A method and system for improving mass spectrometer resolution based on data analysis

Abstract

The present invention discloses a method and system for improving the resolution of a mass spectrometer based on data analysis, and relates to the technical field of data processing, including training a resolution factor model; determining the basic information of a compound to be tested based on an original mass spectrum and an original fragmentation map, determining the target signal-to-noise ratio of the original mass spectrum based on the mass spectrum of a standard compound, and denoising the original mass spectrum by a noise reduction method; determining a baseline reference area based on the denoised original mass spectrum and the mass spectrum of the standard compound; identifying the various features of peaks in the mass spectrum, and separating overlapping peaks by peak features, performing cluster analysis on all peaks according to a resolution factor; and performing dimensionality reduction processing on a class of peaks whose data dimensions exceed a dimensionality threshold. In this way, data inconsistency caused by baseline drift is reduced or avoided. Finally, the resolution of the mass spectrometer is improved by separating overlapping peaks, clustering peaks, and reducing the dimensionality of peaks, thereby ensuring the efficiency of resolution improvement.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more specifically, to a method and system for improving the resolution of a mass spectrometer based on data analysis. Background Art

[0002] A mass spectrometer is an analytical instrument used to measure the mass and number of ions in an ionized gas sample. Its working principle is based on converting the compounds in the sample into gaseous ions, which are then separated and detected based on the mass and charge ratio (m / z) of the ions. Mass spectrometers are widely used in chemistry, biochemistry, drug development, environmental science, forensics, and many other scientific fields. Through the data generated by mass spectrometers, scientists can gain a deep understanding of the composition and properties of substances, thereby promoting the development of scientific research and applications.

[0003] In the prior art, the resolution of a mass spectrometer is often improved by improving the software and hardware, but this improvement method is costly and time-consuming, resulting in poor adaptability and low efficiency of resolution improvement. The relevant data generated by the mass spectrometer can directly or indirectly reflect the resolution, and the resolution of the mass spectrometer can be improved by means of data analysis and processing.

[0004] Therefore, how to provide a method for improving the resolution of a mass spectrometer through data analysis is a technical problem that needs to be solved. Summary of the invention

[0005] The purpose of the present invention is to solve the technical problems of high cost and poor efficiency of resolution improvement in the prior art, and to propose a method for improving the resolution of a mass spectrometer based on data analysis.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for improving the resolution of a mass spectrometer based on data analysis, comprising:

[0008] A resolution factor model is trained using data from the mass analyzer, ion optics, ion transmission, and ion flight paths and collision processes. The resolution factor describes the resolving potential of the mass spectrometer under different circumstances.

[0009] Determine the basic information of the compound to be tested based on the original mass spectrum and the original fragmentation map, search for the closest standard compound in the standard compound library based on the basic information of the compound to be tested, determine the target signal-to-noise ratio of the original mass spectrum based on the mass spectrum of the standard compound, and denoise the original mass spectrum by noise reduction means to achieve the target signal-to-noise ratio;

[0010] The baseline reference area is determined based on the original mass spectrum after denoising and the mass spectrum of the standard compound, and the baseline reference area is optimized by the ALS algorithm to obtain the mass spectrum after removing the baseline drift;

[0011] Identify the characteristics of the peaks in the mass spectrum and separate the overlapping peaks by peak characteristics to obtain all the peaks. Perform cluster analysis on all the peaks according to the resolution factor to classify similar peaks.

[0012] Determine the data dimension of each type of peak, and perform dimensionality reduction processing on the peaks whose data dimension exceeds the dimensionality threshold, so as to improve the resolution of the mass spectrometer;

[0013] Among them, the original fragment map is the ion fragment map after activation.

[0014] In some embodiments of the present application, a resolution factor model is trained using data from a mass analyzer, an ion optical system, ion transmission, and ion flight paths and collision processes, including:

[0015] Collect data on the mass analyzer, ion optical system, ion transmission, ion flight path and collision process and corresponding resolution data over a period of time, generate time series data corresponding to each part in chronological order, and determine the influence of each part of the time series data on the resolution data;

[0016] The hierarchical structure is defined as ion source layer-ion transport layer-mass analysis layer-detector layer in sequence, and the time series data of the mass analyzer, ion optical system, ion transport, ion flight path and collision process are inputted correspondingly according to the ion source layer, ion transport layer, mass analysis layer and detector layer;

[0017] The training ratio is determined according to the ratio of the influence of multiple time series data on the resolution data, and each part of the time series data is cut according to the training ratio, and each part of the time series data is cut into a training set and a test set, so as to integrate the training set and the test set of each part of the time series data;

[0018] A multi-layer model whose hierarchical structure is ion source layer-ion transfer layer-mass analysis layer-detector layer is trained through the training set, and the multi-layer model is adjusted and optimized according to the test set.

[0019] In some embodiments of the present application, searching for the closest standard compound in the standard compound library based on the basic information of the compound to be tested includes:

[0020] The basic information includes structural component information and peak characteristics. Multiple standard compounds to be determined are screened out by comparing the structural component information of the compound to be tested with the structural component information of each standard compound in the standard compound library.

[0021] Normalizing the difference in each structural component information between the test compound and the standard compound to be determined, integrating the difference in each structural component information to obtain a comprehensive structural component information difference index, and screening multiple standard compounds to be determined by comprehensive structural component information differences, thereby screening out two categories of first standard compounds to be determined and second standard compounds to be determined;

[0022] Compare the similarities of the peak features of the first undetermined standard compound and the second undetermined standard compound with the test compound in sequence, thereby determining the basic information similarity index in combination with the comprehensive structural component information difference index;

[0023] ;

[0024] in, is the basic information similarity index between the test compound x and the first standard compound to be determined or the second standard compound to be determined y, n is the number of peak features, is the weight corresponding to the i-th peak feature, is the size of the i-th peak feature of the compound to be tested, is the size of the i-th peak feature in the first standard compound to be determined or the second standard compound to be determined, Indicates the similarity of peak features, exp is an exponential function, is a comprehensive structural component information difference index of the first standard compound to be determined or the second standard compound to be determined, is the corresponding constant of the first standard compound to be determined or the second standard compound to be determined;

[0025] The first undetermined standard compound or the second undetermined standard compound with the smallest basic information similarity index is taken as a standard compound closest to the compound to be tested.

[0026] In the following embodiment of the present application, the target signal-to-noise ratio of the original mass spectrum is determined based on the mass spectrum of the standard compound, including:

[0027] Obtaining a mass spectrum of a standard compound, and determining several representative peaks on the mass spectrum, calculating the areas of the representative peaks, and determining the peak adjacent regions according to the types of the representative peaks, determining the signal-to-noise ratio of the peaks by the peak areas and the peak adjacent regions, integrating the signal-to-noise ratios of all representative peaks, and obtaining the signal-to-noise ratio of the mass spectrum of the standard compound;

[0028] A target percentage is obtained by mapping the basic information similarity index of the test compound and the standard compound, and the target signal-to-noise ratio of the original mass spectrum is determined according to the target percentage and the signal-to-noise ratio of the mass spectrum of the standard compound.

[0029] In some embodiments of the present application, determining the baseline reference area according to the denoised original mass spectrum and the mass spectrum of the standard compound includes:

[0030] Determine the minimum signal value in the area around the starting point on the original mass spectrum after denoising, and determine the maximum signal value on the original mass spectrum after denoising, and determine the extension distance by the ratio of the minimum signal value to the maximum signal value;

[0031] Taking the point with the minimum signal value in the area around the starting point as the baseline starting point, extending the baseline starting point according to the extension distance to determine the first baseline area, and determining the second baseline area in the mass spectrum of the standard compound;

[0032] If the first baseline area and the second baseline area intersect, the intersection baseline area is used as the baseline reference area;

[0033] Otherwise, the area between the first baseline area and the second baseline area is used as the baseline reference area.

[0034] In some embodiments of the present application, overlapping peaks are separated by peak characteristics to obtain all peaks, including:

[0035] All peak features were normalized, and IMDA or ICA was used to identify and separate overlapping peaks to obtain all individual peaks.

[0036] In some embodiments of the present application, cluster analysis is performed on all peaks according to the resolution factor, so as to classify similar peaks, including:

[0037] Determine several representative peaks on the mass spectrum, calculate the area of ​​each representative peak, and combine the resolution factor to determine the minimum distance in the clustering algorithm;

[0038] ;

[0039] Among them, L is the minimum distance in the clustering algorithm, is the resolution factor, m is the number of representative peaks, is the area ratio of the j-th peak, is the area of ​​the j-th peak, is the initial minimum distance, represents the initial minimum distance obtained by area average mapping;

[0040] All peaks were clustered according to the minimum distance and similar peaks were classified.

[0041] In some embodiments of the present application, the data dimension in each type of peak is determined, and the type of peak whose data dimension exceeds the dimension threshold is subjected to dimensionality reduction processing, including:

[0042] Evaluate the data dimension of each type of peak, calculate the position and retention time of each type of peak, set the dimension threshold through the standard deviation of the position and retention time, and perform dimensionality reduction processing on the type of peak whose data dimension exceeds the dimension threshold.

[0043] Correspondingly, the present application also provides a mass spectrometer resolution improvement system based on data analysis, comprising:

[0044] A training module for training a resolution factor model using data of a mass analyzer, an ion optical system, ion transmission, and a flight path and collision process of ions, wherein the resolution factor describes the resolution potential of the mass spectrometer under different conditions;

[0045] A determination module is used to determine the basic information of the compound to be tested based on the original mass spectrum and the original fragmentation map, search for the closest standard compound in the standard compound library for the basic information of the compound to be tested, determine the target signal-to-noise ratio of the original mass spectrum based on the mass spectrum of the standard compound, and denoise the original mass spectrum by noise reduction means to achieve the target signal-to-noise ratio;

[0046] A removal module is used to determine the baseline reference area based on the original mass spectrum after denoising and the mass spectrum of the standard compound, and optimize the baseline reference area through the ALS algorithm to obtain a mass spectrum after removing the baseline drift;

[0047] The classification module is used to identify the various features of the peaks in the mass spectrum and separate the overlapping peaks by peak features to obtain all the peaks. All the peaks are clustered according to the resolution factor to classify similar peaks.

[0048] The dimension reduction module is used to determine the data dimension in each type of peak and perform dimension reduction processing on the peaks whose data dimension exceeds the dimension threshold, so as to improve the resolution of the mass spectrometer;

[0049] Among them, the original fragment map is the ion fragment map after activation.

[0050] By applying the above technical scheme, a resolution factor model is trained through the data of the mass analyzer, ion optical system, ion transmission, and ion flight path and collision process, and the resolution factor describes the resolution potential of the mass spectrometer under different circumstances; the basic information of the compound to be tested is determined according to the original mass spectrum and the original fragmentation map, and the closest standard compound is found in the standard compound library for the basic information of the compound to be tested, and the target signal-to-noise ratio of the original mass spectrum is determined based on the mass spectrum of the standard compound, and the original mass spectrum is denoised by noise reduction means to achieve the target signal-to-noise ratio; the baseline reference area is determined according to the denoised original mass spectrum and the mass spectrum of the standard compound, and the baseline reference area is optimized by the ALS algorithm to obtain the mass spectrum after removing the baseline drift; the various features of the peaks in the mass spectrum are identified, and the overlapping peaks are separated by the peak features to obtain all the peaks, and all the peaks are clustered according to the resolution factor to classify similar peaks; the data dimension in each type of peak is judged, and the type of peak whose data dimension exceeds the dimension threshold is reduced in dimension to improve the resolution of the mass spectrometer. This application trains a resolution factor model to assist in the clustering analysis of subsequent peaks, determines the target signal-to-noise ratio of the original mass spectrum based on the mass spectrum of the standard compound, realizes reasonable denoising of the mass spectrum, avoids problems such as distortion due to excessive or too little noise, and improves the data quality of the mass spectrum. The baseline reference area is determined based on the denoised original mass spectrum and the mass spectrum of the standard compound, and the baseline is optimized to reduce or avoid data inconsistencies caused by baseline drift. Finally, the resolution of the mass spectrometer is improved by separating overlapping peaks, clustering peaks, and reducing the dimensionality of peaks, ensuring the efficiency of resolution improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of a process for improving the resolution of a mass spectrometer based on data analysis proposed for the invention;

[0052] Figure 2 A structural schematic diagram of a mass spectrometer resolution enhancement system based on data analysis proposed for the invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0054] The present application embodiment provides a method for improving the resolution of a mass spectrometer based on data analysis. The working process and principle of the mass spectrometer can be summarized as the following steps:

[0055] 1. Sample introduction:

[0056] The sample is first converted into gaseous ions through various ionization techniques. Common ionization techniques include electron impact ionization (EI), electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), etc. Each technique has its applicable sample type and analysis purpose.

[0057] 2. Ion transport:

[0058] The ionized sample enters the ion transmission system, which usually includes components such as ion mirrors and lenses to guide and focus the ions and ensure that they enter the mass analyzer efficiently.

[0059] 3. Quality analysis:

[0060] The mass analyzer is the core part of the mass spectrometer, which separates ions according to their mass / charge ratio (m / z). Common types of mass analyzers include magnetic mass spectrometer, quadrupole, time of flight (TOF), ion trap and Fourier transform ion cyclotron resonance (FT-ICR). Each analyzer works differently, but the goal is to separate ions according to their mass / charge ratio.

[0061] 4. Ion detection:

[0062] The separated ions are sent to a detector, which converts the ions into electrical signals. The detector can be an electron multiplier, a microchannel plate (MCP), or other types of photodetectors.

[0063] 5. Signal recording and processing:

[0064] The signal generated by the detector is recorded and processed by a data system. This data includes the mass and relative intensity of each ion, and ultimately forms a mass spectrum.

[0065] 6. Data Analysis:

[0066] Mass spectra are often analyzed by specialized software to identify and quantify compounds in a sample. Data analysis may include steps such as peak detection, baseline correction, isotope analysis, and fragment analysis.

[0067] 7. Interpretation of results:

[0068] Based on the information provided by the mass spectrum, the composition, structure, relative concentration, etc. of the sample can be interpreted. This may involve comparison with a database, confirmation of known compounds, or structural inference of unknown compounds.

[0069] A mass spectrum is produced by a mass spectrometer and shows the mass to charge ratio (m / z) of different ions in a sample and their relative intensities. In a mass spectrum, the horizontal axis represents the mass to charge ratio (m / z) of the ions, and the vertical axis represents the intensity or number of ions corresponding to each m / z value. Fragmentation diagrams and mass spectra are conceptually related, but they are not exactly the same thing. A fragmentation diagram usually refers to the ion fragmentation spectrum detected by a mass spectrometer after collision-induced dissociation (CID) or other forms of activation. This diagram shows the mass and relative intensity of fragment ions produced by the fragmentation of the original ion (parent ion) under the action of impact or electron bombardment. A mass spectrum is a broader concept that includes any type of mass spectral data that shows the mass and relative intensity of all ions in a sample, whether they are intact molecular ions, fragment ions, isotopic ions, or other forms of ions. A mass spectrum can be an ion spectrum detected directly without any activation, or a fragment ion spectrum obtained after a certain activation method (such as CID).

[0070] like Figure 1 As shown, the method comprises the following steps:

[0071] Step S101 , training a resolution factor model through data of a mass analyzer, an ion optical system, ion transmission, and ion flight path and collision process, wherein the resolution factor describes the resolution potential of the mass spectrometer under different circumstances.

[0072] In this embodiment, the parameters of these processes will affect the resolution, so they are taken into account to train a resolution factor model. Operating parameters of the mass analyzer: In the magnetic mass spectrometer, adjusting the magnetic field strength can affect the degree of deflection of ions in the magnetic field, thereby affecting the resolution. In the quadrupole mass spectrometer, the adjustment of the radio frequency frequency can optimize the focusing and separation of ions in the quadrupole, thereby affecting the resolution. Ion optical system: The design and configuration of ion mirrors and lenses have an important influence on the transmission and focusing of ions. The parameters of these components can affect the transmission efficiency and focusing effect of ions, thereby changing the resolution. Ion source and transmission efficiency: The transmission efficiency of ions in the mass spectrometer directly affects the resolution. By optimizing the ion source parameters and the transmission system, the ion transmission efficiency can be improved, thereby affecting the resolution. Ion flight path and collision process: The flight path of ions in the mass analyzer directly affects the resolution. Flight path parameters, such as the distance between the ion source and the mass analyzer, the flight path length, etc., can affect the resolution. Ions may have mass deviations during the collision process, affecting the resolution.

[0073] In some embodiments of the present application, a resolution factor model is trained using data from a mass analyzer, an ion optical system, ion transmission, and ion flight paths and collision processes, including:

[0074] Collect data on the mass analyzer, ion optical system, ion transmission, ion flight path and collision process and corresponding resolution data over a period of time, generate time series data corresponding to each part in chronological order, and determine the influence of each part of the time series data on the resolution data;

[0075] The hierarchical structure is defined as ion source layer-ion transport layer-mass analysis layer-detector layer in sequence, and the time series data of the mass analyzer, ion optical system, ion transport, ion flight path and collision process are inputted correspondingly according to the ion source layer, ion transport layer, mass analysis layer and detector layer;

[0076] The training ratio is determined according to the ratio of the influence of multiple time series data on the resolution data, and each part of the time series data is cut according to the training ratio, and each part of the time series data is cut into a training set and a test set, so as to integrate the training set and the test set of each part of the time series data;

[0077] A multi-layer model whose hierarchical structure is ion source layer-ion transfer layer-mass analysis layer-detector layer is trained through the training set, and the multi-layer model is adjusted and optimized according to the test set.

[0078] In this embodiment, the hierarchical structure is defined as ion source layer - ion transport layer - mass analysis layer - detector layer in order, because the order of occurrence is such that the specific conditions are fully simulated. The hierarchical structure of the mass spectrometer is defined as follows:

[0079] Ion source layer: contains the parameters of the ion source, such as temperature, voltage, etc.

[0080] Ion transport layer: Contains the parameters of the ion transport system, such as the voltage of the ion mirror and lens.

[0081] Mass analysis layer: Contains the parameters of the mass analyzer, such as voltage, magnetic field strength, etc.

[0082] Detector layer: Contains the parameters of the detector, such as gain, voltage, etc.

[0083] The parameter adjustments of each layer are passed to the next layer through the connection, affecting the resolution of the next layer.

[0084] In this embodiment, the influence of each part of the time series data on the resolution data can be determined by an influence calculation method (Pearson correlation coefficient, etc.). The training ratio is determined according to the ratio of the influence of multiple parts of the time series data on the resolution data, and the relative influence of each part of the time series data is determined to determine the training ratio. Different relative influences correspond to different training ratios. Each part of the time series data is cut according to the training ratio. This training ratio refers to the ratio in time. The data in the first part of time is used as the training set, and the data in the second part of time is used as the test set.

[0085] It should be noted that the specific model training and adjustment optimization process is not the focus of protection of this solution. It is sufficient to achieve the above requirements and it belongs to the protection scope of this application.

[0086] Step S102, determining the basic information of the compound to be tested based on the original mass spectrum and the original fragmentation map, searching for the closest standard compound in the standard compound library for the basic information of the compound to be tested, determining the target signal-to-noise ratio of the original mass spectrum based on the mass spectrum of the standard compound, and denoising the original mass spectrum by noise reduction means to achieve the target signal-to-noise ratio.

[0087] In this embodiment, by comparing the peaks in the original mass spectrum and the original fragmentation map, the basic information of the compound to be tested (structural component information and peak characteristics) can be determined. Here, a preliminary judgment is made to find the closest standard compound, and the target signal-to-noise ratio and baseline area are set by this standard compound to pre-process the original mass spectrum.

[0088] In some embodiments of the present application, searching for the closest standard compound in the standard compound library based on the basic information of the compound to be tested includes:

[0089] The basic information includes structural component information and peak characteristics. Multiple standard compounds to be determined are screened out by comparing the structural component information of the compound to be tested with the structural component information of each standard compound in the standard compound library.

[0090] Normalizing the difference in each structural component information between the test compound and the standard compound to be determined, integrating the difference in each structural component information to obtain a comprehensive structural component information difference index, and screening multiple standard compounds to be determined by comprehensive structural component information differences, thereby screening out two categories of first standard compounds to be determined and second standard compounds to be determined;

[0091] Compare the similarities of the peak features of the first undetermined standard compound and the second undetermined standard compound with the test compound in sequence, thereby determining the basic information similarity index in combination with the comprehensive structural component information difference index;

[0092] ;

[0093] in, is the basic information similarity index between the test compound x and the first standard compound to be determined or the second standard compound to be determined y, n is the number of peak features, is the weight corresponding to the i-th peak feature, is the size of the i-th peak feature of the compound to be tested, is the size of the i-th peak feature in the first standard compound to be determined or the second standard compound to be determined, Indicates the similarity of peak features, exp is an exponential function, is a comprehensive structural component information difference index of the first undetermined standard compound or the second undetermined standard compound, is the corresponding constant of the first standard compound to be determined or the second standard compound to be determined;

[0094] The first undetermined standard compound or the second undetermined standard compound with the smallest basic information similarity index is taken as a standard compound closest to the test compound.

[0095] In this embodiment, the structural component information includes chemical structure, molecular formula, molecular weight, retention time, concentration, etc., and the peak characteristics include width, area, shape, position, intensity ratio, etc. A plurality of standard compounds to be determined are screened out by comparing the structural component information of the compound to be determined with the structural component information of each standard compound in the standard compound library. Here, similar or similar standard compounds to be determined are first screened out by a single structural component information. After integrating the difference of each structural component information, a comprehensive structural component information difference index (weighted sum) is obtained. The comprehensive structural component information difference index of the first standard compound to be determined is less than that of the second standard compound to be determined. The smaller the comprehensive structural component information difference index, the smaller the comprehensive difference, and the closer to the compound to be determined. The similarity of the peak characteristics of the first standard compound to be determined and the second standard compound to be determined are compared in order. Here, the peak characteristic similarity uses Euclidean distance to describe the similarity. The smaller this value is, the more similar it is. The value range is between 1 and 1.34, and the basic information similarity index is expressed by combining the similarities of structural component information and peak characteristics.

[0096] In the following embodiment of the present application, the target signal-to-noise ratio of the original mass spectrum is determined based on the mass spectrum of the standard compound, including:

[0097] Obtaining a mass spectrum of a standard compound, and determining several representative peaks on the mass spectrum, calculating the areas of the representative peaks, and determining the peak adjacent regions according to the types of the representative peaks, determining the signal-to-noise ratio of the peaks by the peak areas and the peak adjacent regions, integrating the signal-to-noise ratios of all representative peaks, and obtaining the signal-to-noise ratio of the mass spectrum of the standard compound;

[0098] A target percentage is obtained by mapping the basic information similarity index of the test compound and the standard compound, and the target signal-to-noise ratio of the original mass spectrum is determined according to the target percentage and the signal-to-noise ratio of the mass spectrum of the standard compound.

[0099] In this example, several representative peaks are: Peaks with larger areas, because they usually represent compounds with higher concentrations, which is particularly important for quantitative analysis. Peaks with clear shapes are selected, which usually means a higher contrast between the signal and noise and a better signal-to-noise ratio. The retention time of the peak, which is similar to the retention time of the compound to be tested, helps to determine the identity of the compound.

[0100] In this embodiment, the type of representative peak determines the peak adjacent area, and different categories correspond to peak adjacent areas of different sizes, and the peak adjacent area refers to the distance around the peak. The signal-to-noise ratio of the peak is determined by the area of ​​the peak and the peak adjacent area, and the ratio of the area to the noise level near the peak obtains the signal-to-noise ratio. The signal-to-noise ratio of the peak is used here to represent the signal-to-noise ratio of the mass spectrum, because the peak can better reflect the composition or other effective information. According to the target percentage * signal-to-noise of the mass spectrum of the standard compound = the target signal-to-noise ratio of the original mass spectrum, because the standard compound and the compound to be tested are different, the target signal-to-noise ratio is set according to the ratio.

[0101] It should be noted that the original mass spectrum is denoised by noise reduction means, and there is no limit on the means used for denoising, as long as the target signal-to-noise ratio can be achieved. Too much noise will lead to errors in analysis, and too little noise may cause signal loss.

[0102] Step S103, determining a baseline reference region according to the denoised original mass spectrum and the mass spectrum of the standard compound, and optimizing the baseline reference region by an ALS algorithm to obtain a mass spectrum after removing the baseline drift.

[0103] In this embodiment, the baseline will automatically change due to some factors, including changes in the laboratory environment (such as temperature, humidity, electromagnetic interference, etc.), aging of the instrument, differences in the sample preparation process, etc. These factors may cause baseline drift, affecting the accuracy and repeatability of mass spectrometry data, thereby affecting the resolution. In order to remove baseline drift, two commonly used baseline correction algorithms, Asymmetric Least Squares (ALS) and AirPLS, can be used. They calculate the baseline value at each time point in an iterative manner and subtract it from the original signal to remove baseline drift. Therefore, it is necessary to select a baseline reference area to facilitate subsequent baseline optimization.

[0104] In some embodiments of the present application, determining the baseline reference area according to the denoised original mass spectrum and the mass spectrum of the standard compound includes:

[0105] Determine the minimum signal value in the area around the starting point on the original mass spectrum after denoising, and determine the maximum signal value on the original mass spectrum after denoising, and determine the extension distance by the ratio of the minimum signal value to the maximum signal value;

[0106] Taking the point with the minimum signal value in the area around the starting point as the baseline starting point, extending the baseline starting point according to the extension distance to determine the first baseline area, and determining the second baseline area in the mass spectrum of the standard compound;

[0107] If the first baseline area and the second baseline area intersect, the intersection baseline area is used as the baseline reference area;

[0108] Otherwise, the area between the first baseline area and the second baseline area is used as the baseline reference area.

[0109] In the present embodiment, on the original mass spectrogram after denoising, the determination of the baseline is usually based on the ordinate, i.e., the y-axis in the mass spectrogram. The baseline represents the signal level when no ions pass through the detector, and a low value is usually set to facilitate the display of the intensity of the ion signal. The reason for selecting the low point part of the ion signal as the baseline starting point is that these areas usually do not have significant ion signal peaks, so they can be used as the starting point of the baseline. When the mass spectrometer starts to collect data, there may be some warm-up or preparation stages, which may cause the signal intensity of the starting point to be non-zero, so the relative signal minimum is found in the surrounding area. The extension distance is the distance of the y-axis, and the ratio of the signal minimum to the signal maximum corresponds to different extension distances, so as to ensure that the baseline area is relatively complete. If there is no intersection between the first baseline area and the second baseline area, the area between the first baseline area and the second baseline area is used as the baseline reference area. In general, even if the two baseline areas do not intersect, they will not differ very far.

[0110] Step S104, identifying the characteristics of the peaks in the mass spectrum, and separating the overlapping peaks by the peak characteristics, so as to obtain all the peaks, and clustering all the peaks according to the resolution factor, so as to classify similar peaks.

[0111] In some embodiments of the present application, overlapping peaks are separated by peak characteristics to obtain all peaks, including:

[0112] All peak features were normalized, and IMDA or ICA was used to identify and separate overlapping peaks to obtain all individual peaks.

[0113] In this embodiment, intuitive discriminant analysis (IMDA) calculates the similarity or distance between each peak based on the peak characteristics. The similarity or distance matrix can help distinguish different peaks. Based on the results of IMDA analysis, overlapping peaks are identified and separated. Independent component analysis (ICA) The ICA algorithm is a signal processing technique that can separate signals mixed by multiple sources.

[0114] In some embodiments of the present application, all peaks are clustered according to the resolution factor, so that similar peaks are classified, including:

[0115] Determine several representative peaks on the mass spectrum, calculate the area of ​​each representative peak, and combine the resolution factor to determine the minimum distance in the clustering algorithm;

[0116] ;

[0117] Among them, L is the minimum distance in the clustering algorithm, is the resolution factor, m is the number of representative peaks, is the area ratio of the j-th peak, is the area of ​​the jth peak, is the initial minimum distance, represents the initial minimum distance obtained by area average mapping;

[0118] All peaks were clustered according to the minimum distance and similar peaks were classified.

[0119] In this embodiment, the percentage of peak area (area ratio) can help determine the minimum distance to a certain extent. The resolution factor can help determine the minimum distance in the clustering algorithm. The minimum distance refers to the minimum number of points that a core point must contain in the DBSCAN algorithm. The resolution factor can be used to guide the selection of the minimum distance, because high resolution usually means that more refined clustering is required. If the resolution factor indicates that the resolution is higher, a smaller minimum distance can be selected to increase the granularity of the clustering. Through cluster analysis, mass spectrometry peaks with similar characteristics can be distinguished. These peaks may come from the same compound or homologues of the compound. Once these peaks are identified and separated, each type of peak can be analyzed separately, thereby improving the resolution.

[0120] Step S105, determining the data dimension of each type of peak, and performing dimensionality reduction processing on a type of peak whose data dimension exceeds the dimensionality threshold, so as to improve the resolution of the mass spectrometer. The original fragment map is the ion fragment map after activation.

[0121] In some embodiments of the present application, the data dimension in each type of peak is determined, and the type of peak whose data dimension exceeds the dimension threshold is subjected to dimensionality reduction processing, including:

[0122] Evaluate the data dimension of each type of peak, calculate the position and retention time of each type of peak, set the dimension threshold through the standard deviation of the position and retention time, and perform dimensionality reduction processing on the type of peak whose data dimension exceeds the dimension threshold.

[0123] In this embodiment, the position of the peak is the mass-to-charge ratio of the mass spectrum, and the retention time refers to the time required for the compound to pass through the chromatographic column in the gas chromatograph. A dimensional threshold is set by the standard deviation of the two, and the position of the peak and the retention time can reflect the separation of the compound. Dimensionality reduction technology can reduce the dimension of the data and remove redundant information in the data. Reducing the data dimension can show the mass spectrum more clearly, thereby improving the resolution.

[0124] It should be noted that the specific cluster analysis process and dimensionality reduction process are not the focus of protection of this solution, and other solutions that can achieve the above requirements are all acceptable. The corresponding relationships or mapping relationships involved in the full text of this application can be obtained from historical experience or mathematical relationships, and all belong to the protection scope of this application.

[0125] By applying the above technical scheme, a resolution factor model is trained through the data of the mass analyzer, ion optical system, ion transmission, and ion flight path and collision process, and the resolution factor describes the resolution potential of the mass spectrometer under different circumstances; the basic information of the compound to be tested is determined according to the original mass spectrum and the original fragmentation map, and the closest standard compound is found in the standard compound library for the basic information of the compound to be tested, and the target signal-to-noise ratio of the original mass spectrum is determined based on the mass spectrum of the standard compound, and the original mass spectrum is denoised by noise reduction means to achieve the target signal-to-noise ratio; the baseline reference area is determined according to the denoised original mass spectrum and the mass spectrum of the standard compound, and the baseline reference area is optimized by the ALS algorithm to obtain the mass spectrum after removing the baseline drift; the various features of the peaks in the mass spectrum are identified, and the overlapping peaks are separated by the peak features to obtain all the peaks, and all the peaks are clustered according to the resolution factor to classify similar peaks; the data dimension in each type of peak is judged, and the type of peak whose data dimension exceeds the dimension threshold is reduced in dimension to improve the resolution of the mass spectrometer. This application trains a resolution factor model to assist in the clustering analysis of subsequent peaks, determines the target signal-to-noise ratio of the original mass spectrum based on the mass spectrum of the standard compound, realizes reasonable denoising of the mass spectrum, avoids problems such as distortion due to excessive or too little noise, and improves the data quality of the mass spectrum. The baseline reference area is determined based on the denoised original mass spectrum and the mass spectrum of the standard compound, and the baseline is optimized to reduce or avoid data inconsistencies caused by baseline drift. Finally, the resolution of the mass spectrometer is improved by separating overlapping peaks, clustering peaks, and reducing the dimensionality of peaks, ensuring the efficiency of resolution improvement.

[0126] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0127] In order to further explain the technical idea of ​​the present invention, the technical solution of the present invention is now described in combination with specific application scenarios.

[0128] Correspondingly, the present application also provides a mass spectrometer resolution enhancement system based on data analysis, such as Figure 2 As shown, including:

[0129] A training module for training a resolution factor model using data of a mass analyzer, an ion optical system, ion transmission, and a flight path and collision process of ions, wherein the resolution factor describes the resolution potential of the mass spectrometer under different conditions;

[0130] A determination module is used to determine the basic information of the compound to be tested based on the original mass spectrum and the original fragmentation map, search for the closest standard compound in the standard compound library for the basic information of the compound to be tested, determine the target signal-to-noise ratio of the original mass spectrum based on the mass spectrum of the standard compound, and denoise the original mass spectrum by noise reduction means to achieve the target signal-to-noise ratio;

[0131] A removal module is used to determine the baseline reference area based on the original mass spectrum after denoising and the mass spectrum of the standard compound, and optimize the baseline reference area through the ALS algorithm to obtain a mass spectrum after removing the baseline drift;

[0132] The classification module is used to identify the various features of the peaks in the mass spectrum and separate the overlapping peaks by peak features to obtain all the peaks. All the peaks are clustered according to the resolution factor to classify similar peaks.

[0133] The dimension reduction module is used to determine the data dimension in each type of peak and perform dimension reduction processing on the peaks whose data dimension exceeds the dimension threshold, so as to improve the resolution of the mass spectrometer;

[0134] Among them, the original fragment map is the ion fragment map after activation.

[0135] Those skilled in the art will appreciate that the modules in the system in the implementation scenario can be distributed in the system of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more systems different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for improving the resolution of a mass spectrometer based on data analysis, characterized in that: include: A resolution factor model is trained by using the data of the mass analyzer, the data of the ion optical system, the data of the ion transmission, the flight path data of the ions and the data of the collision process. The resolution factor describes the resolution potential of the mass spectrometer under different conditions. Determine the basic information of the compound to be tested based on the original mass spectrum and the original fragmentation map, search for the closest standard compound in the standard compound library based on the basic information of the compound to be tested, determine the target signal-to-noise ratio of the original mass spectrum based on the mass spectrum of the standard compound, and denoise the original mass spectrum by noise reduction means to achieve the target signal-to-noise ratio; The baseline reference area is determined based on the original mass spectrum after denoising and the mass spectrum of the standard compound, and the baseline reference area is optimized by the ALS algorithm to obtain the mass spectrum after removing the baseline drift; Identify the characteristics of the peaks in the mass spectrum and separate the overlapping peaks by peak characteristics to obtain all the peaks. Perform cluster analysis on all the peaks according to the resolution factor to classify similar peaks. Determine the data dimension of each type of peak, and perform dimensionality reduction processing on the peaks whose data dimension exceeds the dimensionality threshold, so as to improve the resolution of the mass spectrometer; The training process of the resolution factor model is: Collect the mass analyzer data, ion optical system data, ion transmission data, ion flight path data and collision process data and corresponding resolution data within a period of time, and generate the corresponding time series data of each part in chronological order, and determine the influence of each part of the time series data on the resolution data; The hierarchical structure is defined as ion source layer-ion transport layer-mass analysis layer-detector layer in sequence, and the time series data of the mass analyzer, ion optical system, ion transport, ion flight path and collision process are inputted correspondingly according to the ion source layer, ion transport layer, mass analysis layer and detector layer; The training ratio is determined according to the ratio of the influence of multiple time series data on the resolution data, and each part of the time series data is cut according to the training ratio, and each part of the time series data is cut into a training set and a test set, so as to integrate the training set and the test set of each part of the time series data; A multi-layer model whose hierarchical structure is ion source layer-ion transmission layer-mass analysis layer-detector layer is trained by a training set, and the multi-layer model is adjusted and optimized according to a test set; Among them, the original fragmentation image is the ion fragmentation spectrum after activation, the data of the mass analyzer is the operating parameters of the mass analyzer, the data of the ion optical system is the design parameters of the ion mirror and lens, the data of ion transmission is the ion transmission efficiency, the ion flight path data is the ion flight path parameters, and the data of the collision process is the parameters generated during the ion collision process.

2. The method for improving the resolution of a mass spectrometer based on data analysis according to claim 1, characterized in that: Search for the closest standard compound in the standard compound library based on the basic information of the compound to be tested, including: The basic information includes structural component information and peak characteristics. Multiple standard compounds to be determined are screened out by comparing the structural component information of the compound to be tested with the structural component information of each standard compound in the standard compound library. Normalizing the difference in each structural component information between the test compound and the standard compound to be determined, integrating the difference in each structural component information to obtain a comprehensive structural component information difference index, and screening multiple standard compounds to be determined by comprehensive structural component information differences, thereby screening out two categories of first standard compounds to be determined and second standard compounds to be determined; Compare the similarities of the peak features of the first undetermined standard compound and the second undetermined standard compound with the test compound in sequence, thereby determining the basic information similarity index in combination with the comprehensive structural component information difference index; ； in, is the basic information similarity index between the test compound x and the first standard compound to be determined or the second standard compound to be determined y, n is the number of peak features, is the weight corresponding to the i-th peak feature, is the size of the i-th peak feature of the compound to be tested, is the size of the i-th peak feature in the first standard compound to be determined or the second standard compound to be determined, Indicates the similarity of peak features, exp is an exponential function, is a comprehensive structural component information difference index of the first standard compound to be determined or the second standard compound to be determined, is the corresponding constant of the first standard compound to be determined or the second standard compound to be determined; The first undetermined standard compound or the second undetermined standard compound with the smallest basic information similarity index is taken as a standard compound closest to the compound to be tested.

3. The method for improving the resolution of a mass spectrometer based on data analysis according to claim 2, characterized in that: Determine the target signal-to-noise ratio of the original mass spectrum based on the mass spectrum of the standard compound, including: Obtaining a mass spectrum of a standard compound, and determining several representative peaks on the mass spectrum, calculating the areas of the representative peaks, and determining the peak adjacent regions according to the types of the representative peaks, determining the signal-to-noise ratio of the peaks by the peak areas and the peak adjacent regions, integrating the signal-to-noise ratios of all representative peaks, and obtaining the signal-to-noise ratio of the mass spectrum of the standard compound; A target percentage is obtained by mapping the basic information similarity index of the test compound and the standard compound, and the target signal-to-noise ratio of the original mass spectrum is determined according to the target percentage and the signal-to-noise ratio of the mass spectrum of the standard compound.

4. The method for improving the resolution of a mass spectrometer based on data analysis according to claim 1, characterized in that: The baseline reference area is determined based on the denoised original mass spectrum and the mass spectrum of the standard compound, including: Determine the minimum signal value in the area around the starting point on the original mass spectrum after denoising, and determine the maximum signal value on the original mass spectrum after denoising, and determine the extension distance by the ratio of the minimum signal value to the maximum signal value; Taking the point with the minimum signal value in the area around the starting point as the baseline starting point, extending the baseline starting point according to the extension distance to determine the first baseline area, and determining the second baseline area in the mass spectrum of the standard compound; If the first baseline area and the second baseline area intersect, the intersection baseline area is used as the baseline reference area; Otherwise, the area between the first baseline area and the second baseline area is used as the baseline reference area.

5. The method for improving the resolution of a mass spectrometer based on data analysis according to claim 1, characterized in that: And the overlapping peaks are separated by peak characteristics to obtain all the peaks, including: All peak features were normalized, and IMDA or ICA was used to identify and separate overlapping peaks to obtain all individual peaks.

6. The method for improving the resolution of a mass spectrometer based on data analysis according to claim 1, characterized in that: All peaks are clustered according to the resolution factor to classify similar peaks, including: Determine several representative peaks on the mass spectrum, calculate the area of ​​each representative peak, and combine the resolution factor to determine the minimum distance in the clustering algorithm; ； Among them, L is the minimum distance in the clustering algorithm, is the resolution factor, m is the number of representative peaks, is the area ratio of the j-th peak, is the area of ​​the j-th peak, is the initial minimum distance, represents the initial minimum distance obtained by area average mapping; All peaks were clustered according to the minimum distance and similar peaks were classified.

7. The method for improving the resolution of a mass spectrometer based on data analysis according to claim 1, characterized in that: Determine the data dimension of each type of peak, and perform dimensionality reduction processing on the peaks whose data dimension exceeds the dimensionality threshold, including: Evaluate the data dimension of each type of peak, calculate the position and retention time of each type of peak, set the dimension threshold through the standard deviation of the position and retention time, and perform dimensionality reduction processing on the type of peak whose data dimension exceeds the dimension threshold.

8. A mass spectrometer resolution improvement system based on data analysis, used to implement a mass spectrometer resolution improvement method based on data analysis as described in any one of claims 1 to 7, characterized in that: The system comprises: A training module is used for training a resolution factor model by using data of a mass analyzer, data of an ion optical system, data of ion transmission, data of ion flight paths and data of collision processes, wherein the resolution factor describes the resolution potential of the mass spectrometer under different circumstances; A determination module is used to determine the basic information of the compound to be tested based on the original mass spectrum and the original fragmentation map, search for the closest standard compound in the standard compound library for the basic information of the compound to be tested, determine the target signal-to-noise ratio of the original mass spectrum based on the mass spectrum of the standard compound, and denoise the original mass spectrum by noise reduction means to achieve the target signal-to-noise ratio; A removal module is used to determine the baseline reference area based on the original mass spectrum after denoising and the mass spectrum of the standard compound, and optimize the baseline reference area through the ALS algorithm to obtain a mass spectrum after removing the baseline drift; The classification module is used to identify the various features of the peaks in the mass spectrum and separate the overlapping peaks by peak features to obtain all the peaks. All the peaks are clustered according to the resolution factor to classify similar peaks. The dimension reduction module is used to determine the data dimension in each type of peak and perform dimension reduction processing on the peaks whose data dimension exceeds the dimension threshold, so as to improve the resolution of the mass spectrometer; Among them, the original fragmentation image is the ion fragmentation spectrum after activation, the data of the mass analyzer is the operating parameters of the mass analyzer, the data of the ion optical system is the design parameters of the ion mirror and lens, the data of ion transmission is the ion transmission efficiency, the ion flight path data is the ion flight path parameters, and the data of the collision process is the parameters generated during the ion collision process.

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