Fast screening method of organic compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry and peak stability verification method

Abstract

The application provides a method for rapid screening and peak stability verification of organic matter based on a comprehensive two-dimensional gas chromatograph-mass spectrometer. The method comprises: obtaining sample files of at least two samples; generating a grid file corresponding to each sample file, wherein a first grid of the grid file is used to record the data condition of the corresponding position in the sample file; merging each grid file to obtain an aggregated grid file, wherein the aggregated grid file comprises a plurality of second grids; screening a target grid in the aggregated grid file, and obtaining organic matter data of the target grid, wherein the target grid is a second grid with a detection probability greater than a preset probability, and the detection probability is used to represent the probability that the organic matter has a feature at the corresponding position in each sample file. The application combines the organic matter data of different samples, and screens the target grid according to the data condition of the second grid, thereby realizing batch processing of the organic matter data of multiple samples, shortening the processing time and improving the efficiency.

Description

Technical Field

[0001] This application belongs to the field of data processing technology, and in particular relates to a rapid screening method for organic compounds and a peak stability verification method based on a two-dimensional gas chromatography-mass spectrometry system. Background Technology

[0002] Currently, the processing of organic sample data utilizes database retrieval and comparison methods, which compare the organic data in the sample data with the database to perform qualitative and quantitative processing of the organic data.

[0003] When the amount of data or the number of samples is too large, the processing time for the sample data becomes long and the efficiency is low. Summary of the Invention

[0004] This application provides a rapid screening method for organic compounds and a peak stability verification method based on a two-dimensional gas chromatography-mass spectrometry system, which can solve the problems of long processing time and low efficiency in processing sample data.

[0005] In a first aspect, embodiments of this application provide a rapid screening method for organic compounds based on a two-dimensional gas chromatography-mass spectrometry system, comprising: Obtain sample files for at least two samples, the sample files including organic data of the samples; Generate a grid file corresponding to each of the sample files. The grid file includes a plurality of first grids, which are used to record the data at corresponding positions in the sample files. The individual mesh files are merged to obtain an aggregated mesh file, which includes multiple second meshes; Target grids are filtered in the aggregated grid file, and the organic matter data of the target grids are obtained. The target grid is a second grid with a detection probability greater than a preset probability. The detection probability is used to characterize the probability that the organic matter has features at the corresponding position in each of the sample files.

[0006] In some embodiments, filtering the target mesh in the aggregated mesh file includes: For each of the second grids, the detection probability is determined based on the amount of organic data in the second grid and the number of sample files. The target grid is determined based on the detection probability of each of the second grids.

[0007] In some embodiments, generating the mesh file corresponding to each of the sample files includes: For each of the sample files, the interval and range of each axial dimension of the mesh file are determined according to the tolerance threshold of the sample file. The number of axial dimensions is determined according to the number of time dimensions of the sample file. Multiple coordinate grids are generated based on the intervals and ranges of each axial dimension; For each of the coordinate grids, the data of the sample file at the position of the corresponding coordinate grid is written into the coordinate grid to obtain the first grid, thereby obtaining the grid file.

[0008] In some embodiments, obtaining sample files for at least two samples includes: Obtain raw data from at least two of the samples; for each sample, perform detection on the raw data to obtain at least one feature, and perform time correction on the raw data to obtain corrected data; Based on the corrected data of each sample, a sample file is generated according to the feature parameters of each feature, wherein the organic data includes the feature parameters of each feature.

[0009] In some embodiments, after acquiring the organic matter data of the target grid, the method further includes: The category of the organic matter is determined based on the organic matter data of each target grid.

[0010] In some embodiments, after obtaining the aggregated mesh file, the method further includes: Generate visualizations of each of the individual mesh files and the aggregated mesh file.

[0011] In some embodiments, determining the detection probability based on the amount of organic data in the second grid and the number of sample files includes: The detection probability is obtained by dividing the number of organic data in the second grid by the number of sample files.

[0012] Secondly, embodiments of this application provide a method for verifying peak stability, including: Generate visualizations of each mesh file and the aggregated mesh file; Wherein, the grid file and the aggregated grid file are obtained by the method as described in any one of the first aspects above, and the organic matter data includes characteristic parameters of the signal peaks.

[0013] Thirdly, embodiments of this application provide a rapid organic matter screening device based on a two-dimensional gas chromatography-mass spectrometry system, comprising: An acquisition module is used to acquire sample files of at least two samples, wherein the sample files include organic matter data of the samples; The file processing module is used to generate a grid file corresponding to each of the sample files. The grid file includes a plurality of first grids, which are used to record the data at corresponding positions in the sample files. It is also used to merge the various mesh files to obtain an aggregated mesh file, wherein the aggregated mesh file includes multiple second meshes; The filtering module is used to filter target grids in the aggregated grid file and obtain the organic matter data of the target grids. The target grid is a second grid with a detection probability greater than a preset probability. The detection probability is used to characterize the probability that the organic matter has features at the corresponding position in each of the sample files.

[0014] Fourthly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any one of the first or second aspects above.

[0015] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in any one of the first or second aspects above.

[0016] In a sixth aspect, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to perform the method described in any one of the first or second aspects above.

[0017] The beneficial effects of the embodiments in this application compared with the prior art are: This application embodiment acquires sample files of at least two samples, each sample file containing organic matter data of the samples; generates a grid file corresponding to each sample file, the grid file including multiple first grids, the first grids being used to record the data at corresponding positions in the sample files, so that the organic matter data in the samples can be easily and directly obtained and processed through the grid, and a foundation is provided for subsequent batch processing of organic matter data; merges the grid files to obtain an aggregated grid file, the aggregated grid file including multiple second grids, the second grids being used to record the quantity of organic matter data at corresponding positions; filters the target grid in the aggregated grid file and obtains the organic matter data of the target grid, the target grid being the second grid with a detection probability greater than a preset probability, the detection probability being used to characterize the probability that the organic matter has a characteristic at the corresponding position in each sample file; merges the organic matter data of different samples, and filters the target grid according to the data of the second grid, thereby realizing batch processing of organic matter data of multiple samples, shortening the processing time and improving efficiency.

[0018] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic flowchart of the rapid organic matter screening method based on a two-dimensional gas chromatography-mass spectrometry system provided in the embodiments of this application; Figure 2 This is an example diagram of the software code for reading organic data provided in an embodiment of this application; Figure 3 These are example diagrams of sample files provided in the embodiments of this application; Figure 4 This is an example diagram of the original data of the sample provided in the embodiments of this application; Figure 5 This is an example diagram of the software code for generating mesh files provided in an embodiment of this application; Figure 6 This is an example diagram illustrating the data situation of the second grid provided in an embodiment of this application; Figure 7 This is an example diagram of a table file of the target mesh provided in an embodiment of this application; Figure 8 This is an example diagram of the software code for merging grids and calculating detection probabilities provided in the embodiments of this application; Figure 9 This is an example diagram of the software code for peak stability verification provided in the embodiments of this application; Figure 10 This is an example visualization of the mesh file provided in the embodiments of this application; Figure 11 This is an example visualization of the aggregated mesh file provided in the embodiments of this application; Figure 12 This is a schematic diagram of the structure of the rapid organic matter screening device based on a two-dimensional gas chromatography-mass spectrometry system provided in the embodiments of this application; Figure 13 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0021] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0022] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0023] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0025] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0026] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0027] Currently, processing organic sample data involves using database retrieval and comparison methods. This involves comparing the organic data in the sample data with a database to perform qualitative and quantitative analysis. However, when the amount of data or the sample size is too large, the processing time becomes lengthy and inefficient.

[0028] As an example, when analyzing VOCs (Volatile Organic Compounds) and I / SVOCs (Intermediate / Semi-Volatile Organic Compounds), gas chromatography-mass spectrometry (GC-MS) is used to detect organic matter in the samples, obtaining raw data. Then, data processing software is used to process the raw data to obtain sample files. When the organic data in the sample file contains a large number of features, processing VOCs and I / SVOCs sample data becomes time-consuming and inefficient.

[0029] As an example, when the organic data in the sample file contains too many signal peaks, each signal peak is compared with the database, and each signal peak is subjected to qualitative and quantitative processing.

[0030] This application provides a rapid screening method for organic compounds based on a two-dimensional gas chromatography-mass spectrometry system, which can process sample data in batches, shorten processing time, and improve efficiency.

[0031] Figure 1 This is a schematic flowchart of a rapid organic matter screening method based on a two-dimensional gas chromatography-mass spectrometry system provided in this application embodiment. Figure 1 The method shown includes steps S11 to S14, which will be described in detail below.

[0032] S11: Obtain sample files for at least two samples.

[0033] In this embodiment of the application, the sample file includes organic matter data of the sample.

[0034] The software can be used to read sample files, obtain the sample files of the samples to be processed, and read the organic data of the sample files.

[0035] In some implementations, the sample file is a CSV (Comma-Separated Values) table file. Figure 2 This is an example diagram of the software code for reading organic matter data provided in an embodiment of this application. The software code for obtaining organic matter data by reading data columns from a spreadsheet file is as follows: Figure 2 As shown.

[0036] In some embodiments, step S11 includes: S111: Obtain raw data from at least two samples; for each sample's raw data, perform detection on the raw data to obtain at least one feature, and perform time correction on the raw data to obtain corrected data.

[0037] The raw sample data is obtained after the instrument performs organic matter detection on the sample. After acquiring the raw data of different samples, data preprocessing is performed. Specifically, feature detection is performed on the raw data to initially screen out features. Based on the same time scale, the dimensionality offset of the raw data of different samples is corrected to obtain corrected data. This reduces the deviation between sample data collected by different instruments and equipment, making the samples comparable and enabling subsequent data processing at similar times. This allows for simultaneous processing of sample data collected by different devices, further improving data processing efficiency.

[0038] As an example, characteristics include signal peaks, mass spectrometry morphology, retention index, etc.

[0039] As an example, peak detection was performed on the raw data to initially screen out signal peaks, identifying at least one. Then, using an alkane internal standard mixture, the dimensionality shift of the raw data from different samples was corrected based on the same time standard, yielding corrected data.

[0040] S112: Generate sample files based on the characteristic parameters of each feature for the calibrated data of each sample.

[0041] In the embodiments of this application, the organic matter data includes the characteristic parameters of each feature.

[0042] After obtaining the feature parameters of each feature, export them as a file to obtain the sample file.

[0043] In some embodiments, for each feature, qualitative analysis is performed based on relevant information to obtain qualitative results, which are then used to determine the category of the organic compound. Feature parameters include relevant information and qualitative results for the feature.

[0044] As an example, the feature is the signal peak, and the feature parameters include the one-dimensional retention time (RT1), two-dimensional retention time (RT2), peak area, qualitative results, etc. Figure 3 These are example diagrams of sample files provided in the embodiments of this application. Figure 3 As shown, the corresponding CSV table file contains data columns such as one-dimensional retention time, two-dimensional retention time and peak area, and qualitative results.

[0045] S12: Generate the mesh file corresponding to each sample file.

[0046] In this embodiment of the application, the grid file includes multiple first grids, which are used to record the data at corresponding positions in the sample file.

[0047] In some embodiments, step S12 includes: S121: For each sample file, determine the interval and range of each axial dimension of the mesh file according to the tolerance threshold of the sample file.

[0048] The number of axial dimensions is determined based on the number of time dimensions in the sample file.

[0049] Each sample file is processed individually to create a mesh file. For each sample file, the intervals and ranges of each axial dimension are adjusted according to a tolerance threshold to obtain the required intervals and ranges for each axial dimension.

[0050] It should be noted that, since the intervals and ranges of each axial dimension of the mesh file are determined based on the tolerance threshold, the total number of generated meshes remains unchanged.

[0051] S122: Generate a coordinate grid based on the intervals and ranges of each axial dimension.

[0052] As an example, organic data includes information on multiple signal peaks. The tolerance threshold is the peak tolerance.

[0053] Two-dimensional instruments are used to detect the sample, and the corresponding software is used to analyze and process the sample to obtain a two-dimensional spectrum. Figure 4 This is an example diagram of the original data of the sample provided in the embodiments of this application. For example... Figure 4 As shown, in the two-dimensional spectrum, the red dots represent signal peaks. The intensity of the color indicates the size of the peak area; the redder the color, the larger the peak area, and vice versa. The horizontal axis of the two-dimensional spectrum represents the one-dimensional retention time, and the vertical axis represents the two-dimensional retention time. The two-dimensional spectrum includes both horizontal and vertical axes, and the corresponding grid file also includes both horizontal and vertical axes. The horizontal axis of the grid file represents the one-dimensional retention time, and the vertical axis represents the two-dimensional retention time.

[0054] Based on the peak tolerance, the horizontal axis interval is determined to be 4s, and the vertical axis interval is determined to be 0.1s. The intervals of each axial dimension are consistent with the peak tolerance. Based on the peak tolerance, the horizontal axis range is determined to be [0, 3600s], and the vertical axis range is determined to be [0, 4s].

[0055] In the example, the range and interval of the vertical axis can also be determined based on the peak tolerance and modulation period. The modulation period is set according to the actual scenario requirements.

[0056] Based on the intervals and ranges of each axial dimension, a 900×40 coordinate grid is generated.

[0057] S123: For each coordinate grid, write the data of the sample file at the corresponding coordinate grid position into the coordinate grid to obtain the first grid, and then obtain the grid file.

[0058] Since the axial dimensions of the mesh file correspond to the axial dimensions of the sample file, each coordinate grid corresponds to a position in the sample file. The data from each position in the sample file is then filled into the corresponding coordinate grid to obtain the mesh file.

[0059] As an example, Figure 5 This is an example diagram of the software code for generating mesh files provided in an embodiment of this application. The software code for generating mesh files is as follows: Figure 5 As shown.

[0060] As an example, organic data includes information on multiple signal peaks. In a two-dimensional spectrum, chromatographic peaks are present at some locations and absent at others. Similarly, in the corresponding sample file, chromatographic peaks are present at some locations and absent at others; therefore, the grid cells corresponding to the locations with present chromatographic peaks are filled with data, while the grid cells corresponding to the locations without present chromatographic peaks are empty.

[0061] S13: Merge the various mesh files to obtain an aggregated mesh file, which includes multiple second meshes.

[0062] The grid files are overlaid, and the first grids belonging to the same position are overlaid to obtain the second grid, thus obtaining the aggregated grid file.

[0063] As an example, there are eight grid files. These eight grid files are merged to obtain an aggregated grid file. Each second grid contains data from eight first grids. Figure 6 This is an example diagram illustrating the data situation of the second grid provided in an embodiment of this application. For example... Figure 6 As shown, data is represented by Y. It is possible that all first grids corresponding to the second grid have recorded data, some first grids have no data, and some first grids have no data. Therefore, the number of data contained in each second grid is in the range [0, 8].

[0064] S14: Filter the target grid in the aggregated grid file and obtain the organic matter data of the target grid.

[0065] In some implementations, the aggregated grid file is a CSV table file. Figure 7 This is an example diagram of a table file representing the target mesh provided in an embodiment of this application. For example... Figure 7 As shown, the organic matter data of the target grid is expressed in the form of a CSV table file.

[0066] In this embodiment, the target grid is a second grid with a detection probability greater than a preset probability. The preset probability can be set according to actual needs to obtain the desired result.

[0067] In this embodiment, the detection probability is used to characterize the probability that the organic compound has a characteristic at the corresponding position in each sample file. The higher the detection probability, the higher the probability that the organic compound has a characteristic at the corresponding position in each sample file.

[0068] As an example, Figure 8 This is an example diagram of the software code for merging meshes and calculating detection probabilities provided in an embodiment of this application. The software code for merging meshes and calculating detection probabilities is shown in Figure 8.

[0069] In some embodiments, step S14 includes: S141: For each second grid, determine the detection probability based on the amount of organic data and the number of sample files in the second grid.

[0070] In some implementations, step S141 includes: The detection probability is obtained by dividing the number of organic data in the second grid by the number of sample files.

[0071] The formula for calculating the detection probability is: Detection probability = (Number of organic data / Number of sample files) × 100%.

[0072] As an example, the organic matter data includes signal peak information and has an eight-grid file. Six of the eight first grids superimposed on the second grid are filled with data, meaning there are six data points, and the total number of organic matter data is six. The detection probability = (6 / 8) × 100% = 75%, characterizing the stable peak frequency of organic matter in all samples as 75%.

[0073] S142: Determine the target grid based on the detection probability of each second grid.

[0074] The detection probability of each second grid is compared with the preset probability. The second grid with a higher probability than the preset probability is found, and the target grid is obtained.

[0075] This application embodiment acquires sample files of at least two samples, each sample file containing organic matter data of the samples; generates a grid file corresponding to each sample file, the grid file including multiple first grids, the first grids being used to record the data at corresponding positions in the sample files, so that the organic matter data in the samples can be easily and directly obtained and processed through the grid, and a foundation is provided for subsequent batch processing of organic matter data; merges the grid files to obtain an aggregated grid file, the aggregated grid file including multiple second grids, the second grids being used to record the quantity of organic matter data at corresponding positions; filters the target grid in the aggregated grid file and acquires the organic matter data of each target grid, the target grid being the second grid with a detection probability greater than a preset probability, the detection probability being used to characterize the probability that the organic matter has a characteristic at the corresponding position in each sample file; merges the organic matter data of different samples, and filters the target grid according to the data of the second grid, thereby realizing batch processing of organic matter data of multiple samples, shortening the processing time and improving efficiency.

[0076] In some embodiments, after step S14, the method further includes: S15: Determine the category of organic matter based on the organic matter data of each target grid.

[0077] In some implementations, the organic matter data of the target grid is analyzed to determine the category of organic matter.

[0078] In some implementations, organic data includes qualitative results, based on which the categories of organic matter are determined.

[0079] As an example, organic matter is VOCs, and organic matter data includes qualitative results of signal peaks. Based on the qualitative results of signal peaks, the organic matter is classified as VOCs.

[0080] This application embodiment determines the category of organic matter based on the organic matter data of each target grid, and accurately identifies the category of organic matter based on the organic matter data of multiple samples, thereby improving the identification accuracy.

[0081] This application provides a method for verifying peak stability, which can verify the rationality of signal peak processing and ensure that sample data can be correctly processed in batches.

[0082] S16: Generate visualizations of individual mesh files and aggregated mesh files.

[0083] The mesh file and the aggregated mesh file are obtained by the methods described in the above embodiments, and the organic data includes characteristic parameters of the signal peaks.

[0084] As an example, Figure 9This is an example diagram of the software code for peak stability verification provided in an embodiment of this application. The software code for generating a mesh file visualization is shown below. Figure 9 As shown.

[0085] As an example, a grid filled with one signal peak is identified by a red hollow circle. A grid filled with multiple signal peaks is identified by a blue hollow star. A grid without signal peaks is not identified. A larger identifier indicates a larger characteristic parameter. Figure 10 This is an example visualization of the mesh file provided in an embodiment of this application. For example... Figure 10 As shown in the diagram, the first grid cells filled with one signal peak are marked with a red hollow circle, while the first grid cells without signal peak data are not marked. The grid file required by the user fills each first grid cell with only one signal peak data, indicating that the grid division is reasonable, there are no multiple features assigned to the same grid, and there is no data overlap. If multiple signal peak data are assigned to the same grid, the grid should be re-divided.

[0086] As an example, Figure 11 This is an example visualization of the aggregated mesh file provided in an embodiment of this application. For example... Figure 11 As shown, the second grid with data is marked in red. The higher the detection probability of the second grid, the darker the red color of the grid.

[0087] This application provides intuitive and understandable visualizations by generating visualizations of individual grid files and aggregated grid files, which can verify the rationality of signal peak processing and ensure that sample data can be correctly processed in batches.

[0088] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. Furthermore, the data collection in the above embodiments is compliant, and its use or implementation does not involve any infringement upon public interests.

[0089] For ease of explanation, only the parts related to the embodiments of this application are shown in the methods described in the above embodiments.

[0090] In some embodiments, Figure 12 This is a schematic diagram of the structure of the rapid organic matter screening device based on a two-dimensional gas chromatography-mass spectrometry system provided in the embodiments of this application. Figure 12 As shown, the rapid organic screening device based on a two-dimensional gas chromatography-mass spectrometry system includes: The acquisition module 10 is used to acquire sample files of at least two samples, the sample files including organic data of the samples; The file processing module 11 is used to generate a grid file corresponding to each sample file. The grid file includes multiple first grids, and the first grids are used to record the data at the corresponding positions in the sample file. It is also used to merge various mesh files to obtain an aggregated mesh file, which includes multiple second meshes; The filtering module 12 is used to filter target grids in the aggregated grid file and obtain organic matter data of the target grids. The target grid is the second grid with a detection probability greater than a preset probability. The detection probability is used to characterize the probability that the organic matter has features at the corresponding position in each sample file.

[0091] In some embodiments, the screening module is specifically used to determine the detection probability for each second grid based on the amount of organic data and the number of sample files in the second grid; and to determine the target grid based on the detection probability of each second grid.

[0092] In some embodiments, the screening module is specifically used to divide the number of organic data in the second grid by the number of sample files to obtain the detection probability.

[0093] In some embodiments, the file processing module is specifically used to determine the interval and range of each axial dimension of the grid file according to the tolerance threshold of the sample file for each sample file, wherein the number of axial dimensions is determined according to the number of time dimensions of the sample file; generate a coordinate grid according to the interval and range of each axial dimension; and write the data of the sample file at the position of the corresponding coordinate grid into the coordinate grid for each coordinate grid to obtain the first grid, thereby obtaining the grid file.

[0094] In some embodiments, the acquisition module is specifically used to acquire raw data of at least two samples; for the raw data of each sample, to detect the raw data, to obtain at least one feature, and to perform time correction on the raw data to obtain corrected data; for the corrected data of each sample, to generate a sample file based on the feature parameters of each feature, wherein the organic data includes the feature parameters of each feature.

[0095] In some embodiments, the apparatus further includes: The identification module is used to determine the category of organic matter based on the organic matter data of each target grid.

[0096] In some embodiments, the apparatus includes: The screening module is specifically used to divide the number of organic data in the second grid by the number of sample files to obtain the detection probability.

[0097] In some embodiments, the peak stability verification device includes: The generation module is used to generate visualizations of individual grid files and aggregated grid files.

[0098] The grid files and aggregated grid files are obtained through the rapid organic matter screening method based on a two-dimensional gas chromatography-mass spectrometry system as described in the above embodiments, and the organic matter data includes characteristic parameters of the signal peaks.

[0099] Figure 13 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 13 As shown, the electronic device 2 of this embodiment includes: at least one processor 20 ( Figure 13 (Only one is shown in the diagram), memory 21, and computer program 22 stored in said memory 21 and executable on said at least one processor 20, wherein said processor 20 executes said computer program 22 to implement the steps in any of the above method embodiments.

[0100] The electronic device 2 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. The electronic device 2 may include, but is not limited to, a processor 20 and a memory 21. Those skilled in the art will understand that... Figure 13 This is merely an example of electronic device 2 and does not constitute a limitation on electronic device 2. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0101] The processor 20 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0102] In some embodiments, the memory 21 may be an internal storage unit of the electronic device 2, such as a hard disk or memory of the electronic device 2. In other embodiments, the memory 21 may be an external storage device of the electronic device 2, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 2. Furthermore, the memory 21 may include both internal and external storage units of the electronic device 2. The memory 21 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 21 can also be used to temporarily store data that has been output or will be output.

[0103] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0104] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0105] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps in the above-described method embodiments.

[0106] This application provides a computer program product that, when run on an electronic device, enables the electronic device to implement the steps described in the various method embodiments above.

[0107] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographic device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some cases, the computer-readable medium cannot be an electrical carrier signal or a telecommunication signal.

[0108] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0109] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0110] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0111] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0112] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions 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 this application, and should all be included within the protection scope of this application.

Claims

1. A rapid screening method for organic compounds based on a two-dimensional gas chromatography-mass spectrometry system, characterized in that, include: Obtain sample files for at least two samples, the sample files including organic data of the samples; Generate a grid file corresponding to each of the sample files. The grid file includes a plurality of first grids, which are used to record the data at corresponding positions in the sample files. The individual mesh files are merged to obtain an aggregated mesh file, which includes multiple second meshes; Target grids are filtered in the aggregated grid file, and the organic matter data of the target grids are obtained. The target grid is a second grid with a detection probability greater than a preset probability. The detection probability is used to characterize the probability that the organic matter has features at the corresponding position in each of the sample files. The generation of the mesh file corresponding to each of the sample files includes: For each of the sample files, the interval and range of each axial dimension of the mesh file are determined according to the tolerance threshold of the sample file. The number of axial dimensions is determined according to the number of time dimensions of the sample file. Multiple coordinate grids are generated based on the intervals and ranges of each axial dimension; For each of the coordinate grids, the data of the sample file at the position of the corresponding coordinate grid is written into the coordinate grid to obtain the first grid, thereby obtaining the grid file.

2. The method according to claim 1, characterized in that, The step of filtering the target mesh in the aggregated mesh file includes: For each of the second grids, the detection probability is determined based on the amount of organic data in the second grid and the number of sample files. The target grid is determined based on the detection probability of each of the second grids.

3. The method according to any one of claims 1 to 2, characterized in that, The acquisition of sample files for at least two samples includes: Obtain raw data from at least two of the samples; for each sample, perform detection on the raw data to obtain at least one feature, and perform time correction on the raw data to obtain corrected data; Based on the corrected data of each sample, a sample file is generated according to the feature parameters of each feature, wherein the organic data includes the feature parameters of each feature.

4. The method according to claim 3, characterized in that, After acquiring the organic matter data of the target grid, the method further includes: The category of the organic matter is determined based on the organic matter data of each target grid.

5. The method according to claim 4, characterized in that, Determining the detection probability based on the amount of organic data in the second grid and the number of sample files includes: The detection probability is obtained by dividing the number of organic data in the second grid by the number of sample files.

6. A method for verifying peak stability, characterized in that, include: Generate visualizations of each mesh file and the aggregated mesh file; Wherein, the grid file and the aggregated grid file are obtained by any one of the methods of claims 1 to 5, and the organic matter data includes characteristic parameters of the signal peaks.

7. A rapid organic matter screening device based on a two-dimensional gas chromatography-mass spectrometry system, characterized in that, include: An acquisition module is used to acquire sample files of at least two samples, wherein the sample files include organic data of the samples; The file processing module is used to generate a grid file corresponding to each of the sample files. The grid file includes a plurality of first grids, which are used to record the data at corresponding positions in the sample files. It is also used to merge the various mesh files to obtain an aggregated mesh file, wherein the aggregated mesh file includes multiple second meshes; The filtering module is used to filter target grids in the aggregated grid file and obtain the organic matter data of the target grids. The target grid is a second grid with a detection probability greater than a preset probability. The detection probability is used to characterize the probability that the organic matter has features at the corresponding position in each of the sample files. Specifically, the file processing module is used to determine the interval and range of each axial dimension of the grid file according to the tolerance threshold of the sample file for each sample file, wherein the number of axial dimensions is determined according to the number of time dimensions of the sample file; generate multiple coordinate grids according to the interval and range of each axial dimension; and write the data of the sample file at the position of the corresponding coordinate grid into the coordinate grid for each coordinate grid to obtain the first grid, thereby obtaining the grid file.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 5 or claim 7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 5 or claim 7.

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