Ion mobility spectrometry-mass spectrometry library building method, identification method and device thereof

Abstract

The present disclosure provides an ion mobility spectrometry-mass spectrometry library building method, which can be applied to the technical field of analytical chemistry. The ion mobility spectrometry-mass spectrometry library building method comprises: performing peak searching processing on the preprocessed original data based on a first peak searching algorithm to obtain multiple ion mobility spectrometry-mass spectrometry column peak information; sorting the multiple column peak information based on a first preset sorting rule to output a first sorted result; determining at least one sample characteristic peak from the first sorted result based on each retention time and a preset condition; performing second sorting on the first characteristic peak and the at least one sample characteristic peak based on a second preset sorting rule to generate a second sorted result; and constructing a target spectrum library according to each second sorted result, the name of a first known substance, a first preset threshold value, and a basic spectrum library corresponding to the second sorted result. The present disclosure also provides an ion mobility spectrometry-mass spectrometry library building device, an ion mobility spectrometry-mass spectrometry identification method and device.

Description

Technical Field

[0001] This disclosure relates to the field of analytical chemistry, and specifically to a library preparation method, identification method and apparatus for ion mobility spectrometry-mass spectrometry. Background Technology

[0002] In ion mobility spectroscopy (IMS) and mass spectrometry (MS) coupled systems, ion mobility spectroscopy is typically used for the initial separation of characteristic peaks, followed by a secondary separation using mass spectrometry. For complex mixed samples, ion mobility spectroscopy-mass spectrometry can not only achieve preliminary separation of characteristic peaks but also distinguish highly sensitive isomers, improving qualitative analysis capabilities. Although ion mobility spectroscopy-mass spectrometry can separate characteristic peaks, in realizing the concept of this disclosure, the inventors discovered at least the following problems in the related technology: the number of selectable characteristic peaks is limited, leading to the omission of characteristic peaks, which in turn reduces the accuracy of characteristic peak identification and increases the false negative rate. Summary of the Invention

[0003] In view of the above problems, this disclosure provides a library construction method, identification method, apparatus, equipment, medium and program product for ion mobility spectrometry-mass spectrometry.

[0004] The first aspect of this disclosure provides a library construction method for ion mobility spectrometry-mass spectrometry, comprising: performing peak-finding processing on preprocessed raw data based on a first peak-finding algorithm to obtain multiple ion mobility spectrometry-mass spectrometry peak information, wherein the raw data is obtained by detecting a first known substance using a first detection device; sorting the multiple peak information based on a first preset sorting rule, and outputting a sorted first sorting result, wherein the first sorting result includes multiple pre-selected peaks; determining at least one sample characteristic peak from the first sorting result based on each retention time and preset conditions, wherein each pre-selected peak includes at least one retention time, wherein the retention time represents... The first and last time intervals of the peaks in the pre-selected peaks are defined. Based on the retention time, multiple first sorting results are compared with the first spectrum to obtain a first comparison result. The first spectrum is obtained from multiple pre-processed raw data. If the first comparison result indicates that there is a first characteristic peak in the first spectrum that is different from multiple first sorting results, the first characteristic peak and at least one sample characteristic peak are sorted a second time based on a second preset sorting rule to generate a second sorting result. A target spectrum library is constructed based on each second sorting result, the name of the first known substance, the first preset threshold, and the basic spectrum library corresponding to the second sorting result.

[0005] According to embodiments of this disclosure, the first detection device includes a multi-mode first odor detector.

[0006] According to an embodiment of this disclosure, when generating the first sorting result, based on the first preset sorting rule and different mode data, the multiple column peak information corresponding to the mode data are sorted, and the sorted first sorting result corresponding to each of the mode data is output, wherein the mode data includes at least one of the following: positive ion migration mode, mass spectrometry mode and negative ion migration mode.

[0007] According to an embodiment of this disclosure, determining at least one sample characteristic peak from the first sorting result based on each retention time and preset conditions includes: determining multiple retention times for each of the preselected peaks based on a heatmap, wherein the heatmap is obtained based on the original data and / or data after preprocessing the original data; and, based on each retention time, determining each preselected peak as a sample characteristic peak if the frequency of the preselected peak meets a preset frequency condition and / or the peak intensity of the preselected peak meets a preset intensity condition.

[0008] According to embodiments of this disclosure, the aforementioned preselected peak includes a peak position identifier and a peak intensity.

[0009] According to an embodiment of this disclosure, the process of comparing multiple first sorting results with a first spectrum to obtain a first comparison result includes: determining multiple test peaks from the first spectrum; and comparing the peak position identifier and peak intensity of each test peak with the peak position identifier and peak intensity of each preselected peak to obtain the first comparison result.

[0010] According to embodiments of this disclosure, the second preset sorting rule includes the importance of different characteristic peaks of the aforementioned samples.

[0011] According to an embodiment of this disclosure, when the first comparison result indicates that there is a first characteristic peak in the first spectrum that is different from multiple first sorting results, the first characteristic peak and at least one sample characteristic peak are sorted a second time based on a second preset sorting rule to generate a second sorting result. This includes: when the first comparison result indicates that there is a first characteristic peak in the first spectrum that is different from multiple first sorting results, the first characteristic peak that meets the preset condition is determined as a sample characteristic peak; and the multiple sample characteristic peaks are sorted a second time based on the second preset sorting rule to generate the second sorting result.

[0012] According to an embodiment of this disclosure, before performing the second sorting, the method further includes: comparing the multiple first sorting results with the second spectrum based on each of the above-mentioned retention times to obtain a second comparison result, wherein the second spectrum is obtained based on the multiple of the above-mentioned original data; if the second comparison result shows that there is at least one second characteristic peak in the second spectrum that is different from the multiple of the above-mentioned first sorting results, the second characteristic peak that meets the above-mentioned preset conditions is determined as the sample characteristic peak.

[0013] According to embodiments of this disclosure, constructing a target spectral library based on each of the second sorting results, the name of the first known substance, a first preset threshold, and a basic spectral library corresponding to the second sorting results includes: for each of the second sorting results, calculating the weight of each of the sample characteristic peaks according to the order and peak intensity of the different sample characteristic peaks in the second sorting results; and constructing the target spectral library based on the basic spectral library, according to multiple sample characteristic peaks, the weight corresponding to each of the sample characteristic peaks, the name of the first known substance, and the first preset threshold.

[0014] According to an embodiment of this disclosure, when the target spectrum library includes the first characteristic peak, identification information is added to the first characteristic peak in the target spectrum library.

[0015] According to embodiments of this disclosure, the first preset sorting rule includes sorting based on the frequency and intensity of each column peak information.

[0016] According to an embodiment of this disclosure, the above-mentioned sorting of multiple column peak information corresponding to the above-mentioned pattern data based on the above-mentioned first preset sorting rule and different pattern data, and outputting the first sorting result corresponding to each of the above-mentioned pattern data, includes: classifying the multiple column peak information according to different above-mentioned pattern data to obtain multiple classified column peak information corresponding to each pattern data; for each above-mentioned pattern data, sorting the multiple classified column peak information according to the frequency of different above-mentioned column peak information to obtain multiple sorted column peak information; and when at least two column peak information have the same frequency among the multiple sorted column peak information, sorting the at least two column peak information according to the peak intensity of the at least two column peak information to obtain the first sorting result.

[0017] According to embodiments of this disclosure, the preprocessing described above includes filtering.

[0018] The second aspect of this disclosure also provides a method for identifying ion mobility spectrometry-mass spectrometry, comprising: performing peak-finding processing on each preprocessed initial data based on a second peak-finding algorithm to obtain multiple unknown peaks of ion mobility spectrometry-mass spectrometry, wherein the initial data is obtained by detecting the unknown substance using a second detection device; sequentially matching the multiple unknown peaks with multiple target spectral libraries to obtain multiple matching results corresponding to different target spectral libraries, wherein the target spectral libraries are constructed according to the library construction method described above; for each matching result, if the matching result indicates that the target spectral library corresponding to the matching result includes a sample characteristic peak corresponding to at least one of the unknown peaks, calculating the overall similarity between the multiple unknown peaks of the unknown substance and the sample characteristic peaks of a second known substance in the target spectral library; if the overall similarity satisfies a second preset threshold in the target spectral library, associating the names of the unknown substance and the second known substance as a first result and writing it into a result chain list.

[0019] According to embodiments of this disclosure, the method further includes: displaying the result list using a display device.

[0020] According to an embodiment of this disclosure, when there are multiple unknowns, the method further includes: when there are multiple first results in the result list, sorting the multiple first results to obtain a sorted result list, wherein the multiple first results correspond to different unknowns.

[0021] According to embodiments of this disclosure, the aforementioned plurality of target spectral libraries include an ion mobility spectrum library and a mass spectrometry library; wherein, the step of sequentially matching the plurality of the aforementioned unknown peaks with the plurality of target spectral libraries to obtain a plurality of matching results corresponding to different aforementioned target spectral libraries includes: matching the plurality of the aforementioned unknown peaks of the aforementioned unknown substance with the aforementioned ion mobility spectrum library to obtain a first matching result; and, when the plurality of the aforementioned unknown peaks of the aforementioned unknown substance have been matched with the aforementioned ion mobility spectrum library, matching the plurality of the aforementioned unknown peaks of the aforementioned unknown substance with the aforementioned mass spectrometry library to obtain a second matching result, wherein the aforementioned first matching result and the aforementioned second matching result represent different aforementioned matching results.

[0022] According to an embodiment of this disclosure, the method further includes: when the target spectral library includes a first characteristic peak, obtaining a special peak of the initial data corresponding to each of the unknown peaks; matching the multiple special peaks with the first characteristic peaks in the multiple target spectral libraries respectively to obtain multiple third matching results corresponding to different target spectral libraries, wherein the third matching result represents a matching result that is different from the first matching result and the second matching result.

[0023] According to an embodiment of this disclosure, when the matching result indicates that the target spectral library corresponding to the matching result includes a sample characteristic peak corresponding to at least one of the unknown peaks, calculating the overall similarity between the multiple unknown peaks of the unknown substance and the sample characteristic peaks of the second known substance in the target spectral library includes: when the matching result indicates that at least one of the unknown peaks has a sample characteristic peak corresponding to the unknown peak, calculating a first similarity between each unknown peak and the corresponding sample characteristic peak; and obtaining the overall similarity of the unknown substance based on the multiple first similarities.

[0024] According to embodiments of this disclosure, the second detection device includes a multi-mode second odor detector.

[0025] A third aspect of this disclosure also provides a library construction apparatus for ion mobility spectrometry-mass spectrometry, comprising: a first processing module, configured to perform peak-finding processing on preprocessed raw data based on a first peak-finding algorithm to obtain multiple ion mobility spectrometry-mass spectrometry peak information, wherein the raw data is obtained by detecting a first known substance using a first detection device; a first sorting module, configured to sort the multiple peak information based on a first preset sorting rule and output a sorted first sorting result, wherein the first sorting result includes multiple pre-selected peaks; and a first determining module, configured to determine at least one sample characteristic peak from the first sorting result based on each retention time and preset conditions, wherein each pre-selected peak includes at least one retention time, the retention time representing... The first comparison module is used to compare multiple first sorting results with the first spectrum based on the retention time to obtain a first comparison result, wherein the first spectrum is obtained based on multiple preprocessed raw data; the second sorting module is used to sort the first characteristic peak and at least one sample characteristic peak in the first spectrum according to a second preset sorting rule when the first comparison result shows that there is a first characteristic peak in the first spectrum that is different from multiple first sorting results, to generate a second sorting result; and the construction module is used to construct a target spectrum library based on each second sorting result, the name of the first known substance, the first preset threshold and the basic spectrum library corresponding to the second sorting result.

[0026] A fourth aspect of this disclosure also provides an ion mobility spectrometry-mass spectrometry identification device, comprising: a second processing module, configured to perform peak-finding processing on each preprocessed initial data based on a second peak-finding algorithm to obtain multiple unknown peaks of ion mobility spectrometry-mass spectrometry, wherein the initial data is obtained by detecting an unknown substance using a second detection device; a matching module, configured to sequentially match the multiple unknown peaks with multiple target spectral libraries to obtain multiple matching results corresponding to different target spectral libraries, wherein the target spectral libraries are constructed according to the aforementioned library construction method; a calculation module, configured to, for each matching result, if the matching result indicates that the target spectral library corresponding to the matching result includes a sample characteristic peak corresponding to at least one of the unknown peaks, calculate the overall similarity between the multiple unknown peaks of the unknown substance and the sample characteristic peaks of a second known substance in the target spectral library; and a writing module, configured to, if the overall similarity satisfies a second preset threshold in the target spectral library, associate the names of the unknown substance and the second known substance as a first result and write it into a result chain list.

[0027] A fifth aspect of this disclosure also provides an electronic device, comprising: one or more processors; and a storage device for storing one or more programs, wherein, when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the ion mobility spectrometry-mass spectrometry library construction method or the ion mobility spectrometry-mass spectrometry identification method.

[0028] A sixth aspect of this disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the above-described library construction method or identification method for ion mobility spectrometry-mass spectrometry.

[0029] The seventh aspect of this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described library construction method or identification method for ion mobility spectrometry-mass spectrometry.

[0030] According to embodiments of this disclosure, by finding missed characteristic peaks in the first spectrum and sorting them with other characteristic peaks when the characteristic peak is a sample characteristic peak, the number of missed characteristic peaks is reduced, the recognition rate of the target spectrum library is improved, and the problems of decreased accuracy and increased false negative rate in characteristic peak recognition in related technologies are at least partially overcome. Attached Figure Description

[0031] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0032] Figure 1 A flowchart illustrating a library construction method for ion mobility spectrometry-mass spectrometry according to an embodiment of the present disclosure is shown schematically.

[0033] Figure 2A A schematic diagram of the peaks in the positive mode of ion migration spectrum according to an embodiment of the present disclosure is shown.

[0034] Figure 2B A schematic diagram of the peaks of the negative mode of ion mobility spectra according to an embodiment of the present disclosure is shown.

[0035] Figure 2C A schematic diagram of mass spectrometry peaks according to an embodiment of the present disclosure is shown;

[0036] Figure 3 A flowchart illustrating a library construction method for ion mobility spectrometry-mass spectrometry according to another embodiment of the present disclosure is shown schematically.

[0037] Figure 4 A flowchart illustrating an ion mobility spectrometry-mass spectrometry identification method according to an embodiment of the present disclosure is shown schematically.

[0038] Figure 5 A flowchart illustrating an ion mobility spectrometry-mass spectrometry identification method according to another embodiment of the present disclosure is shown schematically.

[0039] Figure 6 A schematic diagram illustrating the structure of a library preparation apparatus for ion mobility spectrometry-mass spectrometry according to an embodiment of the present disclosure is shown.

[0040] Figure 7 A schematic diagram illustrating the structure of an ion mobility spectrometry-mass spectrometry identification device according to an embodiment of the present disclosure; and

[0041] Figure 8 A block diagram of an electronic device suitable for implementing a library construction method or an ion mobility spectrometry-mass spectrometry identification method according to embodiments of the present disclosure is illustrated. Detailed Implementation

[0042] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0043] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0044] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0045] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0046] It should be noted that the library construction method, identification method and apparatus for ion mobility spectrometry-mass spectrometry determined in the embodiments of this disclosure can be used in the field of analytical chemistry, or in any field other than analytical chemistry, and its specific application field is not limited.

[0047] In ion mobility spectrometry (IMS) and mass spectrometry (MS / MS) integrated systems, qualitative analysis of samples typically employs classic pattern recognition algorithms. This usually involves first manually or automatically extracting characteristic peaks from the IMS and MS spectra of standard samples, and then establishing an IMS / MS standard library based on these peaks. In practical applications, the IMS / MS characteristic peaks of unknown samples can be extracted and compared with the characteristic peaks in the IMS / MS standard library. If the threshold value of the unknown sample's IMS / MS characteristic peak is greater than the threshold value of the IMS / MS standard library's characteristic peak, the recognition result is output.

[0048] However, manually extracting characteristic peaks requires manual observation and recording of peak positions, intensities, and other information. Assigning appropriate weights to these manually extracted peaks typically requires personnel with specialized knowledge and experience, making it not only labor-intensive and time-consuming but also inefficient for database construction. While automated characteristic peak extraction using algorithms can improve database construction efficiency, it usually suffers from the following problems: a limited number of selectable characteristic peaks, leading to peak omissions; the selected characteristic peaks may be interference peaks rather than genuine sample peaks; characteristic peak weights only consider peak intensity and cannot adjust the order and weights based on the importance of the characteristic peaks; and the accuracy in identifying unknown samples decreases while the false negative rate increases.

[0049] In view of this, embodiments of the present disclosure provide a library construction method for ion mobility spectrometry-mass spectrometry, a library construction apparatus for ion mobility spectrometry-mass spectrometry, an identification method for ion mobility spectrometry-mass spectrometry, an identification apparatus for ion mobility spectrometry-mass spectrometry, an electronic device, a readable storage medium, and a computer program product. These can improve the accuracy of characteristic peak identification and reduce the false negative rate. The library construction method of this ion mobility spectrometry-mass spectrometry includes: 1) performing peak-finding processing on preprocessed raw data based on a first peak-finding algorithm to obtain multiple ion mobility spectrometry-mass spectrometry peak information, wherein the raw data is obtained by detecting a first known substance using a first detection device; 2) sorting the multiple peak information based on a first preset sorting rule to output a sorted first sorting result, wherein the first sorting result includes multiple pre-selected peaks; 3) determining at least one sample characteristic peak from the first sorting result based on each retention time and preset conditions, wherein each pre-selected peak includes at least one retention time, the retention time representing the start and end time period of the peak in the pre-selected peak; 4) comparing the multiple first sorting results with a first spectrum based on the retention time to obtain a first comparison result, wherein the first spectrum is obtained from multiple preprocessed raw data; 5) if the first comparison result indicates the presence of a first characteristic peak in the first spectrum that differs from the multiple first sorting results, then performing a second sorting based on a second preset sorting rule to generate a second sorting result; and 6) constructing a target spectral library based on each second sorting result, the name of the first known substance, a first preset threshold, and a basic spectral library corresponding to the second sorting result.

[0050] In the embodiments of this application, firstly, multi-mode odor detectors and other detection devices are used to detect known standard samples of various prohibited substances. Then, ion mobility spectrometry-mass spectrometry (IMS-MS) and other measurement devices are used to measure and analyze the components, measuring characteristic peaks (peaks exceeding a certain threshold or proportion are considered characteristic peaks) and recording the retention time and ion migration time or two-dimensional coordinates of the corresponding characteristic peaks. Next, the characteristic peaks are sorted using the preset sorting rules in various embodiments of this application, and the two-dimensional peak position coordinates (retention time and migration time) corresponding to the sorted characteristic peaks, as well as the substance name and threshold information, are recorded. After integrating the two-dimensional coordinates and corresponding substance names of the characteristic peaks corresponding to the standard samples, a prohibited substance characteristic peak library for the current coupled device is formed. When the coupled device is used to detect unknown analytes in the future, the characteristic peaks of the analyte are obtained. The system compares the two-dimensional peak position coordinates (retention time and migration time) of the characteristic peaks corresponding to the analyte with the two-dimensional coordinates of the characteristic peaks in the prohibited substance characteristic peak library of the coupled device to find similar candidate characteristic peaks, and presents the identified substance names and other information to the user. The so-called characteristic peaks here can be the real and reliable product ion peaks of the sample, excluding characteristic peaks that are affected by system noise or environmental noise.

[0051] Figure 1 A flowchart illustrating a library construction method for ion mobility spectrometry-mass spectrometry according to an embodiment of the present disclosure is shown schematically.

[0052] like Figure 1 As shown, the library construction method of ion mobility spectrometry-mass spectrometry in this embodiment includes operations S101 to S106.

[0053] In operation S101, based on the first peak-finding algorithm, peak-finding processing is performed on the preprocessed raw data to obtain the peak information of multiple ion mobility spectrometry-mass spectrometry. The raw data is obtained by detecting the first known substance using the first detection device.

[0054] In operation S102, based on the first preset sorting rule, the information of multiple column peaks is sorted and the first sorting result is output, wherein the first sorting result includes multiple pre-selected peaks.

[0055] In operation S103, based on each retention time and preset conditions, at least one sample characteristic peak is determined from the first sorting result, wherein each pre-selected peak includes at least one retention time, and the retention time represents the start and end time period of the peak in the pre-selected peak.

[0056] In operation S104, based on the retention time, multiple first sorting results are compared with the first spectrum to obtain the first comparison result, wherein the first spectrum is obtained based on multiple preprocessed raw data.

[0057] In operation S105, if the first comparison result shows that there is a first characteristic peak in the first spectrum that is different from multiple first sorting results, the first characteristic peak and at least one sample characteristic peak are sorted a second time based on the second preset sorting rule to generate a second sorting result.

[0058] In operation S106, a target spectral library is constructed based on each second sorting result, the name of the first known object, the first preset threshold, and the basic spectral library corresponding to the second sorting result.

[0059] According to embodiments of this disclosure, the first peak-finding algorithm can be a full-spectrum automatic peak-finding algorithm, such as an automatic peak-finding algorithm based on the nuclide library method, which is suitable for finding single peaks with high intensity. It can also be an automatic peak-finding algorithm based on the Gaussian product function, or an automatic peak-finding algorithm based on symmetric zero area, which is suitable for detecting weak peaks and overlapping peaks. After obtaining the peak information through the first peak-finding algorithm, relevant storage measures can be taken to save it.

[0060] According to embodiments of this disclosure, the raw data can be two-dimensional image data acquired over a period of time using a gas phase ion mobility spectrometer, a gas phase mass spectrometer, or an ion mobility spectrometer-mass spectrometer. To smooth the peaks in the raw data, preprocessing can be performed. Preprocessing may include filtering, such as using a smoothing filter.

[0061] According to embodiments of this disclosure, the first detection device may include a multi-mode odor detector. The first known substance may be a known standard sample of a variety of contraband items of interest to the first detection device.

[0062] According to an embodiment of this disclosure, when generating a first sorting result, based on a first preset sorting rule and different mode data, multiple column peak information corresponding to the mode data are sorted, and a sorted first sorting result corresponding to each mode data is output, wherein the mode data includes at least one of the following: positive ion migration mode, mass spectrometry mode, and negative ion migration mode.

[0063] Figure 2A A schematic diagram of the peaks in the positive mode of ion migration spectrum according to an embodiment of the present disclosure is shown. Figure 2B A schematic diagram of the peaks of the negative mode of ion mobility spectra according to an embodiment of the present disclosure is shown. Figure 2C A schematic diagram of mass spectrometry peaks according to an embodiment of the present disclosure is shown. Figures 2A to 2C It can be a spectrum obtained in real time, where the horizontal axis of each graph represents migration time or time, and the vertical axis represents signal strength. Figures 2A to 2C In each mode, multiple peaks can be regarded as a set of column peaks. When the peak intensity of a column peak exceeds a certain threshold or proportion, it can be used as a characteristic peak.

[0064] According to embodiments of this disclosure, the first preset sorting rule may include sorting based on the frequency and intensity of each column peak information. Specifically, during sorting, multiple column peak information may be classified according to different mode data, for example, into three categories: positive ion migration mode, mass spectrometry mode, and negative ion migration mode, resulting in multiple classified column peak information corresponding to each mode data; for each mode data, the multiple classified column peak information is sorted according to the frequency of different column peak information to obtain multiple sorted column peak information; if at least two column peak information have the same frequency among the sorted multiple column peak information, at least two column peak information are sorted according to the peak intensity of at least two column peak information to obtain a first sorting result.

[0065] According to embodiments of this disclosure, peak information may include peak retention time, peak migration time, peak intensity, peak frequency, peak mode, etc. The first preset sorting rule may be based on the frequency of occurrence and intensity of characteristic peaks. Preferably, it may be sorted according to the principle of frequency priority. If at least two peaks in the sorted peak information have the same frequency, then at least two peaks are sorted according to their peak intensity. The sorting result may be a list of peak positions, where each peak in the list can be a pre-selected peak. Therefore, the sorting result may also be a list of pre-selected peaks. For example, the sorting result may be a list of pre-selected peaks including N1 (positive ion migration mode or mass spectrometry) and N2 (negative ion migration mode) peak positions, where N1 and N2 are both positive integers.

[0066] According to an embodiment of this disclosure, operation S103 may further include the following operations: determining multiple retention times for each preselected peak based on a heatmap, wherein the heatmap is obtained based on the original data and / or data after preprocessing the original data; based on each retention time, for each preselected peak, if the frequency of the preselected peak meets a preset frequency condition, and / or the peak intensity of the preselected peak meets a preset intensity condition, the preselected peak is determined as a sample characteristic peak.

[0067] According to embodiments of this disclosure, a preselected peak may refer to the result of sorting the peak information; a sample characteristic peak may be one or more preselected peaks that meet the conditions after conditional screening from multiple preselected peaks, and these preselected peaks that meet the conditions are set as sample characteristic peaks.

[0068] According to embodiments of this disclosure, a heatmap may include a raw data heatmap and a preprocessed data heatmap. The raw data heatmap may be a GC-IMS or GC-MS graph that directly displays the raw data; the preprocessed data heatmap may be a GC-IMS or GC-MS graph displayed after preprocessing the raw data.

[0069] According to embodiments of this disclosure, the retention time can also be understood as the start and end time of the appearance of the preselected peak; the preset conditions may include preset frequency conditions and preset intensity conditions. The preset frequency conditions and preset intensity conditions can be determined experimentally and can also be adaptively adjusted according to actual needs. The heatmap can be displayed on a visualization interface. The heatmap can be understood as using different shades of color to represent the position and intensity of the preselected peak. For example, if the color at a certain position is different from the background color, it indicates that there is a preselected peak at that position; the darker the color, the greater the peak intensity.

[0070] According to embodiments of this disclosure, the approximate location and trend of preselected peaks can be determined first based on gas phase ion mobility spectrometry or gas phase mass spectrometry data in the heatmap. Then, the retention time of each preselected peak can be determined. Based on the preselected peak list obtained from the first sorting result, and according to each retention time and each preselected peak, if the frequency of the preselected peak meets a preset frequency condition and / or the peak intensity of the preselected peak meets a preset intensity condition, the preselected peak is identified as a characteristic peak of the sample. If neither the frequency nor the intensity of the preselected peak meets the preset conditions, the preselected peak is removed from the preselected peak list. The preset frequency and preset intensity conditions can be adaptively adjusted according to actual needs.

[0071] According to embodiments of this disclosure, preprocessed data can also be processed in a visual interface. Specifically, a portion of peaks can be automatically selected and displayed using an algorithm, followed by manual secondary selection, confirmation, and storage in the library. If too many peaks are automatically selected, the library construction parameters can be adjusted before automatic extraction. For example, the library construction parameters can be set on the page before automatic extraction. In addition, the automatically selected peak information can be displayed on the interface where manual peak selection is required by setting mode data. Then, a retention time ion mobility spectrum and mass spectrum can be selected one by one to filter out the true sample peaks, missed peaks, and shoulder peaks filtered out by preprocessing. The order of the peaks can be adjusted according to their importance, and the weight of the selected peaks can be calculated using the relevant characteristic peak weight calculation formula. Alternatively, the peak can be directly manipulated to automatically extract peak information and add it to the peak list for further examination and selection. Through the above design, the library construction efficiency is improved, and even non-professionals can operate it, reducing the difficulty of library construction.

[0072] According to embodiments of this disclosure, by processing preprocessed data through a visual interface, and based on the characteristics and trends of the characteristic peaks displayed in the gas phase ion mobility spectrometry and gas phase mass spectrometry, the corresponding gas phase ion mobility spectrometry and gas phase mass spectrometry spectrometry are selected, and the pre-selected characteristic peaks are screened a second time. This can fill in the gaps and select the true sample characteristic peaks as well as the characteristic peaks that were ignored by preprocessing due to manual annotation.

[0073] According to embodiments of this disclosure, the preselected peaks may further include peak position identifiers and peak intensities. Operation S104 may further include the following operations: determining a plurality of test peaks from the first spectrum; comparing the peak position identifier and peak intensity of each test peak with the peak position identifier and peak intensity of each preselected peak to obtain a first comparison result.

[0074] According to embodiments of this disclosure, the peak position identifier can be a unique identifier of the position of a preselected peak at a certain point in a two-dimensional graph composed of retention time and ion shift time; or a unique identifier of the position-related peak position information of a preselected peak at a certain point in a three-dimensional graph composed of retention time, ion shift time, and peak intensity.

[0075] According to embodiments of this disclosure, the first spectrum can be a spectrum obtained by filtering the original data. Multiple test peaks on the first spectrum can be used to check whether any characteristic peaks were missed in the pre-selected peaks. Specifically, each pre-selected peak can include a peak position identifier and a peak intensity. During comparison, a retention time can be determined first. Based on this retention time, the peak position identifier and peak intensity of each test peak and each pre-selected peak are compared to determine whether any characteristic peaks were missed. The first comparison result can include either a missed characteristic peak or no missed characteristic peaks. If a characteristic peak is missed, the test peak can be added to the pre-selected peak list.

[0076] According to an embodiment of this disclosure, operation S105 may further include the following operations: if the first comparison result shows that there is a first characteristic peak in the first spectrum that is different from multiple first sorting results, the first characteristic peak that meets the preset conditions is determined as the sample characteristic peak; based on the second preset sorting rule, the multiple sample characteristic peaks are sorted a second time to generate a second sorting result. The second preset sorting rule may include the importance of different sample characteristic peaks.

[0077] According to an embodiment of this disclosure, in the comparison results, if there is at least one peak that exists only in the first spectrum, and the peak satisfies a preset frequency condition and a preset intensity condition, the peak is understood as the first characteristic peak, and the first characteristic peak is added to the pre-selected peak list and determined as the sample characteristic peak.

[0078] According to embodiments of this disclosure, the second preset sorting rule can be to sort the sample characteristic peaks according to their importance, and to obtain the second sorting result by changing the order of each characteristic peak according to their importance.

[0079] According to an embodiment of this disclosure, before performing the second sorting, the method further includes: comparing multiple first sorting results with a second spectrum based on each retention time to obtain a second comparison result, wherein the second spectrum is obtained based on multiple original data; if the second comparison result shows that there is at least one second characteristic peak in the second spectrum that is different from the multiple first sorting results, the second characteristic peak that meets the preset conditions is determined as the sample characteristic peak.

[0080] According to embodiments of this disclosure, in order to prevent some small sample characteristic peaks from being ignored during the filtering process of the original data, multiple first sorting results can be compared with the second spectrum based on the retention time before the second sorting. Specifically, the peak position identifier and peak intensity in the first sorting results and the second spectrum can be compared respectively. If there is at least one peak that only exists in the second spectrum, and the peak meets the preset frequency condition and preset intensity condition, the peak is understood as the second characteristic peak and added to the pre-selected peak list to be determined as the sample characteristic peak.

[0081] According to embodiments of this disclosure, raw data can be selected in a visual interface. Based on the characteristics and trends displayed by the characteristic peaks of the gas phase ion mobility spectrum and gas phase mass spectrum, the corresponding gas phase ion mobility spectrum and gas phase mass spectrum can be selected. The pre-selected characteristic peaks can be screened a second time to fill in the gaps and select the true sample characteristic peaks as well as the characteristic peaks that were ignored by preprocessing due to manual annotation.

[0082] According to the embodiments of this disclosure, operation S106 may further include the following operations: for each second sorting result, calculate the weight of each sample characteristic peak according to the order and peak intensity of the different sample characteristic peaks in the second sorting result; specifically, the specific calculation process of calculating the peak position weight of the sample characteristic peak according to the peak position order and peak intensity of the sample characteristic peak can be as shown in formulas (1) to (2).

[0083] The weight of the sample characteristic peak can be composed of peak position order weight and peak intensity weight, and its calculation process is shown in formula (1).

[0084] Peaki.weight=GetOrderWeight(w1,i,nPeakCount)+w2peaki.intensity / peaksIntensityTotal (1)

[0085] Where i can identify the peak order, and i can be a positive integer; Peaki.weight can represent the peak order weight of the i-th peak; w1 can represent the total weight of the peak order, and the value of w1 can be from 0.4 to 0.7; w2 can represent the total weight of the peak intensity, and w2 can be as shown in formula (2); nPeakCount can represent the number of peaks; peaki.intensity can represent the intensity of the i-th peak; peaksIntensityTotal can represent the sum of the intensities of all peaks.

[0086] w2 = 1 - w1 (2)

[0087] GetOrderWeight can be a function that obtains the peak weights for each order, and its key calculation process can be shown below.

[0088] If double dbReWeight = 0, it means that the weight value needs to be returned.

[0089] if(dPeakCount≤1), {dbReWeight=w1}; means that if the number of peaks is less than or equal to 1, the returned weight value can be w1.

[0090] else if (dPeakCount = 2), {switch(i){case1{dbReWeight = w1 - w3}; where case1 can represent the first peak, w3 can represent the sum of the weights of the other peaks except the first peak, and the default value can be 0.2; if the number of peaks is equal to 2, the weight of the first peak can be the difference between w1 and w3.

[0091] else if (dPeakCount = 2), {switch(i) {case2 {dbReWeight = w3}; where case2 can represent the second peak; if the number of peaks is equal to 2, the weight of the second peak can be w3.

[0092] else if (dPeakCount≥3), double dbWeightTemp=1×w3÷2, where when the number of peaks is greater than or equal to 3, w3 can represent the sum of the weights of the other peaks except the first peak. The default value can be 0.2. Specifically, it can be understood as dividing w3 into two parts. The first part can be given to the second peak, and the second part can be given to the other peaks except the first and second peaks.

[0093] else if (dPeakCount≥3), {switch(i){case1{dbReWeight=w1-w3}; where case1 can represent the first peak. If the number of peaks is greater than or equal to 3, the weight of the first peak can be the difference between w1 and w3.

[0094] else if (dPeakCount≥3), {switch(i){case2{dbReWeight=dbWeightTemp}; where case2 can represent the second peak. If the number of peaks is greater than or equal to 3, the weight of the second peak can be dbWeightTemp.

[0095] else if (dPeakCount≥3), {switch(i){default{dbReWeight=1.0×dbWeightTemp / (nPeakCount-2)} where default can represent the 3rd peak and other peaks after the 3rd peak. If the number of peaks is greater than or equal to 3, the weight of the 1st peak can be the difference between w1 and w3.

[0096] According to the embodiments of this disclosure, the peak order weight can be divided into three levels. For the first peak, if there is only one peak, the weight of the first peak can be w1. If there are at least two peaks, the weight of the first peak can be w1-w3. The weight of the first peak is relatively the highest and the most important.

[0097] For the second peak, if there are only two peaks, the weight of the second peak can be w3. If there are at least three peaks, the weight of the second peak can be w3÷2. The weight of the second peak is relatively lower than that of the first peak, and its importance is also lower than that of the first peak.

[0098] For the third peak and all other peaks after the third peak, each peak has the same weight, which can be w3÷2÷(nPeakCount-2).

[0099] According to the embodiments of this disclosure, the above calculation method can select the true sample peaks, not miss the feature peaks that are ranked later, and manually mark the peaks that are filtered out by filtering. Then, the order of feature peaks is adjusted according to the importance of the peak position. The weight of the selected feature peaks is calculated using the method shown in formula (1). By using the two dimensions of peak position order and peak intensity to calculate the weight of each feature peak, it is more reasonable and accurate than only considering the peak intensity dimension to calculate the weight of the feature peaks. This can improve the accuracy of identification and reduce the false alarm rate and the missed alarm rate.

[0100] According to embodiments of this disclosure, when the weights of the positive ion migration mode, mass spectrometry mode, and negative ion migration mode have all been calculated and the pre-selected peak list has been updated, a target spectral library can be constructed based on the basic spectral library, according to multiple sample characteristic peaks, the weights corresponding to each sample characteristic peak, the name of the first known substance, and a first preset threshold. The target spectral library may include an ion migration spectral library or a mass spectrometry library.

[0101] According to embodiments of this disclosure, when a first characteristic peak is included in the target spectral library, identification information is added to the first characteristic peak in the target spectral library.

[0102] According to embodiments of this disclosure, the basic spectral library can be a reference library that includes all information associated with the characteristic peaks of the sample, so as to serve as a reference library for subsequent annotation. The first preset peak threshold can be set in advance based on experiments and can be used as a standard for judging unknown substances. For example, assuming the detection threshold of a certain prohibited substance is 0.6, if the detection threshold of an unknown sample exceeds 0.6, the unknown sample is judged as that prohibited substance.

[0103] According to embodiments of this disclosure, characteristic peaks of ion mobility spectrometry (IMS) and mass spectrometry (MS) samples can be written into an IMS library and a MS library, respectively, and identification information can be added to the sample characteristic peaks. When writing to the spectral library, if there are manually labeled peaks (i.e., peaks manually labeled in the original data state because they were ignored or could not be found), the identifier of the manually labeled peak is set to 1 so that it can be processed differently from non-manually labeled peaks during identification.

[0104] According to embodiments of this disclosure, missed characteristic peaks are searched in the first spectrum. If the characteristic peak is a sample characteristic peak, it is sorted with other characteristic peaks, thereby reducing the missed selection of characteristic peaks and improving the recognition rate of the target spectrum library when used.

[0105] According to the embodiments of this disclosure, the beneficial effects of the present invention are that it can select the true sample peaks without missing the characteristic peaks that are ranked later, thereby improving the recognition accuracy; adding manually labeled peaks (unselected peaks that were filtered out by filtering) can improve the recognition rate and accuracy, and reduce the false negative rate; it can also adjust the order of characteristic peaks as needed and increase the weight value of peak position order. By using both peak position order and peak intensity as two dimensions to calculate the weight of each characteristic peak, it is more reasonable and accurate than calculating the weight of characteristic peaks by only considering peak intensity as one dimension; the visual operation improves the efficiency of database construction; and it can be operated by non-professionals, reducing the difficulty of database construction.

[0106] Figure 3 A flowchart illustrating a library construction method for ion mobility spectrometry-mass spectrometry according to another embodiment of the present disclosure is shown schematically.

[0107] like Figure 3 As shown, the method may include S301 to S330.

[0108] In operation S301, the raw data is preprocessed.

[0109] In operation S302, acquire peak information for all columns of gas phase ion mobility spectrometry and gas phase mass spectrometry. The first peak-finding algorithm can be used to acquire and save all peak information after the preprocessed data injection column.

[0110] In operation S303, pre-selected peak information from gas phase ion mobility spectrometry and gas phase mass spectrometry is acquired. The peak information acquired in operation S302 is sorted according to the frequency of occurrence and peak intensity, and N1 (positive ion mobility mode or mass spectrometry) and N2 (negative ion mobility mode) peak positions are output as pre-selected peaks.

[0111] In operation S304, at least one mode data is selected. The mode data includes at least one of positive ion migration mode, mass spectrometry mode, and negative ion migration mode. In one embodiment, operations S301 to S304 can refer to operations S101 to S102, and will not be described again here.

[0112] In operation S305, select preprocessing data. Specifically, the gas phase ion mobility spectrometry and gas phase mass spectrometry data displayed in the visualization interface can be divided into raw data and preprocessed data. You can select preprocessed data first for subsequent operations.

[0113] When operating S306, analyze the heatmap to obtain the approximate location and trend of characteristic peaks.

[0114] In operation S307, select a retention time. Specifically, based on the pre-selected peak list, select the start and end retention times for the characteristic peaks you wish to browse; you can choose one retention time from multiple options.

[0115] When operating S308, view the pre-selected characteristic peak positions on the spectrum. Specifically, you can check whether these are characteristic peaks of the sample.

[0116] In operation S309, determine whether it is a sample characteristic peak. If it is a sample characteristic peak, proceed directly to operation S311; otherwise, start from operation S310.

[0117] In operation S310, the preselected peak position is removed from the preselected peak list.

[0118] In operation S311, it is determined whether the judgment of each peak position in the pre-selected peak list is complete. If the judgment is complete, the subsequent operations continue; if the judgment is not complete, it is necessary to return to start execution from S307. In one embodiment, operations S305 to S311 can refer to operation S103, and will not be described again here.

[0119] In operation S312, select a retention time. Specifically, you can select a retention time from the list of retention times with peaks.

[0120] When operating S313, view the peak positions and intensities on the spectrum. You can compare this to the pre-selected peak list to check for any missed sample characteristic peaks.

[0121] In step S314, check if any sample characteristic peaks were missed. If any sample characteristic peaks were missed, continue with the subsequent steps; otherwise, proceed directly to step S316.

[0122] In operation S315, characteristic peaks are added to the pre-selected peak list. Specifically, in the peak list for the retention time, characteristic peaks can be selectively omitted and added to the pre-selected peak list.

[0123] In operation S316, it is determined whether the peaks within each retention period have been viewed. If the peaks within each retention period have been viewed, the subsequent operations continue; otherwise, the process returns to start from operation S312. In one embodiment, operations S312 to S316 can refer to operation S104, and will not be described again here.

[0124] In operation S317, select raw data. Specifically, the gas phase ion mobility spectrometry and gas phase mass spectrometry data displayed in the visualization interface can be divided into raw data and preprocessed data. In this operation, you can select raw data for subsequent operations. This is to avoid filtering out some small sample characteristic peaks during the preprocessing of raw data, thus missing some characteristic peaks.

[0125] In operation S318, select a retention time.

[0126] When operating S319, view the peak positions and intensities on the spectrum.

[0127] In operation S320, it is determined whether there are any unidentified feature peaks. If there are unidentified feature peaks, the subsequent operations continue; if there are no unidentified feature peaks, the operation starts directly from operation S324.

[0128] In operation S321, determine whether the peak is a characteristic peak of the sample. If the peak is a characteristic peak of the sample, continue with subsequent operations; if the peak is not a characteristic peak of the sample, start directly from operation S324.

[0129] In operation S322, the characteristic peaks of the sample are labeled. Specifically, the peak position and intensity information can be extracted and marked on the spectrum.

[0130] In step S323, add to the preselected peak list. Specifically, the sample characteristic peaks annotated in step S322 can be added to the preselected peak list.

[0131] In operation S324, it is determined whether the peaks within each retention time period have been viewed. If the peaks within each retention time period have been viewed, the subsequent operations continue; otherwise, the process returns to continue from operation S318. In one embodiment, operations S317 to S324 can refer to the operations prior to the second sorting in the above method, and will not be repeated here.

[0132] In operation S325, the order of the pre-selected peaks is adjusted. Specifically, the order of the peaks is determined based on their importance.

[0133] In operation S326, the weights of the pre-selected peak positions are calculated.

[0134] In operation S327, update the list of pre-selected peak positions.

[0135] In operation S328, is all pattern data completed? If all pattern data is completed, continue with subsequent operations; if not, return and continue execution from operation S204.

[0136] In operation S329, enter the substance name and threshold.

[0137] In operation S330, the data is written to the spectral library. Specifically, the characteristic peaks of the ion mobility spectrometry (IMS) and mass spectrometry (MS) samples are written to the IMS library and the MS library, respectively. When writing to the spectral library, if any of the characteristic peaks are manually labeled (i.e., peaks manually labeled because they were ignored or could not be found in the original data state), the identifier of the manually labeled peak is set to 1 so that it can be processed differently from non-manually labeled peaks during identification. In one embodiment, operations S325 to S330 can refer to operations S105 to S106, and will not be repeated here.

[0138] Figure 4 A flowchart illustrating an ion mobility spectrometry-mass spectrometry identification method according to an embodiment of the present disclosure is shown.

[0139] like Figure 4 As shown, the identification method includes operations S401 to S404.

[0140] In operation S401, based on the second peak-finding algorithm, peak-finding processing is performed on each preprocessed initial data to obtain multiple unknown peaks of ion mobility spectrometry-mass spectrometry. The initial data is obtained by detecting unknown substances using the second detection device.

[0141] In operation S402, multiple unknown peaks are matched sequentially with multiple target spectral libraries to obtain multiple matching results corresponding to different target spectral libraries. The target spectral libraries are constructed according to the above library construction method.

[0142] In operation S403, for each matching result, if the matching result indicates that the target spectral library corresponding to the matching result includes a sample characteristic peak corresponding to at least one unknown peak, the overall similarity between the multiple unknown peaks of the unknown substance and the sample characteristic peaks of the second known substance in the target spectral library is calculated.

[0143] In operation S404, if the overall similarity meets the second preset threshold in the target spectral library, the names of the unknown object and the second known object are associated as the first result and written into the result chain list.

[0144] According to embodiments of this disclosure, the second peak-finding algorithm may be the same as or different from the first peak-finding algorithm, and is used to obtain multiple unknown peaks from ion mobility spectrometry-mass spectrometry. Preprocessing may include smoothing filtering, for example, smoothing filtering may be applied to the obtained multiple unknown peaks to remove unnecessary peaks and other invalid information.

[0145] According to embodiments of this disclosure, the second detection device may include a multi-mode odor detector.

[0146] According to an embodiment of this disclosure, operation S402 may further include the following operations: matching multiple unknown peaks of the unknown substance with an ion mobility spectrum library to obtain a first matching result; and matching multiple unknown peaks of the unknown substance with a mass spectrometry library to obtain a second matching result, wherein the first matching result and the second matching result represent different matching results.

[0147] According to an embodiment of this disclosure, operation S403 may further include the following operations: if the matching result indicates that at least one unknown peak has a sample characteristic peak corresponding to the unknown peak, calculate a first similarity between each unknown peak and the corresponding sample characteristic peak; and obtain the overall similarity of the unknown based on the multiple first similarities.

[0148] According to embodiments of this disclosure, the ion mobility spectrometry-mass spectrometry identification method further includes: when the target spectral library includes a first characteristic peak, obtaining a special peak corresponding to each unknown peak in the initial data; matching the multiple special peaks with the first characteristic peaks in multiple target spectral libraries respectively to obtain multiple third matching results corresponding to different target spectral libraries, wherein the third matching result represents a matching result different from the first matching result and the second matching result.

[0149] According to embodiments of this disclosure, the target spectral library can be an ion mobility spectrum library and a mass spectrometry library constructed using the above-described library construction method. During identification, multiple unknown peaks of an unknown substance can be matched against the ion mobility spectrum library and the mass spectrometry library respectively, yielding a first matching result and a second matching result. The matching result may include the name of the identified substance or a substance that did not find a match. A first similarity is calculated between each unknown peak and its corresponding sample characteristic peak. Specifically, the first similarity can be the degree of matching between the unknown peak and the corresponding sample characteristic peak in the ion mobility spectrum library or the mass spectrometry library. For example, after matching the unknown peak with its corresponding sample characteristic peak, the degree of matching is determined to be 0.9, where 0.9 can be used as the aforementioned first similarity.

[0150] According to embodiments of this disclosure, for example, when matching and identifying multiple unknown peaks of an unknown substance with an ion mobility spectrum library, the peak value of the unknown peak is compared with the preset peak threshold of the corresponding sample characteristic peak in the ion mobility spectrum library. If the peak value exceeds the preset peak threshold, the corresponding first matching result is written into the result chain list; if the peak value does not exceed the preset peak threshold, it indicates that no substance was detected, and an identification result of no matching substance can be obtained.

[0151] According to embodiments of this disclosure, for example, when matching and identifying multiple unknown peaks of an unknown substance with a mass spectrometry library, the peak value of the unknown peak is compared with the preset peak threshold of the corresponding sample characteristic peak in the mass spectrometry library. If the peak value exceeds the preset peak threshold, the corresponding second matching result is written into the result chain list; if the peak value does not exceed the preset peak threshold, it indicates that no substance was detected, and an identification result of no matching substance can be obtained.

[0152] According to embodiments of this disclosure, special peaks may be manually labeled during library construction, in the raw data state, due to reasons such as being ignored or not being found. In cases where no matching substance is identified, it is also possible that some peaks with lower intensities were filtered out during data preprocessing, resulting in no matching substance being found. Therefore, in one embodiment, unknown peaks can also be matched and identified with these special peaks. Before matching and identification, it is not necessary to preprocess the unknown peaks. Then, multiple unknown peaks of the unknown substance are matched and identified with the ion mobility spectrometry library and the mass spectrometry library respectively, and a third matching result is obtained using the same method as for obtaining the first and / or second matching results.

[0153] According to an embodiment of this disclosure, in operation S404, the overall similarity can be the sum of all first similarities. The second preset threshold is set based on a second known substance. The second known substance can be a standard sample of multiple prohibited items of interest to the second detection device. The second preset threshold can be the content of each substance contained in the standard sample. If the overall similarity of the unknown substance meets the second preset threshold, the unknown substance can be considered the standard sample, and the names of the unknown substance and the standard sample are compiled into a first result and written into a result list.

[0154] According to an embodiment of this disclosure, when there are multiple unknowns, the method further includes: sorting the multiple first results in the result list to obtain a sorted result list, wherein the multiple first results correspond to different unknowns.

[0155] According to embodiments of this disclosure, the ion mobility spectrometry-mass spectrometry identification method further includes: displaying a result list using a display device.

[0156] According to embodiments of this disclosure, when a substance is identified, and in the case of multiple unknowns, the first results can be sorted. The sorting rules can be based on the hazard level of the unknowns, the alphabetical order of the names of the second known samples, etc., and the results are displayed on a result list, which can be displayed on a device with a display screen for viewing. If no substance is identified, the result list can display "No matching substance identified," thus ending the identification process for the unknowns.

[0157] According to embodiments of this disclosure, before identification, it is first determined whether there are manually labeled peaks in the ion mobility spectrum library or mass spectrometry library. If so, matching and identification are then performed. During identification, no preprocessing is required, and matching and identification are only performed with characteristic peaks of manually labeled standard substances in the ion mobility spectrum library or mass spectrometry library. This method can improve the identification rate, reduce the false alarm rate, and shorten the identification time.

[0158] Figure 5 A flowchart illustrating an ion mobility spectrometry-mass spectrometry identification method according to another embodiment of the present disclosure is shown.

[0159] like Figure 5 As shown, the identification method includes S501 to S520.

[0160] Using the S501, ion mobility spectrometry and mass spectrometry data preprocessing is performed. The measured ion mobility spectrometry and mass spectrometry data can be filtered to obtain preprocessed data.

[0161] During operation S502, the result list is initialized. Specifically, the result list can be cleared to zero to facilitate the differentiation of subsequently added recognition results.

[0162] In operation S503, ion mobility spectrum identification is performed. Specifically, this can be done by matching and identifying the ion mobility spectrum against a library of ion mobility spectra.

[0163] In operation S504, is there a result exceeding the threshold? Specifically, this could be a result identified by matching with an ion mobility spectrum library. If a result exceeds the threshold, continue with subsequent operations; otherwise, proceed directly to operation S506.

[0164] When operating S505, the ion mobility spectrum identification results are written into the result linked list.

[0165] When operating S506, mass spectrometry identification is performed. Specifically, this can involve matching and identifying the mass spectrometry data against a mass spectrometry library.

[0166] In operation S507, is there a result exceeding the threshold? Specifically, this could be a result identified through matching with a mass spectrometry library. If a result exceeds the threshold, continue with subsequent operations; otherwise, proceed directly to operation S509.

[0167] When operating S508, the mass spectrometry identification results are written to the result linked list.

[0168] In operation S509, determine if the number of results in the result list is greater than 0. If the number of results in the result list is greater than 0, proceed directly to operation S519; if the number of results in the result list is not greater than 0, continue execution from operation S510. Because some characteristic peaks may be manually labeled during library construction, and these peaks are likely to be filtered out during preprocessing, it is also necessary to consider whether there are manually labeled peaks in the ion mobility spectrometry library or mass spectrometry library.

[0169] In operation S510, determine if there are artificially labeled peaks in the ion mobility spectrum library. If artificially labeled peaks exist in the ion mobility spectrum library, continue with subsequent operations; if no artificially labeled peaks exist, you can directly start from operation S514.

[0170] In operation S511, special identification of ion mobility spectrometry is performed. Specifically, special identification can be performed without preprocessing the acquired ion mobility spectrometry and mass spectrometry data, by extracting the sample characteristic peaks of unknown substances and matching them with the characteristic peaks of manually labeled standard substances in the ion mobility spectrometry library.

[0171] In operation S512, is there a result exceeding the threshold? Specifically, this could be a result obtained through special identification with an ion mobility spectrum library. If a result exceeding the threshold is obtained, continue with subsequent operations; if a result not exceeding the threshold is obtained, proceed directly to operation S514.

[0172] In operation S513, the special identification results of ion mobility spectra are written into the result linked list.

[0173] In step S514, determine if there are manually labeled peaks in the mass spectrometry library. If manually labeled peaks exist in the mass spectrometry library, continue with subsequent operations; otherwise, you can directly start from step S518.

[0174] In operation S515, special identification of mass spectrometry is performed. Specifically, special identification can be performed without preprocessing the acquired ion mobility spectra and mass spectrometry data, by extracting the sample characteristic peaks of unknown substances and matching them with the characteristic peaks of manually labeled standard substances in a mass spectrometry library.

[0175] In operation S516, is there a result exceeding the threshold? Specifically, this could be a result obtained through special identification with a mass spectrometry library. If a result exceeding the threshold is obtained, continue with subsequent operations; if a result not exceeding the threshold is obtained, proceed directly to operation S518.

[0176] When operating S517, the special identification results of mass spectrometry are written into the result linked list.

[0177] In operation S518, determine if the number of results in the result list is greater than 0. If the number of results in the result list is still not greater than 0, operation S520 can be executed directly, for example, directly displaying substances with no matching. If the number of results in the result list is greater than 0, subsequent operations can be executed, and operation S520 will display the names of substances in the ion mobility spectrometry library and / or mass spectrometry library corresponding to the unknown substance.

[0178] In operation S519, the result linked list is sorted.

[0179] The S520 was operated, and the results were processed.

[0180] According to an embodiment of the present disclosure, in one embodiment, operations S501 to S520 can refer to operations S401 to S404, and will not be described again here.

[0181] It should be noted that, unless it is explicitly stated that there is a sequential order of execution between different operations, or that there is a sequential order of execution between different operations in terms of technical implementation, the execution order between multiple operations may not be significant, and multiple operations may be executed simultaneously.

[0182] Based on the above-described library preparation method using ion mobility spectrometry-mass spectrometry, this disclosure also provides a library preparation apparatus for ion mobility spectrometry-mass spectrometry. The following will be combined with... Figure 5 The device is described in detail.

[0183] Figure 6 A schematic block diagram of a library preparation apparatus for ion mobility spectrometry-mass spectrometry according to an embodiment of the present disclosure is shown.

[0184] like Figure 6 As shown, the ion mobility spectrometry-mass spectrometry library construction device 600 of this embodiment includes a first processing module 610, a first sorting module 620, a first determination module 630, a first comparison module 640, a second sorting module 650, and a construction module 660.

[0185] The first processing module 610 is used to perform peak-finding processing on the preprocessed raw data based on a first peak-finding algorithm to obtain peak information of multiple ion mobility spectrometry-mass spectrometry. The raw data is obtained by detecting a first known substance using a first detection device. In one embodiment, the first processing module 610 can be used to execute the operation S101 described above, which will not be repeated here.

[0186] The first sorting module 620 is used to sort multiple column peak information based on a first preset sorting rule and output a first sorting result, wherein the first sorting result includes multiple pre-selected peaks. In one embodiment, the first sorting module 620 can be used to perform the operation S102 described above, which will not be repeated here.

[0187] The first determining module 630 is configured to determine at least one sample characteristic peak from the first sorting result based on each retention time and preset conditions, wherein each pre-selected peak includes at least one retention time, and the retention time represents the start and end time interval of the peak in the pre-selected peak. In one embodiment, the determining module 630 may be used to perform the operation S103 described above, which will not be repeated here.

[0188] The first comparison module 640 is used to compare multiple first sorting results with a first spectrum based on the retention time to obtain a first comparison result, wherein the first spectrum is obtained from multiple preprocessed raw data. In one embodiment, the first comparison module 640 can be used to perform the operation S104 described above, which will not be repeated here.

[0189] The second sorting module 650 is used to, when the first comparison result indicates the presence of a first characteristic peak in the first spectrum that differs from multiple first sorting results, sort the first characteristic peak and at least one sample characteristic peak a second time based on a second preset sorting rule to generate a second sorting result. In one embodiment, the second sorting module 650 can be used to perform the operation S105 described above, which will not be repeated here.

[0190] The construction module 660 is used to construct a target spectral library based on each second sorting result, the name of the first known object, a first preset threshold, and a basic spectral library corresponding to the second sorting result. In one embodiment, the construction module 660 can be used to perform the operation S106 described above, which will not be repeated here.

[0191] According to embodiments of this disclosure, the first sorting module 620 further includes a third sorting module.

[0192] The third sorting module is used to sort multiple column peak information corresponding to the mode data based on the first preset sorting rules and different mode data when generating the first sorting result, and output the sorted first sorting result corresponding to each mode data. The mode data includes at least one of the following: positive ion migration mode, mass spectrometry mode and negative ion migration mode.

[0193] According to embodiments of this disclosure, the first determining module 630 further includes a first determining unit and a second determining unit.

[0194] The first determining unit is used to determine multiple retention times for each preselected peak based on the heatmap, which is obtained from the original data and the data after preprocessing the original data.

[0195] The second determining unit, based on each retention time, determines each pre-selected peak as a sample characteristic peak if the frequency of the pre-selected peak meets a preset frequency condition and / or the peak intensity of the pre-selected peak meets a preset intensity condition.

[0196] According to embodiments of this disclosure, the first comparison module 640 further includes a third determining unit and a first comparison unit.

[0197] The third determining unit is used to determine multiple test peaks from the first spectrum.

[0198] The first comparison unit compares the peak position and peak intensity of each test peak with the peak position and peak intensity of each preselected peak to obtain the first comparison result.

[0199] According to embodiments of this disclosure, the second sorting module 650 further includes a fourth determining unit and a first sorting unit.

[0200] The fourth determining unit is used to determine the first characteristic peak that meets the preset conditions as the sample characteristic peak when the first comparison result shows that there is a first characteristic peak in the first spectrum that is different from multiple first sorting results.

[0201] The first sorting unit is used to sort the characteristic peaks of multiple samples a second time based on the second preset sorting rules, and generate a second sorting result.

[0202] According to embodiments of this disclosure, the ion mobility spectrometry-mass spectrometry library preparation device 600 may further include a second comparison module and a second determination module.

[0203] The second comparison module is used to compare multiple first sorting results with the second spectrum based on each retention time to obtain a second comparison result, wherein the second spectrum is obtained based on multiple raw data.

[0204] The second determining module is used to determine the second characteristic peak that meets the preset conditions as the sample characteristic peak when the second comparison result shows that there is at least one second characteristic peak in the second spectrum that is different from multiple first sorting results.

[0205] According to embodiments of this disclosure, the construction module 660 further includes a first computing unit and a construction unit.

[0206] The first calculation unit is used to calculate the weight of each sample characteristic peak for each second sorting result, based on the order and peak intensity of the characteristic peaks of different samples in the second sorting result.

[0207] The construction unit is used to construct a target spectrum library based on a basic spectrum library, according to multiple sample characteristic peaks, the weights corresponding to each sample characteristic peak, the name of a first known substance, and a first preset threshold.

[0208] According to embodiments of this disclosure, the ion mobility spectrometry-mass spectrometry library construction apparatus 600 further includes an addition module.

[0209] The add module is used to add identification information to the first characteristic peak in the target spectrum library if the target spectrum library includes the first characteristic peak.

[0210] According to embodiments of this disclosure, the third sorting module further includes a classification subunit, a first sorting subunit, and a second sorting subunit.

[0211] The classification subunit is used to classify multiple column peak information according to different pattern data, and obtain multiple classified column peak information corresponding to each pattern data.

[0212] The first sorting subunit is used to sort the multiple classified column peak information according to the frequency of different column peak information for each pattern data, and obtain multiple sorted column peak information.

[0213] The second sorting subunit is used to sort at least two column peak information based on their peak intensity when at least two column peak information have the same frequency among the sorted column peak information, thereby obtaining the first sorting result.

[0214] Figure 7 A schematic block diagram of an ion mobility spectrometry-mass spectrometry identification device according to an embodiment of the present disclosure is shown.

[0215] like Figure 7 As shown, the ion mobility spectrometry-mass spectrometry identification device 700 of this embodiment includes a second processing module 710, a matching module 720, a calculation module 730, and a writing module 740.

[0216] The second processing module 710 is used to perform peak finding processing on each preprocessed initial data based on the second peak finding algorithm to obtain multiple unknown peaks of ion mobility spectrometry-mass spectrometry. The initial data is obtained by detecting unknown substances using the second detection device.

[0217] The matching module 720 is used to match multiple unknown peaks sequentially with multiple target spectral libraries to obtain multiple matching results corresponding to different target spectral libraries. The target spectral libraries are constructed according to the library construction method described above.

[0218] The calculation module 730 is used to calculate the overall similarity between multiple unknown peaks of an unknown substance and the sample characteristic peaks of a second known substance in the target spectral library for each matching result, provided that the matching result indicates that the target spectral library corresponding to the matching result includes a sample characteristic peak corresponding to at least one unknown peak.

[0219] The writing module 740 is used to associate the names of the unknown object and the second known object as a first result and write them into the result list when the overall similarity meets the second preset threshold in the target spectral library.

[0220] According to embodiments of this disclosure, the ion mobility spectrometry-mass spectrometry identification device 700 further includes a display module.

[0221] The display module is used to display the result linked list using a display device.

[0222] According to embodiments of the present disclosure, the ion mobility spectrometry-mass spectrometry identification device 700 further includes a second sorting unit.

[0223] The second sorting unit is used to sort the multiple first results when there are multiple first results in the result linked list, so as to obtain a sorted result linked list, wherein the multiple first results correspond to different unknowns.

[0224] According to embodiments of this disclosure, the matching module 720 further includes a first matching unit and a second matching unit.

[0225] The first matching unit is used to match multiple unknown peaks of an unknown substance with the ion mobility spectrum library to obtain the first matching result.

[0226] The second matching unit is used to match the multiple unknown peaks of an unknown substance with the mass spectrometry library when the multiple unknown peaks of the unknown substance have been matched with the ion mobility spectrum library, and to obtain the second matching result. The first matching result and the second matching result represent different matching results.

[0227] According to embodiments of the present disclosure, the ion mobility spectrometry-mass spectrometry identification device 700 further includes an acquisition unit and a third matching unit.

[0228] The acquisition unit is used to acquire the special peaks of the initial data corresponding to each unknown peak, provided that the target spectral library includes the first characteristic peak.

[0229] The third matching unit is used to match multiple special peaks with the first characteristic peaks in multiple target spectral libraries respectively, and obtain multiple third matching results corresponding to different target spectral libraries. The third matching result represents a matching result that is different from the first matching result and the second matching result.

[0230] According to embodiments of this disclosure, the calculation module 730 further includes a second calculation unit and a result unit.

[0231] The second calculation unit is used to calculate the first similarity between each unknown peak and the corresponding sample characteristic peak when the matching results indicate that at least one unknown peak has a sample characteristic peak corresponding to the unknown peak.

[0232] The result unit is used to obtain the overall similarity of the unknown based on multiple first similarities.

[0233] According to embodiments of this disclosure, any multiple modules among the first processing module 610, first sorting module 620, first determining module 630, first comparison module 640, second sorting module 650, and construction module 660, or the second processing module 710, matching module 720, calculation module 730, and writing module 740, can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the first processing module 610, the first sorting module 620, the first determining module 630, the first comparing module 640, the second sorting module 650, the construction module 660, or the second processing module 710, the matching module 720, the calculation module 730, and the writing module 740 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in any one of the three methods of software, hardware, and firmware, or in a suitable combination of any of them. Alternatively, at least one of the first processing module 610, the first sorting module 620, the first determining module 630, the first comparison module 640, the second sorting module 650, the construction module 660, or the second processing module 710, the matching module 720, the calculation module 730, and the writing module 740 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0234] It should be noted that the library construction device and the identification device of ion mobility spectrometry-mass spectrometry in the embodiments of this disclosure correspond to the library construction method and the identification method of ion mobility spectrometry-mass spectrometry in the embodiments of this disclosure, respectively. For a detailed description of the library construction device and the identification device of ion mobility spectrometry-mass spectrometry, please refer to the library construction method and the identification method of ion mobility spectrometry-mass spectrometry in the embodiments of this disclosure, and will not be repeated here.

[0235] Figure 8 A block diagram of an electronic device suitable for implementing a library construction method or an ion mobility spectrometry-mass spectrometry identification method according to embodiments of the present disclosure is illustrated.

[0236] like Figure 8As shown, an electronic device 800 according to an embodiment of this disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage portion 808 into a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this disclosure.

[0237] RAM 803 stores various programs and data required for the operation of electronic device 800. Processor 801, ROM 802, and RAM 803 are interconnected via bus 804. Processor 801 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 802 and / or RAM 803. It should be noted that the programs may also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0238] According to embodiments of this disclosure, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to a bus 804. The electronic device 800 may also include one or more of the following components connected to the I / O interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 810 as needed so that computer programs read from it can be installed into the storage section 808 as needed.

[0239] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0240] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 802 and / or RAM 803 and / or one or more memories other than ROM 802 and RAM 803 described above.

[0241] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code enables the computer system to implement the library construction method or the identification method for ion mobility spectrometry-mass spectrometry provided in the embodiments of this disclosure.

[0242] When the computer program is executed by the processor 801, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0243] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 809, and / or installed from a removable medium 811. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0244] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 809, and / or installed from removable medium 811. When the computer program is executed by processor 801, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0245] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0246] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0247] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0248] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A library construction method for ion mobility spectrometry-mass spectrometry, comprising: Based on the first peak-finding algorithm, peak-finding processing is performed on the preprocessed raw data to obtain the peak information of multiple ion mobility spectrometry-mass spectrometry. The raw data is obtained by detecting a first known substance using a first detection device. Based on a first preset sorting rule, the information of multiple column peaks is sorted, and a first sorting result is output, wherein the first sorting result includes multiple pre-selected peaks; Based on each retention time and preset conditions, at least one sample characteristic peak is determined from the first sorting result, wherein each preselected peak includes at least one retention time, and the retention time represents the start and end time period of the peak in the preselected peak; Based on the retention time, multiple first sorting results are compared with a first spectrum to obtain a first comparison result, wherein the first spectrum is obtained based on multiple preprocessed raw data; If the first comparison result shows that there is a first characteristic peak in the first spectrum that is different from multiple first sorting results, the first characteristic peak that meets the preset conditions is determined as the sample characteristic peak, and based on the second preset sorting rule, multiple sample characteristic peaks are sorted a second time to generate a second sorting result. For each of the second sorting results, the weight of each sample characteristic peak is calculated according to the order and peak intensity of the different sample characteristic peaks in the second sorting result. Based on the basic spectral library corresponding to the second sorting result, a target spectral library is constructed according to the multiple sample characteristic peaks, the weight corresponding to each sample characteristic peak, the name of the first known substance, and the first preset threshold.

2. The method according to claim 1, wherein, The first detection device includes a multi-mode first odor detector.

3. The method according to claim 1, wherein, When generating the first sorting result, based on the first preset sorting rule and different mode data, the multiple column peak information corresponding to the mode data are sorted, and the sorted first sorting result corresponding to each mode data is output. The mode data includes at least one of the following: positive ion migration mode, mass spectrometry mode and negative ion migration mode.

4. The method according to claim 1, wherein, The step of determining at least one sample characteristic peak from the first sorting result based on each retention time and preset conditions includes: Multiple retention times for each of the preselected peaks are determined based on a heatmap, wherein the heatmap is obtained based on the original data and / or data after preprocessing the original data; Based on each retention time, for each preselected peak, if the frequency of the preselected peak meets a preset frequency condition and / or the peak intensity of the preselected peak meets a preset intensity condition, the preselected peak is determined as a characteristic peak of the sample.

5. The method according to claim 1, wherein, The preselected peaks include peak position identifiers and peak strengths.

6. The method according to claim 1 or 5, wherein, The step of comparing multiple first sorting results with a first spectrum to obtain a first comparison result includes: Multiple test peaks were determined from the first spectrum; The peak position identifier and peak intensity of each test peak are compared with the peak position identifier and peak intensity of each preselected peak to obtain the first comparison result.

7. The method according to claim 1, wherein, The second preset sorting rule includes the importance of the characteristic peaks of different samples.

8. The method according to claim 1, wherein, Before performing the second sort, it also includes: Based on each retention time, the multiple first sorting results are compared with the second spectrum to obtain a second comparison result, wherein the second spectrum is obtained based on the multiple original data; If the second comparison result indicates that there is at least one second characteristic peak in the second spectrum that is different from multiple first sorting results, the second characteristic peak that meets the preset conditions is determined as the sample characteristic peak.

9. The method according to claim 1, wherein, If the first characteristic peak is included in the target spectrum library, identification information is added to the first characteristic peak in the target spectrum library.

10. The method according to claim 3, wherein, The first preset sorting rule includes sorting based on the frequency and intensity of each column peak information.

11. The method according to claim 3 or 10, wherein, The step of sorting multiple column peak information corresponding to the pattern data based on the first preset sorting rule and different pattern data, and outputting the first sorting result corresponding to each pattern data, includes: Based on different pattern data, multiple peak information are classified to obtain multiple classified peak information corresponding to each pattern data. For each of the pattern data, the multiple classified column peak information is sorted according to the frequency of different column peak information to obtain multiple sorted column peak information; If at least two column peaks in the sorted column peak information have the same frequency, the at least two column peaks are sorted according to their peak strength to obtain the first sorting result.

12. The method according to claim 1, wherein, The preprocessing includes filtering.

13. A method for identifying ion mobility spectrometry-mass spectrometry, comprising: Based on the second peak-finding algorithm, peak-finding processing is performed on each preprocessed initial data to obtain multiple unknown peaks of ion mobility spectrometry-mass spectrometry. The initial data is obtained by detecting unknown substances using a second detection device. The unknown peaks are sequentially matched with multiple target spectral libraries to obtain multiple matching results corresponding to different target spectral libraries, wherein the target spectral libraries are constructed according to any one of claims 1 to 12; For each matching result, if the matching result indicates that the target spectral library corresponding to the matching result includes a sample characteristic peak corresponding to at least one of the unknown peaks, the overall similarity between the multiple unknown peaks of the unknown substance and the sample characteristic peaks of the second known substance in the target spectral library is calculated. If the overall similarity meets the second preset threshold in the target spectral library, the names of the unknown object and the second known object are associated as a first result and written into the result chain list.

14. The method of claim 13, further comprising: The result list is displayed using a display device.

15. The method according to claim 13, wherein when there are multiple unknowns, the method further comprises: When there are multiple first results in the result list, the multiple first results are sorted to obtain a sorted result list, wherein the multiple first results correspond to different unknowns.

16. The method according to claim 13, wherein the plurality of target spectral libraries include an ion mobility spectral library and a mass spectrometry library; in, The step of sequentially matching multiple unknown peaks with multiple target spectral libraries to obtain multiple matching results corresponding to different target spectral libraries includes: The unknown peaks of the unknown substance are matched with the ion mobility spectrum library to obtain the first matching result; When multiple unknown peaks of the unknown substance are matched with the ion mobility spectrum library, the multiple unknown peaks of the unknown substance are matched with the mass spectrum library respectively to obtain a second matching result, wherein the first matching result and the second matching result represent different matching results.

17. The method of claim 16, further comprising: If the target spectrum library includes a first characteristic peak, obtain the special peak of the initial data corresponding to each of the unknown peaks; The multiple special peaks are matched with the first feature peaks in the multiple target spectral libraries respectively to obtain multiple third matching results corresponding to different target spectral libraries, wherein the third matching result represents a matching result that is different from the first matching result and the second matching result.

18. The method according to claim 13, wherein, When the matching result indicates that the target spectral library corresponding to the matching result includes a sample characteristic peak corresponding to at least one of the unknown peaks, the overall similarity between the multiple unknown peaks of the unknown substance and the sample characteristic peaks of the second known substance in the target spectral library is calculated, including: If the matching result indicates that at least one of the unknown peaks has a sample characteristic peak corresponding to the unknown peak, a first similarity between each of the unknown peaks and the corresponding sample characteristic peak is calculated. The overall similarity of the unknown is obtained based on multiple first similarities.

19. The method according to claim 13, wherein, The second detection device includes a multi-mode second odor detector.

20. A library preparation device for ion mobility spectrometry-mass spectrometry, comprising: The first processing module is used to perform peak-finding processing on the preprocessed raw data based on the first peak-finding algorithm to obtain the peak information of multiple ion mobility spectrometry-mass spectrometry, wherein the raw data is obtained by detecting the first known substance using the first detection device; The first sorting module is used to sort multiple column peak information based on a first preset sorting rule and output a sorted first sorting result, wherein the first sorting result includes multiple pre-selected peaks. The first determining module is used to determine at least one sample characteristic peak from the first sorting result based on each retention time and preset conditions, wherein each preselected peak includes at least one retention time, and the retention time represents the start and end time period of the peak in the preselected peak; The first comparison module is used to compare multiple first sorting results with a first spectrum based on the retention time to obtain a first comparison result, wherein the first spectrum is obtained based on multiple preprocessed raw data; The second sorting module is used to, when the first comparison result indicates the presence of a first characteristic peak in the first spectrum that differs from multiple first sorting results, determine the first characteristic peak that meets the preset conditions as a sample characteristic peak, and, based on a second preset sorting rule, perform a second sorting on multiple sample characteristic peaks to generate a second sorting result; and The construction module is used to calculate the weight of each sample characteristic peak for each second sorting result according to the order and peak intensity of the different sample characteristic peaks in the second sorting result, and construct a target spectrum library based on the basic spectrum library corresponding to the second sorting result, according to multiple sample characteristic peaks, the weight corresponding to each sample characteristic peak, the name of the first known substance and a first preset threshold.

21. An ion mobility spectrometry-mass spectrometry identification device, comprising: The second processing module is used to perform peak finding processing on each preprocessed initial data based on the second peak finding algorithm to obtain multiple unknown peaks of ion mobility spectrometry-mass spectrometry, wherein the initial data is obtained by detecting unknown substances using the second detection device; A matching module is used to sequentially match multiple unknown peaks with multiple target spectral libraries to obtain multiple matching results corresponding to different target spectral libraries, wherein the target spectral libraries are constructed according to the method of any one of claims 1 to 12; The calculation module is configured to, for each matching result, calculate the overall similarity between multiple unknown peaks of the unknown substance and the sample characteristic peaks of a second known substance in the target spectral library, provided that the matching result indicates that the target spectral library corresponding to the matching result includes a sample characteristic peak corresponding to at least one of the unknown peaks; and The writing module is used to associate the name of the unknown object with the name of the second known object as a first result and write it into the result list when the overall similarity meets the second preset threshold in the target spectral library.

22. An electronic device, comprising: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors perform the library construction method according to any one of claims 1 to 12 or the identification method according to any one of claims 13 to 19.

23. A computer-readable storage medium having stored executable instructions thereon, which, when executed by a processor, cause the processor to perform the library construction method according to any one of claims 1 to 12 or the identification method according to any one of claims 13 to 19.

24. A computer program product comprising a computer program that, when executed by a processor, implements the library construction method according to any one of claims 1 to 12 or the identification method according to any one of claims 13 to 19.

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