Method for identifying producing area of aronia melanocarpa fruits

Abstract

The invention provides a production place identification method of aronia melanocarpa fruits. The method comprises the following steps: S101, extracting metabolites of the aronia melanocarpa fruits; s102, carrying out liquid chromatography-mass spectrometry detection on the metabolite, and identifying the characteristic peak of the metabolite; s103, comparing the metabolite characteristic peak with the standard peak value of the metabolite of the aronia melanocarpa fruit in each producing area, and determining the producing area of the aronia melanocarpa fruit. The aronia melanocarpa has unique rich nutritional value and excellent anti-oxidation function, but the components of the aronia melanocarpa are not comprehensively known, and the planting field of the aronia melanocarpa cannot be determined based on the aronia melanocarpa, so that the metabolite of the aronia melanocarpa from different producing areas is obviously different, and the planting field of the aronia melanocarpa can be determined by analyzing the metabolite of the aronia melanocarpa fruit. And the production place information of the aronia melanocarpa fruits is determined, so that the production place traceability of the aronia melanocarpa fruits can be realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biological metabolism analysis, and relates to a method for identifying the producing area of astringent crabapple fruit. BACKGROUND

[0002] Black currant is a kind of not old berry, which has the following functions: (1) Not old berry contains a large amount of phenolic substances, which can help delay the aging process and prevent the occurrence of various chronic diseases such as cardiovascular disease, cancer, and neurodegenerative disease. It can also improve skin condition, reduce wrinkle and pigment production, and make skin smoother and more delicate, maintaining skin elasticity and luster. (2) Anthocyanins and other ingredients in not old berry can reduce the content of cholesterol and triglyceride in blood, and increase the level of high-density lipoprotein cholesterol ("good cholesterol"), which can help prevent the formation of atherosclerosis and reduce the risk of cardiovascular disease. (3) Flavonoids in not old berry have the effect of dilating blood vessels and reducing vascular resistance, which can help lower blood pressure and maintain stable blood pressure. (4) Anthocyanins in not old berry have a special protective effect on the eyes. It can promote the regeneration of rhodopsin in retinal cells, prevent retinal diseases and glaucoma, and relieve eye fatigue and dryness. (5) Not old berry is rich in vitamin C, vitamin B, minerals and various bioactive ingredients, which can provide necessary support for the immune system, enhance the activity and function of immune cells, and improve the body's immunity. (6) The anti-inflammatory activity of various ingredients (flavonoids, phenolic substances, etc.) in not old berry can help treat chronic inflammatory diseases such as arthritis and intestinal inflammation, relieve inflammation symptoms, reduce pain and discomfort, and improve the quality of life of patients. (7) Dietary fiber (stachyose, etc.) in not old berry can promote intestinal peristalsis, improve digestive function, promote the absorption of nutrients, prevent intestinal diseases, and relieve indigestion and gastrointestinal dysfunction. It can also reduce the risk of colon cancer and other intestinal malignancies. (8) Although some ingredients (flavonoids, proanthocyanidins) in not old berry cannot be directly used as anticancer drugs, they have potential value in preventing tumor occurrence and assisting tumor treatment, and can be used as part of the comprehensive anticancer strategy in combination with other treatment methods to improve the treatment effect and quality of life of cancer patients. (9) Antioxidants (flavonoids, etc.) in not old berry can prevent and improve neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease, delay disease progression, improve cognitive ability and self-care ability, and maintain brain health and prevent memory decline and cognitive decline in the elderly. (10) Some ingredients (anthocyanins, etc.) in not old berry can improve insulin sensitivity, and appropriate consumption of not old berry may help control blood sugar and reduce blood sugar fluctuations in diabetic patients or pre-diabetic individuals. Not old berry has rich nutritional value and excellent antioxidant function, but the understanding of its ingredients is not comprehensive, and the planting area of not old berry cannot be determined based on not old berry. SUMMARY

[0003] The application provides a method for identifying the origin of astringent crabapple fruit.

[0004] The application provides a method for identifying the origin of astringent crabapple fruit, comprising:

[0005] S101. Extracting metabolites of astringent crabapple fruit;

[0006] S102. Detecting the metabolites by liquid chromatography-mass spectrometry, and identifying characteristic peaks of the metabolites;

[0007] S103. Comparing the characteristic peaks of the metabolites with standard peak values of metabolites of astringent crabapple fruit from various origins, and determining the origin of the astringent crabapple fruit.

[0008] Further, if the astringent crabapple fruit is a liquid II type sample, the S2. extracting metabolites of astringent crabapple fruit comprises:

[0009] S1011. Taking the sample from a-80℃ refrigerator, thawing, and vortexing for 10s until there is no ice block in the sample;

[0010] S1012. Taking 200ul of the sample and adding it into a corresponding numbered 1.5ml centrifuge tube, and adding 200ul of 70% methanol containing an internal standard extraction solution; the internal standard extraction solution is prepared by dissolving 1mg of a standard in 1ml of 70% methanol water to prepare a 1000ug / ml standard stock solution, and further diluting the 1000ug / ml stock solution with 70% methanol to prepare a 250ug / ml internal standard solution;

[0011] S1013. Vortexing for 15min at 12000r / min and 4℃, and centrifuging for 3min;

[0012] S1014. Transferring the supernatant, filtering with a microporous filter, and storing in an injection bottle for LC-MS / MS detection.

[0013] Further, if the sample in S102 is less than 200ul, the internal standard extraction solution is added according to a volume ratio of 1:1.

[0014] Further, if the astringent crabapple fruit is a dry sample, the S2. extracting metabolites of astringent crabapple fruit comprises:

[0015] S1011. Vacuum freeze-drying the biological sample for 63h;

[0016] S1012. Grinding to a powder state by using a grinder;

[0017] S1013. Take 50 mg of sample powder and add 1200 μL of 70% methanol water internal standard extraction solution pre-cooled at 20°C. The internal standard extraction solution is prepared by dissolving 1 mg of standard in 1 mL of 70% methanol water to prepare a 1000 μg / mL standard stock solution, and then further diluting the 1000 μg / mL stock solution with 70% methanol to prepare a 250 μg / mL internal standard solution;

[0018] S1014. Vortex every 30 minutes for 30 seconds, a total of 6 times.

[0019] S1015. After centrifugation, the supernatant is aspirated, the sample is filtered with a microporous filter membrane, and is stored in a sample injection bottle for UPLC-MS / MS analysis.

[0020] Further, if the sample powder is less than 50 mg, the standard stock solution is added at a ratio of 1200 μL of extraction agent per 50 mg of sample.

[0021] Further, the liquid phase conditions of S103 are as follows:

[0022] (1) Chromatographic column: Agilent SB-C18 1.8 μm, 2.1 mm*100 mm;

[0023] (2) Mobile phase: A phase is ultrapure water containing 0.1% formic acid, and B phase is 0.1% formic acid acetonitrile;

[0024] (3) Elution gradient: 0.00 min B phase ratio is 5%, B phase ratio is linearly increased to 95% within 9.00 min, and maintained at 95% for 1 min, 10.00-11.10 min, B phase ratio is reduced to 5%, and balanced at 5% for 14 min;

[0025] (4) Flow rate 0.35 mL / min; column temperature 40°C; sample injection amount 2 μL.

[0026] Further, the mass spectrometry conditions of S103 are as follows:

[0027] Electrospray ion source temperature 500°C; ion spray voltage 5500V / -4500V; ion source gas I, gas II and gas curtain are set to 50, 60 and 25 psi respectively, collision induced ionization parameters are set to high; QQQ scan uses MRM mode, and the collision gas is set to medium.

[0028] Further, the characteristic peaks of the metabolites in S103 include at least the characteristic peaks of one of the indigotin glycoside metabolites, 1-hexanol rhamnose glycoside metabolites, 3-hydroxysebacic acid metabolites, and 2-methyl malic acid metabolites.

[0029] Further, the method for establishing the standard peak of the fruit metabolite of Aronia melanocarpa in S4 comprises:

[0030] S201. Take Aronia melanocarpa fresh fruits from various origins, and select multiple samples from the same origin to form a sample group of the same origin;

[0031] S202. Extract metabolites of Aronia melanocarpa fresh fruits in each sample;

[0032] S203. Perform liquid-phase-mass spectrometry detection on the metabolites to obtain peak values of each metabolite;

[0033] S204. Process and analyze the peak values of each metabolite of different origins to confirm the characteristic metabolite peaks corresponding to each origin.

[0034] Further, the method for establishing the standard peak of the fruit metabolite of Aronia melanocarpa in S4 comprises:

[0035] S2041. Perform cluster analysis on the peak values of metabolites of each sample group, and perform repeated correlation evaluation, select samples with strong correlation between each sample in the same sample group as subsequent analysis samples;

[0036] S2042. Determine metabolite peaks with large differences between sample groups and small differences within sample groups, and take such metabolite peaks as characteristic metabolite peaks corresponding to each origin.

[0037] Compared with the prior art, the metabolites of Aronia melanocarpa from different origins have significant differences, and the origin information of the fruit of Aronia melanocarpa is determined by analyzing the fruit metabolites, so that the origin traceability of the fruit of Aronia melanocarpa can be realized. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 The sample overall cluster diagram of the embodiment of the application;

[0039] Figure 2 The sample correlation diagram of the embodiment of the application;

[0040] Figure 3 The group principal component analysis diagram of the embodiment of the application;

[0041] Figure 4 The OPLS-DA score diagram of the embodiment of the application;

[0042] Figure 5 The OPLS-DA S-plot score diagram of the embodiment of the application;

[0043] Figure 6 The metabolite content difference dynamic distribution diagram of the embodiment of the application;

[0044] Figure 7 Wayne diagram of each group of different metabolites in the embodiments of the present application. DETAILED DESCRIPTION

[0045] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present application, not all.

[0046] Step 1. In order to establish the standard peak of the fruit metabolites of the present application, the fresh fruits of the Aronia melanocarpa from multiple producing areas and the Aronia melanocarpa ferment fruit after fermentation for 30-50 days were collected, and multiple samples were selected from the same producing area to form a sample group of the same producing area, and the specific contents are shown in the following table:

[0047] Table 1 List of 8 sample plants

[0048]

[0049] Note: HY represents fresh fruit samples collected in Haiyang; MP represents fresh fruit samples collected in Muping (the same below).

[0050] Step 2. Sample extraction of the Aronia melanocarpa fruit in the embodiments of the present application, the specific process includes two kinds as follows:

[0051] 2.1. Liquid II sample

[0052] (1) Take the sample out of the-80℃ refrigerator, thaw it, and mix it evenly in the sample without ice blocks, and vortex for 10s.

[0053] (2) Take 200μL of the sample and add it to the corresponding numbered 1.5mL centrifuge tube, add 200μL of 70% methanol containing internal standard extraction solution (if less than 200μL, add extraction solution according to the ratio of 1:1 (V / V)), and the internal standard extraction solution is prepared by dissolving 1mg standard in 1mL 70% methanol water to prepare 1000μg / mL standard stock solution, and further diluting 1000μg / mL stock solution with 70% methanol to prepare 250μg / mL internal standard solution.

[0054] (3) Vortex for 15min, 12000r / min, 4℃, centrifuge for 3min.

[0055] (4) Take the supernatant, filter it with a microporous filter membrane (0.22μm pore size), and store it in a sample bottle for LC-MS / MS detection.

[0056] 2.2. Dry sample extraction

[0057] (1) The biological sample is placed in a freeze dryer (Scientz-100F) for vacuum freeze drying for 63h;

[0058] (2) Grinding (30 Hz, 1.5 min) to powder by a grinder (MM 400, Retsch);

[0059] (3) 50 mg of sample powder was weighed by an electronic balance (MS105DM), and 1200 μL of 70% methanol water internal standard extraction solution pre-cooled at 20°C was added (if less than 50 mg, 1200 μL of extraction agent was added per 50 mg of sample), and the internal standard extraction solution was prepared by dissolving 1 mg of standard in 1 mL of 70% methanol water to prepare a 1000 μg / mL standard stock solution, and the 1000 μg / mL stock solution was further diluted with 70% methanol to prepare a 250 μg / mL internal standard solution;

[0060] (4) Vortexing once every 30 minutes for 30 seconds, a total of 6 times;

[0061] (5) After centrifugation (12000 rpm for 3 minutes), the supernatant was aspirated, the sample was filtered with a microporous filter membrane (0.22 μm pore size), and was stored in an injection bottle for UPLC-MS / MS analysis.

[0062] Specifically, in the embodiments of the present application, each sample is extracted by dry sample extraction.

[0063] Step 3. The chromatography mass spectrometry collection conditions of the fruit samples of the present application are as follows:

[0064] 3.1. The main data collection instrument system includes an ultra performance liquid chromatography (UPLC) (ExionLC AD, https: / / sciex.com.cn / ) and a tandem mass spectrometry (MS / MS). TM

[0065] 3.2. The main liquid phase conditions of the embodiments of the present application include:

[0066] (1) Chromatographic column: Agilent SB-C18 1.8 μm, 2.1 mm*100 mm;

[0067] (2) Mobile phase: A phase is ultrapure water (0.1% formic acid is added), and B phase is acetonitrile (0.1% formic acid is added);

[0068] ​(3) Elution gradient: at 0.00 min, the B phase ratio was 5%, which increased linearly to 95% within 9.00 min and maintained at 95% for 1 min. From 10.00 to 11.10 min, the B phase ratio was reduced to 5% and equilibrated at 5% for 14 min.

[0069] (4) Flow rate: 0.35 mL / min; column temperature: 40°C; injection volume: 2 μL.

[0070] 3.3. The mass spectrometry conditions of the embodiment of the present invention mainly include:

[0071] The electrospray ionization (ESI) source temperature was 500°C; the ion spray voltage (IS) was 5500 V (positive ion mode) / -4500 V (negative ion mode); the ion source gas I (GSI), gas II (GSII), and curtain gas (CUR) were set to 50, 60, and 25 psi, respectively, and the collision-induced ionization parameter was set to high. QQQ scanning was performed in MRM mode, and the collision gas (nitrogen) was set to medium. The declustering potential (DP) and collision energy (CE) of each MRM transition were further optimized. A specific set of MRM transitions was monitored in each period based on the metabolites eluting during that period.

[0072] The metabolite qualitative and quantitative principles of the embodiments of the present invention are based on the Maiwei metabolic self-built database MWDB (metware database), and the substance is qualitatively identified according to the secondary spectrum information. During the analysis, the isotope signals, repeated signals containing K+ ions, Na+ ions, NH4+ ions, and repeated signals of fragment ions of other larger molecular weight substances are removed.

[0073] For the obtained mass spectra, data preprocessing is also required, including data filling and screening methods: missing values ​​are filled with 1 / 5 of the minimum value of each row (metabolite), and then the CV value of the QC sample is calculated, and substances with a CV value less than 0.5 are retained.

[0074] Step 4: Analyze and process the detected values, including:

[0075] 4.1 Qualitative and quantitative analysis of metabolites

[0076] The mass spectrometry data was processed by software Analyst 1.6.3. The following table shows the total ion current (TIC, the sum of the intensity of all ions at each time point in the mass spectrum diagram versus time) and the MRM metabolite detection multi-peak diagram (multi-substance extraction ion current spectrum, XIC) of the mixed sample quality control (QC) sample. The horizontal coordinate is the retention time (RT) of the metabolite detection, and the vertical coordinate is the ion current intensity of the ion detection (the intensity unit is cps, count per second). The results are as follows:

[0077] The composition of metabolites has sample specificity, and different types of samples contain different categories and proportions of metabolites; in addition, the composition of metabolites will change in different processes or biological processes. By analyzing the proportion of metabolite composition, the distribution of main metabolites in the sample can be investigated as a whole.

[0078] The proportion of each type of substance detected in the fruit and enzyme of Aronia melanocarpa is as follows:

[0079] Table 2 Metabolite category composition

[0080]

[0081] Among them, the number of anthocyanins (one kind of flavonoids) and proanthocyanidins (one kind of tannins) is 27 and 10, including 20 kinds of vitamins such as vitamin C and B, in addition to anthocyanins, each Aronia melanocarpa sample contains 393 kinds of flavonoids other than anthocyanins.

[0082] For different samples, the number of metabolites detected in the fruit of Aronia melanocarpa collected in Haiyang is 2357, while that in Muping is 2361.

[0083]

[0084]

[0085] From the number of nutritional ingredients, 25 kinds of anthocyanins were detected in the fresh fruit collected in Haiyang, and 10 kinds of proanthocyanidins were detected in the fresh fruit collected in Muping. However, after fermentation for 12 hours, the contents of anthocyanins and proanthocyanidins were 23 and 6, respectively. The number of vitamins in the fresh fruit of the two places was 18. The number of phenolic acids in the fresh fruit of Haiyang and Muping was 281 and 285, respectively. The number of flavonoids including anthocyanins in the fresh fruit of Haiyang and Muping was 378 and 374, respectively.

[0086] 4.2 Cluster analysis

[0087] The cluster analysis diagram of each sample is as follows: Figure 1As shown in the figure, the horizontal direction is the sample name, the vertical direction is the metabolite information, Group is the grouping, and different colors are different values filled after normalization of different relative contents (red represents high content, and green represents low content). HY represents the fresh fruit samples collected in Haiyang; MP represents the fresh fruit samples collected in Muping (the same below).

[0088] 4.2.1 First, the data is processed by UV (unit variance scaling), and cluster heat map analysis is performed on all samples, and R program script is used to draw cluster heat map. From Figure 2 It can be seen that the material content difference between Haiyang and Muping is small, and the several substances with higher content are terpenes, flavones, phenolic acids, and lipids, quinones, and steroids.

[0089] 4.2.2 Repeated correlation evaluation, through the correlation analysis between samples, the biological repeatability between samples in the group can be observed. At the same time, the higher the correlation coefficient of the samples in the group relative to the samples between the groups, the more reliable the obtained differential metabolites are. Pearson's Correlation Coefficient r is used as an evaluation index of biological repeatability correlation. Pearson's Correlation Coefficient is calculated by using the built-in cor function of R software, and |r| is closer to 1, indicating that the correlation between two repeated samples is stronger. See Figure 2 , the vertical and diagonal lines in the figure respectively represent the sample names of different samples, different colors represent different Pearson correlation coefficients, the redder the color, the stronger the positive correlation, the greener the color, the weaker the correlation, the bluer the color, the stronger the negative correlation, and the correlation coefficient between the two samples is marked in the square.

[0090] 4.3 Data result analysis

[0091] 4.3.1 Group principal component analysis, before difference analysis, first, the principal component analysis is performed on the group samples to be compared, to observe the variation degree between the difference groups and the samples in the group.

[0092] As shown in Figure 3 , PC1 represents the first principal component, PC2 represents the second principal component, and the percentage represents the explanation rate of the principal component to the data set; each point in the figure represents a sample, and samples in the same group are represented by the same color, and Group is the grouping.

[0093] It can be seen from Figure 3 that the metabolic product difference between groups is significant, and the difference within the group is small, and the data can be used for further analysis of differential metabolites in the next step.

[0094] 4.3.2 Orthogonal partial least squares discriminant analysis (OPLS-DA) Although principal component analysis (PCA) can effectively extract the main information, it is not sensitive to variables with small correlation. Partial least squares discriminant analysis (PLS-DA) can solve this problem. PLS-DA is a multivariate statistical analysis method of supervised pattern recognition, which extracts components from independent variables X and dependent variables Y respectively, and then calculates the correlation between the components. Compared with PCA, PLS-DA can maximize the group discrimination, which is beneficial to find the differential metabolites. Orthogonal partial least squares discriminant analysis (OPLS-DA) combines orthogonal signal correction (OSC) and PLS-DA method, which can decompose the X matrix information into two types of information related to Y and unrelated to Y, and screen the differential variables by removing the unrelated differences. OPLS-DA is used after log2 transformation and centering of the original data. X is the sample quantitative information matrix, and Y is the sample grouping information matrix.

[0095] When modeling OPLS-DA, the X matrix information is decomposed into two types of information related to Y and unrelated to Y, where the variable information related to Y is the predictive principal component, and the variable information unrelated to Y is the orthogonal principal component. According to the OPLS-DA model analysis of metabolome data, the score plot of each group is drawn, which further shows the differences between each group.

[0096] As shown in Figure 4 , the horizontal axis represents the predictive principal component, and the horizontal direction can show the gap between groups; the vertical axis represents the orthogonal principal component, and the vertical direction can show the difference within the group; the percentage represents the explanatory rate of the component to the data set. Each point in the figure represents a sample, and samples in the same group are represented by the same color. Group is the grouping. Among them, QC is the mixed sample of fresh fruits from two places.

[0097] As can be seen from the figure, the intra-group difference (distance) of each combination for difference analysis is small, and the inter-group gap is large, which indicates that each group is suitable for difference analysis, especially as shown in the J plot. The reliability of the evaluation model can be seen in the next section.

[0098] 4.3.3 OPLS-DA S-plot

[0099] Figure 5 The S-plot of OPLS-DA, the horizontal axis represents the covariance of the principal component and the metabolite, and the vertical axis represents the correlation coefficient of the principal component and the metabolite. The metabolites closer to the right upper corner and the left lower corner indicate that the difference is more significant. The red points indicate that the VIP value of these metabolites is greater than 1, and the green points indicate that the VIP value of these metabolites is less than or equal to 1.

[0100] Table 4 Statistics of the number of differential metabolites

[0101]

[0102] Depend on Figure 5 As shown in Table 4, the types and contents of metabolites in different fresh fruit samples, whether from Haiyang or Muping, were significantly different.

[0103] 4.4 Dynamic distribution of metabolite content differences

[0104] In order to more clearly and intuitively display the overall metabolic differences, the fold change (FC) values ​​of the metabolites in the comparison group were calculated. After calculation, they were arranged from small to large according to the FC values, and a dynamic distribution diagram of the metabolite content differences was drawn, and the top 10 up-regulated and down-regulated metabolites were marked.

[0105] Figure 6 The horizontal axis represents the cumulative number of substances arranged from small to large according to the difference multiple, and the vertical axis represents the logarithm of the difference multiple with base 2. Each point represents a substance, the green point represents the top 10 substances that have been downgraded, and the red point represents the top 10 substances that have been upgraded.

[0106] Depend on Figure 6 As shown in Table 4, between the HY and MP groups, the top ten compounds that were up-regulated were mainly some triterpenes, diterpenes or free fatty acids, while the top ten compounds that were down-regulated included some flavonoids, flavonoids, amino acids and their derivatives, which may provide a certain reference for our selection of planting sites.

[0107] 4.5 Differential metabolite screening

[0108] Metabolomics data are characterized by high dimensionality and massive amounts of data. Therefore, it is necessary to combine univariate statistical analysis and multivariate statistical analysis methods, and analyze from multiple angles based on the characteristics of the data, so as to accurately mine differential metabolites. Univariate statistical analysis methods include hypothesis testing and fold change (FC) analysis. Multivariate statistical analysis methods include principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), etc. The variable importance projection (VIP) obtained based on the OPLS-DA model (biological replicates ≥ 3) can preliminarily screen out metabolites that differ between different groups. At the same time, the P-value / FDR (biological replicates ≥ 2) or FC value of the univariate analysis can be combined to further screen out differential metabolites. The screening criteria for differential metabolites in this project are as follows.

[0109] For comparisons between two groups: 1. Select metabolites with a VIP value greater than 1. The VIP value indicates the strength of the inter-group difference in the corresponding metabolite in the classification of each group of samples in the model. Metabolites with a VIP value greater than 1 are generally considered to have significant differences.

[0110] 2. Select metabolites with a fold change ≥ 2 and a fold change ≤ 0.5. If the difference between the control and experimental groups is 2-fold or greater or 0.5 or less, the difference is considered significant.

[0111] For multiple group comparisons:

[0112] 1. Select metabolites with VIP>1. The VIP value indicates the influence of the inter-group difference of the corresponding metabolite in the classification and discrimination of each group of samples in the model. It is generally believed that metabolites with VIP>1 have significant differences.

[0113] 2. Select metabolites with P-value < 0.05 (ANOVA). If there is a statistically significant difference between the metabolites in different groups, the difference is considered significant.

[0114] Depend on Figure 7 It can be seen that there are four types of differential metabolites among the groups, namely indigoside (indole alkaloids), 1-hexyl rhamnosyl glucoside (alcohol compound), 3-hydroxysebacic acid (free fatty acid), and 2-methylmalic acid (organic acid). In other words, the relative contents of the above four metabolites can be used to distinguish the fresh fruits of Haiyang and Mouping, as shown in the following table.

[0115] Table 5 Comparison of anthocyanins and proanthocyanins in Haizhong, fresh fruit of Muping and fermented 12 hours, fermented 45 days enzyme

[0116]

[0117]

[0118] Note: NA+ represents that the compared group (FW and T) is not detected, NA- represents that the compared group is not detected, NA represents that both groups are not detected, _ represents that there is no significant difference between the average contents of the two groups.

[0119] The above table can be used as the standard peak of metabolites of the fruit of the Aronia melanocarpa based on each component.

[0120] In summary, for different samples, 2357 metabolites were detected in the Aronia melanocarpa fruit collected in Haizhong, and 2361 metabolites were detected in Muping. From the perspective of the number of nutrients, 25 anthocyanins were detected in the fresh fruit collected in Haizhong, and 25 anthocyanins were detected in Muping, and 10 proanthocyanins were detected in both. The number of vitamins in the fresh fruit of the two places is 18. Therefore, the relative content of indoxyl (indole alkaloid), 1-hexanol rhamnosyl glucoside (alcohol compound), 3-hydroxy decanedioic acid (free fatty acid), 2-methyl malic acid (organic acid) and the like can be used to distinguish the types of fresh fruit from different places.

[0121] On the basis of the above content of the embodiment of the present application, a method for identifying the origin of the fruit of the Aronia melanocarpa is also provided, comprising:

[0122] S101. Extracting metabolites of the fruit of the Aronia melanocarpa;

[0123] Specifically, the fruit of the Aronia melanocarpa in the embodiment of the present application is a dry sample, and the processing method comprises:

[0124] S1011. Vacuum freeze-drying the biological sample for 63h;

[0125] S1012. Grinding to powder by using a grinder;

[0126] S1013. Weighing 50mg of sample powder, adding 1200μL of 70% methanol water internal standard extraction solution pre-cooled at 20℃, and the internal standard extraction solution is prepared by dissolving 1mg of standard in 1mL of 70% methanol water to prepare 1000μg / mL of standard stock solution, and further diluting the 1000μg / mL stock solution with 70% methanol to prepare 250μg / mL of internal standard solution; if the sample powder is less than 50mg, the standard stock solution is added according to the proportion of 1200μL of extraction agent per 50mg of sample

[0127] S1014. Vortexing every 30 minutes, each time for 30 seconds, a total of 6 times;

[0128] S1015. After centrifugation, the supernatant was aspirated, the sample was filtered with a microporous filter membrane, and was stored in an injection bottle for UPLC-MS / MS analysis.

[0129] S102. The metabolites were subjected to liquid chromatography-mass spectrometry detection, and metabolite characteristic peaks were identified.

[0130] S103. The metabolite characteristic peaks were compared with the metabolite standard peak values of the Rubus corchorifolius fruits from each origin, and the origin of the Rubus corchorifolius fruits was determined.

[0131] In S103, the metabolite characteristic peaks include characteristic peaks of indigoid metabolites, 1-hexanol rhamnose glycoside metabolites, 3-hydroxysebacic acid metabolites, and 2-methyl malic acid metabolites. The Rubus corchorifolius fruit metabolite standard peak values are the Rubus corchorifolius fruit metabolite standard peak values established by the aforementioned process of the embodiments. According to the comparison of the metabolite characteristic peaks with the Rubus corchorifolius fruit metabolite standard peak values from each origin, if the metabolite characteristic peaks of the sample are closest to the Rubus corchorifolius fruit metabolite standard peak values from one origin, then the origin of the sample can be determined as the corresponding origin of the closest Rubus corchorifolius fruit metabolite standard peak values.

[0132] The embodiments of the present application utilize the significant differences in metabolites of Rubus corchorifolius from different origins, realize the determination of the origin information of the Rubus corchorifolius fruits by analyzing the metabolites of the Rubus corchorifolius fruits, and can realize the origin traceability of the Rubus corchorifolius fruits.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application rather than limit the same, and although the present application has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that the specific embodiments of the present application can be modified or replaced by equivalents after reading the present application, but these modifications or changes do not deviate from the scope of the present application.

Claims

1. A method for identifying the origin of aronia fruit, characterized in that: include: S101. Extraction of metabolites from berry fruit; S102. Performing liquid chromatography-mass spectrometry on the metabolites to identify characteristic peaks of the metabolites; S103. Compare the characteristic peaks of the metabolites with the standard peaks of the metabolites of the berry fruits from various origins to determine the origin of the berry fruits.

2. The method for identifying the origin of the berry according to claim 1, wherein: If the berry fruit is a liquid Class II sample, then S2. extracting metabolites from the berry fruit includes: S1011. Remove the sample from the -80°C freezer, thaw until there is no ice in the sample, and vortex for 10 seconds to mix. S1012. Take 200 μL of sample and add it to the corresponding numbered 1.5 mL centrifuge tube. Add 200 μL of 70% methanol containing internal standard extract. The internal standard extract is prepared by dissolving 1 mg of standard in 1 mL of 70% methanol in water to prepare a 1000 μg / mL standard stock solution. The 1000 μg / mL stock solution is further diluted with 70% methanol to prepare a 250 μg / mL internal standard solution. S1013. Vortex for 15 min, 12,000 rpm, 4°C, and centrifuge for 3 min. S1014. Remove the supernatant, filter it through a microporous filter membrane, and store it in an injection bottle for LC-MS / MS detection.

3. The method for identifying the origin of the berry according to claim 2, wherein: If the sample in S102 is less than 200 μL, the internal standard extract is added in a 1:1 volume ratio.

4. The method for identifying the origin of the berry according to claim 1, wherein: If the berry fruit is a dry sample, then the step S1. extracting metabolites from the berry fruit includes: S1011. The biological samples were freeze-dried under vacuum for 63 h; S1012. Grind to powder using a grinder; S1013. Weigh 50 mg of sample powder and add 1200 μL of -20°C pre-cooled 70% methanol-water internal standard extract. The internal standard extract is prepared by dissolving 1 mg of the standard in 1 mL of 70% methanol-water to prepare a 1000 μg / mL standard stock solution. The 1000 μg / mL stock solution is further diluted with 70% methanol to prepare a 250 μg / mL internal standard solution. S1014. Vortex once every 30 minutes for 30 seconds each time, for a total of 6 times; S1015. After centrifugation, aspirate the supernatant, filter the sample through a microporous filter membrane, and store it in an injection bottle for UPLC-MS / MS analysis.

5. The method for identifying the origin of the berry according to claim 4, wherein: If the sample powder is less than 50 mg, add the standard stock solution at a ratio of 1200 μL of extractant per 50 mg of sample.

6. The method for identifying the origin of the berry according to claim 1, wherein: The liquid phase conditions of S103 are: (1) Chromatographic column: Agilent SB-C18 1.8 μm, 2.1 mm x 100 mm; (2) Mobile phase: Phase A is ultrapure water containing 0.1% formic acid, and phase B is acetonitrile containing 0.1% formic acid; (3) Elution gradient: at 0.00 min, the B phase ratio was 5%, which increased linearly to 95% within 9.00 min and maintained at 95% for 1 min. From 10.00 to 11.10 min, the B phase ratio was reduced to 5% and equilibrated at 5% for 14 min. (4) Flow rate: 0.35 mL / min; column temperature: 40°C; injection volume: 2 μL.

7. The method for identifying the origin of the berry according to claim 6, characterized in that: The mass spectrometry conditions of S103 are: The electrospray ionization source temperature was 500°C; the ion spray voltage was 5500 V / -4500 V; the ion source gas I, gas II, and curtain gas were set to 50, 60, and 25 psi, respectively, and the collision-induced ionization parameter was set to high; the QQQ scan used the MRM mode, and the collision gas was set to medium.

8. The method for identifying the origin of the berry according to claim 1, wherein: The characteristic peaks of metabolites in S103 include at least characteristic peaks of one type of substances selected from the group consisting of indigoside metabolites, 1-hexanol rhamnosyl glucoside metabolites, 3-hydroxysebacic acid metabolites, and 2-methylmalic acid metabolites.

9. The method for identifying the origin of the berry according to claim 1, wherein: The method for establishing the standard peak value of the metabolites of the berry fruit in S4 includes: S201. Take fresh berries from various production areas, and select multiple samples from the same production area to form a sample group from the same production area; S202. Extracting metabolites of fresh berry fruit and berry enzyme fruit from each sample; S203. Performing liquid chromatography-mass spectrometry on the metabolites to obtain peak values ​​of each metabolite; S204. Process and analyze the peak values ​​of each metabolite from different origins to confirm the characteristic peaks of metabolites corresponding to each origin.

10. The method for identifying the origin of the berry according to claim 9, characterized in that: S4. Processing and analyzing the peak values ​​of each metabolite from different origins to confirm the characteristic peaks of metabolites corresponding to each origin, including: S2041. Perform cluster analysis on the metabolite peak values ​​of each sample group and perform repeated correlation evaluation, and select samples with strong correlation between samples in the same sample group as subsequent analysis samples; S2042. Determine metabolite peaks that have large differences between sample groups and small differences within sample groups, and use these metabolite peaks as characteristic metabolite peaks corresponding to each origin.

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