Characteristic metabolite combination for identifying shandong dongying origin ziziphus jujuba mill var. spinosa (bunge) hu ex h. fu and identification method and application thereof

Abstract

The present application belongs to the field of agricultural product quality safety, traceability identification and metabolomics technology, and particularly relates to a characteristic metabolite combination for identifying sour jujube flesh from Dongying, Shandong, and an identification method and application thereof. The characteristic metabolite combination comprises at least three of the following substances: His-Val-Leu-Lys, Ser-Ile-His-Arg, Leu-Thr-Gln-Gln-Leu and Tonkinochromane E. The metabolite combination is consistently and significantly highly expressed in sour jujube flesh from Dongying, Shandong, and is relatively low in expression in sour jujube flesh from other representative producing areas (such as Chaoyang, Liaoning, Anyang, Henan, etc.), and thus can be used as a "chemical fingerprint" for identifying sour jujube flesh from Dongying, Shandong.

Description

Technical Field

[0001] This invention belongs to the field of agricultural product quality and safety, traceability identification and metabolomics technology, specifically involving the identification of characteristic metabolite combinations of jujube pulp from Dongying, Shandong, as well as its identification methods and applications. Background Technology

[0002] The flesh of the jujube (Ziziphus jujuba var. spinosa) is an important food and medicinal resource, rich in polysaccharides, flavonoids, triterpenes, polyphenols, and other bioactive components, possessing health benefits such as antioxidant activity, lipid-lowering effects, and immune regulation. However, the quality and functional activity of jujube flesh are significantly influenced by its place of origin (such as climate, soil, and ecological conditions). The accumulation of active ingredients in jujube flesh from different origins varies significantly, leading to differences in its market value and medicinal efficacy. Dongying, Shandong, located in the Yellow River Delta saline-alkali land ecological zone, may have a unique ecological environment that influences the accumulation of secondary metabolites in jujube flesh, giving it quality characteristics distinct from other origins.

[0003] Currently, the identification of jujube pulp quality largely relies on sensory evaluation or the determination of macro-components such as total sugar, total acid, and vitamin C, lacking objective and precise chemical indicators. Traditional targeted detection methods can only analyze a few known components and cannot reflect its complex chemical basis as a whole. Therefore, developing a method that can systematically and accurately identify the origin of jujube pulp, especially that from Dongying, Shandong, a distinctive producing area, is of great significance for protecting geographical indication products, combating counterfeiting and substandard products, achieving premium pricing for high-quality products, and promoting the high-value utilization of resources.

[0004] Non-targeted metabolomics technology can detect all small molecule metabolites in biological samples without bias. Combined with multivariate statistical analysis, it has become a powerful tool for tracing the origin and identifying the quality of agricultural products. However, no studies have yet published a combination of metabolite markers that can specifically identify the pulp of jujubes from Dongying, Shandong, and corresponding detection methods. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a set of characteristic metabolites that can specifically identify jujube pulp from Dongying, Shandong, an identification method based on the set, and its application.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, this invention provides a characteristic metabolite combination for identifying jujube pulp from Dongying, Shandong Province. This combination comprises at least three of the following substances: His-Val-Leu-Lys, Ser-Ile-His-Arg, Leu-Thr-Gln-Gln-Leu, and Tonkinochromane E. This metabolite combination exhibits consistent and significant high expression in jujube pulp from Dongying, Shandong Province, while its expression levels are relatively low in jujube pulp from other representative producing areas (such as Chaoyang, Liaoning Province, and Anyang, Henan Province). Therefore, it can be used as a "chemical fingerprint" for identifying jujube pulp from Dongying, Shandong Province.

[0007] When a discrimination model is constructed based on the relative content of each metabolite in the characteristic metabolite combination, it is possible to distinguish jujube pulp from Dongying, Shandong Province from jujube pulp from at least one other origin, including Chaoyang, Liaoning Province; Anyang, Henan Province; Xingtai, Hebei Province; Lüliang, Shanxi Province; Jining, Shandong Province; Linfen, Shanxi Province; and Aksu, Xinjiang Province.

[0008] Secondly, this invention provides a method for identifying whether jujube pulp originates from Dongying, Shandong, comprising the following steps: S1. Sample preparation: Obtain the jujube pulp sample to be tested, and extract it to obtain the test solution; S2. Metabolite detection: The test solution was analyzed by non-targeted metabolomics using liquid chromatography-mass spectrometry to obtain metabolite profile data containing qualitative and relative quantitative information of metabolites. S3. Data Analysis: Extract information about each metabolite in the characteristic metabolite combination from the metabolite profile data; S4. Origin identification: Based on the extracted characteristic metabolite combination information, the origin of the jujube pulp sample to be tested is determined by multivariate statistical analysis model or by comparison with the reference metabolite spectrum of Dongying, Shandong.

[0009] In step S1, the extraction is performed by using a 70% methanol aqueous solution to extract the freeze-dried and ground jujube pulp powder, wherein 1500 μL of extraction solution is added for every 30 mg of sample powder.

[0010] In step S2, the liquid chromatography conditions include: using a Waters ACQUITY UPLC HSS T3 column, with ultrapure water containing 0.1% formic acid as mobile phase A and acetonitrile containing 0.1% formic acid as mobile phase B, and eluting at the following gradient: 0 min, A 95%, B 5%; 5 min, A 35%, B 65%; 6 min, A 1%, B 99%; 7.5 min, A 1%, B 99%; 7.6 min, A 95%, B 5%; 10 min, A 95%, B 5%.

[0011] In step S4, the multivariate statistical analysis model is a discriminant model constructed based on principal component analysis or orthogonal partial least squares discriminant analysis.

[0012] Thirdly, the present invention provides a test kit for identifying jujube pulp from Dongying, Shandong Province, comprising reagents, standards and / or a data analysis module for detecting the characteristic metabolite combination, wherein the data analysis module has a built-in reference database or discrimination model based on jujube pulp samples from Dongying, Shandong Province, containing information on the characteristic metabolite combination.

[0013] Fourthly, this invention provides the application of the aforementioned characteristic metabolite combination in the preparation of products for identifying or tracing the origin of jujube pulp from Dongying, Shandong. This invention also provides the application of the aforementioned identification method or kit in identifying the authenticity of jujube pulp's origin, protecting geographical indication products, or screening high-quality jujube pulp raw materials.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention is the first to discover a combination of endogenous small molecule metabolite biomarkers that can specifically characterize the pulp of jujube from Dongying, Shandong. This discovery is based on rigorous non-targeted metabolomics data and multivariate statistical analysis, and has objective data support.

[0015] 2. High accuracy and specificity: Based on the UPLC-MS / MS high-resolution mass spectrometry platform and combined with multivariate statistical models such as OPLS-DA, this invention can accurately capture the unique chemical characteristics of jujube pulp from Dongying, Shandong Province from the overall metabolic profile. The identification results are accurate and reliable, avoiding subjective errors.

[0016] 3. Strong applicability: This invention not only provides a set of identification methods, but also provides core technical basis and objective chemical standards for the protection of the geographical indication product of Shandong Dongying jujube pulp, the classification of quality grades, and the development of high value-added products (such as the development of functional foods rich in specific active ingredients). Attached Figure Description

[0017] Figure 1 A classification diagram of non-targeted metabolites in jujube pulp samples from different origins; Figure 2 This is a score chart of jujube pulp samples from different origins based on principal component analysis; Figure 3 Venn diagram of metabolites from jujube pulp samples from different origins; Figure 4 This is a score map of jujube pulp samples from different production areas based on the OPLS-DA model; Figure 5 The permutation test validation diagram for the OPLS-DA model; Figure 6Hierarchical clustering heatmap of the 50 metabolites with the most significant differential expression; Figure 7 Hierarchical cluster analysis diagram of differential metabolites of alkaloids; Figure 8 Hierarchical cluster analysis diagram of differentially metabolized flavonoids; Figure 9 This is a hierarchical cluster analysis diagram of terpenoid differential metabolites; Figure 10 A hierarchical clustering tree for the samples; Figure 11 A bar chart showing the fold difference between samples from Xingtai, Hebei and Dongying, Shandong. Figure 12 A bar chart showing the fold difference between samples from Anyang, Henan and Dongying, Shandong. Figure 13 A bar chart showing the fold difference between samples from Chaoyang, Liaoning and Dongying, Shandong. Figure 14 A bar chart showing the fold difference between samples from Jining, Shandong and Dongying, Shandong. Figure 15 A bar chart showing the fold difference between samples from Linfen, Shanxi and Dongying, Shandong. Figure 16 A bar chart showing the fold difference between samples from Lüliang, Shanxi and Dongying, Shandong. Figure 17 A bar chart showing the fold difference between samples from Aksu, Xinjiang and Dongying, Shandong. Detailed Implementation

[0018] To make the objectives, technical solutions, and beneficial effects of this invention clearer and more complete, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. However, it should be understood that the specific embodiments of this invention are only for explaining the invention and are not intended to limit the scope of protection of this invention. In the following embodiments, unless specific techniques or conditions are specified, they are performed according to the techniques or conditions described in the literature in the field or according to the product instructions. Reagents or instruments used without specified manufacturers are all conventional products that can be obtained commercially.

[0019] Example 1 This embodiment details the experimental procedures and data analysis methods used to discover the characteristic metabolite combinations described in this invention.

[0020] 1. Materials and Reagents Eight different jujube samples from various origins were compared, with the Shandong Dongying sample (SDDY-1) cultivated in saline-alkali soil as the main research object and core reference, and samples from seven other origins, including Liaoning Chaoyang (LNCY-1), Henan Anyang (HNAY-1), Hebei Xingtai (HBXT-1), Shanxi Lvliang (SXLL-1), Shandong Jining (SDJN-1), Shanxi Linfen (SXLF-1), and Xinjiang Aksu (XJAKS-1).

[0021] Table 1 Information on Standards and Reagents

[0022] Table 2 Instrument and Equipment Information

[0023] 2. Sample extraction process (1) Place the sample in a freeze dryer (Scientz-100F) and freeze dry under vacuum for 63 h; (2) Grind into powder using a grinder (MM400, Retsch) (30Hz, 1.5 minutes); (3) Weigh 30 mg of sample powder using an electronic balance (MS105DM), add 1500 μL of 70% methanol-water internal standard extraction solution pre-cooled at -20℃ (if less than 30 mg, add 1500 μL of extraction solution for every 30 mg sample). 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 further diluting the 1000 μg / mL stock solution with 70% methanol to prepare a 250 μg / mL internal standard solution. (4) Vortex once every 30 minutes, each time lasting 30 seconds, for a total of 6 vortices; (5) After centrifugation (12000 rpm, 3 minutes), aspirate the supernatant, filter the sample with a microporous membrane (0.22 μm pore size), and store it in a sample vial for UPLC-MS / MS analysis. 3. Chromatography-mass spectrometry acquisition conditions 3.1 T3 Chromatographic Conditions (1) Chromatographic column: Waters ACQUITY UPL CHS ST3 Column 1.8µm, 2.1mm*100mm (2) Mobile phase A: ultrapure water (0.1% formic acid); Mobile phase B: acetonitrile (0.1% formic acid) (3) Column temperature: 40°C; flow rate: 0.40 mL / min; injection volume: 4 µL Table 3. Column mobile phase gradient conditions

[0024] 3.2 Mass Spectrometry Conditions Table 4 ABTripleTOF 6600 mass spectrometry conditions

[0025] 4. Data Processing Raw mass spectrometry data were converted to mzML format using ProteoWizard. Peak extraction, alignment, and retention time correction were performed using XCMS. Peaks with a missing rate >50% in each sample group were filtered, and blank values ​​were filled using KNN with 1 / 5 minimum value (1 / 5 minimum value filling for blank values ​​>50%, KNN filling for blank values ​​<50%). Peak area was corrected using the SVR method. Metabolite identification was performed on the corrected and screened peaks using a combination of laboratory-built databases, public libraries, prediction libraries, and metDNA methods. Finally, substances with a comprehensive score of 0.5 or higher and a QC sample CV value less than 0.5 were extracted and identified. Positive and negative patterns were then merged (if duplicate substances were found, the substance with the highest qualitative grade and score was retained), resulting in the all_sample_data.xlsx file.

[0026] Example 2 This embodiment details the results and analysis of experimental data used to discover the characteristic metabolite combinations described in this invention.

[0027] 1. Metabolite distribution characteristics of jujube pulp from different origins Untargeted metabolomics identification of eight groups of jujube pulp was performed using liquid chromatography-tandem mass spectrometry (LC-MS / MS), and a total of 2538 metabolites were detected. Figure 1 The sample contained 467 amino acids and their derivatives, 446 organic acids, 291 benzene and its derivatives, 130 flavonoids, 118 alkaloids, 102 lipids, and 92 terpenoids. Amino acids and their derivatives and organic acids were the two dominant groups, accounting for 18.36% and 17.54% respectively, indicating that jujube pulp has a relatively rich composition of primary metabolites, providing an important material basis for its nutritional quality and flavor. It is worth noting that although secondary metabolites such as flavonoids, alkaloids, and terpenoids accounted for a relatively low proportion, they are closely related to the functional activity and quality formation of jujube pulp, and may be important metabolite categories for distinguishing samples from different origins, especially for elucidating the quality characteristics of SDDY (jujube pulp).

[0028] 2. Principal component analysis of total samples of jujube pulp from different origins Principal component analysis (PCA) can be used to reflect the overall metabolic profile differences and inter-group similarities of samples. PCA results show that ( Figure 2 PC1 and PC2 explained 31.96% and 16.23% of the total variation, respectively. The eight groups of jujube pulp samples from different origins showed a clear separation trend in the two-dimensional space formed by the first two principal components, indicating differences in the overall metabolite composition of jujube pulp from different origins. Among them, JP-SDDY-1 was located in the upper left region, clearly separated from the other seven groups, exhibiting the most unique distribution characteristics. JP-SDJN-1 and JP-HNCY-1 also showed some differentiation from other groups, while JP-HNAY-1, JP-HBXT-1, JP-SXLL-1, JP-SXLF-1, and JP-XJAKS-1 had relatively similar overall distributions. The results indicate that the overall metabolite composition of JP-SDDY-1 differs significantly from samples from other origins, suggesting that it may have more unique quality-related metabolic characteristics.

[0029] 3. Venn diagram Venn plots are used to analyze the distribution of common and unique metabolites among different samples. The results show that ( Figure 3 A total of 4898 metabolites were found in the eight groups of jujube pulp samples from different origins, accounting for the vast majority of the total metabolites detected in each group, indicating a high degree of consistency in the overall metabolic composition of jujube pulp from different origins. The total number of metabolites detected in each group showed little difference: JP-HNAY-1 had 4914 metabolites, JP-SDJN-1, JP-SDDY-1, and JP-LNCY-1 had 4913, 4912, and 4912 metabolites respectively, and the remaining samples ranged from 4905 to 4911 metabolites. Therefore, after excluding the common components, the number of unique metabolites among the eight jujube pulp samples was only between 7 and 16.

[0030] Overall, the number of common metabolites among the samples was much higher than the number of unique metabolites, indicating that the basal metabolic profile of jujube pulp from different origins is highly conserved, and the quality differences are more likely due to the relative accumulation differences of a few key differential metabolites.

[0031] 4. Orthogonal Partial Least Squares Discriminant Analysis Orthogonal partial least squares discriminant analysis (OPLS-DA) is used to filter out noise, thereby improving the analytical power and effectiveness of the model. It can effectively screen for differentially expressed metabolites among eight groups of samples by removing irrelevant differences. For example... Figure 4As shown, the eight groups of jujube pulp samples exhibit clustering and inter-group separation characteristics in the score space composed of T score[1] (8.53%) and Orthogonal T score[1] (40.6%). The OPLS-DA validation plot was used to verify the reliability of the OPLS-DA model and avoid overfitting. Through 200 permutation experiments (p<0.005), R²X = 0.618, R²Y = 0.985, Q² = 0.967 were obtained. All samples were within the 95% confidence interval, there was no overlap between sample points, and the distribution of the statistics after permutation was lower than the corresponding values ​​of the original model.

[0032] The OPLS-DA validation results show ( Figure 5 The eight groups of jujube pulp samples from different origins showed a clear separation trend, indicating differences in their overall metabolomics profiles. JP-SDDY-1, JP-SDJN-1, and JP-LNCY-1 were significantly different from the other groups, while JP-SXLL-1, JP-SXLF-1, and JP-XJAKS-1 showed similar distributions. JP-HNAY-1 and JP-HBXT-1 also showed high similarity. These results indicate that JP-SDDY-1 exhibits significant differences in its overall metabolite composition compared to samples from most origins, demonstrating a certain degree of uniqueness.

[0033] 5. Screening of differentially expressed metabolites Based on the variable importance projections obtained from multivariate statistical analysis, differentially expressed metabolites among the different jujube pulp groups from various origins can be initially screened. Univariate analysis is then used for further screening to ensure statistical significance. The screening criteria for differentially expressed metabolites in this experiment were: metabolites with VIP > 1 and P < 0.05, indicating statistically significant differences between groups. Based on this, the cluster heatmap highlights the 50 key metabolites with the most significant differences in expression patterns. Figure 6 As shown, these differential metabolites are finely divided into 10 major categories, mainly including amino acids and derivatives, alkaloids, lipids, organic acids, phenolic acids, flavonoids, terpenoids, GP, FA and others.

[0034] Clustering heatmaps revealed distinct specific aggregation patterns in metabolite expression across different groups. Samples from the JP-SDDY (Shandong), JP-LNCY (Liaoning), and JP-HNAY (Henan) regions clustered in the lower half of the heatmap, indicating a significant high expression trend for secondary metabolites such as phenolic acids, flavonoids, and terpenoids. The JP-XJAKS (Aksu, Xinjiang) region showed a distinct red high-expression area in the upper part of the heatmap, representing an enrichment region for amino acids and their derivatives, organic acids, and lipids. Meanwhile, the JP-HBXT (Xingtai, Hebei) and JP-SXLF (Shanxi) regions exhibited medium-to-high expression of specific alkaloids and other substances. Based on literature reports on jujube, flavonoids, triterpenoids, and polyphenolic acids are the core active ingredients that enable jujube to exert its sedative, antioxidant, and neuroprotective effects. These natural antioxidants and neuromodulators can effectively scavenge free radicals and regulate the central nervous system, suggesting that jujube pulp with high expression in groups from Shandong, Liaoning, and Henan may have higher antioxidant activity and potential medicinal (sleep-improving) effects. Meanwhile, the Aksu region of Xinjiang, rich in amino acids and organic acids, suggests that it may have unique advantages in basic nutritional value and taste.

[0035] 6. Hierarchical cluster analysis of differentially expressed metabolites Hierarchical cluster analysis was conducted on the differentially expressed metabolites of alkaloids, flavonoids, and terpenoids in eight groups of samples. The results clearly reflected the significant differentiation in overall expression patterns among groups from different production areas. Among them, groups such as Shandong (JP-SDDY) and Aksu, Xinjiang (JP-XJAKS) exhibited specific secondary metabolic profiles that were significantly different from those of other production areas. Figure 7 As shown, in the alkaloid metabolite clusters, most differentially expressed metabolites in the SDDY and HNAY groups were significantly overexpressed and formed independent clusters, especially highly enriched in isoquinoline, indole, and amide compounds; while the XJAKS and LNCY groups showed generally low expression or opposite accumulation patterns for these substances, with regional upregulation only in specific hyoscyamine, pyridine, and piperidine alkaloids. This origin-specific differentiation was also significant in flavonoid metabolites, as shown in the results... Figure 8As shown, the SDDY and HBXT groups exhibited high expression and aggregation of core pharmacologically active components such as flavonoids, flavonols, and dihydroflavonoids, in stark contrast to the XJAKS and SXLF groups, which tended to accumulate isoflavones, chalcones, and anthocyanin-modified flavonoids. This reveals the potential differentiation of metabolic fluxes in flavonoid skeletal synthesis and branching pathways among different samples. Furthermore, observations... Figure 9 Based on the terpene clustering characteristics, it was found that the SDDY and HNAY groups exhibited specific high expression of low-molecular-weight substances such as sesquiterpenes and some diterpenes. This is distinctly different from the characteristics of some groups such as SXLL, which significantly enriched triterpenes and triterpenoid saponins. In summary, the SDDY group, characterized by specific high abundance of isoquinolines / indole alkaloids, core flavonoids / flavonols, and distinctive sesquiterpenes, formed a highly representative differential expression profile among the eight samples. These interwoven secondary metabolic networks not only reflect the comprehensive influence of multiple factors such as the place of origin on the accumulation of substances, but also provide an objective material basis for further evaluating the differences in potential pharmacological activities (such as the association between triterpenoid saponins and sedative-sedative activities) among the samples.

[0036] 7. Analysis of Dominant Metabolites of JP-SDDY-1 To better understand the variations in the fold differences in secondary metabolites of jujube pulp from different origins... Figures 11-17 The bar charts showing the fold-change ratios of metabolites present the expression changes of the top 20 differentially expressed secondary metabolites when compared with Dongying, Shandong (JP-SDDY-1) from seven different producing areas: Xingtai (Hebei), Anyang (Henan), Chaoyang (Liaoning), Jining (Shandong), Linfen (Shanxi), Lüliang (Shanxi), and Aksu (Xinjiang). A bar extending to the right (log2FC>0) indicates a higher content of the metabolite in the compared producing area, i.e., a downward trend in Dongying, Shandong; a bar extending to the left (log2FC<0) indicates a higher content of the metabolite in Dongying, Shandong, i.e., an upward trend in Dongying, Shandong.

[0037] Based on pairwise comparisons between jujube pulp from different origins and JP-SDDY-1, fold change analysis showed that JP-SDDY-1 exhibited a significant enrichment advantage of key differentially expressed metabolites in most comparisons. Particularly in comparisons with JP-XJAKS-1, JP-SXLF-1, JP-HBXT-1, and JP-HNAY-1, almost all or the vast majority of the top differentially expressed metabolites showed an upregulated trend in JP-SDDY-1, indicating a clear advantage in overall metabolic levels. Although some upregulated metabolites originated from samples from certain origins (such as JP-LNCY-1, JP-SDJN-A, and JP-SXLL-1), the overall trend showed that JP-SDDY-1 had a greater number of highly expressed metabolites with larger fold changes, demonstrating its dominant enrichment characteristics in key metabolic components.

[0038] Based on the aforementioned fold change analysis, to further clarify the key advantageous components of JP-SDDY-1, differentially expressed metabolites that repeatedly appeared in comparisons across different origins were screened and summarized. The results showed that metabolites His-Val-Leu-Lys, Ser-Ile-His-Arg, Leu-Thr-Gln-Gln-Leu, and Tonkinochromane E all exhibited a consistent upregulation trend in multiple pairwise comparisons and were consistently highly expressed in JP-SDDY-1, demonstrating good stability and representativeness. This indicates that they are important characteristic metabolites distinguishing JP-SDDY-1 from jujube pulp from other origins.

[0039] Table 5. Fold-change analysis of metabolites between different comparative origins and Dongying, Shandong (log2FC value)

[0040] Example 3 This embodiment illustrates how to use the characteristic metabolite combination discovered in Example 2 to identify the place of origin of the jujube pulp sample to be tested.

[0041] 1. Establish a reference database: (1) Collect samples of jujube pulp from Dongying, Shandong, and other production areas (Chaoyang, Liaoning; Anyang, Henan; Xingtai, Hebei; Lüliang, Shanxi; Jining, Shandong; Linfen, Shanxi; and Aksu, Xinjiang) as training sets.

[0042] (2) Following the method in Example 1, all training set samples were preprocessed and detected by UPLC-MS / MS to obtain metabolite profile data containing qualitative and relative quantitative information of metabolites.

[0043] (3) Extract the relative peak area or relative abundance information of each metabolite in the characteristic metabolite combination (His-Val-Leu-Lys, Ser-Ile-His-Arg, Leu-Thr-Gln-Gln-Leu, Tonkinochromane E) from the metabolite spectrum data.

[0044] (4) Using the extracted data, a reference spectrum of characteristic metabolites from Dongying, Shandong Province was constructed by using multivariate statistical methods such as principal component analysis or orthogonal partial least squares discriminant analysis.

[0045] 2. Identification of the sample to be tested: (1) Sample pretreatment and detection: The jujube pulp sample to be tested was processed and detected by UPLC-MS / MS according to the method in Example 1 to obtain its metabolite spectrum data.

[0046] (2) Feature information extraction: Extract information of each metabolite in the feature metabolite combination from the metabolite spectrum data of the sample to be tested.

[0047] (3) Origin identification: The characteristic metabolite combination information of the sample to be tested is compared with the reference spectrum of Shandong Dongying established in step 1. If the expression pattern of the characteristic metabolite combination in the sample to be tested is highly consistent with the reference spectrum, it can be determined that its origin is Shandong Dongying; if there is a significant difference, it is determined that it is not from Shandong Dongying.

Claims

1. A characteristic metabolite combination for identifying jujube pulp from Dongying, Shandong Province, characterized in that, The characteristic metabolite combination comprises at least three of the following substances: His-Val-Leu-Lys, Ser-Ile-His-Arg, Leu-Thr-Gln-Gln-Leu, and Tonkinochromane E.

2. The characteristic metabolite combination according to claim 1, characterized in that, When a discrimination model is constructed based on the relative content of each metabolite in the characteristic metabolite combination, it is possible to distinguish jujube pulp from Dongying, Shandong Province from jujube pulp from at least one other origin, including Chaoyang, Liaoning Province; Anyang, Henan Province; Xingtai, Hebei Province; Lüliang, Shanxi Province; Jining, Shandong Province; Linfen, Shanxi Province; and Aksu, Xinjiang Province.

3. A method for identifying whether jujube pulp originates from Dongying, Shandong, characterized in that, Includes the following steps: S1. Sample preparation: Obtain the jujube pulp sample to be tested, and extract it to obtain the test solution; S2. Metabolite detection: The test solution was analyzed by non-targeted metabolomics using liquid chromatography-mass spectrometry to obtain metabolite profile data containing qualitative and relative quantitative information of metabolites. S3. Data analysis: Extract information of each metabolite in the characteristic metabolite combination described in claim 1 or 2 from the metabolite profile data obtained in step S2; S4. Origin identification: Based on the information of the characteristic metabolite combination extracted in step S3, the origin of the jujube pulp sample to be tested is determined by multivariate statistical analysis model or by comparison with the reference metabolite spectrum of Dongying, Shandong.

4. The method according to claim 3, characterized in that, In step S1, the extraction is performed by using a 70% methanol aqueous solution to extract the freeze-dried and ground jujube pulp powder, wherein 1500 μL of extraction solution is added for every 30 mg of sample powder.

5. The method according to claim 3, characterized in that, In step S2, the liquid chromatography conditions include: using a Waters ACQUITY UPLC HSS T3 column, with ultrapure water containing 0.1% formic acid as mobile phase A and acetonitrile containing 0.1% formic acid as mobile phase B, and eluting at the following gradient: 0 min, A 95%, B 5%; 5 min, A 35%, B 65%; 6 min, A 1%, B 99%; 7.5 min, A 1%, B 99%; 7.6 min, A 95%, B 5%; 10 min, A 95%, B 5%.

6. The method according to claim 3, characterized in that, In step S4, the multivariate statistical analysis model is a discriminant model constructed based on principal component analysis or orthogonal partial least squares discriminant analysis.

7. A test kit for identifying the pulp of jujubes from Dongying, Shandong Province, characterized in that, It includes reagents, standards, and / or a data analysis module for detecting the characteristic metabolite combination described in claim 1 or 2, wherein the data analysis module has a built-in reference database or discriminant model containing information on the characteristic metabolite combination, established based on jujube pulp samples from Dongying, Shandong.

8. The use of the characteristic metabolite combination of claim 1 or 2 in the preparation of products for identifying or tracing the origin of jujube pulp.

9. The application of the method according to any one of claims 3-6 or the kit according to claim 7 in the identification of the authenticity of the origin of jujube pulp, the protection of geographical indication products, or the screening of high-quality jujube pulp raw materials.

Images