Mass spectrometry method for identifying probiotic metabolite

Abstract

The invention relates to the field of metabolite analysis, in particular to a mass spectrometry method for identifying probiotic metabolite. The method comprises the following steps: carrying out normalization processing on peak intensity in collected original mass spectrum data to obtain mass spectrum data; correcting peak intensity in the mass spectrum data, and screening based on the corrected peak intensity to obtain screened mass spectrum data; performing fragment ion peak and isotope peak recognition and combination operation on the screened mass spectrum data to obtain combined mass spectrum data; based on the combined mass spectrum data, identifying the probiotic metabolite, verifying and confirming the probiotic metabolite identification result, and generating a metabolite identification report. The problems that in the signal processing stage, a fragment ion peak and isotope peak recognition and combination method is not fine enough, and misjudgment may be caused are solved; according to a single-dimensional metabolite matching method, the problem that the matching degree is low easily due to the fact that integrated analysis of signal fluctuation and multi-dimensional features is ignored is solved.

Description

Technical Field

[0001] The present invention relates to the field of metabolite analysis, and in particular to a mass spectrometry analysis method for identifying probiotic metabolites. Background Art

[0002] With the continuous deepening of metabolomics and probiotic research, mass spectrometry has become an important means of identifying metabolites. Modern mass spectrometers have high sensitivity, high resolution and wide dynamic range, which can capture complex compound information in samples and generate mass spectrometry data containing mass-to-charge ratio and peak intensity. In order to improve the accuracy of data processing, researchers continue to explore technologies to optimize data preprocessing, signal correction and feature matching algorithms. For example, polynomial fitting algorithms have been widely used in the correction of baseline drift to reduce the interference of environmental noise and instrument errors on signals. At the same time, the identification and merging of fragment ion peaks and isotope peaks have gradually developed into key steps for data reduction and signal optimization. In addition, multidimensional feature matching algorithms combined with known metabolite information in the database are becoming the mainstream method to improve the accuracy of metabolite identification. Through the above developments, mass spectrometry technology has moved from single signal analysis to multidimensional information fusion, providing strong support for the study of probiotic metabolites.

[0003] However, there are still some key issues that need to be resolved in the above technology. In the signal processing stage, the identification and merging methods of fragment ion peaks and isotope peaks are not sophisticated enough. Simple identification rules and rough threshold control may lead to misjudgment, causing multiple signals of the same metabolite to be mistaken for different substances, or meaningful isotope signals are not correctly identified and merged. In addition, the metabolite matching method of a single dimension (such as mass-to-charge ratio or peak intensity) ignores the integrated analysis of signal fluctuations and multidimensional features, which easily leads to a low degree of matching. Especially in the scenario of many-to-many matching, it cannot fully reflect the overall correlation between sample data and metabolites, and it is urgently needed to be improved through a new algorithm and processing technology. Summary of the invention

[0004] The present invention provides a mass spectrometry analysis method for identifying probiotic metabolites, so as to solve the problems that, in the signal processing stage, the identification and merging methods of fragment ion peaks and isotope peaks are not sophisticated enough, simple identification rules and rough threshold control may lead to misjudgment, so that multiple signals of the same metabolite are mistaken for different substances, or meaningful isotope signals are not correctly identified and merged; the metabolite matching method of a single dimension (such as mass-to-charge ratio or peak intensity) ignores the integrated analysis of signal fluctuations and multidimensional features, which easily leads to a low matching degree, especially in the scenario of many-to-many matching, and cannot fully reflect the overall correlation between sample data and metabolites.

[0005] A mass spectrometry analysis method for identifying probiotic metabolites of the present invention specifically includes the following technical solutions:

[0006] A mass spectrometry analysis method for identifying probiotic metabolites comprises the following steps:

[0007] S1: collecting raw mass spectrum data, normalizing the peak intensity in the raw mass spectrum data to obtain mass spectrum data; correcting the peak intensity in the mass spectrum data to obtain corrected peak intensity; screening the mass spectrum data based on the corrected peak intensity to obtain screened mass spectrum data;

[0008] S2: performing identification and merging operations on the fragment ion peaks and isotope peaks on the screened mass spectrum data to obtain merged mass spectrum data;

[0009] S3: Based on the combined mass spectrometry data, a metabolite multidimensional feature matching algorithm is introduced to identify the probiotic metabolites, obtain the probiotic metabolite identification results, and verify and confirm the probiotic metabolite identification results to generate a metabolite identification report.

[0010] Preferably, the S1 specifically includes:

[0011] Based on the mass-to-charge ratio of the mass spectrometry data, a baseline estimation value is obtained by fitting a cubic polynomial; based on the baseline estimation value, the peak intensity in the mass spectrometry data is corrected to obtain a corrected peak intensity.

[0012] Preferably, the S2 specifically includes:

[0013] The identification and merging operation of the fragment ion peak and the isotope peak adopts a two-stage merging process, first identifying and merging the fragment ion peak to obtain the spectrum data after the first merger; then identifying the isotope peak based on the peak intensity ratio and mass-to-charge ratio difference of the spectrum data after the first merger, and performing a second merger to obtain merged mass spectrum data.

[0014] Preferably, the S2 specifically includes:

[0015] In the process of implementing the identification and merging operation of fragment ion peaks and isotope peaks, the fragment ion peaks are identified based on the mass-to-charge ratio difference of adjacent data points of the screened mass spectrometry data. When the mass-to-charge ratio difference of the adjacent data points meets the fragment ion peak identification condition, it is judged that the adjacent data points belong to the fragment ion peaks of the same metabolite, and the first merging operation is performed to obtain the mass spectrometry data after the first merging.

[0016] Preferably, the S2 specifically includes:

[0017] Isotope peaks are identified based on the ratio of the peak intensities of adjacent data points of the mass spectrum data after the first merger. When the ratio of the peak intensities of the adjacent data points meets the peak intensity identification condition, the adjacent data points are judged to be isotope peaks, and a second merger operation is performed to obtain merged mass spectrum data.

[0018] Preferably, the S3 specifically includes:

[0019] In the implementation process of the metabolite multidimensional feature matching algorithm, the matching value is calculated based on the merged mass spectrometry data and the known metabolite reference values ​​in the database, and a matching matrix is ​​constructed; the matching value calculation formula is as follows:

[0020]

[0021] Among them, D ij is the match value, indicating the merged mass spectrum data The matching relationship between the data point and the jth reference value of the metabolite; The combined mass spectrometry data The mass-to-charge ratio of the data point; r j is the mass-to-charge ratio of the jth reference value of the metabolite; j is the index variable of the metabolite reference value; Δr max is the maximum mass-to-charge ratio deviation allowed; ∈ is a positive number to prevent the denominator from being zero; exp is the exponential operation; The combined mass spectrometry data The peak intensity of each data point; z j is the peak intensity of the jth reference value of the metabolite; σ′ I is the peak intensity standard deviation of the combined mass spectrum data; ξ is the local mass-to-charge ratio fluctuation adjustment coefficient; is the average mass-to-charge ratio of the combined mass spectrum data; σ′ m is the mass-to-charge ratio standard deviation of the combined mass spectral data.

[0022] Preferably, the S3 specifically includes:

[0023] Based on the matching value, the combined mass spectrometry data is matched with the metabolites in the database, and the matching score of the metabolites is calculated. The specific calculation formula is as follows:

[0024]

[0025] Where S is the metabolite matching score; U is the number of data points in the merged mass spectrometry data; F is the number of reference values ​​for the metabolite; is the global mass-to-charge ratio fluctuation adjustment coefficient.

[0026] Preferably, the S3 specifically includes:

[0027] The matching score of each metabolite was sorted from high to low, and the metabolite with the highest matching score was selected as the result of probiotic metabolite identification.

[0028] The beneficial effects of the technical solution of the present invention are:

[0029] 1. The present invention introduces the least squares method to calculate the fitting coefficient, uses a cubic polynomial to fit the baseline estimate, and accurately deducts drift interference; in addition, a weighted adjustment term for the degree of deviation of the mass-to-charge ratio relative to the average mass-to-charge ratio is added to the correction formula, which further optimizes the recognition of weak signals and the accuracy of peak intensity, reduces the impact of noise and background signals on metabolite identification, and effectively improves the sensitivity and reliability of metabolite identification.

[0030] 2. The present invention uses a two-stage merging process to first identify and merge the fragment ion peaks, then determine the isotope peaks based on the peak intensity ratio and mass-to-charge ratio difference, and perform secondary merging to reduce data redundancy and ensure the accuracy and integrity of the data. This not only improves the accuracy of the merged mass spectrometry data, but also enhances the ability to characterize complex metabolites.

[0031] 3. The multi-dimensional feature matching algorithm adopted in the present invention integrates the differences in mass-to-charge ratio, peak intensity and mass-to-charge ratio fluctuation when constructing the matching matrix, and combines the local and global mass-to-charge ratio fluctuation adjustment coefficients to solve the problem of insufficient single-dimensional matching; by calculating and sorting the metabolite matching scores, the metabolites with the highest metabolite matching scores are screened out, providing a scientific and quantitative basis for metabolite identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The present invention is a flow chart of a mass spectrometry analysis method for identifying probiotic metabolites. DETAILED DESCRIPTION

[0033] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0034] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0035] The specific scheme of the mass spectrometry analysis method for identifying probiotic metabolites provided by the present invention is described in detail below with reference to the accompanying drawings.

[0036] Refer to the attached Figure 1 , which shows a flow chart of a mass spectrometry analysis method for identifying probiotic metabolites provided by an embodiment of the present invention, the method comprising the following steps:

[0037] S1: collecting raw mass spectrum data, normalizing the peak intensity in the raw mass spectrum data to obtain mass spectrum data; correcting the peak intensity in the mass spectrum data to obtain corrected peak intensity; screening the mass spectrum data based on the corrected peak intensity to obtain screened mass spectrum data;

[0038] The original mass spectrometer data is collected by using a mass spectrometer. The collected original mass spectrometer data includes mass-to-charge ratio and peak intensity. The mass-to-charge ratio represents the ratio of the mass and charge of each signal in the mass spectrum, and the unit is Dalton; the peak intensity represents the relative abundance of the compound under a specific mass-to-charge ratio. The peak intensity is normalized so that all peak intensities are adjusted to the interval [0, 1] to obtain mass spectrometer data; the mass spectrometer data is represented in the form of point pairs, and the specific expression is: (m1, I1), (m2, I2), ..., (m N , I N ), where (m i , I i ) is the i-th data point of mass spectrum data; m i is the mass-to-charge ratio of the i-th data point of the mass spectrum data; Ii is the peak intensity of the i-th data point of the mass spectrum data; i is the index variable of the data point; N is the total number of data points of the mass spectrum data.

[0039] The signal collected by the mass spectrometer will be disturbed by the baseline drift. In order to reduce the influence of the baseline drift on the peak intensity, the peak intensity is corrected using the baseline estimate obtained by cubic polynomial fitting to obtain the corrected peak intensity. The baseline drift is the low-frequency noise caused by instrument error, environmental changes or background signals, which causes the mass spectrometry data to deviate from the true baseline level, resulting in the false appearance of high or low peak intensity, interfering with the accuracy of the mass spectrometry data, and thus affecting the accuracy of metabolite identification; the baseline estimate is the background signal screened out by cubic polynomial fitting, which is used to approximate the drift trend of the mass-to-charge ratio;

[0040] Based on the baseline estimate, the peak intensity is corrected, that is, the baseline estimate is deducted from the peak intensity, and further adjustments are made according to the degree of deviation of the mass-to-charge ratio to reduce the impact of baseline drift on the peak intensity and improve the accuracy and reliability of metabolite identification.

[0041] The specific calibration process is as follows:

[0042] First, a cubic polynomial is used to fit the baseline estimate, as shown in the following formula:

[0043] R(m)=α3·m3 +α2·m 2 +α1·m+α0

[0044] Wherein, B(m) is the baseline estimate at a given mass-to-charge ratio, which is the fitted background signal used to correct the drift and noise of mass spectrometry data; m represents the input mass-to-charge ratio; α3 is the cubic term coefficient, which is used to capture the complex nonlinear changes of the background signal; α2 is the quadratic term coefficient, which is used to describe the milder nonlinear drift; α1 is the linear term coefficient; α0 is the constant term, which represents the baseline level of the background signal;

[0045] A fitting threshold is set according to a specific implementation scenario, and data points with peak intensities lower than the fitting threshold are used as data for fitting the baseline estimation value. The screened data points with peak intensities lower than the fitting threshold are fitted using the least squares method to calculate the values ​​of the cubic term coefficient, the quadratic term coefficient, the linear term coefficient, and the constant term;

[0046] Furthermore, based on the baseline estimation value, the peak intensity is corrected to obtain the corrected peak intensity. The specific correction formula is as follows:

[0047]

[0048] in, is the peak intensity of the ith data point of the mass spectrum data after correction; Ii is the peak intensity of the ith data point of the mass spectrum data; B(m i ) is the baseline estimate, which represents the mass-to-charge ratio m at the i-th data point in the mass spectrum data. i Baseline estimate at ; is the average mass-to-charge ratio of the mass spectrum data, which is used to evaluate the mass-to-charge ratio m of the i-th data point of the mass spectrum data. i The degree of deviation from the average value of mass-to-charge ratio; σ is the standard deviation of the mass-to-charge ratio of the spectrum data, which is used to measure the discrete degree of the mass-to-charge ratio of the mass spectrum data; ∈ is a positive number to prevent the denominator from being zero, which is set according to the specific implementation scenario;

[0049] The correction formula not only deducts the baseline estimate, but also introduces a weighted adjustment term for the degree of deviation of the mass-to-charge ratio from the average mass-to-charge ratio, which enhances the recognition and optimization of weak signals and improves the accuracy of peak intensity and signal sensitivity.

[0050] Finally, it is necessary to further eliminate low-intensity noise signals or background interference. Specifically, the peak intensity threshold is set using the expert experience method, and screening is performed based on the peak intensity threshold. Data points with corrected peak intensities lower than the peak intensity threshold are eliminated to obtain screened mass spectrometry data, thereby reducing the interference of invalid data.

[0051] S2: performing identification and merging operations on the fragment ion peaks and isotope peaks on the screened mass spectrum data to obtain merged mass spectrum data;

[0052] The fragment ion peaks and isotope peaks were identified and merged on the screened mass spectrometry data to further streamline the signals and improve the accuracy of metabolite identification.

[0053] The fragment ion peak is a characteristic signal generated by the fragmentation of the metabolite during the mass spectrometry analysis process, reflecting the molecular structure information of the metabolite. The fragment peaks of the same metabolite are identified by judging whether the mass-to-charge ratio difference of adjacent data points in the screened mass spectrometry data meets the set conditions, and weighted average merging is performed to reduce data redundancy; the isotope peak is a signal generated by a slight mass shift caused by the isotope elements naturally present in the metabolite. The isotope peak is identified by judging the peak intensity ratio of adjacent data points in the screened mass spectrometry data, and weighted average merging is performed to optimize the peak intensity.

[0054] The specific operation process of identifying and merging fragment ion peaks and isotope peaks is as follows:

[0055] First, based on the mass-to-charge ratio difference of adjacent data points of the screened mass spectrum data, the fragment ion peak is identified; it is determined whether the mass-to-charge ratio difference of adjacent data points of the screened mass spectrum data meets the fragment ion peak identification condition, and the fragment ion peak identification condition is as follows:

[0056]

[0057] in, is the mass-to-charge ratio difference of adjacent data points of the filtered mass spectrum data, indicating the first and The difference in mass-to-charge ratio of the data points; and They are the first and The mass-to-charge ratio of each data point; δ is the basic threshold, which is used to define the basic value of the mass-to-charge ratio error range; is the mass-to-charge ratio standard deviation of the filtered mass spectrometry data; is the total number of data points in the filtered mass spectrometry data; It is a fluctuation correction term, which is used to compensate for the error caused by data collection. The more data points there are, the lower the impact of fluctuations.

[0058] If the mass-to-charge ratio difference meets the above-mentioned fragment ion peak identification conditions, the first data points and The data points belong to the fragment ion peak of the same metabolite;

[0059] Then, the mass-to-charge ratio differences of all adjacent data points of the filtered spectrum data are calculated, and the data points of the fragment ion peaks belonging to the same metabolite are merged for the first time to obtain the mass spectrum data after the first merger. Specifically, the merging operation uses a weighted average method to merge the mass-to-charge ratios, and uses a cumulative method to merge the peak intensities;

[0060] Further, based on the mass spectrum data after the first merger, it is determined whether the peak intensity of adjacent data points of the mass spectrum data after the first merger meets the isotope peak identification condition. The isotope peak identification condition is as follows:

[0061]

[0062] in, and They are the first and The peak intensity of each data point; and They are the first and The mass-to-charge ratio of each data point; is the mass-to-charge ratio standard deviation of the mass spectral data after the first merge;

[0063] If the peak intensity ratio meets the isotope peak identification condition, the first merged mass spectrum data is judged. data points and The data points are isotope peaks;

[0064] Finally, for the isotope peak, the mass-to-charge ratio difference matching technology is used to find the mother peak corresponding to the isotope peak, and a second merger is performed to obtain the merged mass spectrum data; the merging operation uses the weighted average method to merge the mass-to-charge ratios, and the cumulative method to merge the peak intensities. The mass-to-charge ratio difference matching technology is a technology well known to those skilled in the art and will not be described in detail here.

[0065] The identification and merging operation of fragment ion peaks and isotope peaks improves the identification accuracy of fragment ion peaks by introducing fluctuation correction items and dynamically adjusting the mass-to-charge ratio error range. A two-stage merging process is adopted to first identify and merge fragment ion peaks to obtain the spectrum data after the first merger. Then, the isotope peaks are identified based on the peak intensity ratio and mass-to-charge ratio difference of the spectrum data after the first merger, and a second merger is performed to obtain merged mass spectrum data to reduce data redundancy and improve the accuracy of metabolite identification.

[0066] The combined mass spectrometry data represent the comprehensive characteristics of probiotic metabolites, expressed as (m′1, I′1), (m′2, I′2), ..., (m′ U , I′U ),in, The combined mass spectrometry data data points; The first The mass-to-charge ratio of each data point; The first The peak intensity of each data point; is the index variable of the data points of the merged mass spectral data; U is the total number of data points of the merged mass spectral data.

[0067] S3: Based on the combined mass spectrometry data, a metabolite multidimensional feature matching algorithm is introduced to identify the probiotic metabolites, obtain the probiotic metabolite identification results, and verify and confirm the probiotic metabolite identification results to generate a metabolite identification report.

[0068] Based on the combined mass spectrometry data, a metabolite multidimensional feature matching algorithm was introduced. The metabolite multidimensional feature matching algorithm constructed a matching matrix, comprehensively compared the differences in mass-to-charge ratio, peak intensity and mass-to-charge ratio fluctuation, evaluated the matching degree between the combined mass spectrometry data and the known metabolites in the database, and achieved metabolite identification.

[0069] The database stores standard mass spectrometry characteristic data of multiple known metabolites, i.e., metabolite reference values. One metabolite contains multiple metabolite reference values. The metabolite reference values ​​include information such as mass-to-charge ratio and peak intensity, providing a matching reference for the identification of metabolites. The metabolite reference values ​​are metabolite mass spectrometry characteristic data measured under standard experimental conditions, including the mass-to-charge ratio and corresponding peak intensity of each metabolite. The matching matrix is ​​used to represent the matching relationship between the data points of the merged mass spectrometry data and the metabolite reference values ​​in the database. The smaller the value of the matching matrix element, the higher the degree of matching. The specific process of the metabolite multidimensional feature matching algorithm is as follows:

[0070] First, calculate the matching value and construct the matching matrix. The matching value calculation formula is as follows:

[0071]

[0072] in, is the match value, is the match matrix element, indicating the merged mass spectrum data The matching relationship between the data point and the jth reference value of the metabolite; The combined mass spectrometry data The mass-to-charge ratio of the data point; r j is the mass-to-charge ratio of the jth reference value of the metabolite; j is the index variable of the metabolite reference value; Δr max is the maximum mass-to-charge ratio deviation allowed, which is set according to the specific implementation scenario; ∈ is a positive number to prevent the denominator from being zero; exp is an exponential operation; The combined mass spectrometry data The peak intensity of each data point; z j is the peak intensity of the jth reference value of the metabolite; σ′ I is the peak intensity standard deviation of the merged mass spectrometry data; ξ is the local mass-to-charge ratio fluctuation adjustment coefficient, which is used to control the influence of the local mass-to-charge ratio fluctuation of the data point of the merged mass spectrometry data on the matching, and is obtained through experimental tuning; is the average mass-to-charge ratio of the combined mass spectrum data; σ′ m is the mass-to-charge ratio standard deviation of the combined mass spectral data;

[0073] Next, based on the matching value, the matching score of the metabolite is calculated to reflect the overall matching of the combined mass spectrometry data and the metabolite. The matching score of the metabolite not only integrates the difference in mass-to-charge ratio and peak intensity between the combined mass spectrometry data and the metabolite reference value, but also considers the influence of global mass-to-charge ratio fluctuations, providing a quantitative basis for the identification of metabolites;

[0074] The metabolite matching score is calculated as follows:

[0075]

[0076] Among them, S is the metabolite matching score, which is used to measure the matching degree between the combined mass spectrometry data and the corresponding metabolites. The higher the metabolite matching score, the better the matching effect; U is the number of data points in the combined mass spectrometry data; F is the number of reference values ​​of the corresponding metabolites; is the global mass-to-charge ratio fluctuation adjustment coefficient, which is used to control the impact of the global mass-to-charge ratio fluctuation of the merged mass spectrometry data on the metabolite matching score and is obtained through experimental tuning;

[0077] Furthermore, the combined mass spectrometry data were matched with all metabolites in the database, the matching score of each metabolite was calculated, and after sorting the metabolites from high to low according to the metabolite matching score, the metabolite with the highest metabolite matching score was selected as the probiotic metabolite identification result;

[0078] Finally, the identification results of probiotic metabolites are verified and confirmed by combining experimental conditions and expert experience, and the metabolite information (mass-to-charge ratio, peak intensity, metabolite matching score) is organized into a metabolite identification report to display the identification results of the metabolites, providing basic data for subsequent metabolic pathway analysis or functional research.

[0079] In summary, a mass spectrometry analysis method for the identification of probiotic metabolites was completed.

[0080] The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0082] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A mass spectrometry analysis method for identifying probiotic metabolites, characterized in that: The following steps are involved: S1: collecting raw mass spectrum data, normalizing the peak intensity in the raw mass spectrum data to obtain mass spectrum data; correcting the peak intensity in the mass spectrum data to obtain corrected peak intensity; screening the mass spectrum data based on the corrected peak intensity to obtain screened mass spectrum data; S2: performing identification and merging operations on the fragment ion peaks and isotope peaks on the screened mass spectrum data to obtain merged mass spectrum data; S3: Based on the combined mass spectrometry data, a metabolite multidimensional feature matching algorithm is introduced to identify the probiotic metabolites, obtain the probiotic metabolite identification results, and verify and confirm the probiotic metabolite identification results to generate a metabolite identification report.

2. The mass spectrometry analysis method for identifying probiotic metabolites according to claim 1, characterized in that: The S1 specifically includes: Based on the mass-to-charge ratio of the mass spectrometry data, a baseline estimation value is obtained by fitting a cubic polynomial; based on the baseline estimation value, the peak intensity in the mass spectrometry data is corrected to obtain a corrected peak intensity.

3. The mass spectrometry analysis method for identifying probiotic metabolites according to claim 1, characterized in that: The S2 specifically includes: The identification and merging operation of the fragment ion peak and the isotope peak adopts a two-stage merging process, first identifying and merging the fragment ion peak to obtain the spectrum data after the first merger; then identifying the isotope peak based on the peak intensity ratio and mass-to-charge ratio difference of the spectrum data after the first merger, and performing a second merger to obtain merged mass spectrum data.

4. The mass spectrometry analysis method for identifying probiotic metabolites according to claim 3, characterized in that: The S2 specifically includes: In the process of implementing the identification and merging operation of fragment ion peaks and isotope peaks, the fragment ion peaks are identified based on the mass-to-charge ratio difference of adjacent data points of the screened mass spectrometry data. When the mass-to-charge ratio difference of the adjacent data points meets the fragment ion peak identification condition, it is judged that the adjacent data points belong to the fragment ion peaks of the same metabolite, and the first merging operation is performed to obtain the mass spectrometry data after the first merging.

5. The mass spectrometry analysis method for identifying probiotic metabolites according to claim 4, characterized in that: The S2 specifically includes: Isotope peaks are identified based on the ratio of the peak intensities of adjacent data points of the mass spectrum data after the first merger. When the ratio of the peak intensities of the adjacent data points meets the peak intensity identification condition, the adjacent data points are judged to be isotope peaks, and a second merger operation is performed to obtain merged mass spectrum data.

6. The mass spectrometry analysis method for identifying probiotic metabolites according to claim 1, characterized in that: The S3 specifically includes: In the implementation process of the metabolite multidimensional feature matching algorithm, the matching value is calculated based on the merged mass spectrometry data and the known metabolite reference values ​​in the database, and a matching matrix is ​​constructed; the matching value calculation formula is as follows: in, is the match value, indicating the merged mass spectrum data The matching relationship between the data point and the jth reference value of the metabolite; The combined mass spectrometry data The mass-to-charge ratio of the data point; r j is the mass-to-charge ratio of the jth reference value of the metabolite; j is the index variable of the metabolite reference value; Δr max is the maximum mass-to-charge ratio deviation allowed; ∈ is a positive number to prevent the denominator from being zero; exp is the exponential operation; The combined mass spectrometry data The peak intensity of each data point; z j is the peak intensity of the jth reference value of the metabolite; σ′ I is the peak intensity standard deviation of the combined mass spectrum data; ξ is the local mass-to-charge ratio fluctuation adjustment coefficient; is the average mass-to-charge ratio of the combined mass spectrum data; σ′ m is the mass-to-charge ratio standard deviation of the combined mass spectral data.

7. The mass spectrometry analysis method for identifying probiotic metabolites according to claim 6, characterized in that: The S3 specifically includes: Based on the matching value, the combined mass spectrometry data is matched with the metabolites in the database, and the matching score of the metabolites is calculated. The specific calculation formula is as follows: Where S is the metabolite matching score; U is the number of data points in the merged mass spectrometry data; F is the number of reference values ​​for the metabolite; is the global mass-to-charge ratio fluctuation adjustment coefficient.

8. The mass spectrometry analysis method for identifying probiotic metabolites according to claim 7, characterized in that: The S3 specifically includes: The matching score of each metabolite was sorted from high to low, and the metabolite with the highest matching score was selected as the result of probiotic metabolite identification.

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