Multi-dimensional detection method and system for hardness of oat grains

Abstract

The invention discloses a method and a system for detecting hardness of oat grains, and belongs to the technical field of agricultural detection. A multi-dimensional hardness grading system is established by integrating a physical detection module (SKCS single grain determination method), a gene detection module and a physical performance detection module (calcium chloride solvent retention capacity SRC value). Through K-means clustering algorithm processing, the hardness characteristic characterization of the SKCS value, the Vin1 gene expression quantity and the calcium chloride SRC index on the grains is synthesized, the grading standards of the soft, intermediate and hard oat grains are determined, and the problem that existing oat grain hardness detection lacks a unified quantitative standard is solved. The method is suitable for oat breeding screening and processing quality prediction, and a scientific basis is provided for standardized detection.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of agricultural detection, and particularly relates to a multi-dimensional detection method and system for oat kernel hardness. BACKGROUND

[0002] Oat is an important cereal crop and has a wide range of applications in the fields of food, feed and the like. Oat kernel hardness is a core physical index affecting its processing adaptability, and has a direct and important influence on key indexes such as oat crushing efficiency and product quality. In the oat processing industry, accurate detection of oat kernel hardness is of great significance for optimizing processing technology, improving product quality and guiding oat breeding. However, the current detection technology for oat kernel hardness faces many bottlenecks, which seriously restricts the further development of the oat industry. The existing oat kernel hardness detection methods mainly rely on physical detection, such as the common texture analyzer method and the HI grinding method. The existing physical detection methods, the texture analyzer method and the HI grinding method, measure the mechanical response of the oat kernel by simulating human chewing or mechanical compression to evaluate the hardness; the HI grinding method indirectly reflects the hardness based on the crushing of the oat kernel during the grinding process. These physical detection methods highly depend on the setting of equipment parameters and the experience of operators. Due to small differences in equipment parameters and different personal operation habits, the detection results often lack horizontal comparability. This makes it difficult to effectively compare and analyze the oat hardness data obtained in different research or production scenarios, bringing great difficulties to the evaluation of oat quality and the optimization of processing technology. From the molecular level, oat hardness is regulated by related genes such as Vin genes. However, the expression characteristics of oat hardness-related genes are not effectively associated with oat kernel crushing, tabletting and other processing characteristics, making it difficult to effectively screen oat varieties with specific hardness such as soft and hard oats at the breeding stage, and making it difficult to improve oat varieties in a targeted manner. In the cultivation of new oat varieties with ideal hardness characteristics, there is a lack of effective molecular markers and theoretical guidance, which cannot meet the market demand for high-quality oat varieties. In the oat industry, due to the lack of clear hardness classification standards, there is a lack of scientific and unified basis in the process of oat variety screening and processing quality prediction. Different enterprises or research institutions may make judgments according to their own standards, resulting in differences in screening and prediction results, low efficiency. This not only increases the production cost, but also affects the quality control and market circulation of oat products, restricting the overall development of the oat industry.

[0003] In summary, the existing oat kernel hardness detection technology has many deficiencies in method, molecular mechanism and standardization system, etc., and it is difficult to meet the demand of oat industry for accurate and comprehensive detection. Therefore, it has important practical significance and application value to develop a systematic oat kernel hardness detection method and system. SUMMARY

[0004] The common oat kernel hardness detection methods are texture analyzer method and HI grinding method. The above methods are affected by human factors and instrument accuracy, and the results lack comparability and fragmentation. At the molecular level, there is also a lack of gene analysis related to oat kernel hardness. In the oat kernel variety screening and processing quality prediction, due to the lack of unified hardness grading standard, it is difficult to realize accurate detection and standardized grading. The purpose of the present application is to provide a systematic oat kernel hardness detection method and system, which realizes the accurate quantification of physical strength, the expression analysis of hardness related genes, the correlation analysis of chemical components and hardness, and the construction of standardized hardness grading system through multi-method comparison and technology fusion.

[0005] In order to achieve the above purpose, the following technical solutions are adopted in the present application: The present application provides a multi-dimensional detection method for oat kernel hardness, comprising: Step 1, determining the Single Kernel Characterization System (SKCS) value of the oat kernel to be tested as the physical detection index of oat kernel hardness, and taking the population average hardness value as the detection result; Step 2, detecting the relative expression amount of Vin1 gene of the oat kernel to be tested after germination as the gene expression analysis index; Step 3, respectively determining the solvent retention capacity value of the oat flour to be tested as the oat solvent retention capacity detection index; Step 4, based on the physical detection index of oat kernel hardness, the gene expression analysis index and the oat solvent retention capacity detection index, establishing the oat hardness grading standard through the K-means clustering algorithm model, and realizing the accurate grading of oat kernel hardness.

[0006] In step 1, the Single Kernel Characterization System (SKCS) value of the oat kernel to be tested is determined by using a single kernel grain characterization instrument, and the negative value is converted to a positive value interval of 15-30 by a reference offset correction.

[0007] Preferably, the reference offset correction formula is: .

[0008] Preferably, the population average hardness value is the arithmetic mean value of the corrected SKCS values of all single kernels of the same variety.

[0009] In step 2, the relative expression of the Vin gene is detected by qPCR, and the primer sequences are shown in SEQ ID NO. 1~SEQ ID NO. 2.

[0010] Preferably, the environmental conditions for oat kernel germination are 20℃, 95% relative humidity, and 72h lightless germination.

[0011] In step 3, the solvent used to calculate the solvent retention capacity is calcium chloride solution.

[0012] In step 3, the formula for calculating the solvent retention capacity is: SRC(%)=(precipitate weight sample weight)×sample moisture content / sample weight×(1 sample moisture content)×100.

[0013] In step 4, the hardness classification standards for oat kernels are as follows: soft: SKCS value <20.41, Vin1 gene expression ≥1.21, calcium chloride SRC ≤90.13%; intermediate: SKCS value 20.41~26.21, Vin1 gene expression 0.46~1.21, calcium chloride SRC 90.13%~110.97%; hard: SKCS value ≥26.21, Vin1 gene expression ≤0.46, calcium chloride SRC ≥110.97%.

[0014] The above multi-dimensional detection method for oat kernel hardness also includes a verification step of the detection results.

[0015] Preferably, oat kernel samples with known hardness are selected for detection, and the detection results are compared with the known hardness to evaluate the accuracy and reliability of the detection method.

[0016] Further preferably, in the verification step, the known hardness oat kernel samples include three types: soft, intermediate, and hard, and the number of samples of each type is not less than 30.

[0017] A system for implementing the above multi-dimensional detection method for oat kernel hardness includes: A physical detection unit equipped with an SKCS single kernel characteristics tester for measuring the SKCS value of the oat kernels to be tested, and having a data correction function, and finally outputting the group average hardness value as the physical detection result; The gene detection unit comprises a germination culture module, an RNA extraction module, a qPCR amplification module and a data analysis module; the germination culture module provides suitable environmental conditions for oat seed germination; the RNA extraction module extracts RNA from the germinated oat; the qPCR amplification module amplifies the Vin1 gene by using specific primer sequences; and the data analysis module calculates the relative expression of the Vin gene; The chemical detection unit is used for grinding the to-be-detected oat seeds into powder and measuring the solvent retention capacity; The data analysis and grading unit uses a K-means clustering algorithm model to establish an oat hardness grading standard based on the obtained oat seed hardness physical detection indexes, gene expression analysis indexes and oat solvent retention capacity detection indexes, so that the precise grading of the oat seed hardness is realized. The verification unit is used for evaluating the accuracy and reliability of the detection method.

[0018] The application provides application of the above-mentioned multi-dimensional detection method of oat seed hardness in oat variety identification or oat processing industry.

[0019] Compared with the prior art, the application has the following beneficial effects: The oat kernel hardness multidimensional detection method provided by the present application integrates physical detection (SKCS value determination), gene expression analysis (Vin1 gene relative expression amount detection), and chemical detection (solvent retention capacity value determination) multiple dimensions to comprehensively obtain oat kernel hardness related information. The K-means clustering algorithm model is used to establish a grading standard, solving the problem of fragmentation of existing detection methods and the lack of comparability of results, and realizing the accurate grading of oat kernel hardness, providing a unified standard and basis for oat variety selection and processing quality prediction, and improving the comprehensiveness and accuracy of detection. The present application provides a method for detecting the hardness of oat kernels, which obtains oat kernel hardness related data through physical detection (SKCS value determination), gene expression analysis (Vin gene relative expression amount detection), and chemical detection (solvent retention capacity value determination). A grading standard is established by a K-means clustering algorithm model. The combination of the above three methods solves the problem of fragmentation of existing detection methods and the lack of comparability of detection results; the established grading standard can be applied to oat breeding and oat processing, and accurate kernel hardness grading lays a more comprehensive, accurate and unified foundation for subsequent breeding and processing quality prediction. Further, the SKCS single kernel grain characteristics tester is used and the negative value is corrected, and the group average hardness value is taken as the result. This ensures the accuracy and stability of the physical detection index, so that the SKCS value can more truly reflect the hardness physical properties of oat kernels, and provides reliable data support for subsequent grading and analysis; the qPCR technology has high sensitivity and specificity, and can accurately detect gene expression level. The specific primer sequence ensures the accuracy and specificity of amplification, which helps to clarify the correlation between hardness related gene expression and physical properties, and provides an effective means for understanding oat kernel hardness from the gene level; according to the different ranges of SKCS value, Vin1 gene expression amount and calcium chloride SRC value, the oat is divided into three categories: soft, intermediate and hard. This grading standard unifies the evaluation scale of oat hardness, solves the problem of lack of unified hardness grading standard, makes the variety selection and processing quality prediction more efficient and accurate, and provides a clear standard basis for the development of oat industry.

[0020] The system provided by the application realizes standardization of detection, comparability of results and grading precision by multi-dimensional detection and intelligent algorithm fusion. Through SKCS single kernel grain characteristic determination, the data correction function eliminates instrument error, and finally outputs the group average hardness value, avoiding the contingency of single grain detection, and being strongly related to processing quality, providing a reliable basis for variety selection, and ensuring the objectivity and repeatability of the detection results. Through the germination culture module to simulate the growth environment of oats, ensure that the RNA extraction module obtains high-quality RNA, combined with qPCR technology to accurately quantify the expression of Vin1 gene, a quantitative relationship model of "gene expression-physical hardness" is established, which provides a tool for the study of the molecular mechanism of hardness formation. SRC detection is directly related to the water absorption, viscoelasticity and other processing properties of oat flour, which is complementary to the physical hardness detection index, and provides a multi-dimensional evaluation basis for variety selection. Through cluster analysis, a comprehensive grading model of "physics-gene-chemistry" is established, and the correlation of the grading results with the processing quality indicators such as flour yield and oat flake smoothness is significantly improved (for example, the flour yield of hard oat is 10%-15% higher than that of soft oat), which provides a scientific basis for variety selection and processing adaptation. The system realizes the comparability of the detection results, the explainability of the hardness formation and the precision of the variety selection, and provides technical support for the high-quality development of the oat industry. The system realizes the comparability of the detection results, the explainability of the hardness formation and the precision of the variety selection, and provides technical support for the high-quality development of the oat industry. The physical detection realizes the comparability of the detection results, the explainability of the hardness formation and the precision of the variety selection, and provides technical support for the high-quality development of the oat industry. The physical detection realizes the comparability of the detection results, the explainability of the hardness formation and the precision of the variety selection, and provides technical support for the high-quality development of the oat industry. The physical detection realizes the comparability of the detection results, the explainability of the hardness formation and the precision of the variety selection, and provides technical support for the high-quality development of the oat industry. The physical detection realizes the comparability of the detection results, the explainability of the hardness formation and the precision of the variety selection, and provides technical support for the high-quality development of the oat industry.

[0021] The application can be applied to oat variety identification or oat processing industry. In oat variety identification, the varieties meeting specific requirements can be screened according to the hardness grading results; in oat processing industry, the appropriate processing mode and process parameters can be selected according to the hardness grading, so as to improve the quality and production efficiency of oat products and promote the development of oat industry. The hardness detection method can be applied to oat breeding and oat processing industry. In oat breeding, the varieties meeting specific requirements can be screened according to the hardness grading results determined by the method, and then cultivated; in oat processing industry, the oat kernels meeting the processing requirements can be selected according to the hardness grading system, so as to improve the quality and production efficiency of oat products and promote the development of oat industry. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 Figure 6 is a box plot of the expression levels of Vin1, Vin2 and Vin3 genes in different types of oat varieties at home and abroad; Figure 2 Figure 9 is a box plot of solvent retention capacity; Figure 3 Figure 10 is a correlation analysis of solvent retention capacity and hardness. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second" and the like used in the specification and claims are only used to distinguish similar objects, and do not necessarily describe a specific order or sequence; it should be understood that these terms can be interchanged under appropriate circumstances. In addition, the terms "include", "have" and any variations thereof are intended to cover non-exclusive inclusion; for example, a process, method, system, product or device that includes a series of steps or units does not necessarily limit to the explicitly listed steps or units, but can include other steps or units not explicitly listed or inherent thereto.

[0025] Method for calculating population average hardness value: single kernel measurement: the SKCS tester is used to measure the hardness of 100 full kernels randomly selected from each oat variety, and the original force-displacement curve data of each kernel is recorded. The arithmetic mean of the corrected SKCS values of all single kernels of the same variety is the "population average hardness value" of the variety.

[0026] The main experimental instruments for gene detection of the application are as follows: Experimental instruments and equipment

[0027] Example 1 The embodiment provides a multidimensional detection method for oat kernel hardness, and the specific steps are as follows: Screening and determination of physical index detection 1.1 SKCS single kernel measurement method The SKCS single kernel grain property tester is used, the negative value result is corrected to the positive value interval of 15-30 by minimum-maximum value mapping correction according to the characteristics that the hardness of oats is lower than the wheat benchmark value, the group average hardness value is used to represent the kernel breakage resistance, the detection speed is ≥1000 grains / minute, and the method is suitable for large-scale breeding screening.

[0028] 1.2 Texture analyzer method The P36R probe is used to simulate the oral mastication process, and the maximum force (unit: g) of kernel fracture is obtained by compression. Parameter setting: deformation degree 95%, pre-measurement speed 2mm / s, middle measurement speed 1mm / s, post-measurement speed 3mm / s, and 100 grains of each sample are detected.

[0029] 1.3 HI grinding method 20g of sample is weighed, ground through a 40-mesh screen for 1 minute, and the proportion of undersize is calculated. Hardness index (Hardness Index, HI) value = (total weight-undersize weight) / total weight x 100%, and the larger the HI value, the higher the hardness.

[0030] 1.4 Correlation analysis and method determination The data of the three physical detection methods are analyzed, and the corrected value of SKCS is significantly positively correlated with the TPA and HI results (r=0.80**, r=0.51*). HI is positively correlated with TPA (r=0.39); compared with TPA and HI, the positive correlation of SKCS is higher, indicating that SKCS can very reliably reflect the texture characteristics of the sample. The core advantage of SKCS is its ultra-high detection speed and full automation. Traditional TPA needs to prepare the sample, and the single test takes a long time. The HI grinding method usually needs to grind a certain amount of sample and analyze the powder characteristics, and the steps are complicated. SKCS has been widely used in grain quality detection (such as wheat hardness grading), and its standardized process and instrument compatibility are more in line with industry standards, and it is convenient for data comparison. HI and TPA may cause results to fluctuate due to differences in laboratory conditions or operation, and the instrument operation of SKCS reduces such risks, so the following research takes the corrected value of SKCS as the physical hardness index of oats for analysis.

[0031] 2. Gene expression analysis 2.1 Sample preparation 6 samples of oats were taken 10.0 g each for use.

[0032] Grain disinfection: Disinfection with 1% sodium hypochlorite solution (1:3 material to liquid ratio) at room temperature for 30 min. The disinfected oats were rinsed repeatedly with distilled water for 5-7 times, each time with 5 min of water soaking and gentle rubbing until the sodium hypochlorite was rinsed clean (as judged by the absence of the smell of sodium hypochlorite).

[0033] Soaking: Soaking at room temperature for 16 h at a material to liquid ratio of 1:3 (oats:distilled water), until the oats could be squeezed to break and the contents squeezed out were liquid.

[0034] Germination: The soaked oats were spread on gauze placed on a mesh tray, which was soaked with distilled water, and placed in a plant growth chamber at 20°C and 95% relative humidity for germination in the dark for 72 h.

[0035] Sampling: When the embryo shoots grew to a length of about 1 cm, the embryo shoot parts were cut and placed in cryotubes, which were frozen in liquid nitrogen and then stored in a -80°C freezer.

[0036] 2.2 RNA extraction Total RNA was obtained by using RNA extraction kit (ABScript lll RT Master Mix for qPCR with gDNA Remover) and reverse transcribed into cDNA. The specific steps are as follows: liquid nitrogen grinding: put oat embryo into a mortar, pour liquid nitrogen into the mortar, grind the sample into powder, weigh an appropriate amount (100-150 mg) for the next experiment; lysis: add 500 μL of Buffer RL2 preheated at 65°C, add 50 μL of Buffer AB per tube, vortex for 30-60 sec, 65°C water bath for 5 min, occasionally point back and forth 1-2 times, centrifuge the lysate at 13000 rpm (14000 x g) for 10 min, and retain the supernatant; adsorb gDNA and RNA: collect the supernatant into a 1.5 mL RNase-free centrifuge tube, add 0.5 times the volume of anhydrous ethanol, mix well by blowing, and add the mixture to the gDNA adsorption column with the collection tube; centrifuge at 13000 rpm (14000 x g) for 2 min, and discard the filtrate (gDNA and RNA are on the adsorption column). Elute RNA: place the gDNA removal column in a new 1.5 mL RNase-free centrifuge tube, add 500 μL of lysis solution RL1, centrifuge at 13000 rpm (14000 x g) for 30 sec, and collect the filtrate (gDNA is on the column, and RNA is in the filtrate). Adjust the environment of the column: add 0.5 times the volume of anhydrous ethanol to the filtrate, mix well by blowing, and adsorb RNA: transfer the filtrate to the RNase-free adsorption column with the collection tube, centrifuge at 13000 rpm (14000 x g) for 2 min, and discard the filtrate (RNA is on the adsorption column); wash the RNA: ① remove protein: add 700 μL of deproteinization solution PR2 to the adsorption column, stand at room temperature for 1 min, centrifuge at 13000 rpm (14000 x g) for 30 sec, and discard the filtrate (RNA is on the adsorption column). ② remove salt ions: add 500 μL of rinse solution WB2 (add 48 mL of anhydrous ethanol to the WB1 bottle for the first time) to the adsorption column, centrifuge at 13000 rpm (14000 x g) for 30 sec, and discard the filtrate (RNA is on the adsorption column) ③ repeat ② once ④ remove rinse solution: centrifuge the empty tube at 13000 rpm (14000 x g) for 2 min, and discard the filtrate (RNA is on the adsorption column); elute RNA: add 30-100 μL of RNase-free ddH2O to the center of the adsorption column, stand at room temperature for 2 min, and centrifuge at 13000 rpm (14000 x g) for 1 min (RNA is in the filtrate).

[0037] 2.3 RNA reverse transcribed into cDNA Reaction mixtures were prepared on ice according to the following table, with a total volume of 10 μL. To ensure accuracy of the reaction mix preparation, the master mix should be prepared in n+2 quantities, then dispensed into each reaction tube, and the RNA added last.

[0038] Reverse transcription reagents

[0039] Briefly centrifuge to collect the RNA at the bottom of the tube, incubate at 42°C for 2 min, then cool on ice. Then add the following components for the reverse transcription reaction (total volume is 20 μL) Reverse transcription reagents

[0040] 2.4 RT-PCR reaction mix To ensure accuracy of the reaction mix preparation, the master mix should be prepared in n+2 quantities, then dispensed into each reaction tube, and the cDNA added first to avoid quenching of the fluorescent dye.

[0041] PCR reagents

[0042] Reaction program

[0043] qPCR detection: β-actin was used as the internal reference gene, and the primers described in Table 2 were used for RT-PCR amplification. Reaction system: 2x SYBR Green Mix 10 μL, 0.4 μL of each upstream and downstream primer, 1 μL of cDNA template, and ddH2O to make up to 20 μL. Reaction program: 95°C pre-denaturation for 30 s, 95°C denaturation for 5 s, 60°C annealing for 30 s, for a total of 40 cycles. The relative expression of the Vin1, Vin2, and Vin3 genes was calculated by 2 -ΔΔCt method.

[0044] Table 2: Gene primer sequences

[0045] 2.5 Analysis of gene expression results and method screening The grains of 40 oat varieties with different hardness were subjected to germination treatment, and the relative expression of the Vin1, Vin2, and Vin3 genes was determined, with the results shown in Figure 1 .

[0046] Vin1 gene expression: The expression of Vin1 gene in foreign oat varieties is lower than that in domestic oat varieties, and the expression of Vin1 gene in naked oat is relatively concentrated; this shows that there are obvious differences in the expression of Vin1 gene in oat varieties of different sources and types, and domestic oat varieties have an advantage in the expression of Vin1 gene, while naked oat is relatively stable in the expression of Vin1 gene. Variety difference: The expression of Carrolup, Bayu No. 18, and Bayu No. 17 is highest (2.52, 2.4, and 2.31, respectively), and the expression of Mule, Beile, and Wuyu No. 1 is lowest (0.1, 0.16, and 0.24, respectively), and there is a significant difference in the expression of Vin1 gene among different oat varieties. Average expression: The average expression of Vin1 gene is 1.17, which is at a certain level, but the distribution of each variety around the average value is quite different.

[0047] Vin2 gene expression: Vin2 is the most stable, and the expression of Vin2 gene in foreign oat varieties is higher than that in domestic oat varieties; the expression of Bayu No. 22, Mortloxk, and Mitika is highest (1.84, 1.09, and 1.08, respectively), and the expression of Wuyu No. 1, Mengyan No. 1, and Weidu No. 5 is lowest (0.11, 0.11, and 0.12, respectively), and there is diversity in the expression of Vin2 gene among different oat varieties; the average expression is 0.60, which is relatively lower than the average expression of Vin1 gene, and the fluctuation of the expression of each variety around the average value reflects its stability.

[0048] Vin3 gene expression: The dispersion of Vin3 in domestic oat is significantly higher than that of other indicators; the overall fluctuation is large. The expression of Yan 2015, Weidu No. 5, and Bayu No. 18 is highest (5.53, 5.35, and 4.76, respectively), and the expression of Bayu No. 7, Xingyan No. 1, and Xingyan No. 3 is lowest (0.13, 0.16, and 0.17, respectively). This shows that there is a significant difference in the expression of Vin3 gene among different varieties; the average expression is 1.88, which is relatively higher than the expression of Vin1 and Vin2, and the expression span is relatively large, indicating that the expression of Vin3 gene in different oat varieties is more complex and diverse.

[0049] Overall, through the determination and analysis of the expression of Vin1, Vin2, and Vin3 genes in 40 oat grains after germination, it can be seen that different genes have different expressions in oat varieties of different sources and types, and these differences provide an important data basis for further study of the genetic characteristics, variety improvement, and related physiological mechanisms of oat.

[0050] The correlation between the hardness and basic indicators of 40 kinds of oats was analyzed using the SKCS single grain grain property tester and the Vin gene index. The indicators with significant correlation were subjected to principal component analysis. The expression level of Vin1 gene was negatively correlated with the SKCS hardness (r=-0.88, p<0.01), and the expression level of Vin2 gene was negatively correlated with the SKCS hardness (r=-0.30). Combined with the previous research results that the introduction of wheat homologous genes into wheat can reduce the hardness of wheat, it was further verified that there was an intrinsic relationship between the Vin gene and the hardness of grains.

[0051] Therefore, according to the conclusion, a method for accurately predicting the hardness of oats can be established, i.e., the Vin gene detection method. The expression level of the Vin gene is detected by extracting the RNA of the germinated oats, reverse transcribing it into cDNA, and then detecting the gene expression level by qPCR to accurately predict the hardness of oats, thereby guiding the breeding of oat grains.

[0052] 3. Solvent retention capacity (SRC) determination 3.1 Reagents and equipment: prepare five solvents including water, 50% sucrose, 1 mol / L calcium chloride, 5% sodium carbonate, and 5% lactic acid, and use a full-automatic solvent retention capacity tester.

[0053] 3.2 Operation steps: weigh 5 g of oat powder (passed through an 80-mesh sieve) into a centrifuge tube, add 25 g of solvent (water, 50% sucrose, 5% lactic acid, 1 mol / L calcium chloride, and 5% sodium carbonate), mix well, centrifuge at 1000 g for 15 minutes, discard the supernatant, and weigh after draining. The calculation formula is: SRC (%) = (sediment weight sample weight) × sample moisture content / sample weight × (1 sample moisture content) × 100.

[0054] 3.3 Solvent retention capacity and correlation analysis The five solvent retention capacities of 40 different oat powders are as follows: Figure 2The SRC of the SRCs of the different solvents was in the order of CaCl2< water < sucrose < Na2CO3< lactic acid, indicating that the different solvents interacted with the oat flour to different extents, and the binding capacity of lactic acid with the oat flour was relatively the strongest, while the binding capacity of CaCl2 with the oat flour was relatively the weakest. The SRC of water had the largest variation range, ranging from 64.2% to 124.47%, and the coefficient of variation was 17.07, indicating that the retention capacity of different oat flour for water was extremely significantly different; the SRC of sucrose ranged from 78.92% to 116.24%, and the coefficient of variation was 9.57, which was lower than that of water, indicating that the stability of the interaction between sucrose and the oat flour was better; the SRC of lactic acid ranged from 108.59% to 155.24%, and the coefficient of variation was 9.20, and the retention capacity of the oat flour for lactic acid was relatively small among different samples. The SRC of Na2CO3 ranged from 71.12% to 128.92%, and the coefficient of variation was 13.68, which was at a medium level; the SRC of CaCl2 ranged from 77.27% to 131.11%, and the coefficient of variation was 16.14, which was relatively high, indicating that the retention capacity of different oat flour for CaCl2 was relatively large, and the factors affecting the combination with CaCl2 in the oat flour varied greatly among different samples.

[0055] The correlation analysis between the solvent retention capacity and the hardness index of the oat is shown in Table 2. Figure 3 As shown in Table 2, the SKCS hardness index of the oat was significantly positively correlated with the SRC of CaCl2 (p<0.01, 0.68), and the coefficient of variation (16.14%) could distinguish different hardness samples and avoid excessive fluctuation of the SRC of water, and the data stability was more suitable as an evaluation index. Moreover, the SRC of CaCl2 was significantly positively correlated with the content of β-glucan (r=0.43). The β-glucan content of the hard oat was high, and the molecular weight was larger, and there were more binding sites for Ca 2+ , resulting in a significant positive correlation between the SRC of CaCl2 and the hardness. It was speculated that the content of β-glucan was related to the formation of the hardness of the oat. The hardness of the oat was mainly determined by the tightness of the endosperm structure, and the content and molecular weight of β-glucan as a key component of the endosperm cell wall directly affected the mechanical strength of the grain. Studies have shown that the β-glucan content of the hard oat (such as the Belle variety, which was up to 6.37%) was higher, and the intermolecular cross-linking was more compact, thereby enhancing the anti-crushing ability of the grain (positively correlated with the SKCS hardness, r=0.49). Ca 2+ in CaCl2 could form ion-dipole interactions with the carboxyl and hydroxyl groups of β-glucan, promote the intermolecular cross-linking of β-glucan, and thereby improve the water retention capacity of the system. Therefore, the higher the SRC value of CaCl2, the higher the content of β-glucan, the more compact the structure, and the greater the hardness of the grain.

[0056] Based on the above correlation analysis and influence mechanism, it is reasonable to use the solvent retention capacity of calcium chloride as one of the indicators of oat hardness evaluation system, because it can indirectly reflect the content and structure of β-glucan, and thus be related to oat hardness, providing an effective indicator basis for accurately evaluating oat hardness.

[0057] 4. Data Correlation and Hardness Grading 4.1 Setting the core parameters for K-means cluster analysis The initialization method uses the "K-means++" algorithm to select the initial cluster centers. It selects samples that are far from the selected centers as new centers through probability distribution, avoiding the instability of clustering results caused by random initialization, reducing the number of iterations and improving convergence efficiency.

[0058] The Euclidean distance is chosen as the metric for similarity between samples, and the formula is: Where x and y represent the multidimensional detection data of two samples (such as physical hardness value, Vin1 gene expression level, and calcium chloride SRC value), and n is the number of dimensions (n=3 in this system). Euclidean distance can intuitively reflect the linear difference of data in multidimensional space and is suitable for cluster analysis of quantitative indicators.

[0059] The optimal number of clusters is determined by combining the elbow method and the silhouette coefficient to determine the optimal K value: Elbow method: Calculate the total sum of squared errors (SSE) for different K values ​​(usually 2-10). When K=3, the SSE curve shows a clear inflection point ("elbow"), indicating that the clustering effect is optimal at this time. Silhouette coefficient: When K=3, the average silhouette coefficient is close to 0.7 (range [-1,1]), indicating that the sample clustering has good cohesion and separation. Finally, the hardness of oats is divided into three categories: soft, medium and hard.

[0060] 4.2 Data Preprocessing Steps Samples exceeding the instrument's range in physical testing (e.g., SKCS values ​​deviating abnormally from the 15-30 range), invalid gene expression Ct values ​​(>35), or repeated measurement deviations of chemical SRC values ​​>5% were removed. For missing values, if the missing percentage was <5%, the mean of the same batch of samples was used for filling; if the missing percentage was >5%, the sample was directly removed to ensure data integrity. When the Z-score of a certain indicator was >3 or <-3, it was determined to be an outlier and the median replacement method was used to avoid interference from extreme values ​​on the clustering results. The physical hardness value (15-30), relative Vin gene expression level (dimensionless), and calcium chloride SRC value (%) were Min-Max standardized and transformed to the [0,1] interval using the following formula: wherein, x’ is the new value obtained after Min - Max standardization processing, whose value range is converted to the interval [0, 1], x is a specific numerical value in the original data, i.e., the original index value that needs to be standardized, min(X) is the minimum value in all numerical values in the entire data set, X represents a set composed of all data of the index, and max(X) is the maximum value in all numerical values in the entire data set, X represents a set composed of all data of the index. The dimension difference of different indexes is eliminated to ensure that the weight of each dimension data is balanced in the cluster analysis. X

[0061] 5. Coordination with the remaining modules Data input: receiving the corrected hardness value of the physical detection module, the relative expression amount of the Vin1 gene of the genetic detection module, and the calcium chloride SRC value of the chemical detection module, forming a three-dimensional data set.

[0062] Algorithm output: dividing the samples into 3 hardness grades through clustering, and outputting the critical value of each grade (such as softness ≤20, intermediate type 20-25, and hardness ≥25).

[0063] Iterative optimization: if the clustering result of the new sample is poor in stability (such as the silhouette coefficient <0.5), the initial center is readjusted or the preprocessing step is optimized to ensure the universality of the grading standard.

[0064] K-means cluster analysis was performed on the above-mentioned indexes of 40 oat kernels, the number of clusters was set to 3, and the number of iterations was set to 10. The cluster center and case number of each index are shown in Table 3. According to the critical value of each cluster category, the test materials were divided into soft, intermediate, and hard oat kernels. The grading standards of SKCS hardness value, Vin1 gene, and calcium chloride solvent retention capacity (SRC) are shown in Table 4: SKCS kernel hardness determination method classification: softness ≤20.41, 20.41 < intermediate type < 26.21, and hardness ≥26.21.

[0065] Vin1 gene expression classification: softness ≥1.21, 0.46 < intermediate type < 1.21, and hardness ≤0.46.

[0066] Calcium chloride (SRC) classification: softness ≤85.13%, 85.13% < intermediate type < 110.97%, and hardness ≥110.97%.

[0067] Table 3: K-means cluster analysis of oat kernel hardness prediction indexes

[0068] Table 4: Oat kernel grading standards ​

[0069] Detection system composition Physical detection module: SKCS single kernel tester (precision ±0.1N), equipped with data automatic acquisition interface.

[0070] Gene detection module: including RNA extractor (throughput ≥96 samples / time), qPCR amplifier (real-time fluorescent quantification), gel imaging system, supporting high-throughput gene expression analysis.

[0071] Solvent detection module: multi-channel full-automatic SRC detector, single processing ≥12 samples, equipped with solvent recovery device to reduce pollution.

[0072] Data analysis module: customized software integrates correlation analysis, principal component analysis (PCA), K-means clustering algorithm, and supports automatic generation of detection report.

[0073] The application will be further described in detail below in combination with the drawings; Example 2: soft oat detection (Banyu No. 1) Physical detection: SKCS value 18.2; Gene detection: Vin1 expression 1.35; Solvent detection: calcium chloride SRC 88.5%; Classification result: soft oat (SKCS <20.41, Vin1≥1.21, calcium chloride SRC≤90.13%).

[0074] Example 3: hard oat detection (Wuyu No. 1) Physical detection: SKCS value 28.7; Gene detection: Vin1 expression 0.32; Solvent detection: calcium chloride SRC 118.3%; Classification result: hard oat (SKCS≥26.21, Vin1≤0.46, calcium chloride SRC≥110.97%).

[0075] Through the above analysis, the application provides a multi-technology synergistic advantage: physical detection provides intuitive hardness data, genetic analysis reveals genetic basis, and SRC determination analyzes chemical correlation, forming a "phenotype-genotype-chemical type" three-in-one detection system.

[0076] Standardized classification system: Through K-means clustering, a quantitative standard is established, which solves the defects of traditional methods relying on subjective judgment, and is suitable for breeding nursery material screening, processing enterprise raw material acceptance and other scenes.

[0077] Molecular mechanism breakthrough: high expression of Vin1 gene significantly reduces hardness (r=-0.88).

[0078] Detection efficiency is improved: the SKCS single kernel test method is combined with automatic data analysis, the complete detection cycle of a sample is less than or equal to 4 hours, the efficiency is improved by more than 50% compared with the traditional method, and the reagent consumption is reduced by 30%.

[0079] The above is only to illustrate the technical idea of the present application, and cannot limit the protection scope of the present application. Any modification made according to the technical idea of the present application on the basis of the technical scheme falls within the protection scope of the claims of the present application.

Claims

1. A multi-dimensional detection method for oat grain hardness, characterized in that, include: Step 1: Measure the individual grain characteristics of the oat kernels to be tested, which will be used as the physical test index of oat kernel hardness, and the population average hardness value will be used as the test result. Step 2: Detect the relative expression level of the Vin1 gene after germination of the oat grains to be tested, as an indicator for gene expression analysis; Step 3: Measure the solvent retention capacity of the oat grain powder to be tested, and use it as the indicator of oat solvent retention capacity. Step 4: Based on the physical test indicators of oat grain hardness, gene expression analysis indicators, and oat solvent retention capacity test indicators, an oat hardness grading standard is established using the K-means clustering algorithm model to achieve accurate grading of oat grain hardness.

2. The method for multi-dimensional detection of oat grain hardness according to claim 1, characterized in that, In step 1, the single grain characteristics of the oat grains to be tested are measured using a single grain grain characteristic tester, and the negative values ​​are converted into a positive value range of 15-30 through reference offset correction.

3. The method for multi-dimensional detection of oat grain hardness according to claim 1, characterized in that, In step 2, the relative expression level of the Vin1 gene was detected by qPCR, and the designed primer sequences are shown in SEQ ID NO.1~SEQ ID NO.

2.

4. The method for detecting the hardness of oat kernels according to claim 1, characterized in that, In step 3, the solvent used to maintain the solvent retention capacity is a calcium chloride solution.

5. The method for detecting the hardness of oat kernels according to claim 1, characterized in that, In step 3, the solvent retention capacity calculation formula is: SRC(%) = (weight of precipitate) / (weight of precipitate) (sample weight) × sample moisture content / sample weight × (1 (Sample moisture content) × 100.

6. The method for detecting the hardness of oat kernels according to claim 1, characterized in that, In step 4, the oat hardness grading standards are as follows: Soft: SKCS value < 20.41, Vin1 gene expression level ≥ 1.21, calcium chloride SRC ≤ 90.13%; Intermediate: SKCS value 20.41-26.21, Vin1 gene expression level 0.46-1.21, calcium chloride SRC 90.13%-110.97%; Hard: SKCS value ≥26.21, Vin gene expression level ≤0.46, calcium chloride SRC ≥110.97%.

7. A method for detecting the hardness of oat kernels according to any one of claims 1 to 6, characterized in that, It also includes a verification step for the test results, which involves selecting oat kernel samples with known hardness, conducting tests, comparing the test results with the known hardness, and evaluating the accuracy and reliability of the test method.

8. The method for detecting the hardness of oat kernels according to claim 7, characterized in that, In the verification step, the selected oat kernel samples with known hardness include three types: soft, medium, and hard, and the number of samples of each type is no less than 30.

9. A system for implementing the method for detecting the hardness of oat kernels according to any one of claims 1 to 8, characterized in that, include: Physical testing unit: Equipped with an SKCS single grain grain characteristic tester, used to measure the SKCS value of the oat grains to be tested, and has a data correction function, and finally outputs the population average hardness value as the physical testing result. Gene detection unit: includes germination culture module, RNA extraction module, qPCR amplification module and data analysis module; the germination culture module provides suitable environmental conditions for oat grain germination; the RNA extraction module extracts RNA from the germinated oats; The qPCR amplification module amplifies the Vin1 gene using specific primer sequences; The data analysis module calculates the relative expression level of the Vin1 gene; The chemical detection unit is used to grind the oat grains to be tested into powder and determine their solvent retention capacity. The data analysis and grading unit, based on the acquired physical test indicators of oat grain hardness, gene expression analysis indicators, and oat solvent retention capacity test indicators, uses the K-means clustering algorithm model to establish an oat hardness grading standard, thereby achieving accurate grading of oat grain hardness. The verification unit is used to evaluate the accuracy and reliability of the detection method.

10. The application of the method for detecting oat grain hardness according to any one of claims 1-8 in oat variety identification or oat processing industry.

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