Data processing method and device of ion mobility spectrometry, and electronic equipment

Abstract

The application discloses a data processing method and device of ion mobility spectrometry and electronic equipment. The method comprises the following steps: removing pseudo characteristic peaks in a target spectrum to obtain target characteristic peaks, wherein the target spectrum comprises a plurality of pseudo characteristic peaks and a plurality of target characteristic peaks; performing region growing according to the target characteristic peaks to determine a plurality of sequences, wherein the plurality of target characteristic peaks in the same sequence belong to the same substance, and one target characteristic peak corresponds to one sequence. The problem that the efficiency is low and the accuracy is poor when the ion mobility spectrometry is used for substance identification in the related art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of substance analysis, in particular, to an ion mobility spectrum data processing method and device and electronic equipment. BACKGROUND

[0002] Ion mobility spectrum (IMS) detection technology is an effective chemical substance analysis method, which has been widely applied to trace detection of explosives, drugs and the like in airports, ports, stations and the like, and the detection accuracy can generally reach micrograms or even nanograms or less. The spectrum data of ion mobility spectrum contains rich substance information, including gas chromatography retention time, ion mobility time and ion intensity and the like. The data processing system processes the ion mobility spectrum signal output by the mobility tube and identifies the substance.

[0003] The threshold setting of the detection method in the prior art is an empirical value, which is greatly affected by human subjectivity, or cannot be applied to many different parameters and different devices, resulting in high false detection rate and missed detection rate. Moreover, the setting of the artificial threshold value will affect the detection limit of the device, resulting in the problem that peaks are detected but cannot be identified. The influence of noise and pseudo noise cannot be suppressed, the algorithm accuracy is not high, and the subjective threshold value in the scheme is an artificial experience value, the detection result is greatly affected by subjectivity, which increases the probability of false detection and missed detection.

[0004] In view of the problem that in the related art, when substance identification is performed by using ion mobility spectrum, experience of artificial identification is relied on, which is not only low in efficiency but also poor in accuracy, no effective solution has been proposed so far. SUMMARY

[0005] The main purpose of the present application is to provide an ion mobility spectrum data processing method and device and electronic equipment, so as to solve the problem that in the related art, when substance identification is performed by using ion mobility spectrum, experience of artificial identification is relied on, which is not only low in efficiency but also poor in accuracy.

[0006] In order to achieve the above purpose, according to one aspect of the present application, an ion mobility spectrum data processing method is provided, comprising: removing pseudo characteristic peaks in a target spectrum to obtain target characteristic peaks, wherein the target spectrum includes a plurality of pseudo characteristic peaks and a plurality of target characteristic peaks; performing region growing according to the target characteristic peaks to determine a plurality of sequences, wherein a plurality of target characteristic peaks in the same sequence belong to the same substance, and one target characteristic peak corresponds to one sequence.

[0007] Optionally, the pseudo characteristic peaks in the target spectrum are removed to obtain the target characteristic peaks, including: determining the intensity and peak width of all characteristic peaks in the target spectrum; clustering all characteristic peaks according to the intensity and peak width to determine a plurality of categories of characteristic peaks, wherein the plurality of categories belong to the pseudo characteristic peaks or the target characteristic peaks; removing the characteristic peaks belonging to the categories of the pseudo characteristic peaks, and retaining the characteristic peaks belonging to at least one category of the target characteristic peaks.

[0008] Optionally, the intensity and peak width of all characteristic peaks in the target spectrum are determined, including: obtaining an ion mobility spectrum detected by an ion mobility spectrometer as the target spectrum; obtaining an intermediate spectrum after preprocessing and correction of the target spectrum; and determining parameters of all characteristic peaks according to the intermediate spectrum, wherein the parameters include the intensity and peak width.

[0009] Optionally, the target characteristic peaks are used for region growing to determine a plurality of sequences, including: selecting a position of a characteristic peak in the plurality of target characteristic peaks as a starting point; performing region growing according to the starting point and a preset mask to determine a plurality of target characteristic peaks belonging to the same substance as the selected characteristic peak, to form a sequence corresponding to the substance; removing the target characteristic peaks forming the sequence, and continuing to select a characteristic peak in the remaining target characteristic peaks as a starting point for region growing, until all target characteristic peaks form corresponding sequences, to obtain the plurality of sequences.

[0010] Optionally, the target characteristic peaks belonging to the same substance as the selected characteristic peak are determined by region growing according to the starting point and a preset mask to form a sequence corresponding to the substance, including: determining the pixel coordinates of the starting point; aligning a set position of the mask with the pixel coordinates, and performing region growing upwards or downwards to determine whether the mask covers the positions of other target characteristic peaks; adding the target characteristic peaks covered by the mask to the corresponding positions in the sequence; aligning the set position of the mask with the pixel positions of the target characteristic peaks added to the sequence, and continuing region growing, until the mask does not cover the positions of other target characteristic peaks, to obtain the sequence to which the target characteristic peaks corresponding to the starting point belong.

[0011] Optionally, the target characteristic peaks covered by the mask are added to the corresponding positions in the sequence, including: in the case that the number of target characteristic peaks covered by the mask is one, directly adding the target characteristic peak covered to the sequence; in the case that the number of target characteristic peaks covered by the mask is a plurality, adding a target characteristic peak with the highest priority in the plurality of target characteristic peaks to the sequence according to a preset priority; wherein the priority includes at least one of the following: the category of the target characteristic peak, the peak type of the target characteristic peak, and the Euclidean distance between the target characteristic peak and the starting point.

[0012] Optionally, determining the pixel coordinate of the starting point comprises: binarizing ion migration time and ion retention time of the target feature peak in the target spectrum to obtain a binary spectrum; and determining the pixel coordinate of the starting point according to the binary spectrum, wherein the abscissa of the pixel coordinate is the ion migration time, and the ordinate of the pixel coordinate is the ion retention time.

[0013] Optionally, after the region growing according to the target feature peak is performed to determine a plurality of sequences, the method further comprises: obtaining an ion migration time array according to ion migration time corresponding to the target feature peak in each sequence; determining a predicted ion migration time of a current position in the ion migration time array according to a preset number of ion migration times adjacent to the current position; determining a deviation between the predicted ion migration time and an actual ion migration time of the current position in the ion migration time array; and removing the actual ion migration time of the current position from the ion migration time array in a case where the deviation exceeds a preset threshold.

[0014] To achieve the above object, according to another aspect of the present application, there is provided a data processing apparatus for ion mobility spectrum, comprising: a removing module configured to remove pseudo feature peaks in a target spectrum to obtain target feature peaks, wherein the target spectrum comprises a plurality of pseudo feature peaks and a plurality of target feature peaks; and a growing module configured to perform region growing according to the target feature peaks to determine a plurality of sequences, wherein a plurality of target feature peaks in a same sequence belong to a same substance, and one target feature peak corresponds to one sequence.

[0015] According to another aspect of the present application, there is also provided a computer readable storage medium for storing a program, wherein the program performs the data processing method for ion mobility spectrum according to any one of the above embodiments.

[0016] According to another aspect of the present application, there is also provided an electronic device comprising one or more processors and a memory, wherein the memory is configured to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method for ion mobility spectrum according to any one of the above embodiments.

[0017] By the present application, the following steps are adopted: removing false characteristic peaks in a target spectrum to obtain target characteristic peaks, wherein the target spectrum includes multiple false characteristic peaks and multiple target characteristic peaks; performing region growing according to the target characteristic peaks to determine multiple sequences, wherein the multiple target characteristic peaks in the same sequence belong to the same substance, and one target characteristic peak corresponds to one sequence. By removing the false characteristic peaks, the purpose of removing noise is achieved. By using the region growing method, the target characteristic peaks of the same substance in different frame numbers are counted into the same sequence. In this way, each sequence corresponds to one substance, so the human eye does not need to identify the characteristic peaks that should belong to the same substance. When the substance needs to be identified, the substance can be identified according to the characteristics of the ion migration time array corresponding to the sequence, so as to achieve the purpose of automatically extracting the sequence of the characteristic peaks of the same substance in the ion migration spectrum. The substance can be more conveniently identified according to the sequence, and the technical effects of improving the efficiency and accuracy of identifying the substance according to the ion migration spectrum are achieved. Furthermore, the problem that in the related art, when the substance is identified by using the ion migration spectrum, the experience of human identification is relied on, which is not only low in efficiency but also poor in accuracy is solved. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application and are incorporated in and constitute a part of this application. The embodiments of this application illustrated in the drawings, and their description, are presented to explain the application and not to limit or define it. In the drawings:

[0019] Figure 1 is a flowchart of a data processing method of an ion migration spectrum according to an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of an intermediate spectrum according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of all characteristic peaks in the intermediate spectrum according to an embodiment of the present application;

[0022] Figure 4 is a schematic diagram of the intermediate spectrum after removing false characteristic peaks according to an embodiment of the present application;

[0023] Figure 5 is a schematic diagram of the final processing result according to an embodiment of the present application;

[0024] Figure 6 is a schematic diagram of the retention time and intensity curve of the characteristic peaks of the final processing result according to an embodiment of the present application;

[0025] Figure 7 is a schematic diagram of a data processing device of an ion migration spectrum according to an embodiment of the present application;

[0026] Figure 8 is a schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] It should be noted that the embodiments and features of the present application can be combined with each other if there is no conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0028] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0030] The present application will be described below in conjunction with preferred implementation steps, Figure 1 is a flowchart of a data processing method of ion mobility spectrometry according to an embodiment of the present application, as shown in the figure, the method comprises the following steps: Figure 1

[0031] Step S101, eliminating the pseudo characteristic peaks in the target spectrum to obtain target characteristic peaks, wherein the target spectrum includes a plurality of pseudo characteristic peaks and a plurality of target characteristic peaks;

[0032] Step S102, region growing according to the target characteristic peaks to determine a plurality of sequences, wherein the plurality of target characteristic peaks in the same sequence belong to the same substance, and one target characteristic peak corresponds to one sequence.

[0033] ​By the above steps, by eliminating the pseudo characteristic peak, the purpose of removing noise is achieved, and the target characteristic peaks of the same substance in different frame numbers are counted into the same sequence by using the region growing method. Thus, each sequence corresponds to a substance, and the human eye is not needed to identify the characteristic peaks that should belong to the same substance. When identifying the substance, only the characteristics of the ion migration time array corresponding to the sequence are needed to identify the substance, achieving the purpose of automatically extracting the sequence of characteristic peaks of the same substance in the ion mobility spectrum. The substance is more conveniently identified according to the sequence, and the technical effects of improving the efficiency and accuracy of identifying the substance according to the ion mobility spectrum are achieved. Thus, the problem of relying on human identification experience when identifying the substance by using the ion mobility spectrum in the related art is solved, which is not only low in efficiency but also poor in accuracy.

[0034] The execution subject of the above steps can be a processor, a calculator, a server, and the like, a device having data operation and data analysis processing capability, and in addition, can be an apparatus having the above-mentioned device having data operation, analysis, and processing capability, such as a computer having a processor, a smart phone, a wearable device, and the like, a data system having a server, an operation system, and the like. The above-mentioned execution subject can be provided on the ion mobility spectrum detection device, can be connected to the ion mobility spectrum detection device, and can process and visualize the ion mobility spectrum detected by the ion mobility spectrum detection device. Or it can also be a processing device provided with the ion mobility spectrum detection device, and the purpose of processing the ion mobility spectrum can be achieved by running the above-mentioned steps.

[0035] The above-mentioned target spectrum can be the ion mobility spectrum detected by the ion mobility spectrum detection device. After the ion mobility spectrum detection device detects the ion mobility spectrum, the ion mobility spectrum can be preprocessed, such as single-frame smoothing processing and baseline correction. After single-frame smoothing processing, the noise inside the image can be effectively filtered out. After baseline correction, the ion mobility spectrum has higher accuracy. Thus, the error of the ion mobility spectrum is reduced, and the accuracy of data processing of the ion mobility spectrum is improved.

[0036] The above-mentioned target spectrum includes a plurality of characteristic peaks, as shown in the figure. Figure 3 The above-mentioned pseudo characteristic peak is a characteristic peak in the target spectrum that is considered to be generated by noise. The target characteristic peak can include a qualified characteristic peak generated by a substance ion, or can be a potential characteristic peak generated by a substance particle. Different characteristic peaks have different parameters, including corresponding ion migration time, ion retention time, ion intensity, ion half-peak width, and the like.

[0037] The region growing refers to a process of developing a set of pixels or regions into a larger region. Starting from a set of seed points, the region growing from these points is by merging adjacent pixels with similar properties such as intensity, gray level, texture color, etc. to each seed point. The region growing is performed according to the target feature peak, that is, the region growing is performed with the coordinate point at the position of the target feature peak as a seed point to find target feature peaks belonging to a sequence with the target feature peak. The feature peaks belonging to a sequence represent the same substance in the ion mobility spectrum. As to what the substance is, the substance can be identified through the parameters of the plurality of target feature peaks corresponding to the substance.

[0038] It should be noted that as the substances in an ion mobility spectrum are generally multiple, the final sequence is also multiple, and one target feature peak can only correspond to one sequence. When performing region growing, all pixel points of the ion mobility spectrum are traversed, the target feature peaks added to the sequence are deleted, and the region growing is continued based on the remaining target feature peaks.

[0039] In step S101, the pseudo feature peaks in the target spectrum are optionally removed to obtain the target feature peaks, including: determining the intensity and peak width of all feature peaks in the target spectrum; clustering all feature peaks according to the intensity and peak width to determine a plurality of categories of feature peaks, wherein the plurality of categories belong to pseudo feature peaks or target feature peaks; and removing the feature peaks of the categories belonging to pseudo feature peaks and retaining the feature peaks of at least one category belonging to target feature peaks.

[0040] The peak width can also be a half peak width. When clustering, a clustering algorithm is first selected, such as a K-Means clustering algorithm. The K-Means clustering algorithm is used to cluster a two-dimensional data set composed of an ion intensity data set and an ion half peak width data set. Here, there are three categories. Specifically, in the two-dimensional data set composed of the ion intensity data set and the ion half peak width data set, three data sets are randomly selected as initial clustering centers, the samples in the sample set are assigned to the nearest cluster according to the minimum distance principle, and the sample mean in each cluster is used as a new clustering center. Repeat the previous steps until the clustering center no longer changes. The three categories represent qualified feature peaks, potential feature peaks, and pseudo feature peaks, respectively. The qualified feature peaks are the determined target feature peaks, the potential feature peaks are the feature peaks between the determined feature peaks and the noise, and the qualified feature peaks and the potential feature peaks are collectively referred to as target feature peaks. The pseudo feature peaks, that is, the noise, need to be directly removed. Removing the pseudo feature peaks can achieve the purpose of denoising and improve the accuracy of the feature peaks in the ion mobility spectrum.

[0041] Optionally, the determining the intensity and the peak width of all the characteristic peaks in the target spectrum includes: obtaining an ion mobility spectrum detected by an ion mobility spectrometer as the target spectrum; obtaining an intermediate spectrum after pre-processing and correction of the target spectrum; and determining parameters of all the characteristic peaks according to the intermediate spectrum, wherein the parameters include the intensity and the peak width.

[0042] The ion mobility spectrum collected by the ion mobility spectrum detection device is regarded as a target spectrum, and the target spectrum is processed by single-frame smoothing and baseline correction to obtain an intermediate spectrum. According to the intermediate spectrum, the ion mobility time, ion retention time, ion intensity, ion half-peak width and other parameter information of each characteristic peak can be calculated.

[0043] In step S102, the multiple sequences are determined by region growing according to the target characteristic peaks, including: selecting a position of a target characteristic peak as a starting point; performing region growing according to the starting point and a preset mask to determine multiple target characteristic peaks belonging to the same substance as the selected target characteristic peak, to form a sequence corresponding to the substance; and removing the target characteristic peaks forming the sequence, and continuing to select a characteristic from the remaining target characteristic peaks as a starting point to perform region growing, until all the target characteristic peaks form corresponding sequences, to obtain the multiple sequences.

[0044] The above selecting a position of a target characteristic peak as a starting point can select a target characteristic peak with the largest intensity, which is more obvious and more conducive to identification and analysis. The size of the mask can be a preset value, and in this embodiment, the size of the mask is 2*n, with a unit of pixels, and n is an odd number. The starting point is aligned with a k-pixel position in the mask, and k=(1+n) / 2, that is, the starting point is at the central pixel position of the mask.

[0045] Since one target characteristic peak can only correspond to one sequence of one substance, after adding the found target characteristic peak to the corresponding sequence, the target characteristic peaks forming the sequence are removed. A characteristic is continuously selected from the remaining target characteristic peaks as a starting point to perform region growing, and the selection principle can be the same, for example, selecting a target characteristic peak with the largest intensity, or the selection principle can be different. Until all the pixel points of the ion mobility spectrum are searched, and the corresponding multiple target characteristic peaks form corresponding sequences, to obtain multiple sequences corresponding to different substances.

[0046] Optionally, the region growing is performed according to the starting point and the preset mask to determine a plurality of target characteristic peaks belonging to the same substance as the selected characteristic peak, and a sequence corresponding to the substance is composed, including: determining the pixel coordinates of the starting point; aligning the set position of the mask with the pixel coordinates, and performing region growing upwards or downwards to determine whether the mask covers the positions of other target characteristic peaks; adding the target characteristic peaks covered by the mask to the corresponding positions in the sequence; aligning the set position of the mask with the pixel positions of the target characteristic peaks added to the sequence, and continuing the region growing until the mask does not cover the positions of other target characteristic peaks, to obtain the sequence to which the target characteristic peak corresponding to the starting point belongs.

[0047] Since the starting point is usually located in the middle position of the ion mobility spectrum, there are pixels above and below it, and the region growing is divided into two processes, i.e., upwards and downwards, which can be performed in sequence or simultaneously. Specifically, when the region growing is performed according to the starting point and the preset mask, two cases are considered.

[0048] When the region growing is performed upwards, the starting point can be aligned with the k pixel positions of the next row of the mask, and whether the pixel positions of the previous row hit the target characteristic peaks can be used to find the target characteristic peaks belonging to the same sequence as the target characteristic peak of the starting point. When the region growing is performed downwards, the starting point is aligned with the k pixel positions of the previous row of the mask, and whether the pixel positions of the next row hit the target characteristic peaks can be used to find the target characteristic peaks belonging to the same sequence as the target characteristic peak of the starting point.

[0049] Optionally, adding the target characteristic peaks covered by the mask to the corresponding positions in the sequence includes: in the case that the number of target characteristic peaks covered by the mask is one, directly adding the covered target characteristic peak to the sequence; in the case that the number of target characteristic peaks covered by the mask is multiple, adding the target characteristic peak with the highest priority among the multiple target characteristic peaks to the sequence according to a preset priority; wherein the priority includes at least one of the following: the category of the target characteristic peak, the peak type of the target characteristic peak, and the Euclidean distance between the target characteristic peak and the starting point.

[0050] It should be noted that in the ion mobility spectrum, the target characteristic peaks of some substances with close ion mobility times can be relatively close, and in the region growing process, the mask covering method can be used to find the target characteristic peaks, and one target characteristic peak can be covered at a time. At this time, a closest target characteristic peak needs to be determined according to the screening condition, so as to determine the target characteristic peak corresponding to the current starting point as a characteristic peak belonging to the same sequence.

[0051] According to the category of the target characteristic peak as a priority, a qualified characteristic peak can be selected, in the case that the number of the qualified characteristic peaks is multiple, the peak type of the target characteristic peak can be selected, the peak type of the characteristic peak corresponding to the maximum intensity initial coordinate point is selected as the same peak type, in the case that the characteristic peaks of the same peak type are multiple, the characteristic peak with the closest Euclidean distance to the starting point (d0, r0) can be selected. That is, the above priority conditions can be used alone or in combination, and the order of the combined screening is not limited to the above order, and can be adjusted according to actual conditions.

[0052] Optionally, the pixel coordinates of the starting point are determined by: binarizing the ion migration time and the ion retention time of the target characteristic peak in the target spectrum to obtain a binary spectrum; and determining the pixel coordinates of the starting point according to the binary spectrum, wherein the horizontal coordinate of the pixel coordinates is the ion migration time, and the vertical coordinate of the pixel coordinates is the ion retention time. The ion migration time and the ion retention time coordinates of the target characteristic peak and the potential characteristic peak are assigned a value of 1, and the other coordinate regions are assigned a value of 0, to complete the binarization conversion to obtain the binary spectrum. The region growing based on the binary spectrum can improve the accuracy of the region growing.

[0053] Optionally, after the plurality of sequences are determined by the region growing according to the target characteristic peak in step S102, the method further includes: obtaining an ion migration time array according to the ion migration time corresponding to the target characteristic peak in each sequence; determining a predicted ion migration time of a current position according to a preset number of ion migration times adjacent to the current position in the ion migration time array; determining a deviation between the predicted ion migration time and an actual ion migration time of the current position in the ion migration time array; and removing the actual ion migration time of the current position from the ion migration time array in the case that the deviation exceeds a preset threshold.

[0054] According to the ion migration time corresponding to the target characteristic peak in each sequence, the ion migration time array is composed, and the ion migration time of the theoretical current position is predicted by the weighted average of the first n ion migration times of the current position in the ion migration time array. The predicted value of the ion migration time is as follows.

[0055]

[0056] In the formula, predictD is the predicted value of the ion migration time of the current position, PeakVal i-3 is the weight of the ion migration time of the i-3 position, D i-3 is the ion migration time of the i-3 position, and i-3 is the position 3 positions before the current position, and n=3 at this time. PeakVal i-2is the weight of ion migration time of i-2 position, D i-2 is the ion migration time of i-2 position, i-2 is the position 2 before the current position. i-1 is the weight of ion migration time of i-1 position, D i-1 is the ion migration time of i-1 position, i-1 is the position 1 before the current position.

[0057] Here, a mask with a size of 1*3 is selected, if the deviation between the predicted value and the actual value of the current position reaches the preset threshold Th, it is considered that the point is not the current migration time characteristic peak, and the weight PeakVal is the corresponding ion intensity. That is, if the deviation between predictD and D reaches the preset threshold Th h , it is considered that the target characteristic peak corresponding to the current position is not the current migration time characteristic peak. Therefore, the multiple sequences obtained by region growing are further optimized to screen out interfering target characteristic peaks, improve the accuracy of the sequences, and then provide the accuracy of the subsequent substance identification, and further provide the accuracy of the subsequent substance identification.

[0058] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in a different order.

[0059] It should be noted that the present application also provides an optional embodiment, which will be described in detail below.

[0060] The present embodiment provides a spectrum processing method of ion mobility spectrum based on image processing and time series analysis. According to the intensity and half-peak width information of the spectrum, a clustering algorithm is used to find the target characteristic peak and eliminate noise. Then, the region growing knowledge in image processing and time series analysis are used to complete the trajectory peak calculation of qualified characteristic peaks. The present invention does not need to set a threshold value artificially, and can adapt to different parameters of a single device and the adaptability of multiple devices, reducing the optimization workload of parameters in the later stage. After being free from the restriction of absolute intensity threshold, more retention time can be obtained to some extent, which can be more accurate for quantitative analysis and minimum detection limit detection. Compared with the traditional single frame intensity and position matching method, the time series model considers the motion trend of the characteristic peak migration time in the time domain, which can remove the misjudgment of noise and pseudo noise, and can also be compatible with the deviation of qualified characteristic peaks within a certain tolerance. In addition, the method also has the characteristics of low complexity and fast operation speed, and has the prospect of being applied to real-time ion mobility spectrum processing.

[0061] Specifically, the steps of the spectrum processing method are as follows:

[0062] 1) The ion mobility spectrum collected by ion mobility spectrometry is regarded as a target spectrum, and the spectrum after single-frame smoothing and baseline correction is an intermediate spectrum, as shown in Figure 2 Figure 2 is a schematic diagram of the intermediate spectrum according to the embodiments of the present application;

[0063] 2) Through the intermediate spectrum, the ion mobility time, ion retention time, ion intensity, and ion half-peak width information of each characteristic peak can be calculated, as shown in Figure 3 Figure 3 is a schematic diagram of all characteristic peaks in the intermediate spectrum according to the embodiments of the present application;

[0064] 3) The two-dimensional data set composed of the ion intensity data set and the ion half-peak width data set is clustered by the K-Means clustering algorithm, which is divided into three categories, representing qualified characteristic peaks, potential characteristic peaks, and pseudo characteristic peaks, respectively. The qualified characteristic peaks are 100% determined target peaks, the potential characteristic peaks are characteristic peaks between the determined characteristic peaks and the noise, which will be confirmed again according to the time series model, and the pseudo characteristic peaks are noise, which need to be directly removed, as shown in Figure 4 Figure 4 is a schematic diagram of the intermediate spectrum after removing the pseudo characteristic peaks according to the embodiments of the present application.

[0065] In the clustering process, three data sets are randomly selected as initial clustering centers in the two-dimensional data set composed of the ion intensity data set and the ion half-peak width data set, and the samples in the sample set are distributed to the nearest cluster according to the minimum distance principle. The sample mean in each cluster is used as the new cluster center, and the above steps are repeated until the cluster center no longer changes.

[0066] It should be noted that the K-Means clustering algorithm described above is only an optional example, and other clustering algorithms can also be used.

[0067] 4) Through the results calculated in 3), the ion mobility time and ion retention time coordinates of the target characteristic peaks including qualified characteristic peaks and potential characteristic peaks are assigned a value of 1, and other coordinate regions are assigned a value of 0, to complete the binary conversion and obtain a binary spectrum;

[0068] 5) The region growing calculation is performed on the binary spectrum. The specific method is to generate a mask with a size of 2*n, and n is an odd number, as shown in Table 1. Table 1 is a mask structure table, and k represents the position of the center pixel of each row of the mask, k=(1+n) / 2.

[0069] Table 1 Mask structure table

[0070] [WC 11 ]]> [WC 12 ]]> …… [WC 1k ]]> …… [WC 1n ]]> [WC 21 ]]> [WC 22 ]]> …… [WC 2k ]]> …… [WC 2n ]]>

[0071] ​​​By combining ion intensity information with the binary spectrum, the initial coordinate point (d0, r0) of the maximum intensity in the binary spectrum is found. d0 is the x-axis (ion migration time), and r0 is the y-axis (ion retention time, i.e., the number of frames). It should be noted that in this embodiment, the x-axis and y-axis of the target spectrum, intermediate spectrum, and binary spectrum are the same; that is, the x-axis represents ion migration time, and the y-axis represents ion retention time, which can also be understood as the number of spectrum frames. The pixel values ​​represent the peak intensity. Therefore, selecting the target feature peak with the highest intensity facilitates analysis and searching, and also allows for more accurate searching.

[0072] During the upward search, the position of the point (d0, r0) in the binary spectrum and W 2k Corresponding to, i.e., mask W 11 ~W 1n At this point, corresponding to the data from the previous frame, if W is in the binary spectrum... 11 ~W 1n If a qualified characteristic peak or a pseudo-characteristic peak (d1, r1) is found within the range, then the ion migration time array D = [d0, d1] and the retention time array R = [r0, r1]. Similarly, when searching downwards, the position of point p0 in the binary spectrum and W are... 1k Correspondingly, if W in the binary spectrum 21 ~W 2n The presence of qualified characteristic peaks or false characteristic peaks (d) within the range -1 r -1 ), then let the ion migration time array D = [d -1 ,d0], retention time array R = [r -1 A special consideration is that if multiple matching feature peaks occur within the mask area, the priority is as follows: select qualified feature peaks; if there are multiple qualified feature peaks, select feature peaks with the same peak shape as the initial coordinate point of maximum intensity; if there are multiple feature peaks of the same peak shape, select the feature peak with the closest Euclidean distance to point (d0, r0). These priority conditions can be used individually or in combination, and the order of selection is not limited to the above order and can be adjusted according to the actual situation. The region growing calculation ends when all pixels in the binary spectrum have been calculated.

[0073] 6) Based on the information in each ion migration time array D, the theoretical ion migration time at the current position is predicted by the weighted average of the previous n ion migration times. Here, a 1*3 mask is selected. If the deviation between the predicted value and the actual value at the current position reaches a preset threshold T... h If the peak value is not the characteristic peak of the current migration time, then the weight PeakVal is the corresponding ion intensity.

[0074]

[0075] wherein predictD is a predicted value of ion mobility time at the current position, PeakVal i-3 is a weight of ion mobility time at i-3 position, D i-3 is ion mobility time at i-3 position, i-3 is a position 3 before the current position, and n = 3. PeakVal i-2 is a weight of ion mobility time at i-2 position, D i-2 is ion mobility time at i-2 position, i-2 is a position 2 before the current position. PeakVal i-1 is a weight of ion mobility time at i-1 position, D i-1 is ion mobility time at i-1 position, i-1 is a position 1 before the current position.

[0076] The final result is shown in Figure 5 and Figure 6 , wherein Figure 5 is a schematic diagram of the final processing result provided according to the embodiments of the present application, Figure 6 is a schematic diagram of the retention time and intensity curve of the characteristic peak of the final processing result provided according to the embodiments of the present application. The final result can be used to judge and identify the corresponding substance according to the retention time and intensity curve.

[0077] The embodiments of the present application further provide an ion mobility spectrum data processing device. It should be noted that the ion mobility spectrum data processing device of the embodiments of the present application can be used to execute the ion mobility spectrum data processing method provided by the embodiments of the present application. The ion mobility spectrum data processing device provided by the embodiments of the present application is described below.

[0078] Figure 7 is a schematic diagram of an ion mobility spectrum data processing device provided according to the embodiments of the present application, as shown in Figure 7 , the device comprises a rejection module 72 and a growth module 74, which are described in detail below.

[0079] The rejection module 72 is configured to reject false characteristic peaks in a target spectrum to obtain target characteristic peaks, wherein the target spectrum comprises a plurality of false characteristic peaks and a plurality of target characteristic peaks. The growth module 74 is connected to the rejection module 72 and is configured to perform region growth according to the target characteristic peaks to determine a plurality of sequences, wherein the plurality of target characteristic peaks in the same sequence belong to the same substance, and one target characteristic peak corresponds to one sequence.

[0080] The data processing device of ion mobility spectrum provided by the embodiment of the present application can achieve the purpose of removing noise by eliminating pseudo characteristic peaks, and can use the method of region growth to count the target characteristic peaks of the same substance in different frames into the same sequence, so that each sequence corresponds to a substance, and the human eye is not needed to identify the characteristic peaks belonging to the same substance, and when the substance needs to be identified, the substance can be identified according to the characteristics of the ion mobility time array corresponding to the sequence, so as to achieve the purpose of automatically extracting the sequence of the characteristic peaks of the same substance in the ion mobility spectrum, and more conveniently identify the substance according to the sequence, and the technical effect of improving the efficiency and accuracy of identifying the substance according to the ion mobility spectrum is achieved, and the problem that the experience of artificial identification is relied on when the substance is identified by using the ion mobility spectrum in the related art is solved, and the efficiency is low and the accuracy is poor.

[0081] The data processing device of ion mobility spectrum includes a processor and a memory, and the above-mentioned elimination module 72, growth module 74 and the like are stored in the memory as program units, and the corresponding functions are realized by the processor executing the above-mentioned program units stored in the memory.

[0082] The processor includes a core, and the corresponding program units are called from the memory by the core. One or more than one core can be set, and the problem that the experience of artificial identification is relied on when the substance is identified by using the ion mobility spectrum in the related art is solved by adjusting the core parameters, and the efficiency is low and the accuracy is poor.

[0083] The memory can include a non-permanent memory in a computer readable medium, a random access memory (RAM) and / or a non-volatile memory such as a read-only memory (ROM) or a flash memory (flash RAM), and the memory includes at least one memory chip.

[0084] The embodiment of the present application provides a computer readable storage medium, which stores a program, and the program is executed by a processor to realize the data processing method of ion mobility spectrum.

[0085] The embodiment of the present application provides a processor, which is used to run a program, and the program is executed to perform the data processing method of ion mobility spectrum.

[0086] Figure 8 is a schematic diagram of an electronic device provided by the embodiment of the present application, as Figure 8 The embodiment of the present application provides an electronic device 80, which includes a processor, a memory and a program stored in the memory and capable of running on the processor, and the processor performs the steps of any method when the program is executed.

[0087] The device in the present application can be a server, a PC, a PAD, a mobile phone and the like.

[0088] The application also provides a computer program product adapted to perform the program of any of the above method steps when executed on a data processing device of an ion mobility spectrometer.

[0089] Those skilled in the art will appreciate that embodiments of the application can be supplied as methods, systems, or computer program products. Accordingly, the application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can be embodied in the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage media, etc.) having computer usable program code embodied therein.

[0090] The application is described herein with reference to the Figures, which illustrate the described methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each of the processes and / or functions illustrated in the Figures, and combinations of them, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of an ion mobility spectrometer data processing device, such as a general purpose computer, special purpose computer, embedded processor, or other programmable ion mobility spectrometer data processing device, to produce a machine, so that the instructions, which execute via the processor of the computer or other programmable ion mobility spectrometer data processing device, create means for implementing the functions / acts specified in the flowchart block or blocks. Figure 1 one or more functions specified in the flowchart block or blocks. Figure 1 one or more functions specified in the flowchart block or blocks.

[0091] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable ion mobility spectrometer data processing devices to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function / act specified in the flowchart block or blocks. Figure 1 one or more functions specified in the flowchart block or blocks. Figure 1 one or more functions specified in the flowchart block or blocks.

[0092] These computer program instructions can also be loaded onto a computer or other programmable ion mobility spectrometer data processing devices to cause a series of operational steps to be performed on the computer or other programmable ion mobility spectrometer data processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable ion mobility spectrometer data processing devices provide steps for implementing the function / act specified in the flowchart block or blocks. Figure 1 one or more functions specified in the flowchart block or blocks. Figure 1 Figure 1 one or more functions specified in the flowchart block or blocks.

[0093] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0094] Memory can include non-persistent memory, Random Access Memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory, etc. in a computer readable medium. Memory is an example of computer readable media.

[0095] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media such as modulated data signals and carrier waves.

[0096] It should also be noted that the terms "comprising", "containing", or any other similar term are intended to encompass non-exclusive inclusions such that a process, method, article, or apparatus that comprises a list of elements does not include those elements solely, but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0097] Those skilled in the art will understand that embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product implemented on one or more computer available storage media (including, but not limited to, disk memory, CD-ROMs, optical storage media, etc.) containing computer usable program code.

[0098] The above merely provides the embodiments of the present application and is not intended to limit the present application. Various modifications and changes can be made by those skilled in the art to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of claims of the present application.

Claims

1. A data processing method for ion mobility spectrometry, characterized in that, include: The false feature peaks in the target spectrum are removed to obtain the target feature peaks. The target spectrum includes multiple false feature peaks and multiple target feature peaks. The target spectrum is an ion mobility spectrum acquired by an ion mobility spectrometry detection device. Region growth is performed based on the target characteristic peaks to determine multiple sequences, wherein multiple target characteristic peaks in the same sequence belong to the same substance, and one target characteristic peak corresponds to one sequence. The step of determining multiple sequences by performing region growing based on the target feature peak includes: Select the location of one characteristic peak from multiple target characteristic peaks as the starting point; Based on the starting point and the preset mask, region growth is performed to determine multiple target feature peaks that belong to the same substance as the selected feature peaks, forming a sequence corresponding to the substance. The target feature peaks that make up the sequence are removed, and features are selected from the remaining target feature peaks as starting points for region growing until multiple target feature peaks form corresponding sequences, resulting in multiple sequences.

2. The method according to claim 1, characterized in that, After removing the pseudo-feature peaks in the target spectrum, the target feature peaks are obtained, including: Determine the intensity and peak width of all characteristic peaks in the target spectrum; Based on the intensity and peak width, all feature peaks are clustered to determine multiple categories of feature peaks, wherein the multiple categories belong to the pseudo feature peaks or the target feature peaks, respectively. Feature peaks belonging to the category of the pseudo-feature peaks are removed, and feature peaks belonging to at least one category of the target feature peaks are retained.

3. The method according to claim 2, characterized in that, Determining the intensity and peak width of all characteristic peaks in the target spectrum includes: The target spectrum is obtained by acquiring the ion mobility spectrometer detected by the ion mobility spectrometer. After preprocessing and correcting the target spectrum, an intermediate spectrum is obtained; Based on the intermediate spectrum, the parameters of all characteristic peaks are determined, wherein the parameters include the intensity and peak width.

4. The method according to claim 1, characterized in that, Based on the starting point and a preset mask, region growth is performed to determine multiple target feature peaks belonging to the same substance as the selected feature peaks, forming a sequence corresponding to the substance, including: Determine the pixel coordinates of the starting point; Align the set position of the mask with the pixel coordinates, perform region growth upwards or downwards, and determine whether the mask covers the location of other target feature peaks. Add the target feature peak covered by the mask to the corresponding position in the sequence; Align the set position of the mask with the pixel position of the target feature peak added to the sequence, and continue region growth until the mask does not cover the position of other target feature peaks, thus obtaining the sequence to which the target feature peak corresponding to the starting point belongs.

5. The method according to claim 4, characterized in that, Adding the target feature peak covered by the mask to the corresponding position in the sequence includes: If the number of target feature peaks covered by the mask is one, the covered target feature peak is directly added to the sequence; When the number of target feature peaks covered by the mask is multiple, the target feature peak with the highest priority among the multiple target feature peaks is added to the sequence according to a preset priority. The priority includes at least one of the following: the category of the target feature peak, the peak shape of the target feature peak, and the Euclidean distance between the target feature peak and the starting point.

6. The method according to claim 4, characterized in that, Determining the pixel coordinates of the starting point includes: The ion migration time and ion retention time of the target characteristic peak in the target spectrum are binarized to obtain a binary spectrum; The pixel coordinates of the starting point are determined based on the binary spectrum, wherein the horizontal coordinate of the pixel coordinates is the ion migration time, and the vertical coordinate of the pixel coordinates is the ion retention time.

7. The method according to any one of claims 1 to 6, characterized in that, After determining multiple sequences by performing region growing based on the target feature peak, the method further includes: Based on the ion migration time corresponding to the target feature peak in each sequence, an ion migration time array is obtained; The predicted ion migration time of the current position is determined based on a preset number of ion migration times preceding a current position in the ion migration time array. Determine the deviation between the predicted ion migration time and the actual ion migration time of the current position in the ion migration time array; If the deviation exceeds a preset threshold, the actual ion migration time at the current position is removed from the ion migration time array.

8. A data processing device for ion mobility spectrometry, characterized in that, include: The elimination module is used to remove pseudo-feature peaks in the target spectrum to obtain the target feature peaks. The target spectrum includes multiple pseudo-feature peaks and multiple target feature peaks. The target spectrum is an ion mobility spectrum acquired by an ion mobility spectrometry detection device. The growth module is used to perform region growth based on the target feature peaks to determine multiple sequences, wherein multiple target feature peaks in the same sequence belong to the same substance, and one target feature peak corresponds to one sequence; the process of performing region growth based on the target feature peaks to determine multiple sequences includes: Select the location of one characteristic peak from multiple target characteristic peaks as the starting point; Based on the starting point and the preset mask, region growth is performed to determine multiple target feature peaks that belong to the same substance as the selected feature peaks, forming a sequence corresponding to the substance. The target feature peaks that make up the sequence are removed, and features are selected from the remaining target feature peaks as starting points for region growing until multiple target feature peaks form corresponding sequences, resulting in multiple sequences.

9. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the ion mobility spectrometry data processing method according to any one of claims 1 to 7.

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