Method for identifying yinjiaping honey based on metabolomics technology

Abstract

The application discloses a method for identifying Yinjiaping honey based on metabolomics technology, and comprises the following steps: Yinjiaping honey and known group honey are detected by using UPLC-Q-TOF-MS respectively, Yinjiaping honey data and known group honey data are obtained, after the Yinjiaping honey data and the known group honey data are pretreated, compound components of the Yinjiaping honey and the known group honey are obtained respectively, principal component analysis and orthogonal partial least squares discriminant analysis are performed on the compound components of the Yinjiaping honey and the known group honey, then, differential metabolites of the Yinjiaping honey and the known group honey are screened through V+S diagram, a PLS discriminant model is established according to the differential metabolites, differential metabolites of a to-be-detected honey are determined, and are brought into the PLS discriminant model, prediction values of the to-be-detected honey are obtained, and whether the to-be-detected honey is Yinjiaping honey is determined according to a preset prediction value range; the application determines characteristic markers of Yinjiaping honey and establishes a discriminant model, realizes effective identification of authenticity of Yinjiaping honey, and has high accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of food adulteration, in particular to a method for identifying Yinjiaping honey based on metabolomics technology. BACKGROUND

[0002] Honey is a natural sweet substance with high nutritional and medicinal value, especially high-quality characteristic honey is deeply recognized and favored by consumers.

[0003] Yinjiaping honey is a characteristic honey. The production of Yinjiaping honey strictly follows the ancient method, uses the traditional beehive of Sanxia barrel, adheres to the custom of cutting honey before and after the lunar white dew festival, takes honey only once a year, insists on the technology of cutting the honeycomb and pressing, and has unique quality and flavor. As a high-quality characteristic honey in China, Yinjiaping honey has great market value.

[0004] Currently, methods commonly used for identifying honey adulteration include stable carbon isotope ratio analysis (SCIRA), thin layer chromatography (TLC), high-performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD), gas chromatography-mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC), high-performance liquid chromatography-isotope mass spectrometry (HPLC-IRMS), nuclear magnetic resonance technology (NMR), and near-infrared spectroscopy (NIRS). The existing methods have many disadvantages for determining honey adulteration or the phenomenon of using inferior goods as good ones, and cannot specifically identify the authenticity of Yinjiaping honey.

[0005] Therefore, it is necessary to establish a method for identifying Yinjiaping honey. SUMMARY

[0006] In view of this, the present application provides a method for identifying Yinjiaping honey based on metabolomics technology, which is used to solve the problem of how to identify the authenticity of Yinjiaping honey.

[0007] To achieve the above technical purposes, the present application adopts the following technical solutions:

[0008] In a first aspect, the present application provides a method for identifying Yinjiaping honey based on metabolomics technology, comprising the following steps:

[0009] UPLC-Q-TOF-MS is used to detect Yinjiaping honey and known group honey respectively, to obtain Yinjiaping honey data and known group honey data. After pretreatment of Yinjiaping honey data and known group honey data, the compound components of Yinjiaping honey and known group honey are obtained respectively;

[0010] The compound components of Yinjiaping honey and known group honey are subjected to principal component analysis and orthogonal partial least squares discriminant analysis, and then the differential metabolites of Yinjiaping honey and known group honey are screened through V+S diagram.

[0011] establishing a PLS discriminant model according to the differential metabolites;

[0012] determining the differential metabolites of the to-be-tested honey, bringing the differential metabolites into the PLS discriminant model to obtain a predicted value of the to-be-tested honey, and determining whether the to-be-tested honey is Yinjiaping honey according to a preset predicted value range.

[0013] Preferably, the parameters of UPLC-Q-TOF-MS for detecting Yinjiaping honey and known group honey are as follows:

[0014] Chromatographic conditions: the chromatographic column is a reversed-phase chromatographic column; the mobile phase is 0.1% formic acid aqueous solution (V:V) for A and chromatographic grade methanol for B; the flow rate is 0.3 mL / min; the injection amount is 3 μL; and gradient elution is 0 min, 5% B; 2 min, 5% B; 25 min, 100% B; 26 min, 5% B; and 30 min, 5% B.

[0015] Mass spectrometry conditions: the positive and negative ion mode spray voltages are 5500 V and -4500 V, respectively; the de-clustering voltages are 60 V and -60 V, respectively; the capillary temperature is 550℃; the sheath gas flow rate is 55 Arb; the auxiliary gas flow rate is 55 Arb; the retention time is 250 ms in the full-scan TOF MS mode, and the retention time is 70 ms in the MS / MS mode; and the scanning range is 50-750. m / z

[0016] Preferably, the pretreatment process includes peak extraction, retention time correction and peak alignment, and the compound components are identified by using databases including HMDB, MassBank and PubChem; and the software used for the pretreatment is Progenesis QI software.

[0017] Preferably, the conditions for screening the differential metabolites by V+S diagram are p [1]>0.05, p (correlation) [1]>0.5 and VIP>2.

[0018] Preferably, the differential metabolites are also screened or determined as characteristic markers.

[0019] Preferably, the step of determining or screening the differential metabolites as characteristic markers is: drawing a heat map of the differential metabolites by using the Hiplot website, screening the corresponding potential markers from the differential metabolites, drawing an ROC curve of the potential markers by using SPSS 27.0 software, calculating the AUC, and selecting the potential marker with AUC>0.7 as a characteristic marker.

[0020] ​Preferably, the steps for establishing the PLS discriminant model are as follows: Select a portion of Yinjiaping honey and known group honey samples as the training set, assign 1 and 0 to the classification variables of Yinjiaping honey samples and known group honey samples in the training set, respectively, and use the differential metabolites of each honey sample in the training set as independent variables and the classification variables as dependent variables to establish the PLS discriminant model.

[0021] Preferably, the step of determining whether the honey to be tested is Yinjiaping honey based on the predicted value range is as follows: if 0.5 < predicted value < 1.5, then the honey to be tested is Yinjiaping honey; if -0.5 < predicted value < 0.5, then the honey to be tested is not Yinjiaping honey.

[0022] Preferably, the known group of honey includes one or more of the following: linden honey, acacia honey, vitex honey, sweet potato honey, jujube honey, tallow tree honey, rubber honey, and rapeseed honey.

[0023] Secondly, this application provides a characteristic marker for Yinjiaping honey, which includes kynurenic acid and abscisic acid. D - One or more of pantothenic acid, sinapic acid, sebacic acid, kaempferol glucuronide, 3,4,5-trimethoxycinnamic acid, 4-hydroxybenzoic acid, 4-hydroxyquinoline, and alanine-phenylalanine.

[0024] The beneficial effects of this application are as follows: This invention is based on the non-targeted metabolomics technology of ultra-high performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) to screen differential metabolites of Yinjiaping honey, and evaluates the working performance of potential biomarkers through receiver operating characteristic (ROC) curves, thus identifying characteristic biomarkers of Yinjiaping honey. At the same time, a partial least squares (PLS) discriminant model for Yinjiaping honey is established using the characteristic biomarkers. The established discriminant model can effectively identify the authenticity of Yinjiaping honey. The identification method established in this application is simple to operate, has few steps, is easy to learn, has high repeatability, low detection cost, fast analysis speed, and high accuracy.

[0025] The present invention provides a method for screening characteristic markers of Yinjiaping honey. Relying on modern analytical instruments with high throughput, high resolution, and high precision, the method can comprehensively and accurately analyze the components of Yinjiaping honey. The obtained characteristic markers can be used to identify Yinjiaping honey accurately and reliably. Attached Figure Description

[0026] Figure 1 PCA and OPLS-DA analyses were performed on honey samples; among which, Figure 1 A and 1B are PCA diagrams under positive and negative ion modes, respectively. Figure 1 C and Figure 1 D represents the OPLS-DA diagrams under positive and negative ion modes, respectively; Figure 1 E and Figure 1F represents the OPLS-DA substitution test plots under positive and negative ion modes, respectively;

[0027] Figure 2 This is a V+S diagram; where Figure 2 A, Figure 2 B represents the V+S diagrams in positive ion mode and negative ion mode, respectively;

[0028] Figure 3 A thermogram of differential metabolites in Yinjiaping honey;

[0029] Figure 4 ROC curves for characteristic markers of Yinjiaping honey;

[0030] Figure 5 This is a graph showing the relationship between the actual and predicted values ​​in PLS analysis. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0032] Metabolomics technology relies on high-throughput, high-resolution, and high-precision modern analytical instruments to extract information and analyze results through massive data processing. Compared with traditional physicochemical detection and previous instrumental analysis methods, metabolomics technology has significant advantages in food identification. Metabolomics technology can achieve high-throughput simultaneous identification of multiple targets and all components in food, greatly improving detection throughput and efficiency, and can also screen and identify biomarkers, making it a new generation of technological means to solve food identification problems.

[0033] Based on this, this application was created.

[0034] This application provides a method for identifying Yinjiaping honey based on metabolomics technology, including the following steps:

[0035] S1. UPLC-Q-TOF-MS was used to detect Yinjiaping honey and known honey groups to obtain raw data, and Progenesis QI software was used to preprocess the raw data to identify the compound components in each honey sample;

[0036] S2. Principal component analysis and orthogonal partial least squares discriminant analysis were performed on the compound components of each honey sample, and then the differential metabolites of each honey sample were screened by V+S diagram;

[0037] S3. Establish a PLS discrimination model based on differential metabolites;

[0038] S4. Measure the differential metabolites of the honey to be tested and input them into the PLS discriminant model to obtain the predicted value of the honey to be tested; determine whether the honey to be tested is Yinjiaping honey based on the range of predicted values.

[0039] Step S1 is the data processing procedure to obtain the compound components of each honey sample.

[0040] Step S2 involves multivariate statistical analysis and screening of differentially expressed metabolites. Principal component analysis (PCA) is an unsupervised model that reduces the dimensionality of data by projecting variables onto principal components with the largest variance, thus achieving the purpose of multivariate statistical analysis. This application uses SIMCA 14.1 software to perform PCA on small molecule compounds in honey samples, and uses the scores of the first two principal components to create a scatter plot. Orthogonal partial least squares discriminant analysis (OPLS-DA) is a supervised multivariate statistical analysis method that can filter out irrelevant variables in the matrix, maximize the difference between different samples, and highlight inter-group differences. To avoid overfitting, 200 permutation tests are performed. If Q... 2 The intersection with the vertical axis is below 0, and the value of the point on the left is lower than the original value on the right, indicating that the model has good accuracy and is not overfitting; differentially expressed metabolites among different groups of samples are screened using V+S plots. The V+S plot combines a projected importance (VIP) plot and an S-plot; a higher VIP value indicates a more important variable; the farther a variable is from the center in the S-plot, the higher its concentration. Furthermore, the correlation coefficient ( p (corr) represents the degree of correlation between the principal components and the variables, and the covariance ( p () indicates the correlation between the principal component and the variable.

[0041] Step S3: In order to verify the role of the characteristic markers of Yinjiaping honey in distinguishing unknown samples, the SIMCA14.1 software was used to establish a PLS discrimination model for Yinjiaping honey on a randomly selected portion of the samples (e.g., 2 / 3), and the remaining independent honey samples (e.g., 1 / 3) were classified to evaluate the discrimination accuracy of the model.

[0042] Step S4 involves inputting the differential metabolites of the honey to be tested into the PLS discrimination model for authenticity identification.

[0043] In some embodiments, the parameters for detecting Yinjiaping honey and known group honey by UPLC-Q-TOF-MS are as follows: Chromatographic conditions: reverse-phase column; mobile phase: A is 0.1% formic acid aqueous solution (V:V), B is chromatographic grade methanol; flow rate is 0.3 mL / min; injection volume is 3 μL; gradient elution: 0 min, 5% B; 2 min, 5% B; 25 min, 100% B; 26 min, 5% B; 30 min, 5% B; mass spectrometry conditions: spray voltages for positive and negative ion modes are 5500 V and -4500 V, respectively; declustering voltages are 60 V and -60 V, respectively; capillary temperature is 550℃; sheath gas flow rate is 55 Arb; auxiliary gas flow rate is 55 Arb; residence time in full scan TOF MS mode is 250 ms; residence time in MS / MS mode is 70 ms; scan range is [missing information]. m / z 50-750.

[0044] In some embodiments, the preprocessing procedure includes peak extraction, retention time correction and peak alignment, and identification of compound components using databases including HMDB, MassBank and PubChem.

[0045] In some embodiments, the criteria for screening differentially expressed metabolites using the V+S plot are | p [1]|>0.05,| p (corr)[1]|>0.5 and VIP>2.

[0046] The V+S plot combines the projected importance (VIP) plot and the S-plot. A higher VIP value indicates a more important variable; in the S-plot, the farther a variable is from the center, the higher its abundance. Furthermore, the correlation coefficient ( p (corr) represents the degree of correlation between the principal components and the variables, and the covariance ( p () indicates the correlation between the principal component and the variable.

[0047] In some embodiments, differential metabolites are identified or screened as characteristic biomarkers.

[0048] In some embodiments, the steps for identifying or screening differential metabolites as characteristic biomarkers are as follows: plotting heatmaps of differential metabolites using the Hiplot website and screening corresponding potential biomarkers from the differential metabolites; plotting ROC curves of potential biomarkers using SPSS 27.0 software and calculating AUC; and selecting potential biomarkers with AUC > 0.7 as characteristic biomarkers.

[0049] In this embodiment, a further verification or screening step for differential metabolites is as follows: a heatmap is drawn based on the differential metabolites, a receiver operating characteristic (ROC) curve is drawn based on the markers in the heatmap, and the area under the curve is calculated. The ROC curve is used to determine whether the differential metabolites can be used as characteristic markers of Yinjiaping honey. The heatmap visualizes the data by mapping values ​​in the data matrix to colors according to certain rules, using color changes to present the global content changes of multiple samples intuitively. It can also show the relationship between the contents of multiple samples. The heatmap is used to discover potential biomarkers for Yinjiaping honey. The receiver operating characteristic (ROC) curve is widely regarded as the most objective and statistically effective method for evaluating the performance of biomarkers. The area under the curve (AUC) can be used to evaluate the effectiveness of the model. The closer the AUC is to 1, the stronger the discriminative ability of the model. After screening potential biomarkers, the discriminative ability of the potential biomarkers is further evaluated based on the AUC. The larger the AUC of the biomarker, the greater the probability that the model classifies it correctly and it can be used as a biomarker. SPSS 27.0 software is used to plot ROC curves for the potential biomarkers screened in the heatmap and calculate the area under the curve. If the AUC of the potential biomarker is greater than 0.7, it can be used as a characteristic biomarker of Yinjiaping honey.

[0050] In some embodiments, the steps for establishing a PLS discriminant model are as follows: Select a portion of Yinjiaping honey and known group honey samples as the training set, assign 1 and 0 to the classification variables of Yinjiaping honey samples and known group honey samples in the training set, respectively, and use the differential metabolites of each honey sample in the training set as independent variables and the classification variables as dependent variables to establish a PLS discriminant model.

[0051] In some embodiments, the step of determining whether the honey to be tested is Yinjiaping honey based on the predicted value range is as follows: if 0.5 < predicted value < 1.5, then the honey to be tested is Yinjiaping honey; if -0.5 < predicted value < 0.5, then the honey to be tested is not Yinjiaping honey.

[0052] In some embodiments, the known group of honey includes one or more of linden honey, acacia honey, vitex honey, sweet potato honey, jujube honey, tallow tree honey, rubber honey, and rapeseed honey.

[0053] This application provides a characteristic marker for Yinjiaping honey, which includes kynurenic acid and abscisic acid. D - One or more of pantothenic acid, sinapic acid, sebacic acid, kaempferol glucuronide, 3,4,5-trimethoxycinnamic acid, 4-hydroxybenzoic acid, 4-hydroxyquinoline, and alanine-phenylalanine.

[0054] The following specific embodiments further illustrate this solution.

[0055] Raw material source:

[0056] Yinjiaping Honey: Locally sourced Yinjiaping honey was collected and grouped into groups, numbered sequentially from Y1 to Y51.

[0057] Known group of honey: commercially available non-Yinjiaping honey, grouped sequentially as DZ1-DZ52.

[0058] Reagents and equipment: Methanol (chromatographic grade, Thermo Fisher Scientific (China) Co., Ltd.); Formic acid (chromatographic grade, Thermo Fisher Scientific (China) Co., Ltd.); Pipettes (Thermo Fisher Scientific (China) Co., Ltd.); Analytical balance (Mettler-Toledo Instruments (Shanghai) Co., Ltd.); Milli-Q ultrapure water preparation system (Wuhan Bellamy's Biotechnology Co., Ltd.); Vortex mixer (Shanghai Chuyue Electromechanical Technology Co., Ltd.); Triple TOF 5600 high-resolution time-of-flight mass spectrometer (AB Sciex, USA).

[0059] Example 1

[0060] A method for identifying Yinjiaping honey based on metabolomics technology includes the following steps:

[0061] S1. Accurately weigh 5 g of each numbered honey sample into a 50 mL centrifuge tube, add 20 mL of acidified water (pH 2) to dissolve, vortex for 2 min to mix thoroughly, then activate the Oasis HLB solid-phase extraction column successively with 6 mL of pure methanol and 6 mL of acidified water (pH 2). After passing the honey sample solution through the solid-phase extraction column, remove the remaining sugars and unadsorbed compounds with 6 mL of acidified water (pH 2), collect with pure methanol, redissolve the collected solution with methanol, filter through a 0.45 μm organic filter membrane into a 2 mL amber liquid chromatography bottle for UPLC-Q-TOF-MS non-targeted metabolomics detection; take 10 μL of solution from each sample into a 10 mL centrifuge tube. In a 10 mL centrifuge tube, the samples were mixed thoroughly to prepare a quality control (QC) sample. Before sample sequence analysis, two injections of the blank sample washing system were administered, followed by one injection of the QC sample equilibration system. Throughout the analysis, one injection of the QC sample and one injection of the blank sample were administered every 10 honey samples to monitor instrument stability. The chromatographic conditions were as follows: reverse-flow column; mobile phase: A was 0.1% formic acid aqueous solution (V:V), B was chromatographic grade methanol; flow rate was 0.3 mL / min; injection volume was 3 μL; gradient elution: 0 min, 5% B; 2 min, 5% B; 25 min, 100% B; 26 min, 5% B; 30 min, 5% B; mass spectrometry conditions: positive and negative ion mode spray voltages were 5500 V and -4500 V, respectively; declustering voltages were 60 V and -60 V, respectively; capillary temperature was 550℃; sheath gas flow rate was 55 Arb; auxiliary gas flow rate was 55 Arb; and residence time in full scan TOF MS mode was 250 °C. In MS / MS mode, the dwell time is 70 ms, and the scan range is [missing information]. m / z 50-750; After the above process, UPLC-Q-TOF-MS was used to detect Yinjiaping honey and honey of known groups, and raw data were obtained. The raw UPLC-Q-TOF-MS data of the honey samples were imported into Progenesis QI software for preprocessing. The preprocessing process included peak extraction, retention time correction, peak alignment, etc., and the compound components in each honey sample were identified using databases such as HMDB, MassBank, and PubChem.

[0062] S2. Principal component analysis (PCA) was performed on small molecule compounds in 103 honey samples using SIMCA 14.1. Scatter plots were created using the scores of the first two principal components. The PCA analysis results based on the first two principal components are shown below. Figure 1 ( Figure 1 A and 1B are PCA diagrams under positive and negative ion modes, respectively. Figure 1 C and Figure 1 D represents the OPLS-DA diagrams under positive and negative ion modes, respectively; Figure 1 E andFigure 1 F represents the OPLS-DA substitution test score graph under positive and negative ion modes, respectively. Figure 1 A and Figure 1 B indicates that the honey samples from the Yinjiaping honey group and the known group can be clearly separated by the first principal component. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to filter irrelevant variables in the matrix, maximizing the difference between different samples and highlighting the inter-group differences. OPLS-DA results showed significant differences in chemical composition between the two groups. In positive ion mode, the model could explain 98.5% (R²) of the differences between the Yinjiaping honey group and the known group. 2 The difference of Y) Figure 1 C); In negative ion mode, the model can explain 98.6% (R0.05) the differences between the Yinjiaping honey group and known groups. 2 The difference of Y, ( Figure 1 D), and Q in both positive and negative ion modes 2 Both values ​​are close to 1, indicating that the OPLS-DA model can effectively distinguish between the two groups of samples. To avoid overfitting, a permutation test (n=200) was used to test the model. The results show that R0 in the positive ion mode... 2 = (0.0, 0.0594), Q 2 = (0.0, -0.253) Figure 1 E); In negative ion mode, R 2 = (0.0, 0.138), Q 2 = (0.0, -0.274) Figure 1 F), and the rightmost original R 2 and Q 2 The values ​​of Q are all greater than the values ​​of the scatter plots after the left permutation test, and Q 2 All intercept values ​​were below 0, indicating that the model was not overfitting. Next, differentially metabolites among different sample groups were screened using V+S plots. The screening criteria for differentially metabolites were: | p [1]|>0.05,| p (corr)[1]|>0.5, VIP>2, the result is as follows Figure 2 As shown, the V+S plot combines a projected importance (VIP) plot and an S-plot. A higher VIP value indicates a more important variable; in the S-plot, the farther a variable is from the center, the higher its abundance. Furthermore, the correlation coefficient ( p (corr) represents the degree of correlation between the principal components and the variables, and the covariance ( p The symbol () indicates the correlation between the principal component and the variable; analysis revealed that the differentially metabolites in Yinjiaping honey included: kynurenic acid, abscisic acid, DPantothenic acid, sinapic acid, sebacic acid, kaempferol glucuronide, 3,4,5-trimethoxycinnamic acid, 4-hydroxybenzoic acid, 4-hydroxyquinoline, alanine-phenylalanine, differential metabolites and related parameters are shown in Table 1.

[0063] Table 1. Differential metabolites and related parameters of Yinjiaping honey

[0064]

[0065] Further screening was conducted on the differential metabolites of the Yinjiaping honey: heatmaps were generated using Hiplot, and potential biomarkers were screened from the differential metabolites. The results are as follows: Figure 3 As shown, the distribution of differential metabolites between Yinjiaping honey and known honey groups can be observed more intuitively. The heatmap results show that the honey samples are divided into two categories, corresponding to Yinjiaping honey and known honey groups, respectively. ROC curves for the potential biomarkers were then plotted using SPSS 27.0 software, and the results are as follows. Figure 4 As shown, the AUC was calculated, and potential biomarkers with AUC > 0.7 were selected as characteristic biomarkers; kynurenic acid, abscisic acid, D The ROC curve areas for ten potential biomarkers of Yinjiaping honey—pantothenic acid, sinapic acid, sebacic acid, kaempferol glucuronide, 3,4,5-trimethoxycinnamic acid, 4-hydroxybenzoic acid, 4-hydroxyquinoline, and alanine-phenylalanine—were 0.998, 0.975, 0.995, 0.945, 0.982, 0.996, 0.982, 0.988, 0.968, and 0.990, respectively. The ROC curve analysis results are as follows: Figure 4 As shown, the curves of all 10 compounds are above the reference line, and the AUC of all compounds is greater than 0.90, indicating that these 10 compounds can be used as characteristic markers of Yinjiaping honey.

[0066] S3. Randomly select 2 / 3 of the samples from the Yinjiaping honey group and the known honey group (34 samples from the Yinjiaping honey group and 35 samples from the known group) as the training set, and the remaining 1 / 3 samples (17 samples) as the validation set. Assign values ​​of 1 and 0 to the categorical variables of the honey samples from the Yinjiaping honey group and the known group in the training set, respectively. Using the differential metabolites of each honey sample in the training set as independent variables and the categorical variables as dependent variables, perform regression analysis to establish a PLS discriminant model between the data and the categorical variables. The steps to determine whether the honey to be tested is Yinjiaping honey based on the predicted value range are as follows: if 0.5 < predicted value < 1.5, then the honey to be tested is Yinjiaping honey; if -0.5 < predicted value < 0.5, then the honey to be tested is not Yinjiaping honey. The results are as follows: Figure 5 As shown in A; the correlation coefficient R between the true and predicted values ​​of the categorical variables in the PLS discriminant model. 2The root mean square error (RMSEE) was 0.9521, the root mean square error of cross-validation (RMSEcv) was 0.111833, and the root mean square error of cross-validation (RMSEcv) was 0.136015, indicating that the model has good discrimination accuracy. The straight line in the figure is the regression line between the model training set and the actual classification. The predicted value of the categorical variable of the real group samples should be close to 1 and in the range of 0.5 to 1.5; the predicted value of the categorical variable of the known group samples should be close to 0 and in the range of -0.5 to 0.5. To verify the classification ability of the PLS discriminative model, the model was used to classify 17 samples (unknown samples) in the validation set that did not participate in the model building. The model's discriminative ability is represented by the root mean square error of prediction (RMSEP). Figure 5 As shown in B, the RMSEP is 0.132563, indicating that honey samples from different groups can be clearly distinguished. The 17 samples in the upper right corner represent the grouping variable as 1, and the 17 samples in the lower left corner represent the grouping variable as 0, which exactly correspond to their respective groups and are all correctly identified. The identification accuracy rate is 100%, indicating that the established model is effective. This demonstrates that the PLS discriminant model established based on the characteristic markers of Yinjiaping honey can effectively identify whether honey samples belong to Yinjiaping honey.

[0067] This invention utilizes non-targeted metabolomics technology based on ultra-high performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) to screen differential metabolites in Yinjiaping honey. The performance of potential biomarkers was evaluated using receiver operating characteristic (ROC) curves, identifying characteristic biomarkers for Yinjiaping honey. Furthermore, a partial least squares (PLS) discriminant model for Yinjiaping honey was established using these characteristic biomarkers. This model effectively distinguishes genuine from counterfeit Yinjiaping honey. The identification method developed in this application is simple to operate, involves few steps, is easy to learn, has high repeatability, low detection cost, fast analysis speed, and high accuracy.

[0068] The present invention provides a method for screening characteristic markers of Yinjiaping honey. Relying on modern analytical instruments with high throughput, high resolution, and high precision, the method can comprehensively and accurately analyze the components of Yinjiaping honey. The obtained characteristic markers can be used to identify Yinjiaping honey accurately and reliably.

[0069] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying Yinjiaping honey based on metabolomics technology, characterized in that, Includes the following steps: UPLC-Q-TOF-MS was used to detect Yinjiaping honey and known group honey respectively, and the data of Yinjiaping honey and known group honey were obtained. After preprocessing the data of Yinjiaping honey and known group honey, the compound components of Yinjiaping honey and known group honey were obtained respectively. Principal component analysis and orthogonal partial least squares discriminant analysis were performed on the compound components of Yinjiaping honey and known honey groups. Then, the differential metabolites of Yinjiaping honey and known honey groups were screened by V+S diagram. A PLS discrimination model was established based on the differential metabolites. Differential metabolites of the honey to be tested are measured and input into the PLS discrimination model to obtain the predicted value of the honey to be tested. Then, the honey to be tested is determined to be Yinjiaping honey according to the preset predicted value range. The steps for establishing the PLS discriminant model are as follows: Select a portion of the samples from the Yinjiaping honey and known honey groups as the training set. Assign categorical variables of the Yinjiaping honey samples and known honey samples in the training set to 1 and 0, respectively. Use the differential metabolites of each honey sample in the training set as independent variables and the categorical variables as dependent variables to establish the PLS discriminant model. The steps for determining whether the honey to be tested is Yinjiaping honey based on the predicted value range are as follows: if 0.5 < predicted value < 1.5, then the honey to be tested is Yinjiaping honey; if -0.5 < predicted value < 0.5, then the honey to be tested is not Yinjiaping honey. The known honey groups include one or more of the following: linden honey, acacia honey, vitex honey, sweet potato honey, jujube honey, tallow tree honey, rubber honey, and rapeseed honey. Differential metabolites include kynurenic acid and abscisic acid. D - Pantothenic acid, sinapic acid, sebacic acid, kaempferol glucuronide, 3,4,5-trimethoxycinnamic acid, 4-hydroxybenzoic acid, 4-hydroxyquinoline and alanine-phenylalanine.

2. The method for identifying Yinjiaping honey based on metabolomics technology according to claim 1, characterized in that, The parameters for detecting Yinjiaping honey and known honey groups using UPLC-Q-TOF-MS are as follows: Chromatographic conditions: reversed-phase column; mobile phase: A was 0.1% formic acid aqueous solution V:V, B was chromatographic grade methanol; flow rate was 0.3 mL / min; injection volume was 3 μL; gradient elution: 0 min, 5% B; 2 min, 5% B; 25 min, 100% B; 26 min, 5% B; 30 min, 5% B; Mass spectrometry conditions: Spray voltages for positive and negative ion modes were 5500 V and -4500 V, respectively; declustering voltages were 60 V and -60 V, respectively; capillary temperature was 550℃; sheath gas flow rate was 55 Arb; auxiliary gas flow rate was 55 Arb; residence time in full-scan TOF MS mode was 250 ms; residence time in MS / MS mode was 70 ms; scan range was [missing information]. m / z 50-750.

3. The method for identifying Yinjiaping honey based on metabolomics technology according to claim 1, characterized in that, The pretreatment process includes peak extraction, retention time correction, and peak alignment, and the identification of compound components is performed using databases including HMDB, MassBank, and PubChem; the software used for the pretreatment is Progenesis QI software.

4. The method for identifying Yinjiaping honey based on metabolomics technology according to claim 1, characterized in that, The conditions for screening differentially metabolites using the V+S plot are as follows: p [1]|>0.05,| p (corr)[1]|>0.5 and VIP>2.

5. The method for identifying Yinjiaping honey based on metabolomics technology according to claim 1, characterized in that, It also includes identifying or screening the differentially metabolites as characteristic biomarkers.

6. The method for identifying Yinjiaping honey based on metabolomics technology according to claim 5, characterized in that, The steps for identifying or screening the differential metabolites as characteristic biomarkers are as follows: a heatmap of the differential metabolites is plotted using the Hiplot website, and corresponding potential biomarkers are screened from the differential metabolites. Then, ROC curves are plotted for the potential biomarkers using SPSS 27.0 software, and the AUC is calculated. Potential biomarkers with AUC > 0.7 are selected as characteristic biomarkers.

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