Visual non-target metabonomics origin tracing method for strong liquor

Abstract

The invention relates to the field of strong wine detection, and discloses a strong wine visual non-target metabonomics origin traceability method, which comprises the following steps: firstly, collecting strong wine original data, digitizing and outputting the collected strong wine original data to obtain strong wine output data; performing preliminary analysis by using a clustering analysis method to obtain first analysis data; performing secondary analysis on the first analysis data by using a principal component analysis mode to obtain second analysis data; and finally analyzing the second analysis data by partial least square discriminant analysis or orthogonal partial least square discriminant analysis, and screening, comparing and identifying the characteristic compounds to obtain the non-target metabonomics origin traceability place of the strong wine. According to the method, the problems that a non-target metabonomics traceability technology easy to visually display does not exist in the prior art, and a standardized operation instruction integrating efficient pretreatment, non-target screening, rapid traceability, accurate confirmation and visual display does not exist are solved.

Description

Technical Field

[0001] This invention relates to the field of spirits testing, and more particularly to a visual non-target metabolomics method for tracing the origin of spirits. Background Technology

[0002] Spirits, also known as distilled spirits, are produced during the fermentation process of alcoholic beverages. The ethanol solution produced kills the yeast, preventing further fermentation. Therefore, the highest ethanol concentration in fermented alcoholic beverages is only 10%–15%. However, alcohol has a boiling point of 78.2℃. By heating the beverage to a temperature exceeding the boiling point of alcohol but below that of water, alcohol vapor escapes. Condensation yields an ethanol solution with a concentration of 80%–90% or higher, which can then be blended to produce high-concentration spirits.

[0003] With the continuous improvement of people's living standards, the production of spirits, mainly baijiu, brandy, whiskey, and vodka, has developed significantly. my country is a major consumer of spirits, importing products such as brandy and whiskey, while also exporting domestic specialty products such as baijiu.

[0004] In customs technical enforcement, how to quickly and accurately trace the origin of key cross-border products and further verify their authenticity has always been a key technical challenge. Especially for the booming cross-border e-commerce business of spirits, there is an urgent need to research a rapid and accurate origin traceability technology for application in daily supervision. Summary of the Invention

[0005] This invention aims to provide a visual non-target metabolomics method for tracing the origin of spirits, which solves the problem that there is no readily intuitive non-target metabolomics traceability technology in the existing technology, forming a standardized operating procedure that integrates efficient preprocessing, non-targeted screening, rapid traceability, accurate verification, and visual display.

[0006] To achieve the above objectives, the present invention provides the following method:

[0007] This invention provides a method for tracing the origin of spirits through visual non-targeted metabolomics:

[0008] S1: Collect raw data of spirits, digitize the collected raw data of spirits and output it to obtain the output data of spirits;

[0009] S2: Use cluster analysis to perform preliminary analysis on the output data of the spirits to obtain the first analysis data;

[0010] S3: Use principal component analysis to perform secondary analysis on the first analytical data to obtain the second analytical data;

[0011] S4: Use partial least squares discriminant analysis or orthogonal partial least squares discriminant analysis to perform final analysis on the second analytical data to obtain the final analytical data;

[0012] S5: Based on the final analysis data, the characteristic compounds are screened and compared for identification to obtain the non-target metabolomics origin of the spirits.

[0013] Preferably, the step of collecting raw data of spirits includes: collecting spirits, mixing spirits and ultrapure water in a ratio of 1:4 to obtain a spirits sample; screening for non-target metabolites using a gas chromatography system to obtain raw data of the spirits, with the following gas chromatography conditions: column: DB-5MS type flexible quartz capillary column 30m×0.25mm×0.25μm; temperature program: initial temperature 40℃, hold for 1.0min, increase to 250℃ at a rate of 5℃ / min, hold for 10.0min; injection port temperature 250℃; carrier gas: helium; flow rate: 1.0mL / min; split ratio: 10:1; mass spectrometry conditions: electron impact ionization source; ionization energy: 70eV; ion source temperature: 230℃.

[0014] Preferably, the step of digitizing and outputting the collected raw data of the spirits to obtain the spirits output data includes: exporting the raw data of the spirits through a specific format required by the multivariate analysis software to obtain the spirits exported data; performing data preprocessing on the spirits exported data to obtain spirits processed data; extracting and identifying characteristic peaks from the spirits processed data, performing result analysis and output, and obtaining the spirits output data.

[0015] Preferably, the step of preprocessing the exported data of the spirits to obtain processed spirits data includes: removing noise from the exported data of the spirits using a linear filtering algorithm to obtain a first-level spectrum of the exported spirits data, wherein the linear filtering algorithm is:

[0016] L=A T B;

[0017] Where A is the filter coefficient matrix, A TLet B be the transpose of the filter coefficient matrix, B be the pixel grayscale matrix of the spirits exported data covered by the filter, and L be the convolution matrix of the spirits exported data. The spirits are then time-shifted based on the primary spectrum of the spirits exported data. The preparation time and addition time periods of each component are calculated based on the state and content of each component in the primary spectrum. The composition intervals of each component in the spirits are defined based on the preparation time and addition time periods. The preparation time, addition time periods, and composition intervals of each component are then normalized to obtain the processed spirits data.

[0018] Preferably, the step of using cluster analysis to perform preliminary analysis on the output data of the spirits to obtain the first analytical data includes: dividing the output data of the spirits into K groups, and randomly selecting K objects as initial cluster centers; calculating the distance between each remaining object and each seed cluster center, and assigning it to the cluster center closest to it; for each assigned object, the cluster center is recalculated based on the existing samples in the cluster; the termination condition is that no object is reassigned to a different cluster or no cluster center changes, so that the loss function corresponding to the clustering result is minimized, and the first analytical data is obtained.

[0019] Preferably, the step of performing secondary analysis on the first analytical data using principal component analysis to obtain the second analytical data includes: standardizing the first analytical data for index data; determining the correlation between indicators in the standardized first analytical data to determine the number of principal components; organizing the first analytical data into several samples, converting the three-dimensional data into two-dimensional data with columns as dimensions and rows as samples, then centering all features, calculating the average value of each feature, and subtracting the mean value of each feature from the mean value of all samples; calculating the covariance of the centered matrix to obtain the covariance matrix of all samples in all dimensions, calculating the eigenvalues ​​and corresponding eigenvectors of the covariance matrix, and arranging the resulting eigenvalue matrix in descending order on the diagonal of the eigenvalue matrix; projecting the original features onto the selected eigenvectors to obtain the dimensionality-reduced K-dimensional features, performing classification to achieve secondary analysis, and obtaining the second analytical data.

[0020] Preferably, the step of using partial least squares discriminant analysis or orthogonal partial least squares discriminant analysis to perform final analysis on the second analytical data to obtain the final analytical data includes: centering and standardizing the second analytical data to ensure that the data in the second analytical data have the same scale; finding the direction with the largest covariance between the explanatory variables and the response variables through iterative calculation; establishing a linear regression model through the direction with the largest covariance between the explanatory variables and the response variables; evaluating the performance of the linear regression model through cross-validation and optimizing the model by adjusting the number of latent variables; and inputting the second analytical data into the linear regression model for final analysis to obtain the final analytical data.

[0021] Preferably, the step of screening characteristic compounds based on the final analysis data includes: screening potential characteristic compounds of spirits in the final analysis data; determining whether the precise mass-to-charge ratio (MMR) of the characteristic compounds is within the range of the precise MMR of characteristic compounds from the non-target metabolomics origin of spirits; if the precise MMR of the characteristic compounds is within the range of the precise MMR of characteristic compounds from the non-target metabolomics origin of spirits, then recording the precise MMR of the characteristic compounds and the retention time of the characteristic compounds; if the precise MMR of the characteristic compounds is not within the range of the precise MMR of characteristic compounds from the non-target metabolomics origin of spirits, then removing them from the list.

[0022] Preferably, the comparative identification step includes: extracting characteristic compounds from the final analysis data after screening; analyzing the corresponding chemical equations, chemical formulas, and chemical structural formulas of the characteristic compounds; retrieving the chemical equations, chemical formulas, and chemical structural formulas of the characteristic compounds from a database using big data analytics, and estimating the characteristic compounds; comparing and verifying the estimated characteristic compounds with similar compounds in the existing database; if the characteristic compounds are the same as the similar compounds, the non-target metabolomics origin of the spirits is obtained.

[0023] Preferably, the step of retrieving the chemical equation, chemical formula, and chemical structural formula of the characteristic compound from a database using big data and then deducing the characteristic compound includes: obtaining the precise mass number, isotope distribution, and fragment ion fraction of the characteristic compound by analyzing its mass spectrum to deduce the chemical formula; combining the structural information of the characteristic compound with the chemical formula to screen different chemical structural formulas to obtain candidate compounds; and comparing and analyzing the candidate compounds with the chemical equation, chemical formula, and structural formula against the mass spectrum to deduce the characteristic compound.

[0024] The beneficial effects of this invention are as follows: By utilizing gas chromatography-mass spectrometry (GC-MS) technology and exploring methodologies, this invention has constructed a simple, efficient, and rapid non-target metabolic screening technology for spirits; PCA cluster analysis is performed on the data, and after dimensionality reduction, the sample data is intended to be effectively distinguished according to the place of origin. Simultaneously, a spirits origin traceability and visualization technology based on non-target metabolomics is established; the technology is used to verify and promote the application of counterfeit and substandard imported and exported spirits seized in routine technical enforcement, aiming to establish a cross-border spirits origin traceability and visualization technology suitable for nationwide customs promotion, thus solving the pain points and difficulties in the customs technical enforcement process. Attached Figure Description

[0025] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0026] Figure 1 A flowchart illustrating a method for tracing the origin of spirits using visual non-target metabolomics, provided in an embodiment of the present invention.

[0027] Figure 2 This is a schematic diagram illustrating the clustering analysis method provided in an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0030] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0031] Customs is the first line of defense in the supervision of cross-border spirits trade. As the scale of cross-border spirits trade gradually expands, the risks of customs supervision are also increasing.

[0032] In customs technical enforcement, how to quickly and accurately trace the origin of key cross-border products and further verify their authenticity has always been a key technical challenge. Especially for the booming cross-border e-commerce business of spirits, there is an urgent need to research a rapid and accurate origin traceability technology for application in daily supervision.

[0033] This invention aims to provide a visual non-target metabolomics method for tracing the origin of spirits, which solves the problem that there is no readily intuitive non-target metabolomics traceability technology in the existing technology, forming a standardized operating procedure that integrates efficient preprocessing, non-targeted screening, rapid traceability, accurate verification, and visual display.

[0034] like Figure 1 and Figure 2 As shown in the figure, a specific embodiment of the present invention provides a method for tracing the origin of spirits through visual non-target metabolomics, including the following steps:

[0035] S1: Collect raw data of spirits, digitize the collected raw data of spirits and output it to obtain the output data of spirits.

[0036] In this embodiment of the invention, strong liquor was collected and mixed with ultrapure water at a ratio of 1:4 to obtain a strong liquor sample. Non-target metabolite screening was performed using gas chromatography to obtain raw data for the strong liquor. The gas chromatography conditions were set as follows: a DB-5MS type flexible quartz capillary column (30m × 0.25mm × 0.25μm); temperature program: initial temperature 40℃, held for 1.0 min, increased to 250℃ at a rate of 5℃ / min, held for 10.0 min; injection... The inlet temperature was 250℃; the carrier gas was helium; the flow rate was 1.0 mL / min; the split ratio was 10:1; the mass spectrometry conditions were: electron impact ionization source; ionization energy 70 eV; ion source temperature 230℃; the raw data of spirits were exported in a specific format required by the multivariate analysis software to obtain the exported data of spirits; the exported data of spirits were preprocessed to obtain the processed data of spirits; noise was removed from the exported data of spirits using a linear filtering algorithm to obtain the first-order spectrum of the exported data of spirits. The linear filtering algorithm was as follows:

[0037] L=A T B;

[0038] Where A is the filter coefficient matrix, A T Let B be the transpose of the filter coefficient matrix, B be the pixel grayscale matrix of the spirits exported data covered by the filter, and L be the convolution matrix of the spirits exported data. The spirits are time-shifted based on the primary spectrum of the exported data. The preparation time and addition time of each component are estimated based on the state and content of each component in the primary spectrum. The composition intervals of each component in the spirits are defined based on the preparation time and addition time of each component. The preparation time, addition time of each component, and composition intervals of each component are normalized to obtain the processed spirits data. Feature peak extraction and identification are performed on the processed spirits data, and the results are analyzed and output to obtain the output spirits data.

[0039] S2: Use cluster analysis to perform preliminary analysis on the output data of spirits to obtain the first analysis data.

[0040] In this embodiment of the invention, by dividing the output data of strong liquor into K groups, K objects are randomly selected as the initial cluster centers; the distance between each of the remaining objects and each seed cluster center is calculated, and each object is assigned to the cluster center closest to it. Each time an object is assigned, the cluster center is recalculated based on the existing samples in the cluster. The termination condition is that no object is reassigned to a different cluster or no cluster center changes, so that the loss function corresponding to the clustering result is minimized, and the first parsed data is obtained.

[0041] S3: Use principal component analysis to perform secondary analysis on the first analytical data to obtain the second analytical data.

[0042] In this embodiment of the invention, the first analytical data is standardized by index data; the correlation between the indicators in the standardized first analytical data is determined to identify the number of principal components; the first analytical data is organized into several samples, and the three-dimensional data is converted into two-dimensional data by using columns as dimensions and rows as samples; then all features are centered, and the average value of each feature is calculated. For all samples, the mean of each feature is subtracted; the covariance of the centered matrix is ​​calculated to obtain the covariance matrix of all samples in all dimensions; the eigenvalues ​​and corresponding eigenvectors of the covariance matrix are calculated, and the resulting eigenvalue matrix is ​​arranged in descending order on the diagonal of the eigenvalue matrix; the original features are projected onto the selected eigenvectors to obtain the dimensionality-reduced K-dimensional features, which are then classified to achieve secondary analysis and obtain the second analytical data.

[0043] S4: Use partial least squares discriminant analysis or orthogonal partial least squares discriminant analysis to perform final analysis on the second analytical data to obtain the final analytical data.

[0044] In this embodiment of the invention, the second analytical data is centered and standardized to ensure that the data in the second analytical data have the same scale; through iterative calculation, the direction with the largest covariance between the explanatory variables and the response variables is found; a linear regression model is established based on the direction with the largest covariance between the explanatory variables and the response variables; the performance of the linear regression model is evaluated by cross-validation, and the number of latent variables is adjusted to optimize the model; the second analytical data is input into the linear regression model for final analysis to obtain the final analytical data.

[0045] S5: Based on the final analysis data, characteristic compounds are screened and compared for identification to obtain the non-target metabolomics origin of spirits.

[0046] In this embodiment of the invention, potential characteristic compounds of spirits in the final analysis data are screened; it is determined whether the precise mass-to-charge ratio (MMR) of the characteristic compounds is within the range of the precise MMR of characteristic compounds from the non-target metabolomics origin of the spirits; if the precise MMR of the characteristic compounds is within the range of the precise MMR of characteristic compounds from the non-target metabolomics origin of the spirits, the precise MMR and retention time of the characteristic compounds are recorded; if the precise MMR of the characteristic compounds is not within the range of the precise MMR of characteristic compounds from the non-target metabolomics origin of the spirits, they are screened out; characteristic compounds are extracted from the final analysis data after screening; and the corresponding chemical formulas are analyzed according to the characteristic compounds. The process involves: obtaining chemical equations, chemical formulas, and chemical structural formulas of characteristic compounds; retrieving these formulas from a database using big data analytics to infer the characteristic compounds; obtaining precise mass numbers, isotopic distributions, and fragment ion fractions from the mass spectra of characteristic compounds to deduce their chemical formulas; synthesizing the structural information of characteristic compounds using their chemical formulas and screening among different chemical structural formulas to obtain candidate compounds; comparing and analyzing the candidate compounds with their chemical equations, chemical formulas, and structural formulas against their mass spectra to infer the characteristic compounds; and comparing and verifying the inferred characteristic compounds with similar compounds in existing databases. If the characteristic compounds are identical to the similar compounds, the non-target metabolomics origin of the spirits is determined.

[0047] The beneficial effects of this invention are as follows: By utilizing gas chromatography-mass spectrometry (GC-MS) technology and exploring methodologies, this invention has constructed a simple, efficient, and rapid non-target metabolic screening technology for spirits; PCA cluster analysis is performed on the data, and after dimensionality reduction, the sample data is intended to be effectively distinguished according to the place of origin. Simultaneously, a spirits origin traceability and visualization technology based on non-target metabolomics is established; the technology is used to verify and promote the application of counterfeit and substandard imported and exported spirits seized in routine technical enforcement, aiming to establish a cross-border spirits origin traceability and visualization technology suitable for nationwide customs promotion, thus solving the pain points and difficulties in the customs technical enforcement process.

[0048] The above descriptions are merely embodiments of the present invention. Commonly known technical solutions or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for tracing the origin of spirits through visual non-targeted metabolomics, characterized in that, The method includes: S1: Collect raw data of spirits, digitize the collected raw data of spirits and output it to obtain the output data of spirits; S2: Use cluster analysis to perform preliminary analysis on the output data of the spirits to obtain the first analysis data; S3: Use principal component analysis to perform secondary analysis on the first analytical data to obtain the second analytical data; S4: Use partial least squares discriminant analysis or orthogonal partial least squares discriminant analysis to perform final analysis on the second analytical data to obtain the final analytical data; S5: Based on the final analysis data, the characteristic compounds are screened and compared for identification to obtain the non-target metabolomics origin of the spirits.

2. The method for tracing the origin of spirits using visual non-target metabolomics according to claim 1, characterized in that, The steps for collecting raw data on spirits include: Collect strong liquor and mix it with ultrapure water at a ratio of 1:4 to obtain a strong liquor sample; Non-target metabolite screening was performed using gas chromatography to obtain raw data for spirits. The gas chromatography conditions were set as follows: The chromatographic column was a DB-5MS type flexible quartz capillary column, 30m × 0.25mm × 0.25μm. Temperature program: Initial temperature 40℃, hold for 1.0 min, increase to 250℃ at a rate of 5℃ / min, hold for 10.0 min; injection port temperature 250℃; The carrier gas is helium; the flow rate is 1.0 mL / min; the split ratio is 10:

1. Mass spectrometry conditions: electron impact ionization source; ionization energy 70 eV; ion source temperature 230 °C.

3. The method for tracing the origin of spirits using visual non-target metabolomics according to claim 1, characterized in that, The step of digitizing and outputting the collected raw data of the spirits to obtain the output data of the spirits includes: The raw data of the spirits is exported using a specific format required by the multivariate analysis software to obtain the exported data of the spirits. The exported data of the spirits is preprocessed to obtain the spirits processing data; The characteristic peaks of the processed spirits are extracted and identified, and the results are analyzed and output to obtain the spirits output data.

4. The method for tracing the origin of spirits using visual non-target metabolomics according to claim 3, characterized in that, The step of preprocessing the exported data of the spirits to obtain processed spirits data includes: The noise is removed from the derived data of the spirits using a linear filtering algorithm to obtain the first-level spectrum of the derived data of the spirits. The linear filtering algorithm is as follows: L=A T B; Where A is the filter coefficient matrix, A T Let B be the transpose of the filter coefficient matrix, B be the pixel grayscale matrix of the spirits exported data covered by the filter, and L be the convolution matrix of the spirits exported data. The time progression of the spirits is calculated based on the primary spectrum of the derived data of the spirits, and the preparation time and addition time of each component are estimated based on the state and content of each component in the primary spectrum of the derived data of the spirits. The composition intervals of each component of the spirit are defined based on the preparation time of the spirit and the time periods for adding each component. The preparation time of the spirit, the time periods for adding each component, and the composition intervals of each component of the spirit are then normalized to obtain the spirit processing data.

5. The method for tracing the origin of spirits using visual non-target metabolomics according to claim 3, characterized in that, The step of using cluster analysis to perform preliminary analysis on the output data of the spirits to obtain the first analytical data includes: By dividing the output data of the spirits into K groups, K objects are randomly selected as the initial cluster centers. The distance between each of the remaining objects and each seed cluster center is calculated, and the object is assigned to the nearest cluster center. Each time an object is assigned, the cluster center is recalculated based on the existing samples in the cluster. The termination condition is that no object is reassigned to a different cluster or no cluster center changes, so that the loss function corresponding to the clustering result is minimized, and the first parsed data is obtained.

6. The method for tracing the origin of spirits using visual non-target metabolomics according to claim 1, characterized in that, The step of performing secondary analysis on the first analytical data using principal component analysis to obtain the second analytical data includes: Standardize the first parsed data into indicator data; The correlation between indicators is determined in the first parsed data after standardization of the indicator data, and the number of principal components is determined. The first parsed data is organized into several samples. The three-dimensional data is converted into two-dimensional data according to the column as the dimension and the row as the sample. Then, all features are centered and the average value of each feature is calculated. For all samples, each feature is subtracted from its own mean. Calculate the covariance of the centered matrix to obtain the covariance matrix of all samples in all dimensions. Calculate the eigenvalues ​​and corresponding eigenvectors of the covariance matrix. The resulting eigenvalue matrix is ​​arranged in descending order on the diagonal of the eigenvalue matrix. The original features are projected onto the selected feature vector to obtain the dimensionality-reduced K-dimensional features. Classification is then performed to achieve secondary analysis, resulting in the second analytical data.

7. The method for tracing the origin of spirits using visual non-target metabolomics according to claim 6, characterized in that, The step of using partial least squares discriminant analysis or orthogonal partial least squares discriminant analysis to perform final analysis on the second analytical data to obtain the final analytical data includes: The second parsed data is centered and standardized to ensure that the data in the second parsed data have the same scale; By iterative calculation, the direction of maximum covariance between explanatory and response variables is found; A linear regression model is established by maximizing the covariance between the explanatory and response variables. The performance of the linear regression model was evaluated using cross-validation, and the model was optimized by adjusting the number of latent variables. The second analytical data is input into the linear regression model for final analysis to obtain the final analytical data.

8. The method for tracing the origin of spirits using visual non-target metabolomics according to claim 7, characterized in that, The step of screening characteristic compounds based on the final analysis data includes: The potential characteristic compounds of spirits in the final analyzed data are screened. Determine whether the precise mass-to-charge ratio of the characteristic compound's ions is within the precise mass-to-charge ratio of the characteristic compound's ions from the non-target metabolomics origin of the spirit; If the precise mass-to-charge ratio of the characteristic compound is within the range of the precise mass-to-charge ratio of the characteristic compound from the non-target metabolomics origin of the spirit, then the precise mass-to-charge ratio of the characteristic compound and the retention time of the characteristic compound are recorded. If the precise mass-to-charge ratio of the characteristic compound is not within the precise mass-to-charge ratio of the characteristic compound from the non-target metabolomics origin of the spirit, it will be screened out.

9. The method for tracing the origin of spirits using visual non-target metabolomics according to claim 1, characterized in that, The comparative identification steps include: Extract characteristic compounds from the final analyzed data after filtering; Based on the characteristic compounds, the corresponding chemical equations, chemical formulas, and chemical structural formulas are analyzed; The chemical equations, chemical formulas, and chemical structural formulas of the characteristic compounds are retrieved from a database using big data analytics, and the characteristic compounds are then deduced. The inferred characteristic compound is compared and verified with similar compounds in the existing database. If the characteristic compound is the same as the similar compound, the non-target metabolomics origin of the spirit is obtained.

10. The method for tracing the origin of spirits using visual non-target metabolomics according to claim 9, characterized in that, The step of retrieving the chemical equation, chemical formula, and chemical structural formula of the characteristic compound from a database using big data analytics, and then deducing the characteristics of the compound, includes: The chemical formula is deduced by analyzing the mass spectrum of the characteristic compound to obtain the precise mass number, isotopic distribution, and fragment ion fraction. By synthesizing the structural information of the characteristic compounds using the chemical formulas, candidate compounds are obtained through screening among different chemical structural formulas. The candidate compounds are compared and analyzed with their chemical equations, chemical formulas, structural formulas, and mass spectra to deduce the characteristic compounds.

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