n main components whose eigenvalues ​​corresponding to the main components are greater than 1. In the present invention, the number of main component factors is preferably 5.

[0031] In the present invention, the ratio of the main component factor to the eigenvalue is preferably used to calculate the main component score model (chestnut comprehensive quality evaluation model); the weight of the main component factor = variance contribution rate / cumulative contribution rate. In the present invention, the chestnut comprehensive quality evaluation model is Q = 0.317F1 + 0.238F2 + 0.178F3 + 0.156F4 + 0.120F5, where Q represents the comprehensive score of chestnut quality; F1 to F5 represent the scores of the five main component factors respectively:

[0032] F1=0.077X1+0201X2+0.096X3+0.441X4-0.089X5-0.097X6-0.033X7+0.397X8-0.096X9+0.164X 10 -0.063X 11 +0.122X 12 ;

[0033] F2=-0.089X1+-0.075X2+0.502X3+0.138X4-0.431X5-0.081X6-0.006X7-0.081X8-0.155X9-0.228X 10-0.192X 11 +0.151X 12 ;

[0034] F3=0.188X1-0.111X2+0.028X3-0.133X4-0.053X5-0.034X6+0.464X7+0.051X8+0.11X9-0.537X 10 +0.394X 11 -0.067X 12 ;

[0035] F4=-0.113X1-0.355X2+0.153X3-0.054X4-0.18X5+0.59X6+0.373X7-0.073X8-0.101X9+0.001X 10 -0.211X 11 -0.127X 12 ;

[0036] F5=-0.437X1-0.197X2+0.105X3-0.054X4-0.028X5-0.023X6+0.082X7-0.025X8+0.448X9+0.06X 10 +0.053X 11 +0.563X 12 ;

[0037] X1 is the standardized index of fruit shape index, X2 is the standardized index of browning index, X3 is the standardized index of elasticity, X4 is the standardized index of starch, X5 is the standardized index of Ni, X6 is the standardized index of Ca, X7 is the standardized index of reducing sugar, X8 is the standardized index of energy value, X9 is the standardized index of ascorbic acid, X 10 is the normalized index of soluble solids, X 11 is the normalized index of cohesion, X 12 It is the standardized index of semi-flavonoids.

[0038] In the present invention, the standardized index is preferably obtained by using SPSS software to standardize the key indicators of the determination. When using SPSS software for standardization, select data analysis description and save the indicator as a standardized variable.

[0039] The technical solutions provided by the present invention are described in detail below in conjunction with the embodiments, but they should not be construed as limiting the protection scope of the present invention.

[0040] In the following embodiments, unless otherwise specified, all of them are conventional methods.

[0041] Unless otherwise specified, the materials and reagents used in the following examples can be obtained from commercial sources.

[0042] Example 1

[0043] A method for comprehensively evaluating the quality of chestnuts of different varieties from different origins comprises the following steps:

[0044] (1) The following 35 quality indices of chestnuts of different varieties from different origins as shown in Table 1 were measured respectively: transverse diameter, longitudinal diameter, fruit shape index, brightness (L*), red-green value (a*), yellow-blue degree (b*), browning index, hardness, cohesion, chewiness, elasticity, adhesiveness, moisture, fat, protein, starch, energy value, soluble solids, soluble protein, amylose, reducing sugar, soluble sugar, ascorbic acid (also known as vitamin C), flavonoids, total phenols, Na, Cu, Zn, Fe, Al, Mg, Mn, K, Ca and Ni.

[0045] The chestnut horizontal diameter and vertical diameter are measured by using a vernier caliper to measure the chestnut horizontal diameter and vertical diameter with an accuracy of 0.02mm. For specific standards, see Figure 1 The fruit shape index was calculated as the ratio of the fruit's horizontal diameter to its vertical diameter. Each index was measured 30 times (n=30).

[0046] Chestnut color measurement: Use a colorimeter to measure the L*, a*, and b* values ​​of the flat part of the randomly selected chestnut kernels (without the seed coat). Calculate the browning index BI based on the L*, a*, and b* values, and repeat five times for each group of measurements (n=5).

[0047] Chestnut texture determination: After the chestnuts are shelled and peeled, they are cut into regular squares of 1×1×0.5 cm and placed on the stage of the food property analyzer using a disc extrusion probe (P / 50) for TPA extrusion testing. Each group is tested 5 times (n=5). The specific parameters are set as a test speed of 60 mm / min, a test depth of 30%, and a trigger force of 2.5 N. The TPA parameters obtained from the measured texture characteristic curve calculated by the TPA software are: hardness, cohesion, elasticity, chewiness, and stickiness.

[0048] Moisture content determination: randomly select three chestnuts, cut the chestnut kernels into thin slices of uniform thickness, place them in a dried glass dish, put them in a 105℃ oven for 30 minutes, then bake them at 65℃ to constant weight, measure the dry mass and record it, and calculate the moisture content of a single fruit.

[0049] Fat content determination: refer to GB 5009.6-2016 "Determination of fat in foods".

[0050] Protein content determination: refer to national standard GB 5009.5-2016.

[0051] Starch content determination: according to the instructions of BC0700 kit.

[0052] Determination of amylose content: refer to the national standard GB / T 15683-2008 "Determination of amylose content in rice".

[0053] Calculation of energy value: The core nutrients in food include protein, fat, and carbohydrates. The energy capacity of protein is 17kJ / g, fat is 37kJ / g, and carbohydrates is 17kJ / g. The energy value is calculated by the following formula:

[0054] Energy value (kJ / 100g) = (protein mass + starch mass) × 17 + fat mass × 37.

[0055] Soluble solids determination: Soluble solids content was determined using a refractometer.

[0056] Soluble protein was determined using the Coomassie brilliant blue method.

[0057] Soluble sugar determination: anthrone sulfuric acid method was used.

[0058] Reducing sugar determination: Determined by DNS method.

[0059] Determination of vitamin C content: refer to GB 5009.86-2016 "Determination of vitamin C in food" 。

[0060] Determination of total phenol content: Weigh 5 mg of gallic acid standard and prepare gallic acid standard solutions with concentrations of 0, 4, 8, 12, and 16 mg / mL. Pipette 0.2 mL of the standard solution into a volumetric flask, and add 0.6 mL of deionized water to each volumetric flask. Subsequently, add 0.2 mL of Folin phenol solution and shake well. After 5 minutes, add 0.8 mL of 7.5% sodium carbonate solution, dilute to 2 mL with distilled water, and protect from light for 2 hours. Then measure the absorbance at 760 nm, and draw a standard curve with absorbance as the ordinate and concentration as the abscissa. After weighing 0.2 g of chestnut sample, add it to a 10 mL centrifuge tube. Add 60% ethanol and ultrasonically treat for 1 hour. Centrifuge the mixture at 9000 r / min for 5 minutes, collect the supernatant, draw 0.2 mL of the extract, add it to a volumetric flask, and dilute to 2 mL. Treat at room temperature and avoid light for 2 hours. The absorbance was then measured at 760 nm and the result was substituted into the standard equation to obtain the total phenol content.

[0061] Flavonoid content determination: Weigh 9 mg of rutin standard to prepare a standard solution of 150 ug / mL. Take 0.00, 0.25, 0.50, 1.00, and 2.00 mL of rutin standard solution to prepare the corresponding final rutin concentrations of 0, 3.75, 7.50, 15, and 30 μg / mL, and add 0.40 mL of 5% sodium nitrite. Let stand for 6 minutes in the dark, then add 0.6 mL of 10% aluminum nitrate solution. Let stand for 6 minutes in the dark, add 1.6 mL of 20% sodium hydroxide solution, dilute to volume with 60% ethanol, and treat in the dark for 15 minutes. Measure the absorbance at 510 m and draw a standard curve. Weigh 0.2 g of chestnut sample and add it to a 10 mL centrifuge tube. Add 60% ethanol and ultrasonicate for 60 minutes. Subsequently, centrifuge at 10000 r / min for 5 minutes to collect the supernatant for use. Take 2.0 mL of supernatant and dilute to the scale with 60% ethanol. The absorbance was measured at 510 nm.

[0062] Determination of mineral element content: Add 0.5±0.0005g of chestnut powder and 5mL of concentrated nitric acid to a 50mL centrifuge tube and acidify overnight, then digest with a heavy metal digester for 1h, add 2mL of H2O2, continue digesting for 1h, and drive the acid to 1.5mL. Dilute to 50±0.5mL with ultrapure water, pass through a 0.25μm membrane, accurately mark for analysis. The elements Na, Cu, Zn, Fe, Al, Mg, Mn, K, Ca and Ni were determined using an inductively coupled plasma optical emission spectrometry system (ICP-OES) (ThermoI CAP 7000).

[0063] Table 1 Information on chestnut varieties

[0064] serial number variety Origin serial number variety Origin 1 Purpur Tangshan Zunhua 13 Aventurine Pearl Tangshan Qianxi 2 Aventurine Pearl Tangshan Zunhua 14 Castanea chinensis Tangshan Qianxi 3 Tafon Tangshan Zunhua 15 Yan Kui Tangshan Qianxi 4 Abundant Tangshan Zunhua 16 Abundant Tangshan Qianxi 5 Big red Tangshan Zunhua 17 short branches Tangshan Qianxi 6 Yan Li Tangshan Zunhua 18 Yan Li Tangshan Qianxi 7 Zunyu Tangshan Zunhua 19 Big red Tangshan Qianxi 8 Yanshan Red Tangshan Zunhua 20 Yanshan Zaofeng Tangshan Qianxi 9 Short thorn Tangshan Zunhua 21 Yanshan Zaofeng Chengde Kuancheng 10 Yanshan Zaofeng Tangshan Zunhua 22 Old chestnut tree Chengde Kuancheng 11 Zunda Li Tangshan Zunhua 23 Big red Chengde Kuancheng 12 Yanfeng Tangshan Qianxi 24 Big blue crack Chengde Kuancheng

[0065] (2) Based on the quality indicators measured in step (1), hierarchical clustering analysis is performed to screen key indicators. Specifically, the clustering method is average, the distance type is correlation, and the method is distance sum. Based on the correlation, the most representative indicator is automatically selected from the 35 quality indicators. The results are as follows Figure 1 As shown in the figure, different colors represent different categories, and the 35 chestnut quality indicators are grouped into 12 categories. The most representative indicators of each category are fruit shape index, browning index, starch, elasticity, cohesion, energy value, soluble solids, reducing sugar, ascorbic acid, flavonoids, Ca, and Ni, indicating that these 12 quality indicators are the key indicators of chestnut quality.

[0066] (3) The principal component analysis method was used to analyze the key indicators, obtain the main component loads and determine the number of main components. The specific steps are as follows: Analyze the values ​​of 12 key indicators of 24 samples to obtain the data matrix; obtain the principal component analysis rotation component matrix, as shown in Table 2; calculate its eigenvalues ​​and corresponding standard eigenvectors; calculate the main component contribution rate and cumulative contribution rate, as shown in Table 3; calculate the main component loads and determine the number of main components; calculate the component score coefficient matrix, as shown in Table 4. The principle of extracting the number of main components is: the first n main components whose corresponding eigenvalues ​​are greater than 1.

[0067] Table 2 Rotated component matrix of principal component analysis of chestnut comprehensive quality indexes

[0068] PC1 PC2 PC3 PC4 PC5 Fruit shape index 0.198 0.003 0.324 -0.237 -0.616 Browning Index 0.443 -0.141 -0.075 0.485 -0.144 elasticity 0.246 0.790 0.073 0.151 0.038 starch 0.910 0.228 -0.039 -0.026 0.049 Ni -0.234 0.756 -0.096 -0.339 -0.131 Ca -0.140 0.026 -0.081 0.779 0.091 Reducing sugar 0.140 -0.079 0.627 0.526 0.165 Energy value 0.868 -0.113 0.205 -0.036 -0.018 ascorbic acid -0.169 -0.370 0.081 -0.011 0.624 Soluble solids 0.179 -0.413 -0.703 0.086 0.199 Cohesion 0.240 -0.289 0.564 -0.234 0.046 Flavonoids 0.292 0.119 0.092 -0.049 0.699

[0069] Table 3 Chestnut characteristic value, variance percentage, cumulative contribution rate

[0070] Eigenvalue Percentage of variance Cumulative contribution rate% 1 2.703 22.527 22.527 2 2.029 16.911 39.438 3 1.151 12.622 52.060 4 1.329 11.079 63.139 5 1.028 8.564 71.103

[0071] Table 4. Chestnut comprehensive quality evaluation component score coefficient matrix

[0072] PC1 PC2 PC3 PC4 PC5 Fruit shape index 0.077 -0.089 0.188 -0.113 -0.437 Browning Index 0.201 -0.075 -0.111 0.355 -0.197 elasticity 0.096 0.502 0.028 0.153 0.105 starch 0.441 0.138 -0.133 -0.054 0.054 Ni -0.089 0.431 -0.053 -0.18 0.028 Ca -0.097 0.081 -0.034 0.59 -0.023 Reducing sugar -0.033 0.006 0.464 0.373 0.082 Energy value 0.397 -0.081 0.051 -0.073 -0.025 ascorbic acid -0.096 -0.155 0.110 -0.101 0.448 Soluble solids 0.164 -0.228 -0.537 -0.001 0.06 Cohesion 0.063 -0.192 0.394 -0.211 0.053 Flavonoids 0.122 0.151 0.067 -0.127 0.563

[0073] From the above results, it can be seen that 5 principal components are extracted from the 12 key indicators of chestnut, as shown in Table 3. The first principal component contains 22.53% of the chestnut information, the second principal component contains 16.91% of the chestnut information, the third principal component contains 12.62% of the chestnut information, the fourth principal component contains 11.08% of the chestnut information, and the fifth principal component contains 8.56% of the chestnut information. The cumulative contribution rate of the first five principal components reaches 71.10%, which can reflect most of the information of chestnut fruit.

[0074] (4) A chestnut comprehensive quality evaluation model was constructed using the main component factors. Specifically, the main component model was calculated by taking the ratio of the five main components to the eigenvalues ​​(the main component model was the chestnut comprehensive quality evaluation model).

[0075] The weight of the main factor = variance contribution rate / cumulative contribution rate. The ratio of the main component to the eigenvalue is used to calculate the main component model:

[0076] F n =PCn1 xX1+PCn2xX2+PCn3 xX3+...+PCn12 xX 12

[0077] Where PCn represents the eigenvector of the corresponding score matrix; F nrepresents the score of the main components of chestnut; Xn represents the key quality indicators of chestnut: X1 is the standardized index of fruit shape index, X2 is the standardized index of browning index, X3 is the standardized index of elasticity, X4 is the standardized index of starch, X5 is the standardized index of Ni, X6 is the standardized index of Ca, X7 is the standardized index of reducing sugar, X8 is the standardized index of energy value, X9 is the standardized index of ascorbic acid, X 10 is the normalized index of soluble solids, X 11 is the normalized index of cohesion, X 12 It is the standardized index of flavonoids.

[0078] The comprehensive evaluation model of chestnut quality is: Q = F1*W1+F2*W2+F3*W3+F4*W4+F5*W5, where W1~W5 are the weight coefficients of the five main factors divided by principal component analysis, Q represents the comprehensive score of chestnut quality, and F1~F5 represents the scores of the five principal components; that is, Q = 0.317F1+0.238F2+0.178F3+0.156F4+0.120F5,

[0079] F1=0.077X1+0201X2+0.096X3+0.441X4-0.089X5-0.097X6-0.033X7+0.397X8-0.096X9+0.164X 10 -0.063X 11 +0.122X 12 ;

[0080] F2=-0.089X1+-0.075X2+0.502X3+0.138X4-0.431X5-0.081X6-0.006X7-0.081X8-0.155X9-0.228X 10 -0.192X 11 +0.151X 12 ;

[0081] F3=0.188X1-0.111X2+0.028X3-0.133X4-0.053X5-0.034X6+0.464X7+0.051X8+0.11X9-0.537X 10 +0.394X 11 -0.067X 12 ;

[0082] F4=-0.113X1-0.355X2+0.153X3-0.054X4-0.18X5+0.59X6+0.373X7-0.073X8-0.101X9+0.001X 10 -0.211X11 -0.127X 12 ;

[0083] F5=-0.437X1-0.197X2+0.105X3-0.054X4-0.028X5-0.023X6+0.082X7-0.025X8+0.448X9+0.06X 10 +0.053X 11 +0.563X 12 ;

[0084] X1 is the standardized index of fruit shape index, X2 is the standardized index of browning index, X3 is the standardized index of elasticity, X4 is the standardized index of starch, X5 is the standardized index of Ni, X6 is the standardized index of Ca, X7 is the standardized index of reducing sugar, X8 is the standardized index of energy value, X9 is the standardized index of ascorbic acid, X 10 is the normalized index of soluble solids, X 11 is the normalized index of cohesion, X 12 It is the standardized index of semi-flavonoids.

[0085] (5) Ranking the quality of each chestnut based on the comprehensive evaluation model of chestnut quality: The 12 key indicators measured were standardized by SPSS software (select data analysis description and save the indicators as standardized variables), and the standardized indicators were substituted into the comprehensive evaluation model of chestnut quality. The results are shown in Table 5. The results show that the top five varieties are: Yanshan Zaofeng in Kuancheng, Chengde, Dabanhong in Kuancheng, Chengde, Zunhua Zunda Li in Tangshan, Qianxi Shuofeng in Tangshan, and Daqinglie in Kuancheng, Chengde. The comprehensive scores were 0.731, 0.556, 0.460, 0.429, and 0.416, respectively. All scores were tested by KS normality test, with P value of 0.20>0.05, which conforms to normal distribution.

[0086] Table 5 Comprehensive quality scores of chestnuts

[0087] variety <![CDATA[F1]]> <![CDATA[F2]]> <![CDATA[F3]]> <![CDATA[F4]]> <![CDATA[F5]]> Q 1 -0.116 -0.557 0.214 0.013 -0.651 -0.225 2 2.215 -0.323 -0.702 -1.669 -0.967 0.053 3 -1.408 -2.466 -1.718 -0.191 -0.462 -1.409 4 -0.650 0.860 -1.208 1.295 -0.068 0.013 5 0.758 0.899 -0.388 -0.323 0.604 0.413 6 -0.694 0.209 0.022 0.242 -0.776 -0.214 7 -1.047 -0.656 0.156 -0.132 -0.438 -0.521 8 -1.121 0.131 0.315 0.368 0.976 -0.048 9 -0.558 -0.769 0.976 0.325 -1.991 -0.412 10 -0.563 0.018 -0.868 2.531 -0.254 0.048 11 -0.350 2.007 0.007 0.661 -0.327 0.460 12 -0.088 1.980 -0.717 0.158 -0.650 0.285 13 -0.619 -0.416 0.749 -1.536 -0.574 -0.470 14 0.166 -0.174 -0.888 -0.151 -1.073 -0.316 15 0.263 -1.489 -0.716 -0.702 1.263 -0.353 16 1.129 0.854 -0.881 -0.323 0.638 0.430 17 -0.584 -1.593 0.378 -0.690 1.857 -0.356 18 0.604 0.989 -0.105 -0.458 -0.264 0.299 19 0.809 0.448 -0.503 -0.759 1.072 0.292 20 0.041 -0.879 -0.086 -0.150 -0.205 -0.274 21 1.049 0.299 0.437 0.325 1.674 0.731 22 -0.188 0.440 1.206 0.488 -0.018 0.332 23 0.724 -0.330 2.198 0.188 0.216 0.556 24 0.292 0.508 1.214 -0.238 0.260 0.416

[0088] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for comprehensive quality evaluation of chestnuts of different varieties from different origins, characterized in that: The method includes the following steps: determining the indices that affect the comprehensive quality of chestnut samples, using hierarchical cluster analysis to determine the key indices that affect the comprehensive quality of chestnut samples; using principal component analysis to obtain the main component loads and determine the number of main component factors; constructing a chestnut comprehensive quality evaluation model based on the main component factors; and ranking the quality of each variety of chestnut based on the comprehensive quality evaluation model.

2. The method according to claim 1, characterized in that The different origins include Tangshan Zunhua, Tangshan Qianxi and Chengde Kuancheng; the different varieties include purple amber, aventurine pearl, Tafeng, Shuofeng, Dabanhong, Yanli, Zunyu, Yanshanhong, Short Ci, Yanshan Zaofeng, Zunda Li, Yanfeng, Shisheng Li, Yankui, Short Branch, Laoshu Li and Daqingchuang.

3. The method according to claim 1, characterized in that The indicators that affect the comprehensive quality of chestnut samples include transverse diameter, longitudinal diameter, fruit shape index, brightness, red-green value, yellow-blue degree, browning index, hardness, cohesion, chewiness, elasticity, viscosity, moisture, fat, protein, starch, energy value, soluble solids, soluble protein, amylose, reducing sugar, soluble sugar, ascorbic acid, flavonoids, total phenols, Na, Cu, Zn, Fe, Al, Mg, Mn, K, Ca and Ni.

4. The method according to claim 1, characterized in that: When using hierarchical cluster analysis, the clustering mode is average, the distance type is correlation, and the method is distance sum.

5. The method according to claim 1, characterized in that The key indicators include fruit shape index, browning index, starch, elasticity, cohesion, energy value, soluble solids, reducing sugar, ascorbic acid, flavonoids, Ca and Ni.

6. The method according to claim 1, characterized in that When determining the number of principal component factors, the principle for extracting the number of principal component factors is: the first n principal components whose eigenvalues ​​corresponding to the principal components are greater than 1.

7. The method according to claim 1, characterized in that The number of the main component factors is 5.

8. The method according to claim 1, characterized in that The principal component score model is calculated by the ratio of the principal component factor to the eigenvalue; the weight of the principal component factor = variance contribution rate / cumulative contribution rate.

9. The method according to claim 1, characterized in that: The chestnut comprehensive quality evaluation model is Q=0.317F1+0.238F2+0.178F3+0.156F4+0.120F5, where Q represents the comprehensive score of chestnut quality; F1-F5 represent the scores of the five main components respectively: F1=0.077X1+0201X2+0.096X3+0.441X4-0.089X5-0.097X6-0.033X7+0.397X8-0.0 96X9+0.164X 10 -0.063X 11 +0.122X 12 ; F2=-0.089X1+-0.075X2+0.502X3+0.138X4-0.431X5-0.081X6-0.006X7-0.081X8-0. 155X9-0.228X 10 -0.192X 11 +0.151X 12 ; F3=0.188X1-0.111X2+0.028X3-0.133X4-0.053X5-0.034X6+0.464X7+0.051X8+0. 11X9-0.537X 10 +0.394X 11 -0.067X 12 ; F4=-0.113X1-0.355X2+0.153X3-0.054X4-0.18X5+0.59X6+0.373X7-0.073X8-0.10 1X9+0.001X 10 -0.211X 11 -0.127X 12 ; F5=-0.437X1-0.197X2+0.105X3-0.054X4-0.028X5-0.023X6+0.082X7-0.025X8+0. 448X9+0.06X 10 +0.053X 11 +0.563X 12 ; X1 is the standardized index of fruit shape index, X2 is the standardized index of browning index, X3 is the standardized index of elasticity, X4 is the standardized index of starch, X5 is the standardized index of Ni, X6 is the standardized index of Ca, X7 is the standardized index of reducing sugar, X8 is the standardized index of energy value, X9 is the standardized index of ascorbic acid, X 10 is the normalized index of soluble solids, X 11 is the standardized index of cohesion, X 12 It is the standardized index of semi-flavonoids.

10. The method according to claim 9, characterized in that The standardized index is obtained by standardizing the key indicators of the measurement using SPSS software.

Images