Far embryo end scraping method and near-infrared data processing method for measuring total phenol content of seeds

Abstract

The invention discloses a far embryo end scraping method and a near infrared data processing method for determining the total phenol content of seeds. The far embryo end scraping method comprises the following steps: screening and sampling samples; removing oil interference components; medium and high polarity components are extracted; and finally, measuring the total phenol content of the seeds. According to the infrared data processing method, unground unhulled peanut seed kernels are directly used as a near infrared spectrum data acquisition background, a special far embryo end scraping method is adopted to measure the content of total phenols in peanuts, and a data process is constructed for optimization; for common peanuts and rare hybrid breeding germplasm resources, phenolic components with the content lower than 1% in hybrid breeding early generation materials have consistent compatibility. The research results of the invention can provide technical support for rapid screening of peanut resources with high total phenol content and efficient cultivation of peanut varieties with high total phenol content.

Description

[0001] This application is a divisional application of the following patent application: Invention title: Near-infrared data model capable of analyzing rare germplasm resources and trace amounts of peanut total phenol content; Application number: 202410415055.2; Application date: 2024-04-08. Technical Field

[0002] This invention relates to data modeling and analysis techniques for biological materials, and in particular to a method for scraping from the distal embryonic end and processing near-infrared data for determining the total phenol content of seeds. Background Technology

[0003] Peanuts are an important oilseed and economic crop in my country, and a significant source of various edible functional components, playing a vital role in the country's national economy and social development. my country's peanut germplasm improvement and hybridization breeding began in the 1950s and has undergone five updates to date. With the increasing emphasis on green and high-quality food among consumers, my country's peanut varieties are currently undergoing their sixth update. This update is characterized by the replacement of ordinary oleic acid varieties with high-oleic acid varieties, extending shelf life, and improving health benefits, continuously meeting the growing living standards and health needs of the people. Studies have found that compared to ordinary peanuts, high-oleic acid peanuts can play a positive role in the prevention and control of various diseases such as metabolic syndrome, obesity, cardiovascular disease, and diabetes. This further narrows the quality gap between peanut oil, long hailed as "Chinese olive oil," and olive oil. However, while olive oil is widely recognized for its health benefits due to its high oleic acid content, its rich content of phenolic compounds is another important factor. Phenolic compounds in edible oils not only help extend shelf life but also reduce the degradation of lipid nutrients during cooking, which is crucial for improving the nutritional value and quality of edible oils.

[0004] Phenolic compounds are a large class of substances including various aromatic secondary metabolites in plants, encompassing flavonoids, phenolic acids, phenylpropane, and quinine, among others. These compounds are often produced in the early stages of plant development and / or when stimulated by various microorganisms, and their content is influenced by factors such as genotype, geography, and climate. Statistics show that in the past 20 years, the CNKI and PubMed databases have published 4987 and 4121 research articles on phenolic compounds, respectively, with the number of publications showing an upward trend. Among these articles in the CNKI database, 950 were funded by national-level grants. This indicates that the development and utilization of phenolic compounds has become a hot topic of widespread interest both domestically and internationally. The relevant technical references for this invention include: ① Wang Chuantang, Zhang Jiancheng, Tang Yueyi, Yu Shutao, Wang Qiang, Liu Feng, Li Qiu. Current status and prospects of high-oleic soybean breeding in China. Shandong Agricultural Sciences, 2018, 50(6): 171-176. ② Jurgoński A, Fotschki B, Juśkiewicz J. Disparate metabolic effects of blackcurrant seed oil in rats fed a basal and obesogenic diet. European Journal of Nutrition, 2015, 54(6): 991-999. ③ Huth PJ, Fulgoni VL, Larson BT. A systematic review of high-oleic vegetable oil substitutes for other fats and oils on cardiovascular disease risk factors: implications for novel high-oleic soybean oils. Advances in Nutrition, 2015, 6(6): 674-693. ④ Schwingshackl L, Lampousi A, Portillo M, Romaguera D,Hoffmann G, Boeing H. Olive oil in the prevention and management of type 2diabetes mellitus: a systematic review and meta-analysis of cohort studies and intervention trials.Nutrition & Diabetes, 2017, 7(4): e262-e262. ⑤ Wang Chuantang, Zhang Jiancheng, Tang Yueyi, Yu Shutao, Wang Qiang, Liu Feng, Li Qiu. Current status and prospects of high oleic acid olive oil breeding in China. Shandong Agricultural Sciences, 2018, 50(6): 171-176. ⑥ Veloso ACA, Rodrigues N, Ouarouer Y, Zaghdoudi K, Pereira JA, Peres AM. A Kinetic‐Thermodynamic study of the effect of the cultivar / total phenols on the oxidative stability of olive oils. Journal of the American Oil Chemists' Society, 2020, 97(6): 625-636. ⑦ Winkel-Shirley B. Flavonoid biosynthesis: a colorful model for genetics, biochemistry, cellbiology, and biotechnology. Plant Physiology, 2001, 126:485-493.⑧ SobolevVS, Horn BW, Potter TL, Deyrup ST, Gloer JB. Production of stilbenoids andphenolic acids by the peanut plant at early stages of growth. Journal ofAgriculture and Food Chemistry, 2006, 54(10): 3505-3511.⑨ Devi MC, Reddy MN.Phenolic acid metabolism of groundnut (Arachis hypogaea L.) plants inoculatedwith VAM fungus and Rhizobium. Plant Growth Regulation, 2002, 37(2): 151-156.⑩ Sobolev VS.Production of phytoalexins in peanut (Arachis hypogaea) seedelicited by selected microorganisms. Journal of Agriculture and FoodChemistry, 2013, 61(8): 1850-1858. etc. .

[0005] Meanwhile, in addition to being rich in oils and proteins, peanut kernels also contain phenolic compounds necessary for plant defense against disasters and for maintaining human health. Yang et al. preliminarily identified more than 30 phenolic compounds from seven different peanut varieties in my country. An online study explored the impact of the ecological environment on the total phenolic content of peanut kernels, finding that genetic differences are a significant factor influencing this content. Sixteen cultivated varieties from Shandong, Henan, Hebei, and Sichuan provinces showed total phenolic content (expressed as gallic acid equivalent) ranging from 1.82 mg / g to 3.23 mg / g under eight different environmental conditions, with relatively stable differences among varieties. This suggests that targeted breeding of peanut varieties rich in phenolic compounds is feasible. Mondal et al. used peanut kernels with the seed coat removed as the research object and measured the total phenol content (expressed as gallic acid equivalent) of the recombinant inbred line population (RIL) of the cross between cultivated variety VG 9514 and TAG 24 in 2010 and 2011, respectively, which were 1.0 mg / g ~ 2.3 mg / g and 0.7 mg / g ~ 2.1 mg / g, with an average content of 1.65 mg / g. Using the total phenol content in peanut kernels as the phenotype, quantitative trait locus (QTL) analysis obtained one related QTL. It can be seen that the total phenol content in peanut kernels is a heritable quantitative trait. How to conduct high-throughput, large-scale detection and analysis of the total phenol content of resource materials and breeding progeny has become a key technology for breeding new peanut varieties with high total phenol content.

[0006] Currently, the determination of total phenol content in plants generally employs visible spectrophotometry, which requires sample crushing and complex pretreatment, resulting in long processing times and low detection efficiency. However, high-throughput, large-scale detection and analysis of total phenol content in resource materials and breeding progeny is a key technology for breeding new peanut varieties with high total phenol content, and is crucial for improving the breeding efficiency of high-total-phenol peanut varieties. Near-infrared spectroscopy, with its non-destructive, rapid, and efficient characteristics, has been widely used in the analysis of peanut oil content, fatty acids, protein, and amino acids, but there are no reports on near-infrared prediction models for total phenol content in peanut kernels. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a method for scraping the distal embryo end and a near-infrared data processing method for determining the total phenol content of seeds.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows.

[0009] The distal embryo scraping method for determining the total phenol content of seeds includes the following steps:

[0010] (1) Sample screening and sampling;

[0011] (2) Removal of oily and greasy interfering components;

[0012] (3) Extraction of medium- to high-polarity components;

[0013] Finally, the total phenol content of the seeds was determined by plotting a standard curve based on the extracted components, blank control, and standards.

[0014] As a preferred technical solution of the present invention, the following steps are included:

[0015] (1) Sample screening and sampling: Select mature, plump, medium-sized kernels that are free from sprouting, damage, and disease spots; take kernels, scrape no more than 1 / 4 of the distal embryo end of each seed with a blade, repeat the operation several times, mix the resulting kernel powder, and set aside for later use;

[0016] (2) Removal of interfering components such as oils; place the kernel powder in a centrifuge tube, weigh it accurately, add n-hexane, insert the centrifuge tube into a foam float plate, soak it at room temperature, then perform ultrasonic-assisted extraction, cool to room temperature, centrifuge at room temperature, separate the n-hexane layer, and use it for fatty acid determination; the precipitate is then added to n-hexane twice with a pipette, and mixed vigorously until there are no particles or lumps at the bottom of the centrifuge tube, then extracted with ultrasonic assistance, and then degreased a second time; after cooling to room temperature, centrifuge, separate the n-hexane layer, and the defatted kernel powder is obtained;

[0017] (3) Extraction of medium and high polarity components: Add methanol / ethanol aqueous solution to the precipitate after discarding hexane in the previous step, shake vigorously or mix with ultrasonic assistance until there are no particles or clumps at the bottom of the centrifuge tube; soak at room temperature, then extract with ultrasonic assistance, cool to room temperature, centrifuge at room temperature, and take the supernatant to obtain the extract rich in medium and high polarity components to be tested.

[0018] Finally, the total phenol content of the seeds was determined by plotting a standard curve based on the extracted components, blank control, and standards.

[0019] As a preferred technical solution of the present invention, the following steps are included:

[0020] (1) Sample screening and sampling: Select mature, plump, medium-sized kernels that are free from sprouting, damage, and disease spots; take 10 to 20 kernels, scrape off no more than 1 / 4 of the distal embryo end of each seed with a blade, repeat the operation several times, mix the resulting kernel powder, and set aside.

[0021] (2) Removal of interfering components such as oils; Take 0.1 g of kernel powder and place it in a specific centrifuge tube (the allowable range is 0.1000~0.1050 g), accurately weigh it, add 1.6 ml of n-hexane, insert the centrifuge tube into a foam float plate, and soak at room temperature for 2 h, shaking vigorously once every 30 min during this period; use ultrasound at 250 W and 40 kHz to assist extraction for 20 min, shaking vigorously once every 5 min during this period; after cooling to room temperature, centrifuge at 5000 rpm for 5 min at room temperature to separate the n-hexane layer for fatty acid determination; the precipitate is again extracted with 1.6 mL of n-hexane twice using a pipette, and vigorously mixed until there are no particles or lumps at the bottom of the centrifuge tube, then use ultrasound at 250 W and 40 kHz to assist extraction for 20 min, shaking vigorously once every 5 min during this period for a second defatting; after cooling to room temperature, centrifuge at 3000 rpm for 5 min at room temperature, and after separating the n-hexane layer, the defatted kernel powder is obtained;

[0022] (3) Extraction of medium- and high polarity components: Add methanol / ethanol aqueous solution to the precipitate from which hexane was discarded in the previous step, and mix vigorously or with ultrasonic assistance for 5 min, shaking vigorously once every 30 s until there are no particles or clumps at the bottom of the centrifuge tube; soak at room temperature for 2 h, shaking vigorously once every 30 min; extract with ultrasonic assistance at 250 W and 40 kHz for 20 min, shaking vigorously once every 5 min; after cooling to room temperature, centrifuge at 13000 rpm for 15 min at room temperature, and take the supernatant to obtain the extract rich in medium- and high polarity components to be tested;

[0023] Finally, the total phenol content of the seeds was determined by plotting a standard curve based on the extracted components, blank control, and standards.

[0024] As a preferred technical solution of the present invention, in step (2), the specific centrifuge tube is constructed as a polypropylene centrifuge tube with a bottom apex angle of 120°±10°, avoiding the use of round-bottomed or pointed-conical-bottomed centrifuge tubes; in order to reduce precipitate caking and adapt to multiple extractions of precipitate while ensuring centrifugation efficiency, not losing precipitate and fully separating supernatant.

[0025] As a preferred embodiment of the present invention, in step (2), the precipitate should not be lost during the separation of the n-hexane layer; and the residual n-hexane should be minimized during the second separation of the n-hexane layer; and the volatilization of n-hexane should be prohibited before adding the methanol aqueous solution.

[0026] As a preferred technical solution of the present invention, in step (3), if the content of seed protein in the final extract is higher than the limit value, the extract should be refrigerated overnight to fully precipitate the protein before subsequent determination.

[0027] As a preferred technical solution of the present invention, in step (3), for the selection of the type and concentration of methanol / ethanol aqueous solution, the determination of phenolic components is carried out using a 50% methanol aqueous solution.

[0028] As a preferred technical solution of the present invention, in step (3), the material-liquid ratio is determined based on the actual content of the component to be tested and the sensitivity that the subsequent detection method can achieve. For the determination of phenolic components, the material-liquid ratio is selected as 1:15, that is, 1.5 ml of methanol / ethanol aqueous solution is added for every 0.1 g of kernel.

[0029] A near-infrared data processing method compatible with rare germplasm resources and trace peanut total phenol content analysis is proposed. This method directly uses multiple unground peanut kernel samples with skin as the background, performs multiple sample loading and near-infrared spectrum measurements to obtain mean near-infrared spectral data, and simultaneously uses the distal embryo scraping method to determine the total phenol content of all peanut samples to obtain multi-sample total phenol content data. Based on this, the model structure is constructed and optimized according to the following process:

[0030] For the mean near-infrared spectral data of the obtained peanut multi-sample modeling materials, a first-order filtering algorithm is constructed according to the following process: specifying wavelength λ as the basic continuous spatial variable of linear self-variation, obtaining the corresponding variable of the original near-infrared spectral data f(λ) of the obtained peanut multi-sample modeling materials according to the differential correspondence of wavelength λ, and then assigning the corresponding variable of f(λ) to the differential variable of wavelength λ, thereby enhancing the chemical information in the spectral data and reducing the influence of background noise. The first-order filtering algorithm externally highlights the intrinsic slope rate of the spectral curve.

[0031] Furthermore, the above-mentioned primary filtering algorithm is iterated as needed to obtain a secondary filtering algorithm, which highlights the curvature changes inherent in the spectral curve.

[0032] Based on this, the mean of the spectral data of the peanut samples is subtracted and the result is divided by the standard deviation to correct the scattering effect in the spectral data and its associated data variation.

[0033] This method has consistent compatibility with phenolic components with a content of less than 1% in common peanuts, rare hybrid breeding germplasm resources, and early generation hybrid breeding materials.

[0034] As a preferred technical solution of the present invention, the method for obtaining the mean near-infrared spectral data is as follows: using unground peanut kernels with skin as a background, multiple sample loadings and near-infrared spectral measurements are performed. The peanut material used for modeling is naturally air-dried to a moisture content of less than 5%. Mature, plump, undamaged, and unmolded peanut seeds are selected as the test samples and placed at a constant temperature of about 25°C for more than 48 hours. The near-infrared quality analyzer is used to perform spectral measurements on the samples, with a scanning wavelength range of 950–1650 nm and a resolution of 5 nm. After the instrument is preheated, the sample to be tested is placed flat into the sample cup, fully covering the bottom mirror surface. The sample loading is repeated 3 times, and 3 measurements are performed each time. A total of 9 near-infrared spectra of peanut kernels are obtained for each sample, and the average spectrum is taken.

[0035] The beneficial effects of adopting the above technical solution are as follows: This invention is the first to construct a data analysis model with consistent compatibility for phenolic components with a content of less than 1% in common peanuts, rare hybrid breeding germplasm resources, and early-generation hybrid breeding materials. Using 189 superior peanut varieties as materials, this invention directly uses unground peanut kernels with skin as the background for near-infrared spectral data acquisition and employs a specialized distal embryo scraping method to determine the total phenolic content of peanuts, thus constructing a professional and effective data model for predicting and analyzing the content of peanut phenolic components. The accuracy of the constructed model has been verified using 10 different peanut varieties. The research results of this invention can provide technical support for the rapid screening of peanut resources with high total phenolic content and the efficient breeding of peanut varieties with high total phenolic content.

[0036] This invention enables the collection and analysis of samples with total phenol content ranging from 0.206% to 0.434%, and also allows for content prediction using near-infrared spectroscopy. The content of the analyte in this study is low, falling into the category of trace components, which greatly expands the application range of near-infrared spectroscopy and successfully applies near-infrared spectroscopy to the study of solid trace components.

[0037] The fitting model of this invention incorporates a variety of data algorithms, which can eliminate errors caused by sample inhomogeneity, high-frequency random noise, baseline drift, stray light, etc., effectively improving the reliability of the model.

[0038] The experimental method and data model of this invention have been verified to be able to directly use uncrushed peanuts with skin as the background for spectral acquisition and model construction, which is extremely convenient and progressive in terms of technical practicality.

[0039] This invention establishes a dedicated distal embryonic end scraping sampling method for phenol content determination. In breeding processes, the number of seeds harvested through hybridization and self-pollination is often limited. Traditional quality determination methods typically require large quantities of seeds and involve destructive operations such as crushing, often necessitating over a year of seed propagation to achieve quality testing, thus extending the breeding cycle of new varieties. The sampling method developed in this invention uses a small amount of seeds with minimal damage. Because it does not affect seed germination activity, it makes quality comparison between the same seed and its offspring possible. It is suitable for studies on the effects of environmental stresses such as drought, salinity, and microorganisms on quality; and for screening hybrid breeding germplasm resources, especially early-generation materials for hybrid breeding.

[0040] This invention employs specific centrifuge tubes for phenol content determination. These tubes are constructed of polypropylene with a conical bottom angle of 120°±10°, avoiding the use of round-bottomed or pointed-bottom centrifuge tubes. This design reduces precipitate caking and accommodates multiple extractions of the precipitate while ensuring centrifugation efficiency, preserving the precipitate, and fully separating the supernatant. Another key technical point is that the hexane should not be evaporated before adding the extraction solvent. This is because hexane has extremely low solubility in methanol / ethanol aqueous solution. During extraction, a small amount of hexane will not affect the extraction efficiency of medium- to high-polarity components, and even after centrifugation, the hexane layer will not affect the absorption of the intermediate solution. Conversely, if the hexane is evaporated before adding the extraction solvent, the centrifuge tube, already swollen and deformed by the hexane during defatting, will further deform, affecting the tube's seal and causing loss of the extraction solution, thus affecting the accuracy of the measurement results. Alternatively, during ultrasonic-assisted extraction, water may seep into the water bath, contaminating the sample.

[0041] This invention makes a core improvement to the oil removal process in the determination of phenol content, especially by limiting the final centrifugation parameters to 3000 rpm for 5 min after cooling to room temperature to separate the hexane layer and obtain defatted kernel powder. These centrifugation parameters have been repeatedly optimized and are suitable for subsequent operations in this method. The reason is that if the centrifugation speed and time are not properly selected for the second defatting step, two problems can easily occur: ① If the centrifugation speed is too low, the precipitate will be too loose, and during the separation of the hexane layer, the precipitate will easily be poured out with the hexane solution, affecting the precision and accuracy of the method; ② If the centrifugation speed is too high, the precipitate will be too compact. After adding a methanol / ethanol aqueous solution, because the viscosity of the methanol / ethanol aqueous solution is higher than that of hexane, the precipitate will clump together, greatly reducing the contact area between the solvent and the sample, thus reducing the extraction efficiency and resulting in lower measurement results.

[0042] Referring to the detailed embodiments described below, the various technical advantages of the present invention are further elaborated in detail. Attached Figure Description

[0043] Figure 1 Photographs of the near-infrared instrument and sample cup used in this invention.

[0044] Figure 2 Distribution of total phenol content in peanut kernels used for modeling.

[0045] Figure 3 Near-infrared spectrum of peanut kernels.

[0046] Figure 4 Near-infrared spectrum of cleaned peanut kernel powder.

[0047] Figure 5 A schematic diagram illustrating the correlation between the predicted total phenol content and the chemical value of peanut kernels and cleaned peanut powder, verified externally.

[0048] Figure 6 A schematic diagram of the application side of the peanut kernel prediction model.

[0049] Figure 7 A schematic diagram of the application of the peanut kernel powder prediction model. Detailed Implementation

[0050] The following embodiments illustrate the present invention in detail. All raw materials and equipment used in the present invention are conventional commercially available products and can be directly obtained through market purchase. It should be understood that, when used in this specification and appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. It should also be understood that the term "and / or" as used in this specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. As used in this specification and appended claims, the term "if" can be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" can be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0051] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance. References to "one embodiment" or "some embodiments" in this application mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0052] Example 1, Test Materials

[0053] This study used 159 superior peanut varieties (S001-S159) as modeling materials to construct a near-infrared model for the total phenolic content of peanut kernels. Twenty peanut varieties (ST01-ST20) were selected for external model validation to evaluate the model's accuracy. All experimental materials were provided by the Peanut Research Laboratory of the Institute of Grain and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences.

[0054] Example 2: Determination of near-infrared spectroscopy

[0055] All peanut materials were naturally air-dried to a moisture content below 5%. Mature, plump, and undamaged peanut seeds were selected as test samples and kept at a constant temperature of approximately 25°C for at least 48 hours. The samples were then subjected to spectral analysis using a DA7200 near-infrared quality analyzer (Borton GmbH, Sweden), with a scanning wavelength range of 950–1650 nm and a resolution of 5 nm. After the instrument had preheated, the test samples were placed flat into the sample cup, ensuring that the bottom mirror surface was fully covered (see Appendix). Figure 1 The sample was loaded three times, and the measurements were performed three times for each sample. A total of nine near-infrared spectra of peanut kernels were obtained from each sample, and the average spectrum was used for model construction. Subsequently, the seed coat of the peanut kernels was removed, and the kernels were ground and passed through a 20-mesh sieve. Near-infrared spectra were then collected using the same method, and the average spectrum was used for model construction.

[0056] Example 3: Determination of total phenol content

[0057] Instruments and equipment: analytical balance, electric thermostatic drying oven, pipette, volumetric flasks, centrifuge tubes (2.0 ml centrifuge tubes recommended), etc. Reagents: n-hexane, methanol, distilled water. Detailed operating procedures and technical parameters are provided below.

[0058] ① Sample screening. Select mature, plump kernels that are of moderate size and free from sprouting, damage, and disease spots.

[0059] ② Sampling. Take 10-20 kernels, and scrape no more than 1 / 4 of the distal embryo end of each seed with a scalpel blade. Repeat this process several times. Mix the resulting kernel powder and set aside.

[0060] ③ Removal of interfering components such as oils. Accurately weigh approximately 0.1 g of kernel powder into a specific 2.0 ml centrifuge tube (allowable range 0.1000~0.1050 g), add 1.6 ml of n-hexane, insert the centrifuge tube into a foam float plate, and soak at room temperature for 2 h, shaking vigorously every 30 min during this time. Perform ultrasonic-assisted extraction for 20 min (250 W, 40 kHz), shaking vigorously every 5 min during this time. After cooling to room temperature, centrifuge at 5000 rpm for 5 min at room temperature to separate the n-hexane layer, which can be used for fatty acid determination. Add 1.6 mL of n-hexane again to the precipitate (adjust a 1000 μL pipette to 800 μL, add twice), mix vigorously until there are no particles or clumps at the bottom of the centrifuge tube, and then perform ultrasonic-assisted extraction for 20 min (250 W, 40 kHz), shaking vigorously every 5 min during this time, for a second defatting process. After cooling to room temperature, centrifuge at 3000 rpm for 5 minutes at room temperature. After separating the n-hexane layer, the defatted kernel powder is obtained.

[0061] It is worth noting that this study can complete the entire sample extraction process in a single 2.0 ml centrifuge tube, consuming a small volume of reagents per sample, making it more environmentally friendly than traditional extraction methods. The key problem it addresses is that traditional defatting methods typically involve glass containers, large sample volumes, and often include filtration and rotary evaporation, leading to varying degrees of sample loss and even degradation of the analyte, significantly reducing extraction recovery rates and affecting method accuracy. This method uses a specific 2.0 ml centrifuge tube constructed of polypropylene with a conical bottom (120°±10° apex angle). This design, as shown in the figure, avoids typical round or conical bottoms, ensuring centrifugation efficiency and allowing for thorough separation of the supernatant without loss of precipitate. Using appropriate centrifugation speeds, the precipitate is less prone to caking, providing excellent properties and ample space for multiple extractions. On the other hand, the centrifuge tubes are made of polypropylene (PP), and after two hexane extractions, their deformation remains within a controllable range, preventing problems such as leakage, deformation, or inability to fit into a standard-sized centrifuge.

[0062] In addition, as detailed below, another key technical point of this method is that the hexane should not be evaporated before adding the extraction solvent. The reason is that hexane has extremely low solubility in methanol / ethanol aqueous solution. During the extraction process, a small amount of hexane mixed in will not affect the extraction efficiency of medium and high polar components. Even after centrifugation, the hexane layer will not affect the absorption of the intermediate solution. On the contrary, if the hexane is evaporated before adding the extraction solvent, the centrifuge tube, which has swollen and deformed due to the hexane during the defatting process, will further deform, affecting the sealing of the centrifuge tube, causing the loss of the extraction solution during the extraction process and affecting the accuracy of the measurement results. Alternatively, during ultrasonic-assisted extraction, water may seep into the water bath, contaminating the sample.

[0063] Equally important is the final centrifugation process: cooling to room temperature and then centrifuging at 3000 rpm for 5 minutes at room temperature. These centrifugation parameters have been repeatedly optimized and are suitable for subsequent operations in this method. The reason is that if the centrifugation speed and time are not chosen appropriately for the second defatting step, two problems can easily occur: A) If the centrifugation speed is too low, the precipitate will be too loose, and during the separation of the hexane layer, the precipitate will easily be poured out with the hexane solution, affecting the precision and accuracy of the method; B) If the centrifugation speed is too high, the precipitate will be too compact. After adding a methanol / ethanol aqueous solution, because the viscosity of the methanol / ethanol aqueous solution is higher than that of hexane, the precipitate will clump together, greatly reducing the contact area between the solvent and the sample, thus reducing the extraction efficiency and resulting in lower measurement results.

[0064] During operation, the following precautions should be taken: when separating the n-hexane layer, avoid losing the precipitate; when separating the n-hexane layer for the second time, minimize the residual n-hexane; and do not evaporate the n-hexane before adding the methanol / ethanol aqueous solution.

[0065] ④ Extraction of medium-to-high polar components. To the precipitate from the previous step (after discarding hexane), add an appropriate concentration of methanol / ethanol aqueous solution at a suitable solid-liquid ratio. Mix vigorously or with ultrasonic assistance (250 W, 40 kHz) for 5 min, shaking vigorously every 30 s until no particles or clumps remain at the bottom of the centrifuge tube. Soak at room temperature for 2 h, shaking vigorously every 30 min. Extract with ultrasonic assistance for 20 min, shaking vigorously every 5 min. After cooling to room temperature, centrifuge at 13000 rpm for 15 min at room temperature. Collect the supernatant to obtain the extract rich in medium-to-high polar components. If the kernel protein content is high, refrigerate overnight at 4℃ to fully precipitate the protein before subsequent determination.

[0066] The key issue addressed in this process design is that for crops with high protein content, such as soybeans, protein components can affect the determination of plant polyphenols and other components, and can also contaminate chromatographic columns and ion sources. Therefore, this method uses low-temperature precipitation of proteins instead of adding protein precipitation reagents. This method utilizes the characteristic that proteins denature and precipitate at low temperatures and in organic solvents. Its advantage is that it does not change the original composition of the sample and avoids introducing other impurities during the purification process, which could cause unnecessary interference with the determination results.

[0067] It is worth noting that this study focuses on the determination of total phenol content in peanuts, but the method of this invention is also applicable to the processing and determination of various materials. Therefore: (1) Select the type and concentration of methanol / ethanol aqueous solution according to the type of component to be tested. For example, 50% methanol aqueous solution can be selected for the determination of phenolic components, and 50% ethanol aqueous solution can be selected for the determination of sugar components; (2) Select an appropriate material-liquid ratio according to the actual content of the component to be tested and the sensitivity that the subsequent detection method can achieve. For example, a material-liquid ratio of 1:15 can be selected for the determination of phenolic components, that is, 1.5 ml of methanol / ethanol aqueous solution is added for every 0.1 g of kernels.

[0068] ⑤ Finally, for the preparation of blank controls and standard curves, refer to existing industry techniques. A general technical route is summarized below. First, prepare peanut kernel extract. Accurately weigh approximately 0.1 g of peanut powder sample used for near-infrared spectroscopy, place it in a 2 mL centrifuge tube, defatt with n-hexane twice, then accurately add 50% methanol aqueous solution (v / v) at a ratio of 1 g:15 mL (w / v), mix well, soak for 30 min, and extract with ultrasonic assistance at room temperature (250 W, 40 kHz) for 30 min, shaking once every 10 min. Centrifuge at 12000 r / min for 10 min at room temperature. Accurately measure 1 mL of the supernatant into a 1.5 mL centrifuge tube, centrifuge at 12000 r / min for 20 min, and collect the supernatant to obtain the peanut kernel extract. Further, determine the total phenol content of the peanut kernel extract using visible spectrophotometry. Take 0.4 ml of peanut kernel extract, add 0.4 ml of Folin-Ciocalteu reagent and 0.8 ml of 7.5% Na₂CO₃ solution, and finally add 2.4 ml of distilled water. Mix well and react at room temperature in the dark for 2 h. Measure the absorbance at 755 nm. Separately, take 0.4 ml of 50% methanol aqueous solution (v / v) and proceed in the same manner as a blank control. Accurately weigh 0.1 g of gallic acid reference standard, dissolve and dilute it in 50% methanol aqueous solution (v / v) to a 10 ml volumetric flask, mix well, and obtain a gallic acid stock solution with a concentration of 10 mg / mL. Take an appropriate amount of sucrose stock solution and dilute it with 50% ethanol aqueous solution (v / v) to obtain gallic acid series reference solutions of 50, 100, 150, 200, 250, and 300 μg / mL, respectively, for the construction of the gallic acid standard curve. The total phenolic content of peanut kernels is expressed as gallic acid equivalent (%), calculated using the following formula: Total phenolic content (%) = Gallic acid equivalent concentration (μg / g) × Solid-liquid ratio (1g:15 mL) × Dilution factor × 0.1. In this formula, the gallic acid equivalent concentration is the concentration of the sample calculated by substituting the sample absorbance value into the gallic acid standard curve; the dilution factor is the number of times the sample absorbance value needs to fall within the range of absorbance values ​​on the gallic acid standard curve, typically 4; and 0.1 is a unit conversion factor. Each sample is measured in triplicate, and the average value is used for model construction and validation.

[0069] In summary, the improved process presented in this embodiment has the following characteristics:

[0070] 1. This sampling method, employing the distal embryonic end scraping technique, does not affect seed germination activity and is suitable for hybrid breeding germplasm resources, especially for screening early-generation materials. The key problem it solves is that the number of seeds harvested through hybridization and self-pollination is often small during breeding. Traditional quality testing methods typically require large quantities of seeds and involve destructive operations such as crushing, often requiring over a year of seed propagation to achieve quality testing, thus prolonging the breeding cycle of new varieties. This method uses a small amount of seeds with minimal damage. Because it does not affect seed germination activity, it makes quality comparison between the same seed and its offspring possible, making it particularly suitable for studies on the effects of environmental stresses such as drought, salinity, and microorganisms on quality.

[0071] 2. This method is also applicable to the analysis of oilseed crops, including samples of soybeans, peanuts, and walnuts. The key problem it addresses is that traditional crushing and sieving processes during oilseed crop sampling can easily cause oil precipitation, commonly known as "oil release," which affects the sample composition and significantly interferes with the determination of oil components. The sampling method used in this approach ensures sufficient sample dispersion while preserving as much of the original sample composition as possible and minimizing the contact time between the sample and air, thus avoiding oxidation and other degradation reactions of the oil components.

[0072] 3. Applicable to the analysis of oil-rich nuts and snacks such as pistachios, macadamia nuts, and almonds.

[0073] 4. Applicable to the determination of the content of medium to high polar substances such as sugars, anthocyanins, flavonoids, and phenolic acids in samples.

[0074] 5. Suitable for sample pretreatment in spectrophotometry, high performance liquid chromatography, ultra-high performance liquid chromatography, mass spectrometry and other analytical methods.

[0075] 6. The supernatant from the defatting treatment is suitable for gas chromatography analysis of fatty acids.

[0076] 7. Suitable for simultaneous extraction and determination of large batches of samples. Compared with traditional extraction methods, it has a higher throughput. After weighing, an operator can extract up to 100 samples per day, which better reduces the inter-batch analysis error of large batches of samples.

[0077] 8. The entire extraction process can be completed in a 2.0 ml centrifuge tube. The volume of reagents consumed per sample is small, making it more environmentally friendly than traditional extraction methods.

[0078] 9. The recovered rate of the analyte in the obtained extract is high and the repeatability is good, which can meet the requirements of the content determination method.

[0079] Example 4: Construction and optimization of the near-infrared model for total phenol content

[0080] First, for the mean near-infrared spectral data of the obtained peanut multi-sample modeling materials, a first-order filtering algorithm is constructed according to the following process: specifying wavelength λ as the basic continuous spatial variable with linear self-variation, obtaining the corresponding variable of the original near-infrared spectral data f(λ) of the aforementioned peanut multi-sample modeling materials according to the differential correspondence of wavelength λ, and then assigning the corresponding variable of f(λ) to the differential variable of wavelength λ, thereby enhancing the chemical information in the spectral data and reducing the influence of background noise. The first-order filtering algorithm highlights the intrinsic slope rate of the spectral curve. Further, the filtering is terminated or the above first-order filtering algorithm is iterated as needed to obtain a second-order filtering algorithm. The second-order filtering highlights the intrinsic curvature change of the spectral curve.

[0081] Based on this, the following data process is constructed: S(λ) = (F(λ) - μ) / σ, where F(λ) is the original spectral data f(λ) or the spectral data processed by a first-order filtering algorithm and / or a second-order filtering algorithm, μ is the mean of the sample spectral data, and σ is the standard deviation. This data process subtracts the mean of the spectral data of the peanut sample and divides it by the standard deviation to correct the scattering effect in the spectral data and its associated data variation.

[0082] Finally, external validation of the near-infrared model for total phenol content was performed. Ten different peanut kernel varieties were randomly selected, and the total phenol content in the kernels was predicted using the model constructed in this study. The total phenol content was then determined by visible spectrophotometry, and the differences and correlations between the near-infrared predicted values ​​and the chemically determined values ​​were compared.

[0083] Experimental results and data analysis of Example 5 and Examples 2-4

[0084] The chemical determination results of total phenol content in peanut kernels are shown below. The distribution map of total phenol content in peanut kernels from the modeled peanut material is as follows. Figure 2 As shown, the average total phenol content was 0.327%, ranging from 0.204% to 0.439%, with a coefficient of variation of 16.19%. This indicates that the total phenol content distribution range of the modeled peanut material is relatively wide, meeting the requirements for constructing a near-infrared model of total phenol content.

[0085] Near-infrared spectral analysis of peanut kernels. The near-infrared spectra of the modeling materials, net peanut kernel powder and peanut kernels, collected in this study are shown below. Figure 3 A and Figure 4 A, the near-infrared spectra after the first filtering algorithm and the S(λ) function processing are respectively as follows: Figure 3 B and Figure 4As shown in B, the near-infrared curves of each sample exhibit multiple absorption peaks in the range of 950–1650 nm. Although the trends are roughly the same, the peak values ​​of each sample show significant differences.

[0086] Regarding the construction and internal validation results of the near-infrared model: The chemical values ​​of total phenol content in peanut kernels and clean peanut powder were fitted with the collected near-infrared spectral data, respectively, and a prediction model was established using the PLS method. The correlation coefficients (R) were 0.9303 and 0.9167, respectively, and the root mean square errors (RMSEP) were 0.019 and 0.021, respectively. Figure 5 The model has a high coefficient of determination and a small root mean square error, indicating that the model can effectively predict the sucrose content in peanut kernels.

[0087] External validation results of the near-infrared model: Twenty peanut varieties were randomly selected as external validation samples. Chemical values ​​were measured, and their near-infrared spectra were substituted into the established model to obtain predicted values. A scatter plot depicting the correlation between the predicted total phenol content and the chemical values ​​was plotted. Figure 5 The correlation coefficient between the predicted value of peanut kernels and the chemical value was R=0.9435, and the correlation coefficient between the net peanut kernel powder and the chemical value was R=0.9480. This indicates that the predicted values ​​of total phenol content obtained by the above two models are accurate and can be used to replace the chemical determination method for non-destructive, rapid, and high-throughput screening of peanut resources and hybrid offspring.

[0088] Based on the above embodiments, it is evident that, compared to some existing studies, such as those by Chen Miao et al. which argued that peanut seed coat color affects the accuracy of near-infrared prediction models and therefore used a colorimeter to classify peanut germplasm into three categories based on seed coat color: blackish-purple, red, and pink, near-infrared models were constructed for the total sugar, soluble sugar, and sucrose content in the kernels, respectively. Compared to these existing technical approaches, this invention removes the seed coat from peanut kernels to obtain clean peanut kernels before constructing the near-infrared model. The study found that the peanut seed coat color has a relatively small impact on the near-infrared prediction results of the total phenol content. The near-infrared models for clean peanut kernel powder and peanut kernels showed comparable predictive effects for total phenol content, indicating that the near-infrared prediction model based on unpeeled and unground peanut kernels is also widely applicable and can meet the needs of non-destructive, rapid, and high-throughput screening of peanut resources and hybrid offspring.

[0089] In summary, this study compared peanut samples in two different physical states: clean peanut kernel powder and whole peanut kernels. A novel method for determining total phenolic content was developed, and a multivariate fitting numerical analysis model was constructed. The study found that the physical state of peanut kernels has little impact on the near-infrared model prediction results for total phenolic content. Both the clean peanut kernel powder and whole peanut kernel near-infrared prediction models for total phenolic content exhibit good accuracy and stability. Since whole peanut kernels can be used for non-destructive detection of total phenolic content, this method is more suitable for rapid, high-throughput screening of peanut resources and hybrid offspring.

[0090] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0091] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0092] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for scraping from the distal embryonic end for determining the total phenolic content of seeds, characterized in that: Includes the following steps: (1) Sample screening and sampling; (2) Removal of oily and greasy interfering components; (3) Extraction of medium- to high-polarity components; Finally, the total phenol content of the seeds was determined by plotting a standard curve based on the extracted components, blank control, and standards.

2. The method for determining the total phenolic content of seeds by scraping from the distal embryonic end according to claim 1, characterized in that: Includes the following steps: (1) Sample screening and sampling: Select mature, plump, medium-sized kernels that are free from sprouting, damage, and disease spots; take kernels, scrape no more than 1 / 4 of the distal embryo end of each seed with a blade, repeat the operation several times, mix the resulting kernel powder, and set aside for later use; (2) Removal of interfering components such as oils; place the kernel powder in a centrifuge tube, weigh it accurately, add n-hexane, insert the centrifuge tube into a foam float plate, soak it at room temperature, then perform ultrasonic-assisted extraction, cool to room temperature, centrifuge at room temperature, separate the n-hexane layer, and use it for fatty acid determination; the precipitate is then added to n-hexane twice with a pipette, and mixed vigorously until there are no particles or lumps at the bottom of the centrifuge tube, then extracted with ultrasonic assistance, and then degreased a second time; after cooling to room temperature, centrifuge, separate the n-hexane layer, and the defatted kernel powder is obtained; (3) Extraction of medium and high polarity components: Add methanol / ethanol aqueous solution to the precipitate after discarding hexane in the previous step, shake vigorously or mix with ultrasonic assistance until there are no particles or clumps at the bottom of the centrifuge tube; soak at room temperature, then extract with ultrasonic assistance, cool to room temperature, centrifuge at room temperature, and take the supernatant to obtain the extract rich in medium and high polarity components to be tested. Finally, the total phenol content of the seeds was determined by plotting a standard curve based on the extracted components, blank control, and standards.

3. The method for determining the total phenolic content of seeds by scraping from the distal embryonic end according to claim 1, characterized in that: Includes the following steps: (1) Sample screening and sampling: Select mature, plump, medium-sized kernels that are free from sprouting, damage, and disease spots; take 10 to 20 kernels, scrape off no more than 1 / 4 of the distal embryo end of each seed with a blade, repeat the operation several times, mix the resulting kernel powder, and set aside. (2) Removal of interfering components such as oils; Take 0.1 g of kernel powder and place it in a specific centrifuge tube (the allowable range is 0.1000~0.1050 g), accurately weigh it, add 1.6 ml of n-hexane, insert the centrifuge tube into a foam float plate, and soak at room temperature for 2 h, shaking vigorously once every 30 min during this period; use ultrasound at 250 W and 40 kHz to assist extraction for 20 min, shaking vigorously once every 5 min during this period; after cooling to room temperature, centrifuge at 5000 rpm for 5 min at room temperature to separate the n-hexane layer for fatty acid determination; the precipitate is again extracted with 1.6 mL of n-hexane twice using a pipette, and vigorously mixed until there are no particles or lumps at the bottom of the centrifuge tube, then use ultrasound at 250 W and 40 kHz to assist extraction for 20 min, shaking vigorously once every 5 min during this period for a second defatting; after cooling to room temperature, centrifuge at 3000 rpm for 5 min at room temperature, and after separating the n-hexane layer, the defatted kernel powder is obtained; (3) Extraction of medium- and high polarity components: Add methanol / ethanol aqueous solution to the precipitate from which hexane was discarded in the previous step, and mix vigorously or with ultrasonic assistance for 5 min, shaking vigorously once every 30 s until there are no particles or clumps at the bottom of the centrifuge tube; soak at room temperature for 2 h, shaking vigorously once every 30 min; extract with ultrasonic assistance at 250 W and 40 kHz for 20 min, shaking vigorously once every 5 min; after cooling to room temperature, centrifuge at 13000 rpm for 15 min at room temperature, and take the supernatant to obtain the extract rich in medium- and high polarity components to be tested; Finally, the total phenol content of the seeds was determined by plotting a standard curve based on the extracted components, blank control, and standards.

4. The method for determining the total phenolic content of seeds by scraping from the distal embryonic end according to claim 3, characterized in that: In step (2), the specific centrifuge tube is constructed of polypropylene and has a conical bottom angle of 120°±10°, avoiding the use of round-bottomed or pointed-bottom centrifuge tubes; in order to reduce precipitate caking and adapt to multiple extractions of precipitate while ensuring centrifugation efficiency, not losing precipitate and fully separating supernatant.

5. The method for determining the total phenolic content of seeds by scraping from the distal embryonic end according to claim 3, characterized in that: In step (2), the precipitate should not be lost during the separation of the n-hexane layer; and the residual n-hexane should be minimized during the second separation of the n-hexane layer; and the n-hexane should not be evaporated before adding the methanol aqueous solution.

6. The method for determining the total phenolic content of seeds by scraping from the distal embryonic end according to claim 3, characterized in that: In step (3), if the content of kernel protein in the final extract is higher than the limit value, it should be refrigerated overnight to fully precipitate the protein before subsequent determination.

7. The method for determining the total phenolic content of seeds by scraping from the distal embryonic end according to claim 3, characterized in that: In step (3), regarding the selection of the type and concentration of methanol / ethanol aqueous solution, a 50% methanol aqueous solution is selected for the determination of phenolic components.

8. The method for determining the total phenolic content of seeds by scraping from the distal embryonic end according to claim 3, characterized in that: In step (3), regarding the material-liquid ratio, based on the actual content of the component to be tested and the sensitivity that the subsequent detection method can achieve, the material-liquid ratio for the determination of phenolic components is selected as 1:15, that is, 1.5 ml of methanol / ethanol aqueous solution is added for every 0.1 g of kernels.

9. A near-infrared data processing method compatible with rare germplasm resources and trace peanut total phenol content analysis, characterized in that: This method directly uses multiple samples of unground peanut kernels with skin as a baseline, performing multiple loading and near-infrared spectral measurements to obtain mean near-infrared spectral data. Simultaneously, based on the distal embryo scraping method, the total phenolic content of all peanut samples is determined to obtain multi-sample total phenolic content data. Based on this, the model structure is constructed and optimized according to the following process: For the mean near-infrared spectral data of the obtained peanut multi-sample modeling materials, a first-order filtering algorithm is constructed according to the following process: specifying wavelength λ as the basic continuous spatial variable of linear self-variation, obtaining the corresponding variable of the original near-infrared spectral data f(λ) of the obtained peanut multi-sample modeling materials according to the differential correspondence of wavelength λ, and then assigning the corresponding variable of f(λ) to the differential variable of wavelength λ, thereby enhancing the chemical information in the spectral data and reducing the influence of background noise. The first-order filtering algorithm externally highlights the intrinsic slope rate of the spectral curve. Furthermore, the above-mentioned primary filtering algorithm is iterated as needed to obtain a secondary filtering algorithm, which highlights the curvature changes inherent in the spectral curve. Based on this, the mean of the spectral data of the peanut samples is subtracted and the result is divided by the standard deviation to correct the scattering effect in the spectral data and its associated data variation. This method has consistent compatibility with phenolic components with a content of less than 1% in common peanuts, rare hybrid breeding germplasm resources, and early generation hybrid breeding materials.

10. The near-infrared data processing method according to claim 9, which is compatible with the analysis of rare germplasm resources and trace amounts of total phenolic content in peanuts, is characterized in that: The method for obtaining the mean near-infrared spectral data is as follows: using unground peanut kernels with skin as a background, multiple sample loadings and near-infrared spectral measurements were performed. The peanut material used for modeling was naturally air-dried to a moisture content of less than 5%. Mature, plump, undamaged, and unmolded peanut seeds were selected as the test samples and placed at a constant temperature of about 25°C for more than 48 hours. The near-infrared quality analyzer was used to perform spectral measurements on the samples, with a scanning wavelength range of 950–1650 nm and a resolution of 5 nm. After the instrument was preheated, the sample to be tested was placed flat into the sample cup, fully covering the bottom mirror surface. The sample loading was repeated 3 times, and 3 measurements were performed for each sample loading. A total of 9 near-infrared spectra of peanut kernels were obtained for each sample, and the average spectrum was taken.

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