Geographically indicated beef product identification method

Abstract

The invention provides a geographical indication beef product identification method, and relates to the technical field of beef traceability identification. Collecting a geographically indicated beef sample and a non-geographically indicated beef sample; carrying out sample pretreatment on all the collected samples under the same condition to obtain a metabolite extracting solution; carrying out non-targeted metabonomics determination on the metabolite extracting solution through high performance liquid chromatography-tandem high-resolution mass spectrometry to obtain original data; performing a series of data preprocessing operations such as peak alignment, retention time correction and peak area extraction on the original data to obtain a metabolome data array; through multivariate statistical analysis and univariate statistical analysis, difference analysis is carried out on the geographically indicated beef and the non-geographically indicated beef, and a classification identification model is constructed, so that the identification of the geographically indicated beef product is realized, the production place of the geographically indicated beef product can be accurately identified, and a technical guarantee is provided for beef traceability identification.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of beef identification and traceability detection, and in particular to a geographical indication beef product identification method based on non-targeted metabolomics characteristics. BACKGROUND

[0002] Horqin cattle belongs to the excellent breed of Chinese Simmental cattle (grassland group). However, as the popularity of Horqin cattle increases at home and abroad, a large number of counterfeit and inferior products appear on the market. These counterfeit products usually use fake geographical indication certification marks, which seriously damages the brand of Horqin beef and the rights and interests of consumers. Therefore, a method for quickly identifying the geographical indication product Horqin beef is urgently needed.

[0003] Currently, there are few reports on the identification of different beef based on non-targeted metabolomics. For the identification of beef, nucleic acid detection technologies such as SNP and SSR markers, stable isotope technology, and mineral fingerprint characteristics technology are mainly used for identification. The advantage of nucleic acid detection technology is the identification of species, which is not suitable for the identification of geographical indication beef products. Stable isotope technology reflects the influence of environmental factors such as climate (precipitation, temperature), soil, etc. on beef quality, and has limited ability to distinguish micro-geographical scales (such as different plots within the same county). Mineral fingerprint characteristics technology relies on the element migration law of the soil-plant system, geological background, and human interference such as fertilization and irrigation, and also has limited distinguishing ability. Metabolomics technology is an important branch of systems biology after proteomics, genomics, and transcriptomics. It uses high-throughput detection technology and multivariate data processing as a means to quickly and comprehensively identify and distinguish different beef from the omics level by studying the changes of small molecule metabolites. Therefore, it can achieve accurate differentiation of different beef.

[0004] Based on this, the present application is proposed. SUMMARY

[0005] The purpose of the present application is to provide a geographical indication beef product traceability identification method. A new method for quickly and comprehensively detecting and identifying beef products that does not exist in the prior art is provided.

[0006] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows: A geographical indication beef product traceability identification method, the method comprising the following steps: (1) Pre-treat the geographical indication beef product and the non-geographical indication beef product under the same conditions to obtain a metabolite extract; (2) Perform non-targeted metabolomics determination on the metabolite extract by high performance liquid chromatography coupled with high resolution mass spectrometry to obtain metabolomic data; (3) Peak extraction and correction are performed on the metabolic profile data to obtain relative peak area and retention time of all metabolites; (4) Principal component analysis, partial least squares analysis and univariate statistical analysis are performed to screen differential markers and construct a PCA discrimination model to distinguish geographical indication beef products and non-geographical indication beef products.

[0007] The application applies non-targeted metabolomics technology to analyze metabolic products in geographical indication beef products and non-geographical indication beef products, comprehensively reflects different components in the samples, and screens differential metabolic markers by combining with multivariate statistical analysis methods.

[0008] In one embodiment, the geographical indication beef product is selected from Tongliao Xian Company's Horqin beef.

[0009] In one embodiment, the non-geographical indication beef product is selected from Shanxi Xian Company's beef and imported Xian Company's beef.

[0010] In one embodiment, in step (1), the sample pretreatment process comprises weighing 150 mg of beef sample in a 2 mL centrifuge tube, adding 1 mL of methanol water extract, ultrasonicating for 5-20 min, vortexing for 5-10 min, centrifuging at 10000 rpm for 20-30 min, and then filtering the upper clear liquid on a filter membrane and then bottling for metabolic analysis.

[0011] Preferably, the filter membrane is an organic filter membrane, and the pore size of the filter membrane is 0.22 μm.

[0012] In one embodiment, in step (4), the variable importance VIP value>1, significance <0.05, and difference fold>2 or <0.5 are used as screening conditions to obtain differential markers.

[0013] In one embodiment, the high-performance liquid chromatography coupled with high-resolution mass spectrometry is performed in two modes of positive ions and negative ions to obtain differential markers in the two modes of positive ions and negative ions.

[0014] In one embodiment, the composition of the mobile phase A of the high-performance liquid chromatography is: a water solution containing 0.1-0.3% formic acid (in the positive ion mode): the composition of the mobile phase A is: a water solution containing 5-10 mM ammonium acetate (in the negative ion mode).

[0015] In one embodiment, the composition of the mobile phase B of the high-performance liquid chromatography is: a methanol solution containing 0.1-0.3% formic acid (in the positive ion mode): the composition of the mobile phase B is: a methanol solution containing 5-10 mM ammonium acetate (in the negative ion mode).

[0016] In one specific embodiment, the gradient elution procedure is as follows:

[0017] The numerical parameters of each of the above mobile phases can fluctuate within a range of ± 5% of the numerical parameter.

[0018] In one embodiment, the flow rate of the gradient elution is 0.2-0.3 mL / min, preferably 0.3 mL / min.

[0019] In one embodiment, the conditions of the high resolution mass spectrometer are as follows: the temperature of the sheath gas is 340 ℃; the flow rate of the sheath gas is 12; the temperature of the gas is 320 ℃; the flow rate of the gas is 8 L / min; the atomization gas is 35 psi; the capillary voltage is 3500 V (+), 3000 V (-); the MS acquisition parameters are 50-1700 Da; the MS scan rate is 5; the iterative Auto MS / MS acquisition parameters are MS / MS acquisition mass range 50-1700 Da, MS / MS scan rate 3, isolation width Medium (~ 4 amu), collision energy 20.00, 40.00, iterative number of injection needles 5; the data storage mode is Centroid.

[0020] In the above conditions, the fluctuation range of each numerical parameter involved is ± 10% of the numerical parameter.

[0021] In one embodiment, 45 potential signature markers are obtained, of which 18 potential signature markers are obtained in the positive ion mode and 27 potential signature markers are obtained in the negative ion mode.

[0022] Application of the differential markers as markers for identifying geographical indication beef products and non-geographical indication beef products.

[0023] Beneficial effects: The present application provides a simple, sensitive and accurate identification method for geographical indication beef products, which is carried out by using a high resolution time-of-flight mass spectrometer, differential markers are screened by analyzing a model, and an identification model is constructed to realize rapid identification of geographical indication beef products.

[0024] The AUC value of the PCA class identification model of the present application is greater than 0.9, indicating that the identification model can effectively identify samples, and the accuracy is more than 95%, which can provide practical technical support for the authenticity identification of geographical indication beef products. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1PCA score plot for discriminating geographical indication beef products and non-geographical indication beef products (wherein H is ordinary beef, J is imported beef, KT is Horqin beef, and QC is quality control sample) provided by the embodiments of the present application; Figure 2 PLS-DA model plot for discriminating geographical indication beef products and non-geographical indication beef products; Figure 3 Volcano plot for discriminating geographical indication beef products and non-geographical indication beef products; Figure 4 PCA-class model Coomans' plot and ROC curve for discriminating geographical indication beef products and non-geographical indication beef products. DETAILED DESCRIPTION

[0026] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and cannot be understood as a limitation of the present application. The instruments and reagents involved in the embodiments of the present application are conventional in the art and can be purchased through conventional commercial channels. The operations involved in the embodiments are conventional technical operations in the art unless otherwise specified.

[0027] Example 1 Screening of metabolic biomarkers for discriminating geographical indication beef products

[0028] 1. Instruments and reagents

[0029] Chromatography: Agilent 1290 Infinity II liquid chromatograph

[0030] Mass spectrometry: Agilent 6546 LC / Q-TOF mass spectrometer

[0031] Chromatographic column: ZORBAX Eclipse C18 (30 x 150 mm, 1.8 μm)

[0032] Low-temperature high-speed centrifuge (Sigma)

[0033] Vortex shaker (IKA)

[0034] Reagents and consumables: Methanol (mass spectrometry grade, Merck-Millipore) Formic acid (chromatography pure, DIKMA) Ammonium acetate (chromatography pure, SIMCA) Water (Watsons) 2. Sample materials Horqin beef collected from a company in Tongliao, ordinary beef collected from a company in Shanxi Province, and imported beef collected from an import company in Jiangsu Province.

[0035] 3. Sample pretreatment: Weigh 150 mg sample into 2 mL centrifuge tube, add 1 mL methanol / water (1 / 1, v / v), ultrasonic for 10 min, vortex for 5 min, centrifuge at 10000 rpm for 20 min, the upper clear liquid is passed through 0.22 μm PTFE filter membrane and bottled for metabolomics analysis.

[0036] 4. Detection method

[0037] (1) Chromatographic conditions

[0038] Agilent 1290 Infinity II liquid chromatograph was used, and the chromatographic column was ZORBAX Eclipse C18 (30×150 mm, 1.8 μm). The column temperature was 40℃; the injection volume was 2 μL; the flow rate was 0.3 mL / min.

[0039] The mobile phase was 0.1% formic acid solution and 0.1% formic acid methanol solution in positive ion mode, and 10 mM ammonium acetate solution and 10 mM ammonium acetate methanol solution in negative ion mode; The gradient elution program of the mobile phase is as follows: Table 2 Gradient elution program of liquid chromatography:

[0040] (2) Mass spectrometry conditions

[0041] Agilent 6546 LC / Q-TOF mass spectrometer was used.

[0042] The mass spectrometry condition parameters are set as follows: Table 3 Agilent 6546 LC / Q-TOFz mass spectrometry condition parameters:

[0043] 5. Data processing and analysis

[0044] (1) Data pretreatment

[0045] The original data was pretreated by MS-DIAL (ver.4.9.221218) software, including baseline correction, peak extraction, peak correction, standardization and normalization, metabolite annotation, etc., to obtain the relative peak area and retention time of all metabolites. The processed data was imported into Excel, the coefficient of variation (CV%) of the QC sample was calculated, compounds exceeding 25% were rejected, feature values with missing values exceeding 50% were rejected, and the remaining missing values were filled with 1 / 5 of the minimum value in all samples.

[0046] (2) Screening of metabolic biomarkers

[0047] Principal component analysis (PCA) was performed on the non-targeted metabolite data to observe the natural clustering trends of samples between and within different groups. From the PCA score plot, such as... Figure 2 As shown in the principal component score plot, the QC samples exhibit good aggregation, indicating stable and reliable instrument detection. The samples are evenly distributed across all regions with no strongly anomalous sample points, suggesting a uniform sample background, the participation of most variables in the model, and model reliability. The three sample groups show good separation. The cumulative contribution of the first three components to the differences is 59.5%. This demonstrates that the PCA model has excellent discriminatory power between the two sample groups, meaning that geographical indication beef products and non-geographical indication beef products can be distinguished using PCA, further illustrating the significant differences among the three sample groups.

[0048] Subsequently, partial least squares analysis was performed to establish a PLS-DA model, and differential biomarkers were screened (screening criteria for differential metabolites: VIP > 1. VIP = Variable importance, the VIP value mainly reflects the importance of a variable to the model). Figure 2 As shown, in the model, R²X(cum) and R²Y(cum) represent the model's explanatory power for the X and Y matrices, respectively, while Q²Y(cum) represents the model's predictive power. The smaller R²X is, and the larger and closer R²Y and Q²Y are to 1, the more stable and reliable the model. In the established PLS-DA model, R²X(cum), R²Y(cum), and Q²Y(cum) are 59.8%, 85.9%, and 84.2%, respectively, indicating that the model is relatively stable and has good predictive ability.

[0049] Furthermore, univariate statistical analysis was performed, and the volcano plot was able to reflect the significance of different metabolites in different groups. P value) and fold change (FC). For example... Figure 3 As shown, the horizontal axis represents the logarithm of the difference multiple, and the vertical axis represents... P The value is the logarithm of the value. Blue and red dots represent significant differences in the metabolite between the two groups of samples, with a fold difference FC < 0.5 or > 2.

[0050] Finally, with a VIP value greater than 1, P Using value < 0.05 and Fold change < 0.5 or > 2 as screening criteria, 45 potential characteristic markers were obtained, of which 18 potential characteristic markers were obtained in positive ion mode and 27 potential characteristic markers were obtained in negative ion mode. Detailed information is shown in Table 4.

[0051] Table 4 Potential Characteristic Markers

[0052] (3) Discrimination model

[0053] The characteristic markers in the application are screened and identified from a large amount of data, have objectivity and accuracy, and can be used for identification of beef geographical indication products.

[0054] Randomly extract 70% of the samples as a training set, perform PCA Class analysis, and establish a PCA Class discrimination model. PCA Class is to establish an independent PCA model according to grouping information, and judge the sample category according to the degree of compliance of the data to different PCA models. We use the characteristic marker data array of the sample to establish an independent PCA model for grouping ( Figure 4 ). The reliability of the model is tested by ROC curve, and the AUC value is the degree of good distinction between the samples in the study group (0.7-0.8 indicates good discrimination, and 0.8-1 indicates excellent discrimination), Figure 4 The AUC values shown are all greater than 0.9, indicating that the discrimination model can effectively discriminate samples.

[0055] Using the PCA Class discrimination model, the remaining 30% of the samples are discriminated, and the discrimination accuracy is more than 95% (Tables 5-7).

[0056] Table 5 Discrimination and classification results of ordinary beef samples (H) and imported beef samples (J) based on the PCA class model

[0057] Table 6 Discrimination and classification results of ordinary beef samples (H) and Horqin beef samples (KT) based on the PCA class model

[0058] Table 7 Discrimination and classification results of imported beef samples (J) and Horqin beef samples (KT) based on the PCA class model

Claims

1. A method for identifying geographical indication beef products, characterized in that, The method includes the following steps: (1) All collected samples were pretreated under the same conditions to obtain metabolite extracts; (2) Non-targeted metabolomics determination of metabolite extracts was performed by high performance liquid chromatography in tandem with high resolution mass spectrometry to obtain raw data; (3) Perform a series of data preprocessing operations on the raw data, such as peak alignment, retention time correction and peak area extraction, to obtain the metabolomics data array; (4) Through multivariate statistical analysis and univariate statistical analysis, the differences between geographical indication beef and non-geographical indication beef are analyzed, differential markers are screened, and a discrimination model is constructed for discrimination.

2. The method according to claim 1, characterized in that, In step (1), the sample pretreatment process includes weighing 150 mg of beef sample into a 2 mL centrifuge tube, adding 1 mL of extraction solution, sonicating for 5–20 min, vortexing for 5–10 min, centrifuging at 10,000 rpm for 20–30 min, filtering the supernatant through a membrane, and bottling it for metabolomics analysis.

3. The method according to claim 2, characterized in that, The extract is methanol-water; preferably, the volume ratio of methanol to water is 1 / 1.

4. The method according to claim 2, characterized in that, The filtration is performed using an organic filter membrane; preferably, the pore size of the filter membrane is 0.22–0.45 μm.

5. The method according to claim 1, characterized in that, The high-performance liquid chromatography-tandem high-resolution mass spectrometry was performed in both positive and negative ion modes to obtain metabolite data arrays under both modes.

6. The method according to claim 5, characterized in that, The chromatography was performed using a 1290 Infinity II liquid chromatograph with a ZORBAX Eclipse C18 column.

7. The method according to claim 5, characterized in that, The high-resolution mass spectrometry was performed using an Agilent 6546 LC / Q-TOF mass spectrometer.

8. The method according to claim 1, characterized in that, In step (4), the multivariate statistical analysis includes constructing a principal component analysis (PCA) model and a partial least squares discriminant analysis (PLS-DA) model.

9. The method according to claim 1, characterized in that, In step (4), differential markers are obtained by using the variable importance VIP value >1, significance level <0.05, and difference fold >2 or <0.5 as screening conditions.

10. The method according to claim 1, characterized in that, In step (4), the identification model is the PCA-Class identification model.

11. The application of the characteristic marker as described in claim 10 as a characteristic marker for distinguishing geographical indication beef products from non-geographical indication beef products.

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