Comprehensive evaluation method for nutritional quality of walnuts

Abstract

The invention discloses a comprehensive evaluation method for walnut nutritional quality, belongs to the technical field of nutrition and food evaluation, and aims to find that 13 characters of walnuts have large genetic difference, and the minimum coefficient of variation is 7.773%. Mutual influence among agronomic traits, yield traits and quality traits of the walnuts is checked. 13 characters are classified into four main component influence factors including quality, yield, fatty acid content, nutrition and the like through main component analysis, the accumulative contribution rate reaches 71.407%, and the highest contribution rate of the fatty acid content influence factors is 33.729%. 175 excellent strains can be divided into three types through clustering analysis, and the three types respectively account for 41.14%, 33.14% and 25.72%. According to the method, phenotype and quality data of superior single plants of the added walnuts are compared on the basis of principal component analysis and clustering analysis for the first time, a more accurate comprehensive evaluation method is explored, a scientific method is expected to be provided for classification and screening of germplasm resources of the Tibetan walnuts, and a theoretical basis and technical support are provided for directional breeding of the Tibetan walnuts.

Description

Technical Field

[0001] The invention belongs to the technical field of nutrition and food evaluation, and particularly relates to a comprehensive evaluation method for the nutritional quality of walnuts. Background Art

[0002] Germplasm resources, also called genetic resources or gene resources, refer to the original materials used to cultivate new varieties. With the development of biotechnology breeding, germplasm resources are not only limited to varieties, mutants, wild species, etc., but also more widely include groups, individuals, organs, tissues, cells, gametes, chromosomes or nucleic acid fragments, etc. In recent years, with the deepening of the world's research on crops and the requirements for the protection and sustainable use of crop genetic diversity, the rational and efficient development and utilization of plant germplasm resources has become one of the research hotspots in the biological field.

[0003] Walnut is one of the four major nuts in the world. It has significant economic, ecological and social benefits and is known as the "oil depot on the tree". China is considered to be one of the origins and distribution centers of walnuts, with rich walnut germplasm resources and a long history of cultivation. At present, China's walnut germplasm resources mainly include excellent varieties, selected asexual lines, excellent individual plants, farmer varieties based on seedlings, and special germplasm resources. Among them, there are hundreds of excellent varieties, and the number of other types of resources has also reached hundreds. Phenotypic traits have always been the key mark for the identification of Juglans species. Comprehensive analysis of phenotypic markers of walnuts can more efficiently and accurately identify excellent germplasm.

[0004] According to statistics from the Food and Agriculture Organization of the United Nations, there are 54 countries in the world that produce walnuts, and China ranks first in the world in both walnut planting area and total output. The "China Forestry and Grassland Statistical Yearbook" shows that China's walnut planting area has reached 8.004×106 hectares, and the total output is 5.404×106 tons, both ranking first in the world. The suitable areas for walnut planting are divided into six major regions: the eastern coastal area, the loess hilly area, the Qinba Mountain area, the Yunnan-Guizhou Plateau area, the Xinjiang oasis area and the Tibet area. Each area is further divided into 2-3 sub-regions based on geographical or ecological conditions. With people's in-depth understanding of the functions and uses of walnuts and the strengthening of walnut resource development, the walnut industry is expected to usher in a broader market prospect.

[0005] The evaluation of walnut germplasm resources is mainly carried out using the genetic diversity levels at different levels. Genetic diversity is the sum of genetic variation between populations and individuals within populations covered by the species, including multi-level genetic variation such as phenotype, biochemistry, chromosomes, proteins and base sequences. Phenotype is a combination of various morphological characteristics and is a representation of biological genetic diversity. The distribution status and characteristics of walnut germplasm resources are the premise and important reference for the development and utilization of resources. The phenotypic traits of its nuts are the most important agronomic traits in breeding. The evaluation of phenotypic traits is of great significance to the development and utilization of resources.

[0006] Tibet is one of the origins of walnuts, with a long history of cultivation and rich germplasm resources. Tibet, known as the "Roof of the World", is divided into a unique walnut distribution area, called the Tibet distribution area, due to its unique and discontinuous distribution characteristics. Research on high-quality walnut plants in Tibet began in the 1980s. In recent years, with the growing attention and support of local governments, research on walnut variety breeding has made progress, accelerating the development of the walnut industry in the region.

[0007] Walnuts are widely distributed in Tibet and show remarkable diversity, with some trees more than a thousand years old. Located in the dry and hot valleys of the Yarlung Zangbo River, Nujiang River and Lancang River, Gacha County is 2,000–3,500 meters above sea level and is one of the main walnut producing areas in Tibet. In December 2023, after an on-site audit by the World Record Certification Agency (WRCA), the walnut forest in Gacha County was officially certified as the "World's Largest Millennium Walnut Forest". Research on the nutritional quality and genetic diversity of Gacha walnuts has laid a solid foundation for the sustainable development and industrial utilization of Tibetan walnut resources.

[0008] So far, there have been some studies on the nutritional quality of walnuts, but systematic research on walnut production in areas such as Gacha County, Tibet is still limited. In addition, there is a lack of relevant guidance for the development of the walnut industry in the region. Therefore, it is of great significance to systematically study the nutritional quality of walnuts in the region and understand its formation factors in order to promote the development of the local walnut industry. Through the investigation of walnut distribution in Gacha area and the determination of nutritional quality indicators, the factors affecting the nutritional quality of walnuts were analyzed in detail, aiming to provide a theoretical basis for the targeted development of the walnut industry in the region. Summary of the invention

[0009] The purpose of the invention is to provide a comprehensive evaluation method for the nutritional quality of walnuts in view of the existing problems.

[0010] The present invention is achieved through the following technical solutions:

[0011] A comprehensive evaluation method for walnut nutritional quality, comprising:

[0012] S1. Determine the phenotypic and nutritional traits of walnuts and make statistical descriptions;

[0013] S2. Rank the individual indicators of the phenotypic traits and nutritional traits measured above;

[0014] S3. Based on the above statistical descriptive data, calculate the variation characteristics of the main quality traits and analyze the genetic variation degree of each phenotypic trait and nutritional trait;

[0015] S4. Perform Pearson correlation analysis and cluster analysis on each trait index, classify the walnuts, and determine the correlation between different traits;

[0016] S5. Use principal component analysis and cluster analysis to comprehensively score the trait index data, and establish a quality evaluation system based on the analysis results.

[0017] Furthermore, the walnut is a Tibetan Gacha walnut.

[0018] Furthermore, the phenotypic trait indicators described in step S1 include: mean of three diameters, single fruit weight, kernel weight, kernel yield rate and shell thickness;

[0019] The nutritional properties include: soluble protein content, crude fat content, linoleic acid (LA) content, oleic acid (OA) content, linolenic acid (ALA) content, palmitic acid (PA) content, stearic acid (SA) content, and ω-6:ω-3.

[0020] Furthermore, the main quality traits described in step S3 include: mean of three diameters, single fruit weight, kernel weight, kernel yield, soluble protein content, crude fat content, linoleic acid (LA) content, oleic acid (OA) content, linolenic acid (ALA) content, palmitic acid (PA) content, stearic acid (SA) content, and ω-6:ω-3.

[0021] Furthermore, the cluster analysis method described in step S4 is: cluster analysis of sample evolutionary tree constructed by UPGMA method is used to divide walnuts into three categories of plants, and excellent plants are selected from each category of plants.

[0022] Furthermore, the three types of individual plants are respectively: Type I, Type II, and Type III.

[0023] Furthermore, the principal component analysis method described in step S5 is: using principal component factor analysis, constructing principal component function expressions F1, F2, F3, F4 and comprehensive score y according to the principal component loading matrix after the data of each trait index is standardized;

[0024] F1=0.024x1+0.042x2+0.231x3+0.221x4+0.159x5+0.240x6+0.229x7+0.082x8-0.046x9-0.090x 10 +0.0001x 11 +0.110x 12 -0.017x 13 ;

[0025] F2=-0.006x1+0.099x2-0.042x3-0.039x4+0.014x5-0.055x6-0.041x7+0.020x8-0.376x9+0.431x 10 +0.327x 11 -0.042x 12 -0.032x 13 ;

[0026] F3=0.133x1+0.125x2-0.078x3-0.057x4+0.262x5-0.055x6-0.043x7+0.362x8+0.077x9+0.141x 10 -0.250x 11 -0.549x 12 +0.083x 13 ;

[0027] F4=-0.423x1+0.453x2+0.034x3+0.001x4-0.161x5+0.037x6-0.008x7-0.066x8-0.079x9-0.006x 10 +0.070x 11 -0.098x 12 +0.609x 13 ;

[0028] y=0.4723F1+0.2332F2+0.1660F3+0.1285F4;

[0029] The final score of the fruit quality is calculated based on the weight of y and sorted in descending order to obtain a comprehensive ranking. The larger the y value, the higher the nutritional quality of the walnut.

[0030] Furthermore, the x 1～13 They are soluble protein, crude fat, linoleic acid (LA), oleic acid (OA), linolenic acid (ALA), palmitic acid (PA), stearic acid (SA), (ω-6:ω-3), mean of the three diameters, single fruit weight, kernel weight, kernel yield, and shell thickness, the values ​​of the reverse trait indicators after standardized processing.

[0031] Compared with the prior art, the present invention has the following advantages:

[0032] 1. The genetic differences of 13 traits of the Jiacha walnut of the present invention are large, and the lowest coefficient of variation is 7.773%. The agronomic traits, yield traits, and quality traits of the Jiacha walnut affect each other. The 13 traits are classified into 4 main component influencing factors such as quality, yield, fatty acid content, and nutrition through principal component analysis, and the cumulative contribution rate reaches 71.407%, among which the contribution rate of the fatty acid content influencing factor is the highest at 33.729%. The 4 main component influencing factors are used to replace the 13 traits to comprehensively score the Jiacha walnut strains, and the highest score is JC174, and the lowest score is JC062. 175 excellent strains can be divided into 3 categories by cluster analysis, accounting for 41.14%, 33.14% and 25.72% respectively. The present invention compares the phenotypic and quality data of the excellent individual strains of Jiacha walnut based on principal component analysis and cluster analysis for the first time, aiming to explore a more accurate comprehensive evaluation method, in order to provide a scientific method for the classification and screening of Tibetan walnut germplasm resources, and provide a theoretical basis and technical support for the directional breeding of Tibetan walnuts.

[0033] 2. The present invention conducts a comprehensive investigation on the Jiacha walnut resources through germplasm resource survey, phenotypic traits, biochemical determination and other aspects, reveals the phenotypic characteristics and diversity of walnut germplasm resources in the region, and statistically analyzes the important agronomic traits of the Jiacha germplasm resources in Tibet with a long history and diverse resources, providing a basis for discovering excellent germplasm and its utilization.

[0034] 3. The present invention uses phenotype and nutritional traits to conduct a comprehensive evaluation of Jiacha walnuts, establishes a screening method suitable for Jiacha walnut germplasm, explores the main influencing factors of walnut nut appearance and quality, screens out germplasm with excellent agronomic traits, provides materials for breeding better new varieties, reveals the genetic diversity of Tibetan Jiacha walnut (Juglans regia L.) germplasm resources and the genetic structure of the population, clarifies the quality characteristics of walnut fruits in Jiacha County, Tibet, and screens walnut plants with the best comprehensive quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a picture of 175 walnut fruits;

[0036] Figure 2 is the standard curve of nucleolar protein content;

[0037] Figure 3 is the GC-MS spectrum of walnut oil;

[0038] Figure 4 The shell thickness data of each individual fruit after reverse processing and the distribution of ω6:ω3 data after central processing, as well as the fatty acid composition ratio diagram of the Jiacha walnut kernel;

[0039] Figure 5This paper conducts correlation analysis among the quality indicators of Jiacha walnuts, conducts comprehensive evaluation scores and rankings, and performs cluster analysis of individual Jiacha walnut plants based on quality indicators. DETAILED DESCRIPTION

[0040] In order to further explain the present invention, it is described below in conjunction with the following specific embodiments.

[0041] Example 1

[0042] Statistical description of trait indicators

[0043] Through field visits and reviewing documents from relevant units, it was found that walnuts in Jiacha County are basically scattered in the area between 3,200 meters and 3,400 meters above sea level on both sides of the Yarlung Zangbo River. According to the terrain, 175 walnut trees with a breast diameter greater than 1.5 meters were selected through drone search.

[0044] Each walnut with green skin was placed in a cool place to dry naturally for 7 days. After the green skin fell off, the green skin was manually removed and cleaned. Then, it was placed in an oven for 48 hours and dried to a moisture content of 8% or less. The walnuts were numbered JC001 to JC175 ( Figure 1 ), collecting 30 nuts from each walnut tree.

[0045] 1. Measurement of Phenotypic Indicators

[0046] The longitudinal diameter, transverse diameter, lateral diameter and shell thickness of the nuts were measured with a vernier caliper with an accuracy of 0.01 mm, and the single fruit weight and kernel mass were determined with an electronic balance with an accuracy of 1 mg. The mean of the three diameters was calculated using formula (1), and the walnut kernel yield was calculated using formula (2). The surface characteristics of the kernel and shell, the difficulty of kernel extraction, and the fullness of the kernel were measured and assigned values ​​according to the walnut nut quality grades of GB / T20398-2021 and T / CSF003-2021 standards.

[0047] (1) Average of three diameters = (longitudinal diameter + transverse diameter + lateral diameter) / 3

[0048] (2) Kernel yield = kernel weight / single fruit weight × 100%

[0049] According to the statistics of fruit traits of individual walnut plants in Jiacha County (Table 1), the maximum values ​​of the fruits of the samples sampled in this study were the average of the three diameters of 46.374 mm (JC174), single fruit weight of 20.645 g (JC174), kernel weight of 10.105 g (JC174), kernel rate of 59.5% (JC062) and shell thickness of 2.652 mm (JC055). Meanwhile, the minimum values ​​of the above indicators were the average of the three diameters of 27.204 mm (JC073), single fruit weight of 6.251 g (JC073), kernel weight of 2.105 g (JC073), kernel rate of 27.9% (JC079) and shell thickness of 1.202 mm (JC107).

[0050] Table 1 Statistical description of the fruit traits of individual walnut trees in Jiacha County

[0051]

[0052]

[0053] Note: The effective number of samples is 175, that is, the maximum value, mean value, standard deviation and other indicators of the three diameters of the 175 sampled walnut trees are statistically described.

[0054] 2. Preparation of Plant Extracts

[0055] 2.1 Determination of protein content

[0056] The protein content was determined using a standard curve prepared using the Coomassie Brilliant Blue method.

[0057] Weigh 0.6 g of nucleolus into a mortar, add 4 mL of distilled water and grind into a homogenate, then wash the mortar with 2 mL of distilled water for two times and transfer them together into a 10 mL centrifuge tube, centrifuge at 12000 r / min for 15 min, discard the precipitate, and the supernatant is the sample extract to be tested.

[0058] Pipette 0.2 mL of sample extract, add 0.8 mL of distilled water and 5 mL of Coomassie Brilliant Blue G-250 reagent respectively, mix thoroughly, let stand for 2 minutes, use No. 0 blank tube as control, use 1 cm colorimetric cup to compare at 595 nm, record the absorbance value, and use the standard curve to check the protein content (μg) in the colorimetric sample extract, repeat 3 times.

[0059] Weigh 0.6g of nucleolus, add 4mL of distilled water to grind, then wash with 2mL of distilled water, transfer together to a 10mL centrifuge tube, and centrifuge at 12000r / min for 15min. Take 0.2mL of supernatant, add 0.8mL of distilled water and 5mL of Coomassie Brilliant Blue G-250 respectively, and mix thoroughly. After standing for 2min, use a microplate reader (Hidex Sense) to check the protein content through the standard curve at 595nm. Repeat 3 times.

[0060] Sample protein content (mg / g fresh weight) = C·V T / V·m·1000; C: protein content of each tube obtained by checking the standard curve (μg); V T : total volume of the extract (mL); V: volume of the extract taken for measurement (mL); m: sampling amount (g).

[0061] 2.2 Determination of the content of main fatty acids

[0062] Weigh 3 g of crushed walnut kernel sample, dry for 4 h, add 90 mL of petroleum ether (boiling range 30°C to 60°C), extract at 60°C for 5 h, recover the petroleum ether, place the round-bottom flask in a 70°C water bath for 5 min, and collect the walnut oil at the bottom.

[0063] Weigh 30 mg of walnut oil sample into a 10 mL centrifuge tube, add 2 mL of hexane to completely dissolve the oil sample. Then add 200 μL of 2 mol / L KOH methanol solution and shake rapidly for 30 seconds for methyl esterification. After standing at room temperature until clear, add about 1 g of anhydrous sodium bisulfate to the solution, shake vigorously, then filter the supernatant with a 22 mm filter membrane, and inject about 1 mL into the sample bottle.

[0064] Thermo Trace 1300, TG-5MS GC column, inlet temperature 250 ° C, carrier gas is high purity helium (purity> 99.999%), carrier gas flow rate is 1.0mL / min, split ratio is 50:1, injection volume is 1μL. The heating program is as follows: the initial temperature is 50 ° C, maintained for 1 minute, then increased to 200 ° C at a rate of 25 ° C / min, maintained for 4 minutes, increased to 230 ° C at a rate of 3 ° C / min, maintained for 5 minutes, and the total time is 26 minutes. The relative content of fatty acid components was quantified by normalization method.

[0065] The standard products of methyl oleate, methyl linoleate, methyl α-linolenate, methyl palmitate and methyl stearate were prepared into master batches with n-hexane at a certain concentration, and then diluted according to a certain concentration gradient. After standing, they were filtered through a 22 mm filter membrane and injected into a vial of about 1 mL. The GC-MS spectrum is shown in FIG. Figure 3 As shown, the area X measured by GC-MS and the known concentration Y of the standard sample generate a standard curve as shown in Table 2.

[0066] Table 2 GC-MS analysis shows the parameters of methyl oleate, methyl linoleate, methyl α-linolenate, methyl palmitate, and methyl stearate

[0067]

[0068] 2.3 Statistical analysis

[0069] First, the measured soluble protein content, crude fat content, mean of three diameters, single fruit weight, kernel weight, kernel yield, shell thickness and fatty acid composition were averaged. Then the shell thickness was reversed ( Figure 4 A), the value of ω-6:ω-3 (linolenic acid:linoleic acid) was centered with 4 as the standard ( Figure 4 B) Surface characteristics, kernel removal, fruit fullness and other indicators were assigned values ​​of 1-4 according to T / CSF003-2021. All phenotypic indicators were standardized.

[0070] Figure 4 A: Reverse processing; B: Centralized processing.

[0071] All experimental data were preliminarily sorted out using Microsoft Excel 2016. The phenotypic indices were calculated as the average of the values ​​measured for 30 samples, and the nutritional quality indices were calculated as the average of three repetitions. Data statistical description, principal component analysis, correlation analysis, and systematic cluster analysis were performed, and graphics were drawn using Office Visio 2021 and Origin 2022.

[0072] Through the data analysis of the nutrient content of single walnut plants in Jiacha County (Table 3), the minimum values ​​of soluble protein content / %, crude fat content / %, linoleic acid (LA) content / %, oleic acid (OA) content / %, linolenic acid (ALA) content / %, palmitic acid (PA) content / %, stearic acid (SA) content / %, ω-6:ω-3 in the 175 samples obtained this time were 2.587% (JC134), 25.741% (JC062), 5.551% (JC083), 0.450% (JC013), 1.695% (JC020), 0.018% (JC081), 0.010% (JC089) and 0.902 (JC056), respectively. At the same time, the maximum values ​​of the above indicators were 21.638% (JC055), 73.074% (JC062), 5.551% (JC083), 0.450% (JC013), 1.695% (JC020), 0.018% (JC081), 0.010% (JC089) and 0.902 (JC056), respectively.

[0073] (JC122), 69.622% (JC008), 19.517% (JC138), 24.589% (JC059), 2.500%

[0074] (JC131), 3.960% (JC172) and 25.968% (JC020).

[0075] Table 3 Statistical description of the nutrient content of walnut plants in Jiacha County

[0076]

[0077] Note: The effective number of samples is 175, that is, the maximum value, mean value, standard deviation and other indicators of the three diameters of the 175 sampled walnut trees are statistically described.

[0078] Example 2

[0079] Ranking of individual indicators of Jiacha walnut

[0080] The average values ​​of the three diameters of the walnut fruit JC174 obtained in the present invention are significantly higher than those of other walnut plants. The plant with the highest single fruit weight and fruit kernel weight is also JC174. The plant with the highest fruit kernel yield rate among all the plants is

[0081] JC062, followed by JC088, both of which had kernel yield rates above 55%. Among the individual plants, the plants with the lowest fruit shell thickness were JC107 and JC131, followed by JC145 (Table 4).

[0082] Among the walnut plants selected in this study, JC055 had the highest soluble protein content, followed by JC138.

[0083] and JC164, the crude fat content was highest in JC122, while the linoleic acid content, oleic acid content, linolenic acid content,

[0084] The rankings of palmitic acid content and stearic acid content are different. The above indicators are ranked by JC008,

[0085] JC138, JC059, JC131, and JC172 ranked highest (Table 5).

[0086] For ω-6:ω-3, this study centralized it, that is, the default value was 4 as the best. The closer the sample ω-6:ω-3 value was to 4, the higher the ranking. The results showed that JC146 ranked the highest, better than other individual strains.

[0087] Table 4 The top five phenotypic trait indexes of the Jiacha walnut strains

[0088]

[0089]

[0090] Note: Values ​​are expressed as mean ± standard error. Different letters represent significant differences at the 0.05 level. The corresponding individual plant names are in brackets below the values ​​of each trait index in the table.

[0091] Table 5 The top five single plants in terms of nutritional content of each nutrient index of Jiacha walnut

[0092]

[0093] Note: Values ​​are expressed as mean ± standard error. Different letters represent significant differences at the 0.05 level. The corresponding individual plant names are in brackets below the values ​​of each trait index in the table.

[0094] Example 3

[0095] Analysis on genetic variation of main genetic traits in walnut

[0096] The genetic variation of the main traits of Jiacha walnut was analyzed. The average three diameters of 175 Jiacha walnut resources were

[0097] 33.143mm, the range is 27.204~46.374, the coefficient of variation is 7.773%; the average single fruit weight is 12.153g,

[0098] The range was 6.251-20.645, with a coefficient of variation of 18.234%; the average kernel weight was 5.276g, with a range of

[0099] 2.105~10.105%, the coefficient of variation is 21.803%; the average kernel rate is 43.551%, the range is

[0100] 27.939%~59.522%, the coefficient of variation was 14.831%; the average content of soluble protein was 10.587%,

[0101] The range is 2.587% to 21.638%, and the coefficient of variation is 28.703%. The average crude fat content is 54.780%.

[0102] The range was 25.741% to 73.074%, and the coefficient of variation was 12.984% (Table 6).

[0103] Table 6 Variation characteristics of main quality traits of 175 Jiacha walnut resources

[0104]

[0105]

[0106] Note: The effective number of samples is 175.

[0107] The fatty acids in walnuts are composed of oleic acid, linoleic acid, linolenic acid, palmitic acid, stearic acid and other fatty acids ( Figure 4 C), of which the average content of oleic acid, linoleic acid and linolenic acid accounts for 67.52% of the total fatty acids in Jiacha walnuts, and is the main unsaturated fatty acid ( Figure 4 D). The average content of linoleic acid was 48.837%, with a range of 5.551% to 69.622%, the average content of oleic acid was 8.738%, with a range of 0.450% to 19.517%, the average content of linolenic acid was 9.942%, with a range of 1.695% to 24.589%, and the coefficients of variation were 28.294%, 48.875% and 50.203% respectively. The average content of palmitic acid in saturated fatty acids was 1.204%, with a range of 0.018% to 2.500%, and the average content of stearic acid was 1.292%, with a range of 0.010% to 3.960%, and the coefficients of variation were 40.490% and 55.356% respectively. The average value of ω-6:ω-3 was 6.150, with a range of 0.902 to 25.968, and the coefficient of variation was 59.426% (Table 6).

[0108] The mean of the three diameters, kernel yield and crude fat are relatively stable in different Jiacha walnut germplasm resources, while single fruit weight, kernel weight, soluble protein and linoleic acid have relatively large variations, and oleic acid, linolenic acid, palmitic acid, stearic acid and ω-6:ω-3 have greater variations. Table 6 calculates the coefficient of variation of 12 indicators of the main quality traits of 175 Jiacha walnuts, from large to small: ω-6:ω-3>stearic acid>linolenic acid>oleic acid>palmitic acid>soluble protein>linoleic acid>kernel weight>single fruit weight>kernel yield>crude fat>mean of the three diameters. Among them, the coefficient of variation of the fatty acid content of the tested Jiacha walnuts is almost greater than 30%, indicating that the genetic variation of fatty acids in the tested Jiacha walnuts is very rich.

[0109] Example 4

[0110] Correlation analysis of various traits of Jiacha walnut

[0111] Pearson correlation analysis was performed on the various trait indicators sampled in the present invention. The soluble protein content was extremely significantly positively correlated with the linolenic acid content, and the crude fat content was extremely significantly positively correlated with linoleic acid, oleic acid, palmitic acid, stearic acid, and the mean of the three diameters; it was significantly positively correlated with linolenic acid, ω6:ω3, and shell thickness; it was significantly negatively correlated with shell surface characteristics, and linoleic acid was extremely significantly positively correlated with oleic acid, linolenic acid, palmitic acid, stearic acid, ω6:ω3, the mean of the three diameters, and kernel weight; it was significantly positively correlated with kernel yield, and oleic acid was extremely significantly positively correlated with linolenic acid, palmitic acid, stearic acid, ω6:ω3, the mean of the three diameters, kernel weight, and kernel yield, and linolenic acid was extremely significantly positively correlated with palmitic acid, stearic acid, ω6:ω3 positive correlation; significantly positively correlated with the mean of the three diameters and single fruit weight, palmitic acid was extremely significantly positively correlated with stearic acid, ω6:ω3, the mean of the three diameters, and kernel weight; significantly positively correlated with the kernel yield rate, stearic acid was extremely significantly positively correlated with ω6:ω3, the mean of the three diameters, and kernel weight; significantly positively correlated with the kernel yield rate, ω6:ω3 was significantly negatively correlated with the kernel yield rate, and the mean of the three diameters was extremely significantly positively correlated with single fruit weight and kernel weight; significantly negatively correlated with the ease of kernel extraction and walnut fullness, single fruit weight was extremely significantly positively correlated with kernel weight; extremely significantly negatively correlated with walnut fullness, kernel weight was extremely significantly positively correlated with kernel yield rate; extremely significantly negatively correlated with walnut fullness, the ease of kernel extraction was significantly positively correlated with walnut fullness ( Figure 5 A).

[0112] Figure 5 SP is the soluble protein content, EE is the crude fat content, LA is the linoleic acid content, OA is the oleic acid content, ALA is the linolenic acid content, PA is the palmitic acid content, SA is the stearic acid content, W is the ratio of linoleic acid to linoleic acid, TDMV is the mean of the three diameters, SW is the single fruit weight, NW is the kernel weight, KW is the kernel yield, KP is the shell thickness, ST is the surface characteristics of the kernel and shell, FD is the difficulty of removing the kernel, and SF is the fullness of the walnut.

[0113] From the above, we can see that there is a relatively significant correlation between the fruit phenotypic traits, fruit quality traits and nutrient content of walnut samples in Jiacha County. There is a certain correlation between the phenotypic traits and the fat and its component content and protein content in the quality traits. There is a negative correlation between the protein content and the fat and its component content in the quality traits.

[0114] Example 5

[0115] Construction of a comprehensive evaluation system for Jiacha walnuts based on principal component analysis

[0116] The quality formation of Jiacha walnuts is relatively complex, and the quality indicators of each individual plant vary, making it difficult to judge the quality intuitively. Therefore, the use of principal component factor analysis to comprehensively score the fruit traits and quality indicators of each individual plant can comprehensively and systematically evaluate the plant quality.

[0117] Table 7 KMO and Bartley sphericity test

[0118]

[0119] Table 8 Contribution rate of principal component variance of each quality index

[0120]

[0121]

[0122] Note: The extraction method is principal component analysis.

[0123] As can be seen from Table 8, the first principal component: the eigenvalue is 4.358, the contribution rate is 33.729%, the palmitic acid, stearic acid, linoleic acid, linolenic acid, oleic acid content, etc. account for a high proportion, which is related to the fatty acid content of the fruit, and is called the fatty acid influencing factor. In the correlation analysis, this type of component is significantly positively correlated with the crude fat content and each other. In actual production, the value of this component should be as large as possible within a certain range. The second principal component: the eigenvalue is 2.165, the contribution rate is 16.654%, the single fruit weight, the mean of the three diameters, and the kernel weight account for a high proportion, which is called the yield influencing factor; high yield can improve market competitiveness and market share, so the higher the value of this principal component, the better. The third principal component: the eigenvalue is 1.541, the contribution rate is 11.853%, the kernel rate, ω-6:ω-3 account for a high proportion, which can be called the nutritional regulation influencing factor. The fourth principal component: the eigenvalue is 1.139, the contribution rate is 9.173%, the shell thickness, crude fat content, and protein content account for a high proportion, and the protein content is negatively correlated with the shell thickness and crude fat content, which can be called the quality influencing factor. The principal component F was constructed based on the principal component loading matrix (Table 10) after the trait index data were standardized. 1-4 The function expression is as follows:

[0124] F1=0.024x1+0.042x2+0.231x3+0.221x4+0.159x5+0.240x6+0.229x7+0.082x8-0.046x9-0.090x 10 +0.0001x 11 +0.110x 12 -0.017x 13 ;

[0125] F2=-0.006x1+0.099x2-0.042x3-0.039x4+0.014x5-0.055x6-0.041x7+0.020x8-0.376x9+0.431x 10 +0.327x 11 -0.042x 12 -0.032x 13 ;

[0126] F3=0.133x1+0.125x2-0.078x3-0.057x4+0.262x5-0.055x6-0.043x7+0.362x8+0.077x9+0.141x 10 -0.250x 11 -0.549x 12 +0.083x 13 ;

[0127] F4=-0.423x1+0.453x2+0.034x3+0.001x4-0.161x5+0.037x6-0.008x7-0.066x8-0.079x9-0.006x 10 +0.070x 11 -0.098x 12 +0.609x 13 ; where x 1-13 is the score coefficient of each component.

[0128] Table 9 Rotated component matrix of trait index data standardization

[0129]

[0130]

[0131] Note: The extraction method is principal component analysis. The rotation method is Kaiser normalization varimax method. aThe rotation converged after 5 iterations. Z: Standardization.

[0132] The components after rotation were classified into four categories, among which the contents of palmitic acid, linoleic acid, stearic acid, oleic acid and linolenic acid were classified into one category, the single fruit weight, the mean of the three diameters and the kernel weight were classified into one category, the kernel yield and ω6:ω3 were classified into one category, and the shell thickness, crude fat content and soluble protein content were classified into one category (Table 9).

[0133] Table 10 Principal component loading matrix after trait index data standardization

[0134]

[0135] Note: Extraction method is principal component analysis. Kaiser normalization and varimax rotation. Z: standardization.

[0136] Table 11 Comprehensive evaluation scores and rankings of fruit quality of each individual plant

[0137]

[0138]

[0139] Note: FAC 1-4 is the score of each component.

[0140] The comprehensive score of each strain was calculated using the following formula: y = (variance contribution of principal component 1) / (cumulative variance contribution of 4 principal components) × FAC 1 + (variance contribution of principal component 2) / (cumulative variance contribution of 4 principal components) × FAC 2 + (variance contribution of principal component 3) / (cumulative variance contribution of 4 principal components) × FAC 3 + (variance contribution of principal component 4) / (cumulative variance contribution of 4 principal components) × FAC 4. Thus, the final score of fruit quality was calculated according to the weighted calculation of y = 0.4723F1 + 0.2332F2 + 0.1660F3 + 0.1285F4 and arranged in descending order to obtain a comprehensive ranking. The top 10 strains were plotted for comparison (Tables 2-11, Figure 5 B). Among the 175 walnut samples from individual plants selected in this study, JC174(1), JC170(2) and JC126(3) ranked the top three, followed by JC131(4), JC162(5), JC151(6), JC052(7), JC172(8), JC050(9) and JC175(10).

[0141] Example 6

[0142] Cluster analysis of Jiacha walnut

[0143] The results of cluster analysis of the sample evolutionary tree constructed using the UPGMA method showed that ( Figure 5C), Jiacha walnut can be divided into 3 types, including JC090, JC082, JC056, JC099, JC095, JC093, JC089, JC083, JC152, JC076, JC081, JC079, JC080, JC084, JC077, JC149, JC147, JC146, JC086, JC159, JC041, JC033, JC030, JC034, JC027, JC023, JC022, JC039, JC114, JC078, JC008, JC009 C002, JC021, JC017, JC087, JC065, JC153, JC150, JC144, JC170, JC148, JC105, JC071, JC047, JC013, JC145, JC010, JC058, JC014, JC035, JC024, JC040, JC038, JC009, JC104, JC026, JC031, and JC018 belong to the same category of individual plants (Class I); JC122, JC029, JC073, JC042, JC103, and JC07 4. JC116, JC121, JC064, JC106, JC100, JC102, JC166, JC135, JC158, JC117, JC057, JC070, JC067, JC136, JC155, JC154, JC01 1. JC142, JC141, JC066, JC028, JC052, JC050, JC132, JC075, JC171, JC051, JC164, JC162, JC172, JC175, JC131, JC036, JC167 , JC088, JC125, JC124, JC085, JC054, JC168, JC127, JC110, JC044, JC015, JC113, JC108, JC137, JC130, JC156, JC140, JC133, JC123, JC169, JC139, JC163, JC098, JC138, JC094, JC161, JC118, JC046, JC143, JC053, JC097, JC165, and JC091 were classified as the second category of individual plants (category II);JC151, JC129, JC012, JC006, JC126, JC111, JC019, JC001, JC160, JC043, JC025, JC00 4. JC032, JC005, JC072, JC059, JC112, JC020, JC016, JC092, JC045, JC134, JC128, JC0 48, JC069, JC055, JC157, JC096, JC049, JC120, JC061, JC174, JC173, JC037, JC003, JC007, JC063, JC060, JC068, JC062, JC109, JC101, JC119, JC115, JC107, etc. are classified as the third type of individual plants (Type III). The quality characteristics of these three types of walnut individual plants are quite different. ;

[0144] The 10 excellent plants in the comprehensive evaluation are JC170 in Class I; JC126, JC131, JC162, JC052, JC172, JC050, and JC175 in Class II; and JC174 and JC151 in Class III. Figure 5 C).

[0145] In summary, the coefficient of variation among the individual walnut plants in Jiacha County is large. The plant ranked first in the mean of the three diameters, single fruit weight, and kernel weight of the walnut fruit selected in the present invention is JC174. The rankings of other indicators are different. The ω-6:ω-3 value of JC146 ranks the highest, which is better than other plants. According to the weighted calculation of the principal component analysis, the top three fruit quality scores are: JC174 (1), JC170 (2), and JC126 (3).

[0146] Through correlation and cluster analysis, it was found that Jiacha walnuts can be roughly divided into three categories, namely nutritional fruits, fresh-eating fruits and balanced fruits. Among the 10 excellent individual plants evaluated comprehensively, JC126, JC131, JC162, JC052, JC172, JC050 and JC175 can be used as breeding materials for oil extraction; JC174 and JC151 can be used as breeding materials for fresh-eating, and JC170 has the excellent properties of both and is the best choice for high-grafting replacement.

[0147] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A comprehensive evaluation method for the nutritional quality of walnuts, characterized in that: include: S1. Determine the phenotypic and nutritional traits of walnuts and make statistical descriptions; S2. Rank the individual indicators of the phenotypic traits and nutritional traits measured above; S3. Based on the above statistical descriptive data, calculate the variation characteristics of the main quality traits and analyze the genetic variation degree of each phenotypic trait and nutritional trait; S4. Perform Pearson correlation analysis and cluster analysis on each trait index, classify the walnuts, and determine the correlation between different traits; S5. Use principal component analysis and cluster analysis to comprehensively score the trait index data, and establish a quality evaluation system based on the analysis results.

2. A comprehensive evaluation method for the nutritional quality of walnuts according to claim 1, characterized in that: The walnut is a Tibetan walnut.

3. A comprehensive evaluation method for walnut nutritional quality according to claim 1, characterized in that: The phenotypic trait indicators described in step S1 include: mean of three diameters, single fruit weight, kernel weight, kernel yield rate and shell thickness; The nutritional properties include: soluble protein content, crude fat content, linoleic acid content, oleic acid content, linolenic acid content, palmitic acid content, stearic acid content, and ω-6:ω-3.

4. A comprehensive evaluation method for walnut nutritional quality according to claim 1, characterized in that: The main quality traits described in step S3 include: mean of three diameters, single fruit weight, kernel weight, kernel yield, soluble protein content, crude fat content, linoleic acid content, oleic acid content, linolenic acid content, palmitic acid content, stearic acid content, and ω-6:ω-3.

5. A comprehensive evaluation method for walnut nutritional quality according to claim 1, characterized in that: The cluster analysis method described in step S4 is: cluster analysis of sample evolution tree constructed by UPGMA method, dividing walnuts into three categories of plants, and selecting excellent plants from each category of plants.

6. A comprehensive evaluation method for walnut nutritional quality according to claim 5, characterized in that: The three types of individual plants are: Type I, Type II, and Type III.

7. A comprehensive evaluation method for walnut nutritional quality according to claim 1, characterized in that: The principal component analysis method described in step S5 is: using principal component factor analysis, constructing principal component function expressions F1, F2, F3, F4 and comprehensive score y according to the principal component loading matrix after the data of each trait index is standardized; F1=0.024x1+0.042x2+0.231x3+0.221x4+0.159x5+0.240x6+0.229x7+0.082 x8-0.046x9-0.090x 10 +0.0001x 11 +0.110x 12 -0.017x 13 ; F2=-0.006x1+0.099x2-0.042x3-0.039x4+0.014x5-0.055x6-0.041x7+0.020 x8-0.376x9+0.431x 10 +0.327x 11 -0.042x 12 -0.032x 13 ; F3=0.133x1+0.125x2-0.078x3-0.057x4+0.262x5-0.055x6-0.043x7+0.362 x8+0.077x9+0.141x 10 -0.250x 11 -0.549x 12 +0.083x 13 ; F4=-0.423x1+0.453x2+0.034x3+0.001x4-0.161x5+0.037x6-0.008x7-0.066 x8-0.079x9-0.006x 10 +0.070x 11 -0.098x 12 +0.609x 13 ; y=0.4723F1+0.2332F2+0.1660F3+0.1285F4; The final score of the fruit quality is calculated based on the weight of y and sorted in descending order to obtain a comprehensive ranking. The larger the y value, the higher the nutritional quality of the walnut.

8. A comprehensive evaluation method for walnut nutritional quality according to claim 7, characterized in that: The x 1～13 They are the values ​​of the reverse trait indicators after standardized processing, namely soluble protein, crude fat, linoleic acid, oleic acid, linolenic acid, palmitic acid, stearic acid, ω-6:ω-3, mean of the three diameters, single fruit weight, kernel weight, kernel yield, and shell thickness.

Images