CN117373565A

CN117373565A - Library construction method, identification method and device for ion mobility spectrometry-mass spectrometry

Info

Publication number: CN117373565A
Application number: CN202210775220.6A
Authority: CN
Inventors: 杨内; 张清军; 李荐民; 李元景; 刘鹏; 魏来; 王岩; 李鸽; 李广勤; 赵艳琴
Original assignee: Tsinghua University; Nuctech Co Ltd
Current assignee: Tsinghua University; Nuctech Co Ltd
Priority date: 2022-07-01
Filing date: 2022-07-01
Publication date: 2024-01-09

Abstract

The present disclosure provides a library construction method of ion mobility spectrometry-mass spectrometry, which can be applied to the technical field of analytical chemistry. The method for establishing the library of the ion mobility spectrometry-mass spectrum comprises the following steps: carrying out peak searching treatment on the preprocessed original data based on a first peak searching algorithm to obtain column peak information of a plurality of ion mobility spectrometry-mass spectra; based on a first preset ordering rule, ordering the plurality of column peak information, and outputting an ordered first ordering result; determining at least one sample characteristic peak from the first ordering result based on each retention time and a preset condition; based on a second preset sorting rule, sorting the first characteristic peak and at least one sample characteristic peak for the second time to generate a second sorting result; and constructing a target spectrum chart library according to each second sequencing result, the name of the first known object, the first preset threshold value and the basic spectrum chart library corresponding to the second sequencing result. The disclosure also provides a library building device of the ion mobility spectrometry-mass spectrometry, and a recognition method and device of the ion mobility spectrometry-mass spectrometry.

Description

Library construction method, identification method and device for ion mobility spectrometry-mass spectrometry

Technical Field

The present disclosure relates to the field of analytical chemistry, and in particular, to a library building method, an identification method, and an apparatus thereof for ion mobility spectrometry-mass spectrometry.

Background

In ion mobility spectrometry (Ion mobility spectroscopy, IMS) and Mass Spectrometry (MS) joint debugging equipment, ion mobility spectrometry is typically used to perform primary resolution of a characteristic peak, and then a mass spectrometer is used to perform secondary resolution of the characteristic peak. Aiming at complex mixed samples, the ion mobility spectrometry-mass spectrometry combined instrument not only can realize the primary separation of characteristic peaks, but also can realize the resolution of high-sensitivity isomers and improve the qualitative analysis capability. Although ion mobility spectrometry can realize separation of characteristic peaks, in the process of realizing the concept of the disclosure, the inventor finds that at least the following problems exist in the related art: the number of the selected characteristic peaks is limited, and the phenomenon of missing characteristic peaks exists, so that the accuracy rate of characteristic peak identification is reduced, and the missing report rate is improved.

Disclosure of Invention

In view of the foregoing, the present disclosure provides a library-building method, an identification method, an apparatus, a device, a medium, and a program product of ion mobility spectrometry-mass spectrometry.

A first aspect of the present disclosure provides a library-building method of ion mobility spectrometry-mass spectrometry, comprising: carrying out peak searching processing on the preprocessed original data based on a first peak searching algorithm to obtain column peak information of a plurality of ion mobility spectrometry-mass spectra, wherein the original data is obtained by detecting a first known object by using first detection equipment; based on a first preset ordering rule, ordering the plurality of column peak information, and outputting an ordered first ordering result, wherein the first ordering result comprises a plurality of preselected peaks; determining at least one sample characteristic peak from the first ranking result based on each retention time and a preset condition, wherein each of the preselected peaks includes at least one of the retention times, the retention times representing start-stop time periods of peaks in the preselected peaks; comparing the plurality of first sequencing results with a first spectrogram based on the retention time to obtain a first comparison result, wherein the first spectrogram is obtained according to the plurality of preprocessed original data; when the first comparison result shows that a first characteristic peak different from a plurality of first sorting results exists in the first spectrogram, based on a second preset sorting rule, sorting the first characteristic peak and the at least one sample characteristic peak for the second time to generate a second sorting result; and constructing a target spectrum chart library according to each second sorting result, the name of the first known object, a first preset threshold value and a basic spectrum chart library corresponding to the second sorting result.

According to an embodiment of the present disclosure, the first detection device comprises a multimode first odor sniffer.

According to an embodiment of the present disclosure, when the first sorting result is generated, sorting a plurality of the column peak information corresponding to the pattern data based on the first preset sorting rule and different pattern data, and outputting the sorted first sorting result corresponding to each pattern data, wherein the pattern data includes at least one of: ion mobility positive mode, mass spectrum mode, and ion mobility negative mode.

According to an embodiment of the present disclosure, the determining at least one sample characteristic peak from the first sorting result based on each retention time and a preset condition includes: determining a plurality of said retention times for each of said preselected peaks based on a heat map derived from said raw data and/or data pre-processed from the raw data; based on each of the retention times, for each of the preselected peaks, determining the preselected peak as one of the sample characteristic peaks if the frequency of the preselected peak satisfies a preset frequency condition and/or the peak intensity of the preselected peak satisfies a preset intensity condition.

According to an embodiment of the present disclosure, the preselected peaks include peak position identification and peak intensity.

According to an embodiment of the present disclosure, comparing the plurality of first ranked results with a first spectrogram to obtain a first comparison result includes: determining a plurality of test peaks from the first spectrum; and comparing the peak position identification and the peak intensity of each inspection peak with the peak position identification and the peak intensity of each preselected peak to obtain the first comparison result.

According to an embodiment of the present disclosure, the second preset ranking rule includes importance levels of different characteristic peaks of the sample.

According to an embodiment of the present disclosure, when the first comparison result indicates that there are first characteristic peaks different from the plurality of first sorting results in the first spectrogram, performing a second sorting on the first characteristic peaks and the at least one sample characteristic peak based on a second preset sorting rule, to generate a second sorting result, including: if the first comparison result shows that the first characteristic peak different from the plurality of first sorting results exists in the first spectrogram, determining the first characteristic peak meeting the preset condition as a sample characteristic peak; and based on the second preset sorting rule, sorting the characteristic peaks of the samples for the second time to generate a second sorting result.

According to an embodiment of the present disclosure, before the second sorting, further comprising: comparing the plurality of first sequencing results with a second spectrogram based on each retention time to obtain a second comparison result, wherein the second spectrogram is obtained according to the plurality of original data; and when the second comparison result shows that at least one second characteristic peak different from the plurality of first sequencing results exists in the second spectrogram, determining the second characteristic peak meeting the preset condition as a sample characteristic peak.

According to an embodiment of the disclosure, the constructing a target spectrum gallery according to each of the second sorting result, the name of the first known object, a first preset threshold value, and a base spectrum gallery corresponding to the second sorting result includes: for each second sorting result, calculating the weight of each sample characteristic peak according to the sequence and peak intensity of different sample characteristic peaks in the second sorting result; based on the basic spectrum gallery, the target spectrum gallery is constructed according to the sample characteristic peaks, the weights corresponding to the sample characteristic peaks, the names of the first known objects and the first preset threshold value.

According to an embodiment of the present disclosure, in a case where the first characteristic peak is included in the target spectrum gallery, identification information is added to the first characteristic peak in the target spectrum gallery.

According to an embodiment of the present disclosure, the first preset ranking rule includes ranking according to the frequency and intensity of each column peak information.

According to an embodiment of the disclosure, the sorting the plurality of column peak information corresponding to the pattern data based on the first preset sorting rule and different pattern data, and outputting the sorted first sorting result corresponding to each pattern data includes: classifying the plurality of column peak information according to different pattern data to obtain a plurality of classified column peak information corresponding to each pattern data; for each pattern data, sorting the plurality of sorted column peak information according to the frequencies of different column peak information to obtain a plurality of sorted column peak information; and when the frequencies of at least two column peak information in the plurality of column peak information after sequencing are the same, sequencing the at least two column peak information according to the peak intensities of the at least two column peak information to obtain the first sequencing result.

According to an embodiment of the present disclosure, the preprocessing described above includes filtering processing.

The second aspect of the present disclosure also provides a method for identifying ion mobility spectrometry-mass spectrometry, comprising: carrying out peak searching processing on each preprocessed initial data based on a second peak searching algorithm to obtain a plurality of unknown peaks of ion mobility spectrometry-mass spectrometry, wherein the initial data is obtained by detecting an unknown object by using second detection equipment; sequentially matching a plurality of unknown peaks with a plurality of target spectrum libraries to obtain a plurality of matching results respectively corresponding to different target spectrum libraries, wherein the target spectrum libraries are constructed according to the library construction method; for each of the matching results, calculating an overall similarity between a plurality of unknown peaks of the unknown substance and sample characteristic peaks of a second known substance in the target spectrum library when the matching result indicates that the target spectrum library corresponding to the matching result includes sample characteristic peaks corresponding to at least one of the unknown peaks; and under the condition that the overall similarity meets a second preset threshold value in the target spectrum gallery, associating the names of the unknown object and the second known object as a first result, and writing the first result into a result linked list.

According to an embodiment of the present disclosure, the above method further includes: and displaying the result linked list by using a display device.

According to an embodiment of the present disclosure, in the case where the unknowns are plural, the method further includes: and when the number of the first results in the result linked list is a plurality of, sorting the plurality of first results to obtain a sorted result linked list, wherein the plurality of first results correspond to different unknowns.

According to an embodiment of the present disclosure, the plurality of target spectrogram libraries include an ion mobility spectrometry library and a mass spectrometry library; wherein the matching the plurality of unknown peaks with the plurality of target spectrum libraries sequentially to obtain a plurality of matching results respectively corresponding to different target spectrum libraries comprises: matching a plurality of unknown peaks of the unknown object with the ion mobility spectrometry library respectively to obtain a first matching result; and when the matching of the plurality of unknown peaks of the unknown substance with the ion mobility spectrogram library is completed, respectively matching the plurality of unknown peaks of the unknown substance with the mass spectrum library to obtain a second matching result, wherein the first matching result and the second matching result respectively represent different matching results.

According to an embodiment of the present disclosure, the above method further includes: under the condition that the target spectrum gallery comprises a first characteristic peak, acquiring a special peak of initial data corresponding to each unknown peak; and respectively matching the special peaks with the first characteristic peaks in the target spectrum libraries to obtain a plurality of third matching results respectively corresponding to different target spectrum libraries, wherein the third matching results represent the matching results different from the first matching results and the second matching results.

According to an embodiment of the present disclosure, when the matching result indicates that the target spectrum library corresponding to the matching result includes a sample characteristic peak corresponding to at least one of the unknown peaks, calculating overall similarity between a plurality of the unknown peaks of the unknown object and a sample characteristic peak of a second known object in the target spectrum library includes: calculating a first similarity between each of the unknown peaks and the corresponding sample characteristic peak when the matching result indicates that at least one of the unknown peaks has the sample characteristic peak corresponding to the unknown peak; and obtaining the overall similarity of the unknown object according to the first similarity.

According to an embodiment of the present disclosure, the second detection device comprises a multimode second odor sniffer.

The third aspect of the present disclosure also provides a library-building device for ion mobility spectrometry-mass spectrometry, comprising: the first processing module is used for carrying out peak searching processing on the preprocessed original data based on a first peak searching algorithm to obtain column peak information of a plurality of ion mobility spectrometry-mass spectra, wherein the original data is obtained by detecting a first known object by using first detection equipment; the first sorting module is used for sorting the plurality of column peak information based on a first preset sorting rule and outputting a sorted first sorting result, wherein the first sorting result comprises a plurality of preselected peaks; a first determining module, configured to determine at least one sample characteristic peak from the first sorting result based on each retention time and a preset condition, where each of the preselected peaks includes at least one retention time, and the retention time represents a start-stop time period of a peak in the preselected peaks; the first comparison module is used for comparing the plurality of first sequencing results with a first spectrogram based on the retention time to obtain a first comparison result, wherein the first spectrogram is obtained according to the plurality of preprocessed original data; the second sorting module is used for sorting the first characteristic peak and the at least one sample characteristic peak for the second time based on a second preset sorting rule under the condition that the first comparison result shows that the first spectrogram has first characteristic peaks different from a plurality of first sorting results, so as to generate a second sorting result; and the construction module is used for constructing a target spectrum gallery according to each second sorting result, the name of the first known object, a first preset threshold value and a basic spectrum gallery corresponding to the second sorting result.

The fourth aspect of the present disclosure also provides an ion mobility spectrometry-mass spectrometry identification device, comprising: the second processing module is used for carrying out peak searching processing on each preprocessed initial data based on a second peak searching algorithm to obtain a plurality of unknown peaks of the ion mobility spectrometry-mass spectrum, wherein the initial data is obtained by detecting the unknown object by using a second detection device; the matching module is used for sequentially matching a plurality of unknown peaks with a plurality of target spectrum libraries to obtain a plurality of matching results which respectively correspond to different target spectrum libraries, wherein the target spectrum libraries are constructed according to the library construction method; a calculation module configured to calculate, for each of the matching results, an overall similarity between a plurality of unknown peaks of the unknown substance and sample characteristic peaks of a second known substance in the target spectrum library, when the matching result indicates that the target spectrum library corresponding to the matching result includes sample characteristic peaks corresponding to at least one of the unknown peaks; and the writing module is used for associating the names of the unknown object and the second known object into a first result and writing the first result into a result linked list under the condition that the overall similarity meets a second preset threshold value in the target spectrum gallery.

The fifth aspect of the present disclosure also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method for creating a library of ion mobility spectrometry-mass spectrometry or the method for identifying ion mobility spectrometry-mass spectrometry.

The sixth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described library-building method of ion mobility spectrometry-mass spectrometry or the identification method of ion mobility spectrometry-mass spectrometry.

The seventh aspect of the present disclosure also provides a computer program product, which comprises a computer program, wherein the computer program realizes the library creating method of the ion mobility spectrometry or the identification method of the ion mobility spectrometry-mass spectrometry when being executed by a processor.

According to the embodiment of the disclosure, the missed characteristic peak is searched in the first spectrogram, and is ranked with other characteristic peaks under the condition that the characteristic peak is a sample characteristic peak, so that the missed characteristic peak is reduced, the recognition rate of the target spectrogram library in use is improved, and the problems of the reduction of the accuracy rate of the characteristic peak recognition and the improvement of the missing report rate in the related art are at least partially overcome.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:

FIG. 1 schematically illustrates a flow chart of a library-building method of ion mobility spectrometry-mass spectrometry according to an embodiment of the present disclosure;

FIG. 2A schematically illustrates a schematic diagram of peaks of an ion mobility spectrometry positive mode according to an embodiment of the present disclosure;

FIG. 2B schematically illustrates a schematic diagram of peaks of a negative mode of ion mobility spectrometry according to an embodiment of the present disclosure;

FIG. 2C schematically shows a schematic diagram of mass spectral peaks according to an embodiment of the disclosure;

FIG. 3 schematically illustrates a flow chart of a library-building method of ion mobility spectrometry-mass spectrometry according to another embodiment of the present disclosure;

fig. 4 schematically illustrates a flow chart of an ion mobility spectrometry-mass spectrometry identification method according to an embodiment of the present disclosure;

fig. 5 schematically illustrates a flow chart of an ion mobility spectrometry-mass spectrometry identification method according to another embodiment of the present disclosure

Fig. 6 schematically shows a block diagram of a library-building device of ion mobility spectrometry-mass spectrometry according to an embodiment of the present disclosure;

fig. 7 schematically illustrates a block diagram of a structure of an ion mobility spectrometry-mass spectrometry identification device according to an embodiment of the present disclosure; and

Fig. 8 schematically illustrates a block diagram of an electronic device suitable for implementing a library-building method of ion mobility spectrometry or an identification method of ion mobility spectrometry according to an embodiment of the disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.

Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

It should be noted that, the library creating method, the identifying method and the device thereof for ion mobility spectrometry determined by the embodiments of the present disclosure may be used in the field of analytical chemistry, and may also be used in any field other than the field of analytical chemistry, and the specific application field is not limited.

In the ion mobility spectrometry and mass spectrometry joint debugging equipment, a classical pattern recognition algorithm is generally used for qualitative analysis of a sample, and an IMS/MS standard library is generally established according to IMS/MS characteristic peaks by manually assisting or automatically extracting the IMS spectrogram and MS characteristic peaks of a standard sample. In the actual application process, the IMS/MS characteristic peak of the unknown sample can be extracted and compared with the characteristic peak of the IMS/MS standard library, and the identification result of the response is output under the condition that the threshold value of the IMS/MS characteristic peak of the unknown sample is larger than that of the IMS/MS standard library.

However, the method for manually extracting the characteristic peaks requires manual observation, records information such as peak positions, peak intensities and the like, adopts the method for manually extracting the characteristic peaks and gives corresponding weights, and generally requires people with professional knowledge and experience, so that the labor and time are consumed, and the library construction efficiency is low. While the method of automatically extracting the characteristic peak by adopting the algorithm can improve the database construction efficiency, the following problems generally exist: the number of the selected characteristic peaks is limited, and the phenomenon of peak leakage exists; the characteristic peak selected may be a sample peak where the interference peak is not real; the characteristic peak weight only considers peak intensity, and the sequence and weight cannot be adjusted according to the importance of the characteristic peak; the accuracy rate of identifying unknown samples is reduced, the report missing rate is increased, and the like.

In view of this, embodiments of the present disclosure provide a library-building method of ion mobility spectrometry, a library-building apparatus of ion mobility spectrometry, an identification method of ion mobility spectrometry, an identification apparatus of ion mobility spectrometry, an electronic device, a readable storage medium, and a computer program product. The accuracy of characteristic peak identification can be improved, and the missing report rate can be reduced. The library construction method of the ion mobility spectrometry-mass spectrometry comprises the steps of carrying out peak searching processing on preprocessed original data based on a first peak searching algorithm to obtain column peak information of a plurality of ion mobility spectrometry-mass spectrometry, wherein the original data is obtained by detecting a first known object by using first detection equipment; based on a first preset ordering rule, ordering the plurality of column peak information, and outputting an ordered first ordering result, wherein the first ordering result comprises a plurality of preselected peaks; determining at least one sample characteristic peak from the first sequencing result based on each retention time and a preset condition, wherein each preselected peak comprises at least one retention time, and the retention time represents a start-stop time period of a wave peak in the preselected peaks; comparing the plurality of first sequencing results with a first spectrogram based on the retention time to obtain a first comparison result, wherein the first spectrogram is obtained according to the plurality of preprocessed original data; under the condition that the first comparison result shows that a first characteristic peak different from a plurality of first sorting results exists in the first spectrogram, the first characteristic peak and at least one sample characteristic peak are sorted for the second time based on a second preset sorting rule, and a second sorting result is generated; and constructing a target spectrum chart library according to each second sequencing result, the name of the first known object, the first preset threshold value and the basic spectrum chart library corresponding to the second sequencing result.

In the embodiment of the application, firstly, detection equipment such as a multimode odor sniffer is utilized to detect known standard samples of various contraband, measurement and analysis components are measured by measurement equipment such as an ion mobility spectrometry-mass spectrometer, characteristic peaks (peak intensities exceeding a certain threshold or proportion are taken as characteristic peaks) are measured, two-dimensional coordinates of retention time and ion mobility time or time corresponding to the characteristic peaks are recorded, then, the characteristic peaks are ranked by utilizing preset ranking rules in each embodiment of the application, and two-dimensional peak position coordinates (retention time and mobility time) corresponding to the ranked characteristic peaks, and information such as substance names and threshold values are recorded. And integrating the two-dimensional coordinates corresponding to the characteristic peaks of the standard sample with the corresponding substance names to form a contraband characteristic peak library of the current combined device. When the combined device is actually used for detecting an unknown object to be detected later, the characteristic peak of the object to be detected is obtained, the system compares the two-dimensional peak position coordinates (retention time and migration time) of the characteristic peak corresponding to the object to be detected with the two-dimensional coordinates of the characteristic peak in the important characteristic peak library of the contraband of the combined device, finds similar candidate characteristic peaks, and presents information such as the identified substance name and the like to a user. The characteristic peak here may be a true and reliable product ion peak of the sample, excluding characteristic peaks of system noise or ambient noise interference.

Fig. 1 schematically shows a flow chart of a library-building method of ion mobility spectrometry-mass spectrometry according to an embodiment of the present disclosure.

As shown in fig. 1, the method for library creation of ion mobility spectrometry-mass spectrometry of this embodiment includes operations S101 to S106.

In operation S101, a peak searching process is performed on the preprocessed raw data based on the first peak searching algorithm, so as to obtain column peak information of a plurality of ion mobility spectrometry-mass spectra, where the raw data is obtained by detecting a first known object with a first detection device.

In operation S102, the plurality of column peak information is ranked based on a first preset ranking rule, and a ranked first ranking result is output, wherein the first ranking result includes a plurality of preselected peaks.

At least one sample characteristic peak is determined from the first ranking result based on each retention time and a preset condition, wherein each preselected peak includes at least one retention time representing a start-stop time period of a peak in the preselected peaks, in operation S103.

In operation S104, comparing the plurality of first sorting results with a first spectrogram based on the retention time to obtain a first comparison result, wherein the first spectrogram is obtained according to the plurality of preprocessed raw data.

In operation S105, if the first comparison result indicates that there is a first characteristic peak different from the plurality of first sorting results in the first spectrogram, the first characteristic peak and at least one sample characteristic peak are sorted for the second time based on a second preset sorting rule, and a second sorting result is generated.

In operation S106, a target spectrum gallery is constructed according to each second sorting result, the name of the first known object, the first preset threshold value and the basic spectrum gallery corresponding to the second sorting result.

According to an embodiment of the disclosure, the first peak searching algorithm may be a full-spectrum automatic peak searching, for example, an automatic peak searching based on a nuclide library method, may be suitable for searching a single peak with high intensity, may be an automatic peak searching method based on a gaussian product function, may be an automatic peak searching method based on a symmetrical zero area, and may be suitable for detecting a weak peak and a heavy peak. After the column peak information is obtained through the first peak searching algorithm, relevant preservation measures can be adopted for preservation.

According to embodiments of the present disclosure, the raw data may be two-dimensional image data acquired over a period of time by gas phase ion mobility spectrometry, gas phase mass spectrometry, ion mobility spectrometry-mass spectrometry. In order to make the peak shape in the original data smoother, the original data may be preprocessed, and the preprocessing may include filtering, for example, the original data may be processed by adopting a smoothing filtering mode.

According to embodiments of the present disclosure, the first detection device may comprise a multimode odor sniffer. The first known substance may be a known standard sample of a plurality of contraband substances of interest to the first detection apparatus.

According to an embodiment of the present disclosure, when generating a first sorting result, sorting a plurality of column peak information corresponding to pattern data based on a first preset sorting rule and different pattern data, and outputting the sorted first sorting result corresponding to each pattern data, wherein the pattern data includes at least one of: ion mobility positive mode, mass spectrum mode, and ion mobility negative mode.

FIG. 2A schematically illustrates a schematic diagram of peaks of an ion mobility spectrometry positive mode according to an embodiment of the present disclosure; FIG. 2B schematically illustrates a schematic diagram of peaks of a negative mode of ion mobility spectrometry according to an embodiment of the present disclosure; fig. 2C schematically shows a schematic diagram of mass spectral peaks according to an embodiment of the present disclosure. Fig. 2A to 2C are diagrams that may be acquired in real time, where the abscissa in each diagram represents migration time or time and the ordinate represents signal intensity. The plurality of peaks in each mode in fig. 2A to 2C may be regarded as a set of column peaks, and a set of column peaks may be regarded as characteristic peaks when the peak intensities of the column peaks exceed a certain threshold value or ratio.

According to an embodiment of the present disclosure, the first preset ranking rule may include ranking according to the frequency and intensity of each column peak information. Specifically, the sorting can be performed according to different mode data, for example, the sorting can be divided into three major categories of an ion migration positive mode, a mass spectrum mode and an ion migration negative mode, and a plurality of sorted column peak information corresponding to each mode data can be obtained; for each mode data, sorting the plurality of sorted column peak information according to the frequencies of different column peak information to obtain a plurality of sorted column peak information; and under the condition that at least two column peak information in the sorted plurality of column peak information have the same frequency, sorting the at least two column peak information according to the peak intensities of the at least two column peak information to obtain a first sorting result.

According to embodiments of the present disclosure, the column peak information may include contents of retention time of peaks, migration time of peaks, peak intensity, frequency of occurrence of peaks, mode of peaks, and the like. The first preset sorting rule may be sorting according to the occurrence frequency of the characteristic peaks and the intensity of the characteristic peaks, preferably sorting according to the principle of priority of the occurrence frequency, and sorting the at least two pieces of column peak information according to the peak intensities of the at least two pieces of column peak information when the frequencies of the at least two pieces of column peak information are the same in the sorted plurality of pieces of column peak information. The ranking result may be a peak position information list composed of a plurality of peak position information, and each peak in the peak position information list may be a preselected peak, so the ranking result may also be a preselected peak list. For example, the ordering result may be a pre-selected peak list comprising N1 (ion-transfer positive mode or mass spectrum) and N2 (ion-transfer negative mode) peak position information, with N1 and N2 being positive integers.

According to an embodiment of the present disclosure, operation S103 may further include the following operations: determining a plurality of retention times for each preselected peak from a heat map, wherein the heat map is derived from raw data and/or data pre-processed from the raw data; based on each retention time, for each preselected peak, the preselected peak is determined to be a sample characteristic peak in the event that 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.

According to embodiments of the present disclosure, the preselected peaks may refer to the results of sorting the column peak information; the sample characteristic peak may be one or more qualified preselected peaks determined after a conditional screening from a plurality of preselected peaks, and the qualified preselected peaks are set as sample characteristic peaks.

According to embodiments of the present disclosure, the heat map may include a raw data heat map and a pre-processed data heat map. The raw data heatmap may be a GC-IMS or GC-MS map directly showing raw data; the preprocessed data heat map may be a GC-IMS or GC-MS map displayed after preprocessing the raw data.

Retention time may also be understood as the start-stop time at which a preselected peak occurs, in accordance with embodiments of the present disclosure; the preset conditions may include a preset frequency condition and a preset intensity condition. The preset frequency condition and the preset intensity condition can be determined according to experiments, and can be adaptively adjusted according to actual needs. A heat map may be displayed on the visual interface, and a heat map may be understood as representing the position and peak intensity of a preselected peak on the map by different colors, such as the shade of the color, etc., for example, a color at a certain position is different from the background color, which may indicate that the preselected peak is present at that position, and a darker color may indicate that the peak intensity is greater.

According to the embodiment of the disclosure, the approximate position and trend of the occurrence of the preselected peak can be determined according to the gas-phase ion mobility spectrometry or the gas-phase mass spectrometry data in the heat map, so as to determine the retention time of each preselected peak, and then the preselected peak is determined as a sample characteristic peak according to each retention time and each preselected peak on the basis of the list of the preselected peaks of the first sequencing result under the condition that the frequency of the preselected peak meets the preset frequency condition and/or the peak intensity of the preselected peak meets the preset intensity condition. And if the frequency and the intensity of the preselected peak do not meet the preset conditions, removing the preselected peak from the list of the preselected peaks. The preset frequency condition and the preset intensity condition can be adaptively adjusted according to actual needs.

According to the embodiment of the disclosure, the preprocessing data can be processed at the visual interface, specifically, a part of peak positions can be automatically screened and displayed through an algorithm, and then secondary screening, confirmation, storage and warehousing can be manually performed; if the number of the automatically selected peaks is too large, the automatically selected peaks can be automatically extracted after the library establishment parameters are adjusted, for example, the library establishment parameters can be firstly set on a page and then the automatically extracted can be performed; in addition, automatic screening peak position information can be displayed on an interface needing manual peak selection through setting mode data; selecting one retention time ion migration spectrogram and one mass spectrogram one by one, screening out real sample peaks, missed peaks and filtered shoulder peaks filtered by pretreatment, adjusting the sequence of the peaks according to the importance of the peaks, and calculating the weight of the selected peaks by using a calculation formula of the weight of the related characteristic peaks; the peak can be directly operated, peak position information is automatically extracted and added into a peak list for further investigation and selection, and through the design, the library construction efficiency is improved, and non-professional staff can also operate the library construction method, so that the library construction difficulty is reduced.

According to the embodiment of the disclosure, preprocessing data are processed at a visual interface, corresponding gas phase ion mobility spectrometry and gas phase mass spectrometry spectrograms are selected according to the display characteristics and trends of the characteristic peaks of the gas phase ion mobility spectrometry and the gas phase mass spectrometry, and secondary screening is carried out on preselected characteristic peaks, so that missing and leakage can be detected, and real sample characteristic peaks and characteristic peaks which are ignored by preprocessing due to manual marking can be selected.

The preselected peaks may also include peak position identification and peak intensity, according to embodiments of the present disclosure. Operation S104 may further include the following operations: determining a plurality of test peaks from the first spectrum; and comparing the peak position mark and the peak intensity of each inspection peak with the peak position mark and the peak intensity of each preselected peak to obtain a first comparison result.

According to embodiments of the present disclosure, the peak position identification may be a unique identification of the preselected peak at a point and position in a two-dimensional plot of retention time and ion offset time; in a three-dimensional plot of retention time, ion offset time, and peak intensity, a unique identification of position-related peak position information for a peak at a point in the plot is preselected.

According to an embodiment of the present disclosure, the first spectrogram may be a spectrogram obtained by filtering according to the raw data. The plurality of test peaks on the first spectrum may be a condition for checking whether there is a missing characteristic peak among the preselected peaks. Specifically, each of the preselected peaks may include a peak position identification and a peak intensity, and when comparing, a retention time may be first determined, based on which the peak position identification and the peak intensity of each of the inspection peaks and each of the preselected peaks are compared, respectively, to determine whether there is a missing characteristic peak, and the first comparison result may include a missing characteristic peak or a non-missing characteristic peak. If there is a missing feature peak, the test peak may be added to the list of preselected peaks.

According to an embodiment of the present disclosure, operation S105 may further include the following operations: under the condition that the first comparison result shows that a first characteristic peak different from a plurality of first sequencing results exists in the first spectrogram, determining the first characteristic peak meeting the preset condition as a sample characteristic peak; and based on a second preset sorting rule, sorting the characteristic peaks of the plurality of samples for the second time to generate a second sorting result. Wherein the second preset ordering rule may include importance levels of different sample feature peaks.

According to an embodiment of the present disclosure, in the comparison result, if there is at least one peak present only in the first spectrogram, and if the peak satisfies the preset frequency condition and the preset intensity condition, the peak is understood as a first characteristic peak, and the first characteristic peak is added to the pre-selected peak list, and is determined as a sample characteristic peak.

According to an embodiment of the disclosure, the second preset sorting rule may be sorting according to the importance degree of the characteristic peaks of the sample, and the order between each characteristic peak is exchanged according to the importance degree of the characteristic peaks, so as to obtain a second sorting result.

According to an embodiment of the present disclosure, before the second sorting, further comprising: comparing the plurality of first sequencing results with a second spectrogram based on each retention time to obtain a second comparison result, wherein the second spectrogram is obtained according to a plurality of original data; and determining the second characteristic peak meeting the preset condition as the sample characteristic peak under the condition that the second comparison result shows that at least one second characteristic peak which is different from the plurality of first sequencing results exists in the second spectrogram.

According to the embodiment of the disclosure, in order to prevent the original data from omitting a part of small sample characteristic peaks in the filtering process, a plurality of first sorting results can be compared with a second spectrogram according to the retention time before the second sorting, specifically, the peak position identifiers and the peak intensities in the first sorting results and the second spectrogram can be respectively compared, if at least one peak exists in the second spectrogram only, and if the peak meets the preset frequency condition and the preset intensity condition, the peak is understood as a second characteristic peak, and the second characteristic peak is added into a pre-selected peak list to be determined as the sample characteristic peak.

According to the embodiment of the disclosure, the original data can be selected at a visual interface, the corresponding gas phase ion mobility spectrometry and gas phase mass spectrogram can be selected according to the display characteristics and trend of the gas phase ion mobility spectrometry and gas phase mass spectrogram characteristic peaks, the preselected characteristic peaks are screened for the second time, the defect and leakage detection can be performed, and the real sample characteristic peaks and the characteristic peaks which are ignored due to the pretreatment and marked by manpower are selected.

According to an embodiment of the present disclosure, operation S106 may further include the following operations: aiming at each second sorting result, calculating the weight of each sample characteristic peak according to the sequence and peak intensity of different sample characteristic peaks in the second sorting result; specifically, a specific calculation process for calculating the peak position weight of the sample characteristic peak according to the peak position order and the peak intensity of the sample characteristic peak may be as shown in formulas (1) to (2).

The weights of the characteristic peaks of the sample can be composed of peak position sequence weights and peak intensity weights, and the calculation process is shown in a formula (1).

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

Wherein i can identify the peak order, and i can be a positive integer; peak weight may be expressed as the peak position order weight of the i-th peak; w1 may represent the total weight of the peak order, and the value range of w1 may be 0.4 to 0.7; w2 may represent the total weight of the peak intensities, w2 may be as shown in formula (2); nPeakCount may represent the number of peaks; the peak intensity may represent the intensity of the i-th peak; peaksintersity total may represent the sum of the intensities of all peaks.

w2＝1-w1 (2)

The GetOrderWeight may be a function of obtaining peak weights of each order, and the key calculation process may be as follows.

If double dbReWeight =0, it may represent that a weight value needs to be returned.

if (dpeakcount.ltoreq.1), { dbreweight=w1 }; it may be indicated that if the number of peaks is equal to or less than 1, the return weight value may be w1.

elsif (dpeakcount=2), { switch (i) { case1{ dbrebaight=w1-w3 }; where case1 may represent the 1 st peak, w3 may represent the sum of weights of the other peaks except the first peak, and the default value may be 0.2; if the number of peaks is equal to 2, the 1 st peak may output a weight that is the difference between w1 and w 3.

else if (dpeakcount=2), { switch (i) { case2{ dbrebaight=w3 }; wherein case2 may represent peak 2; if the number of peaks is equal to 2, the weight output by the 2 nd peak may be w3.

elsif (dPeakCount. Gtoreq.3), double dbWeightTemp =1×w3++2, wherein when the number of peaks is 3 or more, w3 may represent the sum of weights of other peaks than the first peak, and the default value may be 0.2, specifically, it may be understood that w3 is equally divided into two parts, the first part may give the 2 nd peak and the second part gives other peaks than the 1 st and 2 nd peaks.

else if (dpeakcount.gtoreq.3), { switch (i) { case1{ dbReweight=w1-w3 }; wherein case1 may represent the 1 st peak, and if the number of peaks is greater than or equal to 3, the weight output by the 1 st peak may be the difference between w1 and w3.

else if (dpeakcount.gtoreq.3), { switch (i) { case2{ dbReweight=dbweight temp }; wherein case2 may represent the 2 nd peak, and if the number of peaks is greater than or equal to 3, the weight output by the 2 nd peak may be dbweight temp.

else if (dPeakCount not less than 3), { switch (i) { default { dbReweight=1.0×dbweightTemp/(nPeakCount-2) }, wherein default may represent the 3 rd peak and other peaks after the 3 rd peak, and if the number of peaks is 3 or more, the 1 st peak may output a weight of the difference between w1 and w3.

According to an embodiment of the disclosure, in summary, the peak sequence weights may be divided into three levels, for the 1 st peak, if there is only one peak, the weight of the 1 st peak may be w1, if there are at least two peaks, the weight of the 1 st peak may be w1-w3, and the weight of the 1 st peak is relatively highest, and also relatively most important.

For the 2 nd peak, if there are only two peaks, the weight of the 2 nd peak may be w3, and if there are at least three peaks, the weight of the 2 nd peak may be w3+.2, the weight of the 2 nd peak is lower than the weight of the 1 st peak, and the importance is also lower than the 1 st peak.

The weight of each peak is the same for the 3 rd peak and the other peaks after the 3 rd peak, and may be w3.2.2 (nPeakCount-2).

According to the embodiment of the disclosure, through the calculation method, the real sample peak can be selected, the characteristic peak with the rear sequence is not missed, the peak filtered by filtering is marked manually, the characteristic peak sequence is adjusted according to the importance of the peak position, the weight of the selected characteristic peak is calculated by adopting the method shown in the formula (1), the weight of each characteristic peak is calculated by adopting the two dimensions of the peak position sequence and the peak intensity, and the weight of the characteristic peak is calculated more reasonably and accurately than the weight calculated by only considering the one dimension of the peak intensity, so that the recognition accuracy is improved, and the false alarm rate are reduced.

According to an embodiment of the present disclosure, in a case where the ion migration positive mode, the mass spectrum mode, and the ion migration negative mode each complete the calculation of the weight and update the pre-selected peak list, a target spectrum gallery may be constructed from a plurality of sample characteristic peaks, the weight corresponding to each sample characteristic peak, the name of the first known substance, and a first preset threshold value based on the base spectrum gallery, wherein the target spectrum gallery may include the ion migration spectrum gallery or the mass spectrum gallery.

According to an embodiment of the present disclosure, in the case that the first characteristic peak is included in the target spectrum gallery, identification information is added to the first characteristic peak in the target spectrum gallery.

According to embodiments of the present disclosure, the base spectrum gallery may be a gallery used as a reference, and may include all information associated with the sample characteristic peaks, so as to be a gallery that may be referenced at a later annotation. The first preset peak threshold may be set in advance according to experiments, and may be used as a criterion for determining an unknown substance, for example, assuming that the detection threshold of a certain contraband is 0.6, when the detection threshold of an unknown sample is detected to exceed 0.6, the unknown sample is determined to be the certain contraband.

According to the embodiment of the disclosure, the characteristic peaks of the ion mobility spectrometry and the mass spectrometry sample can be respectively written into an ion mobility spectrometry gallery and a mass spectrometry gallery, and identification information is added to the characteristic peaks of the sample. When writing the spectrum chart library, if there is a manually marked peak in the characteristic peak (i.e. a peak which is manually marked in the original data state because of being ignored or not being found, etc.), the identification of the manually marked peak is set to 1 so that a process different from that of a non-manually marked peak can be performed at the time of identification.

According to the embodiment of the disclosure, the missed feature peak is found in the first spectrogram, and is ranked with other feature peaks under the condition that the feature peak is a sample feature peak, so that the missed feature peak is reduced, and the recognition rate of the target spectrogram library in use is improved.

According to the embodiment of the invention, the real sample peak can be selected, the characteristic peak after sequencing is not missed, and the identification accuracy is improved; the manual labeling peak (the unselected peak filtered by the filtering) is added, so that the recognition rate and the accuracy rate can be improved, and the missing report rate can be reduced; the characteristic peak sequence can be adjusted according to the requirement, and the weight value of the peak position sequence is increased, and the weight of each characteristic peak is calculated by adopting two dimensions of the peak position sequence and the peak intensity, so that the weight of the characteristic peak is more reasonable and accurate than the weight calculated by only considering the dimension of the peak intensity; visual operation is performed, so that the library construction efficiency is improved; can be operated by non-professional personnel, and reduces the difficulty of library establishment.

Fig. 3 schematically illustrates a flow chart of a library-building method of ion mobility spectrometry-mass spectrometry according to another embodiment of the present disclosure.

As shown in fig. 3, the method may include S301 to S330.

In operation S301, raw data is preprocessed.

In operation S302, all column peak information of the gas phase ion mobility spectrometry and the gas phase mass spectrometry is acquired. And acquiring all column peak information after the pretreatment data sample injection column by using a first peak searching algorithm, and storing.

In operation S303, gas phase ion mobility spectrometry, gas phase mass spectrometry pre-selected peak information is acquired. The column peak information acquired in operation S302 is subjected to sorting processing according to the occurrence frequency and peak intensity of the column peak, and N1 (ion-transfer positive mode or mass spectrum) and N2 (ion-transfer negative mode) peak position information can be output as preselected peaks.

At least one mode data is selected in operation S304. The mode data includes at least one of an ion mobility positive mode, a mass spectrum mode, and an ion mobility negative mode. In an embodiment, operations S301 to S304 may refer to operations S101 to S102, which are not described herein.

In operation S305, preprocessing data is selected. Specifically, the gas phase ion mobility spectrometry data and the gas phase mass spectrometry data which can be displayed in the visual interface can be divided into raw data and preprocessing data, and the preprocessing data can be selected for subsequent operation.

In operation S306, the heat map is analyzed. The approximate locations and trends of the characteristic peaks are obtained.

In operation S307, a retention time is selected. Specifically, according to the pre-selected peak list, the start-stop retention time of the characteristic peak to be browsed is selected, and one retention time can be selected from a plurality of retention times.

In operation S308, the preselected characteristic peak positions are viewed on the spectrogram. Specifically, it can be checked whether it is a characteristic peak of the sample.

In operation S309, it is determined whether or not the sample characteristic peak is present. If the sample is the characteristic peak, directly executing operation S311; if not, execution starts at operation S310.

In operation S310, the preselected peak position is removed from the list of preselected peaks.

In operation S311, whether the judgment of each peak position in the pre-selected peak list is completed or not. If the checking is completed, continuing to execute the subsequent operation; if not, execution needs to be returned from S307. In an embodiment, operations S305 to S311 may refer to operation S103, which is not described herein.

In operation S312, a retention time is selected. Specifically, one retention time may be selected from a list of retention times having peak positions.

In operation S313, peak identification and intensity are checked on the spectrogram. The list of preselected peaks may be compared to see if there are missing sample feature peaks.

In operation S314, whether the sample characteristic peak is missed. If the condition of missing the characteristic peak of the sample exists, continuing to execute the subsequent operation; if there is no missing sample feature peak, operation S316 is directly performed.

In operation S315, the characteristic peak is added to the list of preselected peaks. Specifically, in the peak list of the retention time, a missing characteristic peak may be selected and added to the preselected peak list.

In operation S316, whether or not the peak view for each retention time is completed. If the checking of the peak in each retention time is completed, continuing to execute the subsequent operation; if the peak in each retention time is not checked, execution continues from operation S312 back. In an embodiment, operations S312 to S316 refer to operation S104, which are not described herein.

In operation S317, original data is selected. Specifically, the gas phase ion mobility spectrometry data and the gas phase mass spectrometry data which can be displayed in the visual interface can be divided into original data and preprocessing data, and the original data can be selected for subsequent operation in the operation. Some small sample characteristic peaks are filtered out to avoid preprocessing the raw data, thereby missing some characteristic peaks.

In operation S318, a retention time is selected.

In operation S319, the peak position identification and the intensity are checked on the spectrogram.

In operation S320, it is determined whether there is an unidentified characteristic peak. If the unidentified characteristic peak exists, continuing to execute the subsequent operation; if there is no unidentified characteristic peak, execution starts directly from operation S324.

In operation S321, it is determined whether it is a characteristic peak of the sample. If the characteristic peak is the characteristic peak of the sample, continuing to execute the subsequent operation; if the characteristic peak is not the characteristic peak of the sample, the execution is directly started from operation S324.

In operation S322, characteristic peaks of the sample are labeled. Specifically, peak position and intensity information can be extracted and identified on a spectrogram.

In operation S323, the pre-selected peak list is added. Specifically, the sample characteristic peaks labeled in operation S322 may be added to the pre-selected peak list.

In operation S324, whether or not the peak view for each retention time is completed. If the checking of the peak in each retention time is completed, continuing to execute the subsequent operation; if the peak within each retention time is not complete, execution continues from operation S318. In an embodiment, operations S317 to S324 may refer to operations of the above method before the second sorting, and are not described herein.

In operation S325, the order of the preselected peak positions is adjusted. Specifically, the order of the characteristic peaks is called according to the importance degree of the characteristic peaks.

In operation S326, the weights of the preselected peak positions are calculated.

In operation S327, the list of preselected peak positions is updated.

In operation S328, whether all mode data is completed. If all the mode data are completed, continuing to execute the subsequent operation; if all the pattern data are not completed, the return continues from operation S204.

In operation S329, a substance name and a threshold value are input.

In operation S330, a spectrum gallery is written. Specifically, the characteristic peaks of the ion mobility spectrometry and the mass spectrum sample are respectively written into an ion mobility spectrometry library and a mass spectrum library. When writing the spectrum chart library, if there is a manually marked peak in the characteristic peak (i.e. a peak which is manually marked in the original data state because of being ignored or not being found, etc.), the identification of the manually marked peak is set to 1 so that a process different from that of a non-manually marked peak can be performed at the time of identification. In an embodiment, operations S325 to S330 may refer to operations S105 to S106, which are not described herein.

Fig. 4 schematically illustrates a flow chart of an ion mobility spectrometry-mass spectrometry identification method according to an embodiment of the present disclosure.

As shown in fig. 4, the identification method includes operations S401 to S404.

In operation S401, peak searching processing is performed on each preprocessed initial data based on the second peak searching algorithm, to obtain a plurality of unknown peaks of the ion mobility spectrometry-mass spectrum, where the initial data is obtained by detecting the unknown object with the second detection device.

In operation S402, a plurality of unknown peaks are sequentially matched with a plurality of target spectrum libraries to obtain a plurality of matching results corresponding to different target spectrum libraries, wherein the target spectrum libraries are constructed according to the library construction method.

In operation S403, for each matching result, in a case where the matching result indicates that the target spectrum gallery corresponding to the matching result includes a sample characteristic peak corresponding to at least one unknown peak, an overall similarity of a plurality of unknown peaks of the unknown substance to sample characteristic peaks of a second known substance in the target spectrum gallery is calculated.

In operation S404, in the case that the overall similarity meets the second preset threshold in the target spectrum gallery, the names of the unknown object and the second known object are associated as the first result, and written into the result linked list.

According to embodiments of the present disclosure, the second peak finding algorithm may or may not be the same as the first peak finding algorithm for obtaining a plurality of unknown peaks of the ion mobility spectrometry-mass spectrum. The preprocessing may be smoothing filter processing, for example, smoothing filter processing may be performed on a plurality of obtained unknown peaks so as to remove unnecessary invalid information such as peaks.

According to an embodiment of the present disclosure, the second detection device may comprise a multimode odor sniffer.

According to an embodiment of the present disclosure, operation S402 may further include the following operations: respectively matching a plurality of unknown peaks of the unknown object with an ion mobility spectrometry graph library to obtain a first matching result; under the condition that a plurality of unknown peaks of an unknown object are matched with an ion mobility spectrometry gallery, the plurality of unknown peaks of the unknown object are respectively matched with a mass spectrometry gallery to obtain a second matching result, wherein the first matching result and the second matching result respectively represent different matching results.

According to an embodiment of the present disclosure, operation S403 may further include the following operations: calculating a first similarity between each unknown peak and the corresponding sample characteristic peak in the case that the matching result shows that at least one unknown peak has the sample characteristic peak corresponding to the unknown peak; and obtaining the overall similarity of the unknown object according to the first similarities.

According to an embodiment of the present disclosure, the method for identifying ion mobility spectrometry-mass spectrometry further comprises: under the condition that a target spectrum chart library comprises a first characteristic peak, acquiring a special peak of initial data corresponding to each unknown peak; and respectively matching the plurality of special peaks with first characteristic peaks in a plurality of target spectrograms to obtain a plurality of third matching results respectively corresponding to different target spectrograms, wherein the third matching results represent matching results different from the first matching results and the second matching results.

According to an embodiment of the present disclosure, the target spectrum gallery may be an ion mobility spectrum gallery and a mass spectrum gallery constructed according to the above-described library construction method. During identification, a plurality of unknown peaks of the unknown substance can be respectively matched and identified with the ion mobility spectrometry gallery and the mass spectrometry gallery, and a first matching result and a second matching result can be respectively obtained. The matching result may include a substance name that is identified, or a substance that is not identified as a match. The first similarity between each unknown peak and the corresponding sample characteristic peak is calculated, specifically, the first similarity may be a degree of matching between the unknown peak and the corresponding sample characteristic peak in the ion mobility spectrometry gallery or the mass spectrometry gallery, for example, the degree of matching between the unknown peak and the corresponding sample characteristic peak is determined to be 0.9 by matching the unknown peak with the corresponding sample characteristic peak, where 0.9 may be used as the first similarity.

According to the embodiment of the disclosure, for example, when a plurality of unknown peaks of an unknown object are matched and identified with an ion mobility spectrometry chart base, comparing the peak value of the unknown peaks with a preset peak threshold value of a corresponding sample characteristic peak in the ion mobility spectrometry chart base, and if the peak value exceeds the preset peak threshold value, writing a corresponding first matching result into a result linked list; if the predetermined peak threshold value is not exceeded, it may be indicated that no substance is detected, and a recognition result of no matching substance may be obtained.

According to the embodiment of the disclosure, for example, when a plurality of unknown peaks of an unknown object are matched and identified with a mass spectrum gallery, comparing the peak value of the unknown peaks with a preset peak threshold value of a corresponding sample characteristic peak in the mass spectrum gallery, and if the peak value exceeds the preset peak threshold value, writing a corresponding second matching result into a result linked list; if the predetermined peak threshold value is not exceeded, it may be indicated that no substance is detected, and a recognition result of no matching substance may be obtained.

According to embodiments of the present disclosure, a particular peak may be a peak manually noted at the time of library creation, in the original data state, because it is ignored or not found, etc. Under the condition that the identification result of the non-matching substance is obtained, partial peaks with smaller peak intensities can be filtered out during pretreatment of the data, so that no matching substance can be found. Therefore, in one embodiment, the unknown peaks may also be identified as matching such special peaks. Before matching and identification, the unknown peaks do not need to be preprocessed, then a plurality of unknown peaks of the unknown object are respectively matched and identified with an ion mobility spectrometry gallery and a mass spectrometry gallery, and a third matching result is obtained by adopting the same method as that for obtaining the first matching result and/or the second matching result.

According to an embodiment of the present disclosure, the overall similarity may be a sum of all the first similarities in operation S404. The second preset threshold is set based on a second known matter. The second known substance may be a standard sample of a plurality of contraband of interest to the second detection apparatus. The second preset threshold may be the content of each substance contained in the standard sample. And under the condition that the overall similarity of the unknown substance meets a second preset threshold value, the unknown substance can be considered as the standard sample, the names of the unknown substance and the standard sample are sorted into a first result, and the first result is written into a result linked list.

According to an embodiment of the present disclosure, in the case where the unknowns are plural, the method further includes: and under the condition that the number of the first results in the result linked list is a plurality of, sorting the plurality of first results to obtain a sorted result linked list, wherein the plurality of first results correspond to different unknowns.

According to an embodiment of the present disclosure, the method for identifying ion mobility spectrometry-mass spectrometry further comprises: and displaying the result linked list by using a display device.

According to the embodiment of the disclosure, in the case that a substance is identified and in the case that a plurality of unknowns exist, the first result may be ranked, the ranking rule may be ranked according to the risk degree of the unknowns, the first letter order of the names of the samples of the second known substances, and the like, and the result may be displayed on a result linked list, which may be displayed on a device having a display screen for reference. If no substance is identified, the result list may display that no matching substance is identified, so as to end the identification procedure of the unknown substance.

According to the embodiment of the disclosure, before identification, judging whether an ion mobility spectrometry gallery or a mass spectrometry gallery has a manual labeling peak or not, and if so, carrying out matching identification; when the identification is carried out, pretreatment is not required, and the identification is only carried out by matching with characteristic peaks of manually marked standard substances in an ion mobility spectrometry chart base or a mass spectrometry chart base. The method can improve the recognition rate, reduce the false alarm rate and shorten the recognition time.

Fig. 5 schematically illustrates a flow chart of an ion mobility spectrometry-mass spectrometry identification method according to another embodiment of the present disclosure.

As shown in fig. 5, the identification method includes S501 to S520.

In operation S501, ion mobility spectrometry and mass spectrometry data preprocessing are performed. The measured ion mobility spectrometry and mass spectrometry data can be subjected to filtering processing to obtain pre-processing data.

In operation S502, a result list is initialized. Specifically, the result linked list may be cleared to facilitate distinguishing the subsequently added recognition results.

In operation S503, ion mobility spectrometry recognition is performed. Specifically, the matching identification can be performed with an ion mobility spectrometry gallery.

In operation S504, whether there is a result exceeding the threshold. Specifically, the result obtained by the matching recognition with the ion mobility spectrogram library may be obtained. Continuing to execute the subsequent operation if a result exceeding the threshold is obtained; in the case where the result that the threshold value is not exceeded is obtained, execution is directly started from operation S506.

In operation S505, the ion mobility spectrometry recognition result is written into the result linked list.

In operation S506, mass spectrum identification is performed. Specifically, the matching identification can be performed with a mass spectrum gallery.

In operation S507, whether there is a result exceeding the threshold. Specifically, the result obtained by matching and identifying with the mass spectrum gallery can be obtained. Continuing to execute the subsequent operation if a result exceeding the threshold is obtained; in the case where the result that the threshold value is not exceeded is obtained, execution is directly started from operation S509.

In operation S508, the mass spectrum identification result is written into the result linked list.

In operation S509, it is determined whether the number of result linked list results is >0. When the number of results in the result linked list is greater than 0, the method is directly executed from operation S519; in the case where the number of results in the result linked list is not greater than 0, execution continues from operation S510. Because some characteristic peaks may be manually labeled peaks during library construction, the peaks are likely to be filtered out during pretreatment, and thus the situation that whether an ion mobility spectrometry library or a mass spectrometry library has manually labeled peaks needs to be considered.

In operation S510, it is determined that the ion mobility spectrometry library has a manually noted peak. If the artificial labeling peak exists in the ion mobility spectrometry chart library, continuing to execute the subsequent operation; if there is no manually noted peak, execution may begin directly from operation S514.

In operation S511, ion mobility spectrometry specific identification is performed. Specifically, the special identification can be that the obtained ion mobility spectrometry and mass spectrum data are not preprocessed, and the characteristic peaks of the sample of the unknown substance are extracted and matched with the characteristic peaks of the manually marked standard substance in the ion mobility spectrometry chart library for identification.

In operation S512, whether there is a threshold result exceeded. Specifically, the result obtained by performing special recognition with the ion mobility spectrometry library may be mentioned. Continuing to execute the subsequent operation if a result exceeding the threshold is obtained; in the case where the result that the threshold value is not exceeded is obtained, execution is directly started from operation S514.

In operation S513, the ion mobility spectrometry specific identification result is written into the result linked list.

In operation S514, it is determined that the mass spectrum library has a manually noted peak. If the manual labeling peak exists in the mass spectrum library, continuing to execute the subsequent operation; if there is no manually noted peak, execution may begin directly from operation S518.

In operation S515, mass spectrum special recognition is performed. Specifically, the special identification can be that the obtained ion mobility spectrometry and mass spectrum data are not preprocessed, and the characteristic peaks of the sample of the unknown substance are extracted to be matched and identified with the characteristic peaks of the manually marked standard substance in the mass spectrum gallery.

In operation S516, whether there is a threshold result exceeded. Specifically, the result obtained by performing special recognition with the mass spectrum gallery may be mentioned. Continuing to execute the subsequent operation if a result exceeding the threshold is obtained; in the case where the result that the threshold value is not exceeded is obtained, execution is directly started from operation S518.

In operation S517, the mass spectrum special identification result is written into the result linked list.

In operation S518, it is determined whether the number of result linked list results is >0. If the number of results in the result chain is not greater than 0, operation S520 may be performed directly, for example, to directly display no matching substance. If the number of results in the results chain table is greater than 0, the subsequent operations may be continued and the names of the substances in the ion mobility spectrometry library and/or mass spectrometry library corresponding to the unknown may be displayed in operation S520.

In operation S519, the result linked list is ordered.

In operation S520, the result display process.

In an embodiment, operations S501 to S520 may refer to operations S401 to S404, which are not described herein.

It should be noted that, unless there is an execution sequence between different operations or an execution sequence between different operations in technical implementation, the execution sequence between multiple operations may be different, and multiple operations may also be executed simultaneously in the embodiment of the disclosure.

Based on the above method for establishing a library of ion mobility spectrometry-mass spectrometry, the present disclosure also provides a device for establishing a library of ion mobility spectrometry-mass spectrometry. The device will be described in detail below in connection with fig. 5.

Fig. 6 schematically shows a block diagram of a library-building device of ion mobility spectrometry-mass spectrometry according to an embodiment of the present disclosure.

As shown in fig. 6, the library apparatus 600 of the ion mobility spectrometry-mass spectrum of this embodiment includes a first processing module 610, a first sorting module 620, a first determining module 630, a first comparing module 640, a second sorting module 650, and a constructing module 660.

The first processing module 610 is configured to perform peak searching processing on the preprocessed raw data based on a first peak searching algorithm to obtain column peak information of a plurality of ion mobility spectrometry-mass spectra, where the raw data is obtained by detecting a first known object with a first detection device. In an embodiment, the first processing module 610 may be configured to perform the operation S101 described above, which is not described herein.

The first sorting module 620 is configured to sort the plurality of column peak information based on a first preset sorting rule, and output a sorted first sorting result, where the first sorting result includes a plurality of pre-selected peaks. In an embodiment, the first sorting module 620 may be configured to perform the operation S102 described above, which is not described herein.

A 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 a preset condition, where each preselected peak includes at least one retention time, and the retention time represents a start-stop time period of a peak in the preselected peaks. In an embodiment, the determining module 630 may be configured to perform the operation S103 described above, which is not described herein.

The first comparison module 640 is configured to compare the plurality of first ranked results with a first spectrogram based on the retention time, to obtain a first comparison result, where the first spectrogram is obtained according to the plurality of preprocessed raw data. In an embodiment, the first comparing module 640 may be used to perform the operation S104 described above, which is not described herein.

And the second ranking module 650 is configured to, if the first comparison result indicates that there are first characteristic peaks different from the plurality of first ranking results in the first spectrogram, perform a second ranking on the first characteristic peaks and the at least one sample characteristic peak based on a second preset ranking rule, and generate a second ranking result. In an embodiment, the second sorting module 650 may be configured to perform the operation S105 described above, which is not described herein.

And a construction module 660, configured to construct a target spectrum gallery according to each second sorting result, the name of the first known object, the first preset threshold value, and the basic spectrum gallery corresponding to the second sorting result. In an embodiment, the construction module 660 may be configured to perform the operation S106 described above, which is not described herein.

According to an embodiment of the present disclosure, the first ordering module 620 further includes a third ordering module.

The third sorting module is configured to sort, when the first sorting result is generated, the plurality of column peak information corresponding to the mode data based on a first preset sorting rule and different mode data, and output the sorted first sorting result corresponding to each mode data, where the mode data includes at least one of: ion mobility positive mode, mass spectrum mode, and ion mobility negative mode.

According to an embodiment of the present disclosure, the first determining module 630 further includes a first determining unit and a second determining unit.

The first determining unit is used for determining a plurality of retention times of each pre-selected peak according to a heat map, wherein the heat map is obtained according to the original data and the data preprocessed by the original data.

The second determining unit determines, for each of the preselected peaks, the preselected peak as one sample characteristic peak in a case where the frequency of the preselected peak satisfies a preset frequency condition and/or the peak intensity of the preselected peak satisfies a preset intensity condition, based on each retention time.

According to an embodiment of the present disclosure, the first comparison module 640 further includes a third determination unit and a first comparison unit.

The third determination unit is used for determining a plurality of inspection peaks from the first spectrogram.

The first comparison unit compares the peak position identification and the peak intensity of each inspection peak with the peak position identification and the peak intensity of each preselected peak to obtain a first comparison result.

According to an embodiment of the present disclosure, the second ordering module 650 further comprises a fourth determining unit and a first ordering unit.

The fourth determining unit is used for determining the first characteristic peak meeting the preset condition as the sample characteristic peak when the first comparison result shows that the first characteristic peak which is different from the plurality of first sequencing results exists in the first spectrogram.

The first sorting unit is used for sorting the characteristic peaks of the plurality of samples for the second time based on a second preset sorting rule, and generating a second sorting result.

The library apparatus 600 of ion mobility spectrometry-mass spectrometry may further comprise a second comparison module, a second determination module, according to an embodiment of the present disclosure.

The second comparison module is used for comparing the plurality of first sequencing results with a second spectrogram based on each retention time to obtain a second comparison result, wherein the second spectrogram is obtained according to the plurality of original data.

The second determining module is used for determining the second characteristic peak meeting the preset condition as the sample characteristic peak under the condition that the second comparison result shows that at least one second characteristic peak which is different from the first sequencing results exists in the second spectrogram.

According to an embodiment of the present disclosure, the build module 660 further comprises a first computing unit, a build unit.

The first calculating unit is used for calculating the weight of each sample characteristic peak according to the sequence and peak intensity of different sample characteristic peaks in the second sorting result aiming at each second sorting result.

The construction unit is used for constructing a target spectrum chart library based on the basic spectrum chart library according to the plurality of sample characteristic peaks, the weight corresponding to each sample characteristic peak, the name of the first known object and the first preset threshold value.

The library apparatus 600 of ion mobility spectrometry-mass spectrometry further comprises an addition module according to an embodiment of the present disclosure.

The adding module is used for adding identification information to the first characteristic peak in the target spectrum gallery under the condition that the first characteristic peak is included in the target spectrum gallery.

According to an embodiment of the present disclosure, the third sorting module further comprises a sorting subunit, a first sorting subunit, a second sorting subunit.

The classifying subunit is configured to classify the plurality of column peak information according to different mode data, and obtain a plurality of classified column peak information corresponding to each mode data.

The first sorting subunit is configured to sort, for each pattern data, the plurality of sorted column peak information according to frequencies of different column peak information, and obtain a plurality of sorted column peak information.

The second sorting subunit is configured to sort the at least two column peak information according to the peak intensities of the at least two column peak information when frequencies of the at least two column peak information are the same in the sorted plurality of column peak information, so as to obtain a first sorting result.

Fig. 7 schematically shows a block diagram of a structure of an ion mobility spectrometry-mass spectrometry identification device according to an embodiment of the present disclosure.

As shown in fig. 7, the apparatus 700 for identifying ion mobility spectrometry-mass spectrum of this embodiment includes a second processing module 710, a matching module 720, a calculating module 730, and a writing module 740.

The second processing module 710 is configured to perform peak searching processing on each of the preprocessed initial data based on a second peak searching algorithm, so as to obtain a plurality of unknown peaks of the ion mobility spectrometry-mass spectrometry, where the initial data is obtained by detecting the unknown object with a second detection device.

The matching module 720 is configured to match a plurality of unknown peaks with a plurality of target spectrum libraries in sequence, so as to obtain a plurality of matching results corresponding to different target spectrum libraries, where the target spectrum libraries are constructed according to the library building method described above.

A calculating module 730, configured to calculate, for each matching result, an overall similarity between a plurality of unknown peaks of the unknown object and a sample characteristic peak of a second known object in the target spectrum gallery, where the matching result indicates that the target spectrum gallery corresponding to the matching result includes a sample characteristic peak corresponding to at least one unknown peak.

The writing module 740 is configured to associate the names of the unknown object and the second known object as a first result when the overall similarity meets a second preset threshold in the target spectrum gallery, and write the first result into the result linked list.

The ion mobility spectrometry-mass spectrometry identification device 700 according to an embodiment of the present disclosure further includes a display module.

The display module is used for displaying the result linked list by using the display device.

According to an embodiment of the present disclosure, the ion mobility spectrometry-mass spectrometry identification apparatus 700 further comprises a second sorting unit.

The second sorting unit is used for sorting the plurality of first results to obtain a sorted result linked list under the condition that the number of the first results in the result linked list is a plurality of, wherein the plurality of first results correspond to different unknowns.

According to an embodiment of the present disclosure, the matching module 720 further includes a first matching unit and a second matching unit.

The first matching unit is used for respectively matching a plurality of unknown peaks of the unknown object with the ion mobility spectrometry library to obtain a first matching result.

The second matching unit is used for respectively matching the plurality of unknown peaks of the unknown object with the mass spectrum gallery under the condition that the plurality of unknown peaks of the unknown object are matched with the ion mobility spectrometry gallery, so as to obtain a second matching result, wherein the first matching result and the second matching result respectively represent different matching results.

According to an embodiment of the present disclosure, the ion mobility spectrometry-mass spectrometry identification apparatus 700 further comprises an acquisition unit and a third matching unit.

The acquisition unit is used for acquiring special peaks of initial data corresponding to each unknown peak under the condition that the target spectrum gallery comprises the first characteristic peak.

The third matching unit is used for respectively matching the plurality of special peaks with first characteristic peaks in the plurality of target spectrograms to obtain a plurality of third matching results respectively corresponding to different target spectrograms, wherein the third matching results represent matching results different from the first matching results and the second matching results.

According to an embodiment of the present disclosure, the calculation module 730 further includes a second calculation unit and a result unit.

The second calculation unit is used for calculating a first similarity between each unknown peak and the corresponding sample characteristic peak in the case that the matching result shows that at least one unknown peak has the sample characteristic peak corresponding to the unknown peak.

The result unit is used for obtaining the overall similarity of the unknown object according to the first similarities.

According to embodiments of the present disclosure, 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 constructing module 660, or any of the second processing module 710, the matching module 720, the calculating module 730, and the writing module 740 may be combined in one module to be implemented, or any of the modules may be split into a plurality of modules. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the first processing module 610, the first ordering module 620, the first determination module 630, the first comparison module 640, the second ordering module 650, the construction module 660, or the second processing module 710, the matching module 720, the calculation module 730, the writing module 740 may be implemented at least in part 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 a package, an Application Specific Integrated Circuit (ASIC), or any other reasonable manner of integrating or packaging the circuitry, or any other hardware or firmware, or any one of or any suitable combination of three of software, hardware, and firmware. Alternatively, at least one of the first processing module 610, the first ordering module 620, the first determining module 630, the first comparing module 640, the second ordering module 650, the constructing module 660, or the second processing module 710, the matching module 720, the calculating module 730, the writing module 740 may be at least partially implemented as a computer program module, which may perform the corresponding functions when executed.

It should be noted that, in the embodiment of the present disclosure, the library creating device of the ion mobility spectrometry and the identification device of the ion mobility spectrometry are respectively corresponding to the library creating method of the ion mobility spectrometry and the identification device of the ion mobility spectrometry, and the description of the library creating device of the ion mobility spectrometry and the identification device of the ion mobility spectrometry specifically refers to the library creating method of the ion mobility spectrometry and the identification method of the ion mobility spectrometry and will not be described herein.

As shown in fig. 8, an electronic device 800 according to an embodiment of the present disclosure includes a processor 801 that 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 section 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)), or the like. The processor 801 may also include on-board memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing the different actions of the method flows according to embodiments of the disclosure.

In the RAM 803, various programs and data required for the operation of the electronic device 800 are stored. The processor 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. The processor 801 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 802 and/or the RAM 803. Note that the program may be stored in one or more memories other than the ROM 802 and the RAM 803. The processor 801 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.

According to an embodiment of the present disclosure, the electronic device 800 may also include an input/output (I/O) interface 805, the input/output (I/O) interface 805 also being connected to the 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 portion 806 including a keyboard, mouse, etc.; an output portion 807 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 808 including a hard disk or the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. The drive 810 is also connected to the I/O interface 805 as needed. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as needed so that a computer program read out therefrom is mounted into the storage section 808 as needed.

The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present 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.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. When the computer program product runs in a computer system, the program code is used for enabling the computer system to realize a library building method of the ion mobility spectrometry or an identification method of the ion mobility spectrometry.

The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 801. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed, and downloaded and installed in the form of a signal on a network medium, and/or from a removable medium 811 via a communication portion 809. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from a network via the communication section 809, and/or installed from the removable media 811. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 801. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.

The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.

Claims

1. A library-building method of ion mobility spectrometry-mass spectrometry, comprising:

carrying out peak searching processing on the preprocessed original data based on a first peak searching algorithm to obtain column peak information of a plurality of ion mobility spectrometry-mass spectra, wherein the original data is obtained by detecting a first known object by using first detection equipment;

based on a first preset ordering rule, ordering the plurality of column peak information, and outputting an ordered first ordering result, wherein the first ordering result comprises a plurality of preselected peaks;

determining at least one sample characteristic peak from the first sequencing result based on each retention time and a preset condition, wherein each pre-selected peak comprises at least one retention time, and the retention time represents a start-stop time period of a wave peak in the pre-selected peaks;

Comparing the plurality of first sequencing results with a first spectrogram based on the retention time to obtain a first comparison result, wherein the first spectrogram is obtained according to the plurality of preprocessed original data;

when the first comparison result shows that a first characteristic peak different from a plurality of first sorting results exists in the first spectrogram, sorting the first characteristic peak and the at least one sample characteristic peak for the second time based on a second preset sorting rule, and generating a second sorting result;

and constructing a target spectrum chart library according to each second sequencing result, the name of the first known object, a first preset threshold value and a basic spectrum chart library corresponding to the second sequencing result.

2. The method of claim 1, wherein the first detection device comprises a multi-mode first odor sniffer.

3. The method of claim 1, wherein, when generating the first ranking result, ranking the plurality of column peak information corresponding to the pattern data based on the first preset ranking rule and different pattern data, outputting the ranked first ranking result corresponding to each pattern data, wherein the pattern data includes at least one of: ion mobility positive mode, mass spectrum mode, and ion mobility negative mode.

4. The method of claim 1, wherein the determining at least one sample characteristic peak from the first ranked result based on each retention time and a preset condition comprises:

determining a plurality of said retention times for each of said preselected peaks from a heat map derived from said raw data and/or data pre-processed from the raw data;

based on each of the retention times, for each of the preselected peaks, determining the preselected peak as one of the sample characteristic peaks if the frequency of the preselected peak satisfies a preset frequency condition and/or the peak intensity of the preselected peak satisfies a preset intensity condition.

5. The method of claim 1, wherein the preselected peaks include peak position identification and peak intensity.

6. The method according to claim 1 or 5, wherein comparing the plurality of first ranked results with the first spectrogram to obtain a first comparison result comprises:

determining a plurality of test peaks from the first spectrum;

and comparing the peak position identification and the peak intensity of each inspection peak with the peak position identification and the peak intensity of each preselected peak to obtain the first comparison result.

7. The method of claim 1, wherein the second preset ordering rule includes degrees of importance of different ones of the sample characteristic peaks.

8. The method according to claim 1 or 7, wherein, in case the first comparison result indicates that there are first characteristic peaks in the first spectrogram different from the plurality of first ranking results, the second ranking of the first characteristic peaks and the at least one sample characteristic peak based on a second preset ranking rule, generating a second ranking result comprises:

if the first comparison result shows that the first characteristic peaks different from the plurality of first sorting results exist in the first spectrogram, determining the first characteristic peaks meeting the preset conditions as sample characteristic peaks;

and based on the second preset sorting rule, sorting the characteristic peaks of the samples for the second time, and generating a second sorting result.

9. The method of claim 8, wherein prior to performing the second sorting, further comprising:

comparing the plurality of first sequencing results with a second spectrogram based on each retention time to obtain a second comparison result, wherein the second spectrogram is obtained according to the plurality of original data;

And if the second comparison result shows that at least one second characteristic peak different from the plurality of first sequencing results exists in the second spectrogram, determining the second characteristic peak meeting the preset condition as a sample characteristic peak.

10. The method of claim 1, wherein the constructing a target spectrum gallery from each of the second ranked results, the name of the first known object, a first preset threshold, and a base spectrum gallery corresponding to the second ranked results comprises:

for each second sorting result, calculating the weight of each sample characteristic peak according to the sequence and peak intensity of different sample characteristic peaks in the second sorting result;

and constructing the target spectrum gallery according to the sample characteristic peaks, the weights corresponding to each sample characteristic peak, the names of the first known matters and the first preset threshold value based on the basic spectrum gallery.

11. The method according to claim 1, wherein, in the case where the first characteristic peak is included in the target spectrum gallery, identification information is added to the first characteristic peak in the target spectrum gallery.

12. A method according to claim 3, wherein the first preset ordering rule comprises ordering according to frequency and intensity of each column peak information.

13. The method according to claim 3 or 12, wherein the sorting the plurality of column peak information corresponding to the pattern data based on the first preset sort rule and different pattern data, outputting the sorted first sort result corresponding to each pattern data, includes:

classifying a plurality of column peak information according to different mode data to obtain a plurality of classified column peak information corresponding to each mode data;

for each mode data, sorting the plurality of sorted column peak information according to the frequencies of different column peak information to obtain a plurality of sorted column peak information;

and under the condition that at least two column peak information in the sorted plurality of column peak information have the same frequency, sorting the at least two column peak information according to the peak intensities of the at least two column peak information, so as to obtain the first sorting result.

14. The method of claim 1, wherein the preprocessing comprises filtering processing.

15. An ion mobility spectrometry-mass spectrometry identification method, comprising:

carrying out peak searching processing on each preprocessed initial data based on a second peak searching algorithm to obtain a plurality of unknown peaks of ion mobility spectrometry-mass spectrometry, wherein the initial data is obtained by detecting an unknown object by using second detection equipment;

sequentially matching a plurality of unknown peaks with a plurality of target spectrum libraries to obtain a plurality of matching results respectively corresponding to different target spectrum libraries, wherein the target spectrum libraries are constructed according to the method of any one of claims 1 to 14;

for each matching result, calculating the overall similarity of a plurality of unknown peaks of the unknown substance and sample characteristic peaks of a second known substance in the target spectrum diagram library when the matching result shows that the target spectrum diagram library corresponding to the matching result comprises sample characteristic peaks corresponding to at least one unknown peak;

and under the condition that the overall similarity meets a second preset threshold value in the target spectrum gallery, associating the names of the unknown object and the second known object as a first result, and writing the first result into a result linked list.

16. The method of claim 15, further comprising:

and displaying the result linked list by using a display device.

17. The method of claim 15, in the case where the unknown substance is a plurality, the method further comprising:

and under the condition that the number of the first results in the result linked list is a plurality of, sorting the plurality of first results to obtain a sorted result linked list, wherein the plurality of first results correspond to different unknowns.

18. The method of claim 15, the plurality of target spectral libraries comprising an ion mobility spectrometry library and a mass spectrometry library;

the step of sequentially matching a plurality of unknown peaks with a plurality of target spectrum libraries to obtain a plurality of matching results respectively corresponding to different target spectrum libraries, including:

matching a plurality of unknown peaks of the unknown object with the ion mobility spectrometry gallery respectively to obtain a first matching result;

and under the condition that a plurality of unknown peaks of the unknown object are matched with the ion mobility spectrogram library, respectively matching the plurality of unknown peaks of the unknown object with the mass spectrum library to obtain a second matching result, wherein the first matching result and the second matching result respectively represent different matching results.

19. The method of claim 18, further comprising:

under the condition that the target spectrum gallery comprises a first characteristic peak, acquiring a special peak of initial data corresponding to each unknown peak;

and respectively matching the special peaks with the first characteristic peaks in the target spectrum libraries to obtain a plurality of third matching results respectively corresponding to different target spectrum libraries, wherein the third matching results represent the matching results different from the first matching results and the second matching results.

20. The method of claim 15, wherein, in the case where the matching result indicates that the target spectrum gallery corresponding to the matching result includes a sample characteristic peak corresponding to at least one of the unknown peaks, calculating overall similarity of a plurality of the unknown peaks of the unknown substance to sample characteristic peaks of a second known substance in the target spectrum gallery includes:

calculating a first similarity between each of the unknown peaks and the corresponding sample characteristic peak in the case that the matching result indicates that at least one of the unknown peaks has a sample characteristic peak corresponding to the unknown peak;

And obtaining the overall similarity of the unknown object according to the first similarity.

21. The method of claim 15, wherein the second detection device comprises a multimode second odor sniffer.

22. A library-building device for ion mobility spectrometry-mass spectrometry, comprising:

the first processing module is used for carrying out peak searching processing on the preprocessed original data based on a first peak searching algorithm to obtain column peak information of a plurality of ion mobility spectrometry-mass spectra, wherein the original data is obtained by detecting a first known object by using first detection equipment;

the first sorting module is used for sorting the plurality of column peak information based on a first preset sorting rule and outputting a sorted first sorting result, wherein the first sorting result comprises a plurality of preselected peaks;

a first determining module, configured to determine at least one sample characteristic peak from the first sorting result based on each retention time and a preset condition, where each of the preselected peaks includes at least one retention time, and the retention time represents a start-stop time period of a peak in the preselected peaks;

the first comparison module is used for comparing the plurality of first sequencing results with a first spectrogram based on the retention time to obtain a first comparison result, wherein the first spectrogram is obtained according to the plurality of preprocessed original data;

The second sorting module is used for sorting the first characteristic peaks and the at least one sample characteristic peaks for the second time based on a second preset sorting rule under the condition that the first comparison result shows that the first characteristic peaks different from the plurality of first sorting results exist in the first spectrogram, so as to generate a second sorting result; and

the construction module is used for constructing a target spectrum gallery according to each second sorting result, the name of the first known object, a first preset threshold value and a basic spectrum gallery corresponding to the second sorting result.

23. An ion mobility spectrometry-mass spectrometry identification device comprising:

the second processing module is used for carrying out peak searching processing on each preprocessed initial data based on a second peak searching algorithm to obtain a plurality of unknown peaks of the ion mobility spectrometry-mass spectrum, wherein the initial data is obtained by detecting the unknown object by using a second detection device;

the matching module is used for sequentially matching a plurality of unknown peaks with a plurality of target spectrum libraries to obtain a plurality of matching results which respectively correspond to different target spectrum libraries, wherein the target spectrum libraries are constructed according to the method of any one of claims 1 to 14;

A calculation module, configured to calculate, for each of the matching results, overall similarity between a plurality of unknown peaks of the unknown object and sample characteristic peaks of a second known object in the target spectrum gallery, in a case where the matching result indicates that the target spectrum gallery corresponding to the matching result includes sample characteristic peaks corresponding to at least one of the unknown peaks; and

and the writing module is used for associating the name of the unknown object and the name of the second known object into a first result and writing the first result into a result linked list under the condition that the overall similarity meets a second preset threshold value in the target spectrum gallery.

24. An electronic device, comprising:

one or more processors;

storage means for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of banking of any one of claims 1 to 14 or the method of identifying of any one of claims 15 to 21.

25. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the banking method of any one of claims 1 to 14 or the identification method of any one of claims 15 to 21.

26. A computer program product comprising a computer program which, when executed by a processor, implements the library construction method according to any one of claims 1 to 14 or the identification method according to any one of claims 15 to 21.