A method for constructing the antioxidant characteristic fingerprint of wolfberry based on UPLC and multivariate statistical analysis and a method for identifying its origin

Abstract

The present invention provides a method for constructing a characteristic anti-oxidation fingerprint of wolfberry based on UPLC and multivariate statistical analysis and a method for distinguishing the origin of wolfberry. An online UPLC-DPPH-PDA-ESI-Q-TOF / MS coupling technology was established, and the antioxidant components of wolfberry were quickly screened and identified; secondly, a characteristic fingerprint of the antioxidant components of wolfberry was established, and combined with multivariate statistical analysis, the correct identification of wolfberry from different producing areas in China was achieved; finally, the antioxidant markers of wolfberry from different producing areas were screened, and the distribution characteristics of their origin were compared and evaluated. This study provides methodological and technical support for the identification of the origin of wolfberry.

Description

Technical Field

[0001] The present invention relates to the technical field of research on Chinese medicinal ingredients, and in particular to a method for constructing a wolfberry antioxidant characteristic fingerprint spectrum based on UPLC and multivariate statistical analysis and a method for distinguishing the origin of the wolfberry. Background Art

[0002] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention, and should not necessarily be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art.

[0003] Wolfberry is a perennial deciduous shrub with red or light red flowers and bright red fruits. Wolfberry has high nutritional value and medicinal and health-care effects. It is not only a traditional Chinese medicine, but also an important economic crop in China. Modern pharmacological research shows that wolfberry has the effects of improving immunity, anti-oxidation, anti-aging, and lowering blood lipids and cholesterol.

[0004] So far, there are more than 80 varieties of wolfberry in the world, and there are 7 varieties in China, which are mainly distributed in Ningxia, Xinjiang, Inner Mongolia, Qinghai, Shanxi, Hebei and other places. Among them, Ningxia wolfberry is the most famous, and it is recognized as an authentic production area and a Chinese geographical indication protected product (GB / T 19742-2008). Therefore, its price is generally higher than that of other regions. With the increase in people's consumption level, some unscrupulous vendors pass off wolfberries from other regions as Ningxia wolfberries or sell them adulterated to make illegal profits. Therefore, the study of the origin identification method of wolfberry is of great significance.

[0005] In recent years, fingerprints have become an internationally recognized effective means of quality control of traditional Chinese medicine or natural medicine, with the characteristics of integrity, macroscopicity, and fuzziness. At present, it has been widely used in the quality control and origin identification of Chinese herbal medicines, especially the combination of LC-MS and chemometrics. However, most of these studies have not clarified the relationship between chemical composition and biological activity, as well as the distribution characteristics of origin-specific active markers.

[0006] Antioxidation is an important pharmacological activity of wolfberry. Many studies have shown that flavonoids, flavan-3-ols, phenolic acids, carotenoids, polysaccharides and other components in wolfberry have antioxidant activity. In recent years, online HPLC / UPLC-DPPH-PDA-ESI-TOF / MS antioxidant screening technology integrates chromatographic analysis, activity screening and structural identification. It has the advantages of high efficiency, rapidity, sensitivity and good reproducibility, and has been widely used in the screening of antioxidant components in natural products.

[0007] However, there are no reports on the screening of antioxidant components and identification of origin of wolfberry based on this technology. Summary of the invention

[0008] In order to solve the deficiencies of the prior art, the present invention provides a method for constructing a characteristic anti-oxidation fingerprint of wolfberry based on UPLC and multivariate statistical analysis and a method for distinguishing the origin of wolfberry. First, an online UPLC-DPPH-PDA-ESI-Q-TOF / MS coupling technology is established, and the antioxidant components of wolfberry are quickly screened and identified; secondly, a characteristic fingerprint of the antioxidant components of wolfberry is established, and combined with multivariate statistical analysis, the correct identification of wolfberry from different producing areas in China is achieved; finally, the antioxidant markers of wolfberry from different producing areas are screened, and the distribution characteristics of their origins are compared and evaluated.

[0009] In order to achieve the above object, the technical solution of the present invention is:

[0010] The first aspect of the present invention provides a method for constructing an antioxidant characteristic fingerprint of wolfberry based on UPLC and multivariate statistical analysis. First, an online UPLC-PDA-DPPH method is used to screen the antioxidant components of different batches of wolfberry extracts; then ESI-Q-TOF / MS is used to measure and preliminarily identify the antioxidant components by comparing precursor ions, fragment ions and ultraviolet absorption wavelengths with literature; finally, 28 chemical components with potential antioxidant activity and good separation, stability and reproducibility are screened out as common peaks to establish a wolfberry antioxidant characteristic fingerprint.

[0011] The preparation method of the wolfberry extract is as follows: 2 g of wolfberry sample is taken, crushed, placed in a conical flask, 32 mL of methanol is added, ultrasonicated, 8 mL of water is added, ultrasonicated again, and filtered for later use;

[0012] Preferably, the step of adding methanol and ultrasonicating for 25 minutes, and adding water and ultrasonicating for 15 minutes;

[0013] Preferably, the suction filtration uses a 0.22 μm organic filter membrane.

[0014] The DPPH concentration is 2×10 -5 -10×10 -5 mol / L, flow rate is 0.2-0.4mL / min, wavelength of DPPH screening reaction is 517nm; inner diameter of PEEK tube is 0.1-0.25mm, length is 5-20m;

[0015] Preferably, the inner diameter of the PEEK tube is 0.18 mm;

[0016] Preferably, the length of the PEEK tube is 10 m;

[0017] Preferably, the DPPH concentration is 6×10 -5 M;

[0018] Preferably, the flow rate is 0.4 mL / min.

[0019] The HPLC chromatographic conditions are as follows: chromatographic column: ACQUITY UPLC BEH amide, 3.0×100 mm, 1.7 μm; mobile phase: acetonitrile aqueous solution; injection volume: 5 μL; flow rate: 0.4 mL·min-1; wavelength: 310 nm; elution gradient.

[0020] The mobile phase is A: acetonitrile, B: water, 0.8% formic acid + 20mmol ammonium formate;

[0021] Further, the gradient elution is 0-10 min, 90%-90% A; 10-20 min, 90%-89% A; 20-35 min, 89%-89% A; 36-80 min, 85%-85% A; 80-85 min, 85%-81% A; 85-100 min, 81%-81% A; 100-105 min, 81%-79% A; 105-110 min, 79%-78% A; 110-135 min, 78%-78% A; 136-150 min, 76%-76% A; 150-155 min, 76%-73% A; 155-175 min, 73%-70% A; 175-200 min, 70%-62% A.

[0022] The MS conditions: flow rate was 0.4 mL min -1 Electrospray positive and negative ion modes were used, with a nebulizer gas pressure of 45 psi; the drying gas was N2 with a flow rate of 10.0 mL min -1 , drying gas temperature 350°C; capillary voltage 3500V, pyrolysis voltage 175V, cone voltage 65V, full scan mass-to-charge ratio range 50-1100.

[0023] The second aspect of the present invention provides a method for identifying the origin of wolfberry based on the above-mentioned fingerprint construction method and multivariate statistical analysis, comprising the following steps:

[0024] (1) Using PCA principal component analysis or PLS-DA pattern recognition analysis to group wolfberry samples from different origins, accurate classification of wolfberry samples based on origin was achieved;

[0025] (2) Using VIP>1 and one-way ANOVA P<0.05, 8 compounds were screened as antioxidant biomarkers for regional differentiation of wolfberry samples.

[0026] According to the PCA principal component analysis or the PLS-DA pattern recognition analysis, the wolfberry samples were divided into four groups according to the regions.

[0027] The eight compounds are peak 4 rutin, peak 24 rutin dihexoside, peak 10 p-coumaric acid trihexoside, peak 5 dicaffeic quinic acid isomer, peak 12 Quercetin-rhamno-di-hexoside and three unknown compounds peak 14, peak 16 and peak 27.

[0028] The third aspect of the present invention provides the application of the above-mentioned fingerprint construction method or wolfberry origin identification method in wolfberry origin tracing.

[0029] The beneficial effects of the present invention are:

[0030] (1) An online UPLC-DPPH-PDA-ESI-Q-TOF / MS technique was established. This method combines the rapid screening and identification capabilities of the LC-MS-based online screening method for antioxidants, and 28 antioxidants were screened from wolfberry extract, of which 19 were preliminarily identified;

[0031] (2) The characteristic fingerprint of antioxidant components of wolfberry was established;

[0032] (3) The antioxidant characteristic fingerprint was combined with PCA and PLS-DA analysis to achieve accurate classification of wolfberry samples based on their origin;

[0033] (4) Using VIP>1 and one-way ANOVA (P<0.05), eight compounds including rutin (peak 4), rutin dihexose (peak 24), p-coumaric acid trihexose (peak 10), dicaffeoylquinic acid isomer (peak 5), quercetin-rhamno-di-hexoside (peak 12), peak 14, peak 16 and peak 27 were screened as antioxidant markers for the differentiation of wolfberry samples by production area;

[0034] (5) This study provides methods and technical support for the identification of the origin of wolfberry. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0036] Figure 1 The online UPLC-PDA-DPPH method of the present invention is used to screen the antioxidant components of wolfberry (A: UPLC-PDA spectrum of wolfberry; B: UPLC-PDA chromatogram of wolfberry after online antioxidant component screening; C: scavenging ability of the eluate on DPPH free radicals);

[0037] Figure 2 The UPLC-PDA fingerprints of 32 batches of wolfberries from different geographical production areas in China according to the present invention;

[0038] Figure 3 The PCA score diagram of 32 batches of wolfberries from different geographical production areas in China based on the antioxidant component information of the present invention;

[0039] Figure 4 This is a PLS-DA score diagram of 32 batches of wolfberries from different geographical production areas in China based on antioxidant component information of the present invention;

[0040] Figure 5 The permutation test of overfitting of the PLS-DA model of 32 batches of wolfberries from different geographical production areas in China based on the antioxidant component information of the present invention (n=200);

[0041] Figure 6 The VIP score graph (VIP>1.0) of 32 batches of wolfberries from different geographical production areas in China based on the antioxidant component information of the present invention;

[0042] Figure 7 are the peak areas of the four markers of the present invention (peak 4 is rutin; peak 24 is rutin dihexose; peak 10 is p-coumaric acid trihexose; peak 27 is unknown);

[0043] Figure 8 These are the peak areas of the other four markers of the present invention (peak 5 is dicaffeoylquinic acid isomer; peak 12 is Quercetin-rhamno-di-hexoside; peak 14 is unknown; peak 16 is unknown). DETAILED DESCRIPTION

[0044] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0045] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0046] The present invention proposes a method for constructing a wolfberry antioxidant characteristic fingerprint based on UPLC and multivariate statistical analysis. Firstly, an online UPLC-PDA-DPPH method is used to screen antioxidant components of different batches of wolfberry extracts; then ESI-Q-TOF / MS is used to measure and preliminarily identify the antioxidant components by comparing precursor ions, fragment ions and ultraviolet absorption wavelengths with those in the literature; finally, 28 chemical components with potential antioxidant activity and good separation, stability and reproducibility are screened out as common peaks to establish a wolfberry antioxidant characteristic fingerprint.

[0047] In a typical embodiment of the present invention, the preparation method of the wolfberry extract is as follows: take 2g of wolfberry sample, crush it, put it in a conical flask, add 32mL of methanol, sonicate, continue to add 8mL of water, sonicate again; filter it for later use;

[0048] Preferably, the step of adding methanol and ultrasonicating for 25 minutes, and adding water and ultrasonicating for 15 minutes;

[0049] Preferably, the suction filtration uses a 0.22 μm organic filter membrane.

[0050] In a typical embodiment of the present invention, the DPPH concentration is 2×10 -5 -10×10 -5 mol / L, flow rate is 0.2-0.4mL / min, wavelength of DPPH screening reaction is 517nm; inner diameter of PEEK tube is 0.1-0.25mm, length is 5-20m;

[0051] Preferably, the inner diameter of the PEEK tube is 0.18 mm;

[0052] Preferably, the length of the PEEK tube is 10 m;

[0053] Preferably, the DPPH concentration is 6×10 -5 M;

[0054] Preferably, the flow rate is 0.4 mL / min.

[0055] In a typical embodiment of the present invention, the HPLC chromatographic conditions are: chromatographic column: ACQUITY UPLCBEH amide, 3.0×100 mm, 1.7 μm; mobile phase is acetonitrile aqueous solution; injection volume is 5 μL; flow rate is 0.4 mL·min-1; wavelength is 310 nm; elution gradient.

[0056] The mobile phase is A: acetonitrile, B: water, 0.8% formic acid + 20mmol ammonium formate;

[0057] Further, the gradient elution is 0-10 min, 90%-90% A; 10-20 min, 90%-89% A; 20-35 min, 89%-89% A; 36-80 min, 85%-85% A; 80-85 min, 85%-81% A; 85-100 min, 81%-81% A; 100-105 min, 81%-79% A; 105-110 min, 79%-78% A; 110-135 min, 78%-78% A; 136-150 min, 76%-76% A; 150-155 min, 76%-73% A; 155-175 min, 73%-70% A; 175-200 min, 70%-62% A.

[0058] In a typical embodiment of the present invention, the MS conditions are: the flow rate is 0.4 mL·min -1 Electrospray positive and negative ion modes were used, with a nebulizer gas pressure of 45 psi; the drying gas was N2 with a flow rate of 10.0 mL min -1 , drying gas temperature 350°C; capillary voltage 3500V, pyrolysis voltage 175V, cone voltage 65V, full scan mass-to-charge ratio range 50-1100.

[0059] In another typical embodiment of the present invention, a method for identifying the origin of wolfberry based on the above-mentioned fingerprint spectrum construction method and multivariate statistical analysis is provided, comprising the following steps:

[0060] (1) Using PCA principal component analysis or PLS-DA pattern recognition analysis to group wolfberry samples from different origins, accurate classification of wolfberry samples based on origin was achieved;

[0061] (2) Using VIP>1 and one-way ANOVA P<0.05, 8 compounds were screened as antioxidant biomarkers for regional differentiation of wolfberry samples.

[0062] In some examples of this embodiment, the wolfberry samples are divided into four groups according to the regions based on the PCA principal component analysis or the PLS-DA pattern recognition analysis.

[0063] In some examples of this embodiment, the eight compounds are peak 4 rutin, peak 24 rutin dihexoside, peak 10 p-coumaric acid trihexoside, peak 5 dicaffeic quinic acid isomer, peak 12 Quercetin-rhamno-di-hexoside and three unknown compounds peak 14, peak 16 and peak 27.

[0064] In another typical embodiment of the present invention, the application of the above-mentioned fingerprint construction method or the above-mentioned wolfberry origin identification method in wolfberry origin tracing is provided.

[0065] In order to enable those skilled in the art to more clearly understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below in conjunction with specific embodiments.

[0066] Example 1 Fingerprint establishment

[0067] (1) Instruments: Agilent 1260 HPLC (equipped with quaternary pump, autosampler, column oven, DAD detector, ELSD detector), Agilent G6520 quadrupole tandem time-of-flight mass spectrometer (equipped with ESI source, Qualitative Analysis mass spectrometry software), Agilent, USA.

[0068] Acetonitrile, chromatographic grade, from Tianjin Opson Company; formic acid and ammonium formate, chromatographic grade, from Tianjin Komiou Company; Wahaha purified water, from Hangzhou Wahaha Group Co., Ltd. All other reagents were analytical grade.

[0069] The wolfberry samples were from four producing areas, and the specific information is shown in Table 1 below:

[0070] Table 1 Sources of wolfberry samples

[0071]

[0072] (2) Preparation of test samples

[0073] Take 2g of wolfberry sample, crush it, put it in a conical flask, add 32mL of methanol, ultrasonicate for 25min, then add 8mL of water, ultrasonicate for 15min. Filter by suction, and pass the filtrate through a 0.22μm organic filter membrane for later use.

[0074] (3) Preparation of DPPH solution

[0075] Dissolve DPPH solution with 80% acetonitrile to prepare a concentration of 6×10-5 mol / L and store at 4°C for later use.

[0076] (4) Online screening conditions

[0077] ①Liquid chromatography conditions:

[0078] Chromatographic column: ACQUITY UPLC BEH amide (3.0×100 mm, 1.7 μm); mobile phase: A: acetonitrile, B: water (0.8% formic acid + 20 mmol ammonium formate); injection volume: 5 μL; flow rate: 0.4 mL min-1; wavelength: 310 nm; elution gradient: 0-10 min, 90%-90% A; 10-20 min, 90%-89% A; 20-35 min, 89%-89% A; 36-80 min, 85%-85% A; 80-85 min n, 85%-81%A; 85-100min, 81%-81%A; 100-105min, 81%-79%A; 105-110min, 79%-78%A; 110-135min, 78% -78%A; 136-150min, 76%-76%A; 150-155min, 76%-73%A; 155-175min, 73%-70%A; 175-200min, 70-62%A.

[0079] ②Mass spectrometry conditions:

[0080] The flow rate of high-resolution time-of-flight mass spectrometry was 0.4 mL min -1 Electrospray positive and negative ion modes were used, with a nebulizer gas pressure of 45 psi and a drying gas (N2) flow rate of 10.0 mL min -1 , drying gas temperature 350 °C, capillary voltage 3500 V, pyrolysis voltage 175 V, cone voltage 65 V, full scan mass-to-charge ratio (m / z) range 50-1100.

[0081] ③UPLC-DPPH screening conditions:

[0082] The DPPH concentration is 6×10-5M, the flow rate is 0.4mL / min, and the wavelength of the DPPH screening reaction is 517nm. The inner diameter of the PEEK tube is 0.18mm and the length is 10m.

[0083] ④Data analysis:

[0084] All chromatogram analyses were performed using the professional software "Chinese Herbal Medicine Chromatographic Fingerprint Similarity Evaluation System (2012 Edition)". SIMCA-P 13.0 software (SIMCA-P 13.0, Umetrics, Umea, Sweden) was used. SPSS statistical system (SPSS, 22.0, IBM, Chicago, USA) was used for one-way analysis of variance (ANOVA), P < 0.05.

[0085] (5) Online screening and identification of antioxidant ingredients

[0086] ① Optimization of UPLC conditions:

[0087] Waters UPLC-T3 (3×100mm, 1.7μm), Waters UPLC-C18 (3×100mm, 1.7μm), Waters UPLC-BEH amide (3×100mm, 1.7μm) and other different stationary phase types were used to separate the wolfberry extract to obtain better separation effect. The results showed that the Waters UPLC-BEH amide column (3×100mm, 1.7μm) had a better separation effect. Therefore, this column was selected to analyze the wolfberry extract. The separation effects of different mobile phase compositions of methanol-water and acetonitrile-water systems were compared. The results showed that when acetonitrile-water solution was used as the mobile phase, the chromatographic peaks were well separated and the baseline was smooth. In order to improve the chromatographic peak shape and avoid the phenomenon of chromatographic peak tailing, the influence of the proportion of formic acid (0.4%, 0.6%, 0.8%) in the aqueous phase was compared. Finally, acetonitrile-0.8% (v / v) formic acid aqueous solution was used as the elution system to ensure that the chromatographic peaks were sharp and symmetrical. The three-dimensional spectrum on the PDA was used to compare the detection wavelength. The results showed that when 310nm was selected as the detection wavelength, there were more chromatographic peaks, most of which were well separated, with sharp peaks and smooth baselines ( Figure 1 (A)).

[0088] ②Online UPLC-DPPH method to screen antioxidant components:

[0089] In order to make the UPLC-DPPH online screening method have good sensitivity and stability, the study investigated the effects of some key factors (such as DPPH concentration (2×10-5mol / L, 4×10-5mol / L, 6×10-5mol / L, 8×10-5mol / L, 10×10-5mol / L), DPPH flow rate (0.2mL / min~0.4mL / min), reaction line inner diameter (0.10mm, 0.18mm, 0.25mm) and length (5m, 10m, 15m, 20m). The results showed that the PEEK tube with an inner diameter of 0.18mm and a length of 10m was the most suitable reaction coil. At the same time, when the DPPH flow rate was 0.4mL / min and the DPPH concentration was 6×10-5mol / L, its negative peak shape had the characteristics of minimal background noise, stable baseline and large signal-to-noise ratio.

[0090] The optimized online UPLC-DPPH conditions were used to screen antioxidant components such as Figure 1 As shown in (C), there is an obvious inverted peak at 517 nm, which is consistent with most of the chromatographic peaks of wolfberry extract ( Figure 1 (B)), indicating that these compounds have potential antioxidant activity.

[0091] ③ESI-Q-TOF / MS identification of antioxidant components:

[0092] With the help of UPLC-PDA-ESI-Q-TOF / MS, 20 compounds were preliminarily identified by comparing the precursor ions, fragment ions and UV absorption wavelengths with those in the literature. The chemical compositions of the 20 components eluted from UPLC are shown in Table 2.

[0093] Table 2 Characterization of compounds in wolfberry by ESI-TOF / MS

[0094]

[0095]

[0096] ④Establishment of antioxidant characteristic fingerprint and similarity evaluation:

[0097] According to "(2) Preparation of test samples", 32 batches of wolfberry samples were prepared into sample solutions. Then, the samples were analyzed under the online screening conditions developed in this study. 28 compounds with potential antioxidant activity and good separation, stability and reproducibility were screened out as common peaks. The established wolfberry antioxidant characteristic fingerprint is shown in Figure 2. Figure 2 shown.

[0098] The similarity analysis of 32 batches of wolfberry samples was performed using the "Similarity Evaluation System for Chromatographic Fingerprints of Traditional Chinese Medicine" (Chinese Pharmacopoeia 2012 Edition). By comparing the similarity between the sample and the reference fingerprint, the relationship between a set of chromatographic fingerprints can be analyzed. The 28 common peaks with antioxidant activity screened in this study were selected for peak matching, and the common pattern was used as the control fingerprint to evaluate the similarity of 32 batches of wolfberry samples. The results are shown in Table 3.

[0099] It can be seen that the similarity of the 32 samples is between 0.6069 and 0.9944, indicating that the composition of wolfberry from the four production areas in China is significantly different. The similarity of wolfberry samples in Ningxia is the highest, with an average value of 0.9915±0.0033 (within the range of 0.9848-0.9944); Gansu is similar to Ningxia, with an average similarity of 0.9617±0.0355 (within the range of 0.8816-0.9923). These results show that the antioxidant components of wolfberry in Ningxia and Gansu are similar, and it is difficult to distinguish them by combining antioxidant fingerprints with similarity analysis. The similarity in Xinjiang and Qinghai is relatively low, 0.8686±0.1370 (within the range of 0.6069-0.9840) and 0.8298±0.0436 (within the range of 0.7574-0.8820), respectively. These results indicate that the antioxidant components in Xinjiang and Qinghai are significantly different from those in Ningxia and Gansu. In addition, the similarity values ​​of the groups in Xinjiang are quite different, which may be related to the vast geographical area and unique climatic and environmental factors of the planting area. However, further chemometric analysis is needed to accurately distinguish the growing areas of wolfberry.

[0100] Table 3 Similarity of 32 batches of wolfberry samples

[0101]

[0102] Example 2 Method for identifying wolfberry origin by combining multivariate statistical analysis

[0103] (1) PCA and PLS-DA

[0104] In order to achieve effective classification, this study first used unsupervised principal component analysis to distinguish wolfberry samples from different origins in China. The PCA scatter plot is shown in Figure 2. Figure 3 As shown in the figure, it can be seen that the 32 batches of wolfberry samples are divided into 4 groups according to the growing area. The samples from Ningxia, Qinghai and Gansu production areas are well clustered, but the samples from Xinjiang production area are relatively scattered, which is consistent with the similarity evaluation results.

[0105] The supervised PLS-DA pattern recognition analysis was further used to further classify the wolfberry samples. The samples in the four regions were classified satisfactorily, and the samples in each region were well clustered together. The model parameters of R2X, R2Y and Q2 were 0.673, 0.750 and 0.517, respectively, which had good discrimination and prediction capabilities ( Figure 4 ), the discrimination accuracy of the model reached 93.75%. In addition, the permutation test of 200 iterations (R2=0.3029, Q2=-0.3607) showed that the reliability of the model ( Figure 5 ).

[0106] (2) Prediction and comparison of chemical markers of wolfberry from different origins

[0107] VIP (variable importance in projection)>1.0 analysis had a significant impact on the origin differentiation of wolfberry samples. Figure 6 As shown, we selected 8 compounds as potential chemical markers, including rutin (peak 4), rutin dihexoside (peak 24), p-coumaric acid trihexoside (peak 10), di-caffeoylquinic acid isomer (peak 5), quercetin-rhamno-di-hexoside (peak 12) and 3 unknown compounds (peak 14, peak 16 and peak 27).

[0108] The distribution of these compounds is Figure 7 and Figure 8 As shown in the figure, the contents of rutin disodium, p-coumaric acid trisodium, marker 27, di-caffeoylquinic acid isomers, quercetin-rhamnose disodium and marker 16 were the highest in Qinghai, and the contents of rutin and marker 14 were the highest in Xinjiang. There were significant differences between the marker compounds from different origins (one-way ANOVA, P<0.05), suggesting that these antioxidant components are expected to be used as quality markers for the identification of the origin of wolfberry in the four production areas of China.

[0109] In this study, an antioxidant characteristic fingerprint was established based on the UPLC-DPPH-PDA-ESI-Q-TOF / MS online coupling technology, and combined with multivariate statistical analysis, it was successfully applied to the identification of the geographical origins of wolfberries in different regions of China. This method combines the rapid screening and identification capabilities of the LC-MS-based online screening method for antioxidants, and 28 antioxidants were screened out from wolfberry extracts, of which 19 were preliminarily identified. The antioxidant characteristic fingerprint was combined with PCA and PLS-DA analysis to achieve accurate classification of wolfberry samples based on their origin. Using VIP>1 and one-way ANOVA (P<0.05), eight compounds, including rutin (peak 4), rutin dihexose (peak 24), p-coumaric acid trihexose (peak 10), dicaffeoylquinic acid isomers (peak 5), quercetin-rhamno-di-hexoside (peak 12), peak 14, peak 16 and peak 27, were screened as antioxidant markers for the differentiation of wolfberry samples by origin. This study provides methods and technical support for the identification of the origin of wolfberry.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention is described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for constructing a characteristic fingerprint of wolfberry antioxidant based on UPLC, characterized in that: Firstly, the online UPLC-PDA-DPPH method was used to screen the antioxidant components of different batches of wolfberry extracts; then ESI-Q-TOF / MS was used to measure and preliminarily identify the antioxidant components by comparing the precursor ions, fragment ions and ultraviolet absorption wavelengths with the literature; finally, 28 chemical components with potential antioxidant activity and good separation, stability and reproducibility were screened as common peaks to establish the characteristic antioxidant fingerprint of wolfberry; The preparation method of the wolfberry extract is as follows: 2 g of wolfberry sample is taken, crushed, placed in a conical flask, 32 mL of methanol is added, ultrasonicated, 8 mL of water is added, ultrasonicated again, and filtered for later use; UPLC chromatographic conditions were as follows: chromatographic column: ACQUITY UPLC BEH amide, 3.0×100 mm, 1.7 μm; mobile phase: A: acetonitrile, B: water, 0.8% formic acid + 20 mmol ammonium formate; injection volume: 5 μL; flow rate: 0.4 mL / min; wavelength: 310 nm; elution gradient: 0-10 min, 90%-90% A; 10-20 min, 90%-89% A; 20-35 min, 89%-89% A; 36-80 min, 85%-85% A; 80-85 min, 85%-81% A; 85-100 min, 81%-81% A; 100-105 min, 81%-79% A; 105-110 min, 79%-78% A; 110-135 min, 78%-78%A; 136-150 min, 76%-76%A; 150-155 min, 76%-73%A; 155-175 min, 73%-70%A; 175-200 min, 70%-62%A; The DPPH concentration was 2×10 -5 -10×10 -5 mol / L, DPPH flow rate is 0.2-0.4 mL / min, and the wavelength of DPPH screening reaction is 517 nm; the inner diameter of the PEEK tube is 0.1-0.25 mm, and the length is 5-20 m; MS conditions: electrospray positive and negative ion modes were used, respectively, with a nebulizer gas pressure of 45 psi; the drying gas was N2 with a flow rate of 10.0 mL / min and a drying gas temperature of 350 °C; the capillary voltage was 3500 V, the fragmentor voltage was 175 V, the cone voltage was 65 V, and the full scan mass-to-charge ratio range was 50-1100.

2. The method for constructing a UPLC-based wolfberry antioxidant fingerprint according to claim 1, characterized in that: In the preparation method of the wolfberry extract, after adding methanol, ultrasonication is performed for 25 minutes, and after adding water, ultrasonication is performed for 15 minutes; and the filtration uses a 0.22 μm organic filter membrane.

3. The method for constructing a UPLC-based wolfberry antioxidant fingerprint according to claim 1, characterized in that: The inner diameter of the PEEK tube is 0.18 mm; The length of the PEEK tube is 10m; The DPPH concentration is 6×10 -5 M; The DPPH flow rate was 0.4 mL / min.

4. A method for identifying the origin of wolfberry based on the fingerprint construction method and multivariate statistical analysis according to any one of claims 1 to 3, characterized in that: The following steps are involved: To establish the antioxidant characteristic fingerprint of wolfberry; The antioxidant characteristic fingerprint combined with PCA principal component analysis or PLS-DA pattern recognition analysis was used to regionally group wolfberry samples from different origins, so as to achieve accurate classification of wolfberry samples based on their origin.

5. The method for identifying the origin of wolfberry according to claim 4, characterized in that: According to the PCA principal component analysis or the PLS-DA pattern recognition analysis, the wolfberry samples were divided into four groups according to the regions.

6. Application of the fingerprint construction method described in any one of claims 1 to 3 or the wolfberry origin identification method described in any one of claims 4 to 5 in wolfberry origin tracing.

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