Application of metabonomics analysis technology to identification of boxthorn seed oil

Abstract

The invention belongs to the technical field of compound analysis and identification, and discloses application of a metabonomics analysis technology to identification of lycium seed oil. According to the metabonomics analysis technology, the characteristic components of the lycium seed oil are identified through Log2 Fold Channel and VIP values, and the screening conditions for identifying the characteristic components of the lycium seed oil are | Log2 Fold Channel | > 1.5, and VIP < gt >; the method specifically comprises the steps of sample preparation, non-targeted metabonomics analysis, extraction and identification of characteristic peaks, similarity analysis, difference analysis and screening of differential markers. According to the method, a non-targeted metabonomics analysis method is adopted, various metabolic components in the lycium barbarum seed oil can be undiscriminately covered, the limitation that a traditional identification technology only aims at a few indexes is broken through, the unique characteristic components of the lycium barbarum seed oil can be accurately screened out, and the accuracy and specificity of an identification result are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of compound analysis and identification technology, and in particular to the application of a metabolomics analysis technique for identifying wolfberry seed oil. Background Technology

[0002] Goji berries ( Lycium bArbarum L. bArbarum Goji berries (also known as wolfberries) belong to the Solanaceae family and have long been used as a functional food in traditional Chinese medicine and diet. Studies have found that goji berries contain various bioactive compounds, such as polysaccharides, flavonoids, alkaloids, and carotenoids. These components have been proven to have health benefits such as anti-oxidation, anti-aging, anti-fatigue, immune regulation, and anti-diabetic effects. Goji berry seeds are a byproduct of deep processing of goji berries, including drying, and account for about 4% of the wet weight of fresh fruit. They are usually discarded as waste and not fully utilized. In addition to common components of goji berry seeds (such as phosphatidylcholine and phytosterols) and bioactive substances such as superoxide dismutase, vitamin E, β-carotene, and various trace elements, they are also rich in fatty oils, with a content between 18% and 22%. This fatty oil content is lower than that of locust seeds (over 30%). Goji berry seed oil is mainly composed of linoleic acid (about 70%) and oleic acid (about 20%), with palmitic acid, stearic acid, and γ-linolenic acid accounting for a combined 10%. Furthermore, wolfberry seed oil boasts a high unsaturated fatty acid content of up to 88%, with linoleic acid, an essential fatty acid, making up the largest proportion. Essential fatty acids are nutrients indispensable for human life activities but cannot be synthesized by the body. Therefore, humans can obtain linoleic acid from wolfberry seed oil through dietary consumption. Wolfberry seed oil can serve as a beneficial nutrient in the pharmaceutical, food, and cosmetic industries. Modern pharmacological research indicates that wolfberry seed oil possesses various pharmacological activities, including antioxidant, hypoglycemic, cognitive impairment improvement, anti-fatigue, anti-hypoxia, and prevention of night blindness caused by vitamin A deficiency. This substance can also be used as an adjunct therapy to treat cardiovascular and cerebrovascular diseases such as hypertension, hyperlipidemia, and atherosclerosis. However, the quality of wolfberry seed oil products on the market varies greatly, with frequent issues such as adulteration with other cheap vegetable oils and the sale of inferior products.

[0003] Traditional identification methods mainly rely on physicochemical indicators such as refractive index, relative density, and iodine value, as well as fatty acid composition analysis. However, these indicators are singular and only reflect some component characteristics, making accurate identification difficult. The chemical composition of wolfberry seed oil is extremely complex, containing over a thousand chemical components. Current requirements for the authenticity of wolfberry seed oil are unclear, and a scientific identification system based on the overall metabolic level is lacking. Therefore, there is an urgent need to develop a comprehensive and accurate identification technology for wolfberry seed oil. Summary of the Invention

[0004] Based on this, and addressing the problems existing in current wolfberry seed oil identification technology, this invention provides an application of metabolomics analysis technology for identifying wolfberry seed oil. It establishes a comprehensive, non-discriminatory, non-targeted metabolomics analysis method for wolfberry seed oil using metabolomics analysis technology, which can analyze the metabolic components of different varieties of plant oils and accurately identify the characteristic components of wolfberry seed oil.

[0005] This invention is achieved through the following technical solution: This invention provides an application of metabolomics analysis technology for identifying wolfberry seed oil. In the metabolomics analysis technology, characteristic components of wolfberry seed oil are identified through Log2 Fold Change and VIP value. The screening conditions for identifying characteristic components of wolfberry seed oil are |Log2 Fold Change| > 1.5 and VIP > 1.

[0006] In one specific embodiment, the metabolomics analysis technology provided by the present invention for identifying the application of wolfberry seed oil includes the following steps: (1) Sample preparation; (2) Non-targeted metabolomics analysis; (3) Extraction and identification of characteristic peaks; (4) Similarity analysis; (5) Differential analysis and screening of differential markers.

[0007] Preferably, the sample preparation in step (1) is as follows: take 150 μL of wolfberry seed oil and other vegetable oil samples, add 2 mL of extraction solution, extract on a track shaker for 90 min, add 1 mL of ultrapure water to remove protein, centrifuge at 4000 r / min for 10 min, transfer the lower phase to a clean centrifuge tube, blow dry with nitrogen, add 900 μL of methanol to make up the volume, filter with a filter membrane, and then inject the sample. At the same time, prepare QC samples to monitor the detection stability.

[0008] Preferably, the non-targeted metabolomics analysis in step (2) includes component separation using high performance liquid chromatography and analysis using mass spectrometry.

[0009] Preferably, the chromatography and mass spectrometry are performed under the following conditions: Chromatographic conditions: Column: CAPCELL PAK MG C18 Column; Mobile phase A: Acetonitrile:water, 60:40, V / V, containing 10 mmol / L ammonium acetate; Mobile phase B: Isopropanol:acetonitrile, 90:10, V / V, containing 10 mmol / L ammonium acetate; Gradient elution: Binary gradient elution program; Acquisition time: 35 min; Column temperature: 40 ℃; Injection plate temperature: 10 ℃; Injection volume: 2 μL; Gradient elution program: 0–20 min, 37%–98%B; 20–28 min, 98%B; 28–28.1 min, 98%–37%B; 28.1–35 min, 37%–37%B. Mass spectrometry conditions: Ion source: HESI source; Acquisition mode: positive and negative ion detection modes; Scanning mode: Full MS / dd-MS2, Full MS resolution 70000, dd-MS2 resolution 17500; Scanning range: positive ion mode 240~2000 m / z, negative ion mode 200~2000 m / z; Sheath gas flow rate: 35 Arb; Auxiliary gas flow rate: 10 Arb; Spray voltage: 3 kV (+); Ion transmission tube temperature: 280 ℃; Heater temperature: 250 ℃; In MS / MS mode, the collision energies used are step energies: 20, 40, and 60 eV.

[0010] Preferably, the extraction and identification of characteristic peaks in step (3) specifically involves: using LipidSearch software to perform peak alignment, peak extraction, noise reduction, and normalization on the raw data of different plant oils obtained from mass spectrometry analysis. The settings of each parameter include: the mass deviation of compound detection is 4 to 6 ppm, the maximum unknown element composition is set to C90, H190, Br3, C14, K2, N10, O40, P3, and S5, the minimum unknown element composition is set to C and H, the minimum peak intensity is set to 800,000 to 1,200,000, the signal-to-noise ratio (S / N) threshold is 2.5 to 3.5, the mass deviation of primary mass spectrum matching is 4.5 to 5.5 ppm, and the database used for primary mass spectrum matching is selected from the LipidSearch database.

[0011] Preferably, the similarity analysis in step (4) specifically involves performing principal component analysis on all lipids based on the collected data using MetaboAnalyst software.

[0012] Preferably, step (5) of difference analysis and screening of difference markers specifically involves: using the OPLS-DA method to analyze and determine the lipid differences of different samples based on the collected data, and evaluating the lipid VIP score that makes a significant contribution to the differentiation of oils.

[0013] Preferably, in step (5) differential analysis and screening of differential markers, the identification and confirmation of differential metabolites involves matching with secondary mass spectra and confirming compounds with standards.

[0014] Preferably, the secondary mass spectrum is matched with the LipidSearch database. Matching the secondary mass spectrum further improves the accuracy of differential metabolite detection and enhances the overall accuracy of the analytical method.

[0015] The present invention has the following beneficial effects: 1. Comprehensive and accurate identification: Using non-targeted metabolomics analysis methods, it can cover all kinds of metabolic components in wolfberry seed oil without discrimination, breaking through the limitations of traditional identification techniques that only target a few indicators. It can accurately screen out the unique characteristic components of wolfberry seed oil, greatly improving the accuracy and specificity of the identification results.

[0016] 2. Wide applicability and high efficiency: This technology can not only be used to identify the authenticity of wolfberry seed oil, but also to analyze the differences in metabolic components of wolfberry seed oil from different varieties, origins and processing techniques. The operation process is highly standardized and the detection efficiency is better than traditional methods, reducing the manpower and time costs in the identification process.

[0017] 3. Outstanding industrial value: It provides scientific and reliable technical support for the quality control and market supervision of wolfberry seed oil, effectively combats industry malpractices such as selling inferior products as superior ones and adulteration, protects consumer rights, and promotes the standardization and high-quality development of the wolfberry seed oil industry. Attached Figure Description

[0018] Figure 1 These are mass spectra of four lipids from Example 2 of the present invention; Figure 2 The PCA score chart and OPLS-DA score chart are shown in Embodiment 2 of the present invention, where A is the PCA score chart and B to D are the OPLS-DA score charts. Figure 3 This is a VIP rating chart comparing wolfberry seed oil with other vegetable oils in Example 2 of the present invention; Figure 4 This is a cross-validation diagram of Embodiment 2 of the present invention; Figure 5 These are volcano diagrams and Venn diagrams for different groups in Embodiment 2 of the present invention. A to C are volcano diagrams, and D is a Venn diagram. Figure 6 These are heat maps of different groups in Embodiment 2 of the present invention. A is a heat map of the LBSO and PO group, B is a heat map of the LBSO and CO group, and C is a heat map of the LBSO and RO group. Detailed Implementation

[0019] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. Example 1: Materials, Instruments, Reagents and Methods

[0020] Goji berry seed oil (LBSO) samples were purchased from Inavicon and Combibloc, respectively. Three common vegetable oils (rapeseed oil RO, peanut oil PO, and corn oil CO) were purchased from local supermarkets. All subsequent vegetable oil samples underwent the same treatment process to eliminate the impact of the treatment method on oil quality.

[0021] Methanol and acetonitrile, used in HPLC, were purchased from Merck (Darmstadt, Germany). Isopropanol, used in LC-MS, was purchased from Purite (USA). Chloroform (HPLC grade) was purchased from Baiyin Liangyou Chemical Reagent Co., Ltd., and ammonium acetate (HPLC grade) was purchased from Shandong Keyuan Biochemical Co., Ltd. Ultrapure water was supplied by Millipore Milli-Q reference-grade water treatment system, France. Sodium hydroxide was supplied by Tianjin Aubokai Chemical Co., Ltd. Heptane and anhydrous sodium sulfate (analytical grade) were purchased from Tianjin Damao Chemical Reagent Factory. Boron trifluoride in methanol solution was purchased from Beijing Beiweiye Metrology Technology Research Institute. Sodium chloride (analytical grade) was purchased from Sichuan Xilong Technology Co., Ltd.

[0022] Lipid extraction: A glass tube containing 150 mg of weighed oil sample and 2 mL of CHCl3:MeOH (2:1, v / v) was placed on an orbital oscillator and shaken for 90 minutes. The mixture was separated into residue and extract. To extract proteins, 1 mL of ultrapure water was added to the extract. After centrifugation at 4000 rpm for 10 minutes, the lower layer was completely transferred to another clean glass tube and dried under a nitrogen stream. For UPLC-Q-Exactive Orbitrap mass spectrometry analysis, the residue was redissolved in 900 μL of CHCl3:MeOH (2:1, v / v).

[0023] Fatty acid extraction: Add 8 mL of 2% sodium hydroxide methanol solution to the oil sample, connect a reflux condenser, and reflux in an 80°C water bath until the oil droplets disappear. Add 7 mL of 15% boron trifluoride methanol solution to the top of the reflux condenser and continue refluxing in an 80°C water bath for 2 minutes. Rinse the reflux condenser with a small amount of water. Stop heating, remove the flask, and rapidly cool to room temperature. Accurately add 20 mL of n-heptane, shake for 2 minutes, then add saturated sodium chloride aqueous solution and allow to separate into layers. Take approximately 5 mL of the upper n-heptane extract and place it in a 25 mL test tube, add 5 g of anhydrous sodium sulfate, shake for 1 minute, and allow to stand for 5 minutes. Transfer the upper solution to a sample vial for analysis.

[0024] Lipid fraction determination: Lipid profile analysis of the oil samples was performed using a Thermo Fisher Scientific UPLC-Q-Extractive Orbitrap mass spectrometer (California, USA) equipped with a heated electrospray ionization probe. The oil extracts were separated on an MG Ш C18 column (2.1 × 150 mm, 3 μm; CAPCELL PAK) in both negative and positive ion modes. A binary solvent system was used: mobile phase A was acetonitrile-water (60:40, v / v) containing 10 mM ammonium acetate, and mobile phase B was isopropanol-acetonitrile (90:10, v / v) containing 10 mM ammonium acetate. Gradient elution was performed at a flow rate of 0.22 mL / min for 35 minutes: the concentration of phase B increased from 37% to 98% within 20 minutes, then maintained at 98% for 8 minutes, and finally equilibrated with a 37% phase concentration for 7 minutes. The column chamber and sample tray temperatures were maintained at 40°C and 10°C, respectively. Data were acquired in both positive and negative ion modes within the m / z 240–2000 and m / z 200–2000 mass ranges, and data-dependent MS / MS acquisition was performed. Full scan and fragment spectra were acquired at 70,000 and 17,500 resolutions, respectively. Ion source parameters were set as follows: injection voltage 3000 V, capillary temperature 280 °C, heater temperature 250 °C, sheath gas flow rate 35 Arb, and auxiliary gas flow rate 10 Arb. A 2 μL injection volume was used for both positive and negative ion modes. Data analysis and lipid identification were performed using LipidSearch software V5.0 (Thermo Fisher Scientific). All lipid identification was based on MS1 ​​and MS2 spectra, with MS1 mass error <5 ppm and MS2 mass error <8 ppm. Lipids with an m-score <10 and a peak area <1e5 were subsequently excluded. By utilizing the high resolution of Orbitrap / MS, the structures of identified lipids in oil are determined by characteristic product ions of acyl chains and head groups, thereby extracting lipid-targeting ions for reliable identification of each substance. Example 2: Experimental Results and Analysis

[0025] Fatty acid composition analysis of four oils: Over 80% of LBSO's components are unsaturated fatty acids. Besides participating in other important lipid metabolism activities, linoleic acid is crucial for enzyme activity and central nervous system function. LBSO provides an alternative source of this essential fatty acid, as comparative analysis shows its linoleic acid content is significantly higher than PO (30.0%), RO (20.0%), and CO (50%). The human body cannot synthesize linoleic acid and must obtain it through dietary intake. Therefore, LBSO can be used as a dietary supplement or functional food. Its fatty acid composition mainly consists of oleic acid (18–22%), palmitic acid (4.82–6.1%), linoleic acid (61.24–66.64%), palmitoleic acid (0.22–0.34%), linolenic acid (1.16–1.52%), and stearic acid (2.19–2.93%). The content of monounsaturated fatty acids in LBSO is between 18% and 22%, and the ratio of saturated fatty acids to unsaturated fatty acids is approximately 13% and 86%, respectively.

[0026] Note: - indicates "Not detected", C16:1-palmitoleic acid, C17:1-heptadecenoic acid, C18:1n9c-cis-9-oleic acid, C22:1n9-erucic acid, C18:2n6t-linoleic acid, C18:2n6c-linoleic acid, C18:3n6-γ-linolenic acid, C18:3n3-α-linolenic acid, C20:2-eicosadienoic acid, C20:3n3-docosatrienoic acid, C20:4n6-arachidonic acid, C22:2-docosatrienoic acid, C4:0-butyric acid, C6:0-hexanoic acid, C14:0-myristic acid, C15:0-pentadecanoic acid. C16:0-Palmitic acid, C17:0-Heptadecanonic acid, C18:0-Stearic acid, C20:0-Octadecanonic acid, C21:0-Coctodecanoic acid, C22:0-Coctodecanoic acid, C23:0-Tredodecanoic acid, C24:0-Citricacid.

[0027] Compositional Analysis of Four Oils: A comprehensive lipid profile analysis of the four oils was performed using UPLC-Q-Extractive Orbitrap mass spectrometry. Detailed information on the precise relative molecular mass and secondary mass spectrometry fragmentation mode of the lipids was obtained using a combined scanning mode. As shown in Table 1, a total of 480 lipids were identified in the four oils, including 445 glycerides, 5 fatty acids, and 22 phospholipids. The 454 glycerides mainly consisted of 53 diacylglycerols (DG), 393 triacylglycerols (TG), 1 wax ester (WE), and 7 monoacylglycerols. The 22 phospholipids included: 3 phosphatidylcholine (PC), 5 phosphatidylethanolamine (PE), 3 phosphatidyl ethers (PEt), 6 phosphatidylglycerols (PG), 1 phosphatidylinositol (PI), 1 phosphatidylserine (PS), and 2 diacylglycerol pyrophosphates (DAP).

[0028] Table 1 Fatty acid composition of four vegetable oils fatty acid RO CO PO LBSO C16:1 0.17±0.05 0.05±0.06 0.06±0.03 0.28±0.06 C17:1 0.04±0.03 0.01±0.0 0.03±0.02 0.15±0.02 C18:1n9c 58.80±1.98 27.07±0.76 48.14±2.21 20.20±1.72 C22:1n9 0.08±0.06 - 0.61±0.04 - C18:2n6c 18.94±2.84 53.64±2.56 35.32±1.74 63.94±2.70 C18:3n6 0.17±0.15 - 1.33±0.30 0.09±0.05 C18:3n3 11.41±1.65 0.75±0.16 - 1.34±0.18 C20:2 0.10±0.01 0.05±0.02 0.03±0.01 0.20±0.16 C20:3n3 3.73±3.73 - - 0.01±0 C20:4n6 - - 0.01±0.0 - C22:2 - - 0.52±0.10 - C4:0 0.05±0.03 0.01±0.0 - - C6:0 - 5.81±5.11 0.54±0.52 0.33±0.74 C14:0 0.09±0.02 0.03±0.01 0.04±0.02 0.09±0.01 C15:0 0.01±0.01 - - - C16:0 3.79±0.66 10.25±0.91 9.30±0.43 5.46±0.64 C17:0 0.05±0.04 0.03±0.01 0.05±0.01 0.02±0.01 C18:0 1.70±0.26 1.32±0.18 3.17±0.13 2.56±0.37 C20:0 0.36±0.81 - - 4.87±0.60 C21:0 2.32±1.25 0.18±0.16 0.77±0.11 - C22:0 0.22±0.11 - - - C23:0 0.07±0.05 - - 0.45±0.59 C24:0 0.55±0.30 - 0.05±0.02 - Saturated fatty acids 9.20±1.93 17.63±4.19 13.93±0.84 13.78±1.24 Monounsaturated fatty acids 59.11±2.01 27.13±0.81 48.85±2.20 20.63±1.68 Polyunsaturated fatty acids 32.07±2.37 54.44±2.47 37.22±1.82 65.59±2.51 Total unsaturated fatty acids 91.18±4.38 81.57±3.28 86.07±4.02 86.22±4.19 Table 1 shows that the number of carbon atoms and double bonds in the fatty acid chains of different oils and fats varies within a certain range. The total number of carbon atoms in the side chains of lipid fatty acids in the four types of oils ranges from 17 to 80, and the number of double bonds ranges from 0 to 21. For glycerides, DG has 17 to 44 carbon atoms and 0 to 8 double bonds. MG has 18 to 25 carbon atoms and 1 to 6 double bonds. TG has 34 to 80 carbon atoms and 0 to 21 double bonds. Dilinoleic acid (DAP) has 31 to 36 carbon atoms and 8 to 13 double bonds. For phospholipids, phosphocholine (PC) has 38 to 49 carbon atoms and 3 to 7 double bonds. Phosphatidylethanolamine (PE) has 33 to 34 carbon atoms and 0 to 3 double bonds. PEt has 32 carbon atoms and 1 to 3 double bonds. PG has 42 to 49 carbon atoms and 1 to 5 double bonds. PI has 46 carbon atoms and 3 double bonds. PS has 50 carbon atoms and 4 double bonds.

[0029] Lipid identification: Under specific collision energies (CE), selected lipid precursor ions enter the Q2 stage of the mass spectrometer, where collision-induced dissociation (CID) occurs. This process generates specific fragment ions or leads to the neutral loss of specific functional groups in the lipid molecule. This invention employs ultra-high performance liquid chromatography-Q-Extractive Orbitrap mass spectrometry to analyze and identify glycerol lipids and phospholipids in LBSO. Subsequent studies analyzed DG 34:2 | DG 16:0_18:2 and DG 36:6 ​​| DG 18:3_18:3 in diacylglycerols, and TG 54:7 | TG 18:1_18:3_18:3 and TG 54:4 | TG 18:1_18:1_18:2 in triglycerides, and their mass spectrometric behavior and fragmentation mechanisms were analyzed in detail.

[0030] Figure 1 A shows the MS / MS spectra of DG 34:2 | DG 16:0_18:2. Figure 1A shows that m / z 610.5392 corresponds to the protonated parent ion [M+NH4]⁺ of DG 34:2. m / z 575.5014 represents [M+NH4-NH3-H2O]⁺, a fragment ion formed by the simultaneous loss of ammonia and water from the parent ion [M+NH4⁺]. m / z 337.27231 appears as a characteristic diagnostic fragment ion of the fatty acid acyl chain ([M+NH4⁻NH3⁻FA 16:0]⁺). This ion is a monoglyceride fragment ion (18:2 DMAG⁺) formed after the parent ion m / z 610.5392 loses fatty acid FA 16:0. Meanwhile, m / z 313.27249 is the characteristic diagnostic fragment ion of the fatty acid acyl chain ([M + NH4⁻NH3⁻FA 18:2]⁺), specifically the monoglyceride fragment ion 16:0 DMAG⁺ generated when the parent ion m / z 610.5392 loses fatty acid FA18:2. Figure 1 B shows the MS / MS spectra of DG 36:6 ​​| DG 18:3_18:3. m / z 630.5082 is identified as the parent ion [M+NH4]⁺ of DG 36:6, while m / z 595.47229 corresponds to [M+NH4-NH3-H2O]⁺—this fragment ion is formed through the neutral loss of ammonia and water from the parent ion [M+NH4⁺]. m / z 335.25806 represents the characteristic diagnostic fragment ion of the fatty acid acyl chain ([M+NH4-NH3-FA 18:3]+), specifically the monoglyceride fragment ion 18:3 DMAG+—formed when the parent ion m / z 630.5082 loses the FA 18:3 fatty acid. Figure 1 C shows the MS / MS spectra of TG 54:7|TG 18:1_18:3_18:3. According to... Figure 1 C, m / z 894.7545 represents the [M + NH4]⁺ parent ion of this compound, which produces two diester fragment ions through neutral losses of FA 18:1 and FA 18:3—36:6DDAG⁺ (m / z 595.4721) and 36:4DDAG⁺ (m / z 599.5034), respectively. Figure 1 D shows the MS / MS spectra of TG 54:4 | TG18:1_18:1_18:2 in positive ion mode. (By...) Figure 1 As can be seen from D, m / z 900.8015 is the [M+ NH4]⁺ parent ion of this compound. After the removal of FA 18:1 and FA 18:2, two characteristic diester fragment ions are generated: 36:3DDAG⁺ (m / z 601.5190) and 36:2DDAG+ (m / z 603.5347).

[0031] Multivariate statistical analysis: Principal component analysis (PCA) is a multidimensional statistical analysis tool used for unsupervised pattern recognition. It has been effectively used to preliminarily assess overall metabolic differences within and between different sample groups. For example... Figure 2 As shown in Figure A, the PCA score plot clearly distinguishes the four oil samples. The four oils—LBSO, PO, CO, and RO—are separated based on the first two principal components, which together explain 43.4% of the total variance.

[0032] The lipid differences among four types of oils—LBSO, PO, CO, and RO—were determined using the OPLS-DA method. Figure 2 B- Figure 2 As shown in Figure D. Simultaneously, the lipid composition of the four oils was analyzed using MetaboAnalyst 6.0 software, and the lipid VIP score, which significantly contributes to the differentiation of oils, was evaluated. Figure 3 As shown in A, 15 unique lipid molecules (VIP score > 1.34) made a significant contribution to the differentiation. It includes 10 triglycerides (TG) and 5 diacylglycerols (DG), specifically: TG(36:2_22:4), TG(22:1_22:1_18:3), TG(20:1_22:1_18:3), TG(18:0COOH_18:1_18:1), TG(18:0_18:1_18:2), TG(18:2_18:2_18:2), TG(22:1_24:1_18:3), TG(22:1_18:2_18:3), TG(22:1_20:2_18:3), TG(14:1_16:1_18:1), DG(6:0_21:3), DG(16:0_22:0), DG(20:0_18:2), DG(16:0_18:1). DG (20:1-22:1). These lipids can be used to distinguish between PO and LBSO. For example... Figure 3As shown in B, 15 unique lipid molecules made significant contributions (VIP score > 1.69). Among these lipid types, 15 triglycerides (TG) were included, such as TG(22:0_18:1_18:1), TG(22:1_22:1_18:3), TG(20:1_22:1_18:3), TG(20:1_22:1_18:2), TG(24:0_18:2_18:2), TG(22:1_24:1_18:3), TG(22:1_20:2_18: 3) TG(18:0_18:1_22:1), TG(22:1_22:1_18:2), TG(16:0_24:0_18:2), TG(16:0_22:0_18:1), TG(18:1_20:1_22:1), TG(18:1_18:1_22:1), TG(22:0_18:1_18:2), TG(18:1_22:1_18:2). These lipids can be used to distinguish between LBSO and CO. For example... Figure 3 As shown in C, a total of 15 unique lipid molecules were considered to have made significant contributions (VIP score > 1.77). These include 11 triglycerides and 4 diacylglycerols, specifically: TG(20:2_16:0_20:0), TG(18:0_20:0_18:2), DG(18:0_18:2), TG(20:0_18:1_18:1), TG(O-12:1_6:0_18:1), TG(16:0_22:0_18:2), TG(10:0_18:1_18:1), DG(18:1_18:1), TG(20:1_18:2_18:2). TG(20:0_18:2_18:3), DG(20:0_18:1), TG(17:0_18:1_18:3), DG(20:5_20:0), TG(18:0_18:1_22:1), TG(17:1_18:1_18:3). These lipids can be used to distinguish between LBSO and RO.

[0033] The stability and predictive ability of the OPLS-DA model were evaluated 1000 times. R²Y represents the model's interpretability, i.e., the proportion of information that the model can explain within the predefined categorical variable Y. The closer R²Y is to 1, the more information the model can explain between the two classification groups, and the more significant the difference between the two groups. Q²Y reflects the model's interpretability when predicting new data. A higher Q² value indicates better predictive performance. Q² is usually calculated through cross-validation; the closer its value is to 1, the stronger the model's predictive ability and the higher its reliability. The p-values ​​of R²Y and Q² should be observed. If both are less than 0.05, there is no evidence of overfitting. Generally, R² and Q² values ​​greater than 0.5 are considered good, and greater than 0.4 are acceptable. Figure 4A shows Q²=0.978 and R²Y=0.998, with a p-value of 0.008 for both. Generally, a p-value < 0.05 indicates that the model is optimal. Figure 4 B shows Q²=0.909, R²Y=0.995, and both have a P-value of 0.009. Figure 4 C shows Q²=0.886, R²Y=0.999, and both have a P-value of 0.011.

[0034] Analysis of specific lipids in LBSO, PO, CO, and RO samples: In this analysis, lipids with a VIP score ≥1 and an absolute log2-fold change ≥1.0 were considered statistically significant. VIP values ​​were obtained from OPLS-DA data and generated using the R software MetaboAnalyst. Before applying OPLS-DA, the data were logarithmically transformed (base 2) and mean centered. Comparative analyses of LBSO with RO, LBSO with PO, and LBSO with CO identified 34, 83, and 7 upregulated lipids, respectively, and 22, 100, and 82 downregulated lipids, respectively. Figure 5 A- Figure 5 C). Three-group cross-analysis revealed six differentially expressed lipids ( Figure 5 D), all of which are triglycerides, including: TG(36:4_19:0COOH), TG(19:0COOH_18:2_18:2), TG(18:1_19:0COOH_18:3), TG(22:1_20:2_18:3), TG(18:1_18:1_20:1) and TG(18:2_22:0_18:3), which may serve as potential biomarkers.

[0035] In the LBSO and PO groups, and the LBSO and CO groups, 231, 177, and 180 differentially expressed metabolites (VIP value > 1) were obtained, respectively, and the top 25 significantly different metabolites from each group were selected. Figure 6As shown in A, these metabolites can be divided into two categories. A group with significantly upregulated differential lipid components was identified, with 11 components elevated in the PO group. The 11 upregulated lipid components in the PO group include DG(22:0_18:2), DG(18:1_22:1), DG(20:1_22:1), DG(20:1_18:2), DG(16:0_24:0), DG(16:0_22:0), DG(20:5_20:0), DG(16:0_18:1), DG(20:0_18:2), TG(18:0_18:1_18:2), and TG(14:1_16:1_18:1_18:1). In the LBSO group, the expression levels of 14 lipid components were significantly increased, including: TG(18:0COOH_18:1_18:1), TG(20:1_22:1_18:3), TG(22:1_22:1_18:3), TG(22:1_18:2_18:2), TG(9:0CHO_18:1_18:1), TG(10:0_12:0_14:0), TG(22:1_20:2_18:3), DG(6:0_21:3), TG(18:2_18:2_18:2_18:2_18:2), TG(36:2_22:4), TG(22:1_24:1_18:3), and DG(O-18:4_18:1). Figure 6As shown in Figure B, metabolites were classified into two categories: LBSO and CO. Analysis revealed that among the groups with significantly increased specific lipid components, the LBSO group exhibited an increasing trend for 25 lipid components. Among these, 25 were significantly elevated, including TG (22:1_18:2_18:3), TG (18:2_23:0_18:1), TG (22:1_24:1_18:2), TG (22:1_18:3_20:3), DG (O-18:4_18:1), and TG (22:1_18:2_18:2). TG(18:1_22:1_18:3), TG(16:0_18:1_22:1), TG(16:0_22:0_18:2), TG(22:0_18:1_18:2), TG(18:1_20:1_22:1), TG(18:1_18:1_22:1),TG(18:1_22:1_18:2),TG(22:1_22:1_18:2), TG(16:0_24:0_18:2), TG(16:0_22:0_18:1),TG(22:1_20:2_18:3),TG(22:1_24:1_18:3), TG(24:0_18:2_18:2), TG(20:1_22:1_18:2), TG(18:0_18:1_22:1), TG(20:1_22:1_18:3), TG(22:1_22:1_18:3), TG(22:0_18:1_18:1), and TG(42:1_20:6) were increased in the LBSO group. Figure 6 As shown in C, among the groups that showed significant downregulation of differentially expressed lipid components, the LBSO group had downregulation of 11 lipid components. These lipid components included TG(22:1_22:1_18:2), TG(20:1_22:1_18:3), TG(22:1_22:1_18:3), TG(22:1_18:2_18:2), TG(18:1_22:1_18:3), TG(22:1_18:2_18:3), TG(22:1_22:1_18:2), TG(22:1_18:3_20:3), TG(18:1_22:1_18:2), TG(24:0_18:2_18:2), and TG(22:0_18:1_18:2), all of which were downregulated in the LBSO group.

[0036] Although the present invention has been described in detail in the embodiments through general description, specific implementation and experiment, any modifications or improvements that can be made without departing from the core of the present invention shall fall within the scope of protection of the present invention.

Claims

1. An application of a metabolomics analysis technique for identifying wolfberry seed oil, characterized in that, In the metabolomics analysis technique, characteristic components of wolfberry seed oil are identified by Log2 Fold Change and VIP value. The screening conditions for identifying characteristic components of wolfberry seed oil are |Log2 Fold Change| > 1.5 and VIP > 1.

2. The application of the metabolomics analysis technology according to claim 1 for identifying wolfberry seed oil, characterized in that, Includes the following steps: (1) Sample preparation; (2) Non-targeted metabolomics analysis; (3) Extraction and identification of characteristic peaks; (4) Similarity analysis; (5) Differential analysis and screening of differential markers.

3. The application of the metabolomics analysis technique described in claim 2 for identifying wolfberry seed oil, characterized in that, The sample preparation in step (1) is as follows: take 150 μL of each of the wolfberry seed oil and other vegetable oil samples, add 2 mL of extraction solution, extract on a track shaker for 90 min, add 1 mL of ultrapure water to remove protein, centrifuge at 4000 r / min for 10 min, transfer the lower phase to a clean centrifuge tube, blow dry with nitrogen, add 900 μL of methanol to make up the volume, filter with a filter membrane, and then inject the sample. At the same time, prepare QC samples to monitor the detection stability.

4. The application of the metabolomics analysis technique according to claim 2 for identifying wolfberry seed oil, characterized in that, The non-targeted metabolomics analysis in step (2) includes component separation using high performance liquid chromatography and analysis using mass spectrometry.

5. The application of the metabolomics analysis technique according to claim 4 for identifying wolfberry seed oil, characterized in that, The chromatography and mass spectrometry were performed under the following conditions: The chromatographic column was a CAPCELL PAK MG C18 column; mobile phase A was acetonitrile:water, 60:40, V / V, containing 10 mmol / L ammonium acetate; mobile phase B was isopropanol:acetonitrile, 90:10, V / V, containing 10 mmol / L ammonium acetate; gradient elution: binary gradient elution program; acquisition time: 35 min. Column temperature: 40 ℃, injection plate temperature: 10 ℃; injection volume: 2 μL; gradient elution program: 0~20 min, 37%~98%B; 20~28 min, 98%B; 28~28.1 min, 98%~37%B; 28.1~35 min, 37%~37%B; Mass spectrometry conditions: Ion source: HESI source; Acquisition mode: positive and negative ion detection modes; Scanning mode: Full MS / dd-MS2, Full MS resolution 70000, dd-MS2 resolution 17500; Scanning range: positive ion mode 240~2000 m / z, negative ion mode 200~2000 m / z; Sheath gas flow rate: 35 Arb; Auxiliary gas flow rate: 10 Arb; Spray voltage: 3 kV (+); Ion transfer tube temperature: 280 ℃; Heater temperature: 250 ℃; In MS / MS mode, the collision energies used are step energies: 20, 40, and 60 eV.

6. The application of the metabolomics analysis technique according to claim 2 for identifying wolfberry seed oil, characterized in that, The extraction and identification of characteristic peaks in step (3) are as follows: the raw data of different plant oils collected are processed by LipidSearch software to perform peak alignment, peak extraction, noise reduction and normalization on the mass spectrometry data. The settings of each parameter include: the mass deviation of compound detection is 4 to 6 ppm, the maximum unknown element composition is set as C90, H190, Br3, C14, K2, N10, O40, P3 and S5, the minimum unknown element composition is set as C and H, the minimum peak intensity is set as 800,000 to 1,200,000, the signal-to-noise ratio threshold is 2.5 to 3.5, the mass deviation of primary mass spectrum matching is 4.5 to 5.5 ppm, and the database used for primary mass spectrum matching is selected from the LipidSearch database.

7. The application of the metabolomics analysis technique according to claim 2 for identifying wolfberry seed oil, characterized in that, The similarity analysis in step (4) specifically involves performing principal component analysis on all lipids based on the collected data using MetaboAnalyst software.

8. The application of the metabolomics analysis technique according to claim 2 for identifying wolfberry seed oil, characterized in that, The specific steps (5) of differential analysis and screening of differential markers are as follows: based on the collected data, the OPLS-DA method is used to analyze and determine the lipid differences of different samples, and to evaluate the lipid VIP score that makes a significant contribution to the differentiation of oils.

9. The application of the metabolomics analysis technique according to claim 2 for identifying wolfberry seed oil, characterized in that, In step (5) differential analysis and screening of differential markers, the identification and confirmation of differential metabolites involves matching with secondary mass spectra and confirming compounds with standards.

10. The application of the metabolomics analysis technique according to claim 9 for identifying wolfberry seed oil, characterized in that, The secondary mass spectra are matched with the LipidSearch database. This matching of secondary mass spectra further improves the accuracy of differential metabolite detection and enhances the overall accuracy of the analytical method.

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