Characteristic peak identification method based on image processing, computing equipment and storage medium
阅读说明：本技术 基于图像处理的特征峰识别方法及计算设备、存储介质 (Characteristic peak identification method based on image processing, computing equipment and storage medium ) 是由 胡芸 赵杨 惠非琼 张丽 阮艺斌 于 2021-08-11 设计创作，主要内容包括：本发明公开了一种基于图像处理的特征峰识别方法,包括：在气相离子迁移谱图中选取第一图谱；将第一图谱转为二值图像；确定二值图像中所有目标区域的边缘点集,根据边缘点集在第一图谱中获取各目标区域的特征数据,特征数据包括离子强度、色谱保留时间、离子迁移时间；针对每一个目标区域,根据特征数据构建第一特征曲线和第二特征曲线,判断第一特征曲线和第二特征曲线的波峰数量,若第一特征曲线和第二特征曲线的波峰数量均为一个,则获取该目标区域内的最大离子强度及其相应的色谱保留时间和离子迁移时间作为该目标区域的特征峰信息。该方法能够准确、简便地从气相离子迁移谱图中提取出特征峰信息。本发明还公开了一种计算设备和存储介质。(The invention discloses a characteristic peak identification method based on image processing, which comprises the following steps: selecting a first map from gas phase ion mobility maps; converting the first map into a binary image; determining edge point sets of all target areas in the binary image, and acquiring characteristic data of each target area in the first map according to the edge point sets, wherein the characteristic data comprises ion intensity, chromatographic retention time and ion migration time; and aiming at each target region, constructing a first characteristic curve and a second characteristic curve according to the characteristic data, judging the number of wave crests of the first characteristic curve and the second characteristic curve, and if the number of the wave crests of the first characteristic curve and the second characteristic curve is one, acquiring the maximum ion intensity in the target region and the corresponding chromatographic retention time and ion migration time of the maximum ion intensity as the characteristic peak information of the target region. The method can accurately and simply extract the characteristic peak information from the gas phase ion mobility spectrogram. The invention also discloses a computing device and a storage medium.)
1. A characteristic peak identification method based on image processing is used for extracting characteristic peaks from a gas phase ion mobility spectrogram, and is characterized by comprising the following steps:
selecting a first map for representing volatile substances of the sample from the gas phase ion mobility spectrogram;
converting the first map into a binary image;
determining edge point sets of all target areas in the binary image, and acquiring feature data of each target area in the first map according to the edge point sets, wherein the feature data of each target area comprises the ion intensity, the chromatographic retention time and the ion migration time of each point in the target area;
for each target region, constructing a first characteristic curve and a second characteristic curve according to the characteristic data in the target region, respectively judging the number of peaks of the first characteristic curve and the number of peaks of the second characteristic curve, and if the number of peaks of the first characteristic curve and the number of peaks of the second characteristic curve are both one, acquiring the maximum ion intensity in the target region and the corresponding chromatographic retention time and ion migration time of the maximum ion intensity as the characteristic peak information of the target region;
wherein the first characteristic curve is a change curve of a first ion intensity along with chromatographic retention time, and the second characteristic curve is a change curve of a second ion intensity along with ion migration time.
2. The image-processing-based characteristic peak identifying method according to claim 1, wherein, for each of the target regions, when the number of peaks of the first characteristic curve and the number of peaks of the second characteristic curve are respectively judged,
if the number of peaks of the first characteristic curve is multiple and the number of peaks of the second characteristic curve is one or the number of peaks of the first characteristic curve is one and the number of peaks of the second characteristic curve is multiple, further acquiring valleys of the characteristic curve of which the number of peaks is multiple in the first characteristic curve and the second characteristic curve;
dividing the characteristic curve into a plurality of sub-curve segments according to the wave crests and the wave troughs on the characteristic curve with a plurality of wave crests, and acquiring the initial position and the end position of each sub-curve segment; wherein the number of the sub-curve segments is equal to the number of the peaks on the characteristic curve, and each sub-curve segment has one and only one peak;
dividing the target area into a plurality of sub-areas according to the initial position and the end position of each sub-curve segment;
for each sub-region, acquiring the maximum ion intensity in the sub-region and the chromatographic retention time and the ion migration time corresponding to the maximum ion intensity;
the maximum ion intensity of each sub-region and the corresponding chromatographic retention time and ion migration time thereof are the information of each characteristic peak of the target region.
3. The image-processing-based characteristic peak identifying method according to claim 1, wherein, for each of the target regions, when the number of peaks of the first characteristic curve and the number of peaks of the second characteristic curve are respectively judged,
if the number of the wave crests of the first characteristic curve and the number of the wave crests of the second characteristic curve are multiple, respectively acquiring a wave trough of the first characteristic curve and a wave trough of the second characteristic curve;
dividing the first characteristic curve into a plurality of first sub-curve segments according to the peaks and the troughs of the first characteristic curve, and acquiring the initial position and the end position of each first sub-curve segment; wherein the number of the first sub-curve segments is equal to the number of peaks on the first characteristic curve, and each first sub-curve segment has one and only one peak;
dividing the second characteristic curve into a plurality of second sub-curve segments according to the peaks and the troughs of the second characteristic curve, and acquiring the initial position and the final position of each second sub-curve segment; wherein the number of the second sub-curve segments is equal to the number of the peaks on the second characteristic curve, and each second sub-curve segment has one and only one peak;
dividing the target area into a plurality of sub-areas according to the initial position and the end position of each first sub-curve segment and the initial position and the end position of each second sub-curve segment, and acquiring characteristic data of each sub-area;
aiming at each sub-region, establishing a first sub-region characteristic curve and a second sub-region characteristic curve according to the characteristic data of the sub-region;
respectively judging the number of wave crests of the first characteristic curve of the subregion and the number of wave crests of the second characteristic curve of the subregion, and acquiring characteristic peak information in the subregion according to the number of wave crests of the first characteristic curve of the subregion and the number of wave crests of the second characteristic curve of the subregion; the characteristic peak information of the target region comprises characteristic peak information of each sub-region.
4. The method for identifying characteristic peaks based on image processing according to claim 1, wherein the constructing a first characteristic curve and a second characteristic curve according to the characteristic data in the target region comprises:
summing the ion intensities of the points in the target region at the same chromatographic retention time to obtain the first ion intensity corresponding to each chromatographic retention time, and constructing the first characteristic curve according to each first ion intensity;
summing the ion intensities of the points in the target area at the same ion migration time to obtain second ion intensities corresponding to the ion migration times, and constructing the second characteristic curve according to the second ion intensities.
5. The image-processing-based feature peak identification method according to claim 1, wherein the separately determining the number of peaks of the first feature curve and the number of peaks of the second feature curve comprises:
respectively solving a first derivative and a second derivative of the first characteristic curve relative to the chromatographic retention time to obtain a first derivative and a first second derivative;
respectively solving a first derivative and a second derivative of the second characteristic curve relative to the ion migration time to obtain a second first derivative and a second derivative;
determining the number of peaks of the first characteristic curve according to the first-order derivative and the first second-order derivative;
and determining the number of peaks of the second characteristic curve according to the second first-order derivative and the second-order derivative.
6. The image-processing-based characteristic peak identifying method according to claim 5,
determining the number of peaks of the first characteristic curve according to the first-order derivative and the first second-order derivative comprises:
calculating a zero point of the first order derivative;
calculating and judging the value of the first second-order derivative at the zero point of the first-order derivative;
if the value of the first second-order derivative at the zero point of the first-order derivative is less than 0, the point, corresponding to the zero point, on the first characteristic curve is the peak of the first characteristic curve;
determining the number of peaks of the second characteristic curve according to the second first order derivative and the second order derivative comprises:
calculating a zero of the second first derivative;
calculating and judging the value of the second derivative at the zero point of the second first derivative;
if the value of the second-order derivative at the zero point of the second first-order derivative is less than 0, the point on the second characteristic curve corresponding to the zero point is the peak of the second characteristic curve.
7. The image-processing-based feature peak identification method according to claim 1, wherein the first map is converted into the binary image by a threshold transformation method.
8. The image-processing-based feature peak identification method according to claim 1, wherein the edge point set of each of the target regions in the binary image is extracted by using a Canny operator.
9. A computing device, comprising:
a processor adapted to implement various instructions;
a memory adapted to store a plurality of instructions adapted to be loaded by the processor and to perform the image processing based feature peak identification method according to any of claims 1 to 8.
10. A storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method for image processing based feature peak identification according to any one of claims 1 to 8.
Technical Field
The invention relates to the field of application of gas chromatography ion mobility spectrometry technology, in particular to a characteristic peak identification method based on image processing, computing equipment and a storage medium.
Background
The Gas Chromatography-Ion Mobility Spectrometer (GC-IMS) technology combines a Gas Chromatography technology with strong resolution and an Ion Mobility spectrometry technology with high sensitivity, so that the GC-IMS technology greatly enhances the detection sensitivity of the Gas Chromatography, improves the resolution and linear response range of the Ion Mobility spectrometry, and is widely applied to the aspects of environmental monitoring of food, military, chemical engineering and the like. The spectrum data generated by the GC-IMS combined instrument contains abundant substance information, including information quantity such as gas chromatography retention time, ion migration time, ion strength and the like of the substance.
At present, the GC-IMS spectrum is analyzed mainly through software of an instrument, characteristic peaks are selected manually according to the difference of human eyes in corresponding spectrums of different samples, information (such as chromatographic retention time, ion migration time, peak intensity and the like) for marking the characteristic peaks is used as characterization variables, and then qualitative or quantitative analysis is carried out. However, this method of manually identifying the characteristic peak is dependent on the operator to some extent, and thus inevitably results in poor efficiency and accuracy of identifying the characteristic peak.
Disclosure of Invention
The invention mainly aims to solve the problem that the accuracy and efficiency of manually identifying characteristic peaks are poor in the prior art.
In order to achieve the above object, embodiments of the present invention provide a method for identifying a characteristic peak based on image processing, which can avoid errors caused by subjective factors to a certain extent and achieve accurate and fast identification of the characteristic peak. Specifically, the method is used for extracting characteristic peaks from a gas phase ion mobility spectrogram, and comprises the following steps:
selecting a first map for representing volatile substances of a sample from a gas phase ion mobility spectrogram;
converting the first map into a binary image;
determining edge point sets of all target areas in the binary image, and acquiring feature data of each target area in the first map according to the edge point sets, wherein the feature data of each target area comprises the ion intensity, the chromatographic retention time and the ion migration time of each point in the target area;
aiming at each target region, constructing a first characteristic curve and a second characteristic curve according to characteristic data in the target region, respectively judging the number of wave crests of the first characteristic curve and the number of wave crests of the second characteristic curve, and if the number of wave crests of the first characteristic curve and the number of wave crests of the second characteristic curve are both one, acquiring the maximum ion intensity in the target region and corresponding chromatographic retention time and ion migration time of the maximum ion intensity as characteristic peak information of the target region;
wherein the first characteristic curve is a change curve of a first ion intensity along with chromatographic retention time, and the second characteristic curve is a change curve of a second ion intensity along with ion migration time.
By adopting the scheme, the workload of data analysis can be simplified to a certain extent, and convenience is provided for identifying the substance type and the substance stability by utilizing a GC-IMS spectrogram. The method firstly separates the gas chromatography of the substance to be detected, then obtains the ion mobility spectrometry, expresses the fingerprint characteristics of the sample to the maximum extent, and ensures the comprehensiveness, the authenticity and the traceability of the sample data.
In one embodiment of the present invention, when the number of peaks of the first characteristic curve and the number of peaks of the second characteristic curve are determined separately for each target region,
if the number of the wave crests of the first characteristic curve is multiple and the number of the wave crests of the second characteristic curve is one or the number of the wave crests of the first characteristic curve is one and the number of the wave crests of the second characteristic curve is multiple, further acquiring the wave troughs of the characteristic curves of which the number of the wave crests is multiple in the first characteristic curve and the second characteristic curve;
dividing the characteristic curve into a plurality of sub-curve segments according to the wave crests and the wave troughs on the characteristic curve with a plurality of wave crests, and acquiring the initial position and the final position of each sub-curve segment; the number of the sub-curve segments is equal to the number of the wave crests on the characteristic curve, and each sub-curve segment has one and only one wave crest;
dividing the target area into a plurality of sub-areas according to the initial position and the end position of each sub-curve segment;
aiming at each subarea, acquiring the maximum ion intensity in the subarea and the chromatographic retention time and the ion migration time corresponding to the maximum ion intensity;
the maximum ion intensity corresponding to each sub-region and the corresponding chromatographic retention time and ion migration time are the information of each characteristic peak of the target region.
As an embodiment of the present invention, constructing the first characteristic curve and the second characteristic curve according to the characteristic data in the target region includes:
summing the ion intensities of all points in the target area at the same chromatographic retention time to obtain first ion intensities corresponding to the chromatographic retention times, and constructing a first characteristic curve according to the first ion intensities;
and summing the ion intensities of all points in the target area at the same ion migration time to obtain second ion intensities corresponding to all the ion migration times, and constructing a second characteristic curve according to all the second ion intensities.
As an embodiment of the present invention, for each target region, when the number of peaks of the first characteristic curve and the number of peaks of the second characteristic curve are respectively judged,
if the number of the wave crests of the first characteristic curve and the number of the wave crests of the second characteristic curve are multiple, respectively acquiring the wave troughs of the first characteristic curve and the wave troughs of the second characteristic curve;
dividing the first characteristic curve into a plurality of first sub-curve segments according to the wave crests and the wave troughs of the first characteristic curve, and acquiring the initial position and the final position of each first sub-curve segment; the number of the first sub-curve segments is equal to the number of the wave crests on the first characteristic curve, and each first sub-curve segment has only one wave crest;
dividing the second characteristic curve into a plurality of second sub-curve segments according to the wave crests and the wave troughs of the second characteristic curve, and acquiring the initial positions and the end positions of the second sub-curve segments; the number of the second sub-curve segments is equal to the number of the wave crests on the second characteristic curve, and each second sub-curve segment has only one wave crest;
dividing the target area into a plurality of sub-areas according to the initial position and the end position of each first sub-curve segment and the initial position and the end position of each second sub-curve segment, and acquiring characteristic data of each sub-area;
aiming at each sub-region, establishing a first sub-region characteristic curve and a second sub-region characteristic curve according to the characteristic data of the sub-region;
respectively judging the number of wave crests of the first characteristic curve of the subregion and the number of wave crests of the second characteristic curve of the subregion, and acquiring characteristic peak information in the subregion according to the number of wave crests of the first characteristic curve of the subregion and the number of wave crests of the second characteristic curve of the subregion; the characteristic peak information of the target area comprises characteristic peak information of each sub-area.
As an embodiment of the present invention, the determining the number of peaks of the first characteristic curve and the number of peaks of the second characteristic curve respectively includes:
respectively solving a first derivative and a second derivative of the first characteristic curve relative to the retention time of the chromatogram to obtain a first derivative and a first second derivative;
respectively solving a first derivative and a second derivative of the second characteristic curve relative to the ion migration time to obtain a second first derivative and a second derivative;
determining the number of peaks of the first characteristic curve according to the first order derivative and the first second order derivative;
and determining the number of peaks of the second characteristic curve according to the second first-order derivative and the second-order derivative.
As a specific embodiment of the present invention,
determining the number of peaks of the first characteristic curve according to the first derivative and the first second derivative comprises:
calculating a zero point of the first order derivative;
calculating and judging the value of the first second-order derivative at the zero point of the first-order derivative;
if the value of the first second-order derivative at the zero point of the first-order derivative is less than 0, the point, corresponding to the zero point, on the first characteristic curve is the peak of the first characteristic curve;
determining the number of peaks of the second characteristic curve according to the second first order derivative and the second order derivative comprises:
calculating a zero point of the second first derivative;
calculating and judging the value of the second derivative at the zero point of the second first derivative;
if the value of the second derivative at the zero point of the second first derivative is less than 0, the point on the second characteristic curve corresponding to the zero point is the peak of the second characteristic curve.
As an embodiment of the present invention, the first map is converted into a binary image by a threshold transformation method.
According to the method, the Canny operator is used for extracting the edge point set of each target area in the binary image.
Accordingly, the present invention also provides a computing device comprising:
a processor adapted to implement various instructions;
and the memory is suitable for storing a plurality of instructions, and the instructions are suitable for being loaded by the processor and executing the characteristic peak identification method based on the image processing.
Accordingly, the present invention also provides a storage medium storing a plurality of instructions adapted to be loaded by a processor and to execute the above-mentioned image processing-based characteristic peak identification method.
Drawings
FIG. 1 is a flow chart of a method for identifying characteristic peaks provided by the present invention;
FIG. 2 is a two-dimensional gray scale gas chromatography-ion mobility spectrum of a sample provided by the present invention;
FIG. 3 is a binarized representation of a first map of a sample provided by the present invention;
FIG. 4 is a target region summary diagram obtained based on Canny edge detection provided by the present invention;
FIG. 5 is a schematic representation of a target region of the chromatographic retention time direction provided by the present invention as bimodal;
FIG. 6 is a schematic diagram of a target region with double peaks in the time direction of the mobility spectrum provided by the present invention;
FIG. 7 is a summary of the target regions finally detected by the characteristic peak identification method provided by the present invention;
fig. 8 is a summary diagram of the characteristic peak intensities finally obtained by the characteristic peak identification method provided by the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure. While the invention will be described in conjunction with the preferred embodiments, it is not intended that features of the invention be limited to these embodiments. On the contrary, the invention is described in connection with the embodiments for the purpose of covering alternatives or modifications that may be extended based on the claims of the present invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The invention may be practiced without these particulars. Moreover, some of the specific details have been left out of the description in order to avoid obscuring or obscuring the focus of the present invention. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
It should be noted that in this specification, like reference numerals and letters refer to like items in the following drawings, and thus, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings.
The terms "first," "second," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides a characteristic peak identification method, which can be used for extracting a characteristic peak from a gas phase ion mobility spectrogram, specifically, the method comprises the following steps:
step S1: selecting a first spectrum for representing volatile substances of the sample from the gas phase ion mobility spectrum.
In particular, the information in the first atlas may appear in a matrix form. In practice, a GAS chromatography-ion mobility spectrometry instrument (such as the German GAS instruments model number)A headspace-gas chromatography-ion mobility spectrometry instrument) to obtain a gas phase ion mobility spectrometry of a substance to be detected (such as feed liquid, cigarettes, vegetable oil and the like), and then deriving a data matrix (also called as a gas chromatography-ion mobility spectrometry data matrix) of the gas phase ion mobility spectrometry by using software. It should be noted that the data matrix obtained from the gas phase ion mobility spectrometry includes information such as ion mobility time, chromatographic retention time, and ion intensity, and provides more abundant chemical information for subsequent analysis. Alternatively, the data matrix may be in CSV format. Specifically, the row direction of the data matrix represents the mobility spectrum information at a certain chromatographic time, and the column direction of the matrix represents the chromatographic information at a certain migration time point, or it can be understood that each data element in the data matrix represents the ion intensity at a certain chromatographic time point and a certain migration time point, wherein the data of the same row represents the ion intensity at the same chromatographic retention time but different ion migration times, and the data of the same column represents the ion intensity at the same ion migration time but different chromatographic retention times. Specifically, the expression form of the data matrix may be:
wherein X represents a data matrix of gas phase ion mobility spectrometry, RtiRepresents the ith chromatographic retention time point, DtjRepresents the jth ion migration time point, the first row in the matrix represents each ion migration time, momentThe first column in the array represents the retention time, x, of each color spectrumijRepresents the data elements in the matrix located in row i and column j that characterize the ion intensity at a particular chromatographic retention time and a particular ion migration time. Wherein the first row to the mth row are arranged according to chromatographic retention time, and the first column to the nth column are arranged according to ion migration time.
Specifically, the GC-IMS spectrum obtained by the gas phase ion mobility spectrometry method can provide chemical information of volatile components of a substance to be detected (e.g., a feed liquid for cigarette), including information such as chromatographic retention time, ion mobility time, and ion strength, as shown in fig. 2. The first vertical line at 1.0ms of GC-IMS early ion migration time is the water reactive ion peak (i.e., RIP peak). When volatile compounds are present in the ionization region of the IMS, the intensity of the reactive ion peak of the water decreases or disappears. Wherein the position of the characteristic peak is the ion migration time and chromatographic retention time of the characteristic peak. As can be seen from FIG. 2, the feed of this grade had a higher volatile content. Generally speaking, the volatile components of the feed liquid are less, and the method comprises the steps of firstly carrying out gas chromatography separation on the substance to be detected, then obtaining an ion mobility spectrum, carrying out nondestructive analysis on the volatile components of the feed liquid through GC-IMS, expressing the fingerprint characteristics of the sample to the maximum extent, and ensuring the comprehensiveness, authenticity and traceability of sample data. In addition, as the GC-IMS spectrum also contains other information, in order to simplify the step of acquiring the characteristic peak, only the first spectrum which can represent the volatile substances in the GC-IMS spectrum is selected, and optionally, the spectrum of the area corresponding to the ion migration time of 1.06 ms-2.00 ms and the chromatographic retention time of 0-200 s can be selected as the first spectrum.
In specific implementation, a characteristic data matrix related to the first spectrum may be selected from the gas chromatography-ion mobility spectrometry data matrix, and each data element in the characteristic data matrix is an ion intensity corresponding to a range of ion mobility time of 1.06ms to 2.00ms and chromatography retention time of 0s to 200 s. Namely, extracting each ion intensity with the ion migration time of 1.06 ms-2.00 ms and the chromatographic retention time of 0-200 s from the gas chromatography-ion mobility spectrometry data matrix, and constructing a new characteristic data matrix. Similarly, data in the same row of the characteristic data matrix characterizes the respective ion intensities at the same chromatographic retention time but different ion migration times, and data in the same column of the characteristic data matrix characterizes the respective ion intensities at the same ion migration time but different chromatographic retention times. In particular, the characteristic data matrix may be in the form of
Wherein Y represents a characteristic data matrix, RtiRepresents the ith chromatographic retention time point, DtjRepresents the j-th ion migration time point, the first row in the matrix represents each ion migration time, the first column in the matrix represents each spectral retention time, yijAnd the data elements are represented in the ith row and the jth column, wherein the first row to the a th row are arranged according to chromatographic retention time, the first column to the b th column are arranged according to ion migration time, a is less than or equal to m, and b is less than or equal to n.
Specifically, the gas phase ion mobility spectrometry can be obtained by using a headspace-gas chromatography-ion mobility spectrometry technology, and the headspace-gas chromatography-ion mobility spectrometry conditions include:
the headspace sampling conditions were set as follows: incubation temperature 50 deg.C, incubation time 5min, sample volume 500 μ l, and sample needle temperature 80 deg.C;
the gas chromatography-ion mobility spectrometry instrument conditions were set as follows: the chromatographic column is a multi-capillary separation column, the stationary phase OV-5, the temperature of the chromatographic column is 40 ℃, the temperature of the migration tube is 45 ℃, the temperature of the sample inlet is 80 ℃, the temperature of the sample injector-chromatographic column pipeline is 65 ℃, and the carrier gas is high-purity nitrogen;
the flow rate of the carrier gas adopts a program speed increasing mode, wherein the carrier gas speed is 2ml/min in 0-1 min, the carrier gas speed is gradually increased from 2ml/min to 50ml/min in 1-3 min, the carrier gas speed is gradually increased from 50ml/min to 150ml/min in 3-5 min, and the carrier gas speed is maintained at 150ml/min in 5-20 min; mobility Spectrum migration gas flow rate 150 ml/min.
Under the condition, the separation degree of the volatile components of the feed liquid is good, the detected volatile components are more, and the volatile components in the feed liquid can be rapidly collected.
Step S2: and converting the first map into a binary image.
Because the feature data of each target region are directly determined in the first map, the workload of manual identification is too large, the applicant extracts the edge of the target region based on the characteristics of the binary image by converting the first map into the binary image, and the workload of subsequent feature peak identification is simplified.
In particular, the first map may be converted into a binary image using a thresholding method, as shown in fig. 3. Specifically, the threshold may be set to 0.4118, so that a clearer binary image can be obtained, and the extraction of subsequent feature edge points is facilitated.
Step S3: determining edge point sets of all target areas in the binary image, and acquiring feature data of each target area in the first map according to the edge point sets, wherein the feature data of each target area comprises the ion intensity, the chromatographic retention time and the ion migration time of each point in the target area.
Since the first spectrum can represent volatile substance information, but the first spectrum still contains other irrelevant information, in order to further simplify the calculation amount of characteristic peak identification, the applicant skillfully thinks that the information of all target regions containing characteristic peaks in the first spectrum is extracted, so that the calculation amount is greatly reduced. Specifically, the first atlas may be converted into a binary image by using an image threshold transformation method, where the target region and other irrelevant regions may be clearly distinguished from each other in the binary image, which provides convenience for extracting edge points of each target region in the binary image, where an edge point set of each target region in the binary image may be extracted by using a Canny operator, and feature data of each target region may be determined in the first atlas according to the edge points of each target region. Namely, the extraction of the image edge point set is completed by using the Canny operator, the image boundary of the foreground image in the image is determined, and then the relevant information of each target area is found through the boundary position of the characteristic edge, as shown in fig. 4. The method greatly simplifies the identification calculation amount.
As mentioned above, the information in the first map can be presented in the form of a matrix, i.e. the matrix includes the retention time of each spectrum and the ion intensity at each ion migration time, and the acquisition of the characteristic data of each target region can be understood simply as follows:
for each target region, extracting each edge point of the target region, namely finding out an interval of chromatographic retention time and ion migration time corresponding to the target region, and then finding out data in the interval in a data matrix corresponding to the first map, namely that the characteristic data of the target region also appears in a matrix form, which is a sub-data matrix in the data matrix corresponding to the first map. The data in the same row of the subdata matrix represents the respective ion intensities at the same chromatographic retention time but different ion migration times, and the data in the same column represents the respective ion intensities at the same ion migration time but different chromatographic retention times. Further, since the edge point set may not be a regular rectangle, it may not be possible to effectively intercept feature data corresponding to the target region from the data matrix of the first map directly according to each point in the edge point set, in the process of acquiring the feature data, it is further necessary to further acquire a start point and an end point of a chromatogram retention time and a start point and an end point of an ion migration time in the edge point set, and then intercept a sub-matrix located in the start point and the end point of the chromatogram retention time and the start point and the end point of the ion migration time from the data matrix of the first map, where data in the sub-matrix constitutes feature data of the target region.
Specifically, the start point and the end point of the chromatogram retention time in the edge point set are respectively for the minimum value and the maximum value of the chromatogram retention time in the edge point set, and the start point and the end point of the ion migration time in the edge point set are respectively for the minimum value and the maximum value of the ion migration time in the edge point set.
In addition, the maximum value of the ion intensity in the sub-data matrix corresponding to each target area, and the chromatographic retention time and the ion migration time corresponding to the maximum ion intensity can be obtained by utilizing a method of solving the maximum value in the matrix.
Further, although according to the above-mentioned image processing method, the maximum ion intensity and the corresponding chromatographic retention time and ion migration time in each target region can be determined, the applicant found that, since there may be a case where the characteristic peaks coincide, the maximum ion intensity and the corresponding chromatographic retention time in the target region cannot be completely determined as the characteristic peak information, and when the characteristic peaks coincide (i.e., there is more than one characteristic peak in the target region), the maximum ion intensity and the corresponding chromatographic retention time and ion migration time in the target region may deviate from the information of the characteristic peaks. Therefore, it is necessary to determine whether there is coincidence between the characteristic peaks in the target region, that is, determine whether the number of the characteristic peaks in the target region is only one, and only when the number of the characteristic peaks in the target region is 1, the maximum ion intensity in the target region and the corresponding chromatographic retention time and ion migration time are the characteristic peak information in the target region. Based on the above considerations, the following steps are proposed:
step S4: aiming at each target region, constructing a first characteristic curve and a second characteristic curve according to characteristic data in the target region, respectively judging the number of wave crests of the first characteristic curve and the number of wave crests of the second characteristic curve, and if the number of wave crests of the first characteristic curve and the number of wave crests of the second characteristic curve are both one, acquiring the maximum ion intensity in the target region and corresponding chromatographic retention time and ion migration time of the maximum ion intensity as characteristic peak information of the target region; wherein the first characteristic curve is a change curve of a first ion intensity along with chromatographic retention time, and the second characteristic curve is a change curve of a second ion intensity along with ion migration time.
I.e. the information of the characteristic peaks includes the peak intensity, chromatographic retention time and ion migration time of the characteristic peaks. When the number of peaks of the first characteristic curve and the number of peaks of the second characteristic curve are both one in the target region, the peak intensity of the characteristic peak of the target region is the maximum ion intensity in the target region, and the chromatographic retention time and the ion migration time of the characteristic peak are the chromatographic retention time and the ion migration time corresponding to the maximum ion intensity.
Compared with the method for directly identifying the characteristic peak in the GC-IMS spectrogram, the method has the advantages that the specific condition of the characteristic peak is analyzed by combining an image processing technology, the accuracy is higher, the workload of identifying the characteristic peak is simplified to a certain extent, and convenience is brought to the identification of the substance type and the substance stability by utilizing the GC-IMS spectrogram. The method firstly separates the gas chromatography of the substance to be detected, then obtains the ion mobility spectrometry, and carries out nondestructive analysis on the volatile components of the feed liquid through headspace-gas chromatography-ion mobility spectrometry, so as to express the fingerprint characteristics of the sample to the maximum extent and ensure the comprehensiveness, authenticity and traceability of the sample data. Specifically, according to the scheme, each target area in a first map of a GC-IMS map is determined based on an image processing technology, and characteristic data of each target area are obtained, namely the GC-IMS map is simplified into a plurality of target area maps, the data volume of the characteristic data of each target area is obviously reduced relative to the huge data of the GC-IMS map, so that the workload is simplified to a certain extent, then, characteristic peak information in each target area is obtained for each target area, the process is simple and convenient, and the error rate is further reduced; in addition, the scheme further judges whether the target area has only one characteristic peak, and when only one characteristic peak exists, the characteristic peak information in the target area is determined, so that the identification error caused by the superposition of the characteristic peaks is avoided, and the accuracy of characteristic peak identification is enhanced.
Optionally, the image data may be updated, for each target region,
when the number of peaks of the first characteristic curve and the number of peaks of the second characteristic curve are respectively judged, if the number of peaks of the first characteristic curve is multiple and the number of peaks of the second characteristic curve is one or the number of peaks of the first characteristic curve is one or the number of peaks of the second characteristic curve is multiple, further obtaining the valleys of the characteristic curve with the number of peaks of the first characteristic curve and the second characteristic curve being multiple; specifically, the position corresponding to the minimum value in the region between adjacent peaks is the position of the trough.
Dividing the characteristic curve into a plurality of sub-curve segments according to the wave crests and the wave troughs on the characteristic curve with a plurality of wave crests, and acquiring the initial position and the final position of each sub-curve segment; the number of the sub-curve segments is equal to the number of the wave crests on the characteristic curve, and each sub-curve segment has one and only one wave crest;
dividing the target area into a plurality of sub-areas according to the initial position and the end position of each sub-curve segment;
aiming at each subarea, acquiring the maximum ion intensity in the subarea and the chromatographic retention time and the ion migration time corresponding to the maximum ion intensity;
the maximum ion intensity corresponding to each sub-region and the corresponding chromatographic retention time and ion migration time are the information of each characteristic peak of the target region.
Specifically, when the number of peaks of the first characteristic curve is plural and the number of peaks of the second characteristic curve is one or the number of peaks of the first characteristic curve is one or the number of peaks of the second characteristic curve is plural, in other words, when only one of the first characteristic curve and the second characteristic curve is plural, the number of characteristic peaks in each target region is equal to the maximum value of the number of peaks of the first characteristic curve and the number of peaks of the second characteristic curve of the target region.
For example, for any target region, if the number of peaks of the first characteristic curve of the target region is 1 and the number of peaks of the second characteristic curve is 2, then the number of characteristic peaks in the target region is 2; if the number of peaks of the first characteristic curve is 3 and the number of peaks of the second characteristic curve is 1, the number of characteristic peaks in the target region is 3.
Specifically, taking one of the target regions as an example, if the number of peaks on the first characteristic curve in the target region is multiple and the number of peaks on the second characteristic curve is 1, when the first characteristic curve is a double peak or more than a triple peak, peaks of two or more than three peaks need to be calculated. Specifically, the valley position corresponding to the minimum value is found in the adjacent peak area. In the method, a trough position is used to separate two peaks or three peaks to obtain a single peak, and a starting position and an ending position of each single peak are obtained, because a first characteristic curve is divided in the example, then the starting position and the ending position of each single peak specifically correspond to two numerical values of the chromatographic retention time, for the convenience of description, the two chromatographic retention time points are defined as a first chromatographic retention time value and a second chromatographic retention time value, and then a submatrix (namely, the target area is divided into a plurality of subregions) with the chromatographic retention time between the first chromatographic retention time value and the second chromatographic retention time value is determined in a data matrix corresponding to the target area. And then searching the maximum value of the ion intensity and the chromatographic retention time and the ion migration time corresponding to the maximum value of the ion intensity in the matrix data corresponding to the sub-region, namely the characteristic peak information corresponding to the sub-region, wherein the determination methods of the characteristic peak information of other sub-regions are the same. The characteristic peaks of the sub-regions are summarized to be the characteristic peak information in the target region. By the method, the information of all characteristic peaks in the target region can be acquired, and the operation is simple and convenient.
According to the scheme, whether each characteristic peak in the target region is coincided with the ion migration direction in the chromatographic retention direction or not is analyzed, and then the overlapped peaks in the chromatographic retention direction and the ion migration direction are separated, so that the characteristic peak condition in the target region is more accurately judged, and the characteristic peak information is obtained, and accurate characteristic peak information data is provided for qualitatively or quantitatively analyzing the substance to be detected.
Optionally, constructing the first characteristic curve and the second characteristic curve according to the characteristic data in the target region includes:
summing the ion intensities of all points in the target area at the same chromatographic retention time to obtain first ion intensities corresponding to the chromatographic retention times, and constructing a first characteristic curve according to the first ion intensities;
and summing the ion intensities of all points in the target area at the same ion migration time to obtain second ion intensities corresponding to all the ion migration times, and constructing a second characteristic curve according to all the second ion intensities.
Specifically, taking the form of matrix data of the characteristic data of the target region as an example, the data in the same row represents the respective ion intensities at the same chromatographic retention time but different ion migration times, and the data in the same column represents the respective ion intensities at the same ion migration time but different chromatographic retention times. Specifically, when the first characteristic curve is obtained, the data in each row in the data matrix corresponding to the target area may be summed up to obtain first ion intensities corresponding to the chromatographic retention times, and then the first characteristic curve is drawn according to the chromatographic retention times and the first ion intensities corresponding thereto; when the second characteristic curve is obtained, accumulating and summing the data in each column of the characteristic data matrix to obtain second ion intensity corresponding to the ion migration time; a second characteristic curve is plotted as a function of each ion mobility time and its corresponding second ion intensity.
Specifically, due to the influence of noise, there may be an error term in the first ion intensity corresponding to each chromatogram retention time obtained by the accumulation and the second ion intensity corresponding to each ion migration time obtained by the accumulation and the summation, and the error term may be processed by using a mean value filter or a Savitzky-Golay filter to smooth the first characteristic curve and the second characteristic curve.
Optionally, the step of respectively determining the number of peaks of the first characteristic curve and the number of peaks of the second characteristic curve may specifically include the following steps:
solving a first derivative of the first characteristic curve with respect to the chromatographic retention time to obtain a first derivative;
solving a first derivative of the second characteristic curve with respect to the ion migration time to obtain a second first derivative;
respectively calculating zero points of the first order derivative and the second first order derivative;
determining the number of peaks of the first characteristic curve according to the zero point of the first-order derivative, and determining the number of peaks of the second characteristic curve according to the zero point of the second first-order derivative.
That is, the method of determining the number of peaks on the first characteristic curve and the second characteristic curve can be understood as follows:
for the first characteristic curve, the characteristic intensity corresponding to each color spectrum retention time point on the first characteristic curve is subjected to first derivation about the color spectrum retention time to obtain a first order derivative, a function curve of the first order derivative in the color spectrum retention time range corresponding to the characteristic curve is simulated, and the peak condition is searched on the function curve to judge the number of peaks of the first characteristic curve.
For the second characteristic curve, the characteristic intensity corresponding to each ion migration time point on the second characteristic curve is subjected to first derivation with respect to the ion migration time to obtain a second first derivative, a function curve of the second first derivative in the ion migration time range corresponding to the second characteristic curve is simulated, and the peak condition is searched on the function curve to judge the number of peaks of the second characteristic curve.
Optionally, the respectively determining the number of peaks of the first characteristic curve and the number of peaks of the second characteristic curve includes:
respectively solving a first derivative and a second derivative of the first characteristic curve relative to the retention time of the chromatogram to obtain a first derivative and a first second derivative;
respectively solving a first derivative and a second derivative of the second characteristic curve relative to the ion migration time to obtain a second first derivative and a second derivative;
determining the number of peaks of the first characteristic curve according to the first order derivative and the first second order derivative;
and determining the number of peaks of the second characteristic curve according to the second first-order derivative and the second-order derivative.
Specifically, determining the number of peaks of the first characteristic curve according to the first order derivative and the first second order derivative includes:
calculating a zero point of the first order derivative;
calculating and judging the value of the first second-order derivative at the zero point of the first-order derivative;
if the value of the first second-order derivative at the zero point of the first-order derivative is less than 0, the point, corresponding to the zero point, on the first characteristic curve is the peak of the first characteristic curve;
determining the number of peaks of the second characteristic curve according to the second first order derivative and the second order derivative comprises:
calculating a zero point of the second first derivative;
calculating and judging the value of the second derivative at the zero point of the second first derivative;
if the value of the second derivative at the zero point of the second first derivative is less than 0, the point on the second characteristic curve corresponding to the zero point is the peak of the second characteristic curve.
Taking the number of peaks of the first characteristic curve as an example, the above method can be understood as follows:
and carrying out primary derivation and secondary derivation on the characteristic intensity corresponding to each color spectrum retention time point on the first characteristic curve with respect to the color spectrum retention time, respectively obtaining a first order derivative and a first second order derivative, enabling the first order derivative to be zero, obtaining the color spectrum retention time at the moment, substituting the obtained color spectrum retention time into the first second order derivative, calculating the value of the first second order derivative, and if the first second order derivative is less than 0, taking the point as a peak, and counting the number of the peaks.
The method for determining the peak of the second characteristic curve is similar to the method for determining the peak of the first characteristic curve, and is not repeated.
The characteristic peak identification method provided by the invention can automatically identify the characteristic peak of the GC-IMS atlas based on the image characteristics, and can separate the mixed peaks overlapped in the chromatographic retention direction or the ion migration direction to obtain the chromatographic retention time, the ion migration time and the peak intensity of a single peak, and the method is simple and convenient and has high accuracy.
Alternatively, for each target region, when the number of peaks of the first characteristic curve and the number of peaks of the second characteristic curve are respectively judged,
if the number of the wave crests of the first characteristic curve and the number of the wave crests of the second characteristic curve are multiple, respectively acquiring the wave troughs of the first characteristic curve and the wave troughs of the second characteristic curve;
dividing the first characteristic curve into a plurality of first sub-curve segments according to the wave crests and the wave troughs of the first characteristic curve, and acquiring the initial position and the final position of each first sub-curve segment; the number of the first sub-curve segments is equal to the number of the wave crests on the first characteristic curve, and each first sub-curve segment has only one wave crest;
dividing the second characteristic curve into a plurality of second sub-curve segments according to the wave crests and the wave troughs of the second characteristic curve, and acquiring the initial positions and the end positions of the second sub-curve segments; the number of the second sub-curve segments is equal to the number of the wave crests on the second characteristic curve, and each second sub-curve segment has only one wave crest;
dividing the target area into a plurality of sub-areas according to the initial position and the end position of each first sub-curve segment and the initial position and the end position of each second sub-curve segment, and acquiring characteristic data of each sub-area in the first map;
aiming at each sub-region, establishing a first sub-region characteristic curve and a second sub-region characteristic curve according to the characteristic data of the sub-region;
respectively judging the number of wave crests of the first characteristic curve of the subregion and the number of wave crests of the second characteristic curve of the subregion, and acquiring characteristic peak information in the subregion according to the number of wave crests of the first characteristic curve of the subregion and the number of wave crests of the second characteristic curve of the subregion; the characteristic peak information of the target area comprises characteristic peak information of each sub-area.
That is, when the number of peaks of the first characteristic curve and the number of peaks of the second characteristic curve are both multiple (for example, the number of peaks of the first characteristic curve and the number of peaks of the second characteristic curve are both 2 or the number of peaks of the first characteristic curve is 2 and the number of peaks of the second characteristic curve is 3, etc.), first, the peaks and the troughs of the first characteristic curve and the peaks and the troughs of the second characteristic curve are respectively obtained, then, the corresponding curves are divided into a plurality of sub-curve segments according to the peaks and the troughs, the initial position and the end position of each sub-curve segment are determined, and the target region is divided into a plurality of sub-regions according to the positions. For example, for a first sub-curve segment of the first characteristic curve, the initial position and the end position correspond to a certain interval of the chromatographic retention time, and for a second sub-curve segment of the second characteristic curve, the initial position and the end position correspond to a certain interval of the ion migration time. And then acquiring characteristic data of each sub-region according to the plurality of correspondingly determined chromatographic retention time intervals and ion migration time intervals. Specifically, the feature data of each sub-region may be obtained in the first map according to the chromatogram retention time interval and the ion migration time interval, or may be obtained in the data matrix corresponding to the target region according to the chromatogram retention time interval and the ion migration time interval. After the characteristic data of the sub-region is obtained, by using the method which is the same as the method for establishing the first characteristic curve and the second characteristic curve, the first characteristic curve and the second characteristic curve of the sub-region are established according to the characteristic data of the sub-region, the number of wave crests of the first characteristic curve and the second characteristic curve of the sub-region is respectively judged, and then the characteristic peak information in the sub-region is obtained.
Wherein, the process of obtaining the characteristic peak in the sub-region is similar to the process of obtaining the characteristic peak in the target region, specifically,
if the number of peaks of the first characteristic curve of the sub-region and the second characteristic curve of the sub-region is 1, acquiring the maximum ion intensity in the sub-region and the corresponding chromatographic retention time and ion migration time of the maximum ion intensity as the characteristic peak information of the sub-region;
if the number of the wave crests of only one curve in the first characteristic curve of the sub-region and the second characteristic curve of the sub-region is multiple, further dividing the characteristic curve of the sub-region into a plurality of sub-region sub-curve segments according to the wave crests and the wave troughs on the characteristic curve of the sub-region with the multiple wave crests, and acquiring the initial position and the end position of each sub-region sub-curve segment; the number of the sub-region sub-curve segments is equal to the number of the wave crests on the sub-region characteristic curve, and each sub-region sub-curve segment has only one wave crest; dividing the sub-region into a plurality of small regions according to the initial position and the end position of each sub-curve segment of the sub-region; aiming at each small area, acquiring the maximum ion intensity in the small area and the chromatographic retention time and the ion migration time corresponding to the maximum ion intensity; the maximum ion intensity corresponding to each small region and the corresponding chromatographic retention time and ion migration time are the information of each characteristic peak of the sub-region;
if the first characteristic curve of the sub-region and the second characteristic curve of the sub-region are multiple at the same time, the steps are circulated, namely the first characteristic curve of the sub-region and the second characteristic curve of the sub-region are continuously divided into multiple sub-curves according to the peaks and the troughs of the first characteristic curve of the sub-region and the second characteristic curve of the sub-region, the peak conditions of the sub-curves are respectively obtained, and the steps are repeated.
Correspondingly, the invention also provides a computing device, comprising: a processor adapted to implement various instructions; a memory adapted to store a plurality of instructions, the instructions adapted to be loaded by the processor and to perform the method of signature peak identification in any of the embodiments described above.
By adopting the computing equipment of the technical scheme, the workload of identifying the characteristic peak can be simplified, and the accurate identification of the characteristic peak can be realized.
Accordingly, the present invention provides a storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor and to perform the method of signature peak identification in any of the above embodiments.
By adopting the storage medium of the technical scheme, the workload of identifying the characteristic peak can be simplified, and the accurate identification of the characteristic peak can be realized.
The embodiments disclosed herein may be implemented in hardware, software, firmware, or a combination of these implementations. Embodiments of the application may be implemented as computer programs or program code executing on programmable systems comprising at least one processor, a storage system (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Program code may be applied to input instructions to perform the functions described herein and generate output information. The output information may be applied to one or more output devices in a known manner. For purposes of this application, a processing system includes any system having a processor such as, for example, a Digital Signal Processor (DSP), a microcontroller, an Application Specific Integrated Circuit (ASIC), or a microprocessor.
The program code may be implemented in a high level procedural or object oriented programming language to communicate with a processing system. The program code can also be implemented in assembly or machine language, if desired. Indeed, the mechanisms described in this application are not limited in scope to any particular programming language. In any case, the language may be a compiled or interpreted language.
In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, the instructions may be distributed via a network or via other computer readable media. Thus, a machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), including, but not limited to, floppy diskettes, optical disks, read-only memories (CD-ROMs), magneto-optical disks, read-only memories (ROMs), Random Access Memories (RAMs), erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), magnetic or optical cards, flash memory, or a tangible machine-readable memory for transmitting information (e.g., carrier waves, infrared digital signals, etc.) using the internet in an electrical, optical, acoustical or other form of propagated signal. Thus, a machine-readable medium includes any type of machine-readable medium suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).
In the drawings, some features of the structures or methods may be shown in a particular arrangement and/or order. However, it is to be understood that such specific arrangement and/or ordering may not be required. Rather, in some embodiments, the features may be arranged in a manner and/or order different from that shown in the illustrative figures. In addition, the inclusion of a structural or methodical feature in a particular figure is not meant to imply that such feature is required in all embodiments, and in some embodiments, may not be included or may be combined with other features.
It should be noted that, all the modules/units mentioned in the embodiments of the apparatuses in this application are logical modules/units, and physically, one logical module/unit may be one physical module/unit, or may be a part of one physical module/unit, and may also be implemented by a combination of multiple physical modules/units, where the physical implementation manner of the logical modules/units itself is not the most important, and the combination of the functions implemented by the logical modules/units is the key to solve the technical problem proposed in this application. Furthermore, in order to highlight the innovative part of the present application, the above-mentioned embodiments of the apparatus of the present application do not introduce modules/units that are not so closely related to solve the technical problems presented in the present application, which does not indicate that there are no other modules/units in the above-mentioned embodiments of the apparatus.
Examples
(1) Collection and preparation of feed liquid sample
Collecting prepared feed liquid samples for cigarettes: and (3) collecting feed liquid samples from a spice kitchen, diluting each sample by 100 times, taking 500ul of the diluted sample, respectively placing the diluted sample in 20mL headspace sample injection bottles, and sealing by a gland to be detected.
(2) Obtaining GC-IMS spectrogram of each feed liquid sample
A. An analytical instrument: the method adopts a head space-GAS chromatography-ion mobility spectrometry instrument (model number is)；
B. The experimental conditions are as follows:
the headspace sampling conditions were set as follows: incubation temperature 50 deg.C, incubation time 5min, sample volume 500 μ l, and sample needle temperature 80 deg.C;
the gas chromatography-ion mobility spectrometry instrument conditions were set as follows: the chromatographic column is a multi-capillary separation column (MCC), the stationary phase OV-5, the temperature of the chromatographic column is 40 ℃, the temperature of the migration tube is 45 ℃, the temperature of the sample inlet is 80 ℃, the temperature of the sample injector-chromatographic column pipeline is 65 ℃, and the carrier gas is high-purity nitrogen;
the flow rate of the carrier gas adopts a program speed increasing mode, wherein the carrier gas speed is 2ml/min in 0-1 min, the carrier gas speed is gradually increased from 2ml/min to 50ml/min in 1-3 min, the carrier gas speed is gradually increased from 50ml/min to 150ml/min in 3-5 min, and the carrier gas speed is maintained at 150ml/min in 5-20 min; mobility Spectrum migration gas flow rate 150 ml/min.
(3) Analysis of results
(a) The GC-IMS spectra (migration time, retention time, and ionic strength) obtained by the gas phase ion mobility spectrometry method provide chemical information of volatile components of the feed liquid for tobacco, including chromatographic retention time, ion migration time, ionic strength, and the like, as shown in fig. 2. The first vertical line at the early GC-IMS ion mobility time of 1.0ms is the water reactive ion peak. When volatile compounds are present in the ionization region of the IMS, the reactive ion peak intensity of the water decreases or disappears. As can be seen from FIG. 2, the feed of this grade had a higher volatile content. Generally speaking, the volatile components of the feed liquid are less, and the method comprises the steps of firstly carrying out gas chromatography separation on the substance to be detected, then obtaining an ion mobility spectrum, carrying out nondestructive analysis on the volatile components of the feed liquid through GC-IMS, expressing the fingerprint characteristics of the sample to the maximum extent, and ensuring the comprehensiveness, authenticity and traceability of sample data.
(b) Extracting a first map region: the first map is a map of a region corresponding to an ion migration time of 1.06 ms-2.00 ms and a gas chromatography time of 0-200 s.
(c) The first map is converted into a binary image using a thresholding method, as shown in figure 3. The value of the threshold is set to 0.4118.
(d) And (5) finishing the extraction of the image edge points by using a Canny operator, and determining the image boundary of the foreground image in the image.
(e) Finding out relevant data of each target area through the boundary position of the characteristic edge, as shown in FIG. 4;
(f) summing the characteristic data of each target area towards the chromatographic retention direction to obtain a first characteristic curve, summing the characteristic data of each characteristic peak towards the ion migration direction to obtain a second characteristic curve, smoothing the first characteristic curve and the second characteristic curve due to the influence of noise, and identifying whether the characteristic peaks on the first characteristic curve and the second characteristic curve are single peaks or mixed peaks by using a first derivative and a second derivative;
(g) if the first characteristic curve and the second characteristic curve are both single peaks, that is, the maximum value of the ion intensity in the target region and the corresponding chromatographic retention time and ion migration time are obtained, which is the characteristic peak information in the target region, for example, the target region 1 and the target region 2 in fig. 4 both represent the mixed peak condition, where two peaks of the target region 1 overlap in the chromatographic retention direction (as shown in fig. 5, the first characteristic curve of the target region 1 has peaks in the chromatographic retention times 84s-88s and 90s-94 s), that is, the first characteristic curve in the target region 1 is double peaks, and the second characteristic curve is single peak; and two peaks of the target region 2 overlap in the ion migration direction (as shown in fig. 6, peaks exist in the second characteristic curve of the target region 2 within the ion migration time of 1.09ms to 1.11ms and 1.12ms to 1.16 ms), that is, the first characteristic curve of the target region 2 is a single peak, and the second characteristic curve is a double peak. Therefore, it is necessary to divide all the mixed peaks in the target region 1 and the target region 2.
(h) Taking the target area 1 as an example, as shown in fig. 5, the first characteristic curve corresponding to the target area 1 in fig. 4 is a double peak, and therefore, the two peak positions need to be calculated, and then the valley position corresponding to the minimum value is found in the adjacent peak area. Separating the double peaks to obtain a single peak by using the trough position, dividing the target region in the first map by the region where the single peak is located, as shown in fig. 7, dividing the target region 1 into two sub-regions, respectively calculating the maximum ion intensities in the two sub-regions, and obtaining the chromatographic retention time and the ion migration time corresponding to the maximum ion intensities, thereby finally obtaining the characteristic peak information in the target region.
(i) Fig. 8 shows the situation of the identified 28 characteristic peaks, and a vector or a matrix can be constructed subsequently according to the intensities of the characteristic peaks to perform qualitative or quantitative analysis on the sample to be detected.
While the invention has been shown and described with reference to certain embodiments thereof, it will be understood by those skilled in the art that the foregoing is a more particular description of the invention than is described in conjunction with the specific embodiments, and the specific embodiments of the invention should not be considered to be limited to such descriptions. Various changes in form and detail may be made therein by those skilled in the art, including simple deductions or substitutions without departing from the spirit and scope of the invention.